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'''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int ) -> int:
'''simple docstring'''
__lowerCAmelCase = [1]
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0, 0, 0
__lowerCAmelCase = ugly_nums[ia] * 2
__lowerCAmelCase = ugly_nums[ia] * 3
__lowerCAmelCase = ugly_nums[ia] * 5
for _ in range(1 , snake_case_ ):
__lowerCAmelCase = min(snake_case_ , snake_case_ , snake_case_ )
ugly_nums.append(snake_case_ )
if next_num == next_a:
ia += 1
__lowerCAmelCase = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
__lowerCAmelCase = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
__lowerCAmelCase = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f'{ugly_numbers(200) = }')
| 229
|
'''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("""Both a & b of two equations can't be zero.""" )
# Extract the coefficients
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
# Calculate the determinants of the matrices
__lowerCAmelCase = aa * ba - aa * ba
__lowerCAmelCase = ca * ba - ca * ba
__lowerCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("""Infinite solutions. (Consistent system)""" )
else:
raise ValueError("""No solution. (Inconsistent system)""" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__lowerCAmelCase = determinant_x / determinant
__lowerCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 229
| 1
|
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Dict = "autoformer"
_lowerCamelCase :Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self : str , UpperCamelCase : Optional[int] = None , UpperCamelCase : Optional[int] = None , UpperCamelCase : str = "student_t" , UpperCamelCase : str = "nll" , UpperCamelCase : int = 1 , UpperCamelCase : List[int] = [1, 2, 3, 4, 5, 6, 7] , UpperCamelCase : bool = True , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 , UpperCamelCase : Optional[List[int]] = None , UpperCamelCase : Optional[List[int]] = None , UpperCamelCase : int = 64 , UpperCamelCase : int = 2 , UpperCamelCase : int = 2 , UpperCamelCase : int = 2 , UpperCamelCase : int = 2 , UpperCamelCase : int = 32 , UpperCamelCase : int = 32 , UpperCamelCase : str = "gelu" , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : int = 1_00 , UpperCamelCase : float = 0.02 , UpperCamelCase : bool = True , UpperCamelCase : Any=True , UpperCamelCase : int = 10 , UpperCamelCase : int = 25 , UpperCamelCase : int = 3 , **UpperCamelCase : List[Any] , ) -> List[Any]:
"""simple docstring"""
# time series specific configuration
lowerCAmelCase__ : Optional[int] = prediction_length
lowerCAmelCase__ : Optional[Any] = context_length if context_length is not None else prediction_length
lowerCAmelCase__ : List[Any] = distribution_output
lowerCAmelCase__ : Union[str, Any] = loss
lowerCAmelCase__ : List[Any] = input_size
lowerCAmelCase__ : Optional[Any] = num_time_features
lowerCAmelCase__ : int = lags_sequence
lowerCAmelCase__ : Optional[int] = scaling
lowerCAmelCase__ : str = num_dynamic_real_features
lowerCAmelCase__ : str = num_static_real_features
lowerCAmelCase__ : Optional[int] = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(UpperCamelCase ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
lowerCAmelCase__ : List[str] = cardinality
else:
lowerCAmelCase__ : Tuple = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(UpperCamelCase ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
lowerCAmelCase__ : Optional[Any] = embedding_dimension
else:
lowerCAmelCase__ : int = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowerCAmelCase__ : Any = num_parallel_samples
# Transformer architecture configuration
lowerCAmelCase__ : Optional[int] = input_size * len(self.lags_sequence ) + self._number_of_features
lowerCAmelCase__ : int = d_model
lowerCAmelCase__ : Union[str, Any] = encoder_attention_heads
lowerCAmelCase__ : int = decoder_attention_heads
lowerCAmelCase__ : List[str] = encoder_ffn_dim
lowerCAmelCase__ : List[Any] = decoder_ffn_dim
lowerCAmelCase__ : Optional[Any] = encoder_layers
lowerCAmelCase__ : Tuple = decoder_layers
lowerCAmelCase__ : Optional[Any] = dropout
lowerCAmelCase__ : Optional[int] = attention_dropout
lowerCAmelCase__ : Optional[int] = activation_dropout
lowerCAmelCase__ : Optional[Any] = encoder_layerdrop
lowerCAmelCase__ : Dict = decoder_layerdrop
lowerCAmelCase__ : Tuple = activation_function
lowerCAmelCase__ : List[Any] = init_std
lowerCAmelCase__ : Union[str, Any] = use_cache
# Autoformer
lowerCAmelCase__ : int = label_length
lowerCAmelCase__ : Optional[int] = moving_average
lowerCAmelCase__ : List[Any] = autocorrelation_factor
super().__init__(is_encoder_decoder=UpperCamelCase , **UpperCamelCase )
@property
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 212
|
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
_A = logging.getLogger(__name__)
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Union[str, Any] = "token-classification"
def __init__( self : Dict , UpperCamelCase : Any ) -> Optional[int]:
"""simple docstring"""
if type(UpperCamelCase ) == dict:
lowerCAmelCase__ : Optional[int] = Namespace(**UpperCamelCase )
lowerCAmelCase__ : Tuple = import_module("""tasks""" )
try:
lowerCAmelCase__ : Union[str, Any] = getattr(UpperCamelCase , hparams.task_type )
lowerCAmelCase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
lowerCAmelCase__ : Optional[Any] = self.token_classification_task.get_labels(hparams.labels )
lowerCAmelCase__ : Dict = CrossEntropyLoss().ignore_index
super().__init__(UpperCamelCase , len(self.labels ) , self.mode )
def _lowerCAmelCase ( self : int , **UpperCamelCase : List[Any] ) -> str:
"""simple docstring"""
return self.model(**UpperCamelCase )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Any ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Tuple = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
lowerCAmelCase__ : List[str] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCAmelCase__ : Tuple = self(**UpperCamelCase )
lowerCAmelCase__ : List[Any] = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.hparams
for mode in ["train", "dev", "test"]:
lowerCAmelCase__ : Union[str, Any] = self._feature_file(UpperCamelCase )
if os.path.exists(UpperCamelCase ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , UpperCamelCase )
lowerCAmelCase__ : Tuple = torch.load(UpperCamelCase )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
lowerCAmelCase__ : Union[str, Any] = self.token_classification_task.read_examples_from_file(args.data_dir , UpperCamelCase )
lowerCAmelCase__ : Tuple = self.token_classification_task.convert_examples_to_features(
UpperCamelCase , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=UpperCamelCase , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("""Saving features into cached file %s""" , UpperCamelCase )
torch.save(UpperCamelCase , UpperCamelCase )
def _lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : bool = False ) -> DataLoader:
"""simple docstring"""
lowerCAmelCase__ : int = self._feature_file(UpperCamelCase )
logger.info("""Loading features from cached file %s""" , UpperCamelCase )
lowerCAmelCase__ : int = torch.load(UpperCamelCase )
lowerCAmelCase__ : str = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
lowerCAmelCase__ : Any = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
lowerCAmelCase__ : Optional[int] = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
lowerCAmelCase__ : Union[str, Any] = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
lowerCAmelCase__ : Union[str, Any] = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) , batch_size=UpperCamelCase )
def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] ) -> List[str]:
"""simple docstring"""
"""Compute validation""" ""
lowerCAmelCase__ : str = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
lowerCAmelCase__ : List[Any] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCAmelCase__ : Union[str, Any] = self(**UpperCamelCase )
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = outputs[:2]
lowerCAmelCase__ : Optional[Any] = logits.detach().cpu().numpy()
lowerCAmelCase__ : Optional[Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _lowerCAmelCase ( self : Tuple , UpperCamelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : str = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
lowerCAmelCase__ : Any = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
lowerCAmelCase__ : List[str] = np.argmax(UpperCamelCase , axis=2 )
lowerCAmelCase__ : str = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
lowerCAmelCase__ : Any = dict(enumerate(self.labels ) )
lowerCAmelCase__ : str = [[] for _ in range(out_label_ids.shape[0] )]
lowerCAmelCase__ : Optional[Any] = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
lowerCAmelCase__ : Optional[int] = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(UpperCamelCase , UpperCamelCase ),
"""precision""": precision_score(UpperCamelCase , UpperCamelCase ),
"""recall""": recall_score(UpperCamelCase , UpperCamelCase ),
"""f1""": fa_score(UpperCamelCase , UpperCamelCase ),
}
lowerCAmelCase__ : Dict = dict(results.items() )
lowerCAmelCase__ : List[Any] = results
return ret, preds_list, out_label_list
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : List[Any] ) -> Any:
"""simple docstring"""
# when stable
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Any = self._eval_end(UpperCamelCase )
lowerCAmelCase__ : Optional[int] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _lowerCAmelCase ( self : Dict , UpperCamelCase : int ) -> Optional[Any]:
"""simple docstring"""
# updating to test_epoch_end instead of deprecated test_end
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self._eval_end(UpperCamelCase )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
lowerCAmelCase__ : int = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _lowerCAmelCase ( UpperCamelCase : List[str] , UpperCamelCase : Union[str, Any] ) -> List[str]:
"""simple docstring"""
# Add NER specific options
BaseTransformer.add_model_specific_args(UpperCamelCase , UpperCamelCase )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=UpperCamelCase , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=1_28 , type=UpperCamelCase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--labels""" , default="""""" , type=UpperCamelCase , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=UpperCamelCase , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
_A = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
_A = NERTransformer.add_model_specific_args(parser, os.getcwd())
_A = parser.parse_args()
_A = NERTransformer(args)
_A = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
_A = sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
_A = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 212
| 1
|
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_lowercase = 3
def _snake_case ( snake_case__ : int ):
print('Generating primitive root of p' )
while True:
A = random.randrange(3 , snake_case__ )
if pow(snake_case__ , 2 , snake_case__ ) == 1:
continue
if pow(snake_case__ , snake_case__ , snake_case__ ) == 1:
continue
return g
def _snake_case ( snake_case__ : int ):
print('Generating prime p...' )
A = rabin_miller.generate_large_prime(snake_case__ ) # select large prime number.
A = primitive_root(snake_case__ ) # one primitive root on modulo p.
A = random.randrange(3 , snake_case__ ) # private_key -> have to be greater than 2 for safety.
A = cryptomath.find_mod_inverse(pow(snake_case__ , snake_case__ , snake_case__ ) , snake_case__ )
A = (key_size, e_a, e_a, p)
A = (key_size, d)
return public_key, private_key
def _snake_case ( snake_case__ : str , snake_case__ : int ):
if os.path.exists(F'{name}_pubkey.txt' ) or os.path.exists(F'{name}_privkey.txt' ):
print('\nWARNING:' )
print(
F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
'Use a different name or delete these files and re-run this program.' )
sys.exit()
A , A = generate_key(snake_case__ )
print(F'\nWriting public key to file {name}_pubkey.txt...' )
with open(F'{name}_pubkey.txt' , 'w' ) as fo:
fo.write(F'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}' )
print(F'Writing private key to file {name}_privkey.txt...' )
with open(F'{name}_privkey.txt' , 'w' ) as fo:
fo.write(F'{private_key[0]},{private_key[1]}' )
def _snake_case ( ):
print('Making key files...' )
make_key_files('elgamal' , 2048 )
print('Key files generation successful' )
if __name__ == "__main__":
main()
| 74
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(snake_case_ , snake_case_ )
def UpperCamelCase_ ( snake_case_ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = emb.weight.shape
__lowerCAmelCase = nn.Linear(snake_case_ , snake_case_ , bias=snake_case_ )
__lowerCAmelCase = emb.weight.data
return lin_layer
def UpperCamelCase_ ( snake_case_ : Any ) -> Any:
'''simple docstring'''
__lowerCAmelCase = torch.load(snake_case_ , map_location="""cpu""" )
__lowerCAmelCase = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
__lowerCAmelCase = mam_aaa["""model"""]
remove_ignore_keys_(snake_case_ )
__lowerCAmelCase = state_dict["""encoder.embed_tokens.weight"""].shape[0]
__lowerCAmelCase = MaMaaaConfig(
vocab_size=snake_case_ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
__lowerCAmelCase = state_dict["""decoder.embed_tokens.weight"""]
__lowerCAmelCase = MaMaaaForConditionalGeneration(snake_case_ )
model.model.load_state_dict(snake_case_ , strict=snake_case_ )
__lowerCAmelCase = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
_A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
_A : str = parser.parse_args()
_A : Optional[int] = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 229
| 0
|
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _A :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=32 , __UpperCAmelCase=2 , __UpperCAmelCase=3 , __UpperCAmelCase=16 , __UpperCAmelCase=[1, 2, 1] , __UpperCAmelCase=[2, 2, 4] , __UpperCAmelCase=2 , __UpperCAmelCase=2.0 , __UpperCAmelCase=True , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase="gelu" , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-5 , __UpperCAmelCase=True , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=10 , __UpperCAmelCase=8 , ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[str] = parent
__UpperCAmelCase : Union[str, Any] = batch_size
__UpperCAmelCase : Any = image_size
__UpperCAmelCase : Dict = patch_size
__UpperCAmelCase : Dict = num_channels
__UpperCAmelCase : List[Any] = embed_dim
__UpperCAmelCase : str = depths
__UpperCAmelCase : Dict = num_heads
__UpperCAmelCase : str = window_size
__UpperCAmelCase : int = mlp_ratio
__UpperCAmelCase : Union[str, Any] = qkv_bias
__UpperCAmelCase : Dict = hidden_dropout_prob
__UpperCAmelCase : str = attention_probs_dropout_prob
__UpperCAmelCase : Optional[int] = drop_path_rate
__UpperCAmelCase : List[str] = hidden_act
__UpperCAmelCase : Optional[int] = use_absolute_embeddings
__UpperCAmelCase : Any = patch_norm
__UpperCAmelCase : Union[str, Any] = layer_norm_eps
__UpperCAmelCase : Optional[int] = initializer_range
__UpperCAmelCase : Tuple = is_training
__UpperCAmelCase : Any = scope
__UpperCAmelCase : Optional[Any] = use_labels
__UpperCAmelCase : Optional[int] = type_sequence_label_size
__UpperCAmelCase : int = encoder_stride
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase : Tuple = None
if self.use_labels:
__UpperCAmelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __A ( self ) -> Dict:
'''simple docstring'''
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Tuple = SwinvaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : Union[str, Any] = model(__UpperCAmelCase )
__UpperCAmelCase : Tuple = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__UpperCAmelCase : List[Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Any = SwinvaForMaskedImageModeling(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : List[Any] = model(__UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__UpperCAmelCase : Optional[Any] = 1
__UpperCAmelCase : Dict = SwinvaForMaskedImageModeling(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__UpperCAmelCase : str = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : str = self.type_sequence_label_size
__UpperCAmelCase : str = SwinvaForImageClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : Any = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
__UpperCAmelCase : List[Any] = config_and_inputs
__UpperCAmelCase : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _A ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[str] = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE : List[str] = (
{"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : Dict = False
_SCREAMING_SNAKE_CASE : Optional[Any] = False
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
_SCREAMING_SNAKE_CASE : Optional[Any] = False
def __A ( self ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : List[str] = SwinvaModelTester(self )
__UpperCAmelCase : Any = ConfigTester(self , config_class=__UpperCAmelCase , embed_dim=37 )
def __A ( self ) -> Any:
'''simple docstring'''
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
def __A ( self ) -> Dict:
'''simple docstring'''
pass
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : Union[str, Any] = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__UpperCAmelCase : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : Tuple = model_class(__UpperCAmelCase )
__UpperCAmelCase : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : str = [*signature.parameters.keys()]
__UpperCAmelCase : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def __A ( self ) -> int:
'''simple docstring'''
__UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Optional[Any] = True
for model_class in self.all_model_classes:
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Optional[Any] = False
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : int = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : List[Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCAmelCase : str = outputs.attentions
__UpperCAmelCase : Any = len(self.model_tester.depths )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__UpperCAmelCase : Dict = True
__UpperCAmelCase : int = config.window_size**2
__UpperCAmelCase : Any = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : int = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCAmelCase : Dict = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
__UpperCAmelCase : Dict = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__UpperCAmelCase : Any = True
__UpperCAmelCase : Any = True
__UpperCAmelCase : Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : List[str] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
if hasattr(self.model_tester , """num_hidden_states_types""" ):
__UpperCAmelCase : Any = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
__UpperCAmelCase : Optional[int] = 2
self.assertEqual(out_len + added_hidden_states , len(__UpperCAmelCase ) )
__UpperCAmelCase : Tuple = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCAmelCase : List[Any] = outputs.hidden_states
__UpperCAmelCase : List[Any] = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# Swinv2 has a different seq_length
__UpperCAmelCase : List[str] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase : Union[str, Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
__UpperCAmelCase : int = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__UpperCAmelCase : str = reshaped_hidden_states[0].shape
__UpperCAmelCase : Any = (
reshaped_hidden_states[0].view(__UpperCAmelCase , __UpperCAmelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __A ( self ) -> str:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Tuple = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__UpperCAmelCase : Union[str, Any] = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Union[str, Any] = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Tuple = 3
__UpperCAmelCase : str = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__UpperCAmelCase : List[str] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase : str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__UpperCAmelCase : Union[str, Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__UpperCAmelCase : int = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Tuple = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , (padded_height, padded_width) )
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase )
def __A ( self ) -> str:
'''simple docstring'''
__UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
@slow
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : Dict = SwinvaModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Tuple = _config_zero_init(__UpperCAmelCase )
for model_class in self.all_model_classes:
__UpperCAmelCase : List[Any] = model_class(config=__UpperCAmelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@require_vision
@require_torch
class _A ( unittest.TestCase ):
@cached_property
def __A ( self ) -> int:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
if is_vision_available()
else None
)
@slow
def __A ( self ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Tuple = SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
__UpperCAmelCase )
__UpperCAmelCase : Tuple = self.default_image_processor
__UpperCAmelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__UpperCAmelCase : Any = image_processor(images=__UpperCAmelCase , return_tensors="""pt""" ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__UpperCAmelCase : Optional[int] = model(**__UpperCAmelCase )
# verify the logits
__UpperCAmelCase : int = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 350
|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_UpperCamelCase = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class _A ( unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_SCREAMING_SNAKE_CASE : int = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
_SCREAMING_SNAKE_CASE : int = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def __A ( self ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : int = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" )
__UpperCAmelCase : List[Any] = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
__UpperCAmelCase : int = text_classifier("""This is great !""" , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}] )
__UpperCAmelCase : Optional[int] = text_classifier(["""This is great !""", """This is bad"""] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
] , )
__UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" , top_k=1 )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
# Legacy behavior
__UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" , return_all_scores=__UpperCAmelCase )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
__UpperCAmelCase : Dict = text_classifier("""This is great !""" , return_all_scores=__UpperCAmelCase )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]] )
__UpperCAmelCase : str = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=__UpperCAmelCase )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
] , )
__UpperCAmelCase : Any = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=__UpperCAmelCase )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
{"""label""": """LABEL_0""", """score""": 0.504},
{"""label""": """LABEL_0""", """score""": 0.504},
] , )
@require_torch
def __A ( self ) -> Dict:
'''simple docstring'''
import torch
__UpperCAmelCase : Any = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" , device=torch.device("""cpu""" ) , )
__UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
@require_tf
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Any = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""tf""" )
__UpperCAmelCase : int = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
@slow
@require_torch
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : int = pipeline("""text-classification""" )
__UpperCAmelCase : int = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 1.0}] )
__UpperCAmelCase : Union[str, Any] = text_classifier("""This is bad !""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
__UpperCAmelCase : Any = text_classifier("""Birds are a type of animal""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 0.988}] )
@slow
@require_tf
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : str = pipeline("""text-classification""" , framework="""tf""" )
__UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 1.0}] )
__UpperCAmelCase : int = text_classifier("""This is bad !""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
__UpperCAmelCase : str = text_classifier("""Birds are a type of animal""" )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 0.988}] )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Any = TextClassificationPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
return text_classifier, ["HuggingFace is in", "This is another test"]
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : int = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
__UpperCAmelCase : Union[str, Any] = """HuggingFace is in"""
__UpperCAmelCase : Any = text_classifier(__UpperCAmelCase )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
__UpperCAmelCase : Optional[int] = ["""HuggingFace is in """, """Paris is in France"""]
__UpperCAmelCase : Any = text_classifier(__UpperCAmelCase )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}, {"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] , )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
__UpperCAmelCase : Any = text_classifier(__UpperCAmelCase , top_k=__UpperCAmelCase )
__UpperCAmelCase : Any = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [[{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] * N, [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] * N] , )
__UpperCAmelCase : str = {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
__UpperCAmelCase : Optional[int] = text_classifier(__UpperCAmelCase )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , {"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )} , )
self.assertTrue(outputs["""label"""] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
__UpperCAmelCase : Union[str, Any] = [["""HuggingFace is in """, """Paris is in France"""]]
with self.assertRaises(__UpperCAmelCase ):
text_classifier(__UpperCAmelCase )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
__UpperCAmelCase : Tuple = text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]] )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] , )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
| 16
| 0
|
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCAmelCase__ :
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Any:
__lowerCamelCase = data
__lowerCamelCase = [0X67_45_23_01, 0XEF_CD_AB_89, 0X98_BA_DC_FE, 0X10_32_54_76, 0XC3_D2_E1_F0]
@staticmethod
def __A ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> str:
return ((n << b) | (n >> (32 - b))) & 0XFF_FF_FF_FF
def __A ( self : Optional[Any] ) -> Any:
__lowerCamelCase = b'''\x80''' + b'''\x00''' * (63 - (len(self.data ) + 8) % 64)
__lowerCamelCase = self.data + padding + struct.pack('''>Q''' , 8 * len(self.data ) )
return padded_data
def __A ( self : Optional[int] ) -> List[str]:
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] ) -> int:
__lowerCamelCase = list(struct.unpack('''>16L''' , SCREAMING_SNAKE_CASE__ ) ) + [0] * 64
for i in range(16 , 80 ):
__lowerCamelCase = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def __A ( self : Union[str, Any] ) -> List[str]:
__lowerCamelCase = self.padding()
__lowerCamelCase = self.split_blocks()
for block in self.blocks:
__lowerCamelCase = self.expand_block(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
__lowerCamelCase = (b & c) | ((~b) & d)
__lowerCamelCase = 0X5A_82_79_99
elif 20 <= i < 40:
__lowerCamelCase = b ^ c ^ d
__lowerCamelCase = 0X6E_D9_EB_A1
elif 40 <= i < 60:
__lowerCamelCase = (b & c) | (b & d) | (c & d)
__lowerCamelCase = 0X8F_1B_BC_DC
elif 60 <= i < 80:
__lowerCamelCase = b ^ c ^ d
__lowerCamelCase = 0XCA_62_C1_D6
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = (
self.rotate(SCREAMING_SNAKE_CASE__ , 5 ) + f + e + k + expanded_block[i] & 0XFF_FF_FF_FF,
a,
self.rotate(SCREAMING_SNAKE_CASE__ , 30 ),
c,
d,
)
__lowerCamelCase = (
self.h[0] + a & 0XFF_FF_FF_FF,
self.h[1] + b & 0XFF_FF_FF_FF,
self.h[2] + c & 0XFF_FF_FF_FF,
self.h[3] + d & 0XFF_FF_FF_FF,
self.h[4] + e & 0XFF_FF_FF_FF,
)
return ("{:08x}" * 5).format(*self.h )
def __magic_name__ ( ) -> Union[str, Any]:
__lowerCamelCase = B'''Test String'''
assert SHAaHash(__lowerCAmelCase ).final_hash() == hashlib.shaa(__lowerCAmelCase ).hexdigest() # noqa: S324
def __magic_name__ ( ) -> List[Any]:
__lowerCamelCase = argparse.ArgumentParser(description='''Process some strings or files''' )
parser.add_argument(
'''--string''' , dest='''input_string''' , default='''Hello World!! Welcome to Cryptography''' , help='''Hash the string''' , )
parser.add_argument('''--file''' , dest='''input_file''' , help='''Hash contents of a file''' )
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , '''rb''' ) as f:
__lowerCamelCase = f.read()
else:
__lowerCamelCase = bytes(__lowerCAmelCase , '''utf-8''' )
print(SHAaHash(__lowerCAmelCase ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 270
|
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Dict = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class lowerCAmelCase__ ( __lowercase ):
a__ : Any = """mask2former"""
a__ : Dict = ["""swin"""]
a__ : Any = {"""hidden_size""": """hidden_dim"""}
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : Optional[Dict] = None , SCREAMING_SNAKE_CASE__ : int = 2_56 , SCREAMING_SNAKE_CASE__ : int = 2_56 , SCREAMING_SNAKE_CASE__ : int = 2_56 , SCREAMING_SNAKE_CASE__ : int = 10_24 , SCREAMING_SNAKE_CASE__ : str = "relu" , SCREAMING_SNAKE_CASE__ : int = 6 , SCREAMING_SNAKE_CASE__ : int = 10 , SCREAMING_SNAKE_CASE__ : int = 8 , SCREAMING_SNAKE_CASE__ : float = 0.0 , SCREAMING_SNAKE_CASE__ : int = 20_48 , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : int = 4 , SCREAMING_SNAKE_CASE__ : int = 2_55 , SCREAMING_SNAKE_CASE__ : int = 1_00 , SCREAMING_SNAKE_CASE__ : float = 0.1 , SCREAMING_SNAKE_CASE__ : float = 2.0 , SCREAMING_SNAKE_CASE__ : float = 5.0 , SCREAMING_SNAKE_CASE__ : float = 5.0 , SCREAMING_SNAKE_CASE__ : int = 1_25_44 , SCREAMING_SNAKE_CASE__ : float = 3.0 , SCREAMING_SNAKE_CASE__ : float = 0.75 , SCREAMING_SNAKE_CASE__ : float = 0.02 , SCREAMING_SNAKE_CASE__ : float = 1.0 , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : List[int] = [4, 8, 16, 32] , SCREAMING_SNAKE_CASE__ : bool = None , **SCREAMING_SNAKE_CASE__ : Tuple , ) -> str:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''' )
__lowerCamelCase = CONFIG_MAPPING['''swin'''](
image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=SCREAMING_SNAKE_CASE__ , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase = backbone_config.pop('''model_type''' )
__lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
__lowerCamelCase = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
f'''Supported model types: {','.join(self.backbones_supported )}''' )
__lowerCamelCase = backbone_config
__lowerCamelCase = feature_size
__lowerCamelCase = mask_feature_size
__lowerCamelCase = hidden_dim
__lowerCamelCase = encoder_feedforward_dim
__lowerCamelCase = activation_function
__lowerCamelCase = encoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = dim_feedforward
__lowerCamelCase = pre_norm
__lowerCamelCase = enforce_input_projection
__lowerCamelCase = common_stride
__lowerCamelCase = ignore_value
__lowerCamelCase = num_queries
__lowerCamelCase = no_object_weight
__lowerCamelCase = class_weight
__lowerCamelCase = mask_weight
__lowerCamelCase = dice_weight
__lowerCamelCase = train_num_points
__lowerCamelCase = oversample_ratio
__lowerCamelCase = importance_sample_ratio
__lowerCamelCase = init_std
__lowerCamelCase = init_xavier_std
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = feature_strides
__lowerCamelCase = output_auxiliary_logits
__lowerCamelCase = decoder_layers
super().__init__(**SCREAMING_SNAKE_CASE__ )
@classmethod
def __A ( cls : Any , SCREAMING_SNAKE_CASE__ : PretrainedConfig , **SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> List[Any]:
return cls(
backbone_config=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
def __A ( self : Any ) -> Dict[str, any]:
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.backbone_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 270
| 1
|
from abc import ABC, abstractmethod
from typing import List, Optional
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self ) -> Tuple:
"""simple docstring"""
# test for the above condition
self.test()
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = 0
UpperCAmelCase = False
while not completed:
if counter == 1:
self.reset()
UpperCAmelCase = self.advance()
if not self.does_advance(_snake_case ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.update(_snake_case )
counter += 1
if counter > 1_0000:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def snake_case_ ( self ) -> Any:
"""simple docstring"""
raise NotImplementedError(
f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def snake_case_ ( self , _snake_case ) -> Dict:
"""simple docstring"""
raise NotImplementedError(
f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def snake_case_ ( self , _snake_case ) -> str:
"""simple docstring"""
raise NotImplementedError(
f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
raise NotImplementedError(
f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
raise NotImplementedError(
f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def snake_case_ ( self , _snake_case=False ) -> int:
"""simple docstring"""
raise NotImplementedError(
f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , _snake_case ) -> Any:
"""simple docstring"""
super(_snake_case , self ).__init__()
if not isinstance(_snake_case , _snake_case ) or len(_snake_case ) == 0:
raise ValueError(f"""`token_ids` has to be a non-empty list, but is {token_ids}.""" )
if any((not isinstance(_snake_case , _snake_case ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.""" )
UpperCAmelCase = token_ids
UpperCAmelCase = len(self.token_ids )
UpperCAmelCase = -1 # the index of the currently fulfilled step
UpperCAmelCase = False
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def snake_case_ ( self , _snake_case ) -> List[Any]:
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ):
raise ValueError(f"""`token_id` has to be an `int`, but is {token_id} of type {type(_snake_case )}""" )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def snake_case_ ( self , _snake_case ) -> int:
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ):
raise ValueError(f"""`token_id` has to be an `int`, but is {token_id} of type {type(_snake_case )}""" )
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
if self.does_advance(_snake_case ):
self.fulfilled_idx += 1
UpperCAmelCase = True
if self.fulfilled_idx == (self.seqlen - 1):
UpperCAmelCase = True
UpperCAmelCase = completed
else:
# failed to make progress.
UpperCAmelCase = True
self.reset()
return stepped, completed, reset
def snake_case_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase = False
UpperCAmelCase = 0
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def snake_case_ ( self , _snake_case=False ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = PhrasalConstraint(self.token_ids )
if stateful:
UpperCAmelCase = self.seqlen
UpperCAmelCase = self.fulfilled_idx
UpperCAmelCase = self.completed
return new_constraint
class lowercase :
'''simple docstring'''
def __init__( self , _snake_case , _snake_case=True ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = max([len(_snake_case ) for one in nested_token_ids] )
UpperCAmelCase = {}
for token_ids in nested_token_ids:
UpperCAmelCase = root
for tidx, token_id in enumerate(_snake_case ):
if token_id not in level:
UpperCAmelCase = {}
UpperCAmelCase = level[token_id]
if no_subsets and self.has_subsets(_snake_case , _snake_case ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
f""" {nested_token_ids}.""" )
UpperCAmelCase = root
def snake_case_ ( self , _snake_case ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.trie
for current_token in current_seq:
UpperCAmelCase = start[current_token]
UpperCAmelCase = list(start.keys() )
return next_tokens
def snake_case_ ( self , _snake_case ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = self.next_tokens(_snake_case )
return len(_snake_case ) == 0
def snake_case_ ( self , _snake_case ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = list(root.values() )
if len(_snake_case ) == 0:
return 1
else:
return sum([self.count_leaves(_snake_case ) for nn in next_nodes] )
def snake_case_ ( self , _snake_case , _snake_case ) -> Any:
"""simple docstring"""
UpperCAmelCase = self.count_leaves(_snake_case )
return len(_snake_case ) != leaf_count
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , _snake_case ) -> Any:
"""simple docstring"""
super(_snake_case , self ).__init__()
if not isinstance(_snake_case , _snake_case ) or len(_snake_case ) == 0:
raise ValueError(f"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.""" )
if any(not isinstance(_snake_case , _snake_case ) for token_ids in nested_token_ids ):
raise ValueError(f"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.""" )
if any(
any((not isinstance(_snake_case , _snake_case ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.""" )
UpperCAmelCase = DisjunctiveTrie(_snake_case )
UpperCAmelCase = nested_token_ids
UpperCAmelCase = self.trie.max_height
UpperCAmelCase = []
UpperCAmelCase = False
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = self.trie.next_tokens(self.current_seq )
if len(_snake_case ) == 0:
return None
else:
return token_list
def snake_case_ ( self , _snake_case ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ):
raise ValueError(f"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(_snake_case )}""" )
UpperCAmelCase = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def snake_case_ ( self , _snake_case ) -> str:
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ):
raise ValueError(f"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(_snake_case )}""" )
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
if self.does_advance(_snake_case ):
self.current_seq.append(_snake_case )
UpperCAmelCase = True
else:
UpperCAmelCase = True
self.reset()
UpperCAmelCase = self.trie.reached_leaf(self.current_seq )
UpperCAmelCase = completed
return stepped, completed, reset
def snake_case_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase = False
UpperCAmelCase = []
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def snake_case_ ( self , _snake_case=False ) -> int:
"""simple docstring"""
UpperCAmelCase = DisjunctiveConstraint(self.token_ids )
if stateful:
UpperCAmelCase = self.seqlen
UpperCAmelCase = self.current_seq
UpperCAmelCase = self.completed
return new_constraint
class lowercase :
'''simple docstring'''
def __init__( self , _snake_case ) -> Dict:
"""simple docstring"""
UpperCAmelCase = constraints
# max # of steps required to fulfill a given constraint
UpperCAmelCase = max([c.seqlen for c in constraints] )
UpperCAmelCase = len(_snake_case )
UpperCAmelCase = False
self.init_state()
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = None
UpperCAmelCase = [constraint.copy(stateful=_snake_case ) for constraint in self.constraints]
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def snake_case_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
UpperCAmelCase = constraint.advance()
if isinstance(_snake_case , _snake_case ):
token_list.append(_snake_case )
elif isinstance(_snake_case , _snake_case ):
token_list.extend(_snake_case )
else:
UpperCAmelCase = self.inprogress_constraint.advance()
if isinstance(_snake_case , _snake_case ):
token_list.append(_snake_case )
elif isinstance(_snake_case , _snake_case ):
token_list.extend(_snake_case )
if len(_snake_case ) == 0:
return None
else:
return token_list
def snake_case_ ( self , _snake_case ) -> int:
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
UpperCAmelCase , UpperCAmelCase = self.add(_snake_case )
# the entire list of constraints are fulfilled
if self.completed:
break
def snake_case_ ( self , _snake_case ) -> Union[str, Any]:
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ):
raise ValueError(f"""`token_id` should be an `int`, but is `{token_id}`.""" )
UpperCAmelCase , UpperCAmelCase = False, False
if self.completed:
UpperCAmelCase = True
UpperCAmelCase = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.inprogress_constraint.update(_snake_case )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_snake_case ) )
UpperCAmelCase = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
UpperCAmelCase = None
if len(self.pending_constraints ) == 0:
# we're done!
UpperCAmelCase = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_snake_case ):
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = pending_constraint.update(_snake_case )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(_snake_case )
UpperCAmelCase = None
if not complete and stepped:
UpperCAmelCase = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
UpperCAmelCase = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
UpperCAmelCase = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def snake_case_ ( self , _snake_case=True ) -> Dict:
"""simple docstring"""
UpperCAmelCase = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
UpperCAmelCase = [
constraint.copy(stateful=_snake_case ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
UpperCAmelCase = self.inprogress_constraint.copy(stateful=_snake_case )
UpperCAmelCase = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 152
|
import string
def _lowerCAmelCase ( A__: str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
UpperCAmelCase = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCAmelCase = string.ascii_uppercase.find(A__ )
UpperCAmelCase = num - key
if num < 0:
UpperCAmelCase = num + len(string.ascii_uppercase )
UpperCAmelCase = translated + string.ascii_uppercase[num]
else:
UpperCAmelCase = translated + symbol
print(F"""Decryption using Key #{key}: {translated}""" )
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = input('''Encrypted message: ''' )
UpperCAmelCase = message.upper()
decrypt(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 152
| 1
|
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _a ( a :Dict , a :Union[str, Any] , a :Dict , a :str , a :str ) -> List[Any]:
# load base model
a = StableDiffusionPipeline.from_pretrained(a , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
a = load_file(a )
a = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
a = key.split('''.''' )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_''' )[-1].split('''_''' )
a = pipeline.text_encoder
else:
a = key.split('''.''' )[0].split(LORA_PREFIX_UNET + '''_''' )[-1].split('''_''' )
a = pipeline.unet
# find the target layer
a = layer_infos.pop(0 )
while len(a ) > -1:
try:
a = curr_layer.__getattr__(a )
if len(a ) > 0:
a = layer_infos.pop(0 )
elif len(a ) == 0:
break
except Exception:
if len(a ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
a = layer_infos.pop(0 )
a = []
if "lora_down" in key:
pair_keys.append(key.replace('''lora_down''' , '''lora_up''' ) )
pair_keys.append(a )
else:
pair_keys.append(a )
pair_keys.append(key.replace('''lora_up''' , '''lora_down''' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
a = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
a = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(a , a ).unsqueeze(2 ).unsqueeze(3 )
else:
a = state_dict[pair_keys[0]].to(torch.floataa )
a = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(a , a )
# update visited list
for item in pair_keys:
visited.append(a )
return pipeline
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
)
parser.add_argument(
"--lora_prefix_text_encoder",
default="lora_te",
type=str,
help="The prefix of text encoder weight in safetensors",
)
parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
parser.add_argument(
"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
)
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = args.base_model_path
UpperCAmelCase__ = args.checkpoint_path
UpperCAmelCase__ = args.dump_path
UpperCAmelCase__ = args.lora_prefix_unet
UpperCAmelCase__ = args.lora_prefix_text_encoder
UpperCAmelCase__ = args.alpha
UpperCAmelCase__ = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
UpperCAmelCase__ = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 0
|
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __a :
def __init__( self : Union[str, Any] , __magic_name__ : Dict=2 , __magic_name__ : Dict=3 , __magic_name__ : Any=64 , __magic_name__ : List[Any]=None ) -> int:
"""simple docstring"""
UpperCAmelCase_ : Dict = np.random.default_rng(__magic_name__ )
UpperCAmelCase_ : Dict = length
UpperCAmelCase_ : Tuple = rng.normal(size=(length,) ).astype(np.floataa )
UpperCAmelCase_ : str = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : int ) -> Union[str, Any]:
"""simple docstring"""
return self.length
def __getitem__( self : List[Any] , __magic_name__ : int ) -> Optional[int]:
"""simple docstring"""
return {"x": self.x[i], "y": self.y[i]}
class __a (torch.nn.Module ):
def __init__( self : Optional[int] , __magic_name__ : Union[str, Any]=0 , __magic_name__ : List[str]=0 , __magic_name__ : List[str]=False ) -> str:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ : Any = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCAmelCase_ : Any = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCAmelCase_ : Optional[int] = True
def UpperCAmelCase__ ( self : List[str] , __magic_name__ : Union[str, Any]=None ) -> Union[str, Any]:
"""simple docstring"""
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
UpperCAmelCase_ : Optional[Any] = False
return x * self.a[0] + self.b[0]
class __a (torch.nn.Module ):
def __init__( self : Any , __magic_name__ : Any=0 , __magic_name__ : List[str]=0 , __magic_name__ : Any=False ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ : Optional[int] = torch.nn.Parameter(torch.tensor(__magic_name__ ).float() )
UpperCAmelCase_ : str = torch.nn.Parameter(torch.tensor(__magic_name__ ).float() )
UpperCAmelCase_ : Tuple = True
def UpperCAmelCase__ ( self : Any , __magic_name__ : List[Any]=None ) -> Optional[int]:
"""simple docstring"""
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
UpperCAmelCase_ : Dict = False
return x * self.a + self.b
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Optional[int], SCREAMING_SNAKE_CASE__ : int = 16 ) -> List[Any]:
from datasets import load_dataset
from transformers import AutoTokenizer
UpperCAmelCase_ : Any = AutoTokenizer.from_pretrained('''bert-base-cased''' )
UpperCAmelCase_ : Optional[Any] = {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
UpperCAmelCase_ : Union[str, Any] = load_dataset('''csv''', data_files=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Union[str, Any] = datasets['''train'''].unique('''label''' )
UpperCAmelCase_ : int = {v: i for i, v in enumerate(SCREAMING_SNAKE_CASE__ )}
def tokenize_function(SCREAMING_SNAKE_CASE__ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ : Union[str, Any] = tokenizer(
examples['''sentence1'''], examples['''sentence2'''], truncation=SCREAMING_SNAKE_CASE__, max_length=SCREAMING_SNAKE_CASE__, padding='''max_length''' )
if "label" in examples:
UpperCAmelCase_ : List[str] = [label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_ : Tuple = datasets.map(
SCREAMING_SNAKE_CASE__, batched=SCREAMING_SNAKE_CASE__, remove_columns=['''sentence1''', '''sentence2''', '''label'''], )
def collate_fn(SCREAMING_SNAKE_CASE__ : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(SCREAMING_SNAKE_CASE__, padding='''max_length''', max_length=128, return_tensors='''pt''' )
return tokenizer.pad(SCREAMING_SNAKE_CASE__, padding='''longest''', return_tensors='''pt''' )
# Instantiate dataloaders.
UpperCAmelCase_ : Tuple = DataLoader(tokenized_datasets['''train'''], shuffle=SCREAMING_SNAKE_CASE__, collate_fn=SCREAMING_SNAKE_CASE__, batch_size=2 )
UpperCAmelCase_ : Optional[int] = DataLoader(tokenized_datasets['''validation'''], shuffle=SCREAMING_SNAKE_CASE__, collate_fn=SCREAMING_SNAKE_CASE__, batch_size=1 )
return train_dataloader, eval_dataloader
| 125
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Tuple = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenization_rag""": ["""RagTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = [
"""RagModel""",
"""RagPreTrainedModel""",
"""RagSequenceForGeneration""",
"""RagTokenForGeneration""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = [
"""TFRagModel""",
"""TFRagPreTrainedModel""",
"""TFRagSequenceForGeneration""",
"""TFRagTokenForGeneration""",
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
UpperCAmelCase_ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 363
|
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 318
| 0
|
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 0 ) -> int:
lowerCAmelCase__ : Any = right or len(SCREAMING_SNAKE_CASE_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 212
|
import os
from distutils.util import strtobool
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
for e in env_keys:
lowerCAmelCase__ : Union[str, Any] = int(os.environ.get(SCREAMING_SNAKE_CASE_ , -1 ) )
if val >= 0:
return val
return default
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> List[str]:
lowerCAmelCase__ : Optional[int] = os.environ.get(SCREAMING_SNAKE_CASE_ , str(SCREAMING_SNAKE_CASE_ ) )
return strtobool(SCREAMING_SNAKE_CASE_ ) == 1 # As its name indicates `strtobool` actually returns an int...
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="no" ) -> List[str]:
lowerCAmelCase__ : Optional[int] = os.environ.get(SCREAMING_SNAKE_CASE_ , str(SCREAMING_SNAKE_CASE_ ) )
return value
| 212
| 1
|
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def snake_case_ ( __SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
lowercase_ : Dict = 384
if "tiny" in model_name:
lowercase_ : Tuple = [3, 3, 9, 3]
lowercase_ : int = [96, 192, 384, 768]
if "small" in model_name:
lowercase_ : List[Any] = [3, 3, 27, 3]
lowercase_ : List[Any] = [96, 192, 384, 768]
if "base" in model_name:
lowercase_ : Optional[int] = [3, 3, 27, 3]
lowercase_ : Optional[Any] = [128, 256, 512, 1024]
lowercase_ : List[Any] = 512
if "large" in model_name:
lowercase_ : Union[str, Any] = [3, 3, 27, 3]
lowercase_ : Union[str, Any] = [192, 384, 768, 1536]
lowercase_ : Optional[int] = 768
if "xlarge" in model_name:
lowercase_ : str = [3, 3, 27, 3]
lowercase_ : Union[str, Any] = [256, 512, 1024, 2048]
lowercase_ : Union[str, Any] = 1024
# set label information
lowercase_ : Tuple = 150
lowercase_ : Tuple = '''huggingface/label-files'''
lowercase_ : List[str] = '''ade20k-id2label.json'''
lowercase_ : Tuple = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
lowercase_ : Tuple = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase_ : List[str] = {v: k for k, v in idalabel.items()}
lowercase_ : Any = ConvNextConfig(
depths=__SCREAMING_SNAKE_CASE , hidden_sizes=__SCREAMING_SNAKE_CASE , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
lowercase_ : Tuple = UperNetConfig(
backbone_config=__SCREAMING_SNAKE_CASE , auxiliary_in_channels=__SCREAMING_SNAKE_CASE , num_labels=__SCREAMING_SNAKE_CASE , idalabel=__SCREAMING_SNAKE_CASE , labelaid=__SCREAMING_SNAKE_CASE , )
return config
def snake_case_ ( __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
lowercase_ : Optional[int] = []
# fmt: off
# stem
rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''') )
rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''') )
rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''') )
rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'''backbone.stages.{i}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''') )
if i > 0:
rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''') )
rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''') )
rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''') )
rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''') )
rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
] )
# fmt: on
return rename_keys
def snake_case_ ( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Tuple = dct.pop(__SCREAMING_SNAKE_CASE )
lowercase_ : List[Any] = val
def snake_case_ ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
lowercase_ : Tuple = {
'''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
'''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
'''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
'''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
'''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
}
lowercase_ : Any = model_name_to_url[model_name]
lowercase_ : Union[str, Any] = torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''state_dict''']
lowercase_ : Union[str, Any] = get_upernet_config(__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = UperNetForSemanticSegmentation(__SCREAMING_SNAKE_CASE )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
lowercase_ : Union[str, Any] = state_dict.pop(__SCREAMING_SNAKE_CASE )
if "bn" in key:
lowercase_ : List[str] = key.replace('''bn''' , '''batch_norm''' )
lowercase_ : List[Any] = val
# rename keys
lowercase_ : int = create_rename_keys(__SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
model.load_state_dict(__SCREAMING_SNAKE_CASE )
# verify on image
lowercase_ : str = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
lowercase_ : Optional[Any] = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw ).convert('''RGB''' )
lowercase_ : Union[str, Any] = SegformerImageProcessor()
lowercase_ : Any = processor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
with torch.no_grad():
lowercase_ : Tuple = model(__SCREAMING_SNAKE_CASE )
if model_name == "upernet-convnext-tiny":
lowercase_ : Any = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] )
elif model_name == "upernet-convnext-small":
lowercase_ : Union[str, Any] = torch.tensor(
[[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] )
elif model_name == "upernet-convnext-base":
lowercase_ : Any = torch.tensor(
[[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] )
elif model_name == "upernet-convnext-large":
lowercase_ : Optional[Any] = torch.tensor(
[[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] )
elif model_name == "upernet-convnext-xlarge":
lowercase_ : Optional[Any] = torch.tensor(
[[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] )
print('''Logits:''' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if push_to_hub:
print(F'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(F'''openmmlab/{model_name}''' )
processor.push_to_hub(F'''openmmlab/{model_name}''' )
if __name__ == "__main__":
_lowercase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-convnext-tiny",
type=str,
choices=[f"""upernet-convnext-{size}""" for size in ["tiny", "small", "base", "large", "xlarge"]],
help="Name of the ConvNext UperNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_lowercase : Optional[int] = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 264
|
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
lowercase_ : Optional[int] = checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = flatten_dict(__SCREAMING_SNAKE_CASE )
return flax_params
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
lowercase_ : int = {}
lowercase_ : Any = {
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowercase_ : Tuple = {
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowercase_ : Tuple = '''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowercase_ : Optional[Any] = new_key.replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowercase_ : Optional[Any] = new_key.replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowercase_ : List[Any] = re.sub(R'''layers_(\d+)''' , R'''layer.\1''' , __SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowercase_ : str = re.sub(R'''layers_(\d+)''' , R'''layer.\1''' , __SCREAMING_SNAKE_CASE )
lowercase_ : Dict = flax_dict[key]
lowercase_ : Any = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowercase_ : str = torch.from_numpy(converted_dict[key].T )
else:
lowercase_ : str = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : List[Any]=False ):
"""simple docstring"""
lowercase_ : List[str] = get_flax_param(__SCREAMING_SNAKE_CASE )
if not use_large:
lowercase_ : List[str] = PixaStructVisionConfig()
lowercase_ : Optional[Any] = PixaStructTextConfig()
else:
lowercase_ : Optional[int] = PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowercase_ : Dict = PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowercase_ : str = PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__SCREAMING_SNAKE_CASE )
lowercase_ : List[str] = PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE )
lowercase_ : int = rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE )
model.load_state_dict(__SCREAMING_SNAKE_CASE )
lowercase_ : str = AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowercase_ : List[Any] = PixaStructImageProcessor()
lowercase_ : int = PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE )
if use_large:
lowercase_ : Tuple = 4096
lowercase_ : Optional[int] = True
# mkdir if needed
os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
print('''Model saved in {}'''.format(__SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
_lowercase : str = argparse.ArgumentParser()
parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--use_large", action="store_true", help="Use large model.")
parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
_lowercase : Tuple = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 264
| 1
|
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ) -> List[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase =StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
__UpperCamelCase =sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
sd_pipe.set_scheduler('sample_euler' )
__UpperCamelCase ='''A painting of a squirrel eating a burger'''
__UpperCamelCase =torch.manual_seed(0 )
__UpperCamelCase =sd_pipe([prompt] , generator=_snake_case , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
__UpperCamelCase =output.images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase =np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> str:
__UpperCamelCase =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
__UpperCamelCase =sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
sd_pipe.set_scheduler('sample_euler' )
__UpperCamelCase ='''A painting of a squirrel eating a burger'''
__UpperCamelCase =torch.manual_seed(0 )
__UpperCamelCase =sd_pipe([prompt] , generator=_snake_case , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
__UpperCamelCase =output.images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase =np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def _a ( self ) -> int:
__UpperCamelCase =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
__UpperCamelCase =sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
sd_pipe.set_scheduler('sample_dpmpp_2m' )
__UpperCamelCase ='''A painting of a squirrel eating a burger'''
__UpperCamelCase =torch.manual_seed(0 )
__UpperCamelCase =sd_pipe(
[prompt] , generator=_snake_case , guidance_scale=7.5 , num_inference_steps=15 , output_type='np' , use_karras_sigmas=_snake_case , )
__UpperCamelCase =output.images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase =np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 62
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase = 50 ) -> int:
lowercase__ : int = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 16
| 0
|
__UpperCAmelCase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__UpperCAmelCase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__UpperCAmelCase = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def __lowerCamelCase ( __magic_name__ : int , __magic_name__ : int , __magic_name__ : int ):
assert len(str(UpperCAmelCase_ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a__: Tuple =year // 100
a__: Tuple =(5 * (century % 4) + 2) % 7
a__: Optional[int] =year % 100
a__: Tuple =centurian % 12
a__: Optional[Any] =(
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a__: List[Any] =(
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a__: Union[str, Any] =(dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352
|
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase__ ( _a , _a ):
@register_to_config
def __init__( self : str , _a : int = 7_6_8 , ):
super().__init__()
a__: Optional[Any] =nn.Parameter(torch.zeros(1 , _a ) )
a__: List[str] =nn.Parameter(torch.ones(1 , _a ) )
def _lowerCamelCase ( self : Tuple , _a : Optional[Union[str, torch.device]] = None , _a : Optional[torch.dtype] = None , ):
a__: str =nn.Parameter(self.mean.to(_a ).to(_a ) )
a__: List[Any] =nn.Parameter(self.std.to(_a ).to(_a ) )
return self
def _lowerCamelCase ( self : List[Any] , _a : Dict ):
a__: str =(embeds - self.mean) * 1.0 / self.std
return embeds
def _lowerCamelCase ( self : List[Any] , _a : str ):
a__: Optional[Any] =(embeds * self.std) + self.mean
return embeds
| 42
| 0
|
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _a( UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
for i in range(0, UpperCamelCase__ ):
for _ in range(0, n - i - 1 ): # printing spaces
print(''' ''', end='''''' )
for _ in range(0, i + 1 ): # printing stars
print('''* ''', end='''''' )
print()
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
for i in range(UpperCamelCase__, 0, -1 ):
for _ in range(UpperCamelCase__, 0, -1 ): # printing stars
print('''* ''', end='''''' )
print()
for _ in range(n - i + 1, 0, -1 ): # printing spaces
print(''' ''', end='''''' )
def _a( UpperCamelCase__ : Tuple ):
'''simple docstring'''
if n <= 0:
print(''' ... .... nothing printing :(''' )
return
floyd(UpperCamelCase__ ) # upper half
reverse_floyd(UpperCamelCase__ ) # lower half
if __name__ == "__main__":
print(R'| /\ | |- | |- |--| |\ /| |-')
print(R'|/ \| |- |_ |_ |__| | \/ | |_')
a_ = 1
while K:
a_ = int(input('enter the number and , and see the magic : '))
print()
pretty_print(user_number)
a_ = int(input('press 0 to exit... and 1 to continue...'))
print('Good Bye...')
| 152
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[Any] =value
SCREAMING_SNAKE_CASE__ : Node | None =None
SCREAMING_SNAKE_CASE__ : Node | None =None
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , __lowercase : Node ) -> None:
SCREAMING_SNAKE_CASE__ : Any =tree
def __magic_name__ ( self : str , __lowercase : Node | None ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 152
| 1
|
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _lowercase ( __UpperCAmelCase ):
lowercase_ = 42
lowercase_ = 42
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 205
|
"""simple docstring"""
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : List[Any] = 1
for i in range(1, num + 1 ):
fact *= i
return fact
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = 0
while number > 0:
lowerCamelCase : str = number % 10
sum_of_digits += last_digit
lowerCamelCase : Tuple = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def UpperCAmelCase ( a_ = 100 ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = factorial(a_ )
lowerCamelCase : int = split_and_add(a_ )
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 205
| 1
|
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 303
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class __lowercase ( unittest.TestCase ):
def UpperCAmelCase__ (self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Tuple = 1
lowerCamelCase_ : str = 3
lowerCamelCase_ : Dict = (3_2, 3_2)
lowerCamelCase_ : Optional[Any] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(A )
return image
@property
def UpperCAmelCase__ (self ):
torch.manual_seed(0 )
lowerCamelCase_ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
return model
@property
def UpperCAmelCase__ (self ):
torch.manual_seed(0 )
lowerCamelCase_ : Union[str, Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
return model
@property
def UpperCAmelCase__ (self ):
torch.manual_seed(0 )
lowerCamelCase_ : Any = RobertaSeriesConfig(
hidden_size=3_2 , project_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_0_0_6 , )
return RobertaSeriesModelWithTransformation(A )
@property
def UpperCAmelCase__ (self ):
def extract(*A , **A ):
class __lowercase :
def __init__(self ):
lowerCamelCase_ : Any = torch.ones([0] )
def UpperCAmelCase__ (self , A ):
self.pixel_values.to(A )
return self
return Out()
return extract
def UpperCAmelCase__ (self ):
lowerCamelCase_ : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ : List[Any] = self.dummy_cond_unet
lowerCamelCase_ : Any = PNDMScheduler(skip_prk_steps=A )
lowerCamelCase_ : Union[str, Any] = self.dummy_vae
lowerCamelCase_ : List[Any] = self.dummy_text_encoder
lowerCamelCase_ : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
lowerCamelCase_ : Dict = 7_7
lowerCamelCase_ : Union[str, Any] = self.dummy_image.to(A )
lowerCamelCase_ : Union[str, Any] = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
lowerCamelCase_ : Dict = AltDiffusionImgaImgPipeline(
unet=A , scheduler=A , vae=A , text_encoder=A , tokenizer=A , safety_checker=A , feature_extractor=self.dummy_extractor , )
lowerCamelCase_ : Tuple = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=A )
lowerCamelCase_ : int = alt_pipe.to(A )
alt_pipe.set_progress_bar_config(disable=A )
lowerCamelCase_ : Optional[Any] = '''A painting of a squirrel eating a burger'''
lowerCamelCase_ : Optional[Any] = torch.Generator(device=A ).manual_seed(0 )
lowerCamelCase_ : Optional[Any] = alt_pipe(
[prompt] , generator=A , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=A , )
lowerCamelCase_ : int = output.images
lowerCamelCase_ : Union[str, Any] = torch.Generator(device=A ).manual_seed(0 )
lowerCamelCase_ : Union[str, Any] = alt_pipe(
[prompt] , generator=A , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=A , return_dict=A , )[0]
lowerCamelCase_ : List[str] = image[0, -3:, -3:, -1]
lowerCamelCase_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowerCamelCase_ : str = np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Dict = self.dummy_cond_unet
lowerCamelCase_ : Optional[Any] = PNDMScheduler(skip_prk_steps=A )
lowerCamelCase_ : List[Any] = self.dummy_vae
lowerCamelCase_ : Dict = self.dummy_text_encoder
lowerCamelCase_ : Any = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
lowerCamelCase_ : Optional[Any] = 7_7
lowerCamelCase_ : str = self.dummy_image.to(A )
# put models in fp16
lowerCamelCase_ : Optional[int] = unet.half()
lowerCamelCase_ : Dict = vae.half()
lowerCamelCase_ : Union[str, Any] = bert.half()
# make sure here that pndm scheduler skips prk
lowerCamelCase_ : Dict = AltDiffusionImgaImgPipeline(
unet=A , scheduler=A , vae=A , text_encoder=A , tokenizer=A , safety_checker=A , feature_extractor=self.dummy_extractor , )
lowerCamelCase_ : List[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=A )
lowerCamelCase_ : Any = alt_pipe.to(A )
alt_pipe.set_progress_bar_config(disable=A )
lowerCamelCase_ : Tuple = '''A painting of a squirrel eating a burger'''
lowerCamelCase_ : str = torch.manual_seed(0 )
lowerCamelCase_ : Optional[int] = alt_pipe(
[prompt] , generator=A , num_inference_steps=2 , output_type='''np''' , image=A , ).images
assert image.shape == (1, 3_2, 3_2, 3)
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase_ : List[str] = init_image.resize((7_6_0, 5_0_4) )
lowerCamelCase_ : List[Any] = '''BAAI/AltDiffusion'''
lowerCamelCase_ : List[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
A , safety_checker=A , )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase_ : Dict = '''A fantasy landscape, trending on artstation'''
lowerCamelCase_ : Any = torch.manual_seed(0 )
lowerCamelCase_ : Optional[Any] = pipe(
prompt=A , image=A , strength=0.75 , guidance_scale=7.5 , generator=A , output_type='''np''' , )
lowerCamelCase_ : Dict = output.images[0]
lowerCamelCase_ : str = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 7_6_0, 3)
lowerCamelCase_ : Union[str, Any] = np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __lowercase ( unittest.TestCase ):
def UpperCAmelCase__ (self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
lowerCamelCase_ : List[str] = init_image.resize((7_6_8, 5_1_2) )
lowerCamelCase_ : str = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''' )
lowerCamelCase_ : int = '''BAAI/AltDiffusion'''
lowerCamelCase_ : List[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
A , safety_checker=A , )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase_ : Tuple = '''A fantasy landscape, trending on artstation'''
lowerCamelCase_ : List[Any] = torch.manual_seed(0 )
lowerCamelCase_ : Dict = pipe(
prompt=A , image=A , strength=0.75 , guidance_scale=7.5 , generator=A , output_type='''np''' , )
lowerCamelCase_ : List[str] = output.images[0]
assert image.shape == (5_1_2, 7_6_8, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1E-2
| 318
| 0
|
"""simple docstring"""
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE : List[Any] = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , a_ , a_=7 , a_=3 , a_=18 , a_=30 , a_=4_00 , a_=None , a_=True , a_=True , a_=None , ):
'''simple docstring'''
__snake_case : int = size if size is not None else {'height': 20, 'width': 20}
__snake_case : List[Any] = parent
__snake_case : List[Any] = batch_size
__snake_case : List[Any] = num_channels
__snake_case : str = image_size
__snake_case : Optional[Any] = min_resolution
__snake_case : str = max_resolution
__snake_case : List[Any] = size
__snake_case : int = do_normalize
__snake_case : Any = do_convert_rgb
__snake_case : Tuple = [5_12, 10_24, 20_48, 40_96]
__snake_case : Tuple = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[str] = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
__snake_case : Any = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('''RGB''' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11, reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.', )
@require_torch
@require_vision
class _UpperCAmelCase ( _UpperCamelCase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =PixaStructImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Dict = PixaStructImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_UpperCAmelCase , '''do_convert_rgb''' ) )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = self.image_processor_tester.prepare_dummy_image()
__snake_case : Optional[int] = self.image_processing_class(**self.image_processor_dict )
__snake_case : List[str] = 20_48
__snake_case : Dict = image_processor(_UpperCAmelCase , return_tensors='''pt''' , max_patches=_UpperCAmelCase )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1E-3 , rtol=1E-3 ) )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__snake_case : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , Image.Image )
# Test not batched input
__snake_case : Dict = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__snake_case : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__snake_case : Union[str, Any] = image_processor(
_UpperCAmelCase , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__snake_case : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , Image.Image )
# Test not batched input
__snake_case : Dict = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
__snake_case : List[str] = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(_UpperCAmelCase ):
__snake_case : str = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
__snake_case : Union[str, Any] = 'Hello'
__snake_case : str = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=_UpperCAmelCase , header_text=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__snake_case : Union[str, Any] = image_processor(
_UpperCAmelCase , return_tensors='''pt''' , max_patches=_UpperCAmelCase , header_text=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__snake_case : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , numpify=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , np.ndarray )
__snake_case : Any = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__snake_case : Dict = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__snake_case : str = image_processor(
_UpperCAmelCase , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__snake_case : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , torchify=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , torch.Tensor )
# Test not batched input
__snake_case : Union[str, Any] = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__snake_case : Optional[int] = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__snake_case : Union[str, Any] = image_processor(
_UpperCAmelCase , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11, reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.', )
@require_torch
@require_vision
class _UpperCAmelCase ( _UpperCamelCase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =PixaStructImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Tuple = PixaStructImageProcessingTester(self , num_channels=4 )
__snake_case : Tuple = 3
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_UpperCAmelCase , '''do_convert_rgb''' ) )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__snake_case : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , Image.Image )
# Test not batched input
__snake_case : int = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__snake_case : Optional[int] = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__snake_case : Optional[Any] = image_processor(
_UpperCAmelCase , return_tensors='''pt''' , max_patches=_UpperCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 368
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=__snake_case ):
'''simple docstring'''
lowerCamelCase__ =['transformers', 'torch', 'note_seq']
def __init__(self , *a_ , **a_ ):
'''simple docstring'''
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def SCREAMING_SNAKE_CASE (cls , *a_ , **a_ ):
'''simple docstring'''
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def SCREAMING_SNAKE_CASE (cls , *a_ , **a_ ):
'''simple docstring'''
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 24
| 0
|
"""simple docstring"""
def __lowercase ( _a , _a , _a , _a ):
if height >= 1:
move_tower(height - 1 , _a , _a , _a )
move_disk(_a , _a )
move_tower(height - 1 , _a , _a , _a )
def __lowercase ( _a , _a ):
print('''moving disk from''' , _a , '''to''' , _a )
def __lowercase ( ):
snake_case_ : List[Any] = int(input('''Height of hanoi: ''' ).strip() )
move_tower(_a , '''A''' , '''B''' , '''C''' )
if __name__ == "__main__":
main()
| 264
|
"""simple docstring"""
def __lowercase ( _a , _a , _a=False ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case_ : Union[str, Any] = len(set_a.intersection(_a ) )
if alternative_union:
snake_case_ : Any = len(_a ) + len(_a )
else:
snake_case_ : str = len(set_a.union(_a ) )
return intersection / union
if isinstance(_a , (list, tuple) ) and isinstance(_a , (list, tuple) ):
snake_case_ : str = [element for element in set_a if element in set_b]
if alternative_union:
snake_case_ : Tuple = len(_a ) + len(_a )
return len(_a ) / union
else:
snake_case_ : List[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a ) / len(_a )
return len(_a ) / len(_a )
return None
if __name__ == "__main__":
lowercase__ : Any = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowercase__ : Optional[Any] = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 264
| 1
|
"""simple docstring"""
a :int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
a :Union[str, Any] = ["a", "b", "c", "d", "e"]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] = start
# add current to visited
visited.append(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
SCREAMING_SNAKE_CASE__ : List[str] = topological_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# if all neighbors visited add current to sort
sort.append(__lowerCAmelCase )
# if all vertices haven't been visited select a new one to visit
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
for vertice in vertices:
if vertice not in visited:
SCREAMING_SNAKE_CASE__ : Dict = topological_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# return sort
return sort
if __name__ == "__main__":
a :Tuple = topological_sort("a", [], [])
print(sort)
| 365
|
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
a :Dict = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def _lowercase ( __lowerCAmelCase ) -> list[list[int]]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i in range(len(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : Any = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
SCREAMING_SNAKE_CASE__ : List[str] = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(__lowerCAmelCase ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(__lowerCAmelCase ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(__lowerCAmelCase ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
SCREAMING_SNAKE_CASE__ : Dict = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(__lowerCAmelCase )
return next_generation
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> list[Image.Image]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
for _ in range(__lowerCAmelCase ):
# Create output image
SCREAMING_SNAKE_CASE__ : int = Image.new("""RGB""" , (len(cells[0] ), len(__lowerCAmelCase )) )
SCREAMING_SNAKE_CASE__ : List[Any] = img.load()
# Save cells to image
for x in range(len(__lowerCAmelCase ) ):
for y in range(len(cells[0] ) ):
SCREAMING_SNAKE_CASE__ : str = 255 - cells[y][x] * 255
SCREAMING_SNAKE_CASE__ : Optional[Any] = (colour, colour, colour)
# Save image
images.append(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = new_generation(__lowerCAmelCase )
return images
if __name__ == "__main__":
a :Dict = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| 56
| 0
|
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] ) -> int:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) or not all(
isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for number in numbers ):
raise ValueError("numbers must be an iterable of integers" )
A__ = A__ = A__ = numbers[0]
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
# update the maximum and minimum subarray products
A__ = numbers[i]
if number < 0:
A__ , A__ = min_till_now, max_till_now
A__ = max(SCREAMING_SNAKE_CASE_ , max_till_now * number )
A__ = min(SCREAMING_SNAKE_CASE_ , min_till_now * number )
# update the maximum product found till now
A__ = max(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return max_prod
| 68
|
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter) for letter in string.ascii_lowercase]
lowercase : set[int] = {ord(char) for char in VALID_CHARS}
lowercase : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> str | None:
_snake_case = ""
_snake_case = 42
_snake_case = 42
_snake_case = 42
for keychar, cipherchar in zip(cycle(__A ) , __A ):
_snake_case = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(__A )
return decoded
def SCREAMING_SNAKE_CASE__ ( __A ) -> list[str]:
_snake_case = []
for key in product(__A , repeat=3 ):
_snake_case = try_key(__A , __A )
if encoded is not None:
possibles.append(__A )
return possibles
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> list[str]:
return [possible for possible in possibles if common_word in possible.lower()]
def SCREAMING_SNAKE_CASE__ ( __A = "p059_cipher.txt" ) -> int:
_snake_case = 42
_snake_case = 42
_snake_case = 42
_snake_case = 42
_snake_case = Path(__A ).parent.joinpath(__A ).read_text(encoding='utf-8' )
_snake_case = [int(__A ) for number in data.strip().split(',' )]
_snake_case = filter_valid_chars(__A )
for common_word in COMMON_WORDS:
_snake_case = filter_common_word(__A , __A )
if len(__A ) == 1:
break
_snake_case = possibles[0]
return sum(ord(__A ) for char in decoded_text )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42
| 0
|
def UpperCamelCase ( _a , _a , _a ) -> int:
'''simple docstring'''
def count_of_possible_combinations(_a ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(_a )
def UpperCamelCase ( _a , _a , _a ) -> int:
'''simple docstring'''
def count_of_possible_combinations_with_dp_array(
_a , _a ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowercase_ :Any = sum(
count_of_possible_combinations_with_dp_array(target - item , _a )
for item in array )
lowercase_ :int = answer
return answer
lowercase_ :List[Any] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(_a , _a )
def UpperCamelCase ( _a , _a , _a ) -> int:
'''simple docstring'''
lowercase_ :Tuple = [0] * (target + 1)
lowercase_ :List[str] = 1
for i in range(1 , target + 1 ):
for j in range(_a ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : List[str] = 3
SCREAMING_SNAKE_CASE : int = 5
SCREAMING_SNAKE_CASE : int = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 252
|
from __future__ import annotations
from random import random
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ = None ):
lowercase_ :Tuple = value
lowercase_ :Tuple = random()
lowercase_ :Node | None = None
lowercase_ :Node | None = None
def __repr__( self ):
from pprint import pformat
if self.left is None and self.right is None:
return f"'{self.value}: {self.prior:.5}'"
else:
return pformat(
{f"{self.value}: {self.prior:.5}": (self.left, self.right)} , indent=1 )
def __str__( self ):
lowercase_ :Optional[int] = str(self.value ) + ''' '''
lowercase_ :List[str] = str(self.left or '''''' )
lowercase_ :List[Any] = str(self.right or '''''' )
return value + left + right
def UpperCamelCase ( _a , _a ) -> tuple[Node | None, Node | None]:
'''simple docstring'''
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowercase_ , lowercase_ :List[Any] = split(root.left , _a )
return left, root
else:
lowercase_ , lowercase_ :Tuple = split(root.right , _a )
return root, right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowercase_ :Tuple = merge(left.right , _a )
return left
else:
lowercase_ :Optional[int] = merge(_a , right.left )
return right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ :str = Node(_a )
lowercase_ , lowercase_ :Dict = split(_a , _a )
return merge(merge(_a , _a ) , _a )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ , lowercase_ :List[str] = split(_a , value - 1 )
lowercase_ , lowercase_ :Tuple = split(_a , _a )
return merge(_a , _a )
def UpperCamelCase ( _a ) -> None:
'''simple docstring'''
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=''',''' )
inorder(root.right )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
for arg in args.split():
if arg[0] == "+":
lowercase_ :Any = insert(_a , int(arg[1:] ) )
elif arg[0] == "-":
lowercase_ :Optional[int] = erase(_a , int(arg[1:] ) )
else:
print('''Unknown command''' )
return root
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase_ :List[Any] = None
print(
'''enter numbers to create a tree, + value to add value into treap, '''
'''- value to erase all nodes with value. \'q\' to quit. ''' )
lowercase_ :Optional[Any] = input()
while args != "q":
lowercase_ :Union[str, Any] = interact_treap(_a , _a )
print(_a )
lowercase_ :str = input()
print('''good by!''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 252
| 1
|
from __future__ import annotations
def a ( A__ : list[int] , A__ : list[int] , A__ : list[int] , A__ : list[list[str]] , A__ : int , ) -> None:
"""simple docstring"""
_lowercase =len(A__ )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(A__ ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , A__ , A__ , )
def a ( A__ : int ) -> None:
"""simple docstring"""
_lowercase =[]
depth_first_search([] , [] , [] , A__ , A__ )
# Print all the boards
for board in boards:
for column in board:
print(A__ )
print('' )
print(len(A__ ) , 'solutions were found.' )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 205
|
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
_a = ["""input_features"""]
def __init__( self , lowerCAmelCase=80 , lowerCAmelCase=16_000 , lowerCAmelCase=160 , lowerCAmelCase=30 , lowerCAmelCase=400 , lowerCAmelCase=0.0 , lowerCAmelCase=False , **lowerCAmelCase , ) -> Any:
'''simple docstring'''
super().__init__(
feature_size=lowerCAmelCase , sampling_rate=lowerCAmelCase , padding_value=lowerCAmelCase , return_attention_mask=lowerCAmelCase , **lowerCAmelCase , )
_lowercase =n_fft
_lowercase =hop_length
_lowercase =chunk_length
_lowercase =chunk_length * sampling_rate
_lowercase =self.n_samples // hop_length
_lowercase =sampling_rate
_lowercase =mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCAmelCase , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=lowerCAmelCase , norm='slaney' , mel_scale='slaney' , )
def A__ ( self , lowerCAmelCase ) -> np.ndarray:
'''simple docstring'''
_lowercase =spectrogram(
lowerCAmelCase , window_function(self.n_fft , 'hann' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel='log10' , )
_lowercase =log_spec[:, :-1]
_lowercase =np.maximum(lowerCAmelCase , log_spec.max() - 8.0 )
_lowercase =(log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def A__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 0.0 ) -> List[np.ndarray]:
'''simple docstring'''
if attention_mask is not None:
_lowercase =np.array(lowerCAmelCase , np.intaa )
_lowercase =[]
for vector, length in zip(lowerCAmelCase , attention_mask.sum(-1 ) ):
_lowercase =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
_lowercase =padding_value
normed_input_values.append(lowerCAmelCase )
else:
_lowercase =[(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self , lowerCAmelCase , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = "max_length" , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , **lowerCAmelCase , ) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
F''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
_lowercase =isinstance(lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
_lowercase =is_batched_numpy or (
isinstance(lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_lowercase =[np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase , np.ndarray ):
_lowercase =np.asarray(lowerCAmelCase , dtype=np.floataa )
elif isinstance(lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowercase =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowercase =[np.asarray([raw_speech] ).T]
_lowercase =BatchFeature({'input_features': raw_speech} )
# convert into correct format for padding
_lowercase =self.pad(
lowerCAmelCase , padding=lowerCAmelCase , max_length=max_length if max_length else self.n_samples , truncation=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
_lowercase =self.zero_mean_unit_var_norm(
padded_inputs['input_features'] , attention_mask=padded_inputs['attention_mask'] , padding_value=self.padding_value , )
_lowercase =np.stack(padded_inputs['input_features'] , axis=0 )
# make sure list is in array format
_lowercase =padded_inputs.get('input_features' ).transpose(2 , 0 , 1 )
_lowercase =[self._np_extract_fbank_features(lowerCAmelCase ) for waveform in input_features[0]]
if isinstance(input_features[0] , lowerCAmelCase ):
_lowercase =[np.asarray(lowerCAmelCase , dtype=np.floataa ) for feature in input_features]
else:
_lowercase =input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
_lowercase =padded_inputs['attention_mask'][:, :: self.hop_length]
if return_tensors is not None:
_lowercase =padded_inputs.convert_to_tensors(lowerCAmelCase )
return padded_inputs
def A__ ( self ) -> Dict[str, Any]:
'''simple docstring'''
_lowercase =copy.deepcopy(self.__dict__ )
_lowercase =self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 205
| 1
|
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = 'ZinengTang/tvlt-base'
lowerCAmelCase__ : Any = tempfile.mkdtemp()
def _lowerCamelCase ( self : Union[str, Any] , **a : Optional[int] ):
'''simple docstring'''
return TvltImageProcessor.from_pretrained(self.checkpoint , **a )
def _lowerCamelCase ( self : List[str] , **a : Any ):
'''simple docstring'''
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **a )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = self.get_image_processor()
lowerCAmelCase__ : int = self.get_feature_extractor()
lowerCAmelCase__ : List[str] = TvltProcessor(image_processor=a , feature_extractor=a )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , a )
self.assertIsInstance(processor.image_processor , a )
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : int = self.get_image_processor()
lowerCAmelCase__ : str = self.get_feature_extractor()
lowerCAmelCase__ : List[str] = TvltProcessor(image_processor=a , feature_extractor=a )
lowerCAmelCase__ : List[str] = np.ones([12_000] )
lowerCAmelCase__ : Dict = feature_extractor(a , return_tensors='np' )
lowerCAmelCase__ : Dict = processor(audio=a , return_tensors='np' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = self.get_image_processor()
lowerCAmelCase__ : Dict = self.get_feature_extractor()
lowerCAmelCase__ : List[str] = TvltProcessor(image_processor=a , feature_extractor=a )
lowerCAmelCase__ : Optional[int] = np.ones([3, 224, 224] )
lowerCAmelCase__ : List[str] = image_processor(a , return_tensors='np' )
lowerCAmelCase__ : int = processor(images=a , return_tensors='np' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _lowerCamelCase ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : Dict = self.get_image_processor()
lowerCAmelCase__ : List[str] = self.get_feature_extractor()
lowerCAmelCase__ : Dict = TvltProcessor(image_processor=a , feature_extractor=a )
lowerCAmelCase__ : List[str] = np.ones([12_000] )
lowerCAmelCase__ : Optional[Any] = np.ones([3, 224, 224] )
lowerCAmelCase__ : int = processor(audio=a , images=a )
self.assertListEqual(list(inputs.keys() ) , ['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask'] )
# test if it raises when no input is passed
with pytest.raises(a ):
processor()
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.get_image_processor()
lowerCAmelCase__ : Union[str, Any] = self.get_feature_extractor()
lowerCAmelCase__ : List[str] = TvltProcessor(image_processor=a , feature_extractor=a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='`processor` and `image_processor`+`feature_extractor` model input names do not match' , )
| 307
|
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> str:
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(SCREAMING_SNAKE_CASE_ ) % 2) != 0:
raise ValueError(
'Base16 encoded data is invalid:\nData does not have an even number of hex digits.' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(SCREAMING_SNAKE_CASE_ ) <= set('0123456789ABCDEF' ):
raise ValueError(
'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 307
| 1
|
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase : Dict = 1.6021e-19 # units = C
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , ):
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 106
|
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class SCREAMING_SNAKE_CASE__ :
def __init__(self : str , a__ : Dict , a__ : Tuple=None , a__ : List[Any]=None , a__ : Dict=None , a__ : Union[str, Any]="resnet50" , a__ : Dict=3 , a__ : str=32 , a__ : int=3 , a__ : Dict=True , a__ : Any=True , ):
"""simple docstring"""
__snake_case = parent
__snake_case = out_indices if out_indices is not None else [4]
__snake_case = stage_names
__snake_case = out_features
__snake_case = backbone
__snake_case = batch_size
__snake_case = image_size
__snake_case = num_channels
__snake_case = use_pretrained_backbone
__snake_case = is_training
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case = self.get_config()
return config, pixel_values
def a (self : Any ):
"""simple docstring"""
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def a (self : List[Any] , a__ : int , a__ : int ):
"""simple docstring"""
__snake_case = TimmBackbone(config=a__ )
model.to(a__ )
model.eval()
with torch.no_grad():
__snake_case = model(a__ )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def a (self : str ):
"""simple docstring"""
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case = config_and_inputs
__snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
A_ : Union[str, Any] = (TimmBackbone,) if is_torch_available() else ()
A_ : Optional[Any] = {'feature-extraction': TimmBackbone} if is_torch_available() else {}
A_ : List[Any] = False
A_ : Dict = False
A_ : Any = False
A_ : List[Any] = False
def a (self : Tuple ):
"""simple docstring"""
__snake_case = TimmBackboneModelTester(self )
__snake_case = ConfigTester(self , config_class=a__ , has_text_modality=a__ )
def a (self : Any ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a (self : int ):
"""simple docstring"""
__snake_case = '''resnet18'''
__snake_case = '''microsoft/resnet-18'''
__snake_case = AutoBackbone.from_pretrained(a__ , use_timm_backbone=a__ )
__snake_case = AutoBackbone.from_pretrained(a__ )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
__snake_case = AutoBackbone.from_pretrained(a__ , use_timm_backbone=a__ , out_indices=[1, 2, 3] )
__snake_case = AutoBackbone.from_pretrained(a__ , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking''' )
def a (self : str ):
"""simple docstring"""
pass
@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''' )
def a (self : int ):
"""simple docstring"""
pass
@unittest.skip('''TimmBackbone initialization is managed on the timm side''' )
def a (self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def a (self : Optional[int] ):
"""simple docstring"""
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def a (self : int ):
"""simple docstring"""
pass
@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''' )
def a (self : Tuple ):
"""simple docstring"""
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def a (self : int ):
"""simple docstring"""
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def a (self : Optional[Any] ):
"""simple docstring"""
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def a (self : Tuple ):
"""simple docstring"""
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def a (self : Dict ):
"""simple docstring"""
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def a (self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''' )
def a (self : Optional[Any] ):
"""simple docstring"""
pass
@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''' )
def a (self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip('''Safetensors is not supported by timm.''' )
def a (self : Tuple ):
"""simple docstring"""
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def a (self : Tuple ):
"""simple docstring"""
pass
def a (self : Tuple ):
"""simple docstring"""
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case = model_class(a__ )
__snake_case = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case = [*signature.parameters.keys()]
__snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , a__ )
def a (self : Dict ):
"""simple docstring"""
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case = True
__snake_case = self.has_attentions
# no need to test all models as different heads yield the same functionality
__snake_case = self.all_model_classes[0]
__snake_case = model_class(a__ )
model.to(a__ )
__snake_case = self._prepare_for_class(a__ , a__ )
__snake_case = model(**a__ )
__snake_case = outputs[0][-1]
# Encoder-/Decoder-only models
__snake_case = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
__snake_case = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=a__ )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def a (self : Optional[int] ):
"""simple docstring"""
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case = model_class(a__ )
model.to(a__ )
model.eval()
__snake_case = model(**a__ )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
__snake_case = copy.deepcopy(a__ )
__snake_case = None
__snake_case = model_class(a__ )
model.to(a__ )
model.eval()
__snake_case = model(**a__ )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
__snake_case = copy.deepcopy(a__ )
__snake_case = False
__snake_case = model_class(a__ )
model.to(a__ )
model.eval()
__snake_case = model(**a__ )
| 24
| 0
|
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class snake_case__ (A__ ):
"""simple docstring"""
__lowerCAmelCase :"DiagonalGaussianDistribution"
class snake_case__ (A__ , A__ ):
"""simple docstring"""
__lowerCAmelCase :Dict = True
@register_to_config
def __init__( self , __lowercase = 3 , __lowercase = 3 , __lowercase = ("DownEncoderBlock2D",) , __lowercase = ("UpDecoderBlock2D",) , __lowercase = (6_4,) , __lowercase = 1 , __lowercase = "silu" , __lowercase = 4 , __lowercase = 3_2 , __lowercase = 3_2 , __lowercase = 0.1_8_2_1_5 , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
# pass init params to Encoder
a__ : Optional[int] = Encoder(
in_channels=__lowercase , out_channels=__lowercase , down_block_types=__lowercase , block_out_channels=__lowercase , layers_per_block=__lowercase , act_fn=__lowercase , norm_num_groups=__lowercase , double_z=__lowercase , )
# pass init params to Decoder
a__ : str = Decoder(
in_channels=__lowercase , out_channels=__lowercase , up_block_types=__lowercase , block_out_channels=__lowercase , layers_per_block=__lowercase , norm_num_groups=__lowercase , act_fn=__lowercase , )
a__ : Tuple = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
a__ : Dict = nn.Convad(__lowercase , __lowercase , 1 )
a__ : Dict = False
a__ : List[Any] = False
# only relevant if vae tiling is enabled
a__ : int = self.config.sample_size
a__ : int = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
a__ : int = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
a__ : List[str] = 0.2_5
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase=False ) -> List[str]:
"""simple docstring"""
if isinstance(__lowercase , (Encoder, Decoder) ):
a__ : List[Any] = value
def SCREAMING_SNAKE_CASE__( self , __lowercase = True ) -> Dict:
"""simple docstring"""
a__ : Tuple = use_tiling
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
self.enable_tiling(__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : Optional[int] = True
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
a__ : Any = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def SCREAMING_SNAKE_CASE__( self ) -> Dict[str, AttentionProcessor]:
"""simple docstring"""
a__ : List[Any] = {}
def fn_recursive_add_processors(__lowercase , __lowercase , __lowercase ):
if hasattr(__lowercase , """set_processor""" ):
a__ : Optional[int] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F'''{name}.{sub_name}''' , __lowercase , __lowercase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__lowercase , __lowercase , __lowercase )
return processors
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> List[Any]:
"""simple docstring"""
a__ : List[str] = len(self.attn_processors.keys() )
if isinstance(__lowercase , __lowercase ) and len(__lowercase ) != count:
raise ValueError(
F'''A dict of processors was passed, but the number of processors {len(__lowercase )} does not match the'''
F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(__lowercase , __lowercase , __lowercase ):
if hasattr(__lowercase , """set_processor""" ):
if not isinstance(__lowercase , __lowercase ):
module.set_processor(__lowercase )
else:
module.set_processor(processor.pop(F'''{name}.processor''' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F'''{name}.{sub_name}''' , __lowercase , __lowercase )
for name, module in self.named_children():
fn_recursive_attn_processor(__lowercase , __lowercase , __lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = True ) -> AutoencoderKLOutput:
"""simple docstring"""
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__lowercase , return_dict=__lowercase )
if self.use_slicing and x.shape[0] > 1:
a__ : List[Any] = [self.encoder(__lowercase ) for x_slice in x.split(1 )]
a__ : str = torch.cat(__lowercase )
else:
a__ : List[Any] = self.encoder(__lowercase )
a__ : str = self.quant_conv(__lowercase )
a__ : List[str] = DiagonalGaussianDistribution(__lowercase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__lowercase , return_dict=__lowercase )
a__ : List[Any] = self.post_quant_conv(__lowercase )
a__ : Dict = self.decoder(__lowercase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowercase )
@apply_forward_hook
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
if self.use_slicing and z.shape[0] > 1:
a__ : Union[str, Any] = [self._decode(__lowercase ).sample for z_slice in z.split(1 )]
a__ : Dict = torch.cat(__lowercase )
else:
a__ : Tuple = self._decode(__lowercase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase ) -> Dict:
"""simple docstring"""
a__ : Optional[Any] = min(a.shape[2] , b.shape[2] , __lowercase )
for y in range(__lowercase ):
a__ : int = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
"""simple docstring"""
a__ : int = min(a.shape[3] , b.shape[3] , __lowercase )
for x in range(__lowercase ):
a__ : List[str] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = True ) -> AutoencoderKLOutput:
"""simple docstring"""
a__ : Any = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
a__ : int = int(self.tile_latent_min_size * self.tile_overlap_factor )
a__ : Tuple = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
a__ : List[Any] = []
for i in range(0 , x.shape[2] , __lowercase ):
a__ : Optional[Any] = []
for j in range(0 , x.shape[3] , __lowercase ):
a__ : Any = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
a__ : Union[str, Any] = self.encoder(__lowercase )
a__ : Optional[Any] = self.quant_conv(__lowercase )
row.append(__lowercase )
rows.append(__lowercase )
a__ : int = []
for i, row in enumerate(__lowercase ):
a__ : Tuple = []
for j, tile in enumerate(__lowercase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
a__ : List[str] = self.blend_v(rows[i - 1][j] , __lowercase , __lowercase )
if j > 0:
a__ : Optional[int] = self.blend_h(row[j - 1] , __lowercase , __lowercase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__lowercase , dim=3 ) )
a__ : List[Any] = torch.cat(__lowercase , dim=2 )
a__ : str = DiagonalGaussianDistribution(__lowercase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
a__ : Dict = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
a__ : int = int(self.tile_sample_min_size * self.tile_overlap_factor )
a__ : Tuple = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
a__ : Optional[int] = []
for i in range(0 , z.shape[2] , __lowercase ):
a__ : Union[str, Any] = []
for j in range(0 , z.shape[3] , __lowercase ):
a__ : Dict = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
a__ : Union[str, Any] = self.post_quant_conv(__lowercase )
a__ : str = self.decoder(__lowercase )
row.append(__lowercase )
rows.append(__lowercase )
a__ : Optional[int] = []
for i, row in enumerate(__lowercase ):
a__ : Tuple = []
for j, tile in enumerate(__lowercase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
a__ : int = self.blend_v(rows[i - 1][j] , __lowercase , __lowercase )
if j > 0:
a__ : int = self.blend_h(row[j - 1] , __lowercase , __lowercase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__lowercase , dim=3 ) )
a__ : str = torch.cat(__lowercase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = False , __lowercase = True , __lowercase = None , ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
a__ : Tuple = sample
a__ : Optional[int] = self.encode(__lowercase ).latent_dist
if sample_posterior:
a__ : List[str] = posterior.sample(generator=__lowercase )
else:
a__ : int = posterior.mode()
a__ : str = self.decode(__lowercase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowercase )
| 266
|
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowercase : str =logging.getLogger(__name__)
@dataclass
class snake_case__ (A__ ):
"""simple docstring"""
__lowerCAmelCase :Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__lowerCAmelCase :bool = field(default=A__ , metadata={"help": "Whether to SortishSamler or not."} )
__lowerCAmelCase :bool = field(
default=A__ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__lowerCAmelCase :bool = field(default=A__ , metadata={"help": "whether to use adafactor"} )
__lowerCAmelCase :Optional[float] = field(
default=A__ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__lowerCAmelCase :Optional[float] = field(
default=A__ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__lowerCAmelCase :Optional[float] = field(default=A__ , metadata={"help": "Dropout probability. Goes into model.config."} )
__lowerCAmelCase :Optional[float] = field(
default=A__ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__lowerCAmelCase :Optional[str] = field(
default="linear" , metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 266
| 1
|
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( _lowerCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = ["pixel_values"]
def __init__( self : Optional[Any] , _UpperCamelCase : bool = True , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCamelCase : bool = True , _UpperCamelCase : Union[int, float] = 1 / 2_5_5 , _UpperCamelCase : bool = True , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : bool = True , **_UpperCamelCase : Union[str, Any] , ) ->str:
super().__init__(**lowercase_ )
snake_case_ = size if size is not None else {'''shortest_edge''': 2_2_4}
snake_case_ = get_size_dict(lowercase_ , default_to_square=lowercase_ )
snake_case_ = crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
snake_case_ = get_size_dict(lowercase_ , param_name='''crop_size''' )
snake_case_ = do_resize
snake_case_ = size
snake_case_ = resample
snake_case_ = do_rescale
snake_case_ = rescale_factor
snake_case_ = do_center_crop
snake_case_ = crop_size
snake_case_ = do_flip_channel_order
def snake_case__( self : int , _UpperCamelCase : np.ndarray , _UpperCamelCase : Dict[str, int] , _UpperCamelCase : PILImageResampling = PIL.Image.BILINEAR , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Optional[int] , ) ->List[Any]:
snake_case_ = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
snake_case_ = get_resize_output_image_size(lowercase_ , size=size['''shortest_edge'''] , default_to_square=lowercase_ )
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def snake_case__( self : List[str] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Dict[str, int] , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Tuple , ) ->Union[str, Any]:
snake_case_ = get_size_dict(lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(lowercase_ , size=(size['''height'''], size['''width''']) , data_format=lowercase_ , **lowercase_ )
def snake_case__( self : Optional[Any] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Union[int, float] , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : int , ) ->int:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def snake_case__( self : Optional[Any] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None ) ->Optional[int]:
return flip_channel_order(lowercase_ , data_format=lowercase_ )
def snake_case__( self : Dict , _UpperCamelCase : ImageInput , _UpperCamelCase : bool = None , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : PILImageResampling = None , _UpperCamelCase : bool = None , _UpperCamelCase : float = None , _UpperCamelCase : bool = None , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : bool = None , _UpperCamelCase : Optional[Union[str, TensorType]] = None , _UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCamelCase : Dict , ) ->Tuple:
snake_case_ = do_resize if do_resize is not None else self.do_resize
snake_case_ = resample if resample is not None else self.resample
snake_case_ = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
snake_case_ = size if size is not None else self.size
snake_case_ = get_size_dict(lowercase_ , default_to_square=lowercase_ )
snake_case_ = crop_size if crop_size is not None else self.crop_size
snake_case_ = get_size_dict(lowercase_ , param_name='''crop_size''' )
snake_case_ = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
# All transformations expect numpy arrays.
snake_case_ = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
snake_case_ = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_center_crop:
snake_case_ = [self.center_crop(image=lowercase_ , size=lowercase_ ) for image in images]
if do_rescale:
snake_case_ = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
snake_case_ = [self.flip_channel_order(image=lowercase_ ) for image in images]
snake_case_ = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
snake_case_ = {'''pixel_values''': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
def snake_case__( self : Optional[int] , _UpperCamelCase : Optional[int] , _UpperCamelCase : List[Tuple] = None ) ->List[Any]:
snake_case_ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowercase_ ) != len(lowercase_ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(lowercase_ ):
snake_case_ = target_sizes.numpy()
snake_case_ = []
for idx in range(len(lowercase_ ) ):
snake_case_ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=lowercase_ )
snake_case_ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowercase_ )
else:
snake_case_ = logits.argmax(dim=1 )
snake_case_ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 8
|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class a :
@staticmethod
def A_ ( *lowercase_ : int , **lowercase_ : str ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class a ( unittest.TestCase ):
snake_case_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def A_ ( self : Any , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str] ):
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , image_processor=lowercase_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def A_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : int ):
snake_case_ = object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' , threshold=0.0 )
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
import datasets
snake_case_ = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case_ = [
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
]
snake_case_ = object_detector(lowercase_ , threshold=0.0 )
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for outputs in batch_outputs:
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
@require_tf
@unittest.skip('''Object detection not implemented in TF''' )
def A_ ( self : int ):
pass
@require_torch
def A_ ( self : Tuple ):
snake_case_ = '''hf-internal-testing/tiny-detr-mobilenetsv3'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=0.0 )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
] , )
@require_torch
@slow
def A_ ( self : Optional[int] ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : Tuple ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : str ):
snake_case_ = 0.9985
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=lowercase_ )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def A_ ( self : Dict ):
snake_case_ = '''Narsil/layoutlmv3-finetuned-funsd'''
snake_case_ = 0.9993
snake_case_ = pipeline('''object-detection''' , model=lowercase_ , threshold=lowercase_ )
snake_case_ = object_detector(
'''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
] , )
| 56
| 0
|
'''simple docstring'''
from __future__ import annotations
import math
def lowercase__ ( __UpperCamelCase )-> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__UpperCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase__ ( __UpperCamelCase )-> list[int]:
UpperCamelCase = str(__UpperCamelCase )
UpperCamelCase = [n]
for i in range(1 , len(__UpperCamelCase ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowercase__ ( __UpperCamelCase )-> bool:
if len(str(__UpperCamelCase ) ) > 3:
if not is_prime(int(str(__UpperCamelCase )[-3:] ) ) or not is_prime(int(str(__UpperCamelCase )[:3] ) ):
return False
return True
def lowercase__ ( __UpperCamelCase = 11 )-> list[int]:
UpperCamelCase = []
UpperCamelCase = 13
while len(__UpperCamelCase ) != count:
if validate(__UpperCamelCase ):
UpperCamelCase = list_truncated_nums(__UpperCamelCase )
if all(is_prime(__UpperCamelCase ) for i in list_nums ):
list_truncated_primes.append(__UpperCamelCase )
num += 2
return list_truncated_primes
def lowercase__ ( )-> int:
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'{sum(compute_truncated_primes(1_1)) = }')
| 370
|
'''simple docstring'''
from PIL import Image
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Image:
def brightness(__UpperCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(__UpperCamelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
SCREAMING_SNAKE_CASE__ = change_brightness(img, 1_0_0)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 183
| 0
|
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( a_ ):
"""simple docstring"""
UpperCamelCase : Optional[Any] = (PNDMScheduler,)
UpperCamelCase : Dict = (("num_inference_steps", 5_0),)
def __A ( self , **A ) -> Dict:
'''simple docstring'''
lowerCamelCase = {
"""num_train_timesteps""": 10_00,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**A )
return config
def __A ( self , A=0 , **A ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase = dict(self.forward_default_kwargs )
lowerCamelCase = kwargs.pop("""num_inference_steps""" , A )
lowerCamelCase = self.dummy_sample
lowerCamelCase = 0.1 * sample
lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
lowerCamelCase = self.get_scheduler_config(**A )
lowerCamelCase = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals
lowerCamelCase = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
lowerCamelCase = scheduler_class.from_pretrained(A )
new_scheduler.set_timesteps(A )
# copy over dummy past residuals
lowerCamelCase = dummy_past_residuals[:]
lowerCamelCase = scheduler.step_prk(A , A , A , **A ).prev_sample
lowerCamelCase = new_scheduler.step_prk(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
lowerCamelCase = scheduler.step_plms(A , A , A , **A ).prev_sample
lowerCamelCase = new_scheduler.step_plms(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def __A ( self , A=0 , **A ) -> Dict:
'''simple docstring'''
lowerCamelCase = dict(self.forward_default_kwargs )
lowerCamelCase = kwargs.pop("""num_inference_steps""" , A )
lowerCamelCase = self.dummy_sample
lowerCamelCase = 0.1 * sample
lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
lowerCamelCase = self.get_scheduler_config()
lowerCamelCase = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals (must be after setting timesteps)
lowerCamelCase = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
lowerCamelCase = scheduler_class.from_pretrained(A )
# copy over dummy past residuals
new_scheduler.set_timesteps(A )
# copy over dummy past residual (must be after setting timesteps)
lowerCamelCase = dummy_past_residuals[:]
lowerCamelCase = scheduler.step_prk(A , A , A , **A ).prev_sample
lowerCamelCase = new_scheduler.step_prk(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
lowerCamelCase = scheduler.step_plms(A , A , A , **A ).prev_sample
lowerCamelCase = new_scheduler.step_plms(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __A ( self , **A ) -> Tuple:
'''simple docstring'''
lowerCamelCase = self.scheduler_classes[0]
lowerCamelCase = self.get_scheduler_config(**A )
lowerCamelCase = scheduler_class(**A )
lowerCamelCase = 10
lowerCamelCase = self.dummy_model()
lowerCamelCase = self.dummy_sample_deter
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.prk_timesteps ):
lowerCamelCase = model(A , A )
lowerCamelCase = scheduler.step_prk(A , A , A ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
lowerCamelCase = model(A , A )
lowerCamelCase = scheduler.step_plms(A , A , A ).prev_sample
return sample
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = dict(self.forward_default_kwargs )
lowerCamelCase = kwargs.pop("""num_inference_steps""" , A )
for scheduler_class in self.scheduler_classes:
lowerCamelCase = self.get_scheduler_config()
lowerCamelCase = scheduler_class(**A )
lowerCamelCase = self.dummy_sample
lowerCamelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(A , """set_timesteps""" ):
scheduler.set_timesteps(A )
elif num_inference_steps is not None and not hasattr(A , """set_timesteps""" ):
lowerCamelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
lowerCamelCase = dummy_past_residuals[:]
lowerCamelCase = scheduler.step_prk(A , 0 , A , **A ).prev_sample
lowerCamelCase = scheduler.step_prk(A , 1 , A , **A ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
lowerCamelCase = scheduler.step_plms(A , 0 , A , **A ).prev_sample
lowerCamelCase = scheduler.step_plms(A , 1 , A , **A ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def __A ( self ) -> Any:
'''simple docstring'''
for timesteps in [1_00, 10_00]:
self.check_over_configs(num_train_timesteps=A )
def __A ( self ) -> List[str]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=A )
lowerCamelCase = self.scheduler_classes[0]
lowerCamelCase = self.get_scheduler_config(steps_offset=1 )
lowerCamelCase = scheduler_class(**A )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , )
def __A ( self ) -> Dict:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=A , beta_end=A )
def __A ( self ) -> str:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=A )
def __A ( self ) -> List[str]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def __A ( self ) -> int:
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=A )
def __A ( self ) -> List[Any]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ):
self.check_over_forward(num_inference_steps=A )
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = 27
for scheduler_class in self.scheduler_classes:
lowerCamelCase = self.dummy_sample
lowerCamelCase = 0.1 * sample
lowerCamelCase = self.get_scheduler_config()
lowerCamelCase = scheduler_class(**A )
scheduler.set_timesteps(A )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
lowerCamelCase = scheduler.step_prk(A , A , A ).prev_sample
def __A ( self ) -> Dict:
'''simple docstring'''
with self.assertRaises(A ):
lowerCamelCase = self.scheduler_classes[0]
lowerCamelCase = self.get_scheduler_config()
lowerCamelCase = scheduler_class(**A )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = self.full_loop()
lowerCamelCase = torch.sum(torch.abs(A ) )
lowerCamelCase = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 198.1318 ) < 1e-2
assert abs(result_mean.item() - 0.2580 ) < 1e-3
def __A ( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase = self.full_loop(prediction_type="""v_prediction""" )
lowerCamelCase = torch.sum(torch.abs(A ) )
lowerCamelCase = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 67.3986 ) < 1e-2
assert abs(result_mean.item() - 0.0878 ) < 1e-3
def __A ( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = self.full_loop(set_alpha_to_one=A , beta_start=0.01 )
lowerCamelCase = torch.sum(torch.abs(A ) )
lowerCamelCase = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 230.0399 ) < 1e-2
assert abs(result_mean.item() - 0.2995 ) < 1e-3
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = self.full_loop(set_alpha_to_one=A , beta_start=0.01 )
lowerCamelCase = torch.sum(torch.abs(A ) )
lowerCamelCase = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 186.9482 ) < 1e-2
assert abs(result_mean.item() - 0.2434 ) < 1e-3
| 252
|
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCamelCase ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Tuple ):
'''simple docstring'''
if gpta_config_file == "":
lowerCamelCase = GPTaConfig()
else:
lowerCamelCase = GPTaConfig.from_json_file(lowerCamelCase__ )
lowerCamelCase = GPTaModel(lowerCamelCase__ )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
lowerCamelCase = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
lowerCamelCase = pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(f'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(model.state_dict() , lowerCamelCase__ )
print(f'Save configuration file to {pytorch_config_dump_path}' )
with open(lowerCamelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--gpt2_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture."
),
)
UpperCAmelCase : Tuple = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 252
| 1
|
lowerCamelCase__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4E-1_9,
"britishthermalunit_it": 1_055.05_585,
"footpound": 1.35_5818,
}
def UpperCAmelCase_ ( __UpperCAmelCase : str , __UpperCAmelCase : str , __UpperCAmelCase : float ) -> float:
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
SCREAMING_SNAKE_CASE_ = (
f"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
f"Valid values are: {', '.join(__UpperCAmelCase )}"
)
raise ValueError(__UpperCAmelCase )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 210
|
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=768 ):
super().__init__(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = proj_size
SCREAMING_SNAKE_CASE_ = CLIPVisionModel(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = PaintByExampleMapper(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = nn.LayerNorm(config.hidden_size )
SCREAMING_SNAKE_CASE_ = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
SCREAMING_SNAKE_CASE_ = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def lowerCAmelCase_ ( self : Any , _lowerCAmelCase : int , _lowerCAmelCase : int=False ):
SCREAMING_SNAKE_CASE_ = self.model(pixel_values=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = clip_output.pooler_output
SCREAMING_SNAKE_CASE_ = self.mapper(latent_states[:, None] )
SCREAMING_SNAKE_CASE_ = self.final_layer_norm(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.proj_out(_lowerCAmelCase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : int , _lowerCAmelCase : Optional[Any] ):
super().__init__()
SCREAMING_SNAKE_CASE_ = (config.num_hidden_layers + 1) // 5
SCREAMING_SNAKE_CASE_ = config.hidden_size
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = nn.ModuleList(
[
BasicTransformerBlock(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , activation_fn='gelu' , attention_bias=_lowerCAmelCase )
for _ in range(_lowerCAmelCase )
] )
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : Optional[Any] ):
for block in self.blocks:
SCREAMING_SNAKE_CASE_ = block(_lowerCAmelCase )
return hidden_states
| 210
| 1
|
def a_ ( ) -> int:
"""simple docstring"""
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(_A , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 307
|
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE:
def __init__( self: List[str] ) -> Union[str, Any]:
snake_case__ = [
[],
[],
[],
]
def lowerCAmelCase_ ( self: Union[str, Any] , UpperCamelCase: int , UpperCamelCase: int ) -> None:
try:
if len(self.queues[priority] ) >= 1_00:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(UpperCamelCase )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def lowerCAmelCase_ ( self: List[Any] ) -> int:
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self: Union[str, Any] ) -> str:
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class __SCREAMING_SNAKE_CASE:
def __init__( self: Union[str, Any] ) -> Any:
snake_case__ = []
def lowerCAmelCase_ ( self: str , UpperCamelCase: int ) -> None:
if len(self.queue ) == 1_00:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(UpperCamelCase )
def lowerCAmelCase_ ( self: int ) -> int:
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
snake_case__ = min(self.queue )
self.queue.remove(UpperCamelCase )
return data
def __str__( self: Optional[Any] ) -> str:
return str(self.queue )
def a_ ( ) -> List[Any]:
"""simple docstring"""
snake_case__ = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 100 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 128 )
print(_A )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(_A )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def a_ ( ) -> List[Any]:
"""simple docstring"""
snake_case__ = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(100 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(128 )
print(_A )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(_A )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 307
| 1
|
def lowerCamelCase ( a_ ) -> int:
assert isinstance(a_ , a_ ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
lowerCAmelCase_ = F'''The input value of [n={number}] has to be > 0'''
raise ValueError(a_ )
else:
lowerCAmelCase_ = sylvester(number - 1 )
lowerCAmelCase_ = num - 1
lowerCAmelCase_ = num
return lower * upper + 1
if __name__ == "__main__":
print(f'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')
| 14
|
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
lowerCamelCase_ = logging.get_logger(__name__)
def lowerCamelCase ( a_ , a_ , a_ , a_ ) -> Tuple[int, int]:
def constraint_to_multiple_of(a_ , a_ , a_=0 , a_=None ):
lowerCAmelCase_ = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
lowerCAmelCase_ = math.floor(val / multiple ) * multiple
if x < min_val:
lowerCAmelCase_ = math.ceil(val / multiple ) * multiple
return x
lowerCAmelCase_ = (output_size, output_size) if isinstance(a_ , a_ ) else output_size
lowerCAmelCase_ , lowerCAmelCase_ = get_image_size(a_ )
lowerCAmelCase_ , lowerCAmelCase_ = output_size
# determine new height and width
lowerCAmelCase_ = output_height / input_height
lowerCAmelCase_ = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
lowerCAmelCase_ = scale_width
else:
# fit height
lowerCAmelCase_ = scale_height
lowerCAmelCase_ = constraint_to_multiple_of(scale_height * input_height , multiple=a_ )
lowerCAmelCase_ = constraint_to_multiple_of(scale_width * input_width , multiple=a_ )
return (new_height, new_width)
class a_ ( a_ ):
'''simple docstring'''
__a: Union[str, Any] = ['''pixel_values''']
def __init__( self , lowercase_ = True , lowercase_ = None , lowercase_ = PILImageResampling.BILINEAR , lowercase_ = False , lowercase_ = 1 , lowercase_ = True , lowercase_ = 1 / 2_5_5 , lowercase_ = True , lowercase_ = None , lowercase_ = None , **lowercase_ , ) -> None:
'''simple docstring'''
super().__init__(**lowercase_ )
lowerCAmelCase_ = size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
lowerCAmelCase_ = get_size_dict(lowercase_ )
lowerCAmelCase_ = do_resize
lowerCAmelCase_ = size
lowerCAmelCase_ = keep_aspect_ratio
lowerCAmelCase_ = ensure_multiple_of
lowerCAmelCase_ = resample
lowerCAmelCase_ = do_rescale
lowerCAmelCase_ = rescale_factor
lowerCAmelCase_ = do_normalize
lowerCAmelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ = False , lowercase_ = 1 , lowercase_ = PILImageResampling.BICUBIC , lowercase_ = None , **lowercase_ , ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase_ = get_size_dict(lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
lowerCAmelCase_ = get_resize_output_image_size(
lowercase_ , output_size=(size['height'], size['width']) , keep_aspect_ratio=lowercase_ , multiple=lowercase_ , )
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ = None , **lowercase_ , ) -> Dict:
'''simple docstring'''
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ = None , **lowercase_ , ) -> np.ndarray:
'''simple docstring'''
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def _lowercase ( self , lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = ChannelDimension.FIRST , **lowercase_ , ) -> PIL.Image.Image:
'''simple docstring'''
lowerCAmelCase_ = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase_ = size if size is not None else self.size
lowerCAmelCase_ = get_size_dict(lowercase_ )
lowerCAmelCase_ = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
lowerCAmelCase_ = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
lowerCAmelCase_ = resample if resample is not None else self.resample
lowerCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase_ = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase_ = image_std if image_std is not None else self.image_std
lowerCAmelCase_ = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
lowerCAmelCase_ = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
lowerCAmelCase_ = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_rescale:
lowerCAmelCase_ = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
lowerCAmelCase_ = [self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
lowerCAmelCase_ = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
lowerCAmelCase_ = {'pixel_values': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
def _lowercase ( self , lowercase_ , lowercase_ = None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowercase_ ) != len(lowercase_ ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(lowercase_ ):
lowerCAmelCase_ = target_sizes.numpy()
lowerCAmelCase_ = []
for idx in range(len(lowercase_ ) ):
lowerCAmelCase_ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=lowercase_ )
lowerCAmelCase_ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowercase_ )
else:
lowerCAmelCase_ = logits.argmax(dim=1 )
lowerCAmelCase_ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 14
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
parser.add_argument('--model_name', default='roberta-large', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
lowercase_ = parser.parse_args()
if args.model_type == "roberta":
lowercase_ = RobertaForMaskedLM.from_pretrained(args.model_name)
lowercase_ = 'roberta'
elif args.model_type == "gpt2":
lowercase_ = GPTaLMHeadModel.from_pretrained(args.model_name)
lowercase_ = 'transformer'
lowercase_ = model.state_dict()
lowercase_ = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
lowercase_ = state_dict[F'''{prefix}.{param_name}''']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
lowercase_ = F'''{prefix}.embeddings.{w}.weight'''
lowercase_ = state_dict[param_name]
for w in ["weight", "bias"]:
lowercase_ = F'''{prefix}.embeddings.LayerNorm.{w}'''
lowercase_ = state_dict[param_name]
# Transformer Blocks #
lowercase_ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
lowercase_ = state_dict[
F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
]
lowercase_ = state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
lowercase_ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
lowercase_ = state_dict[F'''{layer}''']
if args.vocab_transform:
for w in ["weight", "bias"]:
lowercase_ = state_dict[F'''lm_head.dense.{w}''']
lowercase_ = state_dict[F'''lm_head.layer_norm.{w}''']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
lowercase_ = state_dict[F'''{prefix}.ln_f.{w}''']
lowercase_ = state_dict['lm_head.weight']
print(F'''N layers selected for distillation: {std_idx}''')
print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 266
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
if not postfix_notation:
return 0
__A = {'''+''', '''-''', '''*''', '''/'''}
__A = []
for token in postfix_notation:
if token in operations:
__A , __A = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(__UpperCamelCase ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 266
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
"""facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''xlm-roberta-xl'''
def __init__( self : Any , _UpperCAmelCase : Tuple=250880 , _UpperCAmelCase : List[str]=2560 , _UpperCAmelCase : Union[str, Any]=36 , _UpperCAmelCase : Tuple=32 , _UpperCAmelCase : str=10240 , _UpperCAmelCase : List[str]="gelu" , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Optional[Any]=514 , _UpperCAmelCase : Optional[int]=1 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[str]=1e-05 , _UpperCAmelCase : List[Any]=1 , _UpperCAmelCase : Dict=0 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : Dict="absolute" , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Dict=None , **_UpperCAmelCase : List[Any] , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = type_vocab_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = position_embedding_type
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = classifier_dropout
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def lowercase__ ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase_ = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCAmelCase_ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 241
|
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 241
| 1
|
"""simple docstring"""
from typing import Any
class lowercase :
def __init__( self : Tuple , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : str = data
A_ : List[str] = None
class lowercase :
def __init__( self : Union[str, Any] ):
"""simple docstring"""
A_ : Optional[Any] = None
def a_ ( self : Dict ):
"""simple docstring"""
A_ : Tuple = self.head
while temp is not None:
print(temp.data , end=''' ''' )
A_ : Tuple = temp.next
print()
def a_ ( self : List[Any] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : Optional[Any] = Node(__SCREAMING_SNAKE_CASE )
A_ : str = self.head
A_ : List[Any] = new_node
def a_ ( self : Dict , _lowerCamelCase : List[str] , _lowerCamelCase : int ):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
A_ : Union[str, Any] = self.head
while node_a is not None and node_a.data != node_data_a:
A_ : str = node_a.next
A_ : Dict = self.head
while node_a is not None and node_a.data != node_data_a:
A_ : Union[str, Any] = node_a.next
if node_a is None or node_a is None:
return
A_ , A_ : int = node_a.data, node_a.data
if __name__ == "__main__":
_lowerCamelCase : Optional[int] = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('After swapping')
ll.print_list()
| 167
|
"""simple docstring"""
import string
def lowerCamelCase__ ( _lowerCamelCase : str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
lowerCamelCase_ = ''
for symbol in message:
if symbol in string.ascii_uppercase:
lowerCamelCase_ = string.ascii_uppercase.find(_lowerCamelCase )
lowerCamelCase_ = num - key
if num < 0:
lowerCamelCase_ = num + len(string.ascii_uppercase )
lowerCamelCase_ = translated + string.ascii_uppercase[num]
else:
lowerCamelCase_ = translated + symbol
print(F'''Decryption using Key #{key}: {translated}''' )
def lowerCamelCase__ ( ) -> None:
lowerCamelCase_ = input('Encrypted message: ' )
lowerCamelCase_ = message.upper()
decrypt(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 183
| 0
|
def __lowercase ( ) -> int:
'''simple docstring'''
return 1
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 10 ) + five_pence(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 20 ) + ten_pence(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 50 ) + twenty_pence(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_00 ) + fifty_pence(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_00 ) + one_pound(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE = 2_00 ) -> int:
'''simple docstring'''
return two_pound(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 193
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE_ = """RegNetConfig"""
# Base docstring
SCREAMING_SNAKE_CASE_ = """facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE_ = [1, 1_0_8_8, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE_ = """facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE_ = """tabby, tabby cat"""
SCREAMING_SNAKE_CASE_ = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : Optional[str] = "relu" ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = nn.Convad(
lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=lowerCamelCase__ ,stride=lowerCamelCase__ ,padding=kernel_size // 2 ,groups=lowerCamelCase__ ,bias=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = nn.BatchNormad(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = ACTaFN[activation] if activation is not None else nn.Identity()
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Tuple ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.convolution(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.normalization(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.activation(lowerCamelCase__ )
return hidden_state
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] ,lowerCamelCase__ : RegNetConfig ) -> List[str]:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
SCREAMING_SNAKE_CASE = config.num_channels
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
SCREAMING_SNAKE_CASE = self.embedder(lowerCamelCase__ )
return hidden_state
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : int = 2 ) -> List[str]:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = nn.Convad(lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=1 ,stride=lowerCamelCase__ ,bias=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = nn.BatchNormad(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Tensor ) -> Tensor:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.convolution(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.normalization(lowerCamelCase__ )
return hidden_state
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : int ) -> int:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = nn.AdaptiveAvgPoolad((1, 1) )
SCREAMING_SNAKE_CASE = nn.Sequential(
nn.Convad(lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=1 ) ,nn.Sigmoid() ,)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : Any ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.pooler(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.attention(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_state * attention
return hidden_state
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,lowerCamelCase__ : RegNetConfig ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : int = 1 ) -> str:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = in_channels != out_channels or stride != 1
SCREAMING_SNAKE_CASE = max(1 ,out_channels // config.groups_width )
SCREAMING_SNAKE_CASE = (
RegNetShortCut(lowerCamelCase__ ,lowerCamelCase__ ,stride=lowerCamelCase__ ) if should_apply_shortcut else nn.Identity()
)
SCREAMING_SNAKE_CASE = nn.Sequential(
RegNetConvLayer(lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(lowerCamelCase__ ,lowerCamelCase__ ,stride=lowerCamelCase__ ,groups=lowerCamelCase__ ,activation=config.hidden_act ) ,RegNetConvLayer(lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=1 ,activation=lowerCamelCase__ ) ,)
SCREAMING_SNAKE_CASE = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Optional[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = hidden_state
SCREAMING_SNAKE_CASE = self.layer(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.shortcut(lowerCamelCase__ )
hidden_state += residual
SCREAMING_SNAKE_CASE = self.activation(lowerCamelCase__ )
return hidden_state
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,lowerCamelCase__ : RegNetConfig ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : int = 1 ) -> Optional[int]:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = in_channels != out_channels or stride != 1
SCREAMING_SNAKE_CASE = max(1 ,out_channels // config.groups_width )
SCREAMING_SNAKE_CASE = (
RegNetShortCut(lowerCamelCase__ ,lowerCamelCase__ ,stride=lowerCamelCase__ ) if should_apply_shortcut else nn.Identity()
)
SCREAMING_SNAKE_CASE = nn.Sequential(
RegNetConvLayer(lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(lowerCamelCase__ ,lowerCamelCase__ ,stride=lowerCamelCase__ ,groups=lowerCamelCase__ ,activation=config.hidden_act ) ,RegNetSELayer(lowerCamelCase__ ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(lowerCamelCase__ ,lowerCamelCase__ ,kernel_size=1 ,activation=lowerCamelCase__ ) ,)
SCREAMING_SNAKE_CASE = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = hidden_state
SCREAMING_SNAKE_CASE = self.layer(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.shortcut(lowerCamelCase__ )
hidden_state += residual
SCREAMING_SNAKE_CASE = self.activation(lowerCamelCase__ )
return hidden_state
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,lowerCamelCase__ : RegNetConfig ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 2 ,) -> Tuple:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = RegNetXLayer if config.layer_type == """x""" else RegNetYLayer
SCREAMING_SNAKE_CASE = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,stride=lowerCamelCase__ ,) ,*[layer(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ) for _ in range(depth - 1 )] ,)
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.layers(lowerCamelCase__ )
return hidden_state
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : RegNetConfig ) -> str:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
lowerCamelCase__ ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
SCREAMING_SNAKE_CASE = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(lowerCamelCase__ ,config.depths[1:] ):
self.stages.append(RegNetStage(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,depth=lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Tensor ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = True ) -> BaseModelOutputWithNoAttention:
'''simple docstring'''
SCREAMING_SNAKE_CASE = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
SCREAMING_SNAKE_CASE = hidden_states + (hidden_state,)
SCREAMING_SNAKE_CASE = stage_module(lowerCamelCase__ )
if output_hidden_states:
SCREAMING_SNAKE_CASE = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase__ ,hidden_states=lowerCamelCase__ )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : List[Any] = RegNetConfig
__snake_case : Union[str, Any] = "regnet"
__snake_case : Optional[Any] = "pixel_values"
__snake_case : List[Any] = True
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : int ) -> Any:
'''simple docstring'''
if isinstance(lowerCamelCase__ ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode="""fan_out""" ,nonlinearity="""relu""" )
elif isinstance(lowerCamelCase__ ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : str=False ) -> str:
'''simple docstring'''
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = value
SCREAMING_SNAKE_CASE_ = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
SCREAMING_SNAKE_CASE_ = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , lowerCAmelCase_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : str ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
super().__init__(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = config
SCREAMING_SNAKE_CASE = RegNetEmbeddings(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = RegNetEncoder(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=lowerCamelCase__ ,config_class=_CONFIG_FOR_DOC ,modality="""vision""" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Tensor ,lowerCamelCase__ : Optional[bool] = None ,lowerCamelCase__ : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE = self.embedder(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.encoder(
lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ ,return_dict=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = encoder_outputs[0]
SCREAMING_SNAKE_CASE = self.pooler(lowerCamelCase__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowerCamelCase__ ,pooler_output=lowerCamelCase__ ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , lowerCAmelCase_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Any ,lowerCamelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
super().__init__(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = config.num_labels
SCREAMING_SNAKE_CASE = RegNetModel(lowerCamelCase__ )
# classification head
SCREAMING_SNAKE_CASE = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=lowerCamelCase__ ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Optional[torch.FloatTensor] = None ,lowerCamelCase__ : Optional[torch.LongTensor] = None ,lowerCamelCase__ : Optional[bool] = None ,lowerCamelCase__ : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
'''simple docstring'''
SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE = self.regnet(lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ ,return_dict=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = outputs.pooler_output if return_dict else outputs[1]
SCREAMING_SNAKE_CASE = self.classifier(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
SCREAMING_SNAKE_CASE = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
SCREAMING_SNAKE_CASE = """single_label_classification"""
else:
SCREAMING_SNAKE_CASE = """multi_label_classification"""
if self.config.problem_type == "regression":
SCREAMING_SNAKE_CASE = MSELoss()
if self.num_labels == 1:
SCREAMING_SNAKE_CASE = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
SCREAMING_SNAKE_CASE = loss_fct(lowerCamelCase__ ,lowerCamelCase__ )
elif self.config.problem_type == "single_label_classification":
SCREAMING_SNAKE_CASE = CrossEntropyLoss()
SCREAMING_SNAKE_CASE = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
SCREAMING_SNAKE_CASE = BCEWithLogitsLoss()
SCREAMING_SNAKE_CASE = loss_fct(lowerCamelCase__ ,lowerCamelCase__ )
if not return_dict:
SCREAMING_SNAKE_CASE = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowerCamelCase__ ,logits=lowerCamelCase__ ,hidden_states=outputs.hidden_states )
| 193
| 1
|
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
__a : str = field(default='''audio-classification''' ,metadata={'''include_in_asdict_even_if_is_default''': True} )
__a : ClassVar[Features] = Features({'''audio''': Audio()} )
__a : ClassVar[Features] = Features({'''labels''': ClassLabel} )
__a : str = "audio"
__a : str = "labels"
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if self.label_column not in features:
raise ValueError(F"Column {self.label_column} is not present in features." )
if not isinstance(features[self.label_column] , lowerCAmelCase__ ):
raise ValueError(F"Column {self.label_column} is not a ClassLabel." )
__lowercase = copy.deepcopy(self )
__lowercase = self.label_schema.copy()
__lowercase = features[self.label_column]
__lowercase = label_schema
return task_template
@property
def _SCREAMING_SNAKE_CASE ( self ) -> Dict[str, str]:
'''simple docstring'''
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 210
|
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
__a : Optional[Any] = (KDPMaDiscreteScheduler,)
__a : Dict = 10
def _SCREAMING_SNAKE_CASE ( self , **lowerCAmelCase__ ) -> int:
'''simple docstring'''
__lowercase = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**lowerCAmelCase__ )
return config
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(prediction_type='''v_prediction''' )
__lowercase = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
__lowercase = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = model(lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = output.prev_sample
__lowercase = torch.sum(torch.abs(lowerCAmelCase__ ) )
__lowercase = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 4.693_4286_5017_0972E-07 ) < 1E-2
assert abs(result_mean.item() - 0.0002 ) < 1E-3
def _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
if torch_device == "mps":
return
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
__lowercase = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = model(lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = output.prev_sample
__lowercase = torch.sum(torch.abs(lowerCAmelCase__ ) )
__lowercase = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
if torch_device == "mps":
return
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter.to(lowerCAmelCase__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__lowercase = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = model(lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase = output.prev_sample
__lowercase = torch.sum(torch.abs(lowerCAmelCase__ ) )
__lowercase = torch.mean(torch.abs(lowerCAmelCase__ ) )
if str(lowerCAmelCase__ ).startswith('''cpu''' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
| 210
| 1
|
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class __snake_case ( snake_case_ ):
__lowerCamelCase : Optional[int] = 42
class __snake_case ( nn.Module ):
def __init__( self , snake_case__=3 , snake_case__=3 , snake_case__=("DownEncoderBlock2D",) , snake_case__=(64,) , snake_case__=2 , snake_case__=32 , snake_case__="silu" , snake_case__=True , ) -> int:
'''simple docstring'''
super().__init__()
UpperCAmelCase : str =layers_per_block
UpperCAmelCase : Dict =torch.nn.Convad(
snake_case__ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
UpperCAmelCase : Dict =None
UpperCAmelCase : Union[str, Any] =nn.ModuleList([] )
# down
UpperCAmelCase : Union[str, Any] =block_out_channels[0]
for i, down_block_type in enumerate(snake_case__ ):
UpperCAmelCase : Optional[int] =output_channel
UpperCAmelCase : Union[str, Any] =block_out_channels[i]
UpperCAmelCase : Optional[int] =i == len(snake_case__ ) - 1
UpperCAmelCase : Optional[Any] =get_down_block(
snake_case__ , num_layers=self.layers_per_block , in_channels=snake_case__ , out_channels=snake_case__ , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=snake_case__ , resnet_groups=snake_case__ , attention_head_dim=snake_case__ , temb_channels=snake_case__ , )
self.down_blocks.append(snake_case__ )
# mid
UpperCAmelCase : int =UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=snake_case__ , output_scale_factor=1 , resnet_time_scale_shift='''default''' , attention_head_dim=block_out_channels[-1] , resnet_groups=snake_case__ , temb_channels=snake_case__ , )
# out
UpperCAmelCase : Optional[int] =nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=snake_case__ , eps=1e-6 )
UpperCAmelCase : Any =nn.SiLU()
UpperCAmelCase : List[str] =2 * out_channels if double_z else out_channels
UpperCAmelCase : List[Any] =nn.Convad(block_out_channels[-1] , snake_case__ , 3 , padding=1 )
UpperCAmelCase : Any =False
def UpperCAmelCase__ ( self , snake_case__ ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[Any] =x
UpperCAmelCase : Tuple =self.conv_in(snake_case__ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(snake_case__ ):
def custom_forward(*snake_case__ ):
return module(*snake_case__ )
return custom_forward
# down
if is_torch_version('''>=''' , '''1.11.0''' ):
for down_block in self.down_blocks:
UpperCAmelCase : List[Any] =torch.utils.checkpoint.checkpoint(
create_custom_forward(snake_case__ ) , snake_case__ , use_reentrant=snake_case__ )
# middle
UpperCAmelCase : Dict =torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , snake_case__ , use_reentrant=snake_case__ )
else:
for down_block in self.down_blocks:
UpperCAmelCase : Dict =torch.utils.checkpoint.checkpoint(create_custom_forward(snake_case__ ) , snake_case__ )
# middle
UpperCAmelCase : List[Any] =torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , snake_case__ )
else:
# down
for down_block in self.down_blocks:
UpperCAmelCase : int =down_block(snake_case__ )
# middle
UpperCAmelCase : Union[str, Any] =self.mid_block(snake_case__ )
# post-process
UpperCAmelCase : Any =self.conv_norm_out(snake_case__ )
UpperCAmelCase : int =self.conv_act(snake_case__ )
UpperCAmelCase : List[str] =self.conv_out(snake_case__ )
return sample
class __snake_case ( nn.Module ):
def __init__( self , snake_case__=3 , snake_case__=3 , snake_case__=("UpDecoderBlock2D",) , snake_case__=(64,) , snake_case__=2 , snake_case__=32 , snake_case__="silu" , snake_case__="group" , ) -> int:
'''simple docstring'''
super().__init__()
UpperCAmelCase : List[str] =layers_per_block
UpperCAmelCase : Optional[Any] =nn.Convad(
snake_case__ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
UpperCAmelCase : List[Any] =None
UpperCAmelCase : Dict =nn.ModuleList([] )
UpperCAmelCase : Tuple =in_channels if norm_type == 'spatial' else None
# mid
UpperCAmelCase : Optional[Any] =UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=snake_case__ , output_scale_factor=1 , resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=snake_case__ , temb_channels=snake_case__ , )
# up
UpperCAmelCase : int =list(reversed(snake_case__ ) )
UpperCAmelCase : List[Any] =reversed_block_out_channels[0]
for i, up_block_type in enumerate(snake_case__ ):
UpperCAmelCase : Tuple =output_channel
UpperCAmelCase : Any =reversed_block_out_channels[i]
UpperCAmelCase : Optional[Any] =i == len(snake_case__ ) - 1
UpperCAmelCase : Optional[int] =get_up_block(
snake_case__ , num_layers=self.layers_per_block + 1 , in_channels=snake_case__ , out_channels=snake_case__ , prev_output_channel=snake_case__ , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=snake_case__ , resnet_groups=snake_case__ , attention_head_dim=snake_case__ , temb_channels=snake_case__ , resnet_time_scale_shift=snake_case__ , )
self.up_blocks.append(snake_case__ )
UpperCAmelCase : Dict =output_channel
# out
if norm_type == "spatial":
UpperCAmelCase : Dict =SpatialNorm(block_out_channels[0] , snake_case__ )
else:
UpperCAmelCase : str =nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=snake_case__ , eps=1e-6 )
UpperCAmelCase : List[Any] =nn.SiLU()
UpperCAmelCase : str =nn.Convad(block_out_channels[0] , snake_case__ , 3 , padding=1 )
UpperCAmelCase : int =False
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=None ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =z
UpperCAmelCase : Tuple =self.conv_in(snake_case__ )
UpperCAmelCase : Tuple =next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(snake_case__ ):
def custom_forward(*snake_case__ ):
return module(*snake_case__ )
return custom_forward
if is_torch_version('''>=''' , '''1.11.0''' ):
# middle
UpperCAmelCase : Dict =torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , snake_case__ , snake_case__ , use_reentrant=snake_case__ )
UpperCAmelCase : Any =sample.to(snake_case__ )
# up
for up_block in self.up_blocks:
UpperCAmelCase : Dict =torch.utils.checkpoint.checkpoint(
create_custom_forward(snake_case__ ) , snake_case__ , snake_case__ , use_reentrant=snake_case__ )
else:
# middle
UpperCAmelCase : Optional[Any] =torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =sample.to(snake_case__ )
# up
for up_block in self.up_blocks:
UpperCAmelCase : List[str] =torch.utils.checkpoint.checkpoint(create_custom_forward(snake_case__ ) , snake_case__ , snake_case__ )
else:
# middle
UpperCAmelCase : List[Any] =self.mid_block(snake_case__ , snake_case__ )
UpperCAmelCase : List[Any] =sample.to(snake_case__ )
# up
for up_block in self.up_blocks:
UpperCAmelCase : int =up_block(snake_case__ , snake_case__ )
# post-process
if latent_embeds is None:
UpperCAmelCase : List[str] =self.conv_norm_out(snake_case__ )
else:
UpperCAmelCase : Tuple =self.conv_norm_out(snake_case__ , snake_case__ )
UpperCAmelCase : Optional[Any] =self.conv_act(snake_case__ )
UpperCAmelCase : Optional[int] =self.conv_out(snake_case__ )
return sample
class __snake_case ( nn.Module ):
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__="random" , snake_case__=False , snake_case__=True ) -> Dict:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Tuple =n_e
UpperCAmelCase : Any =vq_embed_dim
UpperCAmelCase : Optional[Any] =beta
UpperCAmelCase : List[str] =legacy
UpperCAmelCase : List[str] =nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
UpperCAmelCase : int =remap
if self.remap is not None:
self.register_buffer('''used''' , torch.tensor(np.load(self.remap ) ) )
UpperCAmelCase : Any =self.used.shape[0]
UpperCAmelCase : Optional[int] =unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
UpperCAmelCase : int =self.re_embed
UpperCAmelCase : Any =self.re_embed + 1
print(
f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
f'''Using {self.unknown_index} for unknown indices.''' )
else:
UpperCAmelCase : Optional[Any] =n_e
UpperCAmelCase : Any =sane_index_shape
def UpperCAmelCase__ ( self , snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : Tuple =inds.shape
assert len(snake_case__ ) > 1
UpperCAmelCase : List[str] =inds.reshape(ishape[0] , -1 )
UpperCAmelCase : Optional[int] =self.used.to(snake_case__ )
UpperCAmelCase : Optional[int] =(inds[:, :, None] == used[None, None, ...]).long()
UpperCAmelCase : Tuple =match.argmax(-1 )
UpperCAmelCase : List[str] =match.sum(2 ) < 1
if self.unknown_index == "random":
UpperCAmelCase : Dict =torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
UpperCAmelCase : Optional[Any] =self.unknown_index
return new.reshape(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> Any:
'''simple docstring'''
UpperCAmelCase : Tuple =inds.shape
assert len(snake_case__ ) > 1
UpperCAmelCase : int =inds.reshape(ishape[0] , -1 )
UpperCAmelCase : str =self.used.to(snake_case__ )
if self.re_embed > self.used.shape[0]: # extra token
UpperCAmelCase : Optional[int] =0 # simply set to zero
UpperCAmelCase : int =torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , snake_case__ )
return back.reshape(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =z.permute(0 , 2 , 3 , 1 ).contiguous()
UpperCAmelCase : List[Any] =z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
UpperCAmelCase : List[str] =torch.argmin(torch.cdist(snake_case__ , self.embedding.weight ) , dim=1 )
UpperCAmelCase : Optional[Any] =self.embedding(snake_case__ ).view(z.shape )
UpperCAmelCase : int =None
UpperCAmelCase : List[str] =None
# compute loss for embedding
if not self.legacy:
UpperCAmelCase : Optional[int] =self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
UpperCAmelCase : Optional[Any] =torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
UpperCAmelCase : Any =z + (z_q - z).detach()
# reshape back to match original input shape
UpperCAmelCase : int =z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
UpperCAmelCase : Tuple =min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
UpperCAmelCase : int =self.remap_to_used(snake_case__ )
UpperCAmelCase : Optional[Any] =min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
UpperCAmelCase : Union[str, Any] =min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Optional[Any]:
'''simple docstring'''
if self.remap is not None:
UpperCAmelCase : Any =indices.reshape(shape[0] , -1 ) # add batch axis
UpperCAmelCase : Optional[int] =self.unmap_to_all(snake_case__ )
UpperCAmelCase : Any =indices.reshape(-1 ) # flatten again
# get quantized latent vectors
UpperCAmelCase : List[str] =self.embedding(snake_case__ )
if shape is not None:
UpperCAmelCase : Tuple =z_q.view(snake_case__ )
# reshape back to match original input shape
UpperCAmelCase : Dict =z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class __snake_case ( snake_case_ ):
def __init__( self , snake_case__ , snake_case__=False ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[Any] =parameters
UpperCAmelCase : Optional[int] =torch.chunk(snake_case__ , 2 , dim=1 )
UpperCAmelCase : Dict =torch.clamp(self.logvar , -30.0 , 20.0 )
UpperCAmelCase : List[str] =deterministic
UpperCAmelCase : Any =torch.exp(0.5 * self.logvar )
UpperCAmelCase : Union[str, Any] =torch.exp(self.logvar )
if self.deterministic:
UpperCAmelCase : str =torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def UpperCAmelCase__ ( self , snake_case__ = None ) -> torch.FloatTensor:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =randn_tensor(
self.mean.shape , generator=snake_case__ , device=self.parameters.device , dtype=self.parameters.dtype )
UpperCAmelCase : Dict =self.mean + self.std * sample
return x
def UpperCAmelCase__ ( self , snake_case__=None ) -> List[Any]:
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=[1, 2, 3] ) -> int:
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
UpperCAmelCase : Dict =np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=snake_case__ )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return self.mean
| 356
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Swinv2ForImageClassification''',
'''Swinv2ForMaskedImageModeling''',
'''Swinv2Model''',
'''Swinv2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 78
| 0
|
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
assert isinstance(lowercase_ , lowercase_ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
A__ = f"""The input value of [n={number}] has to be > 0"""
raise ValueError(lowercase_ )
else:
A__ = sylvester(number - 1 )
A__ = num - 1
A__ = num
return lower * upper + 1
if __name__ == "__main__":
print(F'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')
| 14
|
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = args.threshold
A__ = args.model_name_or_path.rstrip('''/''' )
A__ = args.target_model_path
print(f"""Load fine-pruned model from {model_name_or_path}""" )
A__ = torch.load(os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
A__ = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
A__ = tensor
print(f"""Copied layer {name}""" )
elif "classifier" in name or "qa_output" in name:
A__ = tensor
print(f"""Copied layer {name}""" )
elif "bias" in name:
A__ = tensor
print(f"""Copied layer {name}""" )
else:
if pruning_method == "magnitude":
A__ = MagnitudeBinarizer.apply(inputs=lowercase_ , threshold=lowercase_ )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
A__ = name[:-6]
A__ = model[f"""{prefix_}mask_scores"""]
A__ = TopKBinarizer.apply(lowercase_ , lowercase_ )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
A__ = name[:-6]
A__ = model[f"""{prefix_}mask_scores"""]
A__ = ThresholdBinarizer.apply(lowercase_ , lowercase_ , lowercase_ )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
A__ = name[:-6]
A__ = model[f"""{prefix_}mask_scores"""]
A__ , A__ = -0.1, 1.1
A__ = torch.sigmoid(lowercase_ )
A__ = s * (r - l) + l
A__ = s_bar.clamp(min=0.0 , max=1.0 )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
A__ = os.path.join(
os.path.dirname(lowercase_ ) , f"""bertarized_{os.path.basename(lowercase_ )}""" )
if not os.path.isdir(lowercase_ ):
shutil.copytree(lowercase_ , lowercase_ )
print(f"""\nCreated folder {target_model_path}""" )
torch.save(lowercase_ , os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--pruning_method""",
choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
type=str,
required=True,
help=(
"""Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
""" sigmoied_threshold = Soft movement pruning)"""
),
)
parser.add_argument(
"""--threshold""",
type=float,
required=False,
help=(
"""For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
"""For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
"""Not needed for `l0`"""
),
)
parser.add_argument(
"""--model_name_or_path""",
type=str,
required=True,
help="""Folder containing the model that was previously fine-pruned""",
)
parser.add_argument(
"""--target_model_path""",
default=None,
type=str,
required=False,
help="""Folder containing the model that was previously fine-pruned""",
)
_lowerCamelCase : int = parser.parse_args()
main(args)
| 14
| 1
|
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase ( ) -> int:
lowerCAmelCase_ = 9
lowerCAmelCase_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
lowerCAmelCase_ = kruskal(a_ , a_ )
lowerCAmelCase_ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(a_ ) == sorted(a_ )
| 367
|
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCamelCase ( a_ , a_ , a_ , a_ , a_ ) -> List[Any]:
# load base model
lowerCAmelCase_ = StableDiffusionPipeline.from_pretrained(a_ , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
lowerCAmelCase_ = load_file(a_ )
lowerCAmelCase_ = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
lowerCAmelCase_ = key.split('.' )[0].split(LORA_PREFIX_TEXT_ENCODER + '_' )[-1].split('_' )
lowerCAmelCase_ = pipeline.text_encoder
else:
lowerCAmelCase_ = key.split('.' )[0].split(LORA_PREFIX_UNET + '_' )[-1].split('_' )
lowerCAmelCase_ = pipeline.unet
# find the target layer
lowerCAmelCase_ = layer_infos.pop(0 )
while len(a_ ) > -1:
try:
lowerCAmelCase_ = curr_layer.__getattr__(a_ )
if len(a_ ) > 0:
lowerCAmelCase_ = layer_infos.pop(0 )
elif len(a_ ) == 0:
break
except Exception:
if len(a_ ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
lowerCAmelCase_ = layer_infos.pop(0 )
lowerCAmelCase_ = []
if "lora_down" in key:
pair_keys.append(key.replace('lora_down' , 'lora_up' ) )
pair_keys.append(a_ )
else:
pair_keys.append(a_ )
pair_keys.append(key.replace('lora_up' , 'lora_down' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
lowerCAmelCase_ = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
lowerCAmelCase_ = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(a_ , a_ ).unsqueeze(2 ).unsqueeze(3 )
else:
lowerCAmelCase_ = state_dict[pair_keys[0]].to(torch.floataa )
lowerCAmelCase_ = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(a_ , a_ )
# update visited list
for item in pair_keys:
visited.append(a_ )
return pipeline
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
)
parser.add_argument(
"""--lora_prefix_text_encoder""",
default="""lora_te""",
type=str,
help="""The prefix of text encoder weight in safetensors""",
)
parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
parser.add_argument(
"""--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
)
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
lowerCamelCase_ = parser.parse_args()
lowerCamelCase_ = args.base_model_path
lowerCamelCase_ = args.checkpoint_path
lowerCamelCase_ = args.dump_path
lowerCamelCase_ = args.lora_prefix_unet
lowerCamelCase_ = args.lora_prefix_text_encoder
lowerCamelCase_ = args.alpha
lowerCamelCase_ = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
lowerCamelCase_ = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 14
| 0
|
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
lowercase__ = get_tests_dir("""fixtures""")
lowercase__ = get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
lowercase__ = get_tests_dir("""fixtures/dummy-config.json""")
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : Dict = 0
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : Optional[int] = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h" )
self.assertIsInstance(a_ , a_ )
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : Optional[int] = AutoFeatureExtractor.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
def lowerCamelCase ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase_ : List[Any] = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
lowerCAmelCase_ : Tuple = AutoFeatureExtractor.from_pretrained(a_ ).to_dict()
config_dict.pop("feature_extractor_type" )
lowerCAmelCase_ : Tuple = WavaVecaFeatureExtractor(**a_ )
# save in new folder
model_config.save_pretrained(a_ )
config.save_pretrained(a_ )
lowerCAmelCase_ : int = AutoFeatureExtractor.from_pretrained(a_ )
# make sure private variable is not incorrectly saved
lowerCAmelCase_ : List[str] = json.loads(config.to_json_string() )
self.assertTrue("_processor_class" not in dict_as_saved )
self.assertIsInstance(a_ , a_ )
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : List[Any] = AutoFeatureExtractor.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
def lowerCamelCase ( self : Optional[int] ):
with self.assertRaisesRegex(
a_ , "bert-base is not a local folder and is not a valid model identifier" ):
lowerCAmelCase_ : int = AutoFeatureExtractor.from_pretrained("bert-base" )
def lowerCamelCase ( self : Optional[Any] ):
with self.assertRaisesRegex(
a_ , R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
lowerCAmelCase_ : List[Any] = AutoFeatureExtractor.from_pretrained(a_ , revision="aaaaaa" )
def lowerCamelCase ( self : List[str] ):
with self.assertRaisesRegex(
a_ , "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json." , ):
lowerCAmelCase_ : List[str] = AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model" )
def lowerCamelCase ( self : Optional[Any] ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(a_ ):
lowerCAmelCase_ : Optional[Any] = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(a_ ):
lowerCAmelCase_ : Dict = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
lowerCAmelCase_ : Tuple = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(a_ )
lowerCAmelCase_ : Union[str, Any] = AutoFeatureExtractor.from_pretrained(a_ , trust_remote_code=a_ )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
def lowerCamelCase ( self : Optional[Any] ):
try:
AutoConfig.register("custom" , a_ )
AutoFeatureExtractor.register(a_ , a_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(a_ ):
AutoFeatureExtractor.register(a_ , a_ )
# Now that the config is registered, it can be used as any other config with the auto-API
lowerCAmelCase_ : List[Any] = CustomFeatureExtractor.from_pretrained(a_ )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(a_ )
lowerCAmelCase_ : List[str] = AutoFeatureExtractor.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase ( self : Any ):
class __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : List[Any] = True
try:
AutoConfig.register("custom" , a_ )
AutoFeatureExtractor.register(a_ , a_ )
# If remote code is not set, the default is to use local
lowerCAmelCase_ : Tuple = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase_ : Dict = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase_ : List[Any] = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor" , trust_remote_code=a_ )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
self.assertTrue(not hasattr(a_ , "is_local" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 241
|
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __lowerCamelCase ( A__ , A__ , unittest.TestCase ):
'''simple docstring'''
a_ : Optional[Any] = IFInpaintingSuperResolutionPipeline
a_ : int = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
a_ : List[str] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""} )
a_ : int = PipelineTesterMixin.required_optional_params - {"""latents"""}
def lowerCamelCase ( self : Optional[Any] ):
return self._get_superresolution_dummy_components()
def lowerCamelCase ( self : Optional[Any] , a_ : List[str] , a_ : Union[str, Any]=0 ):
if str(a_ ).startswith("mps" ):
lowerCAmelCase_ : List[Any] = torch.manual_seed(a_ )
else:
lowerCAmelCase_ : str = torch.Generator(device=a_ ).manual_seed(a_ )
lowerCAmelCase_ : List[str] = floats_tensor((1, 3, 16, 16) , rng=random.Random(a_ ) ).to(a_ )
lowerCAmelCase_ : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(a_ ) ).to(a_ )
lowerCAmelCase_ : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(a_ ) ).to(a_ )
lowerCAmelCase_ : Any = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase ( self : List[Any] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCamelCase ( self : Optional[int] ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def lowerCamelCase ( self : Optional[Any] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCamelCase ( self : Tuple ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCamelCase ( self : List[str] ):
self._test_save_load_local()
def lowerCamelCase ( self : Optional[int] ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 241
| 1
|
"""simple docstring"""
from math import sqrt
def lowercase ( a__ : int ) -> bool:
assert isinstance(a__ , a__ ) and (
number >= 0
), "'number' must been an int and positive"
_UpperCamelCase = True
# 0 and 1 are none primes.
if number <= 1:
_UpperCamelCase = False
for divisor in range(2 , int(round(sqrt(a__ ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
_UpperCamelCase = False
break
# precondition
assert isinstance(a__ , a__ ), "'status' must been from type bool"
return status
def lowercase ( a__ : Union[str, Any] ) -> Any:
assert isinstance(a__ , a__ ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
_UpperCamelCase = list(range(2 , n + 1 ) )
_UpperCamelCase = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(a__ ) ):
for j in range(i + 1 , len(a__ ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
_UpperCamelCase = 0
# filters actual prime numbers.
_UpperCamelCase = [x for x in begin_list if x != 0]
# precondition
assert isinstance(a__ , a__ ), "'ans' must been from type list"
return ans
def lowercase ( a__ : int ) -> Union[str, Any]:
assert isinstance(a__ , a__ ) and (n > 2), "'N' must been an int and > 2"
_UpperCamelCase = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(a__ ):
ans.append(a__ )
# precondition
assert isinstance(a__ , a__ ), "'ans' must been from type list"
return ans
def lowercase ( a__ : List[str] ) -> int:
assert isinstance(a__ , a__ ) and number >= 0, "'number' must been an int and >= 0"
_UpperCamelCase = [] # this list will be returns of the function.
# potential prime number factors.
_UpperCamelCase = 2
_UpperCamelCase = number
if number == 0 or number == 1:
ans.append(a__ )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(a__ ):
while quotient != 1:
if is_prime(a__ ) and (quotient % factor == 0):
ans.append(a__ )
quotient /= factor
else:
factor += 1
else:
ans.append(a__ )
# precondition
assert isinstance(a__ , a__ ), "'ans' must been from type list"
return ans
def lowercase ( a__ : Union[str, Any] ) -> str:
assert isinstance(a__ , a__ ) and (
number >= 0
), "'number' bust been an int and >= 0"
_UpperCamelCase = 0
# prime factorization of 'number'
_UpperCamelCase = prime_factorization(a__ )
_UpperCamelCase = max(a__ )
# precondition
assert isinstance(a__ , a__ ), "'ans' must been from type int"
return ans
def lowercase ( a__ : int ) -> Optional[Any]:
assert isinstance(a__ , a__ ) and (
number >= 0
), "'number' bust been an int and >= 0"
_UpperCamelCase = 0
# prime factorization of 'number'
_UpperCamelCase = prime_factorization(a__ )
_UpperCamelCase = min(a__ )
# precondition
assert isinstance(a__ , a__ ), "'ans' must been from type int"
return ans
def lowercase ( a__ : List[Any] ) -> Optional[int]:
assert isinstance(a__ , a__ ), "'number' must been an int"
assert isinstance(number % 2 == 0 , a__ ), "compare bust been from type bool"
return number % 2 == 0
def lowercase ( a__ : Optional[Any] ) -> Any:
assert isinstance(a__ , a__ ), "'number' must been an int"
assert isinstance(number % 2 != 0 , a__ ), "compare bust been from type bool"
return number % 2 != 0
def lowercase ( a__ : List[str] ) -> List[str]:
assert (
isinstance(a__ , a__ ) and (number > 2) and is_even(a__ )
), "'number' must been an int, even and > 2"
_UpperCamelCase = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
_UpperCamelCase = get_prime_numbers(a__ )
_UpperCamelCase = len(a__ )
# run variable for while-loops.
_UpperCamelCase = 0
_UpperCamelCase = None
# exit variable. for break up the loops
_UpperCamelCase = True
while i < len_pn and loop:
_UpperCamelCase = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
_UpperCamelCase = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(a__ , a__ )
and (len(a__ ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def lowercase ( a__ : Any , a__ : List[str] ) -> Union[str, Any]:
assert (
isinstance(a__ , a__ )
and isinstance(a__ , a__ )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
_UpperCamelCase = 0
while numbera != 0:
_UpperCamelCase = numbera % numbera
_UpperCamelCase = numbera
_UpperCamelCase = rest
# precondition
assert isinstance(a__ , a__ ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def lowercase ( a__ : Tuple , a__ : Union[str, Any] ) -> List[str]:
assert (
isinstance(a__ , a__ )
and isinstance(a__ , a__ )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
_UpperCamelCase = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
_UpperCamelCase = prime_factorization(a__ )
_UpperCamelCase = prime_factorization(a__ )
elif numbera == 1 or numbera == 1:
_UpperCamelCase = []
_UpperCamelCase = []
_UpperCamelCase = max(a__ , a__ )
_UpperCamelCase = 0
_UpperCamelCase = 0
_UpperCamelCase = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
_UpperCamelCase = prime_fac_a.count(a__ )
_UpperCamelCase = prime_fac_a.count(a__ )
for _ in range(max(a__ , a__ ) ):
ans *= n
else:
_UpperCamelCase = prime_fac_a.count(a__ )
for _ in range(a__ ):
ans *= n
done.append(a__ )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
_UpperCamelCase = prime_fac_a.count(a__ )
for _ in range(a__ ):
ans *= n
done.append(a__ )
# precondition
assert isinstance(a__ , a__ ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def lowercase ( a__ : Optional[int] ) -> Union[str, Any]:
assert isinstance(a__ , a__ ) and (n >= 0), "'number' must been a positive int"
_UpperCamelCase = 0
_UpperCamelCase = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(a__ ):
ans += 1
# precondition
assert isinstance(a__ , a__ ) and is_prime(
a__ ), "'ans' must been a prime number and from type int"
return ans
def lowercase ( a__ : Tuple , a__ : List[str] ) -> Tuple:
assert (
is_prime(a__ ) and is_prime(a__ ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
_UpperCamelCase = p_number_a + 1 # jump to the next number
_UpperCamelCase = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(a__ ):
number += 1
while number < p_number_a:
ans.append(a__ )
number += 1
# fetch the next prime number.
while not is_prime(a__ ):
number += 1
# precondition
assert (
isinstance(a__ , a__ )
and ans[0] != p_number_a
and ans[len(a__ ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def lowercase ( a__ : int ) -> List[str]:
assert isinstance(a__ , a__ ) and (n >= 1), "'n' must been int and >= 1"
_UpperCamelCase = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(a__ )
# precondition
assert ans[0] == 1 and ans[len(a__ ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def lowercase ( a__ : List[str] ) -> str:
assert isinstance(a__ , a__ ) and (
number > 1
), "'number' must been an int and >= 1"
_UpperCamelCase = get_divisors(a__ )
# precondition
assert (
isinstance(a__ , a__ )
and (divisors[0] == 1)
and (divisors[len(a__ ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def lowercase ( a__ : List[Any] , a__ : Union[str, Any] ) -> Optional[int]:
assert (
isinstance(a__ , a__ )
and isinstance(a__ , a__ )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
_UpperCamelCase = gcd(abs(a__ ) , abs(a__ ) )
# precondition
assert (
isinstance(a__ , a__ )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def lowercase ( a__ : int ) -> Dict:
assert isinstance(a__ , a__ ) and (n >= 0), "'n' must been a int and >= 0"
_UpperCamelCase = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def lowercase ( a__ : str ) -> Optional[Any]:
assert isinstance(a__ , a__ ) and (n >= 0), "'n' must been an int and >= 0"
_UpperCamelCase = 0
_UpperCamelCase = 1
_UpperCamelCase = 1 # this will be return
for _ in range(n - 1 ):
_UpperCamelCase = ans
ans += fiba
_UpperCamelCase = tmp
return ans
| 54
|
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 54
| 1
|
from __future__ import annotations
from math import gcd
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int = 2 , UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 3 , )->int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('''The input value cannot be less than 2''' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
return (pow(UpperCamelCase__ , 2 ) + step) % modulus
for _ in range(UpperCamelCase__ ):
# These track the position within the cycle detection logic.
A__ = seed
A__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
A__ = rand_fn(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
A__ = rand_fn(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
A__ = rand_fn(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
A__ = gcd(hare - tortoise , UpperCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
A__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
a__: int = argparse.ArgumentParser()
parser.add_argument(
'num',
type=int,
help='The value to find a divisor of',
)
parser.add_argument(
'--attempts',
type=int,
default=3,
help='The number of attempts before giving up',
)
a__: Dict = parser.parse_args()
a__: List[str] = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
a__: Optional[int] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 193
|
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
a__: List[str] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''sequence-classification'''
def __init__( self,__lowerCamelCase ):
if type(__lowerCamelCase ) == dict:
A__ = Namespace(**__lowerCamelCase )
A__ = glue_output_modes[hparams.task]
A__ = glue_tasks_num_labels[hparams.task]
super().__init__(__lowerCamelCase,__lowerCamelCase,self.mode )
def UpperCamelCase ( self,**__lowerCamelCase ):
return self.model(**__lowerCamelCase )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
A__ = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
A__ = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
A__ = self(**__lowerCamelCase )
A__ = outputs[0]
A__ = self.trainer.lr_schedulers[0]['''scheduler''']
A__ = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase ( self ):
A__ = self.hparams
A__ = processors[args.task]()
A__ = processor.get_labels()
for mode in ["train", "dev"]:
A__ = self._feature_file(__lowerCamelCase )
if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''',__lowerCamelCase )
else:
logger.info('''Creating features from dataset file at %s''',args.data_dir )
A__ = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
A__ = convert_examples_to_features(
__lowerCamelCase,self.tokenizer,max_length=args.max_seq_length,label_list=self.labels,output_mode=args.glue_output_mode,)
logger.info('''Saving features into cached file %s''',__lowerCamelCase )
torch.save(__lowerCamelCase,__lowerCamelCase )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = False ):
A__ = '''dev''' if mode == '''test''' else mode
A__ = self._feature_file(__lowerCamelCase )
logger.info('''Loading features from cached file %s''',__lowerCamelCase )
A__ = torch.load(__lowerCamelCase )
A__ = torch.tensor([f.input_ids for f in features],dtype=torch.long )
A__ = torch.tensor([f.attention_mask for f in features],dtype=torch.long )
A__ = torch.tensor([f.token_type_ids for f in features],dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
A__ = torch.tensor([f.label for f in features],dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
A__ = torch.tensor([f.label for f in features],dtype=torch.float )
return DataLoader(
TensorDataset(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ),batch_size=__lowerCamelCase,shuffle=__lowerCamelCase,)
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
A__ = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
A__ = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
A__ = self(**__lowerCamelCase )
A__ , A__ = outputs[:2]
A__ = logits.detach().cpu().numpy()
A__ = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase ( self,__lowerCamelCase ):
A__ = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
A__ = np.concatenate([x['''pred'''] for x in outputs],axis=0 )
if self.hparams.glue_output_mode == "classification":
A__ = np.argmax(__lowerCamelCase,axis=1 )
elif self.hparams.glue_output_mode == "regression":
A__ = np.squeeze(__lowerCamelCase )
A__ = np.concatenate([x['''target'''] for x in outputs],axis=0 )
A__ = [[] for _ in range(out_label_ids.shape[0] )]
A__ = [[] for _ in range(out_label_ids.shape[0] )]
A__ = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task,__lowerCamelCase,__lowerCamelCase )}
A__ = dict(results.items() )
A__ = results
return ret, preds_list, out_label_list
def UpperCamelCase ( self,__lowerCamelCase ):
A__ , A__ , A__ = self._eval_end(__lowerCamelCase )
A__ = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase ( self,__lowerCamelCase ):
A__ , A__ , A__ = self._eval_end(__lowerCamelCase )
A__ = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase ( __lowerCamelCase,__lowerCamelCase ):
BaseTransformer.add_model_specific_args(__lowerCamelCase,__lowerCamelCase )
parser.add_argument(
'''--max_seq_length''',default=128,type=__lowerCamelCase,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
),)
parser.add_argument(
'''--task''',default='''''',type=__lowerCamelCase,required=__lowerCamelCase,help='''The GLUE task to run''',)
parser.add_argument(
'''--gpus''',default=0,type=__lowerCamelCase,help='''The number of GPUs allocated for this, it is by default 0 meaning none''',)
parser.add_argument(
'''--overwrite_cache''',action='''store_true''',help='''Overwrite the cached training and evaluation sets''' )
return parser
def UpperCamelCase__( )->Any:
A__ = argparse.ArgumentParser()
add_generic_args(UpperCamelCase__ , os.getcwd() )
A__ = GLUETransformer.add_model_specific_args(UpperCamelCase__ , os.getcwd() )
A__ = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
A__ = os.path.join(
'''./results''' , f"{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}" , )
os.makedirs(args.output_dir )
A__ = GLUETransformer(UpperCamelCase__ )
A__ = generic_train(UpperCamelCase__ , UpperCamelCase__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
A__ = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=UpperCamelCase__ ) )
A__ = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(UpperCamelCase__ )
if __name__ == "__main__":
main()
| 193
| 1
|
def lowercase (SCREAMING_SNAKE_CASE_ : int = 50 ) -> int:
SCREAMING_SNAKE_CASE = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 365
|
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
SCREAMING_SNAKE_CASE = int(number**0.5 )
return number == sq * sq
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> tuple[int, int]:
SCREAMING_SNAKE_CASE = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
SCREAMING_SNAKE_CASE = x_den * y_den * z_den
SCREAMING_SNAKE_CASE = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
top //= hcf
bottom //= hcf
return top, bottom
def lowercase (SCREAMING_SNAKE_CASE_ : int = 35 ) -> int:
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = Fraction(0 )
SCREAMING_SNAKE_CASE = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
SCREAMING_SNAKE_CASE = x_num * y_den + x_den * y_num
SCREAMING_SNAKE_CASE = x_den * y_den
SCREAMING_SNAKE_CASE = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
# n=2
SCREAMING_SNAKE_CASE = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
SCREAMING_SNAKE_CASE = x_den * x_den * y_den * y_den
if is_sq(SCREAMING_SNAKE_CASE_ ) and is_sq(SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
# n=-1
SCREAMING_SNAKE_CASE = x_num * y_num
SCREAMING_SNAKE_CASE = x_den * y_num + x_num * y_den
SCREAMING_SNAKE_CASE = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
# n=2
SCREAMING_SNAKE_CASE = x_num * x_num * y_num * y_num
SCREAMING_SNAKE_CASE = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(SCREAMING_SNAKE_CASE_ ) and is_sq(SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
for num, den in unique_s:
total += Fraction(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 38
| 0
|
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__=2 , UpperCamelCase__=8 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=99 , UpperCamelCase__=16 , UpperCamelCase__=5 , UpperCamelCase__=2 , UpperCamelCase__=36 , UpperCamelCase__="gelu" , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=512 , UpperCamelCase__=16 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=3 , UpperCamelCase__=4 , UpperCamelCase__=None , ) -> Union[str, Any]:
lowerCamelCase : Tuple = parent
lowerCamelCase : Optional[Any] = batch_size
lowerCamelCase : Dict = seq_length
lowerCamelCase : int = is_training
lowerCamelCase : Dict = use_input_mask
lowerCamelCase : Optional[Any] = use_token_type_ids
lowerCamelCase : Optional[int] = use_labels
lowerCamelCase : List[Any] = vocab_size
lowerCamelCase : List[Any] = hidden_size
lowerCamelCase : Dict = num_hidden_layers
lowerCamelCase : Tuple = num_attention_heads
lowerCamelCase : Union[str, Any] = intermediate_size
lowerCamelCase : List[Any] = hidden_act
lowerCamelCase : Tuple = hidden_dropout_prob
lowerCamelCase : List[str] = attention_probs_dropout_prob
lowerCamelCase : List[str] = max_position_embeddings
lowerCamelCase : int = type_vocab_size
lowerCamelCase : Any = type_sequence_label_size
lowerCamelCase : List[str] = initializer_range
lowerCamelCase : Dict = num_labels
lowerCamelCase : Optional[int] = num_choices
lowerCamelCase : Any = scope
def _lowercase ( self ) -> Any:
lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase : Optional[Any] = None
if self.use_input_mask:
lowerCamelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase : Optional[Any] = None
if self.use_token_type_ids:
lowerCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCamelCase : Any = None
lowerCamelCase : List[str] = None
lowerCamelCase : List[Any] = None
if self.use_labels:
lowerCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase : int = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self ) -> List[str]:
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , )
def _lowercase ( self ) -> List[str]:
lowerCamelCase : str = self.get_config()
lowerCamelCase : Optional[Any] = 300
return config
def _lowercase ( self ) -> Optional[int]:
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) : List[str] = self.prepare_config_and_inputs()
lowerCamelCase : List[str] = True
lowerCamelCase : Optional[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
lowerCamelCase : Dict = MraModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCamelCase : List[Any] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
lowerCamelCase : Optional[int] = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
lowerCamelCase : Optional[int] = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> Union[str, Any]:
lowerCamelCase : Union[str, Any] = True
lowerCamelCase : Dict = MraModel(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCamelCase : Optional[int] = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , )
lowerCamelCase : List[Any] = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , )
lowerCamelCase : List[Any] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
lowerCamelCase : Tuple = MraForMaskedLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCamelCase : Union[str, Any] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
lowerCamelCase : str = MraForQuestionAnswering(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCamelCase : int = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , start_positions=UpperCamelCase__ , end_positions=UpperCamelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
lowerCamelCase : str = self.num_labels
lowerCamelCase : Union[str, Any] = MraForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCamelCase : Dict = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
lowerCamelCase : int = self.num_labels
lowerCamelCase : str = MraForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCamelCase : List[str] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
lowerCamelCase : Any = self.num_choices
lowerCamelCase : Dict = MraForMultipleChoice(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowerCamelCase : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCamelCase : Any = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCamelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCamelCase : Optional[int] = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) : Tuple = config_and_inputs
lowerCamelCase : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase__ (lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : List[str] = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Dict = False
lowerCamelCase_ : str = False
lowerCamelCase_ : List[Any] = False
lowerCamelCase_ : Optional[int] = ()
def _lowercase ( self ) -> Union[str, Any]:
lowerCamelCase : Optional[Any] = MraModelTester(self )
lowerCamelCase : Union[str, Any] = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def _lowercase ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase ( self ) -> Tuple:
lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self ) -> List[str]:
lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase : Optional[Any] = type
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self ) -> Dict:
lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def _lowercase ( self ) -> Union[str, Any]:
lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )
def _lowercase ( self ) -> Tuple:
lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )
def _lowercase ( self ) -> int:
lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )
@slow
def _lowercase ( self ) -> int:
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase : int = MraModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@unittest.skip(reason="MRA does not output attentions" )
def _lowercase ( self ) -> Optional[int]:
return
@require_torch
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self ) -> int:
lowerCamelCase : Dict = MraModel.from_pretrained("uw-madison/mra-base-512-4" )
lowerCamelCase : Dict = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
lowerCamelCase : Dict = model(UpperCamelCase__ )[0]
lowerCamelCase : str = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , UpperCamelCase__ )
lowerCamelCase : List[str] = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
@slow
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : Any = MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4" )
lowerCamelCase : List[str] = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
lowerCamelCase : Optional[int] = model(UpperCamelCase__ )[0]
lowerCamelCase : Union[str, Any] = 5_0265
lowerCamelCase : int = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
lowerCamelCase : List[str] = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
@slow
def _lowercase ( self ) -> List[str]:
lowerCamelCase : List[str] = MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3" )
lowerCamelCase : Optional[int] = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
lowerCamelCase : Any = model(UpperCamelCase__ )[0]
lowerCamelCase : int = 5_0265
lowerCamelCase : Optional[Any] = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
lowerCamelCase : Any = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
| 48
|
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = fname.split(os.path.sep )[-1]
return re.search(R'^(.*)_\d+\.jpg$' , lowercase_ ).groups()[0]
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :List[str] , lowercase_ :Dict , lowercase_ :List[str]=None , lowercase_ :Optional[Any]=None ) -> Optional[int]:
UpperCAmelCase = file_names
UpperCAmelCase = image_transform
UpperCAmelCase = label_to_id
def __len__( self :Optional[int] ) -> Optional[Any]:
return len(self.file_names )
def __getitem__( self :int , lowercase_ :str ) -> List[str]:
UpperCAmelCase = self.file_names[idx]
UpperCAmelCase = PIL.Image.open(lowercase_ )
UpperCAmelCase = raw_image.convert('RGB' )
if self.image_transform is not None:
UpperCAmelCase = self.image_transform(lowercase_ )
UpperCAmelCase = extract_label(lowercase_ )
if self.label_to_id is not None:
UpperCAmelCase = self.label_to_id[label]
return {"image": image, "label": label}
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
# Initialize accelerator
if args.with_tracking:
UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='all' , project_dir=args.project_dir )
else:
UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase = config['lr']
UpperCAmelCase = int(config['num_epochs'] )
UpperCAmelCase = int(config['seed'] )
UpperCAmelCase = int(config['batch_size'] )
UpperCAmelCase = config['image_size']
if not isinstance(lowercase_ , (list, tuple) ):
UpperCAmelCase = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , 'isdigit' ):
if args.checkpointing_steps == "epoch":
UpperCAmelCase = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
UpperCAmelCase = int(args.checkpointing_steps )
else:
raise ValueError(
F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
else:
UpperCAmelCase = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
UpperCAmelCase = os.path.split(lowercase_ )[-1].split('.' )[0]
accelerator.init_trackers(lowercase_ , lowercase_ )
# Grab all the image filenames
UpperCAmelCase = [os.path.join(args.data_dir , lowercase_ ) for fname in os.listdir(args.data_dir ) if fname.endswith('.jpg' )]
# Build the label correspondences
UpperCAmelCase = [extract_label(lowercase_ ) for fname in file_names]
UpperCAmelCase = list(set(lowercase_ ) )
id_to_label.sort()
UpperCAmelCase = {lbl: i for i, lbl in enumerate(lowercase_ )}
# Set the seed before splitting the data.
np.random.seed(lowercase_ )
torch.manual_seed(lowercase_ )
torch.cuda.manual_seed_all(lowercase_ )
# Split our filenames between train and validation
UpperCAmelCase = np.random.permutation(len(lowercase_ ) )
UpperCAmelCase = int(0.8 * len(lowercase_ ) )
UpperCAmelCase = random_perm[:cut]
UpperCAmelCase = random_perm[cut:]
# For training we use a simple RandomResizedCrop
UpperCAmelCase = Compose([RandomResizedCrop(lowercase_ , scale=(0.5, 1.0) ), ToTensor()] )
UpperCAmelCase = PetsDataset(
[file_names[i] for i in train_split] , image_transform=lowercase_ , label_to_id=lowercase_ )
# For evaluation, we use a deterministic Resize
UpperCAmelCase = Compose([Resize(lowercase_ ), ToTensor()] )
UpperCAmelCase = PetsDataset([file_names[i] for i in eval_split] , image_transform=lowercase_ , label_to_id=lowercase_ )
# Instantiate dataloaders.
UpperCAmelCase = DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4 )
UpperCAmelCase = DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase = create_model('resnet50d' , pretrained=lowercase_ , num_classes=len(lowercase_ ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCAmelCase = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
UpperCAmelCase = False
for param in model.get_classifier().parameters():
UpperCAmelCase = True
# We normalize the batches of images to be a bit faster.
UpperCAmelCase = torch.tensor(model.default_cfg['mean'] )[None, :, None, None].to(accelerator.device )
UpperCAmelCase = torch.tensor(model.default_cfg['std'] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
UpperCAmelCase = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
UpperCAmelCase = OneCycleLR(optimizer=lowercase_ , max_lr=lowercase_ , epochs=lowercase_ , steps_per_epoch=len(lowercase_ ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = accelerator.prepare(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase = 0
# We also need to keep track of the starting epoch so files are named properly
UpperCAmelCase = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}""" )
accelerator.load_state(args.resume_from_checkpoint )
UpperCAmelCase = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
UpperCAmelCase = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
UpperCAmelCase = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
UpperCAmelCase = os.path.splitext(lowercase_ )[0]
if "epoch" in training_difference:
UpperCAmelCase = int(training_difference.replace('epoch_' , '' ) ) + 1
UpperCAmelCase = None
else:
UpperCAmelCase = int(training_difference.replace('step_' , '' ) )
UpperCAmelCase = resume_step // len(lowercase_ )
resume_step -= starting_epoch * len(lowercase_ )
# Now we train the model
for epoch in range(lowercase_ , lowercase_ ):
model.train()
if args.with_tracking:
UpperCAmelCase = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
UpperCAmelCase = accelerator.skip_first_batches(lowercase_ , lowercase_ )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
UpperCAmelCase = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
UpperCAmelCase = {k: v.to(accelerator.device ) for k, v in batch.items()}
UpperCAmelCase = (batch['image'] - mean) / std
UpperCAmelCase = model(lowercase_ )
UpperCAmelCase = torch.nn.functional.cross_entropy(lowercase_ , batch['label'] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(lowercase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = F"""step_{overall_step}"""
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
UpperCAmelCase = os.path.join(args.output_dir , lowercase_ )
accelerator.save_state(lowercase_ )
model.eval()
UpperCAmelCase = 0
UpperCAmelCase = 0
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
UpperCAmelCase = {k: v.to(accelerator.device ) for k, v in batch.items()}
UpperCAmelCase = (batch['image'] - mean) / std
with torch.no_grad():
UpperCAmelCase = model(lowercase_ )
UpperCAmelCase = outputs.argmax(dim=-1 )
UpperCAmelCase , UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['label']) )
UpperCAmelCase = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
UpperCAmelCase = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}""" )
if args.with_tracking:
accelerator.log(
{
'accuracy': 100 * eval_metric,
'train_loss': total_loss.item() / len(lowercase_ ),
'epoch': epoch,
} , step=lowercase_ , )
if checkpointing_steps == "epoch":
UpperCAmelCase = F"""epoch_{epoch}"""
if args.output_dir is not None:
UpperCAmelCase = os.path.join(args.output_dir , lowercase_ )
accelerator.save_state(lowercase_ )
if args.with_tracking:
accelerator.end_training()
def _lowerCAmelCase ( ):
UpperCAmelCase = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument('--data_dir' , required=lowercase_ , help='The data folder on disk.' )
parser.add_argument('--fp16' , action='store_true' , help='If passed, will use FP16 training.' )
parser.add_argument(
'--mixed_precision' , type=lowercase_ , default=lowercase_ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
parser.add_argument(
'--checkpointing_steps' , type=lowercase_ , default=lowercase_ , help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' , )
parser.add_argument(
'--output_dir' , type=lowercase_ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--resume_from_checkpoint' , type=lowercase_ , default=lowercase_ , help='If the training should continue from a checkpoint folder.' , )
parser.add_argument(
'--with_tracking' , action='store_true' , help='Whether to load in all available experiment trackers from the environment and use them for logging.' , )
parser.add_argument(
'--project_dir' , type=lowercase_ , default='logs' , help='Location on where to store experiment tracking logs` and relevent project information' , )
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
training_function(lowercase_ , lowercase_ )
if __name__ == "__main__":
main()
| 78
| 0
|
from functools import reduce
lowerCamelCase : List[Any] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def snake_case_ ( lowerCAmelCase_ : Optional[int] = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda lowerCAmelCase_ , lowerCAmelCase_ : str(int(a__ ) * int(a__ ) ) , n[i : i + 13] ) )
for i in range(len(a__ ) - 12 ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 369
|
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : Optional[Any] = (DPMSolverSDEScheduler,)
_A : Dict = 10
def lowerCAmelCase ( self : Optional[int] , **__a : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase : Any = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**__a )
return config
def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a )
def lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def lowerCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : Optional[int] = self.scheduler_classes[0]
__lowercase : List[str] = self.get_scheduler_config()
__lowercase : Any = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase : Optional[Any] = self.dummy_model()
__lowercase : str = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase : Optional[Any] = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
__lowercase : Union[str, Any] = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[Any] = model(__a , __a )
__lowercase : Optional[Any] = scheduler.step(__a , __a , __a )
__lowercase : str = output.prev_sample
__lowercase : Optional[Any] = torch.sum(torch.abs(__a ) )
__lowercase : Union[str, Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1E-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1E-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase : Tuple = self.scheduler_classes[0]
__lowercase : Dict = self.get_scheduler_config(prediction_type="""v_prediction""" )
__lowercase : int = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase : Optional[int] = self.dummy_model()
__lowercase : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase : Dict = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
__lowercase : Dict = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[int] = model(__a , __a )
__lowercase : Optional[int] = scheduler.step(__a , __a , __a )
__lowercase : int = output.prev_sample
__lowercase : Optional[Any] = torch.sum(torch.abs(__a ) )
__lowercase : List[str] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1E-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1E-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1E-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3
def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase : Tuple = self.scheduler_classes[0]
__lowercase : Dict = self.get_scheduler_config()
__lowercase : Optional[int] = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
__lowercase : int = self.dummy_model()
__lowercase : Optional[Any] = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__lowercase : int = scheduler.scale_model_input(__a , __a )
__lowercase : List[str] = model(__a , __a )
__lowercase : List[str] = scheduler.step(__a , __a , __a )
__lowercase : int = output.prev_sample
__lowercase : List[Any] = torch.sum(torch.abs(__a ) )
__lowercase : Optional[Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1E-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1E-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase : str = self.scheduler_classes[0]
__lowercase : List[Any] = self.get_scheduler_config()
__lowercase : Tuple = scheduler_class(**__a , use_karras_sigmas=__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
__lowercase : List[str] = self.dummy_model()
__lowercase : Optional[int] = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
__lowercase : str = sample.to(__a )
for t in scheduler.timesteps:
__lowercase : List[Any] = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[Any] = model(__a , __a )
__lowercase : Any = scheduler.step(__a , __a , __a )
__lowercase : Optional[Any] = output.prev_sample
__lowercase : Any = torch.sum(torch.abs(__a ) )
__lowercase : Optional[Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
| 306
| 0
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
def lowerCAmelCase ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
if isinstance(lowercase_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowercase_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowercase_ ):
return [[videos]]
raise ValueError(F'''Could not make batched video from {videos}''' )
class _UpperCamelCase ( UpperCAmelCase__ ):
UpperCAmelCase_ = ["""pixel_values"""]
def __init__( self :Any , lowerCamelCase :bool = True , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :PILImageResampling = PILImageResampling.BILINEAR , lowerCamelCase :bool = True , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :bool = True , lowerCamelCase :Union[int, float] = 1 / 255 , lowerCamelCase :bool = True , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , **lowerCamelCase :Tuple , ) -> None:
super().__init__(**UpperCAmelCase__ )
UpperCAmelCase__ = size if size is not None else {"shortest_edge": 224}
UpperCAmelCase__ = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
UpperCAmelCase__ = crop_size if crop_size is not None else {"height": 224, "width": 224}
UpperCAmelCase__ = get_size_dict(UpperCAmelCase__ , param_name="crop_size" )
UpperCAmelCase__ = do_resize
UpperCAmelCase__ = size
UpperCAmelCase__ = do_center_crop
UpperCAmelCase__ = crop_size
UpperCAmelCase__ = resample
UpperCAmelCase__ = do_rescale
UpperCAmelCase__ = rescale_factor
UpperCAmelCase__ = do_normalize
UpperCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase_ ( self :int , lowerCamelCase :np.ndarray , lowerCamelCase :Dict[str, int] , lowerCamelCase :PILImageResampling = PILImageResampling.BILINEAR , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :Tuple , ) -> np.ndarray:
UpperCAmelCase__ = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
if "shortest_edge" in size:
UpperCAmelCase__ = get_resize_output_image_size(UpperCAmelCase__ , size["shortest_edge"] , default_to_square=UpperCAmelCase__ )
elif "height" in size and "width" in size:
UpperCAmelCase__ = (size["height"], size["width"])
else:
raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def UpperCAmelCase_ ( self :Optional[Any] , lowerCamelCase :np.ndarray , lowerCamelCase :Dict[str, int] , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :Union[str, Any] , ) -> np.ndarray:
UpperCAmelCase__ = get_size_dict(UpperCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(UpperCAmelCase__ , size=(size["height"], size["width"]) , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def UpperCAmelCase_ ( self :List[str] , lowerCamelCase :np.ndarray , lowerCamelCase :Union[int, float] , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :Optional[Any] , ) -> Union[str, Any]:
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def UpperCAmelCase_ ( self :str , lowerCamelCase :np.ndarray , lowerCamelCase :Union[float, List[float]] , lowerCamelCase :Union[float, List[float]] , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :List[Any] , ) -> np.ndarray:
return normalize(UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def UpperCAmelCase_ ( self :List[Any] , lowerCamelCase :ImageInput , lowerCamelCase :bool = None , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :PILImageResampling = None , lowerCamelCase :bool = None , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :bool = None , lowerCamelCase :float = None , lowerCamelCase :bool = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :Optional[ChannelDimension] = ChannelDimension.FIRST , ) -> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ = to_numpy_array(UpperCAmelCase__ )
if do_resize:
UpperCAmelCase__ = self.resize(image=UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ )
if do_center_crop:
UpperCAmelCase__ = self.center_crop(UpperCAmelCase__ , size=UpperCAmelCase__ )
if do_rescale:
UpperCAmelCase__ = self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ )
if do_normalize:
UpperCAmelCase__ = self.normalize(image=UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ )
UpperCAmelCase__ = to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ )
return image
def UpperCAmelCase_ ( self :Dict , lowerCamelCase :ImageInput , lowerCamelCase :bool = None , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :PILImageResampling = None , lowerCamelCase :bool = None , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :bool = None , lowerCamelCase :float = None , lowerCamelCase :bool = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :Optional[Union[str, TensorType]] = None , lowerCamelCase :ChannelDimension = ChannelDimension.FIRST , **lowerCamelCase :Optional[Any] , ) -> PIL.Image.Image:
UpperCAmelCase__ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ = resample if resample is not None else self.resample
UpperCAmelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ = image_std if image_std is not None else self.image_std
UpperCAmelCase__ = size if size is not None else self.size
UpperCAmelCase__ = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
UpperCAmelCase__ = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ = get_size_dict(UpperCAmelCase__ , param_name="crop_size" )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
UpperCAmelCase__ = make_batched(UpperCAmelCase__ )
UpperCAmelCase__ = [
[
self._preprocess_image(
image=UpperCAmelCase__ , do_resize=UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ , do_center_crop=UpperCAmelCase__ , crop_size=UpperCAmelCase__ , do_rescale=UpperCAmelCase__ , rescale_factor=UpperCAmelCase__ , do_normalize=UpperCAmelCase__ , image_mean=UpperCAmelCase__ , image_std=UpperCAmelCase__ , data_format=UpperCAmelCase__ , )
for img in video
]
for video in videos
]
UpperCAmelCase__ = {"pixel_values": videos}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 169
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int = 13 , UpperCAmelCase__ : int = 64 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 3 , UpperCAmelCase__ : int = 3 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 128 , UpperCAmelCase__ : Optional[Any]=[16, 32, 64, 128] , UpperCAmelCase__ : int = 7 , UpperCAmelCase__ : int = 4 , UpperCAmelCase__ : int = 37 , UpperCAmelCase__ : str = "gelu" , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : int = 10 , UpperCAmelCase__ : float = 0.02 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : int = 128 , UpperCAmelCase__ : List[int] = [2, 2, 2, 2] , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 2 , ) ->List[Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = is_training
A__ = use_labels
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = type_sequence_label_size
A__ = initializer_range
A__ = encoder_stride
A__ = num_attention_outputs
A__ = embed_dim
A__ = embed_dim + 1
A__ = resolution
A__ = depths
A__ = hidden_sizes
A__ = dim
A__ = mlp_expansion_ratio
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->str:
'''simple docstring'''
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
A__ = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self : int) ->str:
'''simple docstring'''
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict) ->Dict:
'''simple docstring'''
A__ = TFEfficientFormerModel(config=UpperCAmelCase__)
A__ = model(UpperCAmelCase__ , training=UpperCAmelCase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def SCREAMING_SNAKE_CASE ( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str) ->Union[str, Any]:
'''simple docstring'''
A__ = self.type_sequence_label_size
A__ = TFEfficientFormerForImageClassification(UpperCAmelCase__)
A__ = model(UpperCAmelCase__ , labels=UpperCAmelCase__ , training=UpperCAmelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
A__ = 1
A__ = TFEfficientFormerForImageClassification(UpperCAmelCase__)
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
A__ = model(UpperCAmelCase__ , labels=UpperCAmelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def SCREAMING_SNAKE_CASE ( self : int) ->List[str]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
UpperCAmelCase__ = (
{
'''feature-extraction''': TFEfficientFormerModel,
'''image-classification''': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->List[str]:
'''simple docstring'''
A__ = TFEfficientFormerModelTester(self)
A__ = ConfigTester(
self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37)
def SCREAMING_SNAKE_CASE ( self : int) ->Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''EfficientFormer does not use inputs_embeds''')
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''EfficientFormer does not support input and output embeddings''')
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Optional[Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self : Any) ->Optional[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCAmelCase__)
A__ = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : str) ->Any:
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict):
A__ = model_class(UpperCAmelCase__)
A__ = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__) , training=UpperCAmelCase__)
A__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A__ = getattr(
self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1)
self.assertEqual(len(UpperCAmelCase__) , UpperCAmelCase__)
if hasattr(self.model_tester , '''encoder_seq_length'''):
A__ = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , '''chunk_length''') and self.model_tester.chunk_length > 1:
A__ = seq_length * self.model_tester.chunk_length
else:
A__ = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:]) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
A__ = outputs.decoder_hidden_states
self.asseretIsInstance(UpperCAmelCase__ , (list, tuple))
self.assertEqual(len(UpperCAmelCase__) , UpperCAmelCase__)
A__ = getattr(self.model_tester , '''seq_length''' , UpperCAmelCase__)
A__ = getattr(self.model_tester , '''decoder_seq_length''' , UpperCAmelCase__)
self.assertListEqual(
list(hidden_states[-1].shape[-2:]) , [decoder_seq_length, self.model_tester.hidden_size] , )
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict=False) ->int:
'''simple docstring'''
A__ = super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__)
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Union[str, Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__)
@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet''')
def SCREAMING_SNAKE_CASE ( self : str) ->str:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Any) ->Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)
@slow
def SCREAMING_SNAKE_CASE ( self : Tuple) ->Optional[int]:
'''simple docstring'''
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = TFEfficientFormerModel.from_pretrained(UpperCAmelCase__)
self.assertIsNotNone(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Any) ->str:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
A__ = getattr(self.model_tester , '''seq_length''' , UpperCAmelCase__)
A__ = getattr(self.model_tester , '''encoder_seq_length''' , UpperCAmelCase__)
A__ = getattr(self.model_tester , '''key_length''' , UpperCAmelCase__)
A__ = getattr(self.model_tester , '''chunk_length''' , UpperCAmelCase__)
if chunk_length is not None and hasattr(self.model_tester , '''num_hashes'''):
A__ = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
A__ = True
A__ = False
A__ = True
A__ = model_class(UpperCAmelCase__)
A__ = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__) , training=UpperCAmelCase__)
A__ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCAmelCase__) , self.model_tester.num_attention_outputs)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = model_class(UpperCAmelCase__)
A__ = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__) , training=UpperCAmelCase__)
A__ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCAmelCase__) , self.model_tester.num_attention_outputs)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Optional[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
A__ = model_class(UpperCAmelCase__)
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
A__ = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=UpperCAmelCase__)
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
A__ = model(UpperCAmelCase__)
self.assertTrue(outputs_dict is not None)
def SCREAMING_SNAKE_CASE ( ) -> Any:
"""simple docstring"""
A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[str]:
'''simple docstring'''
return (
EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300''')
if is_vision_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Any:
'''simple docstring'''
A__ = TFEfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300''')
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCAmelCase__ , return_tensors='''tf''')
# forward pass
A__ = model(**UpperCAmelCase__ , training=UpperCAmelCase__)
# verify the logits
A__ = tf.TensorShape((1, 1_000))
self.assertEqual(outputs.logits.shape , UpperCAmelCase__)
A__ = tf.constant([-0.0555, 0.4825, -0.0852])
self.assertTrue(np.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1e-4))
@slow
def SCREAMING_SNAKE_CASE ( self : Dict) ->int:
'''simple docstring'''
A__ = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
'''snap-research/efficientformer-l1-300''')
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCAmelCase__ , return_tensors='''tf''')
# forward pass
A__ = model(**UpperCAmelCase__ , training=UpperCAmelCase__)
# verify the logits
A__ = tf.TensorShape((1, 1_000))
self.assertEqual(outputs.logits.shape , UpperCAmelCase__)
A__ = tf.constant([-0.1312, 0.4353, -1.0499])
self.assertTrue(np.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1e-4))
| 14
| 0
|
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Tuple = prime_factors(UpperCamelCase__ )
if is_square_free(UpperCamelCase__ ):
return -1 if len(UpperCamelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 324
| 1
|
"""simple docstring"""
import re
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = split_input(str_ )
return "".join(
["".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
try:
__SCREAMING_SNAKE_CASE = split_input(lowerCAmelCase_ )
if upper:
__SCREAMING_SNAKE_CASE = "".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
__SCREAMING_SNAKE_CASE = "".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return to_simple_case(lowerCAmelCase_ )
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
try:
__SCREAMING_SNAKE_CASE = to_simple_case(lowerCAmelCase_ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return to_complex_case(lowerCAmelCase_ , lowerCAmelCase_ , "_" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return to_complex_case(lowerCAmelCase_ , lowerCAmelCase_ , "-" )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 54
|
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
a__ : Union[str, Any] = logging.get_logger(__name__)
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = os.getenv("SM_HP_MP_PARAMETERS" , "{}" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
__SCREAMING_SNAKE_CASE = json.loads(lowerCAmelCase_ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
__SCREAMING_SNAKE_CASE = os.getenv("SM_FRAMEWORK_PARAMS" , "{}" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
__SCREAMING_SNAKE_CASE = json.loads(lowerCAmelCase_ )
if not mpi_options.get("sagemaker_mpi_enabled" , lowerCAmelCase_ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("smdistributed" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : str = field(
default="" , metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"} , )
def UpperCAmelCase_ ( self : List[str] ) -> Any:
super().__post_init__()
warnings.warn(
"`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
"`TrainingArguments` instead." , UpperCAmelCase__ , )
@cached_property
def UpperCAmelCase_ ( self : List[str] ) -> "torch.device":
logger.info("PyTorch: setting up devices" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"torch.distributed process group is initialized, but local_rank == -1. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
if self.no_cuda:
__SCREAMING_SNAKE_CASE = torch.device("cpu" )
__SCREAMING_SNAKE_CASE = 0
elif is_sagemaker_model_parallel_available():
__SCREAMING_SNAKE_CASE = smp.local_rank()
__SCREAMING_SNAKE_CASE = torch.device("cuda" , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="smddp" , timeout=self.ddp_timeout_delta )
__SCREAMING_SNAKE_CASE = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
__SCREAMING_SNAKE_CASE = torch.device("cuda" , self.local_rank )
__SCREAMING_SNAKE_CASE = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
__SCREAMING_SNAKE_CASE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="nccl" , timeout=self.ddp_timeout_delta )
__SCREAMING_SNAKE_CASE = torch.device("cuda" , self.local_rank )
__SCREAMING_SNAKE_CASE = 1
if device.type == "cuda":
torch.cuda.set_device(UpperCAmelCase__ )
return device
@property
def UpperCAmelCase_ ( self : Dict ) -> Any:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
return not is_sagemaker_model_parallel_available()
@property
def UpperCAmelCase_ ( self : Tuple ) -> int:
return False
| 54
| 1
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class lowerCAmelCase__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str]=13 , lowerCamelCase__ : Optional[Any]=7 , lowerCamelCase__ : List[str]=True , lowerCamelCase__ : Any=True , lowerCamelCase__ : List[Any]=True , lowerCamelCase__ : Any=True , lowerCamelCase__ : int=99 , lowerCamelCase__ : int=32 , lowerCamelCase__ : List[str]=5 , lowerCamelCase__ : Optional[Any]=4 , lowerCamelCase__ : Optional[int]=37 , lowerCamelCase__ : Tuple="gelu" , lowerCamelCase__ : Any=0.1 , lowerCamelCase__ : Union[str, Any]=0.1 , lowerCamelCase__ : Optional[int]=5_12 , lowerCamelCase__ : Optional[int]=16 , lowerCamelCase__ : str=2 , lowerCamelCase__ : Union[str, Any]=0.0_2 , lowerCamelCase__ : Tuple=4 , ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : List[Any] = batch_size
_UpperCAmelCase : Optional[int] = seq_length
_UpperCAmelCase : int = is_training
_UpperCAmelCase : Dict = use_attention_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : int = use_labels
_UpperCAmelCase : Optional[int] = vocab_size
_UpperCAmelCase : Any = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : Tuple = intermediate_size
_UpperCAmelCase : int = hidden_act
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_UpperCAmelCase : Union[str, Any] = max_position_embeddings
_UpperCAmelCase : Tuple = type_vocab_size
_UpperCAmelCase : List[Any] = type_sequence_label_size
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : Dict = num_choices
def lowerCAmelCase__ ( self : List[Any] ) ->Any:
'''simple docstring'''
_UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Dict = None
if self.use_attention_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : int = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCAmelCase__ ( self : Any ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase : Tuple = self.prepare_config_and_inputs()
_UpperCAmelCase : List[Any] = config_and_inputs
_UpperCAmelCase : str = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class lowerCAmelCase__ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCAmelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase__ ( self : Optional[int] ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : int = FlaxAlbertModelTester(self )
@slow
def lowerCAmelCase__ ( self : Any ) ->List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCAmelCase : List[str] = model_class_name.from_pretrained("albert-base-v2" )
_UpperCAmelCase : Optional[int] = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class lowerCAmelCase__ ( unittest.TestCase ):
@slow
def lowerCAmelCase__ ( self : Tuple ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : str = FlaxAlbertModel.from_pretrained("albert-base-v2" )
_UpperCAmelCase : List[Any] = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
_UpperCAmelCase : Optional[int] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_UpperCAmelCase : Dict = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )[0]
_UpperCAmelCase : List[Any] = (1, 11, 7_68)
self.assertEqual(output.shape , lowerCamelCase__ )
_UpperCAmelCase : str = np.array(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , lowerCamelCase__ , atol=1E-4 ) )
| 355
|
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase = 4_000_000 ):
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase , _UpperCAmelCase : Dict = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase : Any = b, a + b
return sum(__lowerCAmelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 322
| 0
|
import socket
def lowercase ( ) -> List[Any]:
_snake_case : Tuple = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_snake_case : Union[str, Any] = socket.gethostname()
_snake_case : Optional[int] = 12_312
sock.connect((host, port) )
sock.send(b"""Hello server!""" )
with open("""Received_file""" , """wb""" ) as out_file:
print("""File opened""" )
print("""Receiving data...""" )
while True:
_snake_case : List[Any] = sock.recv(1_024 )
if not data:
break
out_file.write(SCREAMING_SNAKE_CASE__ )
print("""Successfully received the file""" )
sock.close()
print("""Connection closed""" )
if __name__ == "__main__":
main()
| 317
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
UpperCAmelCase_ : Any = '''Create a default config file for Accelerate with only a few flags set.'''
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[int]="no" , __magic_name__ : str = default_json_config_file , __magic_name__ : bool = False ) -> str:
"""simple docstring"""
UpperCamelCase :Any = Path(__magic_name__ )
path.parent.mkdir(parents=__magic_name__ , exist_ok=__magic_name__ )
if path.exists():
print(
f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
UpperCamelCase :Dict = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
UpperCamelCase :Optional[Any] = {
"""compute_environment""": """LOCAL_MACHINE""",
"""mixed_precision""": mixed_precision,
}
if torch.cuda.is_available():
UpperCamelCase :Union[str, Any] = torch.cuda.device_count()
UpperCamelCase :List[Any] = num_gpus
UpperCamelCase :Dict = False
if num_gpus > 1:
UpperCamelCase :Any = """MULTI_GPU"""
else:
UpperCamelCase :Any = """NO"""
elif is_xpu_available() and use_xpu:
UpperCamelCase :Optional[Any] = torch.xpu.device_count()
UpperCamelCase :Optional[int] = num_xpus
UpperCamelCase :int = False
if num_xpus > 1:
UpperCamelCase :Union[str, Any] = """MULTI_XPU"""
else:
UpperCamelCase :Union[str, Any] = """NO"""
elif is_npu_available():
UpperCamelCase :List[Any] = torch.npu.device_count()
UpperCamelCase :Optional[Any] = num_npus
UpperCamelCase :Tuple = False
if num_npus > 1:
UpperCamelCase :Optional[Any] = """MULTI_NPU"""
else:
UpperCamelCase :List[Any] = """NO"""
else:
UpperCamelCase :Any = 0
UpperCamelCase :Optional[Any] = True
UpperCamelCase :Optional[Any] = 1
UpperCamelCase :List[str] = """NO"""
UpperCamelCase :int = ClusterConfig(**__magic_name__ )
config.to_json_file(__magic_name__ )
return path
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Dict , __magic_name__ : Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase :Dict = parser.add_parser("""default""" , parents=__magic_name__ , help=__magic_name__ , formatter_class=__magic_name__ )
parser.add_argument(
"""--config_file""" , default=__magic_name__ , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) , dest="""save_location""" , )
parser.add_argument(
"""--mixed_precision""" , choices=["""no""", """fp16""", """bf16"""] , type=__magic_name__ , help="""Whether or not to use mixed precision training. """
"""Choose between FP16 and BF16 (bfloat16) training. """
"""BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.""" , default="""no""" , )
parser.set_defaults(func=__magic_name__ )
return parser
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[Any] ) -> List[str]:
"""simple docstring"""
UpperCamelCase :Optional[Any] = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(f"""accelerate configuration saved at {config_file}""" )
| 38
| 0
|
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCAmelCase__ : Any ='''src/diffusers'''
# Matches is_xxx_available()
UpperCAmelCase__ : List[str] =re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
UpperCAmelCase__ : Tuple =re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
UpperCAmelCase__ : Optional[int] ='''
{0} = None
'''
UpperCAmelCase__ : List[str] ='''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
UpperCAmelCase__ : Any ='''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def _lowercase ( _UpperCAmelCase ) -> Optional[Any]:
lowerCamelCase =_re_backend.findall(_UpperCAmelCase )
if len(_UpperCAmelCase ) == 0:
return None
return "_and_".join(_UpperCAmelCase )
def _lowercase ( ) -> List[str]:
with open(os.path.join(_UpperCAmelCase , """__init__.py""" ) , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase =f.readlines()
# Get to the point we do the actual imports for type checking
lowerCamelCase =0
lowerCamelCase ={}
# Go through the end of the file
while line_index < len(_UpperCAmelCase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
lowerCamelCase =find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith("""else:""" ):
line_index += 1
line_index += 1
lowerCamelCase =[]
# Until we unindent, add backend objects to the list
while line_index < len(_UpperCAmelCase ) and len(lines[line_index] ) > 1:
lowerCamelCase =lines[line_index]
lowerCamelCase =_re_single_line_import.search(_UpperCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(_UpperCAmelCase ) > 0:
lowerCamelCase =objects
else:
line_index += 1
return backend_specific_objects
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> Optional[Any]:
if name.isupper():
return DUMMY_CONSTANT.format(_UpperCAmelCase )
elif name.islower():
return DUMMY_FUNCTION.format(_UpperCAmelCase , _UpperCAmelCase )
else:
return DUMMY_CLASS.format(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( _UpperCAmelCase=None ) -> Optional[int]:
if backend_specific_objects is None:
lowerCamelCase =read_init()
# For special correspondence backend to module name as used in the function requires_modulename
lowerCamelCase ={}
for backend, objects in backend_specific_objects.items():
lowerCamelCase ="""[""" + """, """.join(F"""\"{b}\"""" for b in backend.split("""_and_""" ) ) + """]"""
lowerCamelCase ="""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(_UpperCAmelCase , _UpperCAmelCase ) for o in objects] )
lowerCamelCase =dummy_file
return dummy_files
def _lowercase ( _UpperCAmelCase=False ) -> Dict:
lowerCamelCase =create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
lowerCamelCase ={"""torch""": """pt"""}
# Locate actual dummy modules and read their content.
lowerCamelCase =os.path.join(_UpperCAmelCase , """utils""" )
lowerCamelCase ={
backend: os.path.join(_UpperCAmelCase , F"""dummy_{short_names.get(_UpperCAmelCase , _UpperCAmelCase )}_objects.py""" )
for backend in dummy_files.keys()
}
lowerCamelCase ={}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(_UpperCAmelCase ):
with open(_UpperCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase =f.read()
else:
lowerCamelCase =""""""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F"""Updating diffusers.utils.dummy_{short_names.get(_UpperCAmelCase , _UpperCAmelCase )}_objects.py as the main """
"""__init__ has new objects.""" )
with open(dummy_file_paths[backend] , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
"""The main __init__ has objects that are not present in """
F"""diffusers.utils.dummy_{short_names.get(_UpperCAmelCase , _UpperCAmelCase )}_objects.py. Run `make fix-copies` """
"""to fix this.""" )
if __name__ == "__main__":
UpperCAmelCase__ : List[Any] =argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
UpperCAmelCase__ : Union[str, Any] =parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 358
|
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCAmelCase__ : Union[str, Any] =logging.getLogger(__name__)
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
return (preds == labels).mean()
@dataclass
class __A :
__A = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__A = field(
default=a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__A = field(
default=a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__A = field(
default=a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class __A :
__A = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
__A = field(metadata={"""help""": """Should contain the data files for the task."""} )
__A = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__A = field(
default=a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def _lowercase ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCamelCase =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCamelCase , lowerCamelCase , lowerCamelCase =parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , _UpperCAmelCase )
# Set seed
set_seed(training_args.seed )
try:
lowerCamelCase =processors[data_args.task_name]()
lowerCamelCase =processor.get_labels()
lowerCamelCase =len(_UpperCAmelCase )
except KeyError:
raise ValueError("""Task not found: %s""" % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowerCamelCase =AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
lowerCamelCase =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , )
# Get datasets
lowerCamelCase =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_UpperCAmelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowerCamelCase =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_UpperCAmelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_UpperCAmelCase ) -> Dict:
lowerCamelCase =np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_UpperCAmelCase , p.label_ids )}
# Data collator
lowerCamelCase =DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowerCamelCase =Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=_UpperCAmelCase , eval_dataset=_UpperCAmelCase , compute_metrics=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowerCamelCase ={}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowerCamelCase =trainer.evaluate()
lowerCamelCase =os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_master():
with open(_UpperCAmelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , _UpperCAmelCase , _UpperCAmelCase )
writer.write("""%s = %s\n""" % (key, value) )
results.update(_UpperCAmelCase )
return results
def _lowercase ( _UpperCAmelCase ) -> Union[str, Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 262
| 0
|
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = 'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_ = {
'jukebox-1b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'1b_lyrics/prior_level_2.pth.tar',
],
'jukebox-5b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'5b_lyrics/prior_level_2.pth.tar',
],
}
def lowerCamelCase__ ( A__ : Tuple ):
'''simple docstring'''
if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 10:
__lowerCamelCase = key.replace(""".model.1.bias""" , """.conv1d_1.bias""" )
elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 10:
__lowerCamelCase = key.replace(""".model.1.weight""" , """.conv1d_1.weight""" )
elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 10:
__lowerCamelCase = key.replace(""".model.3.bias""" , """.conv1d_2.bias""" )
elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 10:
__lowerCamelCase = key.replace(""".model.3.weight""" , """.conv1d_2.weight""" )
if "conditioner_blocks.0." in key:
__lowerCamelCase = key.replace("""conditioner_blocks.0""" , """conditioner_blocks""" )
if "prime_prior" in key:
__lowerCamelCase = key.replace("""prime_prior""" , """encoder""" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
__lowerCamelCase = key.replace(""".emb.""" , """.""" )
if key.endswith("""k""" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(""".k""" , """.codebook""" )
if "y_emb." in key:
return key.replace("""y_emb.""" , """metadata_embedding.""" )
if "x_emb.emb." in key:
__lowerCamelCase = key.replace("""0.x_emb.emb""" , """embed_tokens""" )
if "prime_state_ln" in key:
return key.replace("""prime_state_ln""" , """encoder.final_layer_norm""" )
if ".ln" in key:
return key.replace(""".ln""" , """.layer_norm""" )
if "_ln" in key:
return key.replace("""_ln""" , """_layer_norm""" )
if "prime_state_proj" in key:
return key.replace("""prime_state_proj""" , """encoder.proj_in""" )
if "prime_x_out" in key:
return key.replace("""prime_x_out""" , """encoder.lm_head""" )
if "prior.x_out" in key:
return key.replace("""x_out""" , """fc_proj_out""" )
if "x_emb" in key:
return key.replace("""x_emb""" , """embed_tokens""" )
return key
def lowerCamelCase__ ( A__ : List[str] , A__ : Dict , A__ : Optional[Any] , A__ : List[Any] ):
'''simple docstring'''
__lowerCamelCase = {}
import re
__lowerCamelCase = re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
__lowerCamelCase = re.compile(
R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
__lowerCamelCase = re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
__lowerCamelCase = re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
__lowerCamelCase = re.compile(
R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
__lowerCamelCase = re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
__lowerCamelCase = re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)""" )
__lowerCamelCase = re.compile(
R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
__lowerCamelCase = re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)""" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(A__ ):
__lowerCamelCase = re_encoder_block_conv_in.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = int(groups[2] ) * 2 + int(groups[3] )
__lowerCamelCase = f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
__lowerCamelCase = re_encoder_block_conv_in.sub(A__ , A__ )
elif re_encoder_block_resnet.fullmatch(A__ ):
__lowerCamelCase = re_encoder_block_resnet.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = int(groups[2] ) * 2 + int(groups[3] )
__lowerCamelCase = {"""1""": 1, """3""": 2}[groups[-2]]
__lowerCamelCase = f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
__lowerCamelCase = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
__lowerCamelCase = prefix + resnet_block
__lowerCamelCase = re_encoder_block_resnet.sub(A__ , A__ )
elif re_encoder_block_proj_out.fullmatch(A__ ):
__lowerCamelCase = re_encoder_block_proj_out.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
__lowerCamelCase = re_encoder_block_proj_out.sub(A__ , A__ )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(A__ ):
__lowerCamelCase = re_decoder_block_conv_out.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = int(groups[2] ) * 2 + int(groups[3] ) - 2
__lowerCamelCase = f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
__lowerCamelCase = re_decoder_block_conv_out.sub(A__ , A__ )
elif re_decoder_block_resnet.fullmatch(A__ ):
__lowerCamelCase = re_decoder_block_resnet.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = int(groups[2] ) * 2 + int(groups[3] ) - 2
__lowerCamelCase = {"""1""": 1, """3""": 2}[groups[-2]]
__lowerCamelCase = f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
__lowerCamelCase = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
__lowerCamelCase = prefix + resnet_block
__lowerCamelCase = re_decoder_block_resnet.sub(A__ , A__ )
elif re_decoder_block_proj_in.fullmatch(A__ ):
__lowerCamelCase = re_decoder_block_proj_in.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
__lowerCamelCase = re_decoder_block_proj_in.sub(A__ , A__ )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(A__ ):
__lowerCamelCase = re_prior_cond_conv_out.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = int(groups[1] ) * 2 + int(groups[2] ) - 2
__lowerCamelCase = f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
__lowerCamelCase = re_prior_cond_conv_out.sub(A__ , A__ )
elif re_prior_cond_resnet.fullmatch(A__ ):
__lowerCamelCase = re_prior_cond_resnet.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = int(groups[1] ) * 2 + int(groups[2] ) - 2
__lowerCamelCase = {"""1""": 1, """3""": 2}[groups[-2]]
__lowerCamelCase = f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
__lowerCamelCase = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
__lowerCamelCase = prefix + resnet_block
__lowerCamelCase = re_prior_cond_resnet.sub(A__ , A__ )
elif re_prior_cond_proj_in.fullmatch(A__ ):
__lowerCamelCase = re_prior_cond_proj_in.match(A__ )
__lowerCamelCase = regex_match.groups()
__lowerCamelCase = f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
__lowerCamelCase = re_prior_cond_proj_in.sub(A__ , A__ )
# keep original key
else:
__lowerCamelCase = original_key
__lowerCamelCase = replace_key(A__ )
if f'{key_prefix}.{key}' not in model_state_dict or key is None:
print(f'failed converting {original_key} to {key}, does not match' )
# handle missmatched shape
elif value.shape != model_state_dict[f'{key_prefix}.{key}'].shape:
__lowerCamelCase = model_state_dict[f'{key_prefix}.{key}']
print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match' )
__lowerCamelCase = original_key
__lowerCamelCase = original_key
__lowerCamelCase = value
return new_dict
@torch.no_grad()
def lowerCamelCase__ ( A__ : str=None , A__ : List[Any]=None ):
'''simple docstring'''
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/" )[-1]}' ):
__lowerCamelCase = requests.get(f'{PREFIX}{file}' , allow_redirects=A__ )
os.makedirs(f'{pytorch_dump_folder_path}/' , exist_ok=A__ )
open(f'{pytorch_dump_folder_path}/{file.split("/" )[-1]}' , """wb""" ).write(r.content )
__lowerCamelCase = MODEL_MAPPING[model_name.split("""/""" )[-1]]
__lowerCamelCase = JukeboxConfig.from_pretrained(A__ )
__lowerCamelCase = JukeboxModel(A__ )
__lowerCamelCase = []
__lowerCamelCase = {}
for i, dict_name in enumerate(A__ ):
__lowerCamelCase = torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}' )["""model"""]
__lowerCamelCase = {}
for k in old_dic.keys():
if k.endswith(""".b""" ):
__lowerCamelCase = old_dic[k]
elif k.endswith(""".w""" ):
__lowerCamelCase = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
__lowerCamelCase = old_dic[k]
else:
__lowerCamelCase = old_dic[k]
__lowerCamelCase = """vqvae""" if i == 0 else f'priors.{3 - i}'
__lowerCamelCase = fix_jukebox_keys(A__ , model.state_dict() , A__ , A__ )
weight_dict.append(A__ )
__lowerCamelCase = weight_dict.pop(0 )
model.vqvae.load_state_dict(A__ )
for i in range(len(A__ ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(A__ ).mkdir(exist_ok=A__ )
with open(f'{pytorch_dump_folder_path}/mapping.json' , """w""" ) as txtfile:
json.dump(A__ , A__ )
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(A__ )
return weight_dict
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='jukebox-5b-lyrics',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='jukebox-5b-lyrics-converted',
type=str,
help='Path to the output PyTorch model directory.',
)
UpperCAmelCase_ = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 12
|
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ) -> int:
"""simple docstring"""
if isinstance(snake_case__ ,snake_case__ ):
_SCREAMING_SNAKE_CASE = np.full((len(snake_case__ ), sequence_length, 2) ,snake_case__ )
else:
_SCREAMING_SNAKE_CASE = np.full((len(snake_case__ ), sequence_length) ,snake_case__ )
for i, tensor in enumerate(snake_case__ ):
if padding_side == "right":
if isinstance(snake_case__ ,snake_case__ ):
_SCREAMING_SNAKE_CASE = tensor[:sequence_length]
else:
_SCREAMING_SNAKE_CASE = tensor[:sequence_length]
else:
if isinstance(snake_case__ ,snake_case__ ):
_SCREAMING_SNAKE_CASE = tensor[:sequence_length]
else:
_SCREAMING_SNAKE_CASE = tensor[:sequence_length]
return out_tensor.tolist()
def __lowerCamelCase ( snake_case__ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ord(snake_case__ )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
_SCREAMING_SNAKE_CASE = unicodedata.category(snake_case__ )
if cat.startswith("""P""" ):
return True
return False
@dataclass
class __UpperCAmelCase (_UpperCAmelCase ):
__snake_case : PreTrainedTokenizerBase
__snake_case : Union[bool, str, PaddingStrategy] = True
__snake_case : Optional[int] = None
__snake_case : Optional[int] = None
__snake_case : int = -100
__snake_case : str = "pt"
def UpperCamelCase ( self: str , UpperCAmelCase_: Optional[Any] ):
'''simple docstring'''
import torch
_SCREAMING_SNAKE_CASE = """label""" if """label""" in features[0].keys() else """labels"""
_SCREAMING_SNAKE_CASE = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
_SCREAMING_SNAKE_CASE = self.tokenizer.pad(
UpperCAmelCase_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" if labels is None else None , )
if labels is None:
return batch
_SCREAMING_SNAKE_CASE = torch.tensor(batch["""entity_ids"""] ).shape[1]
_SCREAMING_SNAKE_CASE = self.tokenizer.padding_side
if padding_side == "right":
_SCREAMING_SNAKE_CASE = [
list(UpperCAmelCase_ ) + [self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase_ )) for label in labels
]
else:
_SCREAMING_SNAKE_CASE = [
[self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase_ )) + list(UpperCAmelCase_ ) for label in labels
]
_SCREAMING_SNAKE_CASE = [feature["""ner_tags"""] for feature in features]
_SCREAMING_SNAKE_CASE = padding_tensor(UpperCAmelCase_ , -1 , UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = [feature["""original_entity_spans"""] for feature in features]
_SCREAMING_SNAKE_CASE = padding_tensor(UpperCAmelCase_ , (-1, -1) , UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = {k: torch.tensor(UpperCAmelCase_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 306
| 0
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
_lowerCAmelCase : Tuple = "\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"A red cartoon frog, 4k\"\n >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/frog.png\"\n ... )\n\n >>> image = pipe(\n ... image=init_image,\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... strength=0.2,\n ... ).images\n\n >>> image[0].save(\"red_frog.png\")\n ```\n"
def __snake_case ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Any=8 ) -> Dict:
'''simple docstring'''
_UpperCAmelCase : int = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_UpperCAmelCase : Dict = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
def __snake_case ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict=512 , SCREAMING_SNAKE_CASE__ : int=512 ) -> str:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = pil_image.resize((w, h) , resample=Image.BICUBIC , reducing_gap=1 )
_UpperCAmelCase : str = np.array(pil_image.convert("RGB" ) )
_UpperCAmelCase : Any = arr.astype(np.floataa ) / 127.5 - 1
_UpperCAmelCase : List[Any] = np.transpose(SCREAMING_SNAKE_CASE__ , [2, 0, 1] )
_UpperCAmelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 )
return image
class UpperCAmelCase_ ( UpperCamelCase__ ):
def __init__( self : List[Any] , A : UNetaDConditionModel , A : DDPMScheduler , A : VQModel , ):
super().__init__()
self.register_modules(
unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , movq=UpperCamelCase_ , )
_UpperCAmelCase : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def snake_case_ ( self : Optional[Any] , A : str , A : Union[str, Any] , A : int ):
_UpperCAmelCase : Any = min(int(num_inference_steps * strength ) , UpperCamelCase_ )
_UpperCAmelCase : List[str] = max(num_inference_steps - init_timestep , 0 )
_UpperCAmelCase : Union[str, Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def snake_case_ ( self : Any , A : Union[str, Any] , A : Union[str, Any] , A : Optional[Any] , A : Optional[int] , A : Dict , A : int , A : Dict=None ):
if not isinstance(UpperCamelCase_ , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCamelCase_ )}' )
_UpperCAmelCase : str = image.to(device=UpperCamelCase_ , dtype=UpperCamelCase_ )
_UpperCAmelCase : Optional[Any] = batch_size * num_images_per_prompt
if image.shape[1] == 4:
_UpperCAmelCase : int = image
else:
if isinstance(UpperCamelCase_ , UpperCamelCase_ ) and len(UpperCamelCase_ ) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(UpperCamelCase_ )}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
_UpperCAmelCase : str = [
self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(UpperCamelCase_ )
]
_UpperCAmelCase : int = torch.cat(UpperCamelCase_ , dim=0 )
else:
_UpperCAmelCase : str = self.movq.encode(UpperCamelCase_ ).latent_dist.sample(UpperCamelCase_ )
_UpperCAmelCase : Union[str, Any] = self.movq.config.scaling_factor * init_latents
_UpperCAmelCase : List[str] = torch.cat([init_latents] , dim=0 )
_UpperCAmelCase : Tuple = init_latents.shape
_UpperCAmelCase : Optional[int] = randn_tensor(UpperCamelCase_ , generator=UpperCamelCase_ , device=UpperCamelCase_ , dtype=UpperCamelCase_ )
# get latents
_UpperCAmelCase : Optional[Any] = self.scheduler.add_noise(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
_UpperCAmelCase : int = init_latents
return latents
def snake_case_ ( self : str , A : List[Any]=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
_UpperCAmelCase : List[Any] = torch.device(f'cuda:{gpu_id}' )
_UpperCAmelCase : Tuple = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(UpperCamelCase_ , UpperCamelCase_ )
def snake_case_ ( self : int , A : Optional[Any]=0 ):
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
_UpperCAmelCase : List[Any] = torch.device(f'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=UpperCamelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_UpperCAmelCase : Optional[int] = None
for cpu_offloaded_model in [self.unet, self.movq]:
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = cpu_offload_with_hook(UpperCamelCase_ , UpperCamelCase_ , prev_module_hook=UpperCamelCase_ )
# We'll offload the last model manually.
_UpperCAmelCase : Any = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def snake_case_ ( self : List[str] ):
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(UpperCamelCase_ , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(UpperCamelCase_ )
def __call__( self : List[Any] , A : Union[torch.FloatTensor, List[torch.FloatTensor]] , A : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] , A : Union[torch.FloatTensor, List[torch.FloatTensor]] , A : int = 5_1_2 , A : int = 5_1_2 , A : int = 1_0_0 , A : float = 4.0 , A : float = 0.3 , A : int = 1 , A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , A : Optional[str] = "pil" , A : bool = True , ):
_UpperCAmelCase : List[Any] = self._execution_device
_UpperCAmelCase : Tuple = guidance_scale > 1.0
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
_UpperCAmelCase : int = torch.cat(UpperCamelCase_ , dim=0 )
_UpperCAmelCase : Optional[Any] = image_embeds.shape[0]
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
_UpperCAmelCase : Any = torch.cat(UpperCamelCase_ , dim=0 )
if do_classifier_free_guidance:
_UpperCAmelCase : Tuple = image_embeds.repeat_interleave(UpperCamelCase_ , dim=0 )
_UpperCAmelCase : List[str] = negative_image_embeds.repeat_interleave(UpperCamelCase_ , dim=0 )
_UpperCAmelCase : Tuple = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=UpperCamelCase_ )
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
_UpperCAmelCase : Tuple = [image]
if not all(isinstance(UpperCamelCase_ , (PIL.Image.Image, torch.Tensor) ) for i in image ):
raise ValueError(
f'Input is in incorrect format: {[type(UpperCamelCase_ ) for i in image]}. Currently, we only support PIL image and pytorch tensor' )
_UpperCAmelCase : Any = torch.cat([prepare_image(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) for i in image] , dim=0 )
_UpperCAmelCase : List[Any] = image.to(dtype=image_embeds.dtype , device=UpperCamelCase_ )
_UpperCAmelCase : Any = self.movq.encode(UpperCamelCase_ )["latents"]
_UpperCAmelCase : Optional[Any] = latents.repeat_interleave(UpperCamelCase_ , dim=0 )
self.scheduler.set_timesteps(UpperCamelCase_ , device=UpperCamelCase_ )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.get_timesteps(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
_UpperCAmelCase : Optional[int] = timesteps[:1].repeat(batch_size * num_images_per_prompt )
_UpperCAmelCase , _UpperCAmelCase : Dict = downscale_height_and_width(UpperCamelCase_ , UpperCamelCase_ , self.movq_scale_factor )
_UpperCAmelCase : Optional[int] = self.prepare_latents(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , image_embeds.dtype , UpperCamelCase_ , UpperCamelCase_ )
for i, t in enumerate(self.progress_bar(UpperCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
_UpperCAmelCase : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_UpperCAmelCase : Union[str, Any] = {"image_embeds": image_embeds}
_UpperCAmelCase : str = self.unet(
sample=UpperCamelCase_ , timestep=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , added_cond_kwargs=UpperCamelCase_ , return_dict=UpperCamelCase_ , )[0]
if do_classifier_free_guidance:
_UpperCAmelCase , _UpperCAmelCase : Dict = noise_pred.split(latents.shape[1] , dim=1 )
_UpperCAmelCase , _UpperCAmelCase : Any = noise_pred.chunk(2 )
_UpperCAmelCase , _UpperCAmelCase : int = variance_pred.chunk(2 )
_UpperCAmelCase : List[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_UpperCAmelCase : Optional[Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_UpperCAmelCase , _UpperCAmelCase : Any = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase : List[str] = self.scheduler.step(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ , )[0]
# post-processing
_UpperCAmelCase : Dict = self.movq.decode(UpperCamelCase_ , force_not_quantize=UpperCamelCase_ )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
_UpperCAmelCase : int = image * 0.5 + 0.5
_UpperCAmelCase : Dict = image.clamp(0 , 1 )
_UpperCAmelCase : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_UpperCAmelCase : Optional[int] = self.numpy_to_pil(UpperCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase_ )
| 355
|
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Dict = GPTSanJapaneseTokenizer
__SCREAMING_SNAKE_CASE : Optional[int] = False
__SCREAMING_SNAKE_CASE : List[str] = {'do_clean_text': False, 'add_prefix_space': False}
def snake_case_ ( self : Any ):
super().setUp()
# fmt: off
_UpperCAmelCase : Any = ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
# fmt: on
_UpperCAmelCase : Optional[int] = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}} # 😀
_UpperCAmelCase : List[Any] = {"unk_token": "<unk>"}
_UpperCAmelCase : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_UpperCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["emoji_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
with open(self.emoji_file , "w" ) as emoji_writer:
emoji_writer.write(json.dumps(A ) )
def snake_case_ ( self : int , **A : List[str] ):
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **A )
def snake_case_ ( self : int , A : Any ):
_UpperCAmelCase : Optional[Any] = "こんにちは、世界。 \nこんばんは、㔺界。😀"
_UpperCAmelCase : List[Any] = "こんにちは、世界。 \nこんばんは、世界。😀"
return input_text, output_text
def snake_case_ ( self : Optional[Any] , A : str ):
_UpperCAmelCase , _UpperCAmelCase : str = self.get_input_output_texts(A )
_UpperCAmelCase : List[Any] = tokenizer.encode(A , add_special_tokens=A )
_UpperCAmelCase : Union[str, Any] = tokenizer.decode(A , clean_up_tokenization_spaces=A )
return text, ids
def snake_case_ ( self : Any ):
pass # TODO add if relevant
def snake_case_ ( self : Union[str, Any] ):
pass # TODO add if relevant
def snake_case_ ( self : int ):
pass # TODO add if relevant
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : List[Any] = self.get_tokenizer()
# Testing tokenization
_UpperCAmelCase : Optional[int] = "こんにちは、世界。 こんばんは、㔺界。"
_UpperCAmelCase : Dict = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
_UpperCAmelCase : List[Any] = tokenizer.tokenize(A )
self.assertListEqual(A , A )
# Testing conversion to ids without special tokens
_UpperCAmelCase : Optional[int] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
_UpperCAmelCase : List[str] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(A , A )
# Testing conversion to ids with special tokens
_UpperCAmelCase : str = tokens + [tokenizer.unk_token]
_UpperCAmelCase : Any = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
_UpperCAmelCase : str = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(A , A )
def snake_case_ ( self : Any ):
_UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
# Testing tokenization
_UpperCAmelCase : Dict = "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
_UpperCAmelCase : Tuple = "こんにちは、、、、世界。こんばんは、、、、世界。"
_UpperCAmelCase : int = tokenizer.encode(A )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(A )
self.assertEqual(A , A )
@slow
def snake_case_ ( self : Dict ):
_UpperCAmelCase : List[Any] = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
# Testing tokenization
_UpperCAmelCase : List[Any] = "こんにちは、世界。"
_UpperCAmelCase : List[str] = "こんばんは、㔺界。😀"
_UpperCAmelCase : Any = "こんにちは、世界。こんばんは、世界。😀"
_UpperCAmelCase : Union[str, Any] = tokenizer.encode(prefix_text + input_text )
_UpperCAmelCase : Tuple = tokenizer.encode("" , prefix_text=prefix_text + input_text )
_UpperCAmelCase : Optional[int] = tokenizer.encode(A , prefix_text=A )
_UpperCAmelCase : Tuple = tokenizer.decode(A )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(A )
_UpperCAmelCase : Tuple = tokenizer.decode(A )
self.assertEqual(A , A )
self.assertEqual(A , A )
self.assertEqual(A , A )
@slow
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
# Testing tokenization
_UpperCAmelCase : Any = "こんにちは、世界。"
_UpperCAmelCase : List[Any] = "こんばんは、㔺界。😀"
_UpperCAmelCase : Optional[Any] = len(tokenizer.encode(A ) ) - 2
_UpperCAmelCase : List[Any] = len(tokenizer.encode(A ) ) - 2
_UpperCAmelCase : List[str] = [1] + [0] * (len_prefix + len_text + 1)
_UpperCAmelCase : str = [1] * (len_prefix + len_text + 1) + [0]
_UpperCAmelCase : int = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
_UpperCAmelCase : Union[str, Any] = tokenizer(prefix_text + input_text ).token_type_ids
_UpperCAmelCase : Any = tokenizer("" , prefix_text=prefix_text + input_text ).token_type_ids
_UpperCAmelCase : List[Any] = tokenizer(A , prefix_text=A ).token_type_ids
self.assertListEqual(A , A )
self.assertListEqual(A , A )
self.assertListEqual(A , A )
@slow
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
_UpperCAmelCase : Dict = tokenizer.encode("あンいワ" )
_UpperCAmelCase : str = tokenizer.encode("" , prefix_text="あンいワ" )
_UpperCAmelCase : Dict = tokenizer.encode("いワ" , prefix_text="あン" )
self.assertEqual(tokenizer.decode(A ) , tokenizer.decode(A ) )
self.assertEqual(tokenizer.decode(A ) , tokenizer.decode(A ) )
self.assertNotEqual(A , A )
self.assertNotEqual(A , A )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
_UpperCAmelCase : Tuple = [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
_UpperCAmelCase : Tuple = tokenizer(A , padding=A )
_UpperCAmelCase : str = tokenizer.batch_encode_plus(A , padding=A )
# fmt: off
_UpperCAmelCase : str = [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
_UpperCAmelCase : str = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
_UpperCAmelCase : int = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , A )
self.assertListEqual(x_token.token_type_ids , A )
self.assertListEqual(x_token.attention_mask , A )
self.assertListEqual(x_token_a.input_ids , A )
self.assertListEqual(x_token_a.token_type_ids , A )
self.assertListEqual(x_token_a.attention_mask , A )
def snake_case_ ( self : List[Any] ):
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def snake_case_ ( self : int ):
# tokenizer has no padding token
pass
| 202
| 0
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : int = '▁'
lowercase__ : Dict = {'vocab_file': 'sentencepiece.bpe.model'}
lowercase__ : Dict = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
),
}
}
lowercase__ : Optional[Any] = {
'facebook/nllb-200-distilled-600M': 10_24,
}
# fmt: off
lowercase__ : int = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Dict = VOCAB_FILES_NAMES
_snake_case : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Any = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Optional[Any] = ['input_ids', 'attention_mask']
_snake_case : List[int] = []
_snake_case : List[int] = []
def __init__( self : str , lowerCAmelCase__ : str , lowerCAmelCase__ : int="<s>" , lowerCAmelCase__ : str="</s>" , lowerCAmelCase__ : Dict="</s>" , lowerCAmelCase__ : Dict="<s>" , lowerCAmelCase__ : Optional[Any]="<unk>" , lowerCAmelCase__ : Any="<pad>" , lowerCAmelCase__ : List[Any]="<mask>" , lowerCAmelCase__ : str=None , lowerCAmelCase__ : Dict=None , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : Optional[Dict[str, Any]] = None , lowerCAmelCase__ : int=None , lowerCAmelCase__ : str=False , **lowerCAmelCase__ : int , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
_UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
_UpperCamelCase = legacy_behaviour
super().__init__(
bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , src_lang=lowerCAmelCase__ , tgt_lang=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=lowerCAmelCase__ , **lowerCAmelCase__ , )
_UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCAmelCase__ ) )
_UpperCamelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
_UpperCamelCase = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_UpperCamelCase = 1
_UpperCamelCase = len(self.sp_model )
_UpperCamelCase = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase__ )
}
_UpperCamelCase = {v: k for k, v in self.lang_code_to_id.items()}
_UpperCamelCase = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
_UpperCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_UpperCamelCase = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
_UpperCamelCase = src_lang if src_lang is not None else '''eng_Latn'''
_UpperCamelCase = self.lang_code_to_id[self._src_lang]
_UpperCamelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.__dict__.copy()
_UpperCamelCase = None
_UpperCamelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : List[str] , lowerCAmelCase__ : str ) -> str:
'''simple docstring'''
_UpperCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCamelCase = {}
_UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def snake_case__ ( self : int ) -> Dict:
'''simple docstring'''
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def snake_case__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def snake_case__ ( self : str , lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
_UpperCamelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def snake_case__ ( self : List[str] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__ )
_UpperCamelCase = [1] * len(self.prefix_tokens )
_UpperCamelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(lowerCAmelCase__ )) + suffix_ones
return prefix_ones + ([0] * len(lowerCAmelCase__ )) + ([0] * len(lowerCAmelCase__ )) + suffix_ones
def snake_case__ ( self : int , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def snake_case__ ( self : Dict , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
_UpperCamelCase = [self.sep_token_id]
_UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case__ ( self : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] , lowerCAmelCase__ : Optional[str] , **lowerCAmelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
_UpperCamelCase = src_lang
_UpperCamelCase = self(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase = self.convert_tokens_to_ids(lowerCAmelCase__ )
_UpperCamelCase = tgt_lang_id
return inputs
def snake_case__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case__ ( self : Optional[Any] , lowerCAmelCase__ : str ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__ )
def snake_case__ ( self : Any , lowerCAmelCase__ : Union[str, Any] ) -> str:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_UpperCamelCase = self.sp_model.PieceToId(lowerCAmelCase__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def snake_case__ ( self : Any , lowerCAmelCase__ : List[str] ) -> Optional[int]:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def snake_case__ ( self : Any , lowerCAmelCase__ : Any ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = ''''''.join(lowerCAmelCase__ ).replace(lowerCAmelCase__ , ''' ''' ).strip()
return out_string
def snake_case__ ( self : str , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCamelCase = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCAmelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase__ , '''wb''' ) as fi:
_UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (out_vocab_file,)
def snake_case__ ( self : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str = "eng_Latn" , lowerCAmelCase__ : Optional[List[str]] = None , lowerCAmelCase__ : str = "fra_Latn" , **lowerCAmelCase__ : Any , ) -> BatchEncoding:
'''simple docstring'''
_UpperCamelCase = src_lang
_UpperCamelCase = tgt_lang
return super().prepare_seqaseq_batch(lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case__ ( self : Any ) -> str:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def snake_case__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def snake_case__ ( self : List[Any] , lowerCAmelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
_UpperCamelCase = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
_UpperCamelCase = []
_UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
else:
_UpperCamelCase = [self.cur_lang_code]
_UpperCamelCase = [self.eos_token_id]
def snake_case__ ( self : Dict , lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
_UpperCamelCase = self.lang_code_to_id[lang]
if self.legacy_behaviour:
_UpperCamelCase = []
_UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
else:
_UpperCamelCase = [self.cur_lang_code]
_UpperCamelCase = [self.eos_token_id]
| 324
|
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowercase__ : Any = logging.get_logger(__name__)
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : List[str] = None
@experimental
def a__ ( lowercase : Union[str, Any], lowercase : Optional[int], lowercase : Tuple, lowercase : List[Any], lowercase : Dict, lowercase : Union[str, Any], lowercase : Optional[Any] ) -> int:
"""simple docstring"""
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
lowercase, lowercase, lowercase, lowercase, lowercase, lowercase, lowercase )
return _map_with_joblib(lowercase, lowercase, lowercase, lowercase, lowercase, lowercase, lowercase )
def a__ ( lowercase : Dict, lowercase : str, lowercase : Union[str, Any], lowercase : Optional[Any], lowercase : Optional[int], lowercase : Optional[Any], lowercase : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = num_proc if num_proc <= len(lowercase ) else len(lowercase )
_UpperCamelCase = [] # We organize the splits ourselve (contiguous splits)
for index in range(lowercase ):
_UpperCamelCase = len(lowercase ) // num_proc
_UpperCamelCase = len(lowercase ) % num_proc
_UpperCamelCase = div * index + min(lowercase, lowercase )
_UpperCamelCase = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(lowercase ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F"""Error dividing inputs iterable among processes. """
F"""Total number of objects {len(lowercase )}, """
F"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
F"""Spawning {num_proc} processes for {len(lowercase )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
_UpperCamelCase , _UpperCamelCase = None, None
if not disable_tqdm:
_UpperCamelCase , _UpperCamelCase = (RLock(),), tqdm.set_lock
with Pool(lowercase, initargs=lowercase, initializer=lowercase ) as pool:
_UpperCamelCase = pool.map(lowercase, lowercase )
logger.info(F"""Finished {num_proc} processes""" )
_UpperCamelCase = [obj for proc_res in mapped for obj in proc_res]
logger.info(F"""Unpacked {len(lowercase )} objects""" )
return mapped
def a__ ( lowercase : str, lowercase : Tuple, lowercase : List[str], lowercase : List[str], lowercase : Any, lowercase : int, lowercase : Optional[Any] ) -> Any:
"""simple docstring"""
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name, n_jobs=lowercase ):
return joblib.Parallel()(
joblib.delayed(lowercase )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
_UpperCamelCase = None
| 324
| 1
|
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a ) -> Dict:
_a : Any = n
_a : Any = [None] * self.n
_a : Tuple = 0 # index of the first element
_a : str = 0
_a : List[Any] = 0
def __len__( self ) -> int:
return self.size
def __lowercase ( self ) -> bool:
return self.size == 0
def __lowercase ( self ) -> Dict:
return False if self.is_empty() else self.array[self.front]
def __lowercase ( self , _a ) -> int:
if self.size >= self.n:
raise Exception('''QUEUE IS FULL''' )
_a : Union[str, Any] = data
_a : Optional[int] = (self.rear + 1) % self.n
self.size += 1
return self
def __lowercase ( self ) -> Any:
if self.size == 0:
raise Exception('''UNDERFLOW''' )
_a : Optional[int] = self.array[self.front]
_a : Optional[int] = None
_a : Tuple = (self.front + 1) % self.n
self.size -= 1
return temp
| 15
|
import argparse
import os
import re
import packaging.version
a__ = '''examples/'''
a__ = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
'''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
a__ = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
a__ = '''README.md'''
def __UpperCAmelCase ( __a : List[str] ,__a : int ,__a : Optional[Any] ) -> int:
"""simple docstring"""
with open(__a ,'''r''' ,encoding='''utf-8''' ,newline='''\n''' ) as f:
_a : Tuple = f.read()
_a , _a : str = REPLACE_PATTERNS[pattern]
_a : List[str] = replace.replace('''VERSION''' ,__a )
_a : List[Any] = re_pattern.sub(__a ,__a )
with open(__a ,'''w''' ,encoding='''utf-8''' ,newline='''\n''' ) as f:
f.write(__a )
def __UpperCAmelCase ( __a : Any ) -> List[Any]:
"""simple docstring"""
for folder, directories, fnames in os.walk(__a ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('''research_projects''' )
if "legacy" in directories:
directories.remove('''legacy''' )
for fname in fnames:
if fname.endswith('''.py''' ):
update_version_in_file(os.path.join(__a ,__a ) ,__a ,pattern='''examples''' )
def __UpperCAmelCase ( __a : List[Any] ,__a : List[str]=False ) -> int:
"""simple docstring"""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__a ,__a ,__a )
if not patch:
update_version_in_examples(__a )
def __UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
_a : Optional[Any] = '''🤗 Transformers currently provides the following architectures'''
_a : str = '''1. Want to contribute a new model?'''
with open(__a ,'''r''' ,encoding='''utf-8''' ,newline='''\n''' ) as f:
_a : Optional[int] = f.readlines()
# Find the start of the list.
_a : Optional[int] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_a : List[Any] = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
_a : Tuple = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' ,'''https://huggingface.co/docs/transformers/model_doc''' ,)
index += 1
with open(__a ,'''w''' ,encoding='''utf-8''' ,newline='''\n''' ) as f:
f.writelines(__a )
def __UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
with open(REPLACE_FILES['''init'''] ,'''r''' ) as f:
_a : Optional[Any] = f.read()
_a : Optional[Any] = REPLACE_PATTERNS['''init'''][0].search(__a ).groups()[0]
return packaging.version.parse(__a )
def __UpperCAmelCase ( __a : Dict=False ) -> str:
"""simple docstring"""
_a : Optional[Any] = get_version()
if patch and default_version.is_devrelease:
raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
if default_version.is_devrelease:
_a : List[Any] = default_version.base_version
elif patch:
_a : str = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
_a : List[str] = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
_a : Dict = input(F"""Which version are you releasing? [{default_version}]""" )
if len(__a ) == 0:
_a : int = default_version
print(F"""Updating version to {version}.""" )
global_version_update(__a ,patch=__a )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def __UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
_a : str = get_version()
_a : int = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
_a : List[Any] = current_version.base_version
# Check with the user we got that right.
_a : Union[str, Any] = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(__a ) == 0:
_a : List[str] = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(__a )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
a__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 15
| 1
|
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class A_ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Tuple:
debug_launcher(test_script.main )
def UpperCAmelCase__ ( self :Any ) -> Optional[int]:
debug_launcher(test_ops.main )
| 78
|
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322
| 0
|
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Union[str, Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE :Optional[int] = {
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
'tokenizer_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
},
}
SCREAMING_SNAKE_CASE :int = {
'facebook/bart-base': 1024,
'facebook/bart-large': 1024,
'facebook/bart-large-mnli': 1024,
'facebook/bart-large-cnn': 1024,
'facebook/bart-large-xsum': 1024,
'yjernite/bart_eli5': 1024,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = BartTokenizer
def __init__( self : Any ,A : Dict=None ,A : Any=None ,A : List[str]=None ,A : Any="replace" ,A : Dict="<s>" ,A : Optional[int]="</s>" ,A : Any="</s>" ,A : Union[str, Any]="<s>" ,A : int="<unk>" ,A : int="<pad>" ,A : Optional[Any]="<mask>" ,A : List[str]=False ,A : str=True ,**A : Tuple ,):
super().__init__(
A ,A ,tokenizer_file=A ,errors=A ,bos_token=A ,eos_token=A ,sep_token=A ,cls_token=A ,unk_token=A ,pad_token=A ,mask_token=A ,add_prefix_space=A ,trim_offsets=A ,**A ,)
__A = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" ,A ) != add_prefix_space:
__A = getattr(A ,pre_tok_state.pop("type" ) )
__A = add_prefix_space
__A = pre_tok_class(**A )
__A = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__A = "post_processor"
__A = getattr(self.backend_tokenizer ,A ,A )
if tokenizer_component_instance:
__A = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__A = tuple(state["sep"] )
if "cls" in state:
__A = tuple(state["cls"] )
__A = False
if state.get("add_prefix_space" ,A ) != add_prefix_space:
__A = add_prefix_space
__A = True
if state.get("trim_offsets" ,A ) != trim_offsets:
__A = trim_offsets
__A = True
if changes_to_apply:
__A = getattr(A ,state.pop("type" ) )
__A = component_class(**A )
setattr(self.backend_tokenizer ,A ,A )
@property
def UpperCamelCase_ ( self : Any ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCamelCase_ ( self : List[str] ,A : List[Any] ):
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else value
__A = value
def UpperCamelCase_ ( self : Optional[Any] ,*A : List[Any] ,**A : Optional[int] ):
__A = kwargs.get("is_split_into_words" ,A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*A ,**A )
def UpperCamelCase_ ( self : Tuple ,*A : int ,**A : Dict ):
__A = kwargs.get("is_split_into_words" ,A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs." )
return super()._encode_plus(*A ,**A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,A : Optional[str] = None ):
__A = self._tokenizer.model.save(A ,name=A )
return tuple(A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : Tuple ,A : str=None ):
__A = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCamelCase_ ( self : Optional[Any] ,A : List[int] ,A : Optional[List[int]] = None ):
__A = [self.sep_token_id]
__A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 124
|
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[str] ,A : Optional[Any] ,A : List[Any] ):
super().__init__()
# make sure scheduler can always be converted to DDIM
__A = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=A ,scheduler=A )
@torch.no_grad()
def __call__( self : Tuple ,A : int = 1 ,A : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,A : float = 0.0 ,A : int = 50 ,A : Optional[bool] = None ,A : Optional[str] = "pil" ,A : bool = True ,):
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size ,A ):
__A = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
__A = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(A ,A ) and len(A ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(A )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
__A = randn_tensor(A ,generator=A ,device=self.device ,dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(A )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
__A = self.unet(A ,A ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
__A = self.scheduler.step(
A ,A ,A ,eta=A ,use_clipped_model_output=A ,generator=A ).prev_sample
__A = (image / 2 + 0.5).clamp(0 ,1 )
__A = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
__A = self.numpy_to_pil(A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A )
| 124
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__: Optional[int] = logging.get_logger(__name__)
UpperCamelCase__: int = {
"google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
# See all ViT models at https://huggingface.co/models?filter=vit
}
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = """vit"""
def __init__( self : Dict , __snake_case : int=768 , __snake_case : Optional[int]=12 , __snake_case : Any=12 , __snake_case : Optional[Any]=3072 , __snake_case : Any="gelu" , __snake_case : str=0.0 , __snake_case : str=0.0 , __snake_case : Dict=0.02 , __snake_case : Optional[int]=1E-12 , __snake_case : List[str]=224 , __snake_case : Tuple=16 , __snake_case : Dict=3 , __snake_case : List[str]=True , __snake_case : Optional[int]=16 , **__snake_case : Dict , ) -> Optional[Any]:
super().__init__(**__snake_case )
UpperCAmelCase : str = hidden_size
UpperCAmelCase : int = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : str = intermediate_size
UpperCAmelCase : Union[str, Any] = hidden_act
UpperCAmelCase : List[str] = hidden_dropout_prob
UpperCAmelCase : Any = attention_probs_dropout_prob
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Optional[int] = layer_norm_eps
UpperCAmelCase : Union[str, Any] = image_size
UpperCAmelCase : Any = patch_size
UpperCAmelCase : Union[str, Any] = num_channels
UpperCAmelCase : Any = qkv_bias
UpperCAmelCase : Union[str, Any] = encoder_stride
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse("""1.11""" )
@property
def A ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def A ( self : Union[str, Any] ) -> float:
return 1E-4
| 23
|
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCAmelCase ( lowerCAmelCase_ )-> Union[str, Any]:
return EnvironmentCommand()
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def lowercase_ ( __lowercase ) -> List[Any]:
lowerCAmelCase_ : List[str] = parser.add_parser('''env''' )
download_parser.set_defaults(func=__lowercase )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Optional[Any] = huggingface_hub.__version__
lowerCAmelCase_ : str = '''not installed'''
lowerCAmelCase_ : str = '''NA'''
if is_torch_available():
import torch
lowerCAmelCase_ : Any = torch.__version__
lowerCAmelCase_ : str = torch.cuda.is_available()
lowerCAmelCase_ : List[str] = '''not installed'''
if is_transformers_available():
import transformers
lowerCAmelCase_ : Any = transformers.__version__
lowerCAmelCase_ : Optional[Any] = '''not installed'''
if is_accelerate_available():
import accelerate
lowerCAmelCase_ : List[Any] = accelerate.__version__
lowerCAmelCase_ : List[str] = '''not installed'''
if is_xformers_available():
import xformers
lowerCAmelCase_ : Optional[Any] = xformers.__version__
lowerCAmelCase_ : int = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(__lowercase ) )
return info
@staticmethod
def lowercase_ ( __lowercase ) -> str:
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"
| 262
| 0
|
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
lowerCAmelCase : Any ='''base_with_context'''
def UpperCAmelCase_ ( __lowerCamelCase : Dict ,__lowerCamelCase : List[Any] ):
lowercase_ :Union[str, Any] = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
lowercase_ :Optional[int] = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) ,requires_grad=_lowerCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
lowercase_ :Union[str, Any] = weights[F'layers_{lyr_num}']
lowercase_ :Union[str, Any] = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
lowercase_ :List[Any] = ly_weight["""attention"""]
lowercase_ :int = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
lowercase_ :Tuple = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
lowercase_ :List[str] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
lowercase_ :Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
lowercase_ :List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
lowercase_ :Tuple = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
lowercase_ :Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
lowercase_ :Tuple = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
lowercase_ :Optional[int] = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def UpperCAmelCase_ ( __lowerCamelCase : int ,__lowerCamelCase : Union[str, Any] ):
lowercase_ :Union[str, Any] = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )
lowercase_ :int = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) ,requires_grad=_lowerCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
lowercase_ :Union[str, Any] = weights[F'layers_{lyr_num}']
lowercase_ :Tuple = ly_weight["""attention"""]
lowercase_ :Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
lowercase_ :int = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
lowercase_ :Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
lowercase_ :Tuple = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
lowercase_ :Any = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
lowercase_ :int = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
lowercase_ :List[str] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
lowercase_ :Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
lowercase_ :List[str] = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
lowercase_ :Optional[int] = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def UpperCAmelCase_ ( __lowerCamelCase : Optional[Any] ,__lowerCamelCase : int ):
lowercase_ :Optional[Any] = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
lowercase_ :Union[str, Any] = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )
lowercase_ :Optional[Any] = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) ,requires_grad=_lowerCAmelCase )
lowercase_ :int = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
lowercase_ :int = weights[F'layers_{lyr_num}']
lowercase_ :int = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"] ) )
lowercase_ :Any = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )
lowercase_ :Optional[Any] = ly_weight["""self_attention"""]
lowercase_ :int = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
lowercase_ :List[str] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
lowercase_ :List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
lowercase_ :str = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
lowercase_ :str = ly_weight["""MultiHeadDotProductAttention_0"""]
lowercase_ :int = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
lowercase_ :Tuple = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
lowercase_ :str = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
lowercase_ :Any = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
lowercase_ :Optional[int] = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )
lowercase_ :Tuple = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
lowercase_ :Union[str, Any] = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
lowercase_ :List[str] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
lowercase_ :List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
lowercase_ :Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
lowercase_ :int = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )
lowercase_ :Tuple = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T ) )
return model
def UpperCAmelCase_ ( __lowerCamelCase : Dict ):
lowercase_ :str = checkpoints.load_tax_checkpoint(args.checkpoint_path )
lowercase_ :List[Any] = jnp.tree_util.tree_map(onp.array ,_lowerCAmelCase )
lowercase_ :Dict = [
"""from __gin__ import dynamic_registration""",
"""from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
"""diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
"""diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
]
lowercase_ :Optional[Any] = os.path.join(args.checkpoint_path ,".." ,"config.gin" )
lowercase_ :List[str] = inference.parse_training_gin_file(_lowerCAmelCase ,_lowerCAmelCase )
lowercase_ :Dict = inference.InferenceModel(args.checkpoint_path ,_lowerCAmelCase )
lowercase_ :Optional[int] = DDPMScheduler(beta_schedule="squaredcos_cap_v2" ,variance_type="fixed_large" )
lowercase_ :Any = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"] ,vocab_size=synth_model.model.module.config.vocab_size ,d_model=synth_model.model.module.config.emb_dim ,dropout_rate=synth_model.model.module.config.dropout_rate ,num_layers=synth_model.model.module.config.num_encoder_layers ,num_heads=synth_model.model.module.config.num_heads ,d_kv=synth_model.model.module.config.head_dim ,d_ff=synth_model.model.module.config.mlp_dim ,feed_forward_proj="gated-gelu" ,)
lowercase_ :Dict = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims ,targets_context_length=synth_model.sequence_length["targets_context"] ,d_model=synth_model.model.module.config.emb_dim ,dropout_rate=synth_model.model.module.config.dropout_rate ,num_layers=synth_model.model.module.config.num_encoder_layers ,num_heads=synth_model.model.module.config.num_heads ,d_kv=synth_model.model.module.config.head_dim ,d_ff=synth_model.model.module.config.mlp_dim ,feed_forward_proj="gated-gelu" ,)
lowercase_ :List[Any] = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims ,targets_length=synth_model.sequence_length["targets_context"] ,max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time ,d_model=synth_model.model.module.config.emb_dim ,num_layers=synth_model.model.module.config.num_decoder_layers ,num_heads=synth_model.model.module.config.num_heads ,d_kv=synth_model.model.module.config.head_dim ,d_ff=synth_model.model.module.config.mlp_dim ,dropout_rate=synth_model.model.module.config.dropout_rate ,)
lowercase_ :int = load_notes_encoder(ta_checkpoint["target"]["token_encoder"] ,_lowerCAmelCase )
lowercase_ :Union[str, Any] = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] ,_lowerCAmelCase )
lowercase_ :Any = load_decoder(ta_checkpoint["target"]["decoder"] ,_lowerCAmelCase )
lowercase_ :Tuple = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )
lowercase_ :Tuple = SpectrogramDiffusionPipeline(
notes_encoder=_lowerCAmelCase ,continuous_encoder=_lowerCAmelCase ,decoder=_lowerCAmelCase ,scheduler=_lowerCAmelCase ,melgan=_lowerCAmelCase ,)
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
lowerCAmelCase : int =argparse.ArgumentParser()
parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
parser.add_argument(
'''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
)
parser.add_argument(
'''--checkpoint_path''',
default=F'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help='''Path to the original jax model checkpoint.''',
)
lowerCAmelCase : Dict =parser.parse_args()
main(args)
| 371
|
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase : Any ={
'''facebook/maskformer-swin-base-ade''': (
'''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase : int =logging.get_logger(__name__)
class a_ ( _lowerCAmelCase ):
__A = "maskformer"
__A = {"hidden_size": "mask_feature_size"}
__A = ["resnet", "swin"]
__A = ["detr"]
def __init__( self : List[Any] , lowercase : int = 256 , lowercase : int = 256 , lowercase : float = 0.1 , lowercase : bool = False , lowercase : Optional[Dict] = None , lowercase : Optional[Dict] = None , lowercase : float = 0.02 , lowercase : float = 1.0 , lowercase : float = 1.0 , lowercase : float = 1.0 , lowercase : float = 20.0 , lowercase : Optional[bool] = None , **lowercase : Any , ):
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase_ :Any = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(lowercase , lowercase ):
lowercase_ :Optional[int] = backbone_config.pop("model_type" )
lowercase_ :Optional[int] = CONFIG_MAPPING[backbone_model_type]
lowercase_ :int = config_class.from_dict(lowercase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
F'Supported model types: {",".join(self.backbones_supported )}' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase_ :Optional[Any] = DetrConfig()
else:
# verify that the decoder is supported
lowercase_ :Tuple = (
decoder_config.pop("model_type" ) if isinstance(lowercase , lowercase ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F'Transformer Decoder {decoder_type} not supported, please use one of'
F' {",".join(self.decoders_supported )}' )
if isinstance(lowercase , lowercase ):
lowercase_ :str = CONFIG_MAPPING[decoder_type]
lowercase_ :List[str] = config_class.from_dict(lowercase )
lowercase_ :str = backbone_config
lowercase_ :Union[str, Any] = decoder_config
# main feature dimension for the model
lowercase_ :Any = fpn_feature_size
lowercase_ :Optional[int] = mask_feature_size
# initializer
lowercase_ :List[Any] = init_std
lowercase_ :Union[str, Any] = init_xavier_std
# Hungarian matcher && loss
lowercase_ :List[str] = cross_entropy_weight
lowercase_ :int = dice_weight
lowercase_ :List[str] = mask_weight
lowercase_ :Optional[Any] = use_auxiliary_loss
lowercase_ :str = no_object_weight
lowercase_ :int = output_auxiliary_logits
lowercase_ :Optional[Any] = self.decoder_config.encoder_attention_heads
lowercase_ :int = self.decoder_config.num_hidden_layers
super().__init__(**lowercase )
@classmethod
def lowercase__ ( cls : Tuple , lowercase : PretrainedConfig , lowercase : PretrainedConfig , **lowercase : Union[str, Any] ):
"""simple docstring"""
return cls(
backbone_config=lowercase , decoder_config=lowercase , **lowercase , )
def lowercase__ ( self : Optional[Any] ):
"""simple docstring"""
lowercase_ :str = copy.deepcopy(self.__dict__ )
lowercase_ :int = self.backbone_config.to_dict()
lowercase_ :List[Any] = self.decoder_config.to_dict()
lowercase_ :Optional[Any] = self.__class__.model_type
return output
| 147
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
'''tokenization_convbert''': ['''ConvBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ['''ConvBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvBertForMaskedLM''',
'''ConvBertForMultipleChoice''',
'''ConvBertForQuestionAnswering''',
'''ConvBertForSequenceClassification''',
'''ConvBertForTokenClassification''',
'''ConvBertLayer''',
'''ConvBertModel''',
'''ConvBertPreTrainedModel''',
'''load_tf_weights_in_convbert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFConvBertForMaskedLM''',
'''TFConvBertForMultipleChoice''',
'''TFConvBertForQuestionAnswering''',
'''TFConvBertForSequenceClassification''',
'''TFConvBertForTokenClassification''',
'''TFConvBertLayer''',
'''TFConvBertModel''',
'''TFConvBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 89
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
import re
from ..utils import cached_file
# docstyle-ignore
_A : Optional[int] = """
Human: <<task>>
Assistant: """
_A : List[Any] = """huggingface-tools/default-prompts"""
_A : Optional[int] = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def __magic_name__ ( __snake_case : int , __snake_case : List[Any] , __snake_case : Dict="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
lowercase : List[Any] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , __snake_case ) is not None:
return prompt_or_repo_id
lowercase : Optional[int] = cached_file(
__snake_case , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(__snake_case , "r" , encoding="utf-8" ) as f:
return f.read()
| 202
| 0
|
from collections.abc import Callable
def lowerCamelCase__ ( UpperCamelCase__ : Callable[[float], float] , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
'''simple docstring'''
_snake_case = a
_snake_case = b
if function(__lowerCAmelCase ) == 0: # one of the a or b is a root for the function
return a
elif function(__lowerCAmelCase ) == 0:
return b
elif (
function(__lowerCAmelCase ) * function(__lowerCAmelCase ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
_snake_case = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(__lowerCAmelCase ) == 0:
return mid
elif function(__lowerCAmelCase ) * function(__lowerCAmelCase ) < 0:
_snake_case = mid
else:
_snake_case = mid
_snake_case = start + (end - start) / 2.0
return mid
def lowerCamelCase__ ( UpperCamelCase__ : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 353
|
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def lowerCamelCase__ ( UpperCamelCase__ : int ) -> List[str]:
'''simple docstring'''
_snake_case = VideoMAEConfig()
set_architecture_configs(UpperCamelCase__ , UpperCamelCase__ )
if "finetuned" not in model_name:
_snake_case = False
if "finetuned" in model_name:
_snake_case = 'huggingface/label-files'
if "kinetics" in model_name:
_snake_case = 400
_snake_case = 'kinetics400-id2label.json'
elif "ssv2" in model_name:
_snake_case = 174
_snake_case = 'something-something-v2-id2label.json'
else:
raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.' )
_snake_case = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
_snake_case = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_snake_case = idalabel
_snake_case = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( UpperCamelCase__ : str , UpperCamelCase__ : Dict ) -> int:
'''simple docstring'''
if "small" in model_name:
_snake_case = 384
_snake_case = 1_536
_snake_case = 12
_snake_case = 16
_snake_case = 12
_snake_case = 3
_snake_case = 192
_snake_case = 768
elif "large" in model_name:
_snake_case = 1_024
_snake_case = 4_096
_snake_case = 24
_snake_case = 16
_snake_case = 12
_snake_case = 8
_snake_case = 512
_snake_case = 2_048
elif "huge" in model_name:
_snake_case = 1_280
_snake_case = 5_120
_snake_case = 32
_snake_case = 16
_snake_case = 12
_snake_case = 8
_snake_case = 640
_snake_case = 2_560
elif "base" not in model_name:
raise ValueError('Model name should include either "small", "base", "large", or "huge"' )
def lowerCamelCase__ ( UpperCamelCase__ : Any ) -> Tuple:
'''simple docstring'''
if "encoder." in name:
_snake_case = name.replace('encoder.' , '' )
if "cls_token" in name:
_snake_case = name.replace('cls_token' , 'videomae.embeddings.cls_token' )
if "decoder_pos_embed" in name:
_snake_case = name.replace('decoder_pos_embed' , 'decoder.decoder_pos_embed' )
if "pos_embed" in name and "decoder" not in name:
_snake_case = name.replace('pos_embed' , 'videomae.embeddings.position_embeddings' )
if "patch_embed.proj" in name:
_snake_case = name.replace('patch_embed.proj' , 'videomae.embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
_snake_case = name.replace('patch_embed.norm' , 'videomae.embeddings.norm' )
if "decoder.blocks" in name:
_snake_case = name.replace('decoder.blocks' , 'decoder.decoder_layers' )
if "blocks" in name:
_snake_case = name.replace('blocks' , 'videomae.encoder.layer' )
if "attn.proj" in name:
_snake_case = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "bias" not in name:
_snake_case = name.replace('attn' , 'attention.self' )
if "attn" in name:
_snake_case = name.replace('attn' , 'attention.attention' )
if "norm1" in name:
_snake_case = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
_snake_case = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
_snake_case = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
_snake_case = name.replace('mlp.fc2' , 'output.dense' )
if "decoder_embed" in name:
_snake_case = name.replace('decoder_embed' , 'decoder.decoder_embed' )
if "decoder_norm" in name:
_snake_case = name.replace('decoder_norm' , 'decoder.decoder_norm' )
if "decoder_pred" in name:
_snake_case = name.replace('decoder_pred' , 'decoder.decoder_pred' )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
_snake_case = name.replace('norm.weight' , 'videomae.layernorm.weight' )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
_snake_case = name.replace('norm.bias' , 'videomae.layernorm.bias' )
if "head" in name and "decoder" not in name:
_snake_case = name.replace('head' , 'classifier' )
return name
def lowerCamelCase__ ( UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
_snake_case = orig_state_dict.pop(UpperCamelCase__ )
if key.startswith('encoder.' ):
_snake_case = key.replace('encoder.' , '' )
if "qkv" in key:
_snake_case = key.split('.' )
if key.startswith('decoder.blocks' ):
_snake_case = config.decoder_hidden_size
_snake_case = int(key_split[2] )
_snake_case = 'decoder.decoder_layers.'
if "weight" in key:
_snake_case = val[:dim, :]
_snake_case = val[dim : dim * 2, :]
_snake_case = val[-dim:, :]
else:
_snake_case = config.hidden_size
_snake_case = int(key_split[1] )
_snake_case = 'videomae.encoder.layer.'
if "weight" in key:
_snake_case = val[:dim, :]
_snake_case = val[dim : dim * 2, :]
_snake_case = val[-dim:, :]
else:
_snake_case = val
return orig_state_dict
def lowerCamelCase__ ( ) -> Union[str, Any]:
'''simple docstring'''
_snake_case = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
_snake_case = np.load(UpperCamelCase__ )
return list(UpperCamelCase__ )
def lowerCamelCase__ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
_snake_case = get_videomae_config(UpperCamelCase__ )
if "finetuned" in model_name:
_snake_case = VideoMAEForVideoClassification(UpperCamelCase__ )
else:
_snake_case = VideoMAEForPreTraining(UpperCamelCase__ )
# download original checkpoint, hosted on Google Drive
_snake_case = 'pytorch_model.bin'
gdown.cached_download(UpperCamelCase__ , UpperCamelCase__ , quiet=UpperCamelCase__ )
_snake_case = torch.load(UpperCamelCase__ , map_location='cpu' )
if "model" in files:
_snake_case = files['model']
else:
_snake_case = files['module']
_snake_case = convert_state_dict(UpperCamelCase__ , UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
# verify model on basic input
_snake_case = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
_snake_case = prepare_video()
_snake_case = image_processor(UpperCamelCase__ , return_tensors='pt' )
if "finetuned" not in model_name:
_snake_case = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
_snake_case = torch.load(UpperCamelCase__ )
_snake_case = model(**UpperCamelCase__ )
_snake_case = outputs.logits
_snake_case = [
'videomae-small-finetuned-kinetics',
'videomae-small-finetuned-ssv2',
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
'videomae-base-short',
'videomae-base-short-finetuned-kinetics',
'videomae-base',
'videomae-base-finetuned-kinetics',
'videomae-large',
'videomae-large-finetuned-kinetics',
'videomae-huge-finetuned-kinetics',
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
'videomae-base-short-ssv2',
'videomae-base-short-finetuned-ssv2',
'videomae-base-ssv2',
'videomae-base-finetuned-ssv2',
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
_snake_case = torch.Size([1, 400] )
_snake_case = torch.tensor([-0.9291, -0.4061, -0.9307] )
elif model_name == "videomae-small-finetuned-ssv2":
_snake_case = torch.Size([1, 174] )
_snake_case = torch.tensor([0.2671, -0.4689, -0.8235] )
elif model_name == "videomae-base":
_snake_case = torch.Size([1, 1_408, 1_536] )
_snake_case = torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]] )
elif model_name == "videomae-base-short":
_snake_case = torch.Size([1, 1_408, 1_536] )
_snake_case = torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] )
# we verified the loss both for normalized and unnormalized targets for this one
_snake_case = torch.tensor([0.5142] ) if config.norm_pix_loss else torch.tensor([0.6469] )
elif model_name == "videomae-large":
_snake_case = torch.Size([1, 1_408, 1_536] )
_snake_case = torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]] )
elif model_name == "videomae-large-finetuned-kinetics":
_snake_case = torch.Size([1, 400] )
_snake_case = torch.tensor([0.0771, 0.0011, -0.3625] )
elif model_name == "videomae-huge-finetuned-kinetics":
_snake_case = torch.Size([1, 400] )
_snake_case = torch.tensor([0.2433, 0.1632, -0.4894] )
elif model_name == "videomae-base-short-finetuned-kinetics":
_snake_case = torch.Size([1, 400] )
_snake_case = torch.tensor([0.6588, 0.0990, -0.2493] )
elif model_name == "videomae-base-finetuned-kinetics":
_snake_case = torch.Size([1, 400] )
_snake_case = torch.tensor([0.3669, -0.0688, -0.2421] )
elif model_name == "videomae-base-short-ssv2":
_snake_case = torch.Size([1, 1_408, 1_536] )
_snake_case = torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
_snake_case = torch.Size([1, 174] )
_snake_case = torch.tensor([-0.0537, -0.1539, -0.3266] )
elif model_name == "videomae-base-ssv2":
_snake_case = torch.Size([1, 1_408, 1_536] )
_snake_case = torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]] )
elif model_name == "videomae-base-finetuned-ssv2":
_snake_case = torch.Size([1, 174] )
_snake_case = torch.tensor([0.1961, -0.8337, -0.6389] )
else:
raise ValueError(F'''Model name not supported. Should be one of {model_names}''' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , UpperCamelCase__ , atol=1e-4 )
else:
print('Logits:' , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , UpperCamelCase__ , atol=1e-4 )
print('Logits ok!' )
# verify loss, if applicable
if model_name == "videomae-base-short":
_snake_case = outputs.loss
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-4 )
print('Loss ok!' )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print('Pushing to the hub...' )
model.push_to_hub(UpperCamelCase__ , organization='nielsr' )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
UpperCAmelCase_ = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 295
| 0
|
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : int ,A : int ):
__A = n
__A = [None] * self.n
__A = 0 # index of the first element
__A = 0
__A = 0
def __len__( self : Tuple ):
return self.size
def UpperCamelCase_ ( self : int ):
return self.size == 0
def UpperCamelCase_ ( self : str ):
return False if self.is_empty() else self.array[self.front]
def UpperCamelCase_ ( self : Union[str, Any] ,A : Dict ):
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
__A = data
__A = (self.rear + 1) % self.n
self.size += 1
return self
def UpperCamelCase_ ( self : Union[str, Any] ):
if self.size == 0:
raise Exception("UNDERFLOW" )
__A = self.array[self.front]
__A = None
__A = (self.front + 1) % self.n
self.size -= 1
return temp
| 15
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 2
@register_to_config
def __init__( self : str ,A : float = 0.02 ,A : float = 1_00 ,A : float = 1.0_07 ,A : float = 80 ,A : float = 0.05 ,A : float = 50 ,):
# standard deviation of the initial noise distribution
__A = sigma_max
# setable values
__A = None
__A = None
__A = None # sigma(t_i)
def UpperCamelCase_ ( self : str ,A : torch.FloatTensor ,A : Optional[int] = None ):
return sample
def UpperCamelCase_ ( self : Dict ,A : int ,A : Union[str, torch.device] = None ):
__A = num_inference_steps
__A = np.arange(0 ,self.num_inference_steps )[::-1].copy()
__A = torch.from_numpy(A ).to(A )
__A = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__A = torch.tensor(A ,dtype=torch.floataa ,device=A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : torch.FloatTensor ,A : float ,A : Optional[torch.Generator] = None ):
if self.config.s_min <= sigma <= self.config.s_max:
__A = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
__A = 0
# sample eps ~ N(0, S_noise^2 * I)
__A = self.config.s_noise * randn_tensor(sample.shape ,generator=A ).to(sample.device )
__A = sigma + gamma * sigma
__A = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase_ ( self : Dict ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_hat + sigma_hat * model_output
__A = (sample_hat - pred_original_sample) / sigma_hat
__A = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : Optional[int] ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_prev + sigma_prev * model_output
__A = (sample_prev - pred_original_sample) / sigma_prev
__A = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : List[Any] ,A : Dict ,A : List[str] ,A : str ):
raise NotImplementedError()
| 15
| 1
|
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_lowerCamelCase : str = ""
if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):
class SCREAMING_SNAKE_CASE ( tr.AbstractTransform ):
def __init__( self : int, __A : str = " " ):
UpperCAmelCase : Optional[int] = sentence_delimiter
def __magic_name__ ( self : Dict, __A : str ):
return list(__A )
def __magic_name__ ( self : List[Any], __A : List[str] ):
UpperCAmelCase : Union[str, Any] = []
for sent_idx, sentence in enumerate(__A ):
chars.extend(self.process_string(__A ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__A ) - 1:
chars.append(self.sentence_delimiter )
return chars
_lowerCamelCase : Optional[int] = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
_lowerCamelCase : Dict = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
_lowerCamelCase : int = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"
_lowerCamelCase : Union[str, Any] = "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"
_lowerCamelCase : Optional[Any] = "\nComputes CER score of transcribed segments against references.\nArgs:\n references: list of references for each speech input.\n predictions: list of transcribtions to score.\n concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n (float): the character error rate\n\nExamples:\n\n >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n >>> references = [\"this is the reference\", \"there is another one\"]\n >>> cer = datasets.load_metric(\"cer\")\n >>> cer_score = cer.compute(predictions=predictions, references=references)\n >>> print(cer_score)\n 0.34146341463414637\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __magic_name__ ( self : List[str] ):
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'''predictions''': datasets.Value('''string''', id='''sequence''' ),
'''references''': datasets.Value('''string''', id='''sequence''' ),
} ), codebase_urls=['''https://github.com/jitsi/jiwer/'''], reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
'''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
], )
def __magic_name__ ( self : Dict, __A : Any, __A : Optional[Any], __A : Dict=False ):
if concatenate_texts:
return jiwer.compute_measures(
__A, __A, truth_transform=__A, hypothesis_transform=__A, )["wer"]
UpperCAmelCase : Any = 0
UpperCAmelCase : Tuple = 0
for prediction, reference in zip(__A, __A ):
UpperCAmelCase : str = jiwer.compute_measures(
__A, __A, truth_transform=__A, hypothesis_transform=__A, )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 352
|
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''onnx'''] )
@classmethod
def __magic_name__ ( cls : Any, *__A : Any, **__A : Dict ):
requires_backends(cls, ['''onnx'''] )
@classmethod
def __magic_name__ ( cls : Tuple, *__A : List[str], **__A : List[str] ):
requires_backends(cls, ['''onnx'''] )
| 99
| 0
|
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
lowerCamelCase : Any = get_logger()
lowerCamelCase : Optional[dict] = None
class __lowercase (TensorFormatter[Mapping, """jax.Array""", Mapping] ):
"""simple docstring"""
def __init__( self , A=None , A=None , **A ) -> Optional[Any]:
super().__init__(features=A )
import jax
from jaxlib.xla_client import Device
if isinstance(A , A ):
raise ValueError(
f"""Expected {device} to be a `str` not {type(A )}, as `jaxlib.xla_extension.Device` """
"""is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
"""the device with `str()` to get its string identifier that will be internally mapped """
"""to the actual `jaxlib.xla_extension.Device`.""" )
snake_case : Optional[int] = device if isinstance(A , A ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
snake_case : Optional[Any] = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"""Device with string identifier {self.device} not listed among the available """
f"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
f"""device: {str(jax.devices()[0] )}.""" )
snake_case : List[Any] = str(jax.devices()[0] )
snake_case : Optional[Any] = jnp_array_kwargs
@staticmethod
def UpperCAmelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(A ): device for device in jax.devices()}
def UpperCAmelCase ( self , A ) -> List[Any]:
import jax
import jax.numpy as jnp
if isinstance(A , A ) and column:
if all(
isinstance(A , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(A , axis=0 )
return column
def UpperCAmelCase ( self , A ) -> List[str]:
import jax
import jax.numpy as jnp
if isinstance(A , (str, bytes, type(A )) ):
return value
elif isinstance(A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
snake_case : List[Any] = {}
if isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
snake_case : Optional[Any] = {"""dtype""": jnp.intaa}
else:
snake_case : Any = {"""dtype""": jnp.intaa}
elif isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
snake_case : List[Any] = {"""dtype""": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A , PIL.Image.Image ):
snake_case : Optional[int] = np.asarray(A )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
snake_case : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(A , **{**default_dtype, **self.jnp_array_kwargs} )
def UpperCAmelCase ( self , A ) -> Dict:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(A , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(A , """__array__""" ) and not isinstance(A , jax.Array ):
snake_case : Union[str, Any] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def UpperCAmelCase ( self , A ) -> str:
return map_nested(self._recursive_tensorize , A , map_list=A )
def UpperCAmelCase ( self , A ) -> Mapping:
snake_case : Optional[int] = self.numpy_arrow_extractor().extract_row(A )
snake_case : Union[str, Any] = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def UpperCAmelCase ( self , A ) -> "jax.Array":
snake_case : int = self.numpy_arrow_extractor().extract_column(A )
snake_case : Tuple = self.python_features_decoder.decode_column(A , pa_table.column_names[0] )
snake_case : Any = self.recursive_tensorize(A )
snake_case : Any = self._consolidate(A )
return column
def UpperCAmelCase ( self , A ) -> Mapping:
snake_case : Union[str, Any] = self.numpy_arrow_extractor().extract_batch(A )
snake_case : Optional[Any] = self.python_features_decoder.decode_batch(A )
snake_case : str = self.recursive_tensorize(A )
for column_name in batch:
snake_case : int = self._consolidate(batch[column_name] )
return batch
| 124
|
from __future__ import annotations
import math
lowerCamelCase : Optional[int] = '2020.9.26'
lowerCamelCase : int = 'xcodz-dot, cclaus, dhruvmanila'
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ) -> tuple[float, float]:
if not all(isinstance(lowercase ,(float, int) ) for val in locals().values() ):
snake_case : Dict = f"""Input values must either be float or int: {list(locals().values() )}"""
raise TypeError(lowercase )
snake_case : List[str] = ((x * distance) / (z + distance)) * scale
snake_case : Dict = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ) -> tuple[float, float, float]:
if not isinstance(lowercase ,lowercase ):
raise TypeError("""Axis must be a str""" )
snake_case : Tuple = locals()
del input_variables["axis"]
if not all(isinstance(lowercase ,(float, int) ) for val in input_variables.values() ):
snake_case : int = (
"""Input values except axis must either be float or int: """
f"""{list(input_variables.values() )}"""
)
raise TypeError(lowercase )
snake_case : int = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
snake_case : str = x * math.cos(lowercase ) - y * math.sin(lowercase )
snake_case : List[Any] = y * math.cos(lowercase ) + x * math.sin(lowercase )
snake_case : Optional[int] = z
elif axis == "x":
snake_case : Optional[Any] = y * math.cos(lowercase ) - z * math.sin(lowercase )
snake_case : Optional[int] = z * math.cos(lowercase ) + y * math.sin(lowercase )
snake_case : Optional[int] = x
elif axis == "y":
snake_case : List[str] = x * math.cos(lowercase ) - z * math.sin(lowercase )
snake_case : Tuple = z * math.cos(lowercase ) + x * math.sin(lowercase )
snake_case : Optional[int] = y
else:
raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'""" )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
print(f"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")
| 124
| 1
|
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_lowerCAmelCase : Dict = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __magic_name__ ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self :int , *snake_case :str , **snake_case :Tuple ):
'''simple docstring'''
super().__init__(*snake_case , **snake_case )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def SCREAMING_SNAKE_CASE ( self :int , snake_case :int=None ):
'''simple docstring'''
A_ : Tuple = {}
if top_k is not None:
A_ : Union[str, Any] = top_k
return {}, {}, postprocess_params
def __call__( self :List[str] , snake_case :Union[str, List[str], "Image.Image", List["Image.Image"]] , **snake_case :Tuple ):
'''simple docstring'''
return super().__call__(snake_case , **snake_case )
def SCREAMING_SNAKE_CASE ( self :Dict , snake_case :Optional[Any] ):
'''simple docstring'''
A_ : Any = load_image(snake_case )
A_ : Optional[int] = self.image_processor(images=snake_case , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE ( self :Any , snake_case :str ):
'''simple docstring'''
A_ : Tuple = self.model(**snake_case )
return model_outputs
def SCREAMING_SNAKE_CASE ( self :Dict , snake_case :Optional[int] , snake_case :List[str]=5 ):
'''simple docstring'''
if top_k > self.model.config.num_labels:
A_ : Optional[Any] = self.model.config.num_labels
if self.framework == "pt":
A_ : List[Any] = model_outputs.logits.softmax(-1 )[0]
A_ , A_ : Tuple = probs.topk(snake_case )
elif self.framework == "tf":
A_ : List[str] = stable_softmax(model_outputs.logits , axis=-1 )[0]
A_ : Tuple = tf.math.top_k(snake_case , k=snake_case )
A_ , A_ : Any = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f"Unsupported framework: {self.framework}" )
A_ : Union[str, Any] = scores.tolist()
A_ : Union[str, Any] = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case , snake_case )]
| 70
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_lowerCAmelCase : str = logging.get_logger(__name__)
@dataclass
class __magic_name__ :
"""simple docstring"""
def __init__( self :Dict , snake_case :List[str]=False , snake_case :Optional[Any]=False , snake_case :Union[str, Any]=6.0 , snake_case :Union[str, Any]=None , snake_case :Union[str, Any]=False , snake_case :str=False , snake_case :Optional[Any]=None , snake_case :int="fp4" , snake_case :int=False , **snake_case :Optional[Any] , ):
'''simple docstring'''
A_ : int = load_in_abit
A_ : Union[str, Any] = load_in_abit
A_ : str = llm_inta_threshold
A_ : str = llm_inta_skip_modules
A_ : List[Any] = llm_inta_enable_fpaa_cpu_offload
A_ : Optional[int] = llm_inta_has_fpaa_weight
A_ : Optional[int] = bnb_abit_quant_type
A_ : Dict = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
A_ : List[Any] = torch.floataa
elif isinstance(snake_case , snake_case ):
A_ : Any = getattr(snake_case , snake_case )
elif isinstance(snake_case , torch.dtype ):
A_ : Union[str, Any] = bnb_abit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype" )
self.post_init()
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
if not isinstance(self.llm_inta_threshold , snake_case ):
raise ValueError("llm_int8_threshold must be a float" )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , snake_case ):
raise ValueError("llm_int8_skip_modules must be a list of strings" )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , snake_case ):
raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean" )
if not isinstance(self.llm_inta_has_fpaa_weight , snake_case ):
raise ValueError("llm_int8_has_fp16_weight must be a boolean" )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError("bnb_4bit_compute_dtype must be torch.dtype" )
if not isinstance(self.bnb_abit_quant_type , snake_case ):
raise ValueError("bnb_4bit_quant_type must be a string" )
if not isinstance(self.bnb_abit_use_double_quant , snake_case ):
raise ValueError("bnb_4bit_use_double_quant must be a boolean" )
if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes" ) ) >= version.parse(
"0.39.0" ):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version" )
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] ):
'''simple docstring'''
return self.load_in_abit or self.load_in_abit
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def SCREAMING_SNAKE_CASE ( cls :List[str] , snake_case :Dict , snake_case :str , **snake_case :Dict ):
'''simple docstring'''
A_ : str = cls(**snake_case )
A_ : Any = []
for key, value in kwargs.items():
if hasattr(snake_case , snake_case ):
setattr(snake_case , snake_case , snake_case )
to_remove.append(snake_case )
for key in to_remove:
kwargs.pop(snake_case , snake_case )
if return_unused_kwargs:
return config, kwargs
else:
return config
def SCREAMING_SNAKE_CASE ( self :Tuple , snake_case :Union[str, os.PathLike] ):
'''simple docstring'''
with open(snake_case , "w" , encoding="utf-8" ) as writer:
A_ : List[Any] = self.to_dict()
A_ : int = json.dumps(snake_case , indent=2 , sort_keys=snake_case ) + "\n"
writer.write(snake_case )
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
A_ : List[str] = copy.deepcopy(self.__dict__ )
A_ : Optional[int] = str(output["bnb_4bit_compute_dtype"] ).split("." )[1]
return output
def __repr__( self :List[str] ):
'''simple docstring'''
return f"{self.__class__.__name__} {self.to_json_string()}"
def SCREAMING_SNAKE_CASE ( self :List[str] , snake_case :bool = True ):
'''simple docstring'''
if use_diff is True:
A_ : List[str] = self.to_diff_dict()
else:
A_ : int = self.to_dict()
return json.dumps(snake_case , indent=2 , sort_keys=snake_case ) + "\n"
def SCREAMING_SNAKE_CASE ( self :Dict ):
'''simple docstring'''
A_ : List[Any] = self.to_dict()
# get the default config dict
A_ : Optional[Any] = BitsAndBytesConfig().to_dict()
A_ : List[Any] = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
A_ : int = value
return serializable_config_dict
| 70
| 1
|
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
__lowercase = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def lowercase ( A_ )-> list[list[int]]:
'''simple docstring'''
a : str = []
for i in range(len(A_ ) ):
a : str = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
a : Union[str, Any] = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(A_ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(A_ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(A_ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
a : Tuple = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(A_ )
return next_generation
def lowercase ( A_ , A_ )-> list[Image.Image]:
'''simple docstring'''
a : List[str] = []
for _ in range(A_ ):
# Create output image
a : str = Image.new("RGB" , (len(cells[0] ), len(A_ )) )
a : Union[str, Any] = img.load()
# Save cells to image
for x in range(len(A_ ) ):
for y in range(len(cells[0] ) ):
a : Optional[Any] = 255 - cells[y][x] * 255
a : str = (colour, colour, colour)
# Save image
images.append(A_ )
a : Tuple = new_generation(A_ )
return images
if __name__ == "__main__":
__lowercase = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 40
|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class _a :
def __init__(self, SCREAMING_SNAKE_CASE_ = "cpu", SCREAMING_SNAKE_CASE_ = "openai/clip-vit-large-patch14" ) -> None:
UpperCAmelCase_: Optional[Any] = device
UpperCAmelCase_: Optional[Any] = CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
UpperCAmelCase_: Optional[Any] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
UpperCAmelCase_: Optional[Any] = torchvision.transforms.Normalize(self.image_mean, self.image_std )
UpperCAmelCase_: Tuple = torchvision.transforms.Resize(224 )
UpperCAmelCase_: Any = torchvision.transforms.CenterCrop(224 )
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
UpperCAmelCase_: Dict = self.resize(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[int] = self.center_crop(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = self.normalize(SCREAMING_SNAKE_CASE_ )
return images
def __call__(self, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, **SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCAmelCase_: Dict = self.tokenizer(text=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = self.preprocess_img(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class _a ( nn.Module ):
def __init__(self, SCREAMING_SNAKE_CASE_=10, SCREAMING_SNAKE_CASE_=0.0_1, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="image", SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False, ) -> None:
super().__init__()
UpperCAmelCase_: List[Any] = None
UpperCAmelCase_: List[str] = device if device else get_device()
if vqgan:
UpperCAmelCase_: int = vqgan
else:
UpperCAmelCase_: Optional[Any] = load_vqgan(self.device, conf_path=SCREAMING_SNAKE_CASE_, ckpt_path=SCREAMING_SNAKE_CASE_ )
self.vqgan.eval()
if clip:
UpperCAmelCase_: List[str] = clip
else:
UpperCAmelCase_: Any = CLIPModel.from_pretrained("""openai/clip-vit-base-patch32""" )
self.clip.to(self.device )
UpperCAmelCase_: Optional[int] = ProcessorGradientFlow(device=self.device )
UpperCAmelCase_: Optional[int] = iterations
UpperCAmelCase_: List[Any] = lr
UpperCAmelCase_: str = log
UpperCAmelCase_: Tuple = make_grid
UpperCAmelCase_: List[str] = return_val
UpperCAmelCase_: Dict = quantize
UpperCAmelCase_: int = self.vqgan.decoder.z_shape
def __snake_case (self, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=True ) -> List[Any]:
UpperCAmelCase_: Tuple = []
if output_path is None:
UpperCAmelCase_: Optional[int] = """./animation.gif"""
if input_path is None:
UpperCAmelCase_: Tuple = self.save_path
UpperCAmelCase_: List[Any] = sorted(glob(input_path + """/*""" ) )
if not len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
""" function?)""" )
if len(SCREAMING_SNAKE_CASE_ ) == 1:
print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)""" )
UpperCAmelCase_: Dict = total_duration / len(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: str = [frame_duration] * len(SCREAMING_SNAKE_CASE_ )
if extend_frames:
UpperCAmelCase_: List[str] = 1.5
UpperCAmelCase_: List[Any] = 3
for file_name in paths:
if file_name.endswith(""".png""" ):
images.append(imageio.imread(SCREAMING_SNAKE_CASE_ ) )
imageio.mimsave(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, duration=SCREAMING_SNAKE_CASE_ )
print(f'gif saved to {output_path}' )
def __snake_case (self, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None ) -> Optional[int]:
if not (path or img):
raise ValueError("""Input either path or tensor""" )
if img is not None:
raise NotImplementedError
UpperCAmelCase_: List[Any] = preprocess(Image.open(SCREAMING_SNAKE_CASE_ ), target_image_size=256 ).to(self.device )
UpperCAmelCase_: Union[str, Any] = preprocess_vqgan(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_ , *UpperCAmelCase_: str = self.vqgan.encode(SCREAMING_SNAKE_CASE_ )
return z
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
UpperCAmelCase_: List[Any] = self.latent.detach().requires_grad_()
UpperCAmelCase_: Optional[int] = base_latent + transform_vector
if self.quantize:
UpperCAmelCase_ , *UpperCAmelCase_: Optional[Any] = self.vqgan.quantize(SCREAMING_SNAKE_CASE_ )
else:
UpperCAmelCase_: Tuple = trans_latent
return self.vqgan.decode(SCREAMING_SNAKE_CASE_ )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=None ) -> List[str]:
UpperCAmelCase_: Any = self.clip_preprocessor(text=SCREAMING_SNAKE_CASE_, images=SCREAMING_SNAKE_CASE_, return_tensors="""pt""", padding=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: str = self.clip(**SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Dict = clip_outputs.logits_per_image
if weights is not None:
UpperCAmelCase_: Any = similarity_logits * weights
return similarity_logits.sum()
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Any:
UpperCAmelCase_: Dict = self._get_clip_similarity(pos_prompts["""prompts"""], SCREAMING_SNAKE_CASE_, weights=(1 / pos_prompts["""weights"""]) )
if neg_prompts:
UpperCAmelCase_: Tuple = self._get_clip_similarity(neg_prompts["""prompts"""], SCREAMING_SNAKE_CASE_, weights=neg_prompts["""weights"""] )
else:
UpperCAmelCase_: Any = torch.tensor([1], device=self.device )
UpperCAmelCase_: List[str] = -torch.log(SCREAMING_SNAKE_CASE_ ) + torch.log(SCREAMING_SNAKE_CASE_ )
return loss
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
UpperCAmelCase_: Tuple = torch.randn_like(self.latent, requires_grad=SCREAMING_SNAKE_CASE_, device=self.device )
UpperCAmelCase_: str = torch.optim.Adam([vector], lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
UpperCAmelCase_: Optional[int] = self._add_vector(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Any = loop_post_process(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = self._get_CLIP_loss(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
print("""CLIP loss""", SCREAMING_SNAKE_CASE_ )
if self.log:
wandb.log({"""CLIP Loss""": clip_loss} )
clip_loss.backward(retain_graph=SCREAMING_SNAKE_CASE_ )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Dict:
wandb.init(reinit=SCREAMING_SNAKE_CASE_, project="""face-editor""" )
wandb.config.update({"""Positive Prompts""": positive_prompts} )
wandb.config.update({"""Negative Prompts""": negative_prompts} )
wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations} )
if image_path:
UpperCAmelCase_: str = Image.open(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = image.resize((256, 256) )
wandb.log("""Original Image""", wandb.Image(SCREAMING_SNAKE_CASE_ ) )
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
if not prompts:
return []
UpperCAmelCase_: Tuple = []
UpperCAmelCase_: str = []
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase_: Optional[Any] = [prompt.strip() for prompt in prompts.split("""|""" )]
for prompt in prompts:
if isinstance(SCREAMING_SNAKE_CASE_, (tuple, list) ):
UpperCAmelCase_: str = prompt[0]
UpperCAmelCase_: List[str] = float(prompt[1] )
elif ":" in prompt:
UpperCAmelCase_ , UpperCAmelCase_: int = prompt.split(""":""" )
UpperCAmelCase_: int = float(SCREAMING_SNAKE_CASE_ )
else:
UpperCAmelCase_: str = prompt
UpperCAmelCase_: Dict = 1.0
processed_prompts.append(SCREAMING_SNAKE_CASE_ )
weights.append(SCREAMING_SNAKE_CASE_ )
return {
"prompts": processed_prompts,
"weights": torch.tensor(SCREAMING_SNAKE_CASE_, device=self.device ),
}
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=None, ) -> Optional[Any]:
if image_path:
UpperCAmelCase_: Optional[int] = self._get_latent(SCREAMING_SNAKE_CASE_ )
else:
UpperCAmelCase_: str = torch.randn(self.latent_dim, device=self.device )
if self.log:
self._init_logging(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
assert pos_prompts, "You must provide at least one positive prompt."
UpperCAmelCase_: List[Any] = self.process_prompts(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Dict = self.process_prompts(SCREAMING_SNAKE_CASE_ )
if save_final and save_path is None:
UpperCAmelCase_: Optional[int] = os.path.join("""./outputs/""", """_""".join(pos_prompts["""prompts"""] ) )
if not os.path.exists(SCREAMING_SNAKE_CASE_ ):
os.makedirs(SCREAMING_SNAKE_CASE_ )
else:
UpperCAmelCase_: List[str] = save_path + """_""" + get_timestamp()
os.makedirs(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[int] = save_path
UpperCAmelCase_: Optional[Any] = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print("""Original Image""" )
show_pil(custom_to_pil(SCREAMING_SNAKE_CASE_ ) )
UpperCAmelCase_: Tuple = loop_post_process(SCREAMING_SNAKE_CASE_ )
for iter, transformed_img in enumerate(self._optimize_CLIP(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) ):
if show_intermediate:
show_pil(SCREAMING_SNAKE_CASE_ )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, f'iter_{iter:03d}.png' ) )
if self.log:
wandb.log({"""Image""": wandb.Image(SCREAMING_SNAKE_CASE_ )} )
if show_final:
show_pil(SCREAMING_SNAKE_CASE_ )
if save_final:
transformed_img.save(os.path.join(self.save_path, f'iter_{iter:03d}_final.png' ) )
| 147
| 0
|
from __future__ import annotations
class _snake_case :
def __init__( self , _lowerCamelCase , _lowerCamelCase ):
a , a :Union[str, Any] = text, pattern
a , a :List[str] = len(_lowerCamelCase ), len(_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def SCREAMING_SNAKE_CASE__ ( self ):
# searches pattern in text and returns index positions
a :int = []
for i in range(self.textLen - self.patLen + 1 ):
a :Any = self.mismatch_in_text(_lowerCamelCase )
if mismatch_index == -1:
positions.append(_lowerCamelCase )
else:
a :Dict = self.match_in_pattern(self.text[mismatch_index] )
a :int = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
snake_case : List[str] = '''ABAABA'''
snake_case : List[Any] = '''AB'''
snake_case : Dict = BoyerMooreSearch(text, pattern)
snake_case : str = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 281
|
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class _snake_case :
def __init__( self , _lowerCamelCase , ):
a :List[str] = parent
a :Dict = 13
a :Optional[int] = 7
a :Optional[Any] = 30
a :Optional[Any] = self.seq_length + self.mem_len
a :Tuple = 15
a :List[str] = True
a :List[Any] = True
a :List[Any] = 99
a :Optional[Any] = [10, 50, 80]
a :Optional[int] = 32
a :List[Any] = 32
a :Dict = 4
a :List[Any] = 8
a :Optional[Any] = 128
a :Dict = 2
a :List[Any] = 2
a :str = None
a :str = 1
a :List[Any] = 0
a :List[str] = 3
a :str = self.vocab_size - 1
a :Optional[Any] = 0.01
def SCREAMING_SNAKE_CASE__ ( self ):
a :Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a :Tuple = None
if self.use_labels:
a :Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a :Union[str, Any] = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def SCREAMING_SNAKE_CASE__ ( self ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
a :int = TFTransfoXLModel(_lowerCamelCase )
a , a :List[Any] = model(_lowerCamelCase ).to_tuple()
a :List[str] = {'''input_ids''': input_ids_a, '''mems''': mems_a}
a , a :Optional[int] = model(_lowerCamelCase ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
a :str = TFTransfoXLLMHeadModel(_lowerCamelCase )
a , a :Tuple = model(_lowerCamelCase ).to_tuple()
a :Any = {'''input_ids''': input_ids_a, '''labels''': lm_labels}
a , a :Dict = model(_lowerCamelCase ).to_tuple()
a , a :Dict = model([input_ids_a, mems_a] ).to_tuple()
a :str = {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
a , a :Any = model(_lowerCamelCase ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
a :Optional[Any] = TFTransfoXLForSequenceClassification(_lowerCamelCase )
a :Any = model(_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
a :str = self.prepare_config_and_inputs()
((a) , (a) , (a) , (a)) :Optional[int] = config_and_inputs
a :Union[str, Any] = {'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class _snake_case ( _snake_case , _snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
SCREAMING_SNAKE_CASE__ = () if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ = (
{
'feature-extraction': TFTransfoXLModel,
'text-classification': TFTransfoXLForSequenceClassification,
'text-generation': TFTransfoXLLMHeadModel,
'zero-shot': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self ):
a :str = TFTransfoXLModelTester(self )
a :str = ConfigTester(self , config_class=_lowerCamelCase , d_embed=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
self.model_tester.set_seed()
a :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ):
self.model_tester.set_seed()
a :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ):
a :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ):
a , a :Any = self.model_tester.prepare_config_and_inputs_for_common()
a :int = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
a :Any = model_class(_lowerCamelCase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
a :Dict = model.get_output_embeddings()
assert isinstance(_lowerCamelCase , tf.keras.layers.Layer )
a :Dict = model.get_bias()
assert name is None
else:
a :int = model.get_output_embeddings()
assert x is None
a :Optional[int] = model.get_bias()
assert name is None
def SCREAMING_SNAKE_CASE__ ( self ):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a :List[Any] = TFTransfoXLModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_tf
class _snake_case ( unittest.TestCase ):
@unittest.skip('''Skip test until #12651 is resolved.''' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
a :Any = TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
# fmt: off
a :Union[str, Any] = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
a :List[Any] = [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
a :Optional[Any] = model.generate(_lowerCamelCase , max_length=200 , do_sample=_lowerCamelCase )
self.assertListEqual(output_ids[0].numpy().tolist() , _lowerCamelCase )
| 281
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|
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
lowerCAmelCase = '''bert-base-cased'''
lowerCAmelCase = '''google/pegasus-xsum'''
lowerCAmelCase = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCAmelCase = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCAmelCase = '''patrickvonplaten/t5-tiny-random'''
lowerCAmelCase = '''sshleifer/bart-tiny-random'''
lowerCAmelCase = '''sshleifer/tiny-mbart'''
lowerCAmelCase = '''sshleifer/tiny-marian-en-de'''
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Optional[int]:
'''simple docstring'''
__lowercase= '\n'.join(lowercase__ )
Path(lowercase__ ).open('w' ).writelines(lowercase__ )
def _lowerCamelCase( lowercase__ ) -> Tuple:
'''simple docstring'''
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(lowercase__ , F'{split}.source' ) , lowercase__ )
_dump_articles(os.path.join(lowercase__ , F'{split}.target' ) , lowercase__ )
return tmp_dir
class A ( A_ ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def _A (self , lowerCAmelCase ):
__lowercase= AutoTokenizer.from_pretrained(lowerCAmelCase )
__lowercase= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__lowercase= max(len(tokenizer.encode(lowerCAmelCase ) ) for a in ARTICLES )
__lowercase= max(len(tokenizer.encode(lowerCAmelCase ) ) for a in SUMMARIES )
__lowercase= 4
__lowercase= 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__lowercase, __lowercase= 'ro_RO', 'de_DE' # ignored for all but mbart, but never causes error.
__lowercase= SeqaSeqDataset(
lowerCAmelCase , data_dir=lowerCAmelCase , type_path='train' , max_source_length=lowerCAmelCase , max_target_length=lowerCAmelCase , src_lang=lowerCAmelCase , tgt_lang=lowerCAmelCase , )
__lowercase= DataLoader(lowerCAmelCase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(lowerCAmelCase , lowerCAmelCase )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__lowercase= shift_tokens_right(batch['labels'] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def _A (self , lowerCAmelCase ):
__lowercase= AutoTokenizer.from_pretrained(lowerCAmelCase )
__lowercase= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__lowercase= max(len(tokenizer.encode(lowerCAmelCase ) ) for a in ARTICLES )
__lowercase= max(len(tokenizer.encode(lowerCAmelCase ) ) for a in SUMMARIES )
__lowercase= 4
__lowercase= LegacySeqaSeqDataset(
lowerCAmelCase , data_dir=lowerCAmelCase , type_path='train' , max_source_length=2_0 , max_target_length=lowerCAmelCase , )
__lowercase= DataLoader(lowerCAmelCase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 2_0 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def _A (self ):
__lowercase= AutoTokenizer.from_pretrained('facebook/mbart-large-cc25' )
__lowercase= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
__lowercase= tmp_dir.joinpath('train.source' ).open().readlines()
__lowercase= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(lowerCAmelCase , lowerCAmelCase , 1_2_8 , lowerCAmelCase )
__lowercase= {x.name for x in tmp_dir.iterdir()}
__lowercase= {x.name for x in save_dir.iterdir()}
__lowercase= save_dir.joinpath('train.source' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(lowerCAmelCase ) < len(lowerCAmelCase )
assert len(lowerCAmelCase ) == 1
assert len(packed_examples[0] ) == sum(len(lowerCAmelCase ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='This test requires fairseq' )
def _A (self ):
if not FAIRSEQ_AVAILABLE:
return
__lowercase, __lowercase, __lowercase= self._get_dataset(max_len=6_4 )
__lowercase= 6_4
__lowercase= ds.make_dynamic_sampler(lowerCAmelCase , required_batch_size_multiple=lowerCAmelCase )
__lowercase= [len(lowerCAmelCase ) for x in batch_sampler]
assert len(set(lowerCAmelCase ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(lowerCAmelCase ) == len(lowerCAmelCase ) # no dropped or added examples
__lowercase= DataLoader(lowerCAmelCase , batch_sampler=lowerCAmelCase , collate_fn=ds.collate_fn , num_workers=2 )
__lowercase= []
__lowercase= []
for batch in data_loader:
__lowercase= batch['input_ids'].shape
__lowercase= src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__lowercase= np.product(batch['input_ids'].shape )
num_src_per_batch.append(lowerCAmelCase )
if num_src_tokens > (max_tokens * 1.1):
failures.append(lowerCAmelCase )
assert num_src_per_batch[0] == max(lowerCAmelCase )
if failures:
raise AssertionError(f'too many tokens in {len(lowerCAmelCase )} batches' )
def _A (self ):
__lowercase, __lowercase, __lowercase= self._get_dataset(max_len=5_1_2 )
__lowercase= 2
__lowercase= ds.make_sortish_sampler(lowerCAmelCase , shuffle=lowerCAmelCase )
__lowercase= DataLoader(lowerCAmelCase , batch_size=lowerCAmelCase , collate_fn=ds.collate_fn , num_workers=2 )
__lowercase= DataLoader(lowerCAmelCase , batch_size=lowerCAmelCase , collate_fn=ds.collate_fn , num_workers=2 , sampler=lowerCAmelCase )
__lowercase= tokenizer.pad_token_id
def count_pad_tokens(lowerCAmelCase , lowerCAmelCase="input_ids" ):
return [batch[k].eq(lowerCAmelCase ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(lowerCAmelCase , k='labels' ) ) < sum(count_pad_tokens(lowerCAmelCase , k='labels' ) )
assert sum(count_pad_tokens(lowerCAmelCase ) ) < sum(count_pad_tokens(lowerCAmelCase ) )
assert len(lowerCAmelCase ) == len(lowerCAmelCase )
def _A (self , lowerCAmelCase=1_0_0_0 , lowerCAmelCase=1_2_8 ):
if os.getenv('USE_REAL_DATA' , lowerCAmelCase ):
__lowercase= 'examples/seq2seq/wmt_en_ro'
__lowercase= max_len * 2 * 6_4
if not Path(lowerCAmelCase ).joinpath('train.len' ).exists():
save_len_file(lowerCAmelCase , lowerCAmelCase )
else:
__lowercase= 'examples/seq2seq/test_data/wmt_en_ro'
__lowercase= max_len * 4
save_len_file(lowerCAmelCase , lowerCAmelCase )
__lowercase= AutoTokenizer.from_pretrained(lowerCAmelCase )
__lowercase= SeqaSeqDataset(
lowerCAmelCase , data_dir=lowerCAmelCase , type_path='train' , max_source_length=lowerCAmelCase , max_target_length=lowerCAmelCase , n_obs=lowerCAmelCase , )
return ds, max_tokens, tokenizer
def _A (self ):
__lowercase, __lowercase, __lowercase= self._get_dataset()
__lowercase= set(DistributedSortishSampler(lowerCAmelCase , 2_5_6 , num_replicas=2 , rank=0 , add_extra_examples=lowerCAmelCase ) )
__lowercase= set(DistributedSortishSampler(lowerCAmelCase , 2_5_6 , num_replicas=2 , rank=1 , add_extra_examples=lowerCAmelCase ) )
assert idsa.intersection(lowerCAmelCase ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def _A (self , lowerCAmelCase ):
__lowercase= AutoTokenizer.from_pretrained(lowerCAmelCase , use_fast=lowerCAmelCase )
if tok_name == MBART_TINY:
__lowercase= SeqaSeqDataset(
lowerCAmelCase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='train' , max_source_length=4 , max_target_length=8 , src_lang='EN' , tgt_lang='FR' , )
__lowercase= train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__lowercase= SeqaSeqDataset(
lowerCAmelCase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='train' , max_source_length=4 , max_target_length=8 , )
__lowercase= train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(lowerCAmelCase ) == 1 if tok_name == BART_TINY else len(lowerCAmelCase ) == 0
| 295
|
def _lowerCamelCase( lowercase__ , lowercase__ = " " ) -> list:
'''simple docstring'''
__lowercase= []
__lowercase= 0
for index, char in enumerate(lowercase__ ):
if char == separator:
split_words.append(string[last_index:index] )
__lowercase= index + 1
elif index + 1 == len(lowercase__ ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 295
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
"""bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
"""bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
"""bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
"""bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
"""bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
"""bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
"""bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
"""bert-large-uncased-whole-word-masking""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-uncased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
"""bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
"""bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-cased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-uncased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
),
"""wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
# See all BERT models at https://huggingface.co/models?filter=bert
}
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "bert"
def __init__( self , _SCREAMING_SNAKE_CASE=3_0522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-12 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , )->Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
A_ : Any = vocab_size
A_ : int = hidden_size
A_ : int = num_hidden_layers
A_ : Tuple = num_attention_heads
A_ : int = hidden_act
A_ : Union[str, Any] = intermediate_size
A_ : Optional[Any] = hidden_dropout_prob
A_ : Optional[int] = attention_probs_dropout_prob
A_ : Optional[int] = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Any = initializer_range
A_ : List[str] = layer_norm_eps
A_ : Dict = position_embedding_type
A_ : Dict = use_cache
A_ : Tuple = classifier_dropout
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self )->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Optional[Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
A_ : List[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 360
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class _lowerCamelCase :
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=None , )->Any:
'''simple docstring'''
A_ : List[Any] = parent
A_ : int = batch_size
A_ : str = seq_length
A_ : int = is_training
A_ : Any = use_token_type_ids
A_ : Union[str, Any] = use_labels
A_ : Any = vocab_size
A_ : Dict = hidden_size
A_ : Dict = num_hidden_layers
A_ : int = num_attention_heads
A_ : Optional[Any] = intermediate_size
A_ : Dict = hidden_act
A_ : List[str] = hidden_dropout_prob
A_ : List[Any] = attention_probs_dropout_prob
A_ : Union[str, Any] = max_position_embeddings
A_ : Optional[int] = type_vocab_size
A_ : str = type_sequence_label_size
A_ : Tuple = initializer_range
A_ : Union[str, Any] = num_labels
A_ : List[str] = num_choices
A_ : Union[str, Any] = scope
A_ : Any = self.vocab_size - 1
def _snake_case ( self )->Any:
'''simple docstring'''
A_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : Any = None
if self.use_token_type_ids:
A_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ : str = None
A_ : Union[str, Any] = None
A_ : Optional[int] = None
if self.use_labels:
A_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
A_ : Optional[Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
A_ : List[str] = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE )->Tuple:
'''simple docstring'''
A_ : int = OpenAIGPTModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ : int = model(_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE )
A_ : Tuple = model(_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE )
A_ : Any = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE )->List[str]:
'''simple docstring'''
A_ : int = OpenAIGPTLMHeadModel(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ : Tuple = model(_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE )->Optional[int]:
'''simple docstring'''
A_ : List[Any] = OpenAIGPTDoubleHeadsModel(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ : str = model(_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE )->str:
'''simple docstring'''
A_ : Any = self.num_labels
A_ : List[Any] = OpenAIGPTForSequenceClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Optional[Any] = model(_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self )->int:
'''simple docstring'''
A_ : Dict = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Optional[int] = config_and_inputs
A_ : int = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class _lowerCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case = (
{
"feature-extraction": OpenAIGPTModel,
"text-classification": OpenAIGPTForSequenceClassification,
"text-generation": OpenAIGPTLMHeadModel,
"zero-shot": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Dict:
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False )->Optional[int]:
'''simple docstring'''
A_ : Optional[Any] = super()._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
A_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=_SCREAMING_SNAKE_CASE , )
A_ : List[Any] = inputs_dict['''labels''']
A_ : Any = inputs_dict['''labels''']
A_ : Tuple = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=_SCREAMING_SNAKE_CASE , )
A_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_SCREAMING_SNAKE_CASE )
return inputs_dict
def _snake_case ( self )->Any:
'''simple docstring'''
A_ : Any = OpenAIGPTModelTester(self )
A_ : int = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , n_embd=37 )
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Any:
'''simple docstring'''
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_SCREAMING_SNAKE_CASE )
@slow
def _snake_case ( self )->List[str]:
'''simple docstring'''
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Optional[int] = OpenAIGPTModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : Optional[int] = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=_SCREAMING_SNAKE_CASE ) # the president is
A_ : Union[str, Any] = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
A_ : Dict = model.generate(_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE )
self.assertListEqual(output_ids[0].tolist() , _SCREAMING_SNAKE_CASE )
| 65
| 0
|
"""simple docstring"""
def lowercase ( A_ , A_ )-> str:
'''simple docstring'''
a : list[list[str]] = [[] for _ in range(A__ )]
a : Optional[int] = key - 1
if key <= 0:
raise ValueError("Height of grid can\'t be 0 or negative" )
if key == 1 or len(A__ ) <= key:
return input_string
for position, character in enumerate(A__ ):
a : Union[str, Any] = position % (lowest * 2) # puts it in bounds
a : List[Any] = min(A__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(A__ )
a : Any = [''.join(A__ ) for row in temp_grid]
a : Optional[Any] = ''.join(A__ )
return output_string
def lowercase ( A_ , A_ )-> str:
'''simple docstring'''
a : Optional[int] = []
a : str = key - 1
if key <= 0:
raise ValueError("Height of grid can\'t be 0 or negative" )
if key == 1:
return input_string
a : list[list[str]] = [[] for _ in range(A__ )] # generates template
for position in range(len(A__ ) ):
a : List[Any] = position % (lowest * 2) # puts it in bounds
a : Tuple = min(A__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("*" )
a : List[Any] = 0
for row in temp_grid: # fills in the characters
a : Optional[int] = input_string[counter : counter + len(A__ )]
grid.append(list(A__ ) )
counter += len(A__ )
a : Any = '' # reads as zigzag
for position in range(len(A__ ) ):
a : List[Any] = position % (lowest * 2) # puts it in bounds
a : Optional[Any] = min(A__ , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def lowercase ( A_ )-> dict[int, str]:
'''simple docstring'''
a : Dict = {}
for key_guess in range(1 , len(A__ ) ): # tries every key
a : Tuple = decrypt(A__ , A__ )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[Any] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Dict = [
"""NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""NllbMoeForConditionalGeneration""",
"""NllbMoeModel""",
"""NllbMoePreTrainedModel""",
"""NllbMoeTop2Router""",
"""NllbMoeSparseMLP""",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
lowercase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 99
| 0
|
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : str = field(default='''image-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
UpperCamelCase : ClassVar[Features] = Features({'''image''': Image()} )
UpperCamelCase : ClassVar[Features] = Features({'''labels''': ClassLabel} )
UpperCamelCase : str = "image"
UpperCamelCase : str = "labels"
def _lowercase ( self : Tuple , UpperCAmelCase__ : List[str] ) -> str:
if self.label_column not in features:
raise ValueError(f"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] , UpperCAmelCase__ ):
raise ValueError(f"""Column {self.label_column} is not a ClassLabel.""" )
_a : int = copy.deepcopy(self )
_a : Optional[Any] = self.label_schema.copy()
_a : str = features[self.label_column]
_a : Any = label_schema
return task_template
@property
def _lowercase ( self : Any ) -> Dict[str, str]:
return {
self.image_column: "image",
self.label_column: "labels",
}
| 324
|
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_snake_case = logging.getLogger(__name__)
_snake_case = 'pytorch_model.bin'
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The name of the task to train on.'''} , )
UpperCamelCase : Optional[List[str]] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=10 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=100 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Random seed for initialization.'''} , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_a : Union[str, Any] = dataset.filter(lambda UpperCamelCase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_a : Any = int(eval_result * len(UpperCamelCase__ ) )
print(UpperCamelCase__ )
_a : str = dataset.sort("""probability""" , reverse=UpperCamelCase__ )
_a : Any = dataset.select(range(UpperCamelCase__ ) )
_a : Tuple = dataset.remove_columns(["""label""", """probability"""] )
_a : Optional[Any] = dataset.rename_column("""prediction""" , """label""" )
_a : Dict = dataset.map(lambda UpperCamelCase__ : {"label": idalabel[example["label"]]} )
_a : Union[str, Any] = dataset.shuffle(seed=args.seed )
_a : Optional[int] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCamelCase__ , index=UpperCamelCase__ )
else:
dataset.to_json(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_a : Dict = STModelArguments(model_name_or_path=UpperCamelCase__ )
_a : Union[str, Any] = STDataArguments(train_file=UpperCamelCase__ , infer_file=UpperCamelCase__ )
_a : Any = STTrainingArguments(output_dir=UpperCamelCase__ )
_a : Any = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCamelCase__ ).items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for key, value in kwargs.items():
if hasattr(UpperCamelCase__ , UpperCamelCase__ ):
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Sanity checks
_a : Union[str, Any] = {}
_a : Tuple = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_a : int = args.train_file
_a : List[Any] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_a : Union[str, Any] = args.eval_file
for key in data_files:
_a : Optional[Any] = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
_a : str = extension
else:
assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
_a : Tuple = F"""{args.output_dir}/self-train_iter-{{}}""".format
_a : Dict = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCamelCase__ )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : str = None
_a : int = None
_a : str = 0
_a : List[Any] = False
# Show the progress bar
_a : List[Any] = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_a : Union[str, Any] = data_dir_format(UpperCamelCase__ )
assert os.path.exists(UpperCamelCase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_a : str = os.path.join(UpperCamelCase__ , """stage-1""" )
_a : Tuple = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
arguments_dict.update({key: value} )
_a : int = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , UpperCamelCase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_a : Dict = os.path.join(UpperCamelCase__ , """best-checkpoint""" )
_a : List[str] = os.path.join(UpperCamelCase__ , """stage-2""" )
# Update arguments_dict
_a : int = model_path
_a : Dict = data_files["""train"""]
_a : int = current_output_dir
_a : Any = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , UpperCamelCase__ )
_a : List[Any] = iteration
_a : int = data_dir_format(iteration + 1 )
_a : Dict = AutoConfig.from_pretrained(os.path.join(UpperCamelCase__ , """best-checkpoint""" ) )
_a : Union[str, Any] = config.idalabel
_a : Any = os.path.join(UpperCamelCase__ , """eval_results_best-checkpoint.json""" )
_a : Any = os.path.join(UpperCamelCase__ , """test_results_best-checkpoint.json""" )
assert os.path.exists(UpperCamelCase__ )
with open(UpperCamelCase__ , """r""" ) as f:
_a : Tuple = float(json.load(UpperCamelCase__ )[args.eval_metric] )
_a : Dict = os.path.join(UpperCamelCase__ , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(UpperCamelCase__ )
# Loading the dataset from local csv or json files.
_a : List[Any] = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_a : Any = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(UpperCamelCase__ ):
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : List[str] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_a : Any = eval_result
if best_iteration is None:
_a : Union[str, Any] = new_iteration
_a : str = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_a : Union[str, Any] = new_iteration
_a : List[str] = new_eval_result
_a : Optional[Any] = 0
else:
if new_eval_result == best_eval_result:
_a : Tuple = new_iteration
_a : List[Any] = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_a : Union[str, Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , UpperCamelCase__ )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
| 324
| 1
|
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( snake_case_ ):
_lowercase: Optional[int] = (UniPCMultistepScheduler,)
_lowercase: List[str] = (('''num_inference_steps''', 25),)
def lowercase__ ( self : Union[str, Any] , **__snake_case : Dict ) -> List[Any]:
_lowerCAmelCase = {
"""num_train_timesteps""": 10_00,
"""beta_start""": 0.00_01,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
"""solver_type""": """bh2""",
}
config.update(**__snake_case )
return config
def lowercase__ ( self : Dict , __snake_case : Any=0 , **__snake_case : Union[str, Any] ) -> List[str]:
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop("""num_inference_steps""" , __snake_case )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
_lowerCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config(**__snake_case )
_lowerCAmelCase = scheduler_class(**__snake_case )
scheduler.set_timesteps(__snake_case )
# copy over dummy past residuals
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__snake_case )
_lowerCAmelCase = scheduler_class.from_pretrained(__snake_case )
new_scheduler.set_timesteps(__snake_case )
# copy over dummy past residuals
_lowerCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase , _lowerCAmelCase = sample, sample
for t in range(__snake_case , time_step + scheduler.config.solver_order + 1 ):
_lowerCAmelCase = scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
_lowerCAmelCase = new_scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Optional[int] , __snake_case : List[Any]=0 , **__snake_case : Tuple ) -> Tuple:
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop("""num_inference_steps""" , __snake_case )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
_lowerCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**__snake_case )
scheduler.set_timesteps(__snake_case )
# copy over dummy past residuals (must be after setting timesteps)
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__snake_case )
_lowerCAmelCase = scheduler_class.from_pretrained(__snake_case )
# copy over dummy past residuals
new_scheduler.set_timesteps(__snake_case )
# copy over dummy past residual (must be after setting timesteps)
_lowerCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase = scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
_lowerCAmelCase = new_scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Optional[Any] , __snake_case : Optional[Any]=None , **__snake_case : str ) -> Optional[Any]:
if scheduler is None:
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(**__snake_case )
_lowerCAmelCase = scheduler_class(**__snake_case )
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(**__snake_case )
_lowerCAmelCase = scheduler_class(**__snake_case )
_lowerCAmelCase = 10
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__snake_case )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = model(__snake_case , __snake_case )
_lowerCAmelCase = scheduler.step(__snake_case , __snake_case , __snake_case ).prev_sample
return sample
def lowercase__ ( self : Dict ) -> Tuple:
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop("""num_inference_steps""" , __snake_case )
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**__snake_case )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(__snake_case , """set_timesteps""" ):
scheduler.set_timesteps(__snake_case )
elif num_inference_steps is not None and not hasattr(__snake_case , """set_timesteps""" ):
_lowerCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_lowerCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
_lowerCAmelCase = scheduler.timesteps[5]
_lowerCAmelCase = scheduler.timesteps[6]
_lowerCAmelCase = scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
_lowerCAmelCase = scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowercase__ ( self : int ) -> Any:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_lowerCAmelCase = UniPCMultistepScheduler(**self.get_scheduler_config() )
_lowerCAmelCase = self.full_loop(scheduler=__snake_case )
_lowerCAmelCase = torch.mean(torch.abs(__snake_case ) )
assert abs(result_mean.item() - 0.24_64 ) < 1E-3
_lowerCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_lowerCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = self.full_loop(scheduler=__snake_case )
_lowerCAmelCase = torch.mean(torch.abs(__snake_case ) )
assert abs(result_mean.item() - 0.24_64 ) < 1E-3
def lowercase__ ( self : str ) -> Union[str, Any]:
for timesteps in [25, 50, 1_00, 9_99, 10_00]:
self.check_over_configs(num_train_timesteps=__snake_case )
def lowercase__ ( self : Any ) -> Union[str, Any]:
self.check_over_configs(thresholding=__snake_case )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__snake_case , prediction_type=__snake_case , sample_max_value=__snake_case , solver_order=__snake_case , solver_type=__snake_case , )
def lowercase__ ( self : Any ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__snake_case )
def lowercase__ ( self : Any ) -> Any:
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__snake_case , solver_type=__snake_case , prediction_type=__snake_case , )
_lowerCAmelCase = self.full_loop(
solver_order=__snake_case , solver_type=__snake_case , prediction_type=__snake_case , )
assert not torch.isnan(__snake_case ).any(), "Samples have nan numbers"
def lowercase__ ( self : List[Any] ) -> str:
self.check_over_configs(lower_order_final=__snake_case )
self.check_over_configs(lower_order_final=__snake_case )
def lowercase__ ( self : Optional[Any] ) -> Any:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
self.check_over_forward(num_inference_steps=__snake_case , time_step=0 )
def lowercase__ ( self : int ) -> Any:
_lowerCAmelCase = self.full_loop()
_lowerCAmelCase = torch.mean(torch.abs(__snake_case ) )
assert abs(result_mean.item() - 0.24_64 ) < 1E-3
def lowercase__ ( self : Optional[Any] ) -> Dict:
_lowerCAmelCase = self.full_loop(prediction_type="""v_prediction""" )
_lowerCAmelCase = torch.mean(torch.abs(__snake_case ) )
assert abs(result_mean.item() - 0.10_14 ) < 1E-3
def lowercase__ ( self : str ) -> Union[str, Any]:
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(thresholding=__snake_case , dynamic_thresholding_ratio=0 )
_lowerCAmelCase = scheduler_class(**__snake_case )
_lowerCAmelCase = 10
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__snake_case )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = model(__snake_case , __snake_case )
_lowerCAmelCase = scheduler.step(__snake_case , __snake_case , __snake_case ).prev_sample
assert sample.dtype == torch.floataa
def lowercase__ ( self : Any , **__snake_case : List[Any] ) -> List[str]:
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config(**__snake_case )
_lowerCAmelCase = scheduler_class(**__snake_case )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 70
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : List[str] =logging.get_logger(__name__)
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}" )
class UpperCAmelCase ( snake_case_ ):
_lowercase: Any = ['''pixel_values''']
def __init__( self : Tuple , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : bool = True , __snake_case : Union[int, float] = 1 / 2_55 , __snake_case : bool = True , __snake_case : bool = True , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , **__snake_case : str , ) -> None:
super().__init__(**__snake_case )
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 2_56}
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
_lowerCAmelCase = get_size_dict(__snake_case , param_name="""crop_size""" )
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = resample
_lowerCAmelCase = do_rescale
_lowerCAmelCase = rescale_factor
_lowerCAmelCase = offset
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self : int , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
if "shortest_edge" in size:
_lowerCAmelCase = get_resize_output_image_size(__snake_case , size["""shortest_edge"""] , default_to_square=__snake_case )
elif "height" in size and "width" in size:
_lowerCAmelCase = (size["""height"""], size["""width"""])
else:
raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : List[Any] , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__snake_case , size=(size["""height"""], size["""width"""]) , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : np.ndarray , __snake_case : Union[int, float] , __snake_case : bool = True , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> Dict:
_lowerCAmelCase = image.astype(np.floataa )
if offset:
_lowerCAmelCase = image - (scale / 2)
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Optional[int] , __snake_case : np.ndarray , __snake_case : Union[float, List[float]] , __snake_case : Union[float, List[float]] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Tuple , ) -> np.ndarray:
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : List[Any] , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[ChannelDimension] = ChannelDimension.FIRST , ) -> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
_lowerCAmelCase = to_numpy_array(__snake_case )
if do_resize:
_lowerCAmelCase = self.resize(image=__snake_case , size=__snake_case , resample=__snake_case )
if do_center_crop:
_lowerCAmelCase = self.center_crop(__snake_case , size=__snake_case )
if do_rescale:
_lowerCAmelCase = self.rescale(image=__snake_case , scale=__snake_case , offset=__snake_case )
if do_normalize:
_lowerCAmelCase = self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case )
_lowerCAmelCase = to_channel_dimension_format(__snake_case , __snake_case )
return image
def lowercase__ ( self : List[Any] , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[str, TensorType]] = None , __snake_case : ChannelDimension = ChannelDimension.FIRST , **__snake_case : List[str] , ) -> PIL.Image.Image:
_lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase = resample if resample is not None else self.resample
_lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase = offset if offset is not None else self.offset
_lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase = image_std if image_std is not None else self.image_std
_lowerCAmelCase = size if size is not None else self.size
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
_lowerCAmelCase = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase = get_size_dict(__snake_case , param_name="""crop_size""" )
if not valid_images(__snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
_lowerCAmelCase = make_batched(__snake_case )
_lowerCAmelCase = [
[
self._preprocess_image(
image=__snake_case , do_resize=__snake_case , size=__snake_case , resample=__snake_case , do_center_crop=__snake_case , crop_size=__snake_case , do_rescale=__snake_case , rescale_factor=__snake_case , offset=__snake_case , do_normalize=__snake_case , image_mean=__snake_case , image_std=__snake_case , data_format=__snake_case , )
for img in video
]
for video in videos
]
_lowerCAmelCase = {"""pixel_values""": videos}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 70
| 1
|
'''simple docstring'''
import os
import string
import sys
a_ : Any = 1 << 8
a_ : List[Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 2_7,
"up": 6_5 + ARROW_KEY_FLAG,
"down": 6_6 + ARROW_KEY_FLAG,
"right": 6_7 + ARROW_KEY_FLAG,
"left": 6_8 + ARROW_KEY_FLAG,
"mod_int": 9_1,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 5_0,
"delete": 5_1,
"pg_up": 5_3,
"pg_down": 5_4,
}
a_ : List[Any] = KEYMAP["up"]
a_ : List[Any] = KEYMAP["left"]
if sys.platform == "win32":
a_ : Optional[int] = []
a_ : Optional[int] = {
B"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
B"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
B"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
B"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
B"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
B"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
B"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
B"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(1_0):
a_ : Any = ord(str(i))
def _A () -> Optional[int]:
'''simple docstring'''
if os.name == "nt":
import msvcrt
_a = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(lowerCAmelCase__ ) == 0:
# Read the keystroke
_a = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
_a = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
_a = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(lowerCAmelCase__ )
if ord(lowerCAmelCase__ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
_a = chr(KEYMAP['esc'] )
except KeyError:
_a = cha[1]
else:
_a = ch.decode(lowerCAmelCase__ )
else:
_a = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
_a = sys.stdin.fileno()
_a = termios.tcgetattr(lowerCAmelCase__ )
try:
tty.setraw(lowerCAmelCase__ )
_a = sys.stdin.read(1 )
finally:
termios.tcsetattr(lowerCAmelCase__ , termios.TCSADRAIN , lowerCAmelCase__ )
return ch
def _A () -> Any:
'''simple docstring'''
_a = get_raw_chars()
if ord(lowerCAmelCase__ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(lowerCAmelCase__ ) == KEYMAP["esc"]:
_a = get_raw_chars()
if ord(lowerCAmelCase__ ) == KEYMAP["mod_int"]:
_a = get_raw_chars()
if ord(lowerCAmelCase__ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowerCAmelCase__ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(lowerCAmelCase__ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 104
|
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> float:
'''simple docstring'''
_a = 0
while len(lowerCAmelCase__ ) > 1:
_a = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
_a = files.index(min(lowerCAmelCase__ ) )
temp += files[min_index]
files.pop(lowerCAmelCase__ )
files.append(lowerCAmelCase__ )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 104
| 1
|
def lowerCAmelCase_ ( _snake_case : Any ) -> str:
'''simple docstring'''
__magic_name__ : Tuple = len(_snake_case )
for i in range(length - 1 ):
__magic_name__ : Dict = i
for k in range(i + 1 , _snake_case ):
if collection[k] < collection[least]:
__magic_name__ : Tuple = k
if least != i:
__magic_name__ , __magic_name__ : List[str] = (collection[i], collection[least])
return collection
if __name__ == "__main__":
snake_case : Optional[int] = input("Enter numbers separated by a comma:\n").strip()
snake_case : Any = [int(item) for item in user_input.split(",")]
print(selection_sort(unsorted))
| 281
|
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Union[str, Any]=False ) -> List[str]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__magic_name__ : int = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Any , _snake_case : Dict=False ) -> int:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__magic_name__ : int = ""
else:
__magic_name__ : Union[str, Any] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__magic_name__ : Optional[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
__magic_name__ : int = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : Dict = in_proj_weight[
: config.hidden_size, :
]
__magic_name__ : List[str] = in_proj_bias[: config.hidden_size]
__magic_name__ : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__magic_name__ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__magic_name__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
__magic_name__ : int = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
__magic_name__ : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : int , _snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : int = dct.pop(_snake_case )
__magic_name__ : List[Any] = val
def lowerCAmelCase_ ( ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__magic_name__ : List[str] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Any , _snake_case : int=False ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_snake_case , )
__magic_name__ : List[str] = ViTHybridConfig(backbone_config=_snake_case , image_size=384 , num_labels=1000 )
__magic_name__ : str = False
# load original model from timm
__magic_name__ : Union[str, Any] = timm.create_model(_snake_case , pretrained=_snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__magic_name__ : List[Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(_snake_case )
__magic_name__ : Tuple = create_rename_keys(_snake_case , _snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , _snake_case )
__magic_name__ : List[str] = "huggingface/label-files"
__magic_name__ : int = "imagenet-1k-id2label.json"
__magic_name__ : Optional[int] = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type="dataset" ) , "r" ) )
__magic_name__ : int = {int(_snake_case ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : List[str] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
__magic_name__ : List[str] = ViTHybridModel(_snake_case ).eval()
else:
__magic_name__ : str = ViTHybridForImageClassification(_snake_case ).eval()
model.load_state_dict(_snake_case )
# create image processor
__magic_name__ : List[Any] = create_transform(**resolve_data_config({} , model=_snake_case ) )
__magic_name__ : int = transform.transforms
__magic_name__ : List[str] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
__magic_name__ : int = ViTHybridImageProcessor(
do_resize=_snake_case , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_snake_case , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_snake_case , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__magic_name__ : List[Any] = prepare_img()
__magic_name__ : Any = transform(_snake_case ).unsqueeze(0 )
__magic_name__ : Tuple = processor(_snake_case , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_snake_case , _snake_case )
# verify logits
with torch.no_grad():
__magic_name__ : Optional[int] = model(_snake_case )
__magic_name__ : List[str] = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
__magic_name__ : List[str] = timm_model.forward_features(_snake_case )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_snake_case , outputs.pooler_output , atol=1E-3 )
else:
__magic_name__ : Any = timm_model(_snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_snake_case )
if push_to_hub:
print(F'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(F'''ybelkada/{vit_name}''' )
processor.push_to_hub(F'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
snake_case : List[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281
| 1
|
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( _lowercase : Dict[str, torch.Tensor] ):
'''simple docstring'''
__UpperCAmelCase : Any = []
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : Optional[int] = []
for rt in rc.restypes:
__UpperCAmelCase : Tuple = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
__UpperCAmelCase : Any = {name: i for i, name in enumerate(_lowercase )}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14 )
restype_atomaa_to_atomaa_list.append([0] * 37 )
restype_atomaa_mask_list.append([0.0] * 14 )
__UpperCAmelCase : Tuple = torch.tensor(
_lowercase , dtype=torch.intaa , device=protein['''aatype'''].device , )
__UpperCAmelCase : Union[str, Any] = torch.tensor(
_lowercase , dtype=torch.intaa , device=protein['''aatype'''].device , )
__UpperCAmelCase : List[Any] = torch.tensor(
_lowercase , dtype=torch.floataa , device=protein['''aatype'''].device , )
__UpperCAmelCase : Optional[int] = protein['''aatype'''].to(torch.long )
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
__UpperCAmelCase : Any = restype_atomaa_to_atomaa[protein_aatype]
__UpperCAmelCase : Any = restype_atomaa_mask[protein_aatype]
__UpperCAmelCase : str = residx_atomaa_mask
__UpperCAmelCase : int = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
__UpperCAmelCase : Optional[Any] = restype_atomaa_to_atomaa[protein_aatype]
__UpperCAmelCase : str = residx_atomaa_to_atomaa.long()
# create the corresponding mask
__UpperCAmelCase : int = torch.zeros([21, 37] , dtype=torch.floataa , device=protein['''aatype'''].device )
for restype, restype_letter in enumerate(rc.restypes ):
__UpperCAmelCase : int = rc.restype_atoa[restype_letter]
__UpperCAmelCase : Optional[Any] = rc.residue_atoms[restype_name]
for atom_name in atom_names:
__UpperCAmelCase : int = rc.atom_order[atom_name]
__UpperCAmelCase : str = 1
__UpperCAmelCase : Union[str, Any] = restype_atomaa_mask[protein_aatype]
__UpperCAmelCase : Optional[Any] = residx_atomaa_mask
return protein
def _a ( _lowercase : Dict[str, torch.Tensor] ):
'''simple docstring'''
__UpperCAmelCase : int = tree_map(lambda _lowercase : torch.tensor(_lowercase , device=batch['''aatype'''].device ) , _lowercase , np.ndarray )
__UpperCAmelCase : List[Any] = tensor_tree_map(lambda _lowercase : np.array(_lowercase ) , make_atomaa_masks(_lowercase ) )
return out
| 240
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class a ( _a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : torch.FloatTensor
class a ( _a , _a ):
"""simple docstring"""
@register_to_config
def __init__( self : str , snake_case : int = 32 , snake_case : int = 64 , snake_case : int = 20 , snake_case : int = 768 , snake_case : Tuple=77 , snake_case : List[Any]=4 , snake_case : float = 0.0 , snake_case : str = "silu" , snake_case : Optional[str] = None , snake_case : Optional[str] = None , snake_case : Optional[str] = "linear" , snake_case : Optional[str] = "prd" , snake_case : Optional[int] = None , snake_case : Optional[int] = None , snake_case : Optional[int] = None , ) -> List[str]:
super().__init__()
__UpperCAmelCase : List[Any] = num_attention_heads
__UpperCAmelCase : List[str] = attention_head_dim
__UpperCAmelCase : int = num_attention_heads * attention_head_dim
__UpperCAmelCase : List[Any] = additional_embeddings
__UpperCAmelCase : Any = time_embed_dim or inner_dim
__UpperCAmelCase : Any = embedding_proj_dim or embedding_dim
__UpperCAmelCase : Union[str, Any] = clip_embed_dim or embedding_dim
__UpperCAmelCase : List[Any] = Timesteps(snake_case , snake_case , 0 )
__UpperCAmelCase : Optional[Any] = TimestepEmbedding(snake_case , snake_case , out_dim=snake_case , act_fn=snake_case )
__UpperCAmelCase : str = nn.Linear(snake_case , snake_case )
if embedding_proj_norm_type is None:
__UpperCAmelCase : str = None
elif embedding_proj_norm_type == "layer":
__UpperCAmelCase : str = nn.LayerNorm(snake_case )
else:
raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}' )
__UpperCAmelCase : List[Any] = nn.Linear(snake_case , snake_case )
if encoder_hid_proj_type is None:
__UpperCAmelCase : Union[str, Any] = None
elif encoder_hid_proj_type == "linear":
__UpperCAmelCase : Any = nn.Linear(snake_case , snake_case )
else:
raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}' )
__UpperCAmelCase : Dict = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , snake_case ) )
if added_emb_type == "prd":
__UpperCAmelCase : Any = nn.Parameter(torch.zeros(1 , 1 , snake_case ) )
elif added_emb_type is None:
__UpperCAmelCase : List[Any] = None
else:
raise ValueError(
f'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.' )
__UpperCAmelCase : Optional[int] = nn.ModuleList(
[
BasicTransformerBlock(
snake_case , snake_case , snake_case , dropout=snake_case , activation_fn='''gelu''' , attention_bias=snake_case , )
for d in range(snake_case )
] )
if norm_in_type == "layer":
__UpperCAmelCase : Tuple = nn.LayerNorm(snake_case )
elif norm_in_type is None:
__UpperCAmelCase : List[Any] = None
else:
raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.' )
__UpperCAmelCase : Dict = nn.LayerNorm(snake_case )
__UpperCAmelCase : Any = nn.Linear(snake_case , snake_case )
__UpperCAmelCase : Any = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -10_000.0 )
causal_attention_mask.triu_(1 )
__UpperCAmelCase : str = causal_attention_mask[None, ...]
self.register_buffer('''causal_attention_mask''' , snake_case , persistent=snake_case )
__UpperCAmelCase : Tuple = nn.Parameter(torch.zeros(1 , snake_case ) )
__UpperCAmelCase : List[str] = nn.Parameter(torch.zeros(1 , snake_case ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowerCamelCase__ ( self : int ) -> Dict[str, AttentionProcessor]:
__UpperCAmelCase : Optional[Any] = {}
def fn_recursive_add_processors(snake_case : str , snake_case : torch.nn.Module , snake_case : Dict[str, AttentionProcessor] ):
if hasattr(snake_case , '''set_processor''' ):
__UpperCAmelCase : Union[str, Any] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'{name}.{sub_name}' , snake_case , snake_case )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(snake_case , snake_case , snake_case )
return processors
def lowerCamelCase__ ( self : Optional[Any] , snake_case : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ) -> Union[str, Any]:
__UpperCAmelCase : Union[str, Any] = len(self.attn_processors.keys() )
if isinstance(snake_case , snake_case ) and len(snake_case ) != count:
raise ValueError(
f'A dict of processors was passed, but the number of processors {len(snake_case )} does not match the'
f' number of attention layers: {count}. Please make sure to pass {count} processor classes.' )
def fn_recursive_attn_processor(snake_case : str , snake_case : torch.nn.Module , snake_case : int ):
if hasattr(snake_case , '''set_processor''' ):
if not isinstance(snake_case , snake_case ):
module.set_processor(snake_case )
else:
module.set_processor(processor.pop(f'{name}.processor' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'{name}.{sub_name}' , snake_case , snake_case )
for name, module in self.named_children():
fn_recursive_attn_processor(snake_case , snake_case , snake_case )
def lowerCamelCase__ ( self : str ) -> Tuple:
self.set_attn_processor(AttnProcessor() )
def lowerCamelCase__ ( self : Optional[Any] , snake_case : List[Any] , snake_case : Union[torch.Tensor, float, int] , snake_case : torch.FloatTensor , snake_case : Optional[torch.FloatTensor] = None , snake_case : Optional[torch.BoolTensor] = None , snake_case : bool = True , ) -> List[Any]:
__UpperCAmelCase : Any = hidden_states.shape[0]
__UpperCAmelCase : Optional[int] = timestep
if not torch.is_tensor(snake_case ):
__UpperCAmelCase : str = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(snake_case ) and len(timesteps.shape ) == 0:
__UpperCAmelCase : Any = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__UpperCAmelCase : Optional[int] = timesteps * torch.ones(snake_case , dtype=timesteps.dtype , device=timesteps.device )
__UpperCAmelCase : Tuple = self.time_proj(snake_case )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__UpperCAmelCase : int = timesteps_projected.to(dtype=self.dtype )
__UpperCAmelCase : Optional[int] = self.time_embedding(snake_case )
if self.embedding_proj_norm is not None:
__UpperCAmelCase : Optional[Any] = self.embedding_proj_norm(snake_case )
__UpperCAmelCase : str = self.embedding_proj(snake_case )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__UpperCAmelCase : Dict = self.encoder_hidden_states_proj(snake_case )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' )
__UpperCAmelCase : Optional[int] = self.proj_in(snake_case )
__UpperCAmelCase : Optional[Any] = self.positional_embedding.to(hidden_states.dtype )
__UpperCAmelCase : Union[str, Any] = []
__UpperCAmelCase : Optional[Any] = 0
if encoder_hidden_states is not None:
additional_embeds.append(snake_case )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__UpperCAmelCase : Optional[int] = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__UpperCAmelCase : Union[str, Any] = hidden_states[:, None, :]
__UpperCAmelCase : Union[str, Any] = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__UpperCAmelCase : Any = self.prd_embedding.to(hidden_states.dtype ).expand(snake_case , -1 , -1 )
additional_embeds.append(snake_case )
__UpperCAmelCase : Dict = torch.cat(
snake_case , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__UpperCAmelCase : str = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__UpperCAmelCase : Union[str, Any] = F.pad(
snake_case , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__UpperCAmelCase : Optional[int] = hidden_states + positional_embeddings
if attention_mask is not None:
__UpperCAmelCase : List[Any] = (1 - attention_mask.to(hidden_states.dtype )) * -10_000.0
__UpperCAmelCase : str = F.pad(snake_case , (0, self.additional_embeddings) , value=0.0 )
__UpperCAmelCase : Optional[int] = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__UpperCAmelCase : Optional[int] = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__UpperCAmelCase : str = self.norm_in(snake_case )
for block in self.transformer_blocks:
__UpperCAmelCase : Optional[int] = block(snake_case , attention_mask=snake_case )
__UpperCAmelCase : int = self.norm_out(snake_case )
if self.prd_embedding is not None:
__UpperCAmelCase : Optional[int] = hidden_states[:, -1]
else:
__UpperCAmelCase : List[Any] = hidden_states[:, additional_embeddings_len:]
__UpperCAmelCase : Dict = self.proj_to_clip_embeddings(snake_case )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=snake_case )
def lowerCamelCase__ ( self : Optional[int] , snake_case : List[str] ) -> str:
__UpperCAmelCase : Dict = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 240
| 1
|
from statistics import mean
import numpy as np
def __lowercase ( a__ , a__ , a__ , a__ ) -> list:
__SCREAMING_SNAKE_CASE = 0
# Number of processes finished
__SCREAMING_SNAKE_CASE = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
__SCREAMING_SNAKE_CASE = [0] * no_of_process
# List to include calculation results
__SCREAMING_SNAKE_CASE = [0] * no_of_process
# Sort by arrival time.
__SCREAMING_SNAKE_CASE = [burst_time[i] for i in np.argsort(__A )]
__SCREAMING_SNAKE_CASE = [process_name[i] for i in np.argsort(__A )]
arrival_time.sort()
while no_of_process > finished_process_count:
__SCREAMING_SNAKE_CASE = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
__SCREAMING_SNAKE_CASE = arrival_time[i]
__SCREAMING_SNAKE_CASE = 0
# Index showing the location of the process being performed
__SCREAMING_SNAKE_CASE = 0
# Saves the current response ratio.
__SCREAMING_SNAKE_CASE = 0
for i in range(0 , __A ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
__SCREAMING_SNAKE_CASE = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
__SCREAMING_SNAKE_CASE = temp
__SCREAMING_SNAKE_CASE = i
# Calculate the turn around time
__SCREAMING_SNAKE_CASE = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
__SCREAMING_SNAKE_CASE = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def __lowercase ( a__ , a__ , a__ , a__ ) -> list:
__SCREAMING_SNAKE_CASE = [0] * no_of_process
for i in range(0 , __A ):
__SCREAMING_SNAKE_CASE = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
lowerCAmelCase__ : Tuple =5
lowerCAmelCase__ : Union[str, Any] =['''A''', '''B''', '''C''', '''D''', '''E''']
lowerCAmelCase__ : int =[1, 2, 3, 4, 5]
lowerCAmelCase__ : List[str] =[1, 2, 3, 4, 5]
lowerCAmelCase__ : Optional[Any] =calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
lowerCAmelCase__ : Any =calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
for i in range(0, no_of_process):
print(
F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
)
print(F'''average waiting time : {mean(waiting_time):.5f}''')
print(F'''average turn around time : {mean(turn_around_time):.5f}''')
| 257
|
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',
'merges_file': 'merges.txt',
}
UpperCamelCase__ = {
'vocab_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
),
},
'tokenizer_config_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
),
},
'merges_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
),
},
}
UpperCamelCase__ = '</w>'
UpperCamelCase__ = '@@ '
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = set()
UpperCAmelCase__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase__ = char
return pairs
# Speech2Text2 has no max input length
UpperCamelCase__ = {'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : str = VOCAB_FILES_NAMES
__UpperCAmelCase : str = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Dict = ['input_ids', 'attention_mask']
def __init__(self : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Dict="<s>" , __UpperCAmelCase : Tuple="<pad>" , __UpperCAmelCase : str="</s>" , __UpperCAmelCase : int="<unk>" , __UpperCAmelCase : List[str]=False , __UpperCAmelCase : str=None , **__UpperCAmelCase : Optional[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(
unk_token=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , **__UpperCAmelCase , )
UpperCAmelCase__ = do_lower_case
with open(__UpperCAmelCase , encoding="utf-8" ) as vocab_handle:
UpperCAmelCase__ = json.load(__UpperCAmelCase )
UpperCAmelCase__ = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""" )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
else:
with open(__UpperCAmelCase , encoding="utf-8" ) as merges_handle:
UpperCAmelCase__ = merges_handle.read().split("\n" )[:-1]
UpperCAmelCase__ = [tuple(merge.split()[:2] ) for merge in merges]
UpperCAmelCase__ = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
UpperCAmelCase__ = {}
@property
def lowercase_ (self : List[str] ) -> int:
"""simple docstring"""
return len(self.decoder )
def lowercase_ (self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase_ (self : Dict , __UpperCAmelCase : Union[str, Any] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
UpperCAmelCase__ = get_pairs(__UpperCAmelCase )
if not pairs:
return token
while True:
UpperCAmelCase__ = min(__UpperCAmelCase , key=lambda __UpperCAmelCase : self.bpe_ranks.get(__UpperCAmelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase__ , UpperCAmelCase__ = bigram
UpperCAmelCase__ = []
UpperCAmelCase__ = 0
while i < len(__UpperCAmelCase ):
try:
UpperCAmelCase__ = word.index(__UpperCAmelCase , __UpperCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase__ = j
if word[i] == first and i < len(__UpperCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase__ = tuple(__UpperCAmelCase )
UpperCAmelCase__ = new_word
if len(__UpperCAmelCase ) == 1:
break
else:
UpperCAmelCase__ = get_pairs(__UpperCAmelCase )
UpperCAmelCase__ = " ".join(__UpperCAmelCase )
if word == "\n " + BPE_TOKEN_MERGES:
UpperCAmelCase__ = "\n" + BPE_TOKEN_MERGES
if word.endswith(__UpperCAmelCase ):
UpperCAmelCase__ = word.replace(__UpperCAmelCase , "" )
UpperCAmelCase__ = word.replace(" " , __UpperCAmelCase )
UpperCAmelCase__ = word
return word
def lowercase_ (self : Tuple , __UpperCAmelCase : int ) -> Optional[int]:
"""simple docstring"""
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding."
"Make sure to provide `merges.txt` file at instantiation to enable "
"encoding." )
if self.do_lower_case:
UpperCAmelCase__ = text.lower()
UpperCAmelCase__ = text.split()
UpperCAmelCase__ = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(__UpperCAmelCase ).split(" " ) ) )
return split_tokens
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : str ) -> int:
"""simple docstring"""
return self.encoder.get(__UpperCAmelCase , self.encoder.get(self.unk_token ) )
def lowercase_ (self : Any , __UpperCAmelCase : int ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.decoder.get(__UpperCAmelCase , self.unk_token )
return result
def lowercase_ (self : Dict , __UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = " ".join(__UpperCAmelCase )
# make sure @@ tokens are concatenated
UpperCAmelCase__ = "".join(string.split(__UpperCAmelCase ) )
return string
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase__ = os.path.join(
__UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ = os.path.join(
__UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__UpperCAmelCase , ensure_ascii=__UpperCAmelCase ) + "\n" )
UpperCAmelCase__ = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(__UpperCAmelCase , "w" , encoding="utf-8" ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __UpperCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
UpperCAmelCase__ = token_index
writer.write(" ".join(__UpperCAmelCase ) + "\n" )
index += 1
return (vocab_file, merges_file)
| 65
| 0
|
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
snake_case : Tuple = logging.get_logger(__name__)
class _snake_case ( _snake_case ):
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ):
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , _lowerCamelCase , )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
| 281
|
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __lowerCamelCase ( UpperCAmelCase_ : dict ):
"""simple docstring"""
return (data["data"], data["target"])
def __lowerCamelCase ( UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : np.ndarray ):
"""simple docstring"""
a :Optional[Any] = XGBClassifier()
classifier.fit(UpperCAmelCase_ , UpperCAmelCase_ )
return classifier
def __lowerCamelCase ( ):
"""simple docstring"""
a :List[Any] = load_iris()
a , a :Any = data_handling(UpperCAmelCase_ )
a , a , a , a :Tuple = train_test_split(
UpperCAmelCase_ , UpperCAmelCase_ , test_size=0.25 )
a :List[Any] = iris['''target_names''']
# Create an XGBoost Classifier from the training data
a :Optional[int] = xgboost(UpperCAmelCase_ , UpperCAmelCase_ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , display_labels=UpperCAmelCase_ , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 281
| 1
|
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
lowercase__ : str = get_logger(__name__)
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCAmelCase__ : Optional[str] = None ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = (
os.path.join(lowerCAmelCase__ , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_UpperCamelCase = Extractor
def snake_case__ ( self : List[Any] , lowerCAmelCase__ : str ) -> str:
'''simple docstring'''
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_UpperCamelCase = os.path.abspath(lowerCAmelCase__ )
return os.path.join(self.extract_dir , hash_url_to_filename(lowerCAmelCase__ ) )
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : bool ) -> bool:
'''simple docstring'''
return force_extract or (
not os.path.isfile(lowerCAmelCase__ ) and not (os.path.isdir(lowerCAmelCase__ ) and os.listdir(lowerCAmelCase__ ))
)
def snake_case__ ( self : str , lowerCAmelCase__ : str , lowerCAmelCase__ : bool = False ) -> str:
'''simple docstring'''
_UpperCamelCase = self.extractor.infer_extractor_format(lowerCAmelCase__ )
if not extractor_format:
return input_path
_UpperCamelCase = self._get_output_path(lowerCAmelCase__ )
if self._do_extract(lowerCAmelCase__ , lowerCAmelCase__ ):
self.extractor.extract(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return output_path
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@classmethod
@abstractmethod
def snake_case__ ( cls : Any , lowerCAmelCase__ : Union[Path, str] , **lowerCAmelCase__ : int ) -> bool:
'''simple docstring'''
...
@staticmethod
@abstractmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
...
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
_snake_case : List[bytes] = []
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : int ) -> Dict:
'''simple docstring'''
with open(lowerCAmelCase__ , '''rb''' ) as f:
return f.read(lowerCAmelCase__ )
@classmethod
def snake_case__ ( cls : int , lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : bytes = b"" ) -> bool:
'''simple docstring'''
if not magic_number:
_UpperCamelCase = max(len(lowerCAmelCase__ ) for cls_magic_number in cls.magic_numbers )
try:
_UpperCamelCase = cls.read_magic_number(lowerCAmelCase__ , lowerCAmelCase__ )
except OSError:
return False
return any(magic_number.startswith(lowerCAmelCase__ ) for cls_magic_number in cls.magic_numbers )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@classmethod
def snake_case__ ( cls : Tuple , lowerCAmelCase__ : Union[Path, str] , **lowerCAmelCase__ : int ) -> bool:
'''simple docstring'''
return tarfile.is_tarfile(lowerCAmelCase__ )
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
def resolved(lowerCAmelCase__ : str ) -> str:
return os.path.realpath(os.path.abspath(lowerCAmelCase__ ) )
def badpath(lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) ).startswith(lowerCAmelCase__ )
def badlink(lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str ) -> bool:
# Links are interpreted relative to the directory containing the link
_UpperCamelCase = resolved(os.path.join(lowerCAmelCase__ , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=lowerCAmelCase__ )
_UpperCamelCase = resolved(lowerCAmelCase__ )
for finfo in members:
if badpath(finfo.name , lowerCAmelCase__ ):
logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(lowerCAmelCase__ , lowerCAmelCase__ ):
logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(lowerCAmelCase__ , lowerCAmelCase__ ):
logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
_UpperCamelCase = tarfile.open(lowerCAmelCase__ )
tar_file.extractall(lowerCAmelCase__ , members=TarExtractor.safemembers(lowerCAmelCase__ , lowerCAmelCase__ ) )
tar_file.close()
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Union[str, Any] = [b'\x1F\x8B']
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
with gzip.open(lowerCAmelCase__ , '''rb''' ) as gzip_file:
with open(lowerCAmelCase__ , '''wb''' ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase__ , lowerCAmelCase__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Dict = [
b'PK\x03\x04',
b'PK\x05\x06', # empty archive
b'PK\x07\x08', # spanned archive
]
@classmethod
def snake_case__ ( cls : Optional[int] , lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : bytes = b"" ) -> bool:
'''simple docstring'''
if super().is_extractable(lowerCAmelCase__ , magic_number=lowerCAmelCase__ ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(lowerCAmelCase__ , '''rb''' ) as fp:
_UpperCamelCase = _EndRecData(lowerCAmelCase__ )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_UpperCamelCase = fp.read(lowerCAmelCase__ ) # CD is where we expect it to be
if len(lowerCAmelCase__ ) == sizeCentralDir:
_UpperCamelCase = struct.unpack(lowerCAmelCase__ , lowerCAmelCase__ ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
with zipfile.ZipFile(lowerCAmelCase__ , '''r''' ) as zip_file:
zip_file.extractall(lowerCAmelCase__ )
zip_file.close()
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Union[str, Any] = [b'\xFD\x37\x7A\x58\x5A\x00']
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
with lzma.open(lowerCAmelCase__ ) as compressed_file:
with open(lowerCAmelCase__ , '''wb''' ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase__ , lowerCAmelCase__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : List[str] = [b'Rar!\x1a\x07\x00', b'Rar!\x1a\x07\x01\x00'] # RAR_ID # RAR5_ID
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
if not config.RARFILE_AVAILABLE:
raise ImportError('''Please pip install rarfile''' )
import rarfile
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
_UpperCamelCase = rarfile.RarFile(lowerCAmelCase__ )
rf.extractall(lowerCAmelCase__ )
rf.close()
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : List[str] = [b'\x28\xb5\x2F\xFD']
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
if not config.ZSTANDARD_AVAILABLE:
raise ImportError('''Please pip install zstandard''' )
import zstandard as zstd
_UpperCamelCase = zstd.ZstdDecompressor()
with open(lowerCAmelCase__ , '''rb''' ) as ifh, open(lowerCAmelCase__ , '''wb''' ) as ofh:
dctx.copy_stream(lowerCAmelCase__ , lowerCAmelCase__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : str = [b'\x42\x5A\x68']
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
with bza.open(lowerCAmelCase__ , '''rb''' ) as compressed_file:
with open(lowerCAmelCase__ , '''wb''' ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase__ , lowerCAmelCase__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Any = [b'\x37\x7A\xBC\xAF\x27\x1C']
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
if not config.PY7ZR_AVAILABLE:
raise ImportError('''Please pip install py7zr''' )
import pyazr
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
with pyazr.SevenZipFile(lowerCAmelCase__ , '''r''' ) as archive:
archive.extractall(lowerCAmelCase__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : str = [b'\x04\x22\x4D\x18']
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] ) -> None:
'''simple docstring'''
if not config.LZ4_AVAILABLE:
raise ImportError('''Please pip install lz4''' )
import lza.frame
with lza.frame.open(lowerCAmelCase__ , '''rb''' ) as compressed_file:
with open(lowerCAmelCase__ , '''wb''' ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase__ , lowerCAmelCase__ )
class __lowerCAmelCase :
"""simple docstring"""
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
_snake_case : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def snake_case__ ( cls : str ) -> str:
'''simple docstring'''
return max(
len(lowerCAmelCase__ )
for extractor in cls.extractors.values()
if issubclass(lowerCAmelCase__ , lowerCAmelCase__ )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : int ) -> int:
'''simple docstring'''
try:
return MagicNumberBaseExtractor.read_magic_number(lowerCAmelCase__ , magic_number_length=lowerCAmelCase__ )
except OSError:
return b""
@classmethod
def snake_case__ ( cls : Tuple , lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : bool = False ) -> bool:
'''simple docstring'''
warnings.warn(
'''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'infer_extractor_format\' instead.''' , category=lowerCAmelCase__ , )
_UpperCamelCase = cls.infer_extractor_format(lowerCAmelCase__ )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def snake_case__ ( cls : Dict , lowerCAmelCase__ : Union[Path, str] ) -> str: # <Added version="2.4.0"/>
'''simple docstring'''
_UpperCamelCase = cls._get_magic_number_max_length()
_UpperCamelCase = cls._read_magic_number(lowerCAmelCase__ , lowerCAmelCase__ )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(lowerCAmelCase__ , magic_number=lowerCAmelCase__ ):
return extractor_format
@classmethod
def snake_case__ ( cls : Union[str, Any] , lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Union[Path, str] , lowerCAmelCase__ : Optional[str] = None , lowerCAmelCase__ : Optional[BaseExtractor] = "deprecated" , ) -> None:
'''simple docstring'''
os.makedirs(os.path.dirname(lowerCAmelCase__ ) , exist_ok=lowerCAmelCase__ )
# Prevent parallel extractions
_UpperCamelCase = str(Path(lowerCAmelCase__ ).with_suffix('''.lock''' ) )
with FileLock(lowerCAmelCase__ ):
shutil.rmtree(lowerCAmelCase__ , ignore_errors=lowerCAmelCase__ )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): # passed as positional arg
warnings.warn(
'''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'extractor_format\' instead.''' , category=lowerCAmelCase__ , )
_UpperCamelCase = extractor if extractor != '''deprecated''' else extractor_format
else:
_UpperCamelCase = cls.extractors[extractor_format]
return extractor.extract(lowerCAmelCase__ , lowerCAmelCase__ )
else:
warnings.warn(
'''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
'''exception in 3.0.0.''' , category=lowerCAmelCase__ , )
for extractor in cls.extractors.values():
if extractor.is_extractable(lowerCAmelCase__ ):
return extractor.extract(lowerCAmelCase__ , lowerCAmelCase__ )
| 324
|
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any=7 , lowerCAmelCase__ : Optional[Any]=3 , lowerCAmelCase__ : Optional[Any]=30 , lowerCAmelCase__ : Dict=400 , lowerCAmelCase__ : Optional[int]=True , lowerCAmelCase__ : str=None , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : List[str]=[0.5, 0.5, 0.5] , lowerCAmelCase__ : int=[0.5, 0.5, 0.5] , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Union[str, Any]=1 / 255 , lowerCAmelCase__ : Tuple=True , ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = num_channels
_UpperCamelCase = min_resolution
_UpperCamelCase = max_resolution
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean
_UpperCamelCase = image_std
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_pad
def snake_case__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def snake_case__ ( self : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any=False ) -> str:
'''simple docstring'''
if not batched:
_UpperCamelCase = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
_UpperCamelCase , _UpperCamelCase = image.size
else:
_UpperCamelCase , _UpperCamelCase = image.shape[1], image.shape[2]
if w < h:
_UpperCamelCase = int(self.size['''shortest_edge'''] * h / w )
_UpperCamelCase = self.size['''shortest_edge''']
elif w > h:
_UpperCamelCase = self.size['''shortest_edge''']
_UpperCamelCase = int(self.size['''shortest_edge'''] * w / h )
else:
_UpperCamelCase = self.size['''shortest_edge''']
_UpperCamelCase = self.size['''shortest_edge''']
else:
_UpperCamelCase = []
for image in image_inputs:
_UpperCamelCase , _UpperCamelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_UpperCamelCase = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
_UpperCamelCase = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
_snake_case : Union[str, Any] = DeformableDetrImageProcessor if is_vision_available() else None
def snake_case__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = DeformableDetrImageProcessingTester(self )
@property
def snake_case__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''image_std''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_resize''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_rescale''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_pad''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''size''' ) )
def snake_case__ ( self : List[Any] ) -> int:
'''simple docstring'''
_UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
_UpperCamelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def snake_case__ ( self : Tuple ) -> Any:
'''simple docstring'''
pass
def snake_case__ ( self : int ) -> Any:
'''simple docstring'''
_UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_UpperCamelCase , _UpperCamelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCamelCase , _UpperCamelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
_UpperCamelCase = image_processing(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case__ ( self : str ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
_UpperCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_UpperCamelCase , _UpperCamelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCamelCase = image_processing(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
_UpperCamelCase , _UpperCamelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
_UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_UpperCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_UpperCamelCase , _UpperCamelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCamelCase = image_processing(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
_UpperCamelCase , _UpperCamelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def snake_case__ ( self : int ) -> Tuple:
'''simple docstring'''
_UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
_UpperCamelCase = json.loads(f.read() )
_UpperCamelCase = {'''image_id''': 39769, '''annotations''': target}
# encode them
_UpperCamelCase = DeformableDetrImageProcessor()
_UpperCamelCase = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors='''pt''' )
# verify pixel values
_UpperCamelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , lowerCAmelCase__ )
_UpperCamelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , lowerCAmelCase__ , atol=1e-4 ) )
# verify area
_UpperCamelCase = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , lowerCAmelCase__ ) )
# verify boxes
_UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , lowerCAmelCase__ )
_UpperCamelCase = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , lowerCAmelCase__ , atol=1e-3 ) )
# verify image_id
_UpperCamelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , lowerCAmelCase__ ) )
# verify is_crowd
_UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , lowerCAmelCase__ ) )
# verify class_labels
_UpperCamelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , lowerCAmelCase__ ) )
# verify orig_size
_UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , lowerCAmelCase__ ) )
# verify size
_UpperCamelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , lowerCAmelCase__ ) )
@slow
def snake_case__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
_UpperCamelCase = json.loads(f.read() )
_UpperCamelCase = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
_UpperCamelCase = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
_UpperCamelCase = DeformableDetrImageProcessor(format='''coco_panoptic''' )
_UpperCamelCase = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors='''pt''' )
# verify pixel values
_UpperCamelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , lowerCAmelCase__ )
_UpperCamelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , lowerCAmelCase__ , atol=1e-4 ) )
# verify area
_UpperCamelCase = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , lowerCAmelCase__ ) )
# verify boxes
_UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , lowerCAmelCase__ )
_UpperCamelCase = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , lowerCAmelCase__ , atol=1e-3 ) )
# verify image_id
_UpperCamelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , lowerCAmelCase__ ) )
# verify is_crowd
_UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , lowerCAmelCase__ ) )
# verify class_labels
_UpperCamelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , lowerCAmelCase__ ) )
# verify masks
_UpperCamelCase = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , lowerCAmelCase__ )
# verify orig_size
_UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , lowerCAmelCase__ ) )
# verify size
_UpperCamelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , lowerCAmelCase__ ) )
| 324
| 1
|
'''simple docstring'''
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , __a : int = 0 ):
_a = key
def UpperCamelCase__ ( self : List[str] , __a : str , __a : int ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(_UpperCAmelCase , _UpperCAmelCase )
_a = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(_UpperCAmelCase ) ^ key ) for ch in content]
def UpperCamelCase__ ( self : Union[str, Any] , __a : str , __a : int ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(_UpperCAmelCase , _UpperCAmelCase )
_a = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(_UpperCAmelCase ) ^ key ) for ch in content]
def UpperCamelCase__ ( self : Any , __a : str , __a : int = 0 ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(_UpperCAmelCase , _UpperCAmelCase )
_a = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
_a = ""
for ch in content:
ans += chr(ord(_UpperCAmelCase ) ^ key )
return ans
def UpperCamelCase__ ( self : Union[str, Any] , __a : str , __a : int = 0 ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(_UpperCAmelCase , _UpperCAmelCase )
_a = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
_a = ""
for ch in content:
ans += chr(ord(_UpperCAmelCase ) ^ key )
return ans
def UpperCamelCase__ ( self : Any , __a : str , __a : int = 0 ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(_UpperCAmelCase , _UpperCAmelCase )
try:
with open(_UpperCAmelCase ) as fin, open("encrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(_UpperCAmelCase , _UpperCAmelCase ) )
except OSError:
return False
return True
def UpperCamelCase__ ( self : Tuple , __a : str , __a : int ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(_UpperCAmelCase , _UpperCAmelCase )
try:
with open(_UpperCAmelCase ) as fin, open("decrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(_UpperCAmelCase , _UpperCAmelCase ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 367
|
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format='%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=os.environ.get('LOGLEVEL', 'INFO').upper(),
stream=sys.stdout,
)
lowerCAmelCase_ : List[Any] = logging.getLogger(__name__)
lowerCAmelCase_ : List[Any] = {'facebook/bart-base': BartForConditionalGeneration}
lowerCAmelCase_ : int = {'facebook/bart-base': BartTokenizer}
def _lowerCamelCase ( ) -> Union[str, Any]:
_a = argparse.ArgumentParser(description="Export Bart model + Beam Search to ONNX graph." )
parser.add_argument(
"--validation_file" , type=lowercase , default=lowercase , help="A csv or a json file containing the validation data." )
parser.add_argument(
"--max_length" , type=lowercase , default=5 , help="The maximum total input sequence length after tokenization." , )
parser.add_argument(
"--num_beams" , type=lowercase , default=lowercase , help=(
"Number of beams to use for evaluation. This argument will be "
"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."
) , )
parser.add_argument(
"--model_name_or_path" , type=lowercase , help="Path to pretrained model or model identifier from huggingface.co/models." , required=lowercase , )
parser.add_argument(
"--config_name" , type=lowercase , default=lowercase , help="Pretrained config name or path if not the same as model_name" , )
parser.add_argument(
"--device" , type=lowercase , default="cpu" , help="Device where the model will be run" , )
parser.add_argument("--output_file_path" , type=lowercase , default=lowercase , help="Where to store the final ONNX file." )
_a = parser.parse_args()
return args
def _lowerCamelCase ( lowercase : Any , lowercase : Tuple="cpu" ) -> Optional[Any]:
_a = model_dict[model_name].from_pretrained(lowercase ).to(lowercase )
_a = tokenizer_dict[model_name].from_pretrained(lowercase )
if model_name in ["facebook/bart-base"]:
_a = 0
_a = None
_a = 0
return huggingface_model, tokenizer
def _lowerCamelCase ( lowercase : List[str] , lowercase : Tuple , lowercase : int , lowercase : Any , lowercase : Dict ) -> Any:
model.eval()
_a = None
_a = torch.jit.script(BARTBeamSearchGenerator(lowercase ) )
with torch.no_grad():
_a = "My friends are cool but they eat too many carbs."
_a = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1024 , return_tensors="pt" ).to(model.device )
_a = model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , num_beams=lowercase , max_length=lowercase , early_stopping=lowercase , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
lowercase , (
inputs["input_ids"],
inputs["attention_mask"],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , lowercase , opset_version=14 , input_names=["input_ids", "attention_mask", "num_beams", "max_length", "decoder_start_token_id"] , output_names=["output_ids"] , dynamic_axes={
"input_ids": {0: "batch", 1: "seq"},
"output_ids": {0: "batch", 1: "seq_out"},
} , example_outputs=lowercase , )
logger.info("Model exported to {}".format(lowercase ) )
_a = remove_dup_initializers(os.path.abspath(lowercase ) )
logger.info("Deduplicated and optimized model written to {}".format(lowercase ) )
_a = onnxruntime.InferenceSession(lowercase )
_a = ort_sess.run(
lowercase , {
"input_ids": inputs["input_ids"].cpu().numpy(),
"attention_mask": inputs["attention_mask"].cpu().numpy(),
"num_beams": np.array(lowercase ),
"max_length": np.array(lowercase ),
"decoder_start_token_id": np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1E-3 , atol=1E-3 )
logger.info("Model outputs from torch and ONNX Runtime are similar." )
logger.info("Success." )
def _lowerCamelCase ( ) -> Any:
_a = parse_args()
_a = 5
_a = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
_a = torch.device(args.device )
_a , _a = load_model_tokenizer(args.model_name_or_path , lowercase )
if model.config.decoder_start_token_id is None:
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined" )
model.to(lowercase )
if args.max_length:
_a = args.max_length
if args.num_beams:
_a = args.num_beams
if args.output_file_path:
_a = args.output_file_path
else:
_a = "BART.onnx"
logger.info("Exporting model to ONNX" )
export_and_validate_model(lowercase , lowercase , lowercase , lowercase , lowercase )
if __name__ == "__main__":
main()
| 346
| 0
|
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowerCAmelCase__ = '''platform'''
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = PegasusConfig
SCREAMING_SNAKE_CASE : str = {}
SCREAMING_SNAKE_CASE : Tuple = 'gelu'
def __init__( self : Any ,lowercase__ : Tuple ,lowercase__ : Dict=1_3 ,lowercase__ : Optional[int]=7 ,lowercase__ : Tuple=True ,lowercase__ : Optional[int]=False ,lowercase__ : Union[str, Any]=9_9 ,lowercase__ : str=3_2 ,lowercase__ : Tuple=5 ,lowercase__ : List[str]=4 ,lowercase__ : Dict=3_7 ,lowercase__ : Optional[int]=0.1 ,lowercase__ : int=0.1 ,lowercase__ : Union[str, Any]=2_0 ,lowercase__ : Optional[Any]=2 ,lowercase__ : int=1 ,lowercase__ : Optional[Any]=0 ,):
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = eos_token_id
__lowercase = pad_token_id
__lowercase = bos_token_id
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ).clip(3 ,self.vocab_size )
__lowercase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) ,1 )
__lowercase = np.concatenate([input_ids, eos_tensor] ,axis=1 )
__lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
__lowercase = self.config_cls(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,)
__lowercase = prepare_pegasus_inputs_dict(lowercase__ ,lowercase__ ,lowercase__ )
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : str ,lowercase__ : Optional[int] ,lowercase__ : Optional[int] ):
__lowercase = 2_0
__lowercase = model_class_name(lowercase__ )
__lowercase = model.encode(inputs_dict['''input_ids'''] )
__lowercase , __lowercase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__lowercase = model.init_cache(decoder_input_ids.shape[0] ,lowercase__ ,lowercase__ )
__lowercase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype='''i4''' )
__lowercase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
__lowercase = model.decode(
decoder_input_ids[:, :-1] ,lowercase__ ,decoder_attention_mask=lowercase__ ,past_key_values=lowercase__ ,decoder_position_ids=lowercase__ ,)
__lowercase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype='''i4''' )
__lowercase = model.decode(
decoder_input_ids[:, -1:] ,lowercase__ ,decoder_attention_mask=lowercase__ ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=lowercase__ ,)
__lowercase = model.decode(lowercase__ ,lowercase__ )
__lowercase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=F"Max diff is {diff}" )
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : List[str] ,lowercase__ : Union[str, Any] ,lowercase__ : str ):
__lowercase = 2_0
__lowercase = model_class_name(lowercase__ )
__lowercase = model.encode(inputs_dict['''input_ids'''] )
__lowercase , __lowercase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__lowercase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] ,axis=-1 ,)
__lowercase = model.init_cache(decoder_input_ids.shape[0] ,lowercase__ ,lowercase__ )
__lowercase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
__lowercase = model.decode(
decoder_input_ids[:, :-1] ,lowercase__ ,decoder_attention_mask=lowercase__ ,past_key_values=lowercase__ ,decoder_position_ids=lowercase__ ,)
__lowercase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype='''i4''' )
__lowercase = model.decode(
decoder_input_ids[:, -1:] ,lowercase__ ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=lowercase__ ,decoder_position_ids=lowercase__ ,)
__lowercase = model.decode(lowercase__ ,lowercase__ ,decoder_attention_mask=lowercase__ )
__lowercase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=F"Max diff is {diff}" )
def _A ( A__ , A__ , A__ , A__=None , A__=None , ):
"""simple docstring"""
if attention_mask is None:
__lowercase = np.not_equal(A__ , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
__lowercase = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class lowercase_ (lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
SCREAMING_SNAKE_CASE : Tuple = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : Any = False
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = FlaxPegasusModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__lowercase = self._prepare_for_class(lowercase__ ,lowercase__ )
__lowercase = model_class(lowercase__ )
@jax.jit
def encode_jitted(lowercase__ : List[Any] ,lowercase__ : Dict=None ,**lowercase__ : Optional[int] ):
return model.encode(input_ids=lowercase__ ,attention_mask=lowercase__ )
with self.subTest('''JIT Enabled''' ):
__lowercase = encode_jitted(**lowercase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__lowercase = encode_jitted(**lowercase__ ).to_tuple()
self.assertEqual(len(lowercase__ ) ,len(lowercase__ ) )
for jitted_output, output in zip(lowercase__ ,lowercase__ ):
self.assertEqual(jitted_output.shape ,output.shape )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__lowercase = model_class(lowercase__ )
__lowercase = model.encode(inputs_dict['''input_ids'''] ,inputs_dict['''attention_mask'''] )
__lowercase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(lowercase__ : List[Any] ,lowercase__ : Tuple ,lowercase__ : str ):
return model.decode(
decoder_input_ids=lowercase__ ,decoder_attention_mask=lowercase__ ,encoder_outputs=lowercase__ ,)
with self.subTest('''JIT Enabled''' ):
__lowercase = decode_jitted(**lowercase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__lowercase = decode_jitted(**lowercase__ ).to_tuple()
self.assertEqual(len(lowercase__ ) ,len(lowercase__ ) )
for jitted_output, output in zip(lowercase__ ,lowercase__ ):
self.assertEqual(jitted_output.shape ,output.shape )
@slow
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
for model_class_name in self.all_model_classes:
__lowercase = model_class_name.from_pretrained('''google/pegasus-large''' ,from_pt=lowercase__ )
__lowercase = np.ones((1, 1) )
__lowercase = model(lowercase__ )
self.assertIsNotNone(lowercase__ )
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''' )
__lowercase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''' )
__lowercase = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
__lowercase = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
__lowercase = tokenizer(lowercase__ ,return_tensors='''np''' ,truncation=lowercase__ ,max_length=5_1_2 ,padding=lowercase__ )
__lowercase = model.generate(**lowercase__ ,num_beams=2 ).sequences
__lowercase = tokenizer.batch_decode(lowercase__ ,skip_special_tokens=lowercase__ )
assert tgt_text == decoded
| 104
|
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _A ( A__ ):
"""simple docstring"""
__lowercase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(A__ ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
__lowercase = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creates a copy of the matrix with swapped positions of the elements
__lowercase = [[0.0, 0.0], [0.0, 0.0]]
__lowercase , __lowercase = matrix[1][1], matrix[0][0]
__lowercase , __lowercase = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(A__ ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(A__ ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
__lowercase = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creating cofactor matrix
__lowercase = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
__lowercase = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
__lowercase = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
__lowercase = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
__lowercase = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
__lowercase = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
__lowercase = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
__lowercase = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
__lowercase = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
__lowercase = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
__lowercase = array(A__ )
for i in range(3 ):
for j in range(3 ):
__lowercase = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
__lowercase = array(A__ )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(A__ )
# Calculate the inverse of the matrix
return [[float(d(A__ ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''' )
| 104
| 1
|
'''simple docstring'''
def _snake_case ( A , A ) -> float:
return base * power(A , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
__UpperCAmelCase = int(input('''Enter the base: ''').strip())
__UpperCAmelCase = int(input('''Enter the exponent: ''').strip())
__UpperCAmelCase = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
__UpperCAmelCase = 1 / result
print(f"""{base} to the power of {exponent} is {result}""")
| 366
|
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__UpperCAmelCase = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
__UpperCAmelCase = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
__UpperCAmelCase = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def _snake_case ( A , A ) -> List[Any]:
return float((preds == labels).mean() )
def _snake_case ( A , A , A="binary" ) -> int:
lowerCAmelCase__ = simple_accuracy(A , A )
lowerCAmelCase__ = float(fa_score(y_true=A , y_pred=A , average=A ) )
return {
"accuracy": acc,
"f1": fa,
}
def _snake_case ( A , A ) -> List[Any]:
lowerCAmelCase__ = {}
for id_pred, label in zip(A , A ):
lowerCAmelCase__ = F"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
lowerCAmelCase__ = id_pred['''prediction''']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
lowerCAmelCase__ = [(pred, label)]
lowerCAmelCase__ , lowerCAmelCase__ = [], []
for question, preds_labels in question_map.items():
lowerCAmelCase__ , lowerCAmelCase__ = zip(*A )
lowerCAmelCase__ = fa_score(y_true=A , y_pred=A , average='''macro''' )
fas.append(A )
lowerCAmelCase__ = int(sum(pred == label for pred, label in preds_labels ) == len(A ) )
ems.append(A )
lowerCAmelCase__ = float(sum(A ) / len(A ) )
lowerCAmelCase__ = sum(A ) / len(A )
lowerCAmelCase__ = float(fa_score(y_true=A , y_pred=[id_pred['''prediction'''] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None , )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('''int64''' ),
"query": datasets.Value('''int64''' ),
},
"prediction_text": datasets.Value('''string''' ),
},
"references": {
"idx": {
"passage": datasets.Value('''int64''' ),
"query": datasets.Value('''int64''' ),
},
"answers": datasets.Sequence(datasets.Value('''string''' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('''int64''' ),
"paragraph": datasets.Value('''int64''' ),
"question": datasets.Value('''int64''' ),
},
"prediction": datasets.Value('''int64''' ),
},
"references": datasets.Value('''int64''' ),
}
else:
return {
"predictions": datasets.Value('''int64''' ),
"references": datasets.Value('''int64''' ),
}
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ ) -> Dict:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowerCamelCase_ , lowerCamelCase_ )}
elif self.config_name == "cb":
return acc_and_fa(lowerCamelCase_ , lowerCamelCase_ , fa_avg='''macro''' )
elif self.config_name == "record":
lowerCAmelCase__ = [
{
'''qas''': [
{'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
for ref in references
]
}
]
lowerCAmelCase__ = {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
return evaluate_record(lowerCamelCase_ , lowerCamelCase_ )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowerCamelCase_ , lowerCamelCase_ )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowerCamelCase_ , lowerCamelCase_ )}
else:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' )
| 228
| 0
|
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
snake_case : List[Any] = logging.getLogger(__name__)
snake_case : Optional[int] = 50 # max width of layer names
snake_case : Any = 70 # max width of quantizer names
def __lowercase ( __lowerCAmelCase : Tuple ):
a__ = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=__lowerCAmelCase , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=__lowerCAmelCase , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=__lowerCAmelCase , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=__lowerCAmelCase , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=__lowerCAmelCase , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=__lowerCAmelCase , type=__lowerCAmelCase , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=__lowerCAmelCase , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def __lowercase ( __lowerCAmelCase : Union[str, Any] ):
if args.calibrator == "max":
a__ = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
a__ = 'histogram'
elif args.calibrator == "mse":
a__ = 'histogram'
else:
raise ValueError(F'Invalid calibrator {args.calibrator}' )
a__ = QuantDescriptor(num_bits=args.aprec , calib_method=__lowerCAmelCase )
a__ = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(__lowerCAmelCase )
quant_nn.QuantLinear.set_default_quant_desc_weight(__lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : Dict=False ):
logger.info('Configuring Model for Quantization' )
logger.info(F'using quantization package {pytorch_quantization.__file__}' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(__lowerCAmelCase , ['embeddings'] , which='weight' , _disabled=__lowerCAmelCase )
if args.quant_disable:
set_quantizer_by_name(__lowerCAmelCase , [''] , _disabled=__lowerCAmelCase )
if args.quant_disable_keyword:
set_quantizer_by_name(__lowerCAmelCase , args.quant_disable_keyword , _disabled=__lowerCAmelCase )
if args.quant_disable_layer_module:
set_quantizer_by_name(__lowerCAmelCase , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=__lowerCAmelCase )
if args.quant_enable_layer_module:
set_quantizer_by_name(__lowerCAmelCase , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=__lowerCAmelCase )
if args.recalibrate_weights:
recalibrate_weights(__lowerCAmelCase )
if args.fuse_qkv:
fuse_qkv(__lowerCAmelCase , __lowerCAmelCase )
if args.clip_gelu:
clip_gelu(__lowerCAmelCase , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(__lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Optional[int] ):
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F'{name:80}: {module}' )
def __lowercase ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] ):
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(__lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str ):
def fusea(__lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ):
for mod in [qq, qk, qv]:
if not hasattr(__lowerCAmelCase , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
a__ = qq._amax.detach().item()
a__ = qk._amax.detach().item()
a__ = qv._amax.detach().item()
a__ = max(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
qq._amax.fill_(__lowerCAmelCase )
qk._amax.fill_(__lowerCAmelCase )
qv._amax.fill_(__lowerCAmelCase )
logger.info(F' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(F'FUSE_QKV: {name:{name_width}}' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def __lowercase ( __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] ):
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
a__ = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=__lowerCAmelCase )
a__ = mod._input_quantizer._amax.data.detach().item()
logger.info(F'CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}' )
def __lowercase ( __lowerCAmelCase : Optional[Any] ):
for name, mod in model.named_modules():
if hasattr(__lowerCAmelCase , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
a__ = mod.weight.shape[0]
a__ = mod._weight_quantizer._amax.detach()
a__ = torch.ones(__lowerCAmelCase , dtype=amax.dtype , device=amax.device ) * amax
print(F'expanding {name} {amax} -> {mod._weight_quantizer._amax}' )
def __lowercase ( __lowerCAmelCase : Union[str, Any] ):
for name, mod in model.named_modules():
if hasattr(__lowerCAmelCase , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
a__ = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
a__ = set(range(len(mod.weight.size() ) ) ) - axis_set
a__ = pytorch_quantization.utils.reduce_amax(mod.weight , axis=__lowerCAmelCase , keepdims=__lowerCAmelCase ).detach()
logger.info(F'RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}' )
a__ = amax
def __lowercase ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int]=2_5 , __lowerCAmelCase : List[Any]=1_8_0 , __lowerCAmelCase : Tuple=None ):
if ignore is None:
a__ = []
elif not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a__ = [ignore]
a__ = 0
for name, mod in model.named_modules():
if not hasattr(__lowerCAmelCase , 'weight' ):
continue
a__ = max(__lowerCAmelCase , len(__lowerCAmelCase ) )
for name, mod in model.named_modules():
a__ = getattr(__lowerCAmelCase , '_input_quantizer' , __lowerCAmelCase )
a__ = getattr(__lowerCAmelCase , '_weight_quantizer' , __lowerCAmelCase )
if not hasattr(__lowerCAmelCase , 'weight' ):
continue
if type(__lowerCAmelCase ) in ignore:
continue
if [True for s in ignore if type(__lowerCAmelCase ) is str and s in name]:
continue
a__ = F'Act:{input_q.extra_repr()}'
a__ = F'Wgt:{weight_q.extra_repr()}'
a__ = F'{name:{name_width}} {act_str} {wgt_str}'
if len(__lowerCAmelCase ) <= line_width:
logger.info(__lowerCAmelCase )
else:
logger.info(F'{name:{name_width}} {act_str}' )
logger.info(F'{" ":{name_width}} {wgt_str}' )
def __lowercase ( __lowerCAmelCase : Dict ):
a__ = 0
for name, mod in model.named_modules():
if isinstance(__lowerCAmelCase , pytorch_quantization.nn.TensorQuantizer ):
print(F'{name:80} {mod}' )
count += 1
print(F'{count} TensorQuantizers found in model' )
def __lowercase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Dict ):
a__ = getattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if quantizer_mod is not None:
assert hasattr(__lowerCAmelCase , __lowerCAmelCase )
setattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
logger.warning(F'{name} has no {quantizer}' )
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int]="both" , **__lowerCAmelCase : str ):
a__ = F'Warning: changing {which} quantizers of {name:{qname_width}}'
for k, v in kwargs.items():
s += F' {k}={v}'
if which in ["input", "both"]:
set_quantizer(__lowerCAmelCase , __lowerCAmelCase , '_input_quantizer' , __lowerCAmelCase , __lowerCAmelCase )
if which in ["weight", "both"]:
set_quantizer(__lowerCAmelCase , __lowerCAmelCase , '_weight_quantizer' , __lowerCAmelCase , __lowerCAmelCase )
logger.info(__lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Any , __lowerCAmelCase : Tuple , **__lowerCAmelCase : Optional[Any] ):
for name, mod in model.named_modules():
if hasattr(__lowerCAmelCase , '_input_quantizer' ) or hasattr(__lowerCAmelCase , '_weight_quantizer' ):
for n in names:
if re.search(__lowerCAmelCase , __lowerCAmelCase ):
set_quantizers(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(__lowerCAmelCase , __lowerCAmelCase ):
a__ = F'Warning: changing {name:{name_width}}'
for k, v in kwargs.items():
s += F' {k}={v}'
setattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
logger.info(__lowerCAmelCase )
| 240
|
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : Any , __lowerCAmelCase : int ):
# Construct model
if gpta_config_file == "":
a__ = GPTaConfig()
else:
a__ = GPTaConfig.from_json_file(__lowerCAmelCase )
a__ = GPTaModel(__lowerCAmelCase )
# Load weights from numpy
load_tf_weights_in_gpta(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Save pytorch-model
a__ = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
a__ = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(F'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(model.state_dict() , __lowerCAmelCase )
print(F'Save configuration file to {pytorch_config_dump_path}' )
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
snake_case : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--gpt2_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
'''This specifies the model architecture.'''
),
)
snake_case : Any = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 240
| 1
|
"""simple docstring"""
from typing import List
import numpy as np
def __a ( _SCREAMING_SNAKE_CASE ) ->int:
a__: Dict = {key: len(_SCREAMING_SNAKE_CASE ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'Sharding is ambiguous for this dataset: '
+ 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
+ '\n'.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
+ '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
+ 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
) )
a__: Optional[Any] = max(lists_lengths.values() , default=0 )
return max(1 , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[range]:
a__: Optional[int] = []
for group_idx in range(_SCREAMING_SNAKE_CASE ):
a__: Union[str, Any] = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
a__: Optional[int] = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
a__: List[Any] = range(_SCREAMING_SNAKE_CASE , start + num_shards_to_add )
shards_indices_per_group.append(_SCREAMING_SNAKE_CASE )
return shards_indices_per_group
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[dict]:
a__: Any = _number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE )
if num_shards == 1:
return [dict(_SCREAMING_SNAKE_CASE )]
else:
a__: Dict = _distribute_shards(num_shards=_SCREAMING_SNAKE_CASE , max_num_jobs=_SCREAMING_SNAKE_CASE )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(_SCREAMING_SNAKE_CASE ) )
]
def __a ( _SCREAMING_SNAKE_CASE ) ->dict:
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , _SCREAMING_SNAKE_CASE )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->dict:
a__: int = {len(_SCREAMING_SNAKE_CASE ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
a__: Dict = {}
for size in list_sizes:
a__: Optional[int] = list(range(_SCREAMING_SNAKE_CASE ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
a__: List[str] = dict(_SCREAMING_SNAKE_CASE )
for key, value in shuffled_kwargs.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = [value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE )]]
return shuffled_kwargs
| 203
|
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowercase__ = logging.get_logger(__name__)
lowercase__ = Dict[str, Any]
lowercase__ = List[Prediction]
@add_end_docstrings(__lowerCAmelCase )
class __snake_case ( __lowerCAmelCase ):
def __init__( self , *lowercase , **lowercase) -> Dict:
'''simple docstring'''
super().__init__(*lowercase , **lowercase)
if self.framework == "tf":
raise ValueError(f'The {self.__class__} is only available in PyTorch.')
requires_backends(self , 'vision')
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))
def lowerCamelCase_ ( self , **lowercase) -> int:
'''simple docstring'''
a__: Optional[Any] = {}
if "threshold" in kwargs:
a__: Dict = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *lowercase , **lowercase) -> Union[Predictions, List[Prediction]]:
'''simple docstring'''
return super().__call__(*lowercase , **lowercase)
def lowerCamelCase_ ( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = load_image(lowercase)
a__: List[Any] = torch.IntTensor([[image.height, image.width]])
a__: Any = self.image_processor(images=[image] , return_tensors='pt')
if self.tokenizer is not None:
a__: Any = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt')
a__: List[str] = target_size
return inputs
def lowerCamelCase_ ( self , lowercase) -> int:
'''simple docstring'''
a__: Any = model_inputs.pop('target_size')
a__: Union[str, Any] = self.model(**lowercase)
a__: List[str] = outputs.__class__({'target_size': target_size, **outputs})
if self.tokenizer is not None:
a__: Union[str, Any] = model_inputs['bbox']
return model_outputs
def lowerCamelCase_ ( self , lowercase , lowercase=0.9) -> Optional[Any]:
'''simple docstring'''
a__: int = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
a__ , a__: str = target_size[0].tolist()
def unnormalize(lowercase):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 10_00),
(height * bbox[1] / 10_00),
(width * bbox[2] / 10_00),
(height * bbox[3] / 10_00),
]))
a__ , a__: Optional[Any] = model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
a__: str = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
a__: Union[str, Any] = [unnormalize(lowercase) for bbox in model_outputs['bbox'].squeeze(0)]
a__: Dict = ['score', 'label', 'box']
a__: Any = [dict(zip(lowercase , lowercase)) for vals in zip(scores.tolist() , lowercase , lowercase) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
a__: List[str] = self.image_processor.post_process_object_detection(lowercase , lowercase , lowercase)
a__: Tuple = raw_annotations[0]
a__: List[str] = raw_annotation['scores']
a__: int = raw_annotation['labels']
a__: int = raw_annotation['boxes']
a__: List[Any] = scores.tolist()
a__: Any = [self.model.config.idalabel[label.item()] for label in labels]
a__: Dict = [self._get_bounding_box(lowercase) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
a__: Optional[Any] = ['score', 'label', 'box']
a__: List[Any] = [
dict(zip(lowercase , lowercase))
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'])
]
return annotation
def lowerCamelCase_ ( self , lowercase) -> Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
a__ , a__ , a__ , a__: List[Any] = box.int().tolist()
a__: Optional[int] = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 203
| 1
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : List[Any] = {
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
"microsoft/deberta-v2-xlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
),
"microsoft/deberta-v2-xxlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
),
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'deberta-v2'
def __init__( self , _a=128_100 , _a=1_536 , _a=24 , _a=24 , _a=6_144 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=0 , _a=0.02 , _a=1e-7 , _a=False , _a=-1 , _a=0 , _a=True , _a=None , _a=0 , _a="gelu" , **_a , ):
super().__init__(**_a )
__magic_name__ : Union[str, Any] = hidden_size
__magic_name__ : List[Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : List[Any] = intermediate_size
__magic_name__ : Any = hidden_act
__magic_name__ : Union[str, Any] = hidden_dropout_prob
__magic_name__ : str = attention_probs_dropout_prob
__magic_name__ : Union[str, Any] = max_position_embeddings
__magic_name__ : Optional[Any] = type_vocab_size
__magic_name__ : Dict = initializer_range
__magic_name__ : Optional[int] = relative_attention
__magic_name__ : Any = max_relative_positions
__magic_name__ : Dict = pad_token_id
__magic_name__ : str = position_biased_input
# Backwards compatibility
if type(_a ) == str:
__magic_name__ : Dict = [x.strip() for x in pos_att_type.lower().split("|" )]
__magic_name__ : Optional[int] = pos_att_type
__magic_name__ : Union[str, Any] = vocab_size
__magic_name__ : Any = layer_norm_eps
__magic_name__ : Optional[int] = kwargs.get("pooler_hidden_size" , _a )
__magic_name__ : List[Any] = pooler_dropout
__magic_name__ : Union[str, Any] = pooler_hidden_act
class _snake_case ( snake_case ):
@property
def SCREAMING_SNAKE_CASE ( self ):
if self.task == "multiple-choice":
__magic_name__ : Union[str, Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
__magic_name__ : Optional[Any] = {0: "batch", 1: "sequence"}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] )
else:
return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] )
@property
def SCREAMING_SNAKE_CASE ( self ):
return 12
def SCREAMING_SNAKE_CASE ( self , _a , _a = -1 , _a = -1 , _a = -1 , _a = False , _a = None , _a = 3 , _a = 40 , _a = 40 , _a = None , ):
__magic_name__ : Dict = super().generate_dummy_inputs(preprocessor=_a , framework=_a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 281
|
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : str = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowerCAmelCase_ ( _snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
def remove_articles(_snake_case : List[str] ):
return ARTICLES_REGEX.sub(" " , _snake_case )
def white_space_fix(_snake_case : Optional[int] ):
return " ".join(text.split() )
def remove_punc(_snake_case : Optional[int] ):
__magic_name__ : Dict = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_snake_case : str ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_snake_case ) ) ) )
def lowerCAmelCase_ ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(_snake_case ).split()
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Dict ) -> Tuple:
'''simple docstring'''
return int(normalize_answer(_snake_case ) == normalize_answer(_snake_case ) )
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : int ) -> str:
'''simple docstring'''
__magic_name__ : Any = get_tokens(_snake_case )
__magic_name__ : Optional[int] = get_tokens(_snake_case )
__magic_name__ : Tuple = collections.Counter(_snake_case ) & collections.Counter(_snake_case )
__magic_name__ : Tuple = sum(common.values() )
if len(_snake_case ) == 0 or len(_snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
__magic_name__ : Dict = 1.0 * num_same / len(_snake_case )
__magic_name__ : Optional[Any] = 1.0 * num_same / len(_snake_case )
__magic_name__ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = {}
__magic_name__ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : Union[str, Any] = qa["id"]
__magic_name__ : Any = [t for t in qa["answers"]["text"] if normalize_answer(_snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
__magic_name__ : Tuple = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
__magic_name__ : Any = preds[qid]
# Take max over all gold answers
__magic_name__ : List[Any] = max(compute_exact(_snake_case , _snake_case ) for a in gold_answers )
__magic_name__ : int = max(compute_fa(_snake_case , _snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : str = {}
for qid, s in scores.items():
__magic_name__ : Dict = na_probs[qid] > na_prob_thresh
if pred_na:
__magic_name__ : str = float(not qid_to_has_ans[qid] )
else:
__magic_name__ : Optional[int] = s
return new_scores
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : Tuple=None ) -> Tuple:
'''simple docstring'''
if not qid_list:
__magic_name__ : Any = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
__magic_name__ : Tuple = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : str , _snake_case : str ) -> Dict:
'''simple docstring'''
for k in new_eval:
__magic_name__ : int = new_eval[k]
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
plt.step(_snake_case , _snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(_snake_case , _snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_snake_case )
plt.savefig(_snake_case )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Any , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[int]=None , _snake_case : int=None ) -> str:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
__magic_name__ : Optional[int] = 0.0
__magic_name__ : str = 1.0
__magic_name__ : str = 0.0
__magic_name__ : List[str] = [1.0]
__magic_name__ : str = [0.0]
__magic_name__ : Optional[Any] = 0.0
for i, qid in enumerate(_snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
__magic_name__ : List[str] = true_pos / float(i + 1 )
__magic_name__ : Any = true_pos / float(_snake_case )
if i == len(_snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_snake_case )
recalls.append(_snake_case )
if out_image:
plot_pr_curve(_snake_case , _snake_case , _snake_case , _snake_case )
return {"ap": 100.0 * avg_prec}
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Any , _snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(_snake_case ):
os.makedirs(_snake_case )
__magic_name__ : Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
__magic_name__ : Union[str, Any] = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
__magic_name__ : str = {k: float(_snake_case ) for k, v in qid_to_has_ans.items()}
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_snake_case , _snake_case , "pr_exact" )
merge_eval(_snake_case , _snake_case , "pr_f1" )
merge_eval(_snake_case , _snake_case , "pr_oracle" )
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
if not qid_list:
return
__magic_name__ : Dict = [na_probs[k] for k in qid_list]
__magic_name__ : str = np.ones_like(_snake_case ) / float(len(_snake_case ) )
plt.hist(_snake_case , weights=_snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(_snake_case , F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : List[str] , _snake_case : Dict ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
__magic_name__ : List[str] = num_no_ans
__magic_name__ : Dict = cur_score
__magic_name__ : Dict = 0.0
__magic_name__ : Any = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
for i, qid in enumerate(_snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
__magic_name__ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
__magic_name__ : List[Any] = -1
else:
__magic_name__ : Optional[int] = 0
cur_score += diff
if cur_score > best_score:
__magic_name__ : Optional[int] = cur_score
__magic_name__ : List[Any] = na_probs[qid]
return 100.0 * best_score / len(_snake_case ), best_thresh
def lowerCAmelCase_ ( _snake_case : int , _snake_case : str , _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ : List[str] = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ , __magic_name__ : int = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ : Optional[int] = best_exact
__magic_name__ : List[Any] = exact_thresh
__magic_name__ : Dict = best_fa
__magic_name__ : Any = fa_thresh
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
with open(OPTS.data_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
__magic_name__ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
__magic_name__ : Any = json.load(_snake_case )
else:
__magic_name__ : Any = {k: 0.0 for k in preds}
__magic_name__ : str = make_qid_to_has_ans(_snake_case ) # maps qid to True/False
__magic_name__ : Tuple = [k for k, v in qid_to_has_ans.items() if v]
__magic_name__ : Optional[Any] = [k for k, v in qid_to_has_ans.items() if not v]
__magic_name__ , __magic_name__ : Union[str, Any] = get_raw_scores(_snake_case , _snake_case )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case )
if has_ans_qids:
__magic_name__ : int = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "HasAns" )
if no_ans_qids:
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , OPTS.out_image_dir )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(_snake_case , _snake_case )
else:
print(json.dumps(_snake_case , indent=2 ) )
if __name__ == "__main__":
snake_case : int = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 281
| 1
|
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
a :Optional[int] = inspect.getfile(accelerate.test_utils )
a :str = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py'''] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
a :Any = test_metrics
@require_cpu
def SCREAMING_SNAKE_CASE__ ( self ):
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def SCREAMING_SNAKE_CASE__ ( self ):
debug_launcher(self.test_metrics.main )
@require_single_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
self.test_metrics.main()
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
print(F'''Found {torch.cuda.device_count()} devices.''' )
a :List[Any] = ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_A , env=os.environ.copy() )
| 364
|
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _snake_case ( _snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ = MgpstrTokenizer
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# fmt: off
a :int = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
a :List[str] = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
a :Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_lowerCamelCase ) + '''\n''' )
def SCREAMING_SNAKE_CASE__ ( self , **_lowerCamelCase ):
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a :str = '''tester'''
a :Union[str, Any] = '''tester'''
return input_text, output_text
@unittest.skip('''MGP-STR always lower cases letters.''' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
a :List[Any] = self.get_tokenizers(do_lower_case=_lowerCamelCase )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
a :Any = '''[SPECIAL_TOKEN]'''
tokenizer.add_special_tokens({'''cls_token''': special_token} )
a :str = tokenizer.encode([special_token] , add_special_tokens=_lowerCamelCase )
self.assertEqual(len(_lowerCamelCase ) , 1 )
a :Tuple = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
self.assertTrue(special_token not in decoded )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
a , a :Tuple = self.get_input_output_texts(_lowerCamelCase )
a :Tuple = tokenizer.tokenize(_lowerCamelCase )
a :int = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
a :Optional[int] = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
a :Any = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertNotEqual(len(_lowerCamelCase ) , 0 )
a :str = tokenizer.decode(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(text_a.replace(''' ''' , '''''' ) , _lowerCamelCase )
@unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
| 281
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = ['GPTSw3Tokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
_a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 61
|
'''simple docstring'''
import math
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
UpperCAmelCase__ = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str]=1 , **SCREAMING_SNAKE_CASE__ : List[str] ):
'''simple docstring'''
UpperCAmelCase__ = factor * value
UpperCAmelCase__ = value
while not is_prime(SCREAMING_SNAKE_CASE__ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **SCREAMING_SNAKE_CASE__ )
return value
| 346
| 0
|
from math import factorial
def snake_case (UpperCAmelCase__ = 1_0_0 ) -> int:
return sum(int(UpperCAmelCase__ ) for x in str(factorial(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 352
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Dict = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Tuple ='''xglm'''
a : List[Any] =['''past_key_values''']
a : Union[str, Any] ={
'''num_attention_heads''': '''attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowerCamelCase=2_5_6_0_0_8 , _lowerCamelCase=2_0_4_8 , _lowerCamelCase=1_0_2_4 , _lowerCamelCase=4_0_9_6 , _lowerCamelCase=2_4 , _lowerCamelCase=1_6 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0_2 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=2 , _lowerCamelCase=1 , _lowerCamelCase=0 , _lowerCamelCase=2 , **_lowerCamelCase , ):
UpperCamelCase_: Optional[Any] = vocab_size
UpperCamelCase_: Optional[int] = max_position_embeddings
UpperCamelCase_: List[str] = d_model
UpperCamelCase_: List[Any] = ffn_dim
UpperCamelCase_: List[Any] = num_layers
UpperCamelCase_: List[Any] = attention_heads
UpperCamelCase_: Tuple = activation_function
UpperCamelCase_: Tuple = dropout
UpperCamelCase_: Tuple = attention_dropout
UpperCamelCase_: Optional[Any] = activation_dropout
UpperCamelCase_: List[str] = layerdrop
UpperCamelCase_: Any = init_std
UpperCamelCase_: Any = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase_: Union[str, Any] = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 292
| 0
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : List[str] = {
"google/pix2struct-textcaps-base": (
"https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
),
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """pix2struct_text_model"""
UpperCAmelCase = ["""past_key_values"""]
UpperCAmelCase = {
"""hidden_size""": """hidden_size""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self ,a_=50_244 ,a_=768 ,a_=64 ,a_=2_048 ,a_=12 ,a_=12 ,a_=32 ,a_=128 ,a_=0.1 ,a_=1E-6 ,a_=1.0 ,a_="gelu_new" ,a_=0 ,a_=False ,a_=0 ,a_=1 ,a_=False ,a_=True ,**a_ ,) -> Tuple:
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : Tuple = d_kv
_UpperCAmelCase : List[Any] = d_ff
_UpperCAmelCase : str = num_layers
_UpperCAmelCase : Tuple = num_heads
_UpperCAmelCase : int = relative_attention_num_buckets
_UpperCAmelCase : Optional[int] = relative_attention_max_distance
_UpperCAmelCase : List[str] = dropout_rate
_UpperCAmelCase : List[Any] = layer_norm_epsilon
_UpperCAmelCase : Union[str, Any] = initializer_factor
_UpperCAmelCase : List[str] = use_cache
_UpperCAmelCase : str = eos_token_id
_UpperCAmelCase : str = decoder_start_token_id
# for backwards compatibility
_UpperCAmelCase : Dict = dense_act_fn
super().__init__(
pad_token_id=a_ ,eos_token_id=a_ ,decoder_start_token_id=a_ ,tie_word_embeddings=a_ ,is_decoder=a_ ,**a_ ,)
@classmethod
def _snake_case ( cls ,a_ ,**a_ ) -> List[Any]:
cls._set_token_in_kwargs(a_ )
_UpperCAmelCase ,_UpperCAmelCase : List[str] = cls.get_config_dict(a_ ,**a_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_UpperCAmelCase : int = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(a_ ,**a_ )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """pix2struct_vision_model"""
def __init__( self ,a_=768 ,a_=768 ,a_=2_048 ,a_=64 ,a_=12 ,a_=12 ,a_="gelu_new" ,a_=1E-6 ,a_=0.0 ,a_=0.0 ,a_=1E-1_0 ,a_=1.0 ,a_=4_096 ,a_=32 ,a_=128 ,**a_ ,) -> Dict:
super().__init__(**a_ )
_UpperCAmelCase : Union[str, Any] = hidden_size
_UpperCAmelCase : Dict = patch_embed_hidden_size
_UpperCAmelCase : List[Any] = d_ff
_UpperCAmelCase : List[Any] = dropout_rate
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : str = initializer_range
_UpperCAmelCase : Union[str, Any] = initializer_factor
_UpperCAmelCase : List[Any] = attention_dropout
_UpperCAmelCase : Optional[Any] = layer_norm_eps
_UpperCAmelCase : int = dense_act_fn
_UpperCAmelCase : List[str] = seq_len
_UpperCAmelCase : int = relative_attention_num_buckets
_UpperCAmelCase : Any = relative_attention_max_distance
_UpperCAmelCase : Union[str, Any] = d_kv
@classmethod
def _snake_case ( cls ,a_ ,**a_ ) -> List[Any]:
cls._set_token_in_kwargs(a_ )
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = cls.get_config_dict(a_ ,**a_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_UpperCAmelCase : List[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(a_ ,**a_ )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """pix2struct"""
UpperCAmelCase = True
def __init__( self ,a_=None ,a_=None ,a_=1.0 ,a_=0.02 ,a_=False ,a_=False ,a_=True ,**a_ ,) -> str:
super().__init__(tie_word_embeddings=a_ ,is_encoder_decoder=a_ ,**a_ )
if text_config is None:
_UpperCAmelCase : Tuple = {}
logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values.""" )
if vision_config is None:
_UpperCAmelCase : List[Any] = {}
logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values.""" )
_UpperCAmelCase : List[Any] = PixaStructTextConfig(**a_ )
_UpperCAmelCase : str = PixaStructVisionConfig(**a_ )
_UpperCAmelCase : List[Any] = self.text_config.decoder_start_token_id
_UpperCAmelCase : str = self.text_config.pad_token_id
_UpperCAmelCase : Union[str, Any] = self.text_config.eos_token_id
_UpperCAmelCase : Dict = initializer_factor
_UpperCAmelCase : Dict = initializer_range
_UpperCAmelCase : Optional[int] = self.initializer_range
_UpperCAmelCase : Any = self.initializer_range
_UpperCAmelCase : Optional[Any] = is_vqa
@classmethod
def _snake_case ( cls ,a_ ,a_ ,**a_ ) -> Tuple:
return cls(text_config=text_config.to_dict() ,vision_config=vision_config.to_dict() ,**a_ )
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Tuple = copy.deepcopy(self.__dict__ )
_UpperCAmelCase : Dict = self.text_config.to_dict()
_UpperCAmelCase : Optional[int] = self.vision_config.to_dict()
_UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
| 215
|
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
raise ValueError(
"""starting number must be
and integer and be more than 0""" )
if not iterations >= 1:
raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz""" )
a = """"""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__lowerCamelCase )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 228
| 0
|
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = 42
__lowercase = jnp.floataa
__lowercase = True
def lowerCamelCase ( self ):
"""simple docstring"""
super().setup()
_snake_case = nn.Dense(5 , dtype=self.dtype )
def __call__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = super().__call__(*lowerCAmelCase_ , **lowerCAmelCase_ )
_snake_case = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = FlaxBigBirdForNaturalQuestionsModule
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A , __A , __A ) -> int:
def cross_entropy(__A , __A , __A=None ):
_snake_case = logits.shape[-1]
_snake_case = (labels[..., None] == jnp.arange(__A )[None]).astype('f4' )
_snake_case = jax.nn.log_softmax(__A , axis=-1 )
_snake_case = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
_snake_case = reduction(__A )
return loss
_snake_case = partial(__A , reduction=jnp.mean )
_snake_case = cross_entropy(__A , __A )
_snake_case = cross_entropy(__A , __A )
_snake_case = cross_entropy(__A , __A )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __UpperCAmelCase :
__lowercase = """google/bigbird-roberta-base"""
__lowercase = 30_00
__lowercase = 1_05_00
__lowercase = 1_28
__lowercase = 3
__lowercase = 1
__lowercase = 5
# tx_args
__lowercase = 3e-5
__lowercase = 0.0
__lowercase = 2_00_00
__lowercase = 0.0_0_9_5
__lowercase = """bigbird-roberta-natural-questions"""
__lowercase = """training-expt"""
__lowercase = """data/nq-training.jsonl"""
__lowercase = """data/nq-validation.jsonl"""
def lowerCamelCase ( self ):
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=lowerCAmelCase_ )
_snake_case = os.path.join(self.base_dir , self.save_dir )
_snake_case = self.batch_size_per_device * jax.device_count()
@dataclass
class __UpperCAmelCase :
__lowercase = 42
__lowercase = 40_96 # no dynamic padding on TPUs
def __call__( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.collate_fn(lowerCAmelCase_ )
_snake_case = jax.tree_util.tree_map(lowerCAmelCase_ , lowerCAmelCase_ )
return batch
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case , _snake_case = self.fetch_inputs(features['input_ids'] )
_snake_case = {
'input_ids': jnp.array(lowerCAmelCase_ , dtype=jnp.intaa ),
'attention_mask': jnp.array(lowerCAmelCase_ , dtype=jnp.intaa ),
'start_labels': jnp.array(features['start_token'] , dtype=jnp.intaa ),
'end_labels': jnp.array(features['end_token'] , dtype=jnp.intaa ),
'pooled_labels': jnp.array(features['category'] , dtype=jnp.intaa ),
}
return batch
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = [self._fetch_inputs(lowerCAmelCase_ ) for ids in input_ids]
return zip(*lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = [1 for _ in range(len(lowerCAmelCase_ ) )]
while len(lowerCAmelCase_ ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A=None ) -> List[str]:
if seed is not None:
_snake_case = dataset.shuffle(seed=__A )
for i in range(len(__A ) // batch_size ):
_snake_case = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(__A )
@partial(jax.pmap , axis_name='batch' )
def SCREAMING_SNAKE_CASE__ ( __A , __A , **__A ) -> Optional[int]:
def loss_fn(__A ):
_snake_case = model_inputs.pop('start_labels' )
_snake_case = model_inputs.pop('end_labels' )
_snake_case = model_inputs.pop('pooled_labels' )
_snake_case = state.apply_fn(**__A , params=__A , dropout_rng=__A , train=__A )
_snake_case , _snake_case , _snake_case = outputs
return state.loss_fn(
__A , __A , __A , __A , __A , __A , )
_snake_case , _snake_case = jax.random.split(__A )
_snake_case = jax.value_and_grad(__A )
_snake_case , _snake_case = grad_fn(state.params )
_snake_case = jax.lax.pmean({'loss': loss} , axis_name='batch' )
_snake_case = jax.lax.pmean(__A , 'batch' )
_snake_case = state.apply_gradients(grads=__A )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def SCREAMING_SNAKE_CASE__ ( __A , **__A ) -> int:
_snake_case = model_inputs.pop('start_labels' )
_snake_case = model_inputs.pop('end_labels' )
_snake_case = model_inputs.pop('pooled_labels' )
_snake_case = state.apply_fn(**__A , params=state.params , train=__A )
_snake_case , _snake_case , _snake_case = outputs
_snake_case = state.loss_fn(__A , __A , __A , __A , __A , __A )
_snake_case = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class __UpperCAmelCase ( train_state.TrainState ):
__lowercase = struct.field(pytree_node=_lowerCamelCase )
@dataclass
class __UpperCAmelCase :
__lowercase = 42
__lowercase = 42
__lowercase = 42
__lowercase = 42
__lowercase = 42
__lowercase = 42
__lowercase = None
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None ):
"""simple docstring"""
_snake_case = model.params
_snake_case = TrainState.create(
apply_fn=model.__call__ , params=lowerCAmelCase_ , tx=lowerCAmelCase_ , loss_fn=lowerCAmelCase_ , )
if ckpt_dir is not None:
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case = restore_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
_snake_case , _snake_case = build_tx(**lowerCAmelCase_ )
_snake_case = train_state.TrainState(
step=lowerCAmelCase_ , apply_fn=model.__call__ , params=lowerCAmelCase_ , tx=lowerCAmelCase_ , opt_state=lowerCAmelCase_ , )
_snake_case = args
_snake_case = data_collator
_snake_case = lr
_snake_case = params
_snake_case = jax_utils.replicate(lowerCAmelCase_ )
return state
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.args
_snake_case = len(lowerCAmelCase_ ) // args.batch_size
_snake_case = jax.random.PRNGKey(0 )
_snake_case = jax.random.split(lowerCAmelCase_ , jax.device_count() )
for epoch in range(args.max_epochs ):
_snake_case = jnp.array(0 , dtype=jnp.floataa )
_snake_case = get_batched_dataset(lowerCAmelCase_ , args.batch_size , seed=lowerCAmelCase_ )
_snake_case = 0
for batch in tqdm(lowerCAmelCase_ , total=lowerCAmelCase_ , desc=F'Running EPOCH-{epoch}' ):
_snake_case = self.data_collator(lowerCAmelCase_ )
_snake_case , _snake_case , _snake_case = self.train_step_fn(lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ )
running_loss += jax_utils.unreplicate(metrics['loss'] )
i += 1
if i % args.logging_steps == 0:
_snake_case = jax_utils.unreplicate(state.step )
_snake_case = running_loss.item() / i
_snake_case = self.scheduler_fn(state_step - 1 )
_snake_case = self.evaluate(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = {
'step': state_step.item(),
'eval_loss': eval_loss.item(),
'tr_loss': tr_loss,
'lr': lr.item(),
}
tqdm.write(str(lowerCAmelCase_ ) )
self.logger.log(lowerCAmelCase_ , commit=lowerCAmelCase_ )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}' , state=lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = get_batched_dataset(lowerCAmelCase_ , self.args.batch_size )
_snake_case = len(lowerCAmelCase_ ) // self.args.batch_size
_snake_case = jnp.array(0 , dtype=jnp.floataa )
_snake_case = 0
for batch in tqdm(lowerCAmelCase_ , total=lowerCAmelCase_ , desc='Evaluating ... ' ):
_snake_case = self.data_collator(lowerCAmelCase_ )
_snake_case = self.val_step_fn(lowerCAmelCase_ , **lowerCAmelCase_ )
running_loss += jax_utils.unreplicate(metrics['loss'] )
i += 1
return running_loss / i
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = jax_utils.unreplicate(lowerCAmelCase_ )
print(F'SAVING CHECKPOINT IN {save_dir}' , end=' ... ' )
self.model_save_fn(lowerCAmelCase_ , params=state.params )
with open(os.path.join(lowerCAmelCase_ , 'opt_state.msgpack' ) , 'wb' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(lowerCAmelCase_ , 'args.joblib' ) )
joblib.dump(self.data_collator , os.path.join(lowerCAmelCase_ , 'data_collator.joblib' ) )
with open(os.path.join(lowerCAmelCase_ , 'training_state.json' ) , 'w' ) as f:
json.dump({'step': state.step.item()} , lowerCAmelCase_ )
print('DONE' )
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> List[Any]:
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=' ... ' )
with open(os.path.join(__A , 'flax_model.msgpack' ) , 'rb' ) as f:
_snake_case = from_bytes(state.params , f.read() )
with open(os.path.join(__A , 'opt_state.msgpack' ) , 'rb' ) as f:
_snake_case = from_bytes(state.opt_state , f.read() )
_snake_case = joblib.load(os.path.join(__A , 'args.joblib' ) )
_snake_case = joblib.load(os.path.join(__A , 'data_collator.joblib' ) )
with open(os.path.join(__A , 'training_state.json' ) , 'r' ) as f:
_snake_case = json.load(__A )
_snake_case = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A ) -> Union[str, Any]:
_snake_case = num_train_steps - warmup_steps
_snake_case = optax.linear_schedule(init_value=__A , end_value=__A , transition_steps=__A )
_snake_case = optax.linear_schedule(init_value=__A , end_value=1e-7 , transition_steps=__A )
_snake_case = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A , __A ) -> Any:
def weight_decay_mask(__A ):
_snake_case = traverse_util.flatten_dict(__A )
_snake_case = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(__A )
_snake_case = scheduler_fn(__A , __A , __A , __A )
_snake_case = optax.adamw(learning_rate=__A , weight_decay=__A , mask=__A )
return tx, lr
| 371
|
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
lowercase : Tuple = False
lowercase : str = logging.get_logger(__name__)
lowercase : List[str] = "ybelkada/fonts"
def SCREAMING_SNAKE_CASE__ ( ) -> Any:
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F'You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '
'Pix2StructImageProcessor. Please upgrade torch.' )
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> Optional[int]:
requires_backends(__A , ['torch'] )
_check_torch_version()
_snake_case = image_tensor.unsqueeze(0 )
_snake_case = torch.nn.functional.unfold(__A , (patch_height, patch_width) , stride=(patch_height, patch_width) )
_snake_case = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , __A , __A , -1 )
_snake_case = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def SCREAMING_SNAKE_CASE__ ( __A , __A = 36 , __A = "black" , __A = "white" , __A = 5 , __A = 5 , __A = 5 , __A = 5 , __A = None , __A = None , ) -> Image.Image:
requires_backends(__A , 'vision' )
# Add new lines so that each line is no more than 80 characters.
_snake_case = textwrap.TextWrapper(width=80 )
_snake_case = wrapper.wrap(text=__A )
_snake_case = '\n'.join(__A )
if font_bytes is not None and font_path is None:
_snake_case = io.BytesIO(__A )
elif font_path is not None:
_snake_case = font_path
else:
_snake_case = hf_hub_download(__A , 'Arial.TTF' )
_snake_case = ImageFont.truetype(__A , encoding='UTF-8' , size=__A )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
_snake_case = ImageDraw.Draw(Image.new('RGB' , (1, 1) , __A ) )
_snake_case , _snake_case , _snake_case , _snake_case = temp_draw.textbbox((0, 0) , __A , __A )
# Create the actual image with a bit of padding around the text.
_snake_case = text_width + left_padding + right_padding
_snake_case = text_height + top_padding + bottom_padding
_snake_case = Image.new('RGB' , (image_width, image_height) , __A )
_snake_case = ImageDraw.Draw(__A )
draw.text(xy=(left_padding, top_padding) , text=__A , fill=__A , font=__A )
return image
def SCREAMING_SNAKE_CASE__ ( __A , __A , **__A ) -> Dict:
requires_backends(__A , 'vision' )
# Convert to PIL image if necessary
_snake_case = to_pil_image(__A )
_snake_case = render_text(__A , **__A )
_snake_case = max(header_image.width , image.width )
_snake_case = int(image.height * (new_width / image.width) )
_snake_case = int(header_image.height * (new_width / header_image.width) )
_snake_case = Image.new('RGB' , (new_width, new_height + new_header_height) , 'white' )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
_snake_case = to_numpy_array(__A )
if infer_channel_dimension_format(__A ) == ChannelDimension.LAST:
_snake_case = to_channel_dimension_format(__A , ChannelDimension.LAST )
return new_image
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = ["""flattened_patches"""]
def __init__( self , lowerCAmelCase_ = True , lowerCAmelCase_ = True , lowerCAmelCase_ = None , lowerCAmelCase_ = 20_48 , lowerCAmelCase_ = False , **lowerCAmelCase_ , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_ )
_snake_case = patch_size if patch_size is not None else {'height': 16, 'width': 16}
_snake_case = do_normalize
_snake_case = do_convert_rgb
_snake_case = max_patches
_snake_case = is_vqa
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(self.extract_flattened_patches , 'torch' )
_check_torch_version()
# convert to torch
_snake_case = to_channel_dimension_format(lowerCAmelCase_ , ChannelDimension.FIRST )
_snake_case = torch.from_numpy(lowerCAmelCase_ )
_snake_case , _snake_case = patch_size['height'], patch_size['width']
_snake_case , _snake_case = get_image_size(lowerCAmelCase_ )
# maximize scale s.t.
_snake_case = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
_snake_case = max(min(math.floor(scale * image_height / patch_height ) , lowerCAmelCase_ ) , 1 )
_snake_case = max(min(math.floor(scale * image_width / patch_width ) , lowerCAmelCase_ ) , 1 )
_snake_case = max(num_feasible_rows * patch_height , 1 )
_snake_case = max(num_feasible_cols * patch_width , 1 )
_snake_case = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode='bilinear' , align_corners=lowerCAmelCase_ , antialias=lowerCAmelCase_ , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
_snake_case = torch_extract_patches(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = patches.shape
_snake_case = patches_shape[1]
_snake_case = patches_shape[2]
_snake_case = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
_snake_case = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
_snake_case = torch.arange(lowerCAmelCase_ ).reshape([rows, 1] ).repeat(1 , lowerCAmelCase_ ).reshape([rows * columns, 1] )
_snake_case = torch.arange(lowerCAmelCase_ ).reshape([1, columns] ).repeat(lowerCAmelCase_ , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
_snake_case = row_ids.to(torch.floataa )
_snake_case = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
_snake_case = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
_snake_case = torch.nn.functional.pad(lowerCAmelCase_ , [0, 0, 0, max_patches - (rows * columns)] ).float()
_snake_case = to_numpy_array(lowerCAmelCase_ )
return result
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None , **lowerCAmelCase_ ):
"""simple docstring"""
if image.dtype == np.uinta:
_snake_case = image.astype(np.floataa )
# take mean across the whole `image`
_snake_case = np.mean(lowerCAmelCase_ )
_snake_case = np.std(lowerCAmelCase_ )
_snake_case = max(lowerCAmelCase_ , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(lowerCAmelCase_ , mean=lowerCAmelCase_ , std=lowerCAmelCase_ , **lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = ChannelDimension.FIRST , **lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = do_normalize if do_normalize is not None else self.do_normalize
_snake_case = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_snake_case = patch_size if patch_size is not None else self.patch_size
_snake_case = max_patches if max_patches is not None else self.max_patches
_snake_case = self.is_vqa
if kwargs.get('data_format' , lowerCAmelCase_ ) is not None:
raise ValueError('data_format is not an accepted input as the outputs are ' )
_snake_case = make_list_of_images(lowerCAmelCase_ )
if not valid_images(lowerCAmelCase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
_snake_case = [convert_to_rgb(lowerCAmelCase_ ) for image in images]
# All transformations expect numpy arrays.
_snake_case = [to_numpy_array(lowerCAmelCase_ ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError('A header text must be provided for VQA models.' )
_snake_case = kwargs.pop('font_bytes' , lowerCAmelCase_ )
_snake_case = kwargs.pop('font_path' , lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case = [header_text] * len(lowerCAmelCase_ )
_snake_case = [
render_header(lowerCAmelCase_ , header_text[i] , font_bytes=lowerCAmelCase_ , font_path=lowerCAmelCase_ )
for i, image in enumerate(lowerCAmelCase_ )
]
if do_normalize:
_snake_case = [self.normalize(image=lowerCAmelCase_ ) for image in images]
# convert to torch tensor and permute
_snake_case = [
self.extract_flattened_patches(image=lowerCAmelCase_ , max_patches=lowerCAmelCase_ , patch_size=lowerCAmelCase_ )
for image in images
]
# create attention mask in numpy
_snake_case = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
_snake_case = BatchFeature(
data={'flattened_patches': images, 'attention_mask': attention_masks} , tensor_type=lowerCAmelCase_ )
return encoded_outputs
| 160
| 0
|
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
__lowerCAmelCase : Any =[
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def UpperCamelCase ( _lowerCamelCase : Union[str, Any] ):
for pegasus_name, hf_name in PATTERNS:
A__ = k.replace(__snake_case , __snake_case )
return k
def UpperCamelCase ( _lowerCamelCase : dict , _lowerCamelCase : dict ):
A__ = DEFAULTS.copy()
cfg_kwargs.update(__snake_case )
A__ = PegasusConfig(**__snake_case )
A__ = PegasusForConditionalGeneration(__snake_case )
A__ = torch_model.model.state_dict()
A__ = {}
for k, v in tf_weights.items():
A__ = rename_state_dict_key(__snake_case )
if new_k not in sd:
raise ValueError(F"could not find new key {new_k} in state dict. (converted from {k})" )
if "dense" in k or "proj" in new_k:
A__ = v.T
A__ = torch.tensor(__snake_case , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
# make sure embedding.padding_idx is respected
A__ = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1] )
A__ = mapping["shared.weight"]
A__ = mapping["shared.weight"]
A__ = {k: torch.zeros_like(__snake_case ) for k, v in sd.items() if k.endswith("bias" ) and k not in mapping}
mapping.update(**__snake_case )
A__ = torch_model.model.load_state_dict(__snake_case , strict=__snake_case )
A__ = [
k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
]
assert unexpected_missing == [], F"no matches found for the following torch keys {unexpected_missing}"
assert extra == [], F"no matches found for the following tf keys {extra}"
return torch_model
def UpperCamelCase ( _lowerCamelCase : List[str]="./ckpt/aeslc/model.ckpt-32000" ):
A__ = tf.train.list_variables(__snake_case )
A__ = {}
A__ = ["Adafactor", "global_step"]
for name, shape in tqdm(__snake_case , desc="converting tf checkpoint to dict" ):
A__ = any(pat in name for pat in ignore_name )
if skip_key:
continue
A__ = tf.train.load_variable(__snake_case , __snake_case )
A__ = array
return tf_weights
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : str ):
# save tokenizer first
A__ = Path(__snake_case ).parent.name
A__ = task_specific_params[F"summarization_{dataset}"]["max_position_embeddings"]
A__ = PegasusTokenizer.from_pretrained("sshleifer/pegasus" , model_max_length=__snake_case )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__snake_case )
# convert model
A__ = get_tf_weights_as_numpy(__snake_case )
A__ = task_specific_params[F"summarization_{dataset}"]
if dataset == "large":
A__ = task_specific_params
A__ = convert_pegasus(__snake_case , __snake_case )
torch_model.save_pretrained(__snake_case )
A__ = torch_model.state_dict()
sd.pop("model.decoder.embed_positions.weight" )
sd.pop("model.encoder.embed_positions.weight" )
torch.save(__snake_case , Path(__snake_case ) / "pytorch_model.bin" )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] =argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
__lowerCAmelCase : Union[str, Any] =parser.parse_args()
if args.save_dir is None:
__lowerCAmelCase : Any =Path(args.tf_ckpt_path).parent.name
__lowerCAmelCase : Dict =os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 237
|
"""simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_A : int = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( a_, unittest.TestCase ):
__lowerCAmelCase = DebertaVaTokenizer
__lowerCAmelCase = DebertaVaTokenizerFast
__lowerCAmelCase = True
__lowerCAmelCase = True
def __magic_name__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowercase : Any = DebertaVaTokenizer(_a , unk_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self , _a ):
lowercase : int = "this is a test"
lowercase : Tuple = "this is a test"
return input_text, output_text
def __magic_name__ ( self ):
lowercase : List[Any] = "<pad>"
lowercase : List[str] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def __magic_name__ ( self ):
lowercase : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "[PAD]" )
self.assertEqual(len(_a ) , 30_001 )
def __magic_name__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 30_000 )
def __magic_name__ ( self ):
# fmt: off
lowercase : List[str] = " \tHeLLo!how \n Are yoU? "
lowercase : str = ["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
lowercase : Union[str, Any] = DebertaVaTokenizer(_a , do_lower_case=_a )
lowercase : Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
lowercase : str = DebertaVaTokenizerFast(_a , do_lower_case=_a )
lowercase : Optional[Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __magic_name__ ( self ):
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __magic_name__ ( self ):
pass
def __magic_name__ ( self ):
# fmt: off
lowercase : Optional[Any] = "I was born in 92000, and this is falsé."
lowercase : Tuple = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
lowercase : List[Any] = DebertaVaTokenizer(_a , split_by_punct=_a )
lowercase : Tuple = tokenizer.convert_ids_to_tokens(tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
lowercase : Union[str, Any] = DebertaVaTokenizerFast(_a , split_by_punct=_a )
lowercase : Any = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
def __magic_name__ ( self ):
# fmt: off
lowercase : int = "I was born in 92000, and this is falsé."
lowercase : Tuple = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
lowercase : List[str] = DebertaVaTokenizer(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : Optional[Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
lowercase : Union[str, Any] = DebertaVaTokenizerFast(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
def __magic_name__ ( self ):
# fmt: off
lowercase : List[Any] = "I was born in 92000, and this is falsé."
lowercase : Optional[int] = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
lowercase : List[Any] = DebertaVaTokenizer(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : Optional[int] = tokenizer.convert_ids_to_tokens(tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
lowercase : Optional[int] = DebertaVaTokenizerFast(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : Dict = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
def __magic_name__ ( self ):
# fmt: off
lowercase : int = "I was born in 92000, and this is falsé."
lowercase : Dict = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
lowercase : Union[str, Any] = DebertaVaTokenizer(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
lowercase : Union[str, Any] = DebertaVaTokenizerFast(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : Dict = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
def __magic_name__ ( self ):
# fmt: off
lowercase : Dict = " \tHeLLo!how \n Are yoU? "
lowercase : str = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
lowercase : Optional[int] = DebertaVaTokenizer(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : int = tokenizer.convert_ids_to_tokens(tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
lowercase : List[str] = DebertaVaTokenizerFast(_a , do_lower_case=_a , split_by_punct=_a )
lowercase : List[Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
def __magic_name__ ( self ):
lowercase : str = self.get_tokenizer()
lowercase : Dict = self.get_rust_tokenizer()
lowercase : str = "I was born in 92000, and this is falsé."
lowercase : Optional[Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(_a , add_special_tokens=_a ) )
lowercase : Dict = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a , add_special_tokens=_a ) )
self.assertListEqual(_a , _a )
lowercase : str = tokenizer.encode(_a , add_special_tokens=_a )
lowercase : Dict = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
lowercase : Optional[int] = self.get_rust_tokenizer()
lowercase : Tuple = tokenizer.encode(_a )
lowercase : List[str] = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def __magic_name__ ( self ):
lowercase : str = "This is a test"
lowercase : Tuple = [13, 1, 4_398, 25, 21, 1_289]
lowercase : Optional[int] = ["▁", "T", "his", "▁is", "▁a", "▁test"]
lowercase : Optional[Any] = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
lowercase : Any = DebertaVaTokenizer(_a , keep_accents=_a )
lowercase : Dict = DebertaVaTokenizerFast(_a , keep_accents=_a )
lowercase : str = tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
lowercase : str = tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
lowercase : str = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(_a , _a )
lowercase : int = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
lowercase : Any = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
lowercase : Union[str, Any] = rust_tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(_a , _a )
# fmt: off
lowercase : int = "I was born in 92000, and this is falsé."
lowercase : Any = [13, 1, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]
lowercase : List[str] = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
lowercase : str = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
lowercase : Tuple = tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
lowercase : List[Any] = tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
lowercase : str = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(_a , _a )
lowercase : Optional[int] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
lowercase : List[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
lowercase : List[Any] = rust_tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(_a , _a )
def __magic_name__ ( self ):
lowercase : Optional[int] = DebertaVaTokenizer(_a )
lowercase : List[Any] = tokenizer.encode("sequence builders" )
lowercase : Dict = tokenizer.encode("multi-sequence build" )
lowercase : List[Any] = tokenizer.build_inputs_with_special_tokens(_a )
lowercase : Dict = tokenizer.build_inputs_with_special_tokens(_a , _a )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _a )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _a , )
@slow
def __magic_name__ ( self ):
# fmt: off
lowercase : Dict = {"input_ids": [[1, 39_867, 36, 19_390, 486, 27, 35_052, 81_436, 18, 60_685, 1_225, 7, 35_052, 81_436, 18, 9_367, 16_899, 18, 15_937, 53, 594, 773, 18, 16_287, 30_465, 36, 15_937, 6, 41_139, 38, 36_979, 60_763, 191, 6, 34_132, 99, 6, 50_538, 390, 43_230, 6, 34_132, 2_779, 20_850, 14, 699, 1_072, 1_194, 36, 382, 10_901, 53, 7, 699, 1_072, 2_084, 36, 20_422, 630, 53, 19, 105, 3_049, 1_896, 1_053, 16_899, 1_506, 11, 37_978, 4_243, 7, 1_237, 31_869, 200, 16_566, 654, 6, 35_052, 81_436, 7, 55_630, 13_593, 4, 2], [1, 26, 15_011, 13, 667, 8, 1_053, 18, 23_611, 1_237, 72_356, 12_820, 34, 104_134, 1_209, 35, 13_313, 6_627, 21, 202, 347, 7, 164, 2_399, 11, 46, 4_485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_232, 2_864, 15_785, 14_951, 105, 5, 8_581, 1_250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 202
| 0
|
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase ( __snake_case ):
'''simple docstring'''
def snake_case_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_snake_case , '''embed_dim''' ) )
self.parent.assertTrue(hasattr(_snake_case , '''num_heads''' ) )
class lowercase :
'''simple docstring'''
def __init__( self , _snake_case , _snake_case=13 , _snake_case=64 , _snake_case=3 , _snake_case=[16, 48, 96] , _snake_case=[1, 3, 6] , _snake_case=[1, 2, 10] , _snake_case=[7, 3, 3] , _snake_case=[4, 2, 2] , _snake_case=[2, 1, 1] , _snake_case=[2, 2, 2] , _snake_case=[False, False, True] , _snake_case=[0.0, 0.0, 0.0] , _snake_case=0.02 , _snake_case=1e-12 , _snake_case=True , _snake_case=True , _snake_case=2 , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = patch_sizes
UpperCAmelCase = patch_stride
UpperCAmelCase = patch_padding
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = num_labels
UpperCAmelCase = num_channels
UpperCAmelCase = embed_dim
UpperCAmelCase = num_heads
UpperCAmelCase = stride_kv
UpperCAmelCase = depth
UpperCAmelCase = cls_token
UpperCAmelCase = attention_drop_rate
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case ) -> Any:
"""simple docstring"""
UpperCAmelCase = CvtModel(config=_snake_case )
model.to(_snake_case )
model.eval()
UpperCAmelCase = model(_snake_case )
UpperCAmelCase = (self.image_size, self.image_size)
UpperCAmelCase , UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = self.num_labels
UpperCAmelCase = CvtForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
UpperCAmelCase = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE = (
{'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = CvtModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def snake_case_ ( self ) -> Any:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
return
@unittest.skip(reason='''Cvt does not output attentions''' )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
pass
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(_snake_case )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _snake_case )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(_snake_case , _snake_case , _snake_case ):
UpperCAmelCase = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
UpperCAmelCase = model(**self._prepare_for_class(_snake_case , _snake_case ) )
UpperCAmelCase = outputs.hidden_states
UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(_snake_case ) , _snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
def snake_case_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
pass
@slow
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = CvtModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_snake_case )
UpperCAmelCase = self.default_image_processor
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=_snake_case , return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**_snake_case )
# verify the logits
UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _snake_case )
UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _snake_case , atol=1e-4 ) )
| 362
|
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__magic_name__ = logging.get_logger(__name__)
def _lowerCAmelCase ( A__: nn.ModuleList , A__: nn.ModuleList , A__: List[int] ):
'''simple docstring'''
UpperCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(A__ ) == len(A__ ), F"""{len(A__ )} != {len(A__ )}"""
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__magic_name__ = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__magic_name__ = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _lowerCAmelCase ( A__: List[str] , A__: Optional[int] ):
'''simple docstring'''
try:
UpperCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
F""" {n_student}""" )
return list(range(A__ ) )
def _lowerCAmelCase ( A__: Optional[int] , A__: Tuple ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}""" )
elif n_teacher == n_student:
return list(range(A__ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _lowerCAmelCase ( A__: Union[str, PreTrainedModel] , A__: Union[str, Path] = "student" , A__: Union[int, None] = None , A__: Union[int, None] = None , A__: Optional[int]=False , A__: Tuple=None , A__: Any=None , **A__: List[str] , ):
'''simple docstring'''
UpperCAmelCase = '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
assert (e is not None) or (d is not None), _msg
if isinstance(A__ , A__ ):
AutoTokenizer.from_pretrained(A__ ).save_pretrained(A__ ) # purely for convenience
UpperCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(A__ ).eval()
else:
assert isinstance(A__ , A__ ), F"""teacher must be a model or string got type {type(A__ )}"""
UpperCAmelCase = teacher.config.to_diff_dict()
try:
UpperCAmelCase , UpperCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
UpperCAmelCase = teacher_e
if d is None:
UpperCAmelCase = teacher_d
init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d} )
except AttributeError: # T5
if hasattr(teacher.config , '''num_encoder_layers''' ):
UpperCAmelCase , UpperCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
UpperCAmelCase , UpperCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
UpperCAmelCase = teacher_e
if d is None:
UpperCAmelCase = teacher_d
if hasattr(teacher.config , '''num_encoder_layers''' ):
init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d} )
else:
init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(A__ )
# Copy weights
UpperCAmelCase = teacher.config_class(**A__ )
UpperCAmelCase = AutoModelForSeqaSeqLM.from_config(A__ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
UpperCAmelCase = student.load_state_dict(teacher.state_dict() , strict=A__ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
UpperCAmelCase , UpperCAmelCase = list(range(A__ ) ), list(range(A__ ) )
logger.info(
F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
F""" {save_path}""" )
student.save_pretrained(A__ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
UpperCAmelCase = pick_layers_to_copy(A__ , A__ )
if d_layers_to_copy is None:
UpperCAmelCase = pick_layers_to_copy(A__ , A__ )
try:
if hasattr(
A__ , '''prophetnet''' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , A__ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , A__ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , A__ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , A__ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , A__ )
copy_layers(teacher.decoder.block , student.decoder.block , A__ )
logger.info(
F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}""" )
UpperCAmelCase = {
'''teacher_type''': teacher.config.model_type,
'''copied_encoder_layers''': e_layers_to_copy,
'''copied_decoder_layers''': d_layers_to_copy,
}
student.save_pretrained(A__ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 152
| 0
|
UpperCAmelCase : Any = 8.3_1_4_4_5_9_8
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
UpperCAmelCase : Tuple = 300
UpperCAmelCase : Optional[int] = 28
UpperCAmelCase : Tuple = rms_speed_of_molecule(temperature, molar_mass)
print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")
| 95
|
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Dict = "mock-s3-bucket"
__magic_name__ : Any = F'''s3://{mock_bucket}'''
__magic_name__ : str = extract_path_from_uri(_snake_case )
assert dataset_path.startswith("s3://" ) is False
__magic_name__ : Tuple = "./local/path"
__magic_name__ : Optional[Any] = extract_path_from_uri(_snake_case )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : str = is_remote_filesystem(_snake_case )
assert is_remote is True
__magic_name__ : Optional[int] = fsspec.filesystem("file" )
__magic_name__ : int = is_remote_filesystem(_snake_case )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Tuple , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
__magic_name__ : Any = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
__magic_name__ : str = input_paths[compression_fs_class.protocol]
if input_path is None:
__magic_name__ : Dict = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_snake_case )
__magic_name__ : str = fsspec.filesystem(compression_fs_class.protocol , fo=_snake_case )
assert isinstance(_snake_case , _snake_case )
__magic_name__ : int = os.path.basename(_snake_case )
__magic_name__ : Optional[int] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(_snake_case , "r" , encoding="utf-8" ) as f, open(_snake_case , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any] ) -> str:
'''simple docstring'''
__magic_name__ : int = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
__magic_name__ : int = compressed_file_paths[protocol]
__magic_name__ : Tuple = "dataset.jsonl"
__magic_name__ : List[str] = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
__magic_name__ , *__magic_name__ : Optional[Any] = fsspec.get_fs_token_paths(_snake_case )
assert fs.isfile(_snake_case )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : List[str] , _snake_case : Tuple ) -> str:
'''simple docstring'''
__magic_name__ : int = hf_api.dataset_info(_snake_case , token=_snake_case )
__magic_name__ : Optional[Any] = HfFileSystem(repo_info=_snake_case , token=_snake_case )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(_snake_case ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : Optional[Any] = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_snake_case , _snake_case , clobber=_snake_case )
with pytest.warns(_snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_snake_case ) == 1
assert (
str(warning_info[0].message )
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 281
| 0
|
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
for i in range(0 , UpperCamelCase__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in range(0 , i + 1 ): # printing stars
print('* ' , end='' )
print()
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
for i in range(UpperCamelCase__ , 0 , -1 ):
for _ in range(UpperCamelCase__ , 0 , -1 ): # printing stars
print('* ' , end='' )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(' ' , end='' )
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
if n <= 0:
print(' ... .... nothing printing :(' )
return
floyd(UpperCamelCase__ ) # upper half
reverse_floyd(UpperCamelCase__ ) # lower half
if __name__ == "__main__":
print(R"| /\ | |- | |- |--| |\ /| |-")
print(R"|/ \| |- |_ |_ |__| | \/ | |_")
__lowerCamelCase = 1
while K:
__lowerCamelCase = int(input("enter the number and , and see the magic : "))
print()
pretty_print(user_number)
__lowerCamelCase = int(input("press 0 to exit... and 1 to continue..."))
print("Good Bye...")
| 352
|
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> Optional[int]:
A__ = inspect.getfile(accelerate.test_utils )
A__ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_script.py'] )
A__ = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def snake_case__ ( self ) -> int:
A__ = f'''
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
'''.split()
A__ = [sys.executable] + distributed_args
execute_subprocess_async(__UpperCAmelCase ,env=os.environ.copy() )
| 154
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 288
|
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _UpperCAmelCase ( lowercase_ , unittest.TestCase ):
UpperCamelCase = RoFormerTokenizer
UpperCamelCase = RoFormerTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def lowerCamelCase ( self :List[str] ):
super().setUp()
def lowerCamelCase ( self :int , **__UpperCamelCase :List[Any] ):
return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **__UpperCamelCase )
def lowerCamelCase ( self :Tuple , **__UpperCamelCase :Optional[int] ):
return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **__UpperCamelCase )
def lowerCamelCase ( self :Any ):
A = "永和服装饰品有限公司,今天天气非常好"
A = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
return input_text, output_text
def lowerCamelCase ( self :int ):
A = self.get_tokenizer()
A, A = self.get_chinese_input_output_texts()
A = tokenizer.tokenize(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , output_text.split() )
A = tokens + [tokenizer.unk_token]
A = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , __UpperCamelCase )
def lowerCamelCase ( self :str ):
A = self.get_rust_tokenizer()
A, A = self.get_chinese_input_output_texts()
A = tokenizer.tokenize(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , output_text.split() )
A = tokens + [tokenizer.unk_token]
A = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , __UpperCamelCase )
def lowerCamelCase ( self :Any ):
pass
def lowerCamelCase ( self :Tuple ):
pass
def lowerCamelCase ( self :List[str] ):
pass
| 292
| 0
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a__( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase__ = CycleDiffusionPipeline
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"""negative_prompt""",
"""height""",
"""width""",
"""negative_prompt_embeds""",
}
lowercase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
lowercase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase_ ( self : Any ):
torch.manual_seed(0 )
a : Union[str, Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
a : str = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , num_train_timesteps=10_00 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
a : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
a : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
a : List[str] = CLIPTextModel(__snake_case )
a : int = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
a : Tuple = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowercase_ ( self : Optional[int] , __snake_case : Dict , __snake_case : Any=0 ):
a : str = floats_tensor((1, 3, 32, 32) , rng=random.Random(__snake_case ) ).to(__snake_case )
a : Optional[Any] = image / 2 + 0.5
if str(__snake_case ).startswith('mps' ):
a : List[str] = torch.manual_seed(__snake_case )
else:
a : Union[str, Any] = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
a : List[Any] = {
'prompt': 'An astronaut riding an elephant',
'source_prompt': 'An astronaut riding a horse',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'eta': 0.1,
'strength': 0.8,
'guidance_scale': 3,
'source_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self : Optional[int] ):
a : Optional[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
a : int = self.get_dummy_components()
a : str = CycleDiffusionPipeline(**__snake_case )
a : List[str] = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
a : Dict = self.get_dummy_inputs(__snake_case )
a : Union[str, Any] = pipe(**__snake_case )
a : List[Any] = output.images
a : Optional[Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
a : Tuple = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != 'cuda' , 'This test requires a GPU' )
def lowercase_ ( self : int ):
a : List[Any] = self.get_dummy_components()
for name, module in components.items():
if hasattr(__snake_case , 'half' ):
a : Any = module.half()
a : Tuple = CycleDiffusionPipeline(**__snake_case )
a : Any = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
a : str = self.get_dummy_inputs(__snake_case )
a : int = pipe(**__snake_case )
a : Optional[int] = output.images
a : Tuple = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
a : int = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def lowercase_ ( self : List[Any] ):
return super().test_save_load_local()
@unittest.skip('non-deterministic pipeline' )
def lowercase_ ( self : Dict ):
return super().test_inference_batch_single_identical()
@skip_mps
def lowercase_ ( self : int ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowercase_ ( self : Dict ):
return super().test_save_load_optional_components()
@skip_mps
def lowercase_ ( self : List[Any] ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a__( unittest.TestCase ):
def lowercase_ ( self : Tuple ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : Optional[int] ):
a : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
a : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy' )
a : List[str] = init_image.resize((5_12, 5_12) )
a : Dict = 'CompVis/stable-diffusion-v1-4'
a : List[str] = DDIMScheduler.from_pretrained(__snake_case , subfolder='scheduler' )
a : Any = CycleDiffusionPipeline.from_pretrained(
__snake_case , scheduler=__snake_case , safety_checker=__snake_case , torch_dtype=torch.floataa , revision='fp16' )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
pipe.enable_attention_slicing()
a : Union[str, Any] = 'A black colored car'
a : Optional[Any] = 'A blue colored car'
a : int = torch.manual_seed(0 )
a : Optional[Any] = pipe(
prompt=__snake_case , source_prompt=__snake_case , image=__snake_case , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__snake_case , output_type='np' , )
a : Dict = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def lowercase_ ( self : int ):
a : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
a : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy' )
a : str = init_image.resize((5_12, 5_12) )
a : Optional[int] = 'CompVis/stable-diffusion-v1-4'
a : Union[str, Any] = DDIMScheduler.from_pretrained(__snake_case , subfolder='scheduler' )
a : str = CycleDiffusionPipeline.from_pretrained(__snake_case , scheduler=__snake_case , safety_checker=__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
pipe.enable_attention_slicing()
a : Tuple = 'A black colored car'
a : Tuple = 'A blue colored car'
a : List[str] = torch.manual_seed(0 )
a : str = pipe(
prompt=__snake_case , source_prompt=__snake_case , image=__snake_case , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__snake_case , output_type='np' , )
a : Tuple = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 96
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a__( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase__ = CycleDiffusionPipeline
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"""negative_prompt""",
"""height""",
"""width""",
"""negative_prompt_embeds""",
}
lowercase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
lowercase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase_ ( self : Any ):
torch.manual_seed(0 )
a : Union[str, Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
a : str = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , num_train_timesteps=10_00 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
a : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
a : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
a : List[str] = CLIPTextModel(__snake_case )
a : int = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
a : Tuple = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowercase_ ( self : Optional[int] , __snake_case : Dict , __snake_case : Any=0 ):
a : str = floats_tensor((1, 3, 32, 32) , rng=random.Random(__snake_case ) ).to(__snake_case )
a : Optional[Any] = image / 2 + 0.5
if str(__snake_case ).startswith('mps' ):
a : List[str] = torch.manual_seed(__snake_case )
else:
a : Union[str, Any] = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
a : List[Any] = {
'prompt': 'An astronaut riding an elephant',
'source_prompt': 'An astronaut riding a horse',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'eta': 0.1,
'strength': 0.8,
'guidance_scale': 3,
'source_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self : Optional[int] ):
a : Optional[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
a : int = self.get_dummy_components()
a : str = CycleDiffusionPipeline(**__snake_case )
a : List[str] = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
a : Dict = self.get_dummy_inputs(__snake_case )
a : Union[str, Any] = pipe(**__snake_case )
a : List[Any] = output.images
a : Optional[Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
a : Tuple = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != 'cuda' , 'This test requires a GPU' )
def lowercase_ ( self : int ):
a : List[Any] = self.get_dummy_components()
for name, module in components.items():
if hasattr(__snake_case , 'half' ):
a : Any = module.half()
a : Tuple = CycleDiffusionPipeline(**__snake_case )
a : Any = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
a : str = self.get_dummy_inputs(__snake_case )
a : int = pipe(**__snake_case )
a : Optional[int] = output.images
a : Tuple = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
a : int = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def lowercase_ ( self : List[Any] ):
return super().test_save_load_local()
@unittest.skip('non-deterministic pipeline' )
def lowercase_ ( self : Dict ):
return super().test_inference_batch_single_identical()
@skip_mps
def lowercase_ ( self : int ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowercase_ ( self : Dict ):
return super().test_save_load_optional_components()
@skip_mps
def lowercase_ ( self : List[Any] ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a__( unittest.TestCase ):
def lowercase_ ( self : Tuple ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : Optional[int] ):
a : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
a : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy' )
a : List[str] = init_image.resize((5_12, 5_12) )
a : Dict = 'CompVis/stable-diffusion-v1-4'
a : List[str] = DDIMScheduler.from_pretrained(__snake_case , subfolder='scheduler' )
a : Any = CycleDiffusionPipeline.from_pretrained(
__snake_case , scheduler=__snake_case , safety_checker=__snake_case , torch_dtype=torch.floataa , revision='fp16' )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
pipe.enable_attention_slicing()
a : Union[str, Any] = 'A black colored car'
a : Optional[Any] = 'A blue colored car'
a : int = torch.manual_seed(0 )
a : Optional[Any] = pipe(
prompt=__snake_case , source_prompt=__snake_case , image=__snake_case , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__snake_case , output_type='np' , )
a : Dict = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def lowercase_ ( self : int ):
a : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
a : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy' )
a : str = init_image.resize((5_12, 5_12) )
a : Optional[int] = 'CompVis/stable-diffusion-v1-4'
a : Union[str, Any] = DDIMScheduler.from_pretrained(__snake_case , subfolder='scheduler' )
a : str = CycleDiffusionPipeline.from_pretrained(__snake_case , scheduler=__snake_case , safety_checker=__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
pipe.enable_attention_slicing()
a : Tuple = 'A black colored car'
a : Tuple = 'A blue colored car'
a : List[str] = torch.manual_seed(0 )
a : str = pipe(
prompt=__snake_case , source_prompt=__snake_case , image=__snake_case , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__snake_case , output_type='np' , )
a : Tuple = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 96
| 1
|
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : int = len(a_ )
for _ in range(a_ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
UpperCAmelCase__ : Optional[Any] = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
__A =list(range(10, 0, -1))
print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 163
|
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :str , a_ :str) -> bool:
__a : Optional[Any] = get_failure_array(a_)
# 2) Step through text searching for pattern
__a , __a : Union[str, Any] = 0, 0 # index into text, pattern
while i < len(a_):
if pattern[j] == text[i]:
if j == (len(a_) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
__a : List[Any] = failure[j - 1]
continue
i += 1
return False
def __A ( a_ :str) -> list[int]:
__a : List[Any] = [0]
__a : List[Any] = 0
__a : Any = 1
while j < len(a_):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
__a : Any = failure[i - 1]
continue
j += 1
failure.append(a_)
return failure
if __name__ == "__main__":
# Test 1)
A = '''abc1abc12'''
A = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
A = '''alskfjaldsk23adsfabcabc'''
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
A = '''ABABX'''
A = '''ABABZABABYABABX'''
assert kmp(pattern, text)
# Test 3)
A = '''AAAB'''
A = '''ABAAAAAB'''
assert kmp(pattern, text)
# Test 4)
A = '''abcdabcy'''
A = '''abcxabcdabxabcdabcdabcy'''
assert kmp(pattern, text)
# Test 5)
A = '''aabaabaaa'''
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 160
| 0
|
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A__ :
pass
| 101
|
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCAmelCase__ ( UpperCAmelCase__ = N ) -> int:
A_ = -sys.maxsize - 1
for i in range(len(UpperCAmelCase__ ) - 12 ):
A_ = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
A_ = product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 101
| 1
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> int:
"""simple docstring"""
_UpperCAmelCase : Tuple = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_UpperCAmelCase : Stack[int] = Stack()
_UpperCAmelCase : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_UpperCAmelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(_UpperCAmelCase )
elif i == ")":
# RULE 4
_UpperCAmelCase : str = operator_stack.peek()
operator_stack.pop()
_UpperCAmelCase : List[str] = operand_stack.peek()
operand_stack.pop()
_UpperCAmelCase : List[str] = operand_stack.peek()
operand_stack.pop()
_UpperCAmelCase : List[Any] = operators[opr](_UpperCAmelCase , _UpperCAmelCase )
operand_stack.push(_UpperCAmelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : List[str] = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(F'{equation} = {dijkstras_two_stack_algorithm(equation)}')
| 31
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
'kssteven/ibert-roberta-large-mnli': (
'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
),
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """ibert"""
def __init__( self : Optional[int] , __lowercase : List[str]=3_05_22 , __lowercase : Tuple=7_68 , __lowercase : str=12 , __lowercase : Optional[int]=12 , __lowercase : Optional[Any]=30_72 , __lowercase : str="gelu" , __lowercase : List[str]=0.1 , __lowercase : List[Any]=0.1 , __lowercase : List[str]=5_12 , __lowercase : str=2 , __lowercase : Tuple=0.02 , __lowercase : Union[str, Any]=1e-12 , __lowercase : List[Any]=1 , __lowercase : List[str]=0 , __lowercase : Optional[Any]=2 , __lowercase : int="absolute" , __lowercase : Tuple=False , __lowercase : int="none" , **__lowercase : Optional[Any] , ) -> List[Any]:
super().__init__(pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Any =vocab_size
SCREAMING_SNAKE_CASE__ : Dict =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =hidden_act
SCREAMING_SNAKE_CASE__ : List[str] =intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict =type_vocab_size
SCREAMING_SNAKE_CASE__ : Tuple =initializer_range
SCREAMING_SNAKE_CASE__ : str =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Tuple =position_embedding_type
SCREAMING_SNAKE_CASE__ : Any =quant_mode
SCREAMING_SNAKE_CASE__ : Optional[int] =force_dequant
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@property
def __magic_name__ ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ : str ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE__ : Any ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 152
| 0
|
_A = range(2, 20 + 1)
_A = [10**k for k in range(ks[-1] + 1)]
_A = {}
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> int:
UpperCAmelCase__ : List[str] = sum(a_i[j] for j in range(lowerCAmelCase , len(lowerCAmelCase ) ) )
UpperCAmelCase__ : str = sum(a_i[j] * base[j] for j in range(min(len(lowerCAmelCase ) , lowerCAmelCase ) ) )
UpperCAmelCase__ : Optional[Any] = 0, 0
UpperCAmelCase__ : Optional[Any] = n - i
UpperCAmelCase__ : Union[str, Any] = memo.get(lowerCAmelCase )
if sub_memo is not None:
UpperCAmelCase__ : Any = sub_memo.get(lowerCAmelCase )
if jumps is not None and len(lowerCAmelCase ) > 0:
# find and make the largest jump without going over
UpperCAmelCase__ : Optional[int] = -1
for _k in range(len(lowerCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
UpperCAmelCase__ : str = _k
break
if max_jump >= 0:
UpperCAmelCase__ : Optional[int] = jumps[max_jump]
# since the difference between jumps is cached, add c
UpperCAmelCase__ : Any = diff + c
for j in range(min(lowerCAmelCase , len(lowerCAmelCase ) ) ):
UpperCAmelCase__ : Tuple = divmod(lowerCAmelCase , 10 )
if new_c > 0:
add(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
else:
UpperCAmelCase__ : int = []
else:
UpperCAmelCase__ : Union[str, Any] = {c: []}
UpperCAmelCase__ : Union[str, Any] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
UpperCAmelCase__ : Optional[Any] = next_term(lowerCAmelCase , k - 1 , i + dn , lowerCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
UpperCAmelCase__ : Tuple = compute(lowerCAmelCase , lowerCAmelCase , i + dn , lowerCAmelCase )
diff += _diff
dn += terms_jumped
UpperCAmelCase__ : str = sub_memo[c]
# keep jumps sorted by # of terms skipped
UpperCAmelCase__ : Any = 0
while j < len(lowerCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(lowerCAmelCase , (diff, dn, k) )
return (diff, dn)
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> List[Any]:
if i >= n:
return 0, i
if k > len(lowerCAmelCase ):
a_i.extend([0 for _ in range(k - len(lowerCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
UpperCAmelCase__ : Tuple = i
UpperCAmelCase__ : Optional[Any] = 0, 0, 0
for j in range(len(lowerCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
UpperCAmelCase__ : Dict = ds_c + ds_b
diff += addend
UpperCAmelCase__ : Tuple = 0
for j in range(lowerCAmelCase ):
UpperCAmelCase__ : Tuple = a_i[j] + addend
UpperCAmelCase__ : Optional[int] = divmod(lowerCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return diff, i - start_i
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Optional[Any]:
for j in range(lowerCAmelCase , len(lowerCAmelCase ) ):
UpperCAmelCase__ : Optional[Any] = digits[j] + addend
if s >= 10:
UpperCAmelCase__ : Dict = divmod(lowerCAmelCase , 10 )
UpperCAmelCase__ : Any = addend // 10 + quotient
else:
UpperCAmelCase__ : Optional[Any] = s
UpperCAmelCase__ : Tuple = addend // 10
if addend == 0:
break
while addend > 0:
UpperCAmelCase__ : Optional[int] = divmod(lowerCAmelCase , 10 )
digits.append(lowerCAmelCase )
def a__ ( lowerCAmelCase = 10**15 ) -> int:
UpperCAmelCase__ : Optional[int] = [1]
UpperCAmelCase__ : Union[str, Any] = 1
UpperCAmelCase__ : Dict = 0
while True:
UpperCAmelCase__ : Union[str, Any] = next_term(lowerCAmelCase , 20 , i + dn , lowerCAmelCase )
dn += terms_jumped
if dn == n - i:
break
UpperCAmelCase__ : Optional[int] = 0
for j in range(len(lowerCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'''{solution() = }''')
| 369
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_A = {
"""configuration_layoutlmv3""": [
"""LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LayoutLMv3Config""",
"""LayoutLMv3OnnxConfig""",
],
"""processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
"""tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["""LayoutLMv3TokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LayoutLMv3ForQuestionAnswering""",
"""LayoutLMv3ForSequenceClassification""",
"""LayoutLMv3ForTokenClassification""",
"""LayoutLMv3Model""",
"""LayoutLMv3PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLayoutLMv3ForQuestionAnswering""",
"""TFLayoutLMv3ForSequenceClassification""",
"""TFLayoutLMv3ForTokenClassification""",
"""TFLayoutLMv3Model""",
"""TFLayoutLMv3PreTrainedModel""",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["""LayoutLMv3FeatureExtractor"""]
_A = ["""LayoutLMv3ImageProcessor"""]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
_A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 166
| 0
|
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase__ : int = {'vocab_file': 'vocab.json'}
UpperCamelCase__ : List[str] = {
'vocab_file': {
'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
}
}
UpperCamelCase__ : Tuple = {'mgp-str': 27}
class _lowerCAmelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = VOCAB_FILES_NAMES
lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _lowerCamelCase , _lowerCamelCase="[GO]" , _lowerCamelCase="[GO]" , _lowerCamelCase="[s]" , _lowerCamelCase="[GO]" , **_lowerCamelCase ) -> Dict:
super().__init__(
unk_token=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as vocab_handle:
A_ : int = json.load(_SCREAMING_SNAKE_CASE )
A_ : Dict = {v: k for k, v in self.vocab.items()}
@property
def UpperCAmelCase_ ( self ) -> Optional[Any]:
return len(self.vocab )
def UpperCAmelCase_ ( self ) -> str:
return dict(self.vocab , **self.added_tokens_encoder )
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> List[Any]:
A_ : Tuple = []
for s in text:
char_tokens.extend(_SCREAMING_SNAKE_CASE )
return char_tokens
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Optional[int]:
return self.vocab.get(_SCREAMING_SNAKE_CASE , self.vocab.get(self.unk_token ) )
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Optional[int]:
return self.decoder.get(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ) -> Tuple[str]:
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error("""Vocabulary path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE ) )
return
A_ : Dict = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE ) + """\n""" )
return (vocab_file,)
| 344
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : int = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = [
'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
'UniSpeechForCTC',
'UniSpeechForPreTraining',
'UniSpeechForSequenceClassification',
'UniSpeechModel',
'UniSpeechPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
__A : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 154
| 0
|
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCAmelCase ( _lowerCAmelCase , unittest.TestCase ):
a__ : Any = None
a__ : Dict = BloomTokenizerFast
a__ : str = BloomTokenizerFast
a__ : Dict = True
a__ : Optional[Any] = False
a__ : str = "tokenizer_file"
a__ : str = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
def a ( self : Optional[int] ):
super().setUp()
__UpperCAmelCase = BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''' )
tokenizer.save_pretrained(self.tmpdirname )
def a ( self : int , **_lowercase : Union[str, Any] ):
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **_lowercase )
def a ( self : Any ):
__UpperCAmelCase = self.get_rust_tokenizer()
__UpperCAmelCase = ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
__UpperCAmelCase = [[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]
__UpperCAmelCase = tokenizer.batch_encode_plus(_lowercase )['''input_ids''']
self.assertListEqual(_lowercase , _lowercase )
__UpperCAmelCase = tokenizer.batch_decode(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
def a ( self : Union[str, Any] , _lowercase : List[Any]=6 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
__UpperCAmelCase = '''This is a simple input'''
__UpperCAmelCase = ['''This is a simple input 1''', '''This is a simple input 2''']
__UpperCAmelCase = ('''This is a simple input''', '''This is a pair''')
__UpperCAmelCase = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
try:
tokenizer_r.encode(_lowercase , max_length=_lowercase )
tokenizer_r.encode_plus(_lowercase , max_length=_lowercase )
tokenizer_r.batch_encode_plus(_lowercase , max_length=_lowercase )
tokenizer_r.encode(_lowercase , max_length=_lowercase )
tokenizer_r.batch_encode_plus(_lowercase , max_length=_lowercase )
except ValueError:
self.fail('''Bloom Tokenizer should be able to deal with padding''' )
__UpperCAmelCase = None # Hotfixing padding = None
self.assertRaises(_lowercase , tokenizer_r.encode , _lowercase , max_length=_lowercase , padding='''max_length''' )
# Simple input
self.assertRaises(_lowercase , tokenizer_r.encode_plus , _lowercase , max_length=_lowercase , padding='''max_length''' )
# Simple input
self.assertRaises(
_lowercase , tokenizer_r.batch_encode_plus , _lowercase , max_length=_lowercase , padding='''max_length''' , )
# Pair input
self.assertRaises(_lowercase , tokenizer_r.encode , _lowercase , max_length=_lowercase , padding='''max_length''' )
# Pair input
self.assertRaises(_lowercase , tokenizer_r.encode_plus , _lowercase , max_length=_lowercase , padding='''max_length''' )
# Pair input
self.assertRaises(
_lowercase , tokenizer_r.batch_encode_plus , _lowercase , max_length=_lowercase , padding='''max_length''' , )
def a ( self : List[str] ):
__UpperCAmelCase = self.get_rust_tokenizer()
__UpperCAmelCase = load_dataset('''xnli''' , '''all_languages''' , split='''test''' , streaming=_lowercase )
__UpperCAmelCase = next(iter(_lowercase ) )['''premise'''] # pick up one data
__UpperCAmelCase = list(sample_data.values() )
__UpperCAmelCase = list(map(tokenizer.encode , _lowercase ) )
__UpperCAmelCase = [tokenizer.decode(_lowercase , clean_up_tokenization_spaces=_lowercase ) for x in output_tokens]
self.assertListEqual(_lowercase , _lowercase )
def a ( self : Optional[Any] ):
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 358
|
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class _UpperCAmelCase :
def __init__( self : Optional[int] , _lowercase : Any , _lowercase : List[str]=14 , _lowercase : Dict=7 , _lowercase : Optional[int]=True , _lowercase : Optional[int]=True , _lowercase : Any=False , _lowercase : Any=True , _lowercase : List[str]=99 , _lowercase : int=32 , _lowercase : Union[str, Any]=4 , _lowercase : Dict=4 , _lowercase : List[Any]=4 , _lowercase : Dict=37 , _lowercase : Tuple="gelu" , _lowercase : Optional[int]=0.1 , _lowercase : Dict=0.1 , _lowercase : Union[str, Any]=5_12 , _lowercase : int=0.02 , ):
__UpperCAmelCase = parent
__UpperCAmelCase = batch_size
__UpperCAmelCase = seq_length
__UpperCAmelCase = is_training
__UpperCAmelCase = use_input_mask
__UpperCAmelCase = use_token_type_ids
__UpperCAmelCase = use_labels
__UpperCAmelCase = vocab_size
__UpperCAmelCase = hidden_size
__UpperCAmelCase = rotary_dim
__UpperCAmelCase = num_hidden_layers
__UpperCAmelCase = num_attention_heads
__UpperCAmelCase = intermediate_size
__UpperCAmelCase = hidden_act
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = max_position_embeddings
__UpperCAmelCase = initializer_range
__UpperCAmelCase = None
__UpperCAmelCase = vocab_size - 1
__UpperCAmelCase = vocab_size - 1
__UpperCAmelCase = vocab_size - 1
def a ( self : int ):
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase = None
if self.use_input_mask:
__UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCAmelCase = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=_lowercase , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def a ( self : str ):
__UpperCAmelCase = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = config_and_inputs
__UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def a ( self : List[Any] , _lowercase : Tuple , _lowercase : Optional[Any] , _lowercase : List[str] , _lowercase : List[str] ):
__UpperCAmelCase = 20
__UpperCAmelCase = model_class_name(_lowercase )
__UpperCAmelCase = model.init_cache(input_ids.shape[0] , _lowercase )
__UpperCAmelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
__UpperCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__UpperCAmelCase = model(
input_ids[:, :-1] , attention_mask=_lowercase , past_key_values=_lowercase , position_ids=_lowercase , )
__UpperCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
__UpperCAmelCase = model(
input_ids[:, -1:] , attention_mask=_lowercase , past_key_values=outputs_cache.past_key_values , position_ids=_lowercase , )
__UpperCAmelCase = model(_lowercase )
__UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''' )
def a ( self : List[Any] , _lowercase : Optional[int] , _lowercase : Any , _lowercase : Optional[int] , _lowercase : Union[str, Any] ):
__UpperCAmelCase = 20
__UpperCAmelCase = model_class_name(_lowercase )
__UpperCAmelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
__UpperCAmelCase = model.init_cache(input_ids.shape[0] , _lowercase )
__UpperCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__UpperCAmelCase = model(
input_ids[:, :-1] , attention_mask=_lowercase , past_key_values=_lowercase , position_ids=_lowercase , )
__UpperCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
__UpperCAmelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=_lowercase , position_ids=_lowercase , )
__UpperCAmelCase = model(_lowercase , attention_mask=_lowercase )
__UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''' )
@require_flax
class _UpperCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
a__ : Any = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
a__ : List[Any] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def a ( self : List[Any] ):
__UpperCAmelCase = FlaxGPTJModelTester(self )
def a ( self : Any ):
for model_class_name in self.all_model_classes:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(_lowercase , _lowercase , _lowercase , _lowercase )
def a ( self : Union[str, Any] ):
for model_class_name in self.all_model_classes:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
_lowercase , _lowercase , _lowercase , _lowercase )
@tooslow
def a ( self : Tuple ):
__UpperCAmelCase = GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
__UpperCAmelCase = tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=_lowercase , truncation=_lowercase )
__UpperCAmelCase = FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
__UpperCAmelCase = False
__UpperCAmelCase = model.config.eos_token_id
__UpperCAmelCase = jax.jit(model.generate )
__UpperCAmelCase = jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
__UpperCAmelCase = tokenizer.batch_decode(_lowercase , skip_special_tokens=_lowercase )
__UpperCAmelCase = [
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(_lowercase , _lowercase )
@is_pt_flax_cross_test
def a ( self : Tuple ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
__UpperCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__UpperCAmelCase = getattr(_lowercase , _lowercase )
__UpperCAmelCase , __UpperCAmelCase = pt_inputs['''input_ids'''].shape
__UpperCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_lowercase ):
__UpperCAmelCase = 0
__UpperCAmelCase = 1
__UpperCAmelCase = 0
__UpperCAmelCase = 1
__UpperCAmelCase = pt_model_class(_lowercase ).eval()
__UpperCAmelCase = model_class(_lowercase , dtype=jnp.floataa )
__UpperCAmelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _lowercase )
__UpperCAmelCase = fx_state
with torch.no_grad():
__UpperCAmelCase = pt_model(**_lowercase ).to_tuple()
__UpperCAmelCase = fx_model(**_lowercase ).to_tuple()
self.assertEqual(len(_lowercase ) , len(_lowercase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(_lowercase , _lowercase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_lowercase )
__UpperCAmelCase = model_class.from_pretrained(_lowercase , from_pt=_lowercase )
__UpperCAmelCase = fx_model_loaded(**_lowercase ).to_tuple()
self.assertEqual(
len(_lowercase ) , len(_lowercase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(_lowercase , _lowercase ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def a ( self : Any ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
__UpperCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__UpperCAmelCase = getattr(_lowercase , _lowercase )
__UpperCAmelCase = pt_model_class(_lowercase ).eval()
__UpperCAmelCase = model_class(_lowercase , dtype=jnp.floataa )
__UpperCAmelCase = load_flax_weights_in_pytorch_model(_lowercase , fx_model.params )
__UpperCAmelCase , __UpperCAmelCase = pt_inputs['''input_ids'''].shape
__UpperCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_lowercase ):
__UpperCAmelCase = 0
__UpperCAmelCase = 1
__UpperCAmelCase = 0
__UpperCAmelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
__UpperCAmelCase = pt_model(**_lowercase ).to_tuple()
__UpperCAmelCase = fx_model(**_lowercase ).to_tuple()
self.assertEqual(len(_lowercase ) , len(_lowercase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(_lowercase , _lowercase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_lowercase )
__UpperCAmelCase = pt_model_class.from_pretrained(_lowercase , from_flax=_lowercase )
with torch.no_grad():
__UpperCAmelCase = pt_model_loaded(**_lowercase ).to_tuple()
self.assertEqual(
len(_lowercase ) , len(_lowercase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(_lowercase , _lowercase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def a ( self : Tuple ):
for model_class_name in self.all_model_classes:
__UpperCAmelCase = model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
__UpperCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
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