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'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase (__A = 2_000_000):
"""simple docstring"""
_a = [0]
_a = 42
for idx in range(1 , ceil(sqrt(target * 2) * 1.1)):
triangle_numbers.append(triangle_numbers[-1] + idx)
# we want this to be as close as possible to target
_a = 0
# the area corresponding to the grid that gives the product closest to target
_a = 0
# an estimate of b, using the quadratic formula
_a = 42
# the largest integer less than b_estimate
_a = 42
# the largest integer less than b_estimate
_a = 42
# the triangle number corresponding to b_floor
_a = 42
# the triangle number corresponding to b_ceil
_a = 42
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1):
_a = (-1 + sqrt(1 + 8 * target / triangle_a)) / 2
_a = floor(__A)
_a = ceil(__A)
_a = triangle_numbers[b_floor]
_a = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a) < abs(
target - best_product):
_a = triangle_b_first_guess * triangle_a
_a = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a) < abs(
target - best_product):
_a = triangle_b_second_guess * triangle_a
_a = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"""{solution() = }""")
| 211
|
'''simple docstring'''
import random
def lowerCAmelCase (__A):
"""simple docstring"""
_a = num - 1
_a = 0
while s % 2 == 0:
_a = s // 2
t += 1
for _ in range(5):
_a = random.randrange(2 , num - 1)
_a = pow(__A , __A , __A)
if v != 1:
_a = 0
while v != (num - 1):
if i == t - 1:
return False
else:
_a = i + 1
_a = (v**2) % num
return True
def lowerCAmelCase (__A):
"""simple docstring"""
if num < 2:
return False
_a = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(__A)
def lowerCAmelCase (__A = 1_024):
"""simple docstring"""
while True:
_a = random.randrange(2 ** (keysize - 1) , 2 ** (keysize))
if is_prime_low_num(__A):
return num
if __name__ == "__main__":
lowercase_ = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| 211
| 1
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCamelCase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( UpperCAmelCase__ ):
def __init__( self : Any , *lowerCamelCase__ : Union[str, Any] , **lowerCamelCase__ : Optional[int] ) ->None:
'''simple docstring'''
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__ )
| 322
|
'''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
| 1
|
"""simple docstring"""
import functools
from typing import Any
def lowercase ( _snake_case : str , _snake_case : list[str] ) ->bool:
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ) or len(_snake_case ) == 0:
raise ValueError('''the string should be not empty string''' )
if not isinstance(_snake_case , _snake_case ) or not all(
isinstance(_snake_case , _snake_case ) and len(_snake_case ) > 0 for item in words ):
raise ValueError('''the words should be a list of non-empty strings''' )
# Build trie
__snake_case : dict[str, Any] = {}
__snake_case : int = '''WORD_KEEPER'''
for word in words:
__snake_case : List[Any] = trie
for c in word:
if c not in trie_node:
__snake_case : Union[str, Any] = {}
__snake_case : List[str] = trie_node[c]
__snake_case : List[str] = True
__snake_case : Dict = len(_snake_case )
# Dynamic programming method
@functools.cache
def is_breakable(_snake_case : int ) -> bool:
if index == len_string:
return True
__snake_case : List[str] = trie
for i in range(_snake_case , _snake_case ):
__snake_case : Dict = trie_node.get(string[i] , _snake_case )
if trie_node is None:
return False
if trie_node.get(_snake_case , _snake_case ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102
|
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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A : str = logging.get_logger(__name__)
def a__ ( __UpperCamelCase , __UpperCamelCase=False ):
SCREAMING_SNAKE_CASE_ = []
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'''deit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "deit.embeddings.cls_token"),
("dist_token", "deit.embeddings.distillation_token"),
("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "deit.embeddings.position_embeddings"),
] )
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 "deit" from all keys that start with "deit"
SCREAMING_SNAKE_CASE_ = [(pair[0], pair[1][4:]) if pair[1].startswith("deit" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("norm.weight", "deit.layernorm.weight"),
("norm.bias", "deit.layernorm.bias"),
("head.weight", "cls_classifier.weight"),
("head.bias", "cls_classifier.bias"),
("head_dist.weight", "distillation_classifier.weight"),
("head_dist.bias", "distillation_classifier.bias"),
] )
return rename_keys
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False ):
for i in range(config.num_hidden_layers ):
if base_model:
SCREAMING_SNAKE_CASE_ = ""
else:
SCREAMING_SNAKE_CASE_ = "deit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE_ = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE_ = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE_ = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE_ = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE_ = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE_ = in_proj_bias[-config.hidden_size :]
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = dct.pop(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = val
def a__ ( ):
SCREAMING_SNAKE_CASE_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE_ = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = DeiTConfig()
# all deit models have fine-tuned heads
SCREAMING_SNAKE_CASE_ = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
SCREAMING_SNAKE_CASE_ = 1_0_0_0
SCREAMING_SNAKE_CASE_ = "huggingface/label-files"
SCREAMING_SNAKE_CASE_ = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE_ = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = int(deit_name[-6:-4] )
SCREAMING_SNAKE_CASE_ = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("tiny" ):
SCREAMING_SNAKE_CASE_ = 1_9_2
SCREAMING_SNAKE_CASE_ = 7_6_8
SCREAMING_SNAKE_CASE_ = 1_2
SCREAMING_SNAKE_CASE_ = 3
elif deit_name[9:].startswith("small" ):
SCREAMING_SNAKE_CASE_ = 3_8_4
SCREAMING_SNAKE_CASE_ = 1_5_3_6
SCREAMING_SNAKE_CASE_ = 1_2
SCREAMING_SNAKE_CASE_ = 6
if deit_name[9:].startswith("base" ):
pass
elif deit_name[4:].startswith("large" ):
SCREAMING_SNAKE_CASE_ = 1_0_2_4
SCREAMING_SNAKE_CASE_ = 4_0_9_6
SCREAMING_SNAKE_CASE_ = 2_4
SCREAMING_SNAKE_CASE_ = 1_6
# load original model from timm
SCREAMING_SNAKE_CASE_ = timm.create_model(__UpperCamelCase , pretrained=__UpperCamelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE_ = timm_model.state_dict()
SCREAMING_SNAKE_CASE_ = create_rename_keys(__UpperCamelCase , __UpperCamelCase )
for src, dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
read_in_q_k_v(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# load HuggingFace model
SCREAMING_SNAKE_CASE_ = DeiTForImageClassificationWithTeacher(__UpperCamelCase ).eval()
model.load_state_dict(__UpperCamelCase )
# Check outputs on an image, prepared by DeiTImageProcessor
SCREAMING_SNAKE_CASE_ = int(
(2_5_6 / 2_2_4) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
SCREAMING_SNAKE_CASE_ = DeiTImageProcessor(size=__UpperCamelCase , crop_size=config.image_size )
SCREAMING_SNAKE_CASE_ = image_processor(images=prepare_img() , return_tensors="pt" )
SCREAMING_SNAKE_CASE_ = encoding["pixel_values"]
SCREAMING_SNAKE_CASE_ = model(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = timm_model(__UpperCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__UpperCamelCase , outputs.logits , atol=1E-3 )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__UpperCamelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--deit_name",
default="vit_deit_base_distilled_patch16_224",
type=str,
help="Name of the DeiT 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."
)
A : Dict = parser.parse_args()
convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 118
| 0
|
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
_lowerCamelCase : Dict = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def _UpperCAmelCase (UpperCamelCase_ : List[Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int ):
'''simple docstring'''
for attribute in key.split(""".""" ):
_lowerCAmelCase : Optional[int] = getattr(UpperCamelCase_ , UpperCamelCase_ )
if weight_type is not None:
_lowerCAmelCase : Optional[Any] = getattr(UpperCamelCase_ , UpperCamelCase_ ).shape
else:
_lowerCAmelCase : Any = hf_pointer.shape
assert hf_shape == value.shape, (
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}"
)
if weight_type == "weight":
_lowerCAmelCase : Tuple = value
elif weight_type == "weight_g":
_lowerCAmelCase : Optional[int] = value
elif weight_type == "weight_v":
_lowerCAmelCase : str = value
elif weight_type == "bias":
_lowerCAmelCase : str = value
else:
_lowerCAmelCase : Tuple = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def _UpperCAmelCase (UpperCamelCase_ : List[Any] , UpperCamelCase_ : Union[str, Any] ):
'''simple docstring'''
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : Tuple = fairseq_model.state_dict()
_lowerCAmelCase : str = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
_lowerCAmelCase : Any = None
for name, value in fairseq_dict.items():
_lowerCAmelCase : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase : Any = True
elif name.split(""".""" )[0] == "proj":
_lowerCAmelCase : Union[str, Any] = fairseq_model.proj
_lowerCAmelCase : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase : Union[str, Any] = True
if "*" in mapped_key:
_lowerCAmelCase : Union[str, Any] = name.split(UpperCamelCase_ )[0].split(""".""" )[-2]
_lowerCAmelCase : Optional[Any] = mapped_key.replace("""*""" , UpperCamelCase_ )
if "weight_g" in name:
_lowerCAmelCase : List[str] = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase : Tuple = """weight_v"""
elif "bias" in name:
_lowerCAmelCase : Dict = """bias"""
elif "weight" in name:
_lowerCAmelCase : Optional[int] = """weight"""
else:
_lowerCAmelCase : Optional[Any] = None
set_recursively(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
continue
if not is_used:
unused_weights.append(UpperCamelCase_ )
logger.warning(F"Unused weights: {unused_weights}" )
return proj_weight
def _UpperCAmelCase (UpperCamelCase_ : Any , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : str , UpperCamelCase_ : Tuple , UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase : List[str] = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase : Optional[int] = name.split(""".""" )
_lowerCAmelCase : List[str] = int(items[0] )
_lowerCAmelCase : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
_lowerCAmelCase : List[Any] = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
_lowerCAmelCase : Any = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
_lowerCAmelCase : Tuple = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
_lowerCAmelCase : Union[str, Any] = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(UpperCamelCase_ )
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = emb.weight.shape
_lowerCAmelCase : Union[str, Any] = nn.Linear(UpperCamelCase_ , UpperCamelCase_ , bias=UpperCamelCase_ )
_lowerCAmelCase : List[Any] = emb.weight.data
return lin_layer
def _UpperCAmelCase (UpperCamelCase_ : Any ):
'''simple docstring'''
with open(UpperCamelCase_ , """r""" , encoding="""utf-8""" ) as f:
_lowerCAmelCase : Optional[int] = f.readlines()
_lowerCAmelCase : Dict = [line.split(""" """ )[0] for line in lines]
_lowerCAmelCase : Dict = len(UpperCamelCase_ )
_lowerCAmelCase : Optional[int] = {
"""<s>""": 0,
"""<pad>""": 1,
"""</s>""": 2,
"""<unk>""": 3,
}
vocab_dict.update(dict(zip(UpperCamelCase_ , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def _UpperCAmelCase (UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[str] , ):
'''simple docstring'''
_lowerCAmelCase : Any = WavaVecaConfig.from_pretrained(UpperCamelCase_ )
_lowerCAmelCase : Union[str, Any] = SpeechaTextaConfig.from_pretrained(
UpperCamelCase_ , vocab_size=UpperCamelCase_ , decoder_layers=UpperCamelCase_ , do_stable_layer_norm=UpperCamelCase_ )
_lowerCAmelCase : List[str] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
_lowerCAmelCase : Tuple = model[0].eval()
# set weights for wav2vec2 encoder
_lowerCAmelCase : Union[str, Any] = WavaVecaModel(UpperCamelCase_ )
_lowerCAmelCase : Union[str, Any] = recursively_load_weights_wavaveca(model.encoder , UpperCamelCase_ )
_lowerCAmelCase : List[str] = SpeechaTextaForCausalLM(UpperCamelCase_ )
_lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=UpperCamelCase_ )
# set output linear layer
unexpected_keys.remove("""embed_out""" )
_lowerCAmelCase : Dict = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}" )
logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}" )
_lowerCAmelCase : List[Any] = SpeechEncoderDecoderModel(encoder=UpperCamelCase_ , decoder=UpperCamelCase_ )
_lowerCAmelCase : Any = False
# add projection layer
_lowerCAmelCase : List[Any] = nn.Parameter(projection_layer.weight )
_lowerCAmelCase : Union[str, Any] = nn.Parameter(projection_layer.bias )
_lowerCAmelCase : Any = create_vocab_dict(UpperCamelCase_ )
with open(os.path.join(UpperCamelCase_ , """vocab.json""" ) , """w""" ) as fp:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
_lowerCAmelCase : Any = SpeechaTextaTokenizer(os.path.join(UpperCamelCase_ , """vocab.json""" ) )
tokenizer.save_pretrained(UpperCamelCase_ )
_lowerCAmelCase : str = hf_wavavec.config.to_dict()
_lowerCAmelCase : Any = tokenizer.pad_token_id
_lowerCAmelCase : List[str] = tokenizer.bos_token_id
_lowerCAmelCase : Any = tokenizer.eos_token_id
_lowerCAmelCase : Union[str, Any] = """speech_to_text_2"""
_lowerCAmelCase : Any = """wav2vec2"""
_lowerCAmelCase : str = SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_ )
hf_wavavec.save_pretrained(UpperCamelCase_ )
feature_extractor.save_pretrained(UpperCamelCase_ )
if __name__ == "__main__":
_lowerCamelCase : int = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=1_0_2_2_4, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
_lowerCamelCase : Optional[Any] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 159
|
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowerCamelCase : List[str] = logging.get_logger(__name__)
class __snake_case (_a ):
def __init__( self : Optional[Any] , *_UpperCAmelCase : str , **_UpperCAmelCase : Any ) -> None:
'''simple docstring'''
warnings.warn(
"""The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use GLPNImageProcessor instead.""" , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 159
| 1
|
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")
# TF training parameters
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> List[str]:
'''simple docstring'''
return TrainCommand(SCREAMING_SNAKE_CASE_ )
class a__ ( snake_case ):
"""simple docstring"""
@staticmethod
def UpperCamelCase ( lowercase ) -> Any:
'''simple docstring'''
A__ = parser.add_parser("train" , help="CLI tool to train a model on a task." )
train_parser.add_argument(
"--train_data" , type=lowercase , required=lowercase , help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences." , )
train_parser.add_argument(
"--column_label" , type=lowercase , default=0 , help="Column of the dataset csv file with example labels." )
train_parser.add_argument(
"--column_text" , type=lowercase , default=1 , help="Column of the dataset csv file with example texts." )
train_parser.add_argument(
"--column_id" , type=lowercase , default=2 , help="Column of the dataset csv file with example ids." )
train_parser.add_argument(
"--skip_first_row" , action="store_true" , help="Skip the first row of the csv file (headers)." )
train_parser.add_argument("--validation_data" , type=lowercase , default="" , help="path to validation dataset." )
train_parser.add_argument(
"--validation_split" , type=lowercase , default=0.1 , help="if validation dataset is not provided, fraction of train dataset to use as validation dataset." , )
train_parser.add_argument("--output" , type=lowercase , default="./" , help="path to saved the trained model." )
train_parser.add_argument(
"--task" , type=lowercase , default="text_classification" , help="Task to train the model on." )
train_parser.add_argument(
"--model" , type=lowercase , default="bert-base-uncased" , help="Model's name or path to stored model." )
train_parser.add_argument("--train_batch_size" , type=lowercase , default=32 , help="Batch size for training." )
train_parser.add_argument("--valid_batch_size" , type=lowercase , default=64 , help="Batch size for validation." )
train_parser.add_argument("--learning_rate" , type=lowercase , default=3e-5 , help="Learning rate." )
train_parser.add_argument("--adam_epsilon" , type=lowercase , default=1e-08 , help="Epsilon for Adam optimizer." )
train_parser.set_defaults(func=lowercase )
def __init__( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = logging.get_logger("transformers-cli/training" )
A__ = "tf" if is_tf_available() else "torch"
os.makedirs(args.output , exist_ok=lowercase )
A__ = args.output
A__ = args.column_label
A__ = args.column_text
A__ = args.column_id
self.logger.info(F'Loading {args.task} pipeline for {args.model}' )
if args.task == "text_classification":
A__ = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'Loading dataset from {args.train_data}' )
A__ = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
A__ = None
if args.validation_data:
self.logger.info(F'Loading validation dataset from {args.validation_data}' )
A__ = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
A__ = args.validation_split
A__ = args.train_batch_size
A__ = args.valid_batch_size
A__ = args.learning_rate
A__ = args.adam_epsilon
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
raise NotImplementedError
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 68
|
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( A_ : Tuple, A_ : int, A_ : Dict ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = LxmertConfig.from_json_file(A_ )
print(F'''Building PyTorch model from configuration: {config}''' )
_lowerCamelCase : List[str] = LxmertForPreTraining(A_ )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(A_, A_, A_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict(), A_ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowerCAmelCase__ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 72
| 0
|
"""simple docstring"""
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
_SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
class __a ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = ['input_features']
def __init__( self : Union[str, Any] , lowercase_ : Optional[int]=80 , lowercase_ : List[str]=1_6000 , lowercase_ : Union[str, Any]=160 , lowercase_ : List[str]=30 , lowercase_ : Any=400 , lowercase_ : Optional[int]=0.0 , lowercase_ : List[Any]=False , **lowercase_ : List[str] , ):
super().__init__(
feature_size=lowercase_ , sampling_rate=lowercase_ , padding_value=lowercase_ , return_attention_mask=lowercase_ , **lowercase_ , )
UpperCamelCase__ : int =n_fft
UpperCamelCase__ : int =hop_length
UpperCamelCase__ : Union[str, Any] =chunk_length
UpperCamelCase__ : Tuple =chunk_length * sampling_rate
UpperCamelCase__ : Any =self.n_samples // hop_length
UpperCamelCase__ : Tuple =sampling_rate
UpperCamelCase__ : Union[str, Any] =mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowercase_ , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=lowercase_ , norm='''slaney''' , mel_scale='''slaney''' , )
def _lowerCAmelCase ( self : Tuple , lowercase_ : np.array ):
UpperCamelCase__ : Any =spectrogram(
lowercase_ , 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''' , )
UpperCamelCase__ : Any =log_spec[:, :-1]
UpperCamelCase__ : str =np.maximum(lowercase_ , log_spec.max() - 8.0 )
UpperCamelCase__ : Tuple =(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 _lowerCAmelCase ( lowercase_ : List[np.ndarray] , lowercase_ : List[np.ndarray] , lowercase_ : float = 0.0 ):
if attention_mask is not None:
UpperCamelCase__ : Optional[int] =np.array(lowercase_ , np.intaa )
UpperCamelCase__ : Optional[Any] =[]
for vector, length in zip(lowercase_ , attention_mask.sum(-1 ) ):
UpperCamelCase__ : Dict =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
UpperCamelCase__ : Optional[int] =padding_value
normed_input_values.append(lowercase_ )
else:
UpperCamelCase__ : Tuple =[(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self : Dict , lowercase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , lowercase_ : bool = True , lowercase_ : Optional[int] = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : Optional[bool] = None , lowercase_ : Optional[str] = "max_length" , lowercase_ : Optional[int] = None , lowercase_ : Optional[int] = None , lowercase_ : Optional[bool] = None , **lowercase_ : Any , ):
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.''' )
UpperCamelCase__ : str =isinstance(lowercase_ , 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}''' )
UpperCamelCase__ : Optional[int] =is_batched_numpy or (
isinstance(lowercase_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCamelCase__ : Union[str, Any] =[np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowercase_ , np.ndarray ):
UpperCamelCase__ : Union[str, Any] =np.asarray(lowercase_ , dtype=np.floataa )
elif isinstance(lowercase_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCamelCase__ : int =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCamelCase__ : List[str] =[np.asarray([raw_speech] ).T]
UpperCamelCase__ : Union[str, Any] =BatchFeature({'''input_features''': raw_speech} )
# convert into correct format for padding
UpperCamelCase__ : Tuple =self.pad(
lowercase_ , padding=lowercase_ , max_length=max_length if max_length else self.n_samples , truncation=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
UpperCamelCase__ : Any =self.zero_mean_unit_var_norm(
padded_inputs['''input_features'''] , attention_mask=padded_inputs['''attention_mask'''] , padding_value=self.padding_value , )
UpperCamelCase__ : int =np.stack(padded_inputs['''input_features'''] , axis=0 )
# make sure list is in array format
UpperCamelCase__ : List[str] =padded_inputs.get('''input_features''' ).transpose(2 , 0 , 1 )
UpperCamelCase__ : Optional[int] =[self._np_extract_fbank_features(lowercase_ ) for waveform in input_features[0]]
if isinstance(input_features[0] , lowercase_ ):
UpperCamelCase__ : int =[np.asarray(lowercase_ , dtype=np.floataa ) for feature in input_features]
else:
UpperCamelCase__ : Any =input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
UpperCamelCase__ : Optional[Any] =padded_inputs['''attention_mask'''][:, :: self.hop_length]
if return_tensors is not None:
UpperCamelCase__ : Any =padded_inputs.convert_to_tensors(lowercase_ )
return padded_inputs
def _lowerCAmelCase ( self : Union[str, Any] ):
UpperCamelCase__ : Tuple =copy.deepcopy(self.__dict__ )
UpperCamelCase__ : Any =self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 359
|
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
def _lowerCAmelCase ( UpperCAmelCase : bool , UpperCAmelCase : bool ):
'''simple docstring'''
def run_func(UpperCAmelCase : List[str] ):
@wraps(UpperCAmelCase )
def run_in_eager_mode(*UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Dict ):
return func(*UpperCAmelCase , **UpperCAmelCase )
@wraps(UpperCAmelCase )
@tf.function(experimental_compile=UpperCAmelCase )
def run_in_graph_mode(*UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Tuple ):
return func(*UpperCAmelCase , **UpperCAmelCase )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
'''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.''' )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def _lowerCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : int ):
'''simple docstring'''
UpperCamelCase__ : Tuple =random.Random()
UpperCamelCase__ : List[str] =[rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(UpperCAmelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __a ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = "TensorFlow"
@property
def _lowerCAmelCase ( self : int ):
return tf.__version__
def _lowerCAmelCase ( self : List[str] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
# initialize GPU on separate process
UpperCamelCase__ : Optional[int] =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
UpperCamelCase__ : str =self._prepare_inference_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_speed(_inference )
def _lowerCAmelCase ( self : str , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
UpperCamelCase__ : List[str] =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
UpperCamelCase__ : int =self._prepare_train_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_speed(_train )
def _lowerCAmelCase ( self : Any , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowercase_ )
UpperCamelCase__ : Union[str, Any] =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
UpperCamelCase__ : Optional[Any] =self._prepare_inference_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_memory(_inference )
def _lowerCAmelCase ( self : Dict , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowercase_ )
UpperCamelCase__ : Tuple =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
UpperCamelCase__ : List[Any] =self._prepare_train_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_memory(_train )
def _lowerCAmelCase ( self : Union[str, Any] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
UpperCamelCase__ : Optional[Any] =self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
UpperCamelCase__ : Dict =(
hasattr(lowercase_ , '''architectures''' )
and isinstance(config.architectures , lowercase_ )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCamelCase__ : Dict ='''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCamelCase__ : List[str] =__import__('''transformers''' , fromlist=[model_class] )
UpperCamelCase__ : Optional[int] =getattr(lowercase_ , lowercase_ )
UpperCamelCase__ : Optional[int] =model_cls(lowercase_ )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
UpperCamelCase__ : Any =TF_MODEL_MAPPING[config.__class__](lowercase_ )
# encoder-decoder has vocab size saved differently
UpperCamelCase__ : Optional[int] =config.vocab_size if hasattr(lowercase_ , '''vocab_size''' ) else config.encoder.vocab_size
UpperCamelCase__ : List[Any] =random_input_ids(lowercase_ , lowercase_ , lowercase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowercase_ , decoder_input_ids=lowercase_ , training=lowercase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowercase_ , training=lowercase_ )
UpperCamelCase__ : Dict =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def _lowerCAmelCase ( self : Dict , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
UpperCamelCase__ : List[str] =self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.''' )
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
UpperCamelCase__ : Optional[Any] =(
hasattr(lowercase_ , '''architectures''' )
and isinstance(config.architectures , lowercase_ )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCamelCase__ : Tuple ='''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCamelCase__ : List[Any] =__import__('''transformers''' , fromlist=[model_class] )
UpperCamelCase__ : Dict =getattr(lowercase_ , lowercase_ )
UpperCamelCase__ : Tuple =model_cls(lowercase_ )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
UpperCamelCase__ : Optional[int] =TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase_ )
# encoder-decoder has vocab size saved differently
UpperCamelCase__ : str =config.vocab_size if hasattr(lowercase_ , '''vocab_size''' ) else config.encoder.vocab_size
UpperCamelCase__ : Union[str, Any] =random_input_ids(lowercase_ , lowercase_ , lowercase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
UpperCamelCase__ : Optional[Any] =model(lowercase_ , decoder_input_ids=lowercase_ , labels=lowercase_ , training=lowercase_ )[0]
UpperCamelCase__ : Dict =tf.gradients(lowercase_ , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
UpperCamelCase__ : Dict =model(lowercase_ , labels=lowercase_ , training=lowercase_ )[0]
UpperCamelCase__ : List[str] =tf.gradients(lowercase_ , model.trainable_variables )
return gradients
UpperCamelCase__ : List[Any] =encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def _lowerCAmelCase ( self : Tuple , lowercase_ : Union[str, Any] ):
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation''' )
timeit.repeat(lowercase_ , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
UpperCamelCase__ : int =timeit.repeat(
lowercase_ , repeat=self.args.repeat , number=10 , )
return min(lowercase_ ) / 1_0.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def _lowerCAmelCase ( self : Dict , lowercase_ : Callable[[], None] ):
logger.info(
'''Note that TensorFlow allocates more memory than '''
'''it might need to speed up computation. '''
'''The memory reported here corresponds to the memory '''
'''reported by `nvidia-smi`, which can vary depending '''
'''on total available memory on the GPU that is used.''' )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
'''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
''' consumption line by line.''' )
UpperCamelCase__ : Tuple =start_memory_tracing('''transformers''' )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
'''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
''' with `args.memory=False`''' )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
'''py3nvml not installed, we won\'t log GPU memory usage. '''
'''Install py3nvml (pip install py3nvml) to log information about GPU.''' )
UpperCamelCase__ : List[str] ='''N/A'''
else:
logger.info(
'''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
''' running on the same GPU.''' )
# init nvml
nvml.nvmlInit()
func()
UpperCamelCase__ : Optional[Any] =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
UpperCamelCase__ : Dict =nvml.nvmlDeviceGetMemoryInfo(lowercase_ )
UpperCamelCase__ : str =meminfo.used
UpperCamelCase__ : int =Memory(lowercase_ )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
'''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
''' TensorFlow.''' )
UpperCamelCase__ : Union[str, Any] =None
else:
UpperCamelCase__ : Optional[int] =measure_peak_memory_cpu(lowercase_ )
UpperCamelCase__ : Dict =Memory(lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else memory_bytes
if self.args.trace_memory_line_by_line:
UpperCamelCase__ : Tuple =stop_memory_tracing(lowercase_ )
if memory is None:
UpperCamelCase__ : List[Any] =summary.total
else:
UpperCamelCase__ : List[Any] =None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 157
| 0
|
"""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
_UpperCamelCase : List[str] = logging.get_logger(__name__)
def a_ ( _lowerCAmelCase : Tuple ):
'''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_ ( _a):
lowerCamelCase__ : Any = ["pixel_values"]
def __init__( self , a = True , a = None , a = PILImageResampling.BILINEAR , a = True , a = None , a = True , a = 1 / 2_5_5 , a = True , a = True , a = None , a = None , **a , ) -> None:
super().__init__(**a )
lowercase__ : List[Any] = size if size is not None else {'shortest_edge': 2_5_6}
lowercase__ : Dict = get_size_dict(a , default_to_square=a )
lowercase__ : Dict = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
lowercase__ : int = get_size_dict(a , param_name='crop_size' )
lowercase__ : str = do_resize
lowercase__ : Dict = size
lowercase__ : Optional[int] = do_center_crop
lowercase__ : Any = crop_size
lowercase__ : Optional[int] = resample
lowercase__ : str = do_rescale
lowercase__ : str = rescale_factor
lowercase__ : Tuple = offset
lowercase__ : List[Any] = do_normalize
lowercase__ : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _UpperCAmelCase ( self , a , a , a = PILImageResampling.BILINEAR , a = None , **a , ) -> np.ndarray:
lowercase__ : str = get_size_dict(a , default_to_square=a )
if "shortest_edge" in size:
lowercase__ : Optional[Any] = get_resize_output_image_size(a , size['shortest_edge'] , default_to_square=a )
elif "height" in size and "width" in size:
lowercase__ : Union[str, Any] = (size['height'], size['width'])
else:
raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(a , size=a , resample=a , data_format=a , **a )
def _UpperCAmelCase ( self , a , a , a = None , **a , ) -> np.ndarray:
lowercase__ : Optional[Any] = get_size_dict(a )
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(a , size=(size['height'], size['width']) , data_format=a , **a )
def _UpperCAmelCase ( self , a , a , a = True , a = None , **a , ) -> Any:
lowercase__ : Union[str, Any] = image.astype(np.floataa )
if offset:
lowercase__ : Optional[int] = image - (scale / 2)
return rescale(a , scale=a , data_format=a , **a )
def _UpperCAmelCase ( self , a , a , a , a = None , **a , ) -> np.ndarray:
return normalize(a , mean=a , std=a , data_format=a , **a )
def _UpperCAmelCase ( self , a , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = 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.
lowercase__ : List[Any] = to_numpy_array(a )
if do_resize:
lowercase__ : int = self.resize(image=a , size=a , resample=a )
if do_center_crop:
lowercase__ : Optional[Any] = self.center_crop(a , size=a )
if do_rescale:
lowercase__ : Union[str, Any] = self.rescale(image=a , scale=a , offset=a )
if do_normalize:
lowercase__ : List[Any] = self.normalize(image=a , mean=a , std=a )
lowercase__ : Optional[Any] = to_channel_dimension_format(a , a )
return image
def _UpperCAmelCase ( self , a , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = ChannelDimension.FIRST , **a , ) -> PIL.Image.Image:
lowercase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase__ : List[Any] = resample if resample is not None else self.resample
lowercase__ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Dict = offset if offset is not None else self.offset
lowercase__ : Tuple = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Tuple = image_mean if image_mean is not None else self.image_mean
lowercase__ : Any = image_std if image_std is not None else self.image_std
lowercase__ : Optional[int] = size if size is not None else self.size
lowercase__ : List[Any] = get_size_dict(a , default_to_square=a )
lowercase__ : Dict = crop_size if crop_size is not None else self.crop_size
lowercase__ : List[str] = get_size_dict(a , param_name='crop_size' )
if not valid_images(a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
lowercase__ : str = make_batched(a )
lowercase__ : List[Any] = [
[
self._preprocess_image(
image=a , do_resize=a , size=a , resample=a , do_center_crop=a , crop_size=a , do_rescale=a , rescale_factor=a , offset=a , do_normalize=a , image_mean=a , image_std=a , data_format=a , )
for img in video
]
for video in videos
]
lowercase__ : Optional[Any] = {'pixel_values': videos}
return BatchFeature(data=a , tensor_type=a )
| 77
|
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : int = args.pruning_method
lowercase__ : Tuple = args.threshold
lowercase__ : str = args.model_name_or_path.rstrip('/' )
lowercase__ : List[Any] = args.target_model_path
print(f"""Load fine-pruned model from {model_name_or_path}""" )
lowercase__ : Optional[Any] = torch.load(os.path.join(_lowerCAmelCase , 'pytorch_model.bin' ) )
lowercase__ : List[str] = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
lowercase__ : Tuple = tensor
print(f"""Copied layer {name}""" )
elif "classifier" in name or "qa_output" in name:
lowercase__ : List[str] = tensor
print(f"""Copied layer {name}""" )
elif "bias" in name:
lowercase__ : Optional[Any] = tensor
print(f"""Copied layer {name}""" )
else:
if pruning_method == "magnitude":
lowercase__ : Optional[Any] = MagnitudeBinarizer.apply(inputs=_lowerCAmelCase , threshold=_lowerCAmelCase )
lowercase__ : Optional[int] = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
lowercase__ : Optional[Any] = name[:-6]
lowercase__ : Optional[int] = model[f"""{prefix_}mask_scores"""]
lowercase__ : Any = TopKBinarizer.apply(_lowerCAmelCase , _lowerCAmelCase )
lowercase__ : List[Any] = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
lowercase__ : Any = name[:-6]
lowercase__ : Optional[Any] = model[f"""{prefix_}mask_scores"""]
lowercase__ : Tuple = ThresholdBinarizer.apply(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase__ : List[str] = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
lowercase__ : Union[str, Any] = name[:-6]
lowercase__ : Optional[int] = model[f"""{prefix_}mask_scores"""]
lowercase__ , lowercase__ : Tuple = -0.1, 1.1
lowercase__ : Optional[Any] = torch.sigmoid(_lowerCAmelCase )
lowercase__ : Optional[Any] = s * (r - l) + l
lowercase__ : Optional[Any] = s_bar.clamp(min=0.0 , max=1.0 )
lowercase__ : Union[str, Any] = tensor * mask
print(f"""Pruned layer {name}""" )
else:
raise ValueError('Unknown pruning method' )
if target_model_path is None:
lowercase__ : Union[str, Any] = os.path.join(
os.path.dirname(_lowerCAmelCase ) , f"""bertarized_{os.path.basename(_lowerCAmelCase )}""" )
if not os.path.isdir(_lowerCAmelCase ):
shutil.copytree(_lowerCAmelCase , _lowerCAmelCase )
print(f"""\nCreated folder {target_model_path}""" )
torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , 'pytorch_model.bin' ) )
print('\nPruned model saved! See you later!' )
if __name__ == "__main__":
_UpperCamelCase : int = 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",
)
_UpperCamelCase : Dict = parser.parse_args()
main(args)
| 77
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : List[str] = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientFormerConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Union[str, Any] = ['EfficientFormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[str] = [
'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientFormerForImageClassification',
'EfficientFormerForImageClassificationWithTeacher',
'EfficientFormerModel',
'EfficientFormerPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[str] = [
'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFEfficientFormerForImageClassification',
'TFEfficientFormerForImageClassificationWithTeacher',
'TFEfficientFormerModel',
'TFEfficientFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
_lowerCamelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 337
|
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = '''umt5'''
__lowerCAmelCase = ['''past_key_values''']
def __init__(self : Dict , _lowerCAmelCase : Optional[int]=25_0112 , _lowerCAmelCase : int=512 , _lowerCAmelCase : Any=64 , _lowerCAmelCase : int=1024 , _lowerCAmelCase : int=8 , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Optional[int]=6 , _lowerCAmelCase : Optional[int]=32 , _lowerCAmelCase : Any=128 , _lowerCAmelCase : Union[str, Any]=0.1 , _lowerCAmelCase : Optional[int]=1e-6 , _lowerCAmelCase : Dict=1.0 , _lowerCAmelCase : Tuple="gated-gelu" , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Optional[int]="T5Tokenizer" , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[Any]=0 , _lowerCAmelCase : str=1 , _lowerCAmelCase : Union[str, Any]=0 , **_lowerCAmelCase : Union[str, Any] , ):
super().__init__(
is_encoder_decoder=_lowerCAmelCase , tokenizer_class=_lowerCAmelCase , tie_word_embeddings=_lowerCAmelCase , pad_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , **_lowerCAmelCase , )
A = vocab_size
A = d_model
A = d_kv
A = d_ff
A = num_layers
A = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
A = num_heads
A = relative_attention_num_buckets
A = relative_attention_max_distance
A = dropout_rate
A = layer_norm_epsilon
A = initializer_factor
A = feed_forward_proj
A = use_cache
A = self.feed_forward_proj.split("""-""" )
A = act_info[-1]
A = act_info[0] == """gated"""
if len(_lowerCAmelCase ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase ) > 2:
raise ValueError(
F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
if feed_forward_proj == "gated-gelu":
A = """gelu_new"""
@property
def A (self : Optional[Any] ):
return self.d_model
@property
def A (self : List[Any] ):
return self.num_heads
@property
def A (self : Dict ):
return self.num_layers
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def A (self : Optional[Any] ):
A = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
A = """past_encoder_sequence + sequence"""
A = {0: """batch"""}
A = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
A = {0: """batch""", 1: """decoder_sequence"""}
A = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase , direction="""inputs""" )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def A (self : Union[str, Any] ):
return 13
@property
def A (self : Tuple ):
return 5e-4
| 337
| 1
|
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a = logging.get_logger(__name__)
_a = {
'''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}
class A_ ( snake_case__ , snake_case__ ):
_lowercase : Any = 'resnet'
_lowercase : List[Any] = ['basic', 'bottleneck']
def __init__( self : Dict , UpperCAmelCase : Dict=3 , UpperCAmelCase : Any=6_4 , UpperCAmelCase : List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , UpperCAmelCase : int=[3, 4, 6, 3] , UpperCAmelCase : Union[str, Any]="bottleneck" , UpperCAmelCase : Union[str, Any]="relu" , UpperCAmelCase : int=False , UpperCAmelCase : Any=None , UpperCAmelCase : List[Any]=None , **UpperCAmelCase : Tuple , ) -> str:
super().__init__(**UpperCAmelCase )
if layer_type not in self.layer_types:
raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
__lowerCAmelCase: Tuple = num_channels
__lowerCAmelCase: Any = embedding_size
__lowerCAmelCase: Optional[int] = hidden_sizes
__lowerCAmelCase: Optional[int] = depths
__lowerCAmelCase: str = layer_type
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: Optional[int] = downsample_in_first_stage
__lowerCAmelCase: Dict = ['stem'] + [F'''stage{idx}''' for idx in range(1 , len(UpperCAmelCase ) + 1 )]
__lowerCAmelCase , __lowerCAmelCase: Tuple = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase , out_indices=UpperCAmelCase , stage_names=self.stage_names )
class A_ ( snake_case__ ):
_lowercase : Tuple = version.parse('1.11' )
@property
def UpperCAmelCase ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def UpperCAmelCase ( self : int ) -> float:
return 1E-3
| 322
|
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
| 1
|
from __future__ import annotations
def _a ( lowerCamelCase: list[float] ) -> float:
'''simple docstring'''
__A = 0.00
__A = 0
for resistor in resistors:
if resistor <= 0:
__A = F"""Resistor at index {index} has a negative or zero value!"""
raise ValueError(lowerCamelCase )
first_sum += 1 / float(lowerCamelCase )
index += 1
return 1 / first_sum
def _a ( lowerCamelCase: list[float] ) -> float:
'''simple docstring'''
__A = 0.00
__A = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
__A = F"""Resistor at index {index} has a negative value!"""
raise ValueError(lowerCamelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 250
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
),
}
class A_ ( _lowerCamelCase ):
lowerCAmelCase__ = """pix2struct_text_model"""
lowerCAmelCase__ = ["""past_key_values"""]
lowerCAmelCase__ = {
"""hidden_size""": """hidden_size""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__(self :Any , _UpperCamelCase :int=5_0244 , _UpperCamelCase :Optional[Any]=768 , _UpperCamelCase :Optional[Any]=64 , _UpperCamelCase :Dict=2048 , _UpperCamelCase :int=12 , _UpperCamelCase :Optional[int]=12 , _UpperCamelCase :Optional[int]=32 , _UpperCamelCase :Dict=128 , _UpperCamelCase :Tuple=0.1 , _UpperCamelCase :List[str]=1e-6 , _UpperCamelCase :Optional[Any]=1.0 , _UpperCamelCase :Union[str, Any]="gelu_new" , _UpperCamelCase :int=0 , _UpperCamelCase :int=False , _UpperCamelCase :int=0 , _UpperCamelCase :Dict=1 , _UpperCamelCase :Any=False , _UpperCamelCase :Optional[Any]=True , **_UpperCamelCase :Tuple , )-> Dict:
__A = vocab_size
__A = hidden_size
__A = d_kv
__A = d_ff
__A = num_layers
__A = num_heads
__A = relative_attention_num_buckets
__A = relative_attention_max_distance
__A = dropout_rate
__A = layer_norm_epsilon
__A = initializer_factor
__A = use_cache
__A = eos_token_id
__A = decoder_start_token_id
# for backwards compatibility
__A = dense_act_fn
super().__init__(
pad_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , decoder_start_token_id=_UpperCamelCase , tie_word_embeddings=_UpperCamelCase , is_decoder=_UpperCamelCase , **_UpperCamelCase , )
@classmethod
def _lowerCAmelCase (cls :List[str] , _UpperCamelCase :Union[str, os.PathLike] , **_UpperCamelCase :List[Any] )-> "PretrainedConfig":
cls._set_token_in_kwargs(_UpperCamelCase )
__A , __A = cls.get_config_dict(_UpperCamelCase , **_UpperCamelCase )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
__A = 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(_UpperCamelCase , **_UpperCamelCase )
class A_ ( _lowerCamelCase ):
lowerCAmelCase__ = """pix2struct_vision_model"""
def __init__(self :Dict , _UpperCamelCase :Optional[Any]=768 , _UpperCamelCase :List[str]=768 , _UpperCamelCase :Any=2048 , _UpperCamelCase :Tuple=64 , _UpperCamelCase :int=12 , _UpperCamelCase :Optional[int]=12 , _UpperCamelCase :Tuple="gelu_new" , _UpperCamelCase :Dict=1e-6 , _UpperCamelCase :int=0.0 , _UpperCamelCase :int=0.0 , _UpperCamelCase :Union[str, Any]=1e-10 , _UpperCamelCase :Tuple=1.0 , _UpperCamelCase :Tuple=4096 , _UpperCamelCase :List[str]=32 , _UpperCamelCase :Optional[Any]=128 , **_UpperCamelCase :List[str] , )-> Any:
super().__init__(**_UpperCamelCase )
__A = hidden_size
__A = patch_embed_hidden_size
__A = d_ff
__A = dropout_rate
__A = num_hidden_layers
__A = num_attention_heads
__A = initializer_range
__A = initializer_factor
__A = attention_dropout
__A = layer_norm_eps
__A = dense_act_fn
__A = seq_len
__A = relative_attention_num_buckets
__A = relative_attention_max_distance
__A = d_kv
@classmethod
def _lowerCAmelCase (cls :List[str] , _UpperCamelCase :Union[str, os.PathLike] , **_UpperCamelCase :List[str] )-> "PretrainedConfig":
cls._set_token_in_kwargs(_UpperCamelCase )
__A , __A = cls.get_config_dict(_UpperCamelCase , **_UpperCamelCase )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
__A = 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(_UpperCamelCase , **_UpperCamelCase )
class A_ ( _lowerCamelCase ):
lowerCAmelCase__ = """pix2struct"""
lowerCAmelCase__ = True
def __init__(self :List[Any] , _UpperCamelCase :str=None , _UpperCamelCase :int=None , _UpperCamelCase :List[Any]=1.0 , _UpperCamelCase :int=0.0_2 , _UpperCamelCase :List[str]=False , _UpperCamelCase :Optional[Any]=False , _UpperCamelCase :int=True , **_UpperCamelCase :Any , )-> Optional[Any]:
super().__init__(tie_word_embeddings=_UpperCamelCase , is_encoder_decoder=_UpperCamelCase , **_UpperCamelCase )
if text_config is None:
__A = {}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
__A = {}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
__A = PixaStructTextConfig(**_UpperCamelCase )
__A = PixaStructVisionConfig(**_UpperCamelCase )
__A = self.text_config.decoder_start_token_id
__A = self.text_config.pad_token_id
__A = self.text_config.eos_token_id
__A = initializer_factor
__A = initializer_range
__A = self.initializer_range
__A = self.initializer_range
__A = is_vqa
@classmethod
def _lowerCAmelCase (cls :str , _UpperCamelCase :PixaStructTextConfig , _UpperCamelCase :PixaStructVisionConfig , **_UpperCamelCase :Union[str, Any] )-> List[str]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_UpperCamelCase )
def _lowerCAmelCase (self :Union[str, Any] )-> int:
__A = copy.deepcopy(self.__dict__ )
__A = self.text_config.to_dict()
__A = self.vision_config.to_dict()
__A = self.__class__.model_type
return output
| 250
| 1
|
from collections import deque
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
snake_case_ = process_name # process name
snake_case_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
snake_case_ = arrival_time
snake_case_ = burst_time # remaining burst time
snake_case_ = 0 # total time of the process wait in ready queue
snake_case_ = 0 # time from arrival time to completion time
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : int , _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : deque[Process] , _lowerCAmelCase : int , ) -> None:
"""simple docstring"""
# total number of mlfq's queues
snake_case_ = number_of_queues
# time slice of queues that round robin algorithm applied
snake_case_ = time_slices
# unfinished process is in this ready_queue
snake_case_ = queue
# current time
snake_case_ = current_time
# finished process is in this sequence queue
snake_case_ = deque()
def lowerCAmelCase__ ( self : Union[str, Any] ) -> list[str]:
"""simple docstring"""
snake_case_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def lowerCAmelCase__ ( self : Dict , _lowerCAmelCase : list[Process] ) -> list[int]:
"""simple docstring"""
snake_case_ = []
for i in range(len(_lowerCAmelCase ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def lowerCAmelCase__ ( self : Tuple , _lowerCAmelCase : list[Process] ) -> list[int]:
"""simple docstring"""
snake_case_ = []
for i in range(len(_lowerCAmelCase ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : list[Process] ) -> list[int]:
"""simple docstring"""
snake_case_ = []
for i in range(len(_lowerCAmelCase ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def lowerCAmelCase__ ( self : int , _lowerCAmelCase : deque[Process] ) -> list[int]:
"""simple docstring"""
return [q.burst_time for q in queue]
def lowerCAmelCase__ ( self : Dict , _lowerCAmelCase : Process ) -> int:
"""simple docstring"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def lowerCAmelCase__ ( self : int , _lowerCAmelCase : deque[Process] ) -> deque[Process]:
"""simple docstring"""
snake_case_ = deque() # sequence deque of finished process
while len(_lowerCAmelCase ) != 0:
snake_case_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(_lowerCAmelCase )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
snake_case_ = 0
# set the process's turnaround time because it is finished
snake_case_ = self.current_time - cp.arrival_time
# set the completion time
snake_case_ = self.current_time
# add the process to queue that has finished queue
finished.append(_lowerCAmelCase )
self.finish_queue.extend(_lowerCAmelCase ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def lowerCAmelCase__ ( self : Dict , _lowerCAmelCase : deque[Process] , _lowerCAmelCase : int ) -> tuple[deque[Process], deque[Process]]:
"""simple docstring"""
snake_case_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(_lowerCAmelCase ) ):
snake_case_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(_lowerCAmelCase )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
snake_case_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(_lowerCAmelCase )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
snake_case_ = 0
# set the finish time
snake_case_ = self.current_time
# update the process' turnaround time because it is finished
snake_case_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(_lowerCAmelCase )
self.finish_queue.extend(_lowerCAmelCase ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def lowerCAmelCase__ ( self : List[Any] ) -> deque[Process]:
"""simple docstring"""
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
snake_case_ , snake_case_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
SCREAMING_SNAKE_CASE :Any = Process('''P1''', 0, 53)
SCREAMING_SNAKE_CASE :List[str] = Process('''P2''', 0, 17)
SCREAMING_SNAKE_CASE :str = Process('''P3''', 0, 68)
SCREAMING_SNAKE_CASE :Optional[int] = Process('''P4''', 0, 24)
SCREAMING_SNAKE_CASE :Union[str, Any] = 3
SCREAMING_SNAKE_CASE :List[Any] = [17, 25]
SCREAMING_SNAKE_CASE :List[Any] = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})
SCREAMING_SNAKE_CASE :Union[str, Any] = Process('''P1''', 0, 53)
SCREAMING_SNAKE_CASE :List[str] = Process('''P2''', 0, 17)
SCREAMING_SNAKE_CASE :int = Process('''P3''', 0, 68)
SCREAMING_SNAKE_CASE :Dict = Process('''P4''', 0, 24)
SCREAMING_SNAKE_CASE :int = 3
SCREAMING_SNAKE_CASE :Any = [17, 25]
SCREAMING_SNAKE_CASE :List[str] = deque([Pa, Pa, Pa, Pa])
SCREAMING_SNAKE_CASE :Dict = MLFQ(number_of_queues, time_slices, queue, 0)
SCREAMING_SNAKE_CASE :str = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
F'''waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print completion times of processes(P1, P2, P3, P4)
print(
F'''completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
F'''turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print sequence of finished processes
print(
F'''sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}'''
)
| 159
|
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
SCREAMING_SNAKE_CASE :Any = '''bart'''
SCREAMING_SNAKE_CASE :Any = True
@st.cache(allow_output_mutation=lowerCAmelCase_ )
def _lowerCAmelCase ( )->List[Any]:
'''simple docstring'''
if LOAD_DENSE_INDEX:
snake_case_ = AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased" )
snake_case_ = AutoModel.from_pretrained("yjernite/retribert-base-uncased" ).to("cuda:0" )
snake_case_ = qar_model.eval()
else:
snake_case_ , snake_case_ = (None, None)
if MODEL_TYPE == "bart":
snake_case_ = AutoTokenizer.from_pretrained("yjernite/bart_eli5" )
snake_case_ = AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5" ).to("cuda:0" )
snake_case_ = torch.load("seq2seq_models/eli5_bart_model_blm_2.pth" )
sas_model.load_state_dict(save_dict["model"] )
snake_case_ = sas_model.eval()
else:
snake_case_ , snake_case_ = make_qa_sas_model(
model_name="t5-small" , from_file="seq2seq_models/eli5_t5_model_1024_4.pth" , device="cuda:0" )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=lowerCAmelCase_ )
def _lowerCAmelCase ( )->Tuple:
'''simple docstring'''
if LOAD_DENSE_INDEX:
snake_case_ = faiss.StandardGpuResources()
snake_case_ = datasets.load_dataset(path="wiki_snippets" , name="wiki40b_en_100_0" )["train"]
snake_case_ = np.memmap(
"wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat" , dtype="float32" , mode="r" , shape=(wikiaab_passages.num_rows, 128) , )
snake_case_ = faiss.IndexFlatIP(128 )
snake_case_ = faiss.index_cpu_to_gpu(lowerCAmelCase_ , 1 , lowerCAmelCase_ )
wikiaab_gpu_index_flat.add(lowerCAmelCase_ ) # TODO fix for larger GPU
else:
snake_case_ , snake_case_ = (None, None)
snake_case_ = Elasticsearch([{"host": "localhost", "port": "9200"}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=lowerCAmelCase_ )
def _lowerCAmelCase ( )->Union[str, Any]:
'''simple docstring'''
snake_case_ = datasets.load_dataset("eli5" , name="LFQA_reddit" )
snake_case_ = elia["train_eli5"]
snake_case_ = np.memmap(
"eli5_questions_reps.dat" , dtype="float32" , mode="r" , shape=(elia_train.num_rows, 128) )
snake_case_ = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(lowerCAmelCase_ )
return (elia_train, eli5_train_q_index)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :Union[str, Any] = load_indexes()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :Optional[int] = load_models()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :Union[str, Any] = load_train_data()
def _lowerCAmelCase ( lowerCAmelCase_ :List[Any] , lowerCAmelCase_ :List[Any]=10 )->int:
'''simple docstring'''
snake_case_ = embed_questions_for_retrieval([question] , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ , snake_case_ = eli5_train_q_index.search(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ = [elia_train[int(lowerCAmelCase_ )] for i in I[0]]
return nn_examples
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[int] , lowerCAmelCase_ :Optional[int]="wiki40b" , lowerCAmelCase_ :Optional[Any]="dense" , lowerCAmelCase_ :Any=10 )->Union[str, Any]:
'''simple docstring'''
if source == "none":
snake_case_ , snake_case_ = (" <P> ".join(["" for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
snake_case_ , snake_case_ = query_qa_dense_index(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
else:
snake_case_ , snake_case_ = query_es_index(
lowerCAmelCase_ , lowerCAmelCase_ , index_name="english_wiki40b_snippets_100w" , n_results=lowerCAmelCase_ , )
snake_case_ = [
(res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
]
snake_case_ = "question: {} context: {}".format(lowerCAmelCase_ , lowerCAmelCase_ )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda lowerCAmelCase_ : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCAmelCase_ : None),
} )
def _lowerCAmelCase ( lowerCAmelCase_ :List[str] , lowerCAmelCase_ :Optional[int] , lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :int=64 , lowerCAmelCase_ :str=256 , lowerCAmelCase_ :int=False , lowerCAmelCase_ :Optional[int]=2 , lowerCAmelCase_ :Optional[int]=0.9_5 , lowerCAmelCase_ :str=0.8 )->Any:
'''simple docstring'''
with torch.no_grad():
snake_case_ = qa_sas_generate(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , num_answers=1 , num_beams=lowerCAmelCase_ , min_len=lowerCAmelCase_ , max_len=lowerCAmelCase_ , do_sample=lowerCAmelCase_ , temp=lowerCAmelCase_ , top_p=lowerCAmelCase_ , top_k=lowerCAmelCase_ , max_input_length=1_024 , device="cuda:0" , )[0]
return (answer, support_list)
st.title('''Long Form Question Answering with ELI5''')
# Start sidebar
SCREAMING_SNAKE_CASE :Dict = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
SCREAMING_SNAKE_CASE :Optional[int] = '''
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class="img-container"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
''' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
SCREAMING_SNAKE_CASE :Optional[Any] = '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)
SCREAMING_SNAKE_CASE :Tuple = [
'''Answer the question''',
'''View the retrieved document only''',
'''View the most similar ELI5 question and answer''',
'''Show me everything, please!''',
]
SCREAMING_SNAKE_CASE :Any = st.sidebar.checkbox('''Demo options''')
if demo_options:
SCREAMING_SNAKE_CASE :Tuple = st.sidebar.selectbox(
'''''',
action_list,
index=3,
)
SCREAMING_SNAKE_CASE :Optional[int] = action_list.index(action_st)
SCREAMING_SNAKE_CASE :Any = st.sidebar.selectbox(
'''''',
['''Show full text of passages''', '''Show passage section titles'''],
index=0,
)
SCREAMING_SNAKE_CASE :Optional[int] = show_type == '''Show full text of passages'''
else:
SCREAMING_SNAKE_CASE :List[str] = 3
SCREAMING_SNAKE_CASE :Dict = True
SCREAMING_SNAKE_CASE :List[Any] = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
SCREAMING_SNAKE_CASE :str = '''
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
'''
st.sidebar.markdown(retriever_info)
SCREAMING_SNAKE_CASE :str = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
SCREAMING_SNAKE_CASE :Union[str, Any] = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
SCREAMING_SNAKE_CASE :Dict = '''wiki40b'''
SCREAMING_SNAKE_CASE :Optional[int] = '''dense'''
SCREAMING_SNAKE_CASE :str = '''beam'''
SCREAMING_SNAKE_CASE :List[str] = 2
SCREAMING_SNAKE_CASE :int = 64
SCREAMING_SNAKE_CASE :List[str] = 2_56
SCREAMING_SNAKE_CASE :str = None
SCREAMING_SNAKE_CASE :Optional[Any] = None
SCREAMING_SNAKE_CASE :int = st.sidebar.checkbox('''Generation options''')
if generate_options:
SCREAMING_SNAKE_CASE :Optional[Any] = '''
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder\'s output probabilities.
'''
st.sidebar.markdown(generate_info)
SCREAMING_SNAKE_CASE :str = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
SCREAMING_SNAKE_CASE :Optional[Any] = st.sidebar.slider(
'''Minimum generation length''', min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
)
SCREAMING_SNAKE_CASE :Union[str, Any] = st.sidebar.slider(
'''Maximum generation length''', min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
)
if sampled == "beam":
SCREAMING_SNAKE_CASE :List[Any] = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
SCREAMING_SNAKE_CASE :Any = st.sidebar.slider(
'''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
SCREAMING_SNAKE_CASE :Optional[Any] = st.sidebar.slider(
'''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
SCREAMING_SNAKE_CASE :Union[str, Any] = None
# start main text
SCREAMING_SNAKE_CASE :Any = [
'''<MY QUESTION>''',
'''How do people make chocolate?''',
'''Why do we get a fever when we are sick?''',
'''How can different animals perceive different colors?''',
'''What is natural language processing?''',
'''What\'s the best way to treat a sunburn?''',
'''What exactly are vitamins ?''',
'''How does nuclear energy provide electricity?''',
'''What\'s the difference between viruses and bacteria?''',
'''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
'''Why do people like drinking coffee even though it tastes so bad?''',
'''What happens when wine ages? How does it make the wine taste better?''',
'''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
'''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
'''How does New Zealand have so many large bird predators?''',
]
SCREAMING_SNAKE_CASE :Optional[Any] = st.selectbox(
'''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
SCREAMING_SNAKE_CASE :List[Any] = st.text_input('''Enter your question here:''', '''''')
else:
SCREAMING_SNAKE_CASE :str = question_s
if st.button('''Show me!'''):
if action in [0, 1, 3]:
if index_type == "mixed":
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :int = make_support(question, source=wiki_source, method='''dense''', n_results=10)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :Optional[int] = make_support(question, source=wiki_source, method='''sparse''', n_results=10)
SCREAMING_SNAKE_CASE :Optional[Any] = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
SCREAMING_SNAKE_CASE :Union[str, Any] = support_list[:10]
SCREAMING_SNAKE_CASE :int = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :str = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :Any = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == '''sampled'''),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('''### The model generated answer is:''')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
for i, res in enumerate(support_list):
SCREAMING_SNAKE_CASE :Dict = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
SCREAMING_SNAKE_CASE :Tuple = res[1].strip()
if sec_titles == "":
SCREAMING_SNAKE_CASE :Tuple = '''[{}]({})'''.format(res[0], wiki_url)
else:
SCREAMING_SNAKE_CASE :Union[str, Any] = sec_titles.split(''' & ''')
SCREAMING_SNAKE_CASE :Optional[int] = ''' & '''.join(
['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
)
st.markdown(
'''{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'''.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
)
if action in [2, 3]:
SCREAMING_SNAKE_CASE :List[Any] = find_nearest_training(question)
SCREAMING_SNAKE_CASE :List[Any] = nn_train_list[0]
st.markdown(
'''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
)
SCREAMING_SNAKE_CASE :Any = [
'''{}. {}'''.format(i + 1, ''' \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
if i == 0 or sc > 2
]
st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))
SCREAMING_SNAKE_CASE :Optional[int] = '''
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 159
| 1
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class lowerCAmelCase ( UpperCamelCase_ ):
__lowerCamelCase = """unispeech"""
def __init__( self :List[str] , _lowercase :Optional[Any]=32 , _lowercase :Union[str, Any]=7_68 , _lowercase :List[str]=12 , _lowercase :str=12 , _lowercase :Optional[int]=30_72 , _lowercase :Union[str, Any]="gelu" , _lowercase :Any=0.1 , _lowercase :Dict=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :List[str]=0.0 , _lowercase :Tuple=0.0 , _lowercase :Union[str, Any]=0.1 , _lowercase :int=0.1 , _lowercase :Optional[int]=0.02 , _lowercase :str=1e-5 , _lowercase :List[Any]="group" , _lowercase :Optional[int]="gelu" , _lowercase :Any=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , _lowercase :int=(5, 2, 2, 2, 2, 2, 2) , _lowercase :Optional[int]=(10, 3, 3, 3, 3, 2, 2) , _lowercase :List[Any]=False , _lowercase :Dict=1_28 , _lowercase :Optional[int]=16 , _lowercase :List[str]=False , _lowercase :Tuple=True , _lowercase :Union[str, Any]=0.05 , _lowercase :Dict=10 , _lowercase :str=2 , _lowercase :Optional[Any]=0.0 , _lowercase :List[Any]=10 , _lowercase :Optional[int]=0 , _lowercase :Optional[int]=3_20 , _lowercase :Union[str, Any]=2 , _lowercase :Dict=0.1 , _lowercase :Any=1_00 , _lowercase :str=2_56 , _lowercase :List[str]=2_56 , _lowercase :Optional[Any]=0.1 , _lowercase :Optional[int]="mean" , _lowercase :Optional[Any]=False , _lowercase :Tuple=False , _lowercase :str=2_56 , _lowercase :List[Any]=80 , _lowercase :List[str]=0 , _lowercase :Optional[int]=1 , _lowercase :int=2 , _lowercase :Union[str, Any]=0.5 , **_lowercase :List[Any] , ):
'''simple docstring'''
super().__init__(**_a , pad_token_id=_a , bos_token_id=_a , eos_token_id=_a )
lowercase__ = hidden_size
lowercase__ = feat_extract_norm
lowercase__ = feat_extract_activation
lowercase__ = list(_a )
lowercase__ = list(_a )
lowercase__ = list(_a )
lowercase__ = conv_bias
lowercase__ = num_conv_pos_embeddings
lowercase__ = num_conv_pos_embedding_groups
lowercase__ = len(self.conv_dim )
lowercase__ = num_hidden_layers
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = num_attention_heads
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = feat_proj_dropout
lowercase__ = final_dropout
lowercase__ = layerdrop
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
lowercase__ = num_ctc_classes
lowercase__ = vocab_size
lowercase__ = do_stable_layer_norm
lowercase__ = use_weighted_layer_sum
lowercase__ = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase__ = apply_spec_augment
lowercase__ = mask_time_prob
lowercase__ = mask_time_length
lowercase__ = mask_time_min_masks
lowercase__ = mask_feature_prob
lowercase__ = mask_feature_length
lowercase__ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowercase__ = num_codevectors_per_group
lowercase__ = num_codevector_groups
lowercase__ = contrastive_logits_temperature
lowercase__ = feat_quantizer_dropout
lowercase__ = num_negatives
lowercase__ = codevector_dim
lowercase__ = proj_codevector_dim
lowercase__ = diversity_loss_weight
# ctc loss
lowercase__ = ctc_loss_reduction
lowercase__ = ctc_zero_infinity
# pretraining loss
lowercase__ = replace_prob
@property
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 362
|
from collections import namedtuple
import requests
from lxml import html # type: ignore
_snake_case = namedtuple("""covid_data""", """cases deaths recovered""")
def _A ( __magic_name__ = "https://www.worldometers.info/coronavirus/" ):
lowercase__ = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(__magic_name__ ).content ).xpath(__magic_name__ ) )
_snake_case = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 201
| 0
|
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
# Copyright 2021 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.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
_snake_case = '''pytorch_model.bin'''
_snake_case = '''pytorch_model.bin.index.json'''
_snake_case = '''adapter_config.json'''
_snake_case = '''adapter_model.bin'''
_snake_case = '''adapter_model.safetensors'''
_snake_case = '''tf_model.h5'''
_snake_case = '''tf_model.h5.index.json'''
_snake_case = '''model.ckpt'''
_snake_case = '''flax_model.msgpack'''
_snake_case = '''flax_model.msgpack.index.json'''
_snake_case = '''model.safetensors'''
_snake_case = '''model.safetensors.index.json'''
_snake_case = '''config.json'''
_snake_case = '''preprocessor_config.json'''
_snake_case = FEATURE_EXTRACTOR_NAME
_snake_case = '''generation_config.json'''
_snake_case = '''modelcard.json'''
_snake_case = '''▁'''
_snake_case = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
_snake_case = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
_snake_case = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
_snake_case = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def _UpperCamelCase ( snake_case__ ) -> Any:
if version.parse(snake_case__ ) < version.parse(snake_case__ ):
if "dev" in min_version:
__UpperCAmelCase : Dict = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__UpperCAmelCase : str = f'''This example requires a minimum version of {min_version},'''
error_message += f''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 157
| 0
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _A ( __lowercase , unittest.TestCase ):
lowercase__: str = ShapEPipeline
lowercase__: Union[str, Any] = ['''prompt''']
lowercase__: Dict = ['''prompt''']
lowercase__: Union[str, Any] = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
lowercase__: Union[str, Any] = False
@property
def lowercase__ ( self : Tuple ) -> Dict:
"""simple docstring"""
return 32
@property
def lowercase__ ( self : List[Any] ) -> Tuple:
"""simple docstring"""
return 32
@property
def lowercase__ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowercase__ ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
return 8
@property
def lowercase__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__snake_case : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def lowercase__ ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
__snake_case : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModelWithProjection(__magic_name__ )
@property
def lowercase__ ( self : Dict ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
__snake_case : Tuple = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
__snake_case : List[Any] = PriorTransformer(**__magic_name__ )
return model
@property
def lowercase__ ( self : Tuple ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
__snake_case : Optional[Any] = {
"""param_shapes""": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"""d_latent""": self.time_input_dim,
"""d_hidden""": self.renderer_dim,
"""n_output""": 12,
"""background""": (
0.1,
0.1,
0.1,
),
}
__snake_case : Any = ShapERenderer(**__magic_name__ )
return model
def lowercase__ ( self : Tuple ) -> Any:
"""simple docstring"""
__snake_case : int = self.dummy_prior
__snake_case : Optional[int] = self.dummy_text_encoder
__snake_case : str = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_renderer
__snake_case : str = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=10_24 , prediction_type="""sample""" , use_karras_sigmas=__magic_name__ , clip_sample=__magic_name__ , clip_sample_range=1.0 , )
__snake_case : List[Any] = {
"""prior""": prior,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowercase__ ( self : Optional[int] , __magic_name__ : Tuple , __magic_name__ : str=0 ) -> Any:
"""simple docstring"""
if str(__magic_name__ ).startswith("""mps""" ):
__snake_case : int = torch.manual_seed(__magic_name__ )
else:
__snake_case : Dict = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
__snake_case : Any = {
"""prompt""": """horse""",
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowercase__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__snake_case : Optional[Any] = """cpu"""
__snake_case : Optional[int] = self.get_dummy_components()
__snake_case : List[Any] = self.pipeline_class(**__magic_name__ )
__snake_case : Optional[Any] = pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
__snake_case : str = pipe(**self.get_dummy_inputs(__magic_name__ ) )
__snake_case : str = output.images[0]
__snake_case : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : List[Any] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowercase__ ( self : Any ) -> List[str]:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase__ ( self : str ) -> Tuple:
"""simple docstring"""
__snake_case : Optional[Any] = torch_device == """cpu"""
__snake_case : List[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__magic_name__ , relax_max_difference=__magic_name__ , )
def lowercase__ ( self : Optional[int] ) -> str:
"""simple docstring"""
__snake_case : str = self.get_dummy_components()
__snake_case : List[str] = self.pipeline_class(**__magic_name__ )
__snake_case : Any = pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
__snake_case : Optional[int] = 1
__snake_case : Union[str, Any] = 2
__snake_case : Any = self.get_dummy_inputs(__magic_name__ )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Optional[int] = batch_size * [inputs[key]]
__snake_case : Tuple = pipe(**__magic_name__ , num_images_per_prompt=__magic_name__ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
def lowercase__ ( self : str ) -> Optional[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : int ) -> Dict:
"""simple docstring"""
__snake_case : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_np_out.npy""" )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained("""openai/shap-e""" )
__snake_case : Union[str, Any] = pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
__snake_case : Optional[Any] = torch.Generator(device=__magic_name__ ).manual_seed(0 )
__snake_case : Any = pipe(
"""a shark""" , generator=__magic_name__ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__magic_name__ , __magic_name__ )
| 357
|
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 13
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientFormerConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''EfficientFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientFormerForImageClassification''',
'''EfficientFormerForImageClassificationWithTeacher''',
'''EfficientFormerModel''',
'''EfficientFormerPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFEfficientFormerForImageClassification''',
'''TFEfficientFormerForImageClassificationWithTeacher''',
'''TFEfficientFormerModel''',
'''TFEfficientFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'deta'
A : Dict = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=900 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=300 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.25 , **SCREAMING_SNAKE_CASE__ , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = backbone_config.pop('''model_type''' )
lowercase : Any = CONFIG_MAPPING[backbone_model_type]
lowercase : List[Any] = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = backbone_config
lowercase : Union[str, Any] = num_queries
lowercase : Any = max_position_embeddings
lowercase : int = d_model
lowercase : Any = encoder_ffn_dim
lowercase : Optional[int] = encoder_layers
lowercase : Tuple = encoder_attention_heads
lowercase : Optional[Any] = decoder_ffn_dim
lowercase : Optional[int] = decoder_layers
lowercase : int = decoder_attention_heads
lowercase : Any = dropout
lowercase : int = attention_dropout
lowercase : Dict = activation_dropout
lowercase : int = activation_function
lowercase : Dict = init_std
lowercase : List[str] = init_xavier_std
lowercase : Optional[Any] = encoder_layerdrop
lowercase : Tuple = auxiliary_loss
lowercase : Tuple = position_embedding_type
# deformable attributes
lowercase : List[str] = num_feature_levels
lowercase : Tuple = encoder_n_points
lowercase : Optional[int] = decoder_n_points
lowercase : Tuple = two_stage
lowercase : Optional[Any] = two_stage_num_proposals
lowercase : Union[str, Any] = with_box_refine
lowercase : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : str = bbox_cost
lowercase : List[Any] = giou_cost
# Loss coefficients
lowercase : Tuple = mask_loss_coefficient
lowercase : Any = dice_loss_coefficient
lowercase : Dict = bbox_loss_coefficient
lowercase : Tuple = giou_loss_coefficient
lowercase : Union[str, Any] = eos_coefficient
lowercase : Tuple = focal_alpha
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return self.encoder_attention_heads
@property
def __lowerCamelCase ( self ):
return self.d_model
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase : Any = self.backbone_config.to_dict()
lowercase : List[str] = self.__class__.model_type
return output
| 337
| 1
|
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A_ ( _lowerCamelCase , unittest.TestCase ):
lowerCAmelCase__ = PhobertTokenizer
lowerCAmelCase__ = False
def _lowerCAmelCase (self :Union[str, Any] )-> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__A = ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
__A = dict(zip(_UpperCamelCase , range(len(_UpperCamelCase ) ) ) )
__A = ['''#version: 0.2''', '''l à</w>''']
__A = {'''unk_token''': '''<unk>'''}
__A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
for token in vocab_tokens:
fp.write(f"""{token} {vocab_tokens[token]}\n""" )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_UpperCamelCase ) )
def _lowerCAmelCase (self :int , **_UpperCamelCase :List[str] )-> List[Any]:
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **_UpperCamelCase )
def _lowerCAmelCase (self :Optional[Any] , _UpperCamelCase :Tuple )-> Dict:
__A = '''Tôi là VinAI Research'''
__A = '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
return input_text, output_text
def _lowerCAmelCase (self :Optional[int] )-> Tuple:
__A = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__A = '''Tôi là VinAI Research'''
__A = '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
__A = tokenizer.tokenize(_UpperCamelCase )
print(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
__A = tokens + [tokenizer.unk_token]
__A = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , _UpperCamelCase )
| 363
|
import math
def _a ( lowerCamelCase: int ) -> int:
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
__A = F"""Input value of [number={number}] must be an integer"""
raise TypeError(lowerCamelCase )
if number < 1:
__A = F"""Input value of [number={number}] must be > 0"""
raise ValueError(lowerCamelCase )
elif number == 1:
return 3
elif number == 2:
return 5
else:
__A = int(math.log(number // 3 , 2 ) ) + 2
__A = [3, 5]
__A = 2
__A = 3
for block in range(1 , lowerCamelCase ):
for _ in range(lowerCamelCase ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
snake_case__ : Optional[Any] = 0
try:
snake_case__ : int = proth(number)
except ValueError:
print(f'ValueError: there is no {number}th Proth number')
continue
print(f'The {number}th Proth number: {value}')
| 250
| 0
|
'''simple docstring'''
def _A ( snake_case , snake_case ) -> str:
_lowercase : int = ""
for word_or_phrase in separated:
if not isinstance(snake_case , snake_case ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separator
return joined.strip(snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 250
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class a__ ( unittest.TestCase ):
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=7 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=99 , _UpperCamelCase=32 , _UpperCamelCase=5 , _UpperCamelCase=4 , _UpperCamelCase=37 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=16 , _UpperCamelCase=2 , _UpperCamelCase=0.0_2 , _UpperCamelCase=4 , ):
"""simple docstring"""
_lowercase : int = parent
_lowercase : List[str] = batch_size
_lowercase : Tuple = seq_length
_lowercase : Any = is_training
_lowercase : List[Any] = use_attention_mask
_lowercase : Dict = use_token_type_ids
_lowercase : int = use_labels
_lowercase : List[Any] = vocab_size
_lowercase : int = hidden_size
_lowercase : int = num_hidden_layers
_lowercase : str = num_attention_heads
_lowercase : Optional[Any] = intermediate_size
_lowercase : Union[str, Any] = hidden_act
_lowercase : Optional[int] = hidden_dropout_prob
_lowercase : List[str] = attention_probs_dropout_prob
_lowercase : str = max_position_embeddings
_lowercase : Optional[int] = type_vocab_size
_lowercase : List[str] = type_sequence_label_size
_lowercase : str = initializer_range
_lowercase : List[Any] = num_choices
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowercase : Any = None
if self.use_attention_mask:
_lowercase : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
_lowercase : str = None
if self.use_token_type_ids:
_lowercase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowercase : Optional[Any] = RobertaPreLayerNormConfig(
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 , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Tuple = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase , _lowercase : Optional[Any] = config_and_inputs
_lowercase : List[str] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : List[str] = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase , _lowercase : Tuple = config_and_inputs
_lowercase : Any = True
_lowercase : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class a__ ( lowerCamelCase_ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[Any] = True
_SCREAMING_SNAKE_CASE : Tuple = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[Any] = FlaxRobertaPreLayerNormModelTester(self )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowercase : List[str] = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_UpperCamelCase )
_lowercase : Optional[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCamelCase )
@require_flax
class a__ ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : int = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_UpperCamelCase )
_lowercase : Optional[Any] = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] , dtype=jnp.intaa )
_lowercase : Optional[Any] = model(_UpperCamelCase )[0]
_lowercase : Any = [1, 11, 50265]
self.assertEqual(list(output.shape ) , _UpperCamelCase )
# compare the actual values for a slice.
_lowercase : Dict = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCamelCase , atol=1E-4 ) )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[Any] = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_UpperCamelCase )
_lowercase : int = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] , dtype=jnp.intaa )
_lowercase : List[Any] = model(_UpperCamelCase )[0]
# compare the actual values for a slice.
_lowercase : List[str] = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCamelCase , atol=1E-4 ) )
| 250
| 1
|
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__A = logging.get_logger(__name__)
class UpperCAmelCase :
"""simple docstring"""
_UpperCAmelCase :str
_UpperCAmelCase :str = None
@staticmethod
def _snake_case ( ):
raise NotImplementedError
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ):
raise NotImplementedError
def _snake_case ( self , _UpperCAmelCase ):
raise NotImplementedError
def _snake_case ( self ):
if not self.is_available():
raise RuntimeError(
F"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def _snake_case ( cls ):
return F"""`pip install {cls.pip_package or cls.name}`"""
class UpperCAmelCase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase :Any = 'optuna'
@staticmethod
def _snake_case ( ):
return is_optuna_available()
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ):
return run_hp_search_optuna(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
return default_hp_space_optuna(__lowerCAmelCase )
class UpperCAmelCase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase :Optional[int] = 'ray'
_UpperCAmelCase :Dict = '\'ray[tune]\''
@staticmethod
def _snake_case ( ):
return is_ray_available()
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ):
return run_hp_search_ray(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
return default_hp_space_ray(__lowerCAmelCase )
class UpperCAmelCase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase :Optional[Any] = 'sigopt'
@staticmethod
def _snake_case ( ):
return is_sigopt_available()
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ):
return run_hp_search_sigopt(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
return default_hp_space_sigopt(__lowerCAmelCase )
class UpperCAmelCase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase :Optional[Any] = 'wandb'
@staticmethod
def _snake_case ( ):
return is_wandb_available()
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ):
return run_hp_search_wandb(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
return default_hp_space_wandb(__lowerCAmelCase )
__A = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def SCREAMING_SNAKE_CASE__ ( ) -> Tuple:
lowercase__: List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__UpperCAmelCase ) > 0:
lowercase__: Union[str, Any] = available_backends[0].name
if len(__UpperCAmelCase ) > 1:
logger.info(
F"""{len(__UpperCAmelCase )} hyperparameter search backends available. Using {name} as the default.""" )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
F""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 357
|
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__A = "<<<<<<< This should probably be modified because it mentions: "
__A = "=======\n>>>>>>>\n"
__A = [
"TextEncoderConfig",
"ByteTextEncoder",
"SubwordTextEncoder",
"encoder_config",
"maybe_build_from_corpus",
"manual_dir",
]
__A = [
# (pattern, replacement)
# Order is important here for some replacements
(R"tfds\.core", R"datasets"),
(R"tf\.io\.gfile\.GFile", R"open"),
(R"tf\.([\w\d]+)", R"datasets.Value('\1')"),
(R"tfds\.features\.Text\(\)", R"datasets.Value('string')"),
(R"tfds\.features\.Text\(", R"datasets.Value('string'),"),
(R"features\s*=\s*tfds.features.FeaturesDict\(", R"features=datasets.Features("),
(R"tfds\.features\.FeaturesDict\(", R"dict("),
(R"The TensorFlow Datasets Authors", R"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
(R"tfds\.", R"datasets."),
(R"dl_manager\.manual_dir", R"self.config.data_dir"),
(R"self\.builder_config", R"self.config"),
]
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Tuple:
return ConvertCommand(args.tfds_path , args.datasets_directory )
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
@staticmethod
def _snake_case ( _UpperCAmelCase ):
lowercase__: int = parser.add_parser(
'''convert''' , help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.''' , )
train_parser.add_argument(
'''--tfds_path''' , type=_UpperCAmelCase , required=_UpperCAmelCase , help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.''' , )
train_parser.add_argument(
'''--datasets_directory''' , type=_UpperCAmelCase , required=_UpperCAmelCase , help='''Path to the HuggingFace Datasets folder.''' )
train_parser.set_defaults(func=_UpperCAmelCase )
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase ):
lowercase__: List[str] = get_logger('''datasets-cli/converting''' )
lowercase__: Optional[Any] = tfds_path
lowercase__: Dict = datasets_directory
def _snake_case ( self ):
if os.path.isdir(self._tfds_path ):
lowercase__: Optional[Any] = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
lowercase__: Optional[int] = os.path.dirname(self._tfds_path )
else:
raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.''' )
lowercase__: int = os.path.abspath(self._datasets_directory )
self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}""" )
lowercase__: Tuple = []
lowercase__: Dict = []
lowercase__: Any = {}
if os.path.isdir(self._tfds_path ):
lowercase__: Dict = os.listdir(_UpperCAmelCase )
else:
lowercase__: Dict = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(F"""Looking at file {f_name}""" )
lowercase__: Tuple = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Optional[int] = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if not os.path.isfile(_UpperCAmelCase ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info('''Skipping file''' )
continue
with open(_UpperCAmelCase , encoding='''utf-8''' ) as f:
lowercase__: Tuple = f.readlines()
lowercase__: Optional[Any] = []
lowercase__: Dict = False
lowercase__: List[str] = False
lowercase__: List[Any] = []
for line in lines:
lowercase__: List[str] = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
lowercase__: Optional[int] = '''import datasets\n'''
elif "import tensorflow" in out_line:
# order is important here
lowercase__: Dict = ''''''
continue
elif "from absl import logging" in out_line:
lowercase__: Tuple = '''from datasets import logging\n'''
elif "getLogger" in out_line:
lowercase__: Optional[Any] = out_line.replace('''getLogger''' , '''get_logger''' )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
lowercase__: Any = True
lowercase__: str = list(filter(lambda _UpperCAmelCase : e in out_line , _UpperCAmelCase ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_UpperCAmelCase ) + '''\n''' )
out_lines.append(_UpperCAmelCase )
out_lines.append(_UpperCAmelCase )
continue
else:
for pattern, replacement in TO_CONVERT:
lowercase__: List[Any] = re.sub(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
lowercase__: Any = re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)''' , _UpperCAmelCase )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(''',''' ) )
lowercase__: List[str] = '''from . import ''' + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(F"""Error converting {out_line.strip()}""" )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
lowercase__: Optional[Any] = True
out_lines.append(_UpperCAmelCase )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
lowercase__: Dict = f_name.replace('''.py''' , '''''' )
lowercase__: Dict = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Optional[Any] = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
self._logger.info(F"""Adding directory {output_dir}""" )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(_UpperCAmelCase )
if needs_manual_update:
with_manual_update.append(_UpperCAmelCase )
with open(_UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.writelines(_UpperCAmelCase )
self._logger.info(F"""Converted in {output_file}""" )
for utils_file in utils_files:
try:
lowercase__: str = os.path.basename(_UpperCAmelCase )
lowercase__: Union[str, Any] = imports_to_builder_map[f_name.replace('''.py''' , '''''' )]
self._logger.info(F"""Moving {dest_folder} to {utils_file}""" )
shutil.copy(_UpperCAmelCase , _UpperCAmelCase )
except KeyError:
self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually.""" )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.""" )
| 2
| 0
|
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A__ ( unittest.TestCase ):
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = "ylacombe/bark-small"
UpperCamelCase : List[Any] = tempfile.mkdtemp()
UpperCamelCase : Optional[int] = "en_speaker_1"
UpperCamelCase : Tuple = "This is a test string"
UpperCamelCase : Tuple = "speaker_embeddings_path.json"
UpperCamelCase : List[str] = "speaker_embeddings"
def __UpperCamelCase( self , **A_ ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = self.get_tokenizer()
UpperCamelCase : Optional[Any] = BarkProcessor(tokenizer=A_ )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase : Tuple = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
UpperCamelCase : Optional[Any] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
UpperCamelCase : List[Any] = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="(BOS)" , eos_token="(EOS)" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
UpperCamelCase : int = 35
UpperCamelCase : str = 2
UpperCamelCase : Dict = 8
UpperCamelCase : Tuple = {
"semantic_prompt": np.ones(A_ ),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
"fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
UpperCamelCase : Any = processor(text=self.input_string , voice_preset=A_ )
UpperCamelCase : Dict = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(A_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
UpperCamelCase : str = os.path.join(self.tmpdirname , "file.npz" )
np.savez(A_ , **A_ )
UpperCamelCase : Union[str, Any] = processor(text=self.input_string , voice_preset=A_ )
UpperCamelCase : List[Any] = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(A_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
UpperCamelCase : Optional[Any] = processor(text=self.input_string , voice_preset=self.voice_preset )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = self.get_tokenizer()
UpperCamelCase : str = BarkProcessor(tokenizer=A_ )
UpperCamelCase : str = processor(text=self.input_string )
UpperCamelCase : Tuple = tokenizer(
self.input_string , padding="max_length" , max_length=256 , add_special_tokens=A_ , return_attention_mask=A_ , return_token_type_ids=A_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 52
|
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCAmelCase_ ( __UpperCAmelCase: Optional[int] , __UpperCAmelCase: List[Any]=0.999 , __UpperCAmelCase: Tuple="cosine" , ) -> Optional[Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase: List[Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase: List[Any] ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
UpperCamelCase__ : Dict = []
for i in range(__UpperCAmelCase ):
UpperCamelCase__ : Optional[Any] = i / num_diffusion_timesteps
UpperCamelCase__ : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class lowercase__ ( __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
a : Optional[Any] = [e.name for e in KarrasDiffusionSchedulers]
a : Union[str, Any] = 2
@register_to_config
def __init__( self, __magic_name__ = 1000, __magic_name__ = 0.0_0085, __magic_name__ = 0.012, __magic_name__ = "linear", __magic_name__ = None, __magic_name__ = "epsilon", __magic_name__ = "linspace", __magic_name__ = 0, ) -> Tuple:
"""simple docstring"""
if trained_betas is not None:
UpperCamelCase__ : int = torch.tensor(__magic_name__, dtype=torch.floataa )
elif beta_schedule == "linear":
UpperCamelCase__ : Dict = torch.linspace(__magic_name__, __magic_name__, __magic_name__, dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
UpperCamelCase__ : List[str] = (
torch.linspace(beta_start**0.5, beta_end**0.5, __magic_name__, dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
UpperCamelCase__ : str = betas_for_alpha_bar(__magic_name__ )
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )
UpperCamelCase__ : Optional[int] = 1.0 - self.betas
UpperCamelCase__ : List[Any] = torch.cumprod(self.alphas, dim=0 )
# set all values
self.set_timesteps(__magic_name__, __magic_name__, __magic_name__ )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__=None ) -> str:
"""simple docstring"""
if schedule_timesteps is None:
UpperCamelCase__ : Dict = self.timesteps
UpperCamelCase__ : Tuple = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
UpperCamelCase__ : List[str] = 1 if len(__magic_name__ ) > 1 else 0
else:
UpperCamelCase__ : List[Any] = timestep.cpu().item() if torch.is_tensor(__magic_name__ ) else timestep
UpperCamelCase__ : int = self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, ) -> torch.FloatTensor:
"""simple docstring"""
UpperCamelCase__ : Tuple = self.index_for_timestep(__magic_name__ )
if self.state_in_first_order:
UpperCamelCase__ : str = self.sigmas[step_index]
else:
UpperCamelCase__ : Optional[int] = self.sigmas_interpol[step_index]
UpperCamelCase__ : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCamelCase__ ( self, __magic_name__, __magic_name__ = None, __magic_name__ = None, ) -> str:
"""simple docstring"""
UpperCamelCase__ : Dict = num_inference_steps
UpperCamelCase__ : Tuple = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
UpperCamelCase__ : Union[str, Any] = np.linspace(0, num_train_timesteps - 1, __magic_name__, dtype=__magic_name__ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
UpperCamelCase__ : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
UpperCamelCase__ : List[str] = (np.arange(0, __magic_name__ ) * step_ratio).round()[::-1].copy().astype(__magic_name__ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
UpperCamelCase__ : Optional[Any] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
UpperCamelCase__ : List[str] = (np.arange(__magic_name__, 0, -step_ratio )).round().copy().astype(__magic_name__ )
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )
UpperCamelCase__ : int = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
UpperCamelCase__ : Optional[Any] = torch.from_numpy(np.log(__magic_name__ ) ).to(__magic_name__ )
UpperCamelCase__ : Any = np.interp(__magic_name__, np.arange(0, len(__magic_name__ ) ), __magic_name__ )
UpperCamelCase__ : Union[str, Any] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
UpperCamelCase__ : Any = torch.from_numpy(__magic_name__ ).to(device=__magic_name__ )
# interpolate sigmas
UpperCamelCase__ : int = sigmas.log().lerp(sigmas.roll(1 ).log(), 0.5 ).exp()
UpperCamelCase__ : List[str] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
UpperCamelCase__ : str = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(__magic_name__ ).startswith('''mps''' ):
# mps does not support float64
UpperCamelCase__ : Optional[Any] = torch.from_numpy(__magic_name__ ).to(__magic_name__, dtype=torch.floataa )
else:
UpperCamelCase__ : List[Any] = torch.from_numpy(__magic_name__ ).to(__magic_name__ )
# interpolate timesteps
UpperCamelCase__ : str = self.sigma_to_t(__magic_name__ ).to(__magic_name__, dtype=timesteps.dtype )
UpperCamelCase__ : Dict = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]), dim=-1 ).flatten()
UpperCamelCase__ : Optional[int] = torch.cat([timesteps[:1], interleaved_timesteps] )
UpperCamelCase__ : List[str] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
UpperCamelCase__ : Dict = defaultdict(__magic_name__ )
def UpperCamelCase__ ( self, __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
# get log sigma
UpperCamelCase__ : Any = sigma.log()
# get distribution
UpperCamelCase__ : List[str] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
UpperCamelCase__ : int = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
UpperCamelCase__ : Optional[Any] = low_idx + 1
UpperCamelCase__ : str = self.log_sigmas[low_idx]
UpperCamelCase__ : int = self.log_sigmas[high_idx]
# interpolate sigmas
UpperCamelCase__ : List[Any] = (low - log_sigma) / (low - high)
UpperCamelCase__ : str = w.clamp(0, 1 )
# transform interpolation to time range
UpperCamelCase__ : Tuple = (1 - w) * low_idx + w * high_idx
UpperCamelCase__ : int = t.view(sigma.shape )
return t
@property
def UpperCamelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
return self.sample is None
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__, __magic_name__ = True, ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
UpperCamelCase__ : List[str] = self.index_for_timestep(__magic_name__ )
# advance index counter by 1
UpperCamelCase__ : int = timestep.cpu().item() if torch.is_tensor(__magic_name__ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
UpperCamelCase__ : Optional[Any] = self.sigmas[step_index]
UpperCamelCase__ : Union[str, Any] = self.sigmas_interpol[step_index + 1]
UpperCamelCase__ : List[Any] = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
UpperCamelCase__ : Tuple = self.sigmas[step_index - 1]
UpperCamelCase__ : Tuple = self.sigmas_interpol[step_index]
UpperCamelCase__ : Dict = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
UpperCamelCase__ : Optional[int] = 0
UpperCamelCase__ : List[Any] = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
UpperCamelCase__ : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_interpol
UpperCamelCase__ : List[str] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
UpperCamelCase__ : List[Any] = sigma_hat if self.state_in_first_order else sigma_interpol
UpperCamelCase__ : Optional[Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError('''prediction_type not implemented yet: sample''' )
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
UpperCamelCase__ : List[Any] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
UpperCamelCase__ : List[str] = sigma_interpol - sigma_hat
# store for 2nd order step
UpperCamelCase__ : Dict = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
UpperCamelCase__ : List[str] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
UpperCamelCase__ : Union[str, Any] = sigma_next - sigma_hat
UpperCamelCase__ : Union[str, Any] = self.sample
UpperCamelCase__ : Dict = None
UpperCamelCase__ : Optional[int] = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__magic_name__ )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__, ) -> torch.FloatTensor:
"""simple docstring"""
# Make sure sigmas and timesteps have the same device and dtype as original_samples
UpperCamelCase__ : List[str] = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__ ):
# mps does not support float64
UpperCamelCase__ : Union[str, Any] = self.timesteps.to(original_samples.device, dtype=torch.floataa )
UpperCamelCase__ : Tuple = timesteps.to(original_samples.device, dtype=torch.floataa )
else:
UpperCamelCase__ : str = self.timesteps.to(original_samples.device )
UpperCamelCase__ : int = timesteps.to(original_samples.device )
UpperCamelCase__ : Any = [self.index_for_timestep(__magic_name__, __magic_name__ ) for t in timesteps]
UpperCamelCase__ : List[str] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
UpperCamelCase__ : int = sigma.unsqueeze(-1 )
UpperCamelCase__ : List[str] = original_samples + noise * sigma
return noisy_samples
def __len__( self ) -> Any:
"""simple docstring"""
return self.config.num_train_timesteps
| 201
| 0
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
lowercase : str = datasets.utils.logging.get_logger(__name__)
lowercase : Union[str, Any] = ['names', 'prefix']
lowercase : Union[str, Any] = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowercase : List[Any] = ['encoding_errors', 'on_bad_lines']
lowercase : Any = ['date_format']
@dataclass
class A ( datasets.BuilderConfig ):
__magic_name__ = ''','''
__magic_name__ = None
__magic_name__ = '''infer'''
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = True
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = False
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = True
__magic_name__ = True
__magic_name__ = False
__magic_name__ = True
__magic_name__ = None
__magic_name__ = '''.'''
__magic_name__ = None
__magic_name__ = '''"'''
__magic_name__ = 0
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = True
__magic_name__ = True
__magic_name__ = 0
__magic_name__ = True
__magic_name__ = False
__magic_name__ = None
__magic_name__ = 10000
__magic_name__ = None
__magic_name__ = '''strict'''
__magic_name__ = '''error'''
__magic_name__ = None
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
if self.delimiter is not None:
A : Optional[Any] = self.delimiter
if self.column_names is not None:
A : Optional[Any] = self.column_names
@property
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : str = {
'''sep''': self.sep,
'''header''': self.header,
'''names''': self.names,
'''index_col''': self.index_col,
'''usecols''': self.usecols,
'''prefix''': self.prefix,
'''mangle_dupe_cols''': self.mangle_dupe_cols,
'''engine''': self.engine,
'''converters''': self.converters,
'''true_values''': self.true_values,
'''false_values''': self.false_values,
'''skipinitialspace''': self.skipinitialspace,
'''skiprows''': self.skiprows,
'''nrows''': self.nrows,
'''na_values''': self.na_values,
'''keep_default_na''': self.keep_default_na,
'''na_filter''': self.na_filter,
'''verbose''': self.verbose,
'''skip_blank_lines''': self.skip_blank_lines,
'''thousands''': self.thousands,
'''decimal''': self.decimal,
'''lineterminator''': self.lineterminator,
'''quotechar''': self.quotechar,
'''quoting''': self.quoting,
'''escapechar''': self.escapechar,
'''comment''': self.comment,
'''encoding''': self.encoding,
'''dialect''': self.dialect,
'''error_bad_lines''': self.error_bad_lines,
'''warn_bad_lines''': self.warn_bad_lines,
'''skipfooter''': self.skipfooter,
'''doublequote''': self.doublequote,
'''memory_map''': self.memory_map,
'''float_precision''': self.float_precision,
'''chunksize''': self.chunksize,
'''encoding_errors''': self.encoding_errors,
'''on_bad_lines''': self.on_bad_lines,
'''date_format''': self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , SCREAMING_SNAKE_CASE ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class A ( datasets.ArrowBasedBuilder ):
__magic_name__ = CsvConfig
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}' )
A : int = dl_manager.download_and_extract(self.config.data_files )
if isinstance(SCREAMING_SNAKE_CASE , (str, list, tuple) ):
A : str = data_files
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : int = [files]
A : Optional[int] = [dl_manager.iter_files(SCREAMING_SNAKE_CASE ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
A : Tuple = []
for split_name, files in data_files.items():
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : List[str] = [files]
A : List[str] = [dl_manager.iter_files(SCREAMING_SNAKE_CASE ) for file in files]
splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE , gen_kwargs={'''files''': files} ) )
return splits
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
A : Optional[int] = self.config.features.arrow_schema
if all(not require_storage_cast(SCREAMING_SNAKE_CASE ) for feature in self.config.features.values() ):
# cheaper cast
A : List[Any] = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=SCREAMING_SNAKE_CASE )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
A : int = table_cast(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return pa_table
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
A : Union[str, Any] = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
A : int = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(SCREAMING_SNAKE_CASE ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE ) ):
A : Union[str, Any] = pd.read_csv(SCREAMING_SNAKE_CASE , iterator=SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(SCREAMING_SNAKE_CASE ):
A : Dict = pa.Table.from_pandas(SCREAMING_SNAKE_CASE )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE )
except ValueError as e:
logger.error(F'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE )}: {e}' )
raise
| 358
|
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
return 1.0 / (1.0 + np.exp(-_outputs ))
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : Optional[int] = np.max(_outputs , axis=-1 , keepdims=snake_case__ )
A : Any = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=snake_case__ )
class A ( __snake_case ):
__magic_name__ = '''sigmoid'''
__magic_name__ = '''softmax'''
__magic_name__ = '''none'''
@add_end_docstrings(
__snake_case , R'''
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `"default"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `"sigmoid"`: Applies the sigmoid function on the output.
- `"softmax"`: Applies the softmax function on the output.
- `"none"`: Does not apply any function on the output.
''' , )
class A ( __snake_case ):
__magic_name__ = False
__magic_name__ = ClassificationFunction.NONE
def __init__( self , **SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE="" , **SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
A : Optional[Any] = tokenizer_kwargs
A : int = {}
if hasattr(self.model.config , '''return_all_scores''' ) and return_all_scores is None:
A : int = self.model.config.return_all_scores
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) or top_k is None:
A : Union[str, Any] = top_k
A : Dict = False
elif return_all_scores is not None:
warnings.warn(
'''`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'''
''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''' , SCREAMING_SNAKE_CASE , )
if return_all_scores:
A : Optional[int] = None
else:
A : Dict = 1
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : Dict = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
A : int = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
A : str = super().__call__(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
A : Any = '''top_k''' not in kwargs
if isinstance(args[0] , SCREAMING_SNAKE_CASE ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Dict[str, GenericTensor]:
"""simple docstring"""
A : List[Any] = self.framework
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return self.tokenizer(**SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) == 1 and isinstance(inputs[0] , SCREAMING_SNAKE_CASE ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''' )
return self.tokenizer(SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return self.model(**SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=1 , SCREAMING_SNAKE_CASE=True ) -> List[str]:
"""simple docstring"""
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
A : Optional[int] = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
A : Any = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , '''function_to_apply''' ) and function_to_apply is None:
A : Optional[int] = self.model.config.function_to_apply
else:
A : Optional[int] = ClassificationFunction.NONE
A : Any = model_outputs['''logits'''][0]
A : List[Any] = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
A : int = sigmoid(SCREAMING_SNAKE_CASE )
elif function_to_apply == ClassificationFunction.SOFTMAX:
A : Any = softmax(SCREAMING_SNAKE_CASE )
elif function_to_apply == ClassificationFunction.NONE:
A : int = outputs
else:
raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
A : int = [
{'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(SCREAMING_SNAKE_CASE )
]
if not _legacy:
dict_scores.sort(key=lambda SCREAMING_SNAKE_CASE : x["score"] , reverse=SCREAMING_SNAKE_CASE )
if top_k is not None:
A : Union[str, Any] = dict_scores[:top_k]
return dict_scores
| 311
| 0
|
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return (gray > 127) & (gray <= 255)
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = np.zeros_like(_UpperCAmelCase )
lowerCamelCase : Optional[Any] = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
lowerCamelCase : Optional[Any] = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
lowerCamelCase : str = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
lowerCamelCase : Union[str, Any] = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
_snake_case = Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
_snake_case = np.array(Image.open(lena_path))
# kernel to be applied
_snake_case = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
_snake_case = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
_snake_case = Image.fromarray(output).convert('''RGB''')
pil_img.save('''result_dilation.png''')
| 283
|
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase : Optional[Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = XGLMTokenizer
_UpperCAmelCase : List[Any] = XGLMTokenizerFast
_UpperCAmelCase : Optional[int] = True
_UpperCAmelCase : Tuple = True
def _SCREAMING_SNAKE_CASE ( self : Tuple):
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE_: List[Any] = XGLMTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__)
tokenizer.save_pretrained(self.tmpdirname)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
SCREAMING_SNAKE_CASE_: Optional[Any] = "<pad>"
SCREAMING_SNAKE_CASE_: int = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__) , lowerCAmelCase__)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__) , lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: Optional[int] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , "<s>")
self.assertEqual(vocab_keys[1] , "<pad>")
self.assertEqual(len(lowerCAmelCase__) , 1008)
def _SCREAMING_SNAKE_CASE ( self : Any):
self.assertEqual(self.get_tokenizer().vocab_size , 1008)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
SCREAMING_SNAKE_CASE_: Optional[int] = XGLMTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = tokenizer.tokenize("This is a test")
self.assertListEqual(lowerCAmelCase__ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE_: List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
lowerCAmelCase__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
SCREAMING_SNAKE_CASE_: Optional[Any] = tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
self.assertListEqual(
lowerCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE_: List[Any] = tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
self.assertListEqual(
lowerCAmelCase__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Any):
return XGLMTokenizer.from_pretrained("facebook/xglm-564M")
def _SCREAMING_SNAKE_CASE ( self : str):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCAmelCase__ , f.name)
SCREAMING_SNAKE_CASE_: Tuple = XGLMTokenizer(f.name , keep_accents=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = pickle.dumps(lowerCAmelCase__)
pickle.loads(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : str):
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE_: Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE_: List[str] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_: Any = "I was born in 92000, and this is falsé."
SCREAMING_SNAKE_CASE_: Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = rust_tokenizer.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[int] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_: str = tokenizer.encode(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__)
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_: Dict = "Hello World!"
SCREAMING_SNAKE_CASE_: Union[str, Any] = [2, 3_1227, 4447, 35]
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__))
@slow
def _SCREAMING_SNAKE_CASE ( self : int):
SCREAMING_SNAKE_CASE_: Union[str, Any] = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
)
# fmt: off
SCREAMING_SNAKE_CASE_: Optional[Any] = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__))
@slow
def _SCREAMING_SNAKE_CASE ( self : int):
# fmt: off
SCREAMING_SNAKE_CASE_: str = {
"input_ids": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
"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, 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]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="facebook/xglm-564M" , padding=lowerCAmelCase__ , )
| 13
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'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:
__a = [
'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
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 370
|
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
], )
def __UpperCAmelCase ( a_: Tuple, a_: Any ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json", "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
_UpperCAmelCase : List[str] = DatasetInfosDict.from_directory(a_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"dataset_info", [
DatasetInfo(),
DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, ),
], )
def __UpperCAmelCase ( a_: Union[str, Any], a_: DatasetInfo ):
_UpperCAmelCase : Tuple = str(a_ )
dataset_info.write_to_directory(a_ )
_UpperCAmelCase : Any = DatasetInfo.from_directory(a_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(a_, "dataset_info.json" ) )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = DatasetInfo(
description="foo", citation="bar", homepage="https://foo.bar", license="CC0", features=Features({"a": Value("int32" )} ), post_processed={}, supervised_keys=(), task_templates=[], builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train", "num_examples": 42}], download_checksums={}, download_size=1_337, post_processing_size=442, dataset_size=1_234, size_in_bytes=1_337 + 442 + 1_234, )
_UpperCAmelCase : Tuple = dataset_info._to_yaml_dict()
assert sorted(a_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key], (list, dict, int, str) )
_UpperCAmelCase : List[Any] = yaml.safe_dump(a_ )
_UpperCAmelCase : Optional[int] = yaml.safe_load(a_ )
assert dataset_info_yaml_dict == reloaded
def __UpperCAmelCase ( ):
_UpperCAmelCase : str = DatasetInfo()
_UpperCAmelCase : List[str] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict", [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1_337 ),
} ),
], )
def __UpperCAmelCase ( a_: str, a_: DatasetInfosDict ):
_UpperCAmelCase : Union[str, Any] = str(a_ )
dataset_infos_dict.write_to_directory(a_ )
_UpperCAmelCase : Union[str, Any] = DatasetInfosDict.from_directory(a_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_UpperCAmelCase : List[str] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(a_, "README.md" ) )
| 17
| 0
|
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__ = {
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}
class lowercase_ ( lowerCamelCase_ ):
'''simple docstring'''
__snake_case = 'xlm'
__snake_case = {
'hidden_size': 'emb_dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
'n_words': 'vocab_size', # For backward compatibility
}
def __init__( self : Dict , __UpperCAmelCase : List[str]=30_145 , __UpperCAmelCase : List[Any]=2_048 , __UpperCAmelCase : int=12 , __UpperCAmelCase : Optional[int]=16 , __UpperCAmelCase : Optional[Any]=0.1 , __UpperCAmelCase : Optional[int]=0.1 , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : Optional[int]=False , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Any=False , __UpperCAmelCase : List[str]=1 , __UpperCAmelCase : int=True , __UpperCAmelCase : Dict=512 , __UpperCAmelCase : str=2_048**-0.5 , __UpperCAmelCase : Union[str, Any]=1e-1_2 , __UpperCAmelCase : Union[str, Any]=0.02 , __UpperCAmelCase : Union[str, Any]=0 , __UpperCAmelCase : Optional[int]=1 , __UpperCAmelCase : Tuple=2 , __UpperCAmelCase : str=3 , __UpperCAmelCase : Tuple=5 , __UpperCAmelCase : int=True , __UpperCAmelCase : Union[str, Any]="first" , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : Any=None , __UpperCAmelCase : str=True , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : Optional[Any]=5 , __UpperCAmelCase : List[Any]=5 , __UpperCAmelCase : Union[str, Any]=0 , __UpperCAmelCase : Optional[Any]=0 , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : Any=0 , **__UpperCAmelCase : Dict , ) ->List[Any]:
"""simple docstring"""
a = vocab_size
a = emb_dim
a = n_layers
a = n_heads
a = dropout
a = attention_dropout
a = gelu_activation
a = sinusoidal_embeddings
a = causal
a = asm
a = n_langs
a = use_lang_emb
a = layer_norm_eps
a = bos_index
a = eos_index
a = pad_index
a = unk_index
a = mask_index
a = is_encoder
a = max_position_embeddings
a = embed_init_std
a = init_std
a = summary_type
a = summary_use_proj
a = summary_activation
a = summary_proj_to_labels
a = summary_first_dropout
a = start_n_top
a = end_n_top
a = mask_token_id
a = lang_id
if "n_words" in kwargs:
a = kwargs["n_words"]
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , **_UpperCamelCase )
class lowercase_ ( lowerCamelCase_ ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self : int ) ->int:
"""simple docstring"""
if self.task == "multiple-choice":
a = {0: "batch", 1: "choice", 2: "sequence"}
else:
a = {0: "batch", 1: "sequence"}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 0
|
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
_snake_case = logging.get_logger('transformers.models.speecht5')
_snake_case = {
'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
_snake_case = {
'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_snake_case = {
'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
_snake_case = {
'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
_snake_case = {
'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_snake_case = {
'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_snake_case = {
'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
_snake_case = {
'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
_snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
_snake_case = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_snake_case = []
_snake_case = [
'encoder.version',
'encoder.layers.*.norm_k.weight',
'encoder.layers.*.norm_k.bias',
'decoder.version',
'decoder.layers.*.norm_k.weight',
'decoder.layers.*.norm_k.bias',
'decoder.pos_emb.pe_k',
'speech_encoder_prenet.embed_positions._float_tensor',
'text_decoder_prenet.embed_positions._float_tensor',
]
_snake_case = IGNORE_KEYS + [
'encoder.proj',
'text_encoder_prenet.*',
'speech_decoder_prenet.*',
'speech_decoder_postnet.*',
]
_snake_case = IGNORE_KEYS + [
'encoder.proj',
'speech_encoder_prenet.*',
'text_decoder_prenet.*',
'text_decoder_postnet.*',
]
_snake_case = IGNORE_KEYS + [
'encoder.proj',
'text_encoder_prenet.*',
'text_decoder_prenet.*',
'text_decoder_postnet.*',
]
def _A ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> Optional[Any]:
for attribute in key.split("." ):
_lowercase : Dict = getattr(snake_case , snake_case )
if weight_type is not None:
_lowercase : Union[str, Any] = getattr(snake_case , snake_case ).shape
else:
_lowercase : int = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
_lowercase : str = value
elif weight_type == "weight_g":
_lowercase : List[str] = value
elif weight_type == "weight_v":
_lowercase : int = value
elif weight_type == "bias":
_lowercase : Union[str, Any] = value
elif weight_type == "running_mean":
_lowercase : Union[str, Any] = value
elif weight_type == "running_var":
_lowercase : Optional[Any] = value
elif weight_type == "num_batches_tracked":
_lowercase : Tuple = value
else:
_lowercase : int = value
logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )
def _A ( snake_case , snake_case ) -> str:
for key in ignore_keys:
if key.endswith(".*" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
_lowercase , _lowercase : Optional[Any] = key.split(".*." )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def _A ( snake_case , snake_case , snake_case ) -> Tuple:
_lowercase : List[Any] = []
if task == "s2t":
_lowercase : str = hf_model.speechta.encoder.prenet.feature_encoder
_lowercase : List[str] = MAPPING_S2T
_lowercase : Optional[Any] = IGNORE_KEYS_S2T
elif task == "t2s":
_lowercase : List[Any] = None
_lowercase : Tuple = MAPPING_T2S
_lowercase : List[str] = IGNORE_KEYS_T2S
elif task == "s2s":
_lowercase : Union[str, Any] = hf_model.speechta.encoder.prenet.feature_encoder
_lowercase : Optional[Any] = MAPPING_S2S
_lowercase : Tuple = IGNORE_KEYS_S2S
else:
raise ValueError(F'''Unsupported task: {task}''' )
for name, value in fairseq_dict.items():
if should_ignore(snake_case , snake_case ):
logger.info(F'''{name} was ignored''' )
continue
_lowercase : Optional[Any] = False
if "conv_layers" in name:
load_conv_layer(
snake_case , snake_case , snake_case , snake_case , hf_model.config.feat_extract_norm == "group" , )
_lowercase : Optional[int] = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
_lowercase , _lowercase : Optional[int] = key.split(".*." )
if prefix in name and suffix in name:
_lowercase : Dict = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
_lowercase : Optional[Any] = True
if "*" in mapped_key:
_lowercase : int = name.split(snake_case )[0].split("." )[-2]
_lowercase : Union[str, Any] = mapped_key.replace("*" , snake_case )
if "weight_g" in name:
_lowercase : Dict = "weight_g"
elif "weight_v" in name:
_lowercase : Optional[Any] = "weight_v"
elif "bias" in name:
_lowercase : Any = "bias"
elif "weight" in name:
_lowercase : Dict = "weight"
elif "running_mean" in name:
_lowercase : List[Any] = "running_mean"
elif "running_var" in name:
_lowercase : Union[str, Any] = "running_var"
elif "num_batches_tracked" in name:
_lowercase : str = "num_batches_tracked"
else:
_lowercase : str = None
set_recursively(snake_case , snake_case , snake_case , snake_case , snake_case )
continue
if not is_used:
unused_weights.append(snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def _A ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> List[str]:
_lowercase : Optional[int] = full_name.split("conv_layers." )[-1]
_lowercase : Tuple = name.split("." )
_lowercase : Optional[int] = int(items[0] )
_lowercase : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
_lowercase : Union[str, Any] = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
_lowercase : int = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
_lowercase : Optional[int] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
_lowercase : Tuple = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(snake_case )
@torch.no_grad()
def _A ( snake_case , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , ) -> Optional[Any]:
if config_path is not None:
_lowercase : List[str] = SpeechTaConfig.from_pretrained(snake_case )
else:
_lowercase : int = SpeechTaConfig()
if task == "s2t":
_lowercase : List[Any] = config.max_text_positions
_lowercase : Optional[int] = SpeechTaForSpeechToText(snake_case )
elif task == "t2s":
_lowercase : str = 18_76
_lowercase : str = 6_00
_lowercase : List[Any] = config.max_speech_positions
_lowercase : Optional[int] = SpeechTaForTextToSpeech(snake_case )
elif task == "s2s":
_lowercase : Tuple = 18_76
_lowercase : List[Any] = config.max_speech_positions
_lowercase : str = SpeechTaForSpeechToSpeech(snake_case )
else:
raise ValueError(F'''Unknown task name: {task}''' )
if vocab_path:
_lowercase : Any = SpeechTaTokenizer(snake_case , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
_lowercase : Optional[int] = AddedToken("<mask>" , lstrip=snake_case , rstrip=snake_case )
_lowercase : int = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
_lowercase : Dict = SpeechTaFeatureExtractor()
_lowercase : str = SpeechTaProcessor(tokenizer=snake_case , feature_extractor=snake_case )
processor.save_pretrained(snake_case )
_lowercase : Union[str, Any] = torch.load(snake_case )
recursively_load_weights(fairseq_checkpoint["model"] , snake_case , snake_case )
model.save_pretrained(snake_case )
if repo_id:
print("Pushing to the hub..." )
processor.push_to_hub(snake_case )
model.push_to_hub(snake_case )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
'--task',
default='s2t',
type=str,
help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
_snake_case = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 250
| 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=_SCREAMING_SNAKE_CASE )
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = field(default="image-classification", metadata={"include_in_asdict_even_if_is_default": True} )
__lowerCAmelCase = Features({"image": Image()} )
__lowerCAmelCase = Features({"labels": ClassLabel} )
__lowerCAmelCase = "image"
__lowerCAmelCase = "labels"
def SCREAMING_SNAKE_CASE ( self , __A ) -> Tuple:
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] , __A ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
a =copy.deepcopy(self )
a =self.label_schema.copy()
a =features[self.label_column]
a =label_schema
return task_template
@property
def SCREAMING_SNAKE_CASE ( self ) -> Dict[str, str]:
return {
self.image_column: "image",
self.label_column: "labels",
}
| 215
|
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class __A :
"""simple docstring"""
__lowerCAmelCase = 42
# setable values
__lowerCAmelCase = 42
__lowerCAmelCase = 42
__lowerCAmelCase = None
@classmethod
def SCREAMING_SNAKE_CASE ( cls , __A , __A , __A ) -> List[str]:
return cls(common=__A , init_noise_sigma=__A , timesteps=__A )
@dataclass
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = 42
class __A ( _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
__lowerCAmelCase = 42
@property
def SCREAMING_SNAKE_CASE ( self ) -> str:
return True
@register_to_config
def __init__( self , __A = 1000 , __A = 0.0_001 , __A = 0.02 , __A = "linear" , __A = None , __A = "fixed_small" , __A = True , __A = "epsilon" , __A = jnp.floataa , ) -> List[Any]:
a =dtype
def SCREAMING_SNAKE_CASE ( self , __A = None ) -> DDPMSchedulerState:
if common is None:
a =CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
a =jnp.array(1.0 , dtype=self.dtype )
a =jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=__A , init_noise_sigma=__A , timesteps=__A , )
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A = None ) -> jnp.ndarray:
return sample
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A = () ) -> DDPMSchedulerState:
a =self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
a =(jnp.arange(0 , __A ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=__A , timesteps=__A , )
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A=None , __A=None ) -> str:
a =state.common.alphas_cumprod[t]
a =jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
a =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
a =self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
a =jnp.clip(__A , a_min=1E-2_0 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
a =jnp.log(jnp.clip(__A , a_min=1E-2_0 ) )
elif variance_type == "fixed_large":
a =state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
a =jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
a =variance
a =state.common.betas[t]
a =(predicted_variance + 1) / 2
a =frac * max_log + (1 - frac) * min_log
return variance
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A , __A , __A = None , __A = True , ) -> Union[FlaxDDPMSchedulerOutput, Tuple]:
a =timestep
if key is None:
a =jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
a , a =jnp.split(__A , sample.shape[1] , axis=1 )
else:
a =None
# 1. compute alphas, betas
a =state.common.alphas_cumprod[t]
a =jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
a =1 - alpha_prod_t
a =1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
a =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
a =model_output
elif self.config.prediction_type == "v_prediction":
a =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
''' for the FlaxDDPMScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
a =jnp.clip(__A , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
a =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
a =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
a =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
a =jax.random.split(__A , num=1 )
a =jax.random.normal(__A , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(__A , __A , predicted_variance=__A ) ** 0.5) * noise
a =jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
a =pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=__A , state=__A )
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A , __A , ) -> jnp.ndarray:
return add_noise_common(state.common , __A , __A , __A )
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A , __A , ) -> jnp.ndarray:
return get_velocity_common(state.common , __A , __A , __A )
def __len__( self ) -> Optional[int]:
return self.config.num_train_timesteps
| 215
| 1
|
class lowercase__:
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : Union[str, Any]=None ) -> List[str]:
lowercase_ = data
lowercase_ = previous
lowercase_ = next_node
def __str__( self : Tuple ) -> str:
return f'''{self.data}'''
def _lowercase ( self : Any ) -> int:
return self.data
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
return self.next
def _lowercase ( self : Tuple ) -> Any:
return self.previous
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int ) -> str:
lowercase_ = head
def __iter__( self : Union[str, Any] ) -> str:
return self
def _lowercase ( self : List[str] ) -> Tuple:
if not self.current:
raise StopIteration
else:
lowercase_ = self.current.get_data()
lowercase_ = self.current.get_next()
return value
class lowercase__:
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> Any:
lowercase_ = None # First node in list
lowercase_ = None # Last node in list
def __str__( self : Union[str, Any] ) -> Union[str, Any]:
lowercase_ = self.head
lowercase_ = []
while current is not None:
nodes.append(current.get_data() )
lowercase_ = current.get_next()
return " ".join(str(SCREAMING_SNAKE_CASE_ ) for node in nodes )
def __contains__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int ) -> List[Any]:
lowercase_ = self.head
while current:
if current.get_data() == value:
return True
lowercase_ = current.get_next()
return False
def __iter__( self : Dict ) -> Tuple:
return LinkedListIterator(self.head )
def _lowercase ( self : Any ) -> Optional[Any]:
if self.head:
return self.head.get_data()
return None
def _lowercase ( self : Any ) -> Any:
if self.tail:
return self.tail.get_data()
return None
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : Node ) -> None:
if self.head is None:
lowercase_ = node
lowercase_ = node
else:
self.insert_before_node(self.head , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Node ) -> None:
if self.head is None:
self.set_head(SCREAMING_SNAKE_CASE_ )
else:
self.insert_after_node(self.tail , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : int ) -> None:
lowercase_ = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
self.set_head(SCREAMING_SNAKE_CASE_ )
else:
self.set_tail(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Node , SCREAMING_SNAKE_CASE_ : Node ) -> None:
lowercase_ = node
lowercase_ = node.previous
if node.get_previous() is None:
lowercase_ = node_to_insert
else:
lowercase_ = node_to_insert
lowercase_ = node_to_insert
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Node , SCREAMING_SNAKE_CASE_ : Node ) -> None:
lowercase_ = node
lowercase_ = node.next
if node.get_next() is None:
lowercase_ = node_to_insert
else:
lowercase_ = node_to_insert
lowercase_ = node_to_insert
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> None:
lowercase_ = 1
lowercase_ = Node(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.head
while node:
if current_position == position:
self.insert_before_node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return
current_position += 1
lowercase_ = node.next
self.insert_after_node(self.tail , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : int ) -> Node:
lowercase_ = self.head
while node:
if node.get_data() == item:
return node
lowercase_ = node.get_next()
raise Exception('''Node not found''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : str ) -> List[Any]:
if (node := self.get_node(SCREAMING_SNAKE_CASE_ )) is not None:
if node == self.head:
lowercase_ = self.head.get_next()
if node == self.tail:
lowercase_ = self.tail.get_previous()
self.remove_node_pointers(SCREAMING_SNAKE_CASE_ )
@staticmethod
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node ) -> None:
if node.get_next():
lowercase_ = node.previous
if node.get_previous():
lowercase_ = node.next
lowercase_ = None
lowercase_ = None
def _lowercase ( self : Tuple ) -> Tuple:
return self.head is None
def a ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
|
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, 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.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class __lowerCAmelCase :
'''simple docstring'''
def __init__(self : str , UpperCamelCase : Tuple , UpperCamelCase : Optional[int]=99 , UpperCamelCase : Optional[int]=13 , UpperCamelCase : Tuple=16 , UpperCamelCase : Union[str, Any]=7 , UpperCamelCase : List[Any]=True , UpperCamelCase : List[str]=True , UpperCamelCase : str=True , UpperCamelCase : Tuple=False , UpperCamelCase : str=True , UpperCamelCase : Tuple=2 , UpperCamelCase : Optional[int]=32 , UpperCamelCase : Any=4 , UpperCamelCase : Optional[int]=4 , UpperCamelCase : Tuple=30 , UpperCamelCase : str=0 , UpperCamelCase : Tuple=1 , UpperCamelCase : List[Any]=2 , UpperCamelCase : str=None , ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = decoder_seq_length
# For common tests
lowercase__ = self.decoder_seq_length
lowercase__ = is_training
lowercase__ = use_attention_mask
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = d_model
lowercase__ = d_model
lowercase__ = decoder_layers
lowercase__ = decoder_layers
lowercase__ = decoder_ffn_dim
lowercase__ = decoder_attention_heads
lowercase__ = decoder_attention_heads
lowercase__ = eos_token_id
lowercase__ = bos_token_id
lowercase__ = pad_token_id
lowercase__ = decoder_start_token_id
lowercase__ = use_cache
lowercase__ = max_position_embeddings
lowercase__ = None
lowercase__ = decoder_seq_length
lowercase__ = 2
lowercase__ = 1
def UpperCamelCase__ (self : str ):
'''simple docstring'''
lowercase__ = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
lowercase__ = None
if self.use_attention_mask:
lowercase__ = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
lowercase__ = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def UpperCamelCase__ (self : Tuple , UpperCamelCase : List[Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Tuple , UpperCamelCase : List[str] , ):
'''simple docstring'''
lowercase__ = True
lowercase__ = TrOCRDecoder(config=UpperCamelCase ).to(UpperCamelCase ).eval()
lowercase__ = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
lowercase__ = model(UpperCamelCase , use_cache=UpperCamelCase )
lowercase__ = model(UpperCamelCase )
lowercase__ = model(UpperCamelCase , use_cache=UpperCamelCase )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) + 1 )
lowercase__ = outputs['''past_key_values''']
# create hypothetical next token and extent to next_input_ids
lowercase__ = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
lowercase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase__ = model(UpperCamelCase )['''last_hidden_state''']
lowercase__ = model(UpperCamelCase , past_key_values=UpperCamelCase )['''last_hidden_state''']
# select random slice
lowercase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase__ = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
lowercase__ = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 )
def UpperCamelCase__ (self : Optional[Any] ):
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ = config_and_inputs
lowercase__ = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase (lowercase_ , lowercase_ , lowercase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
lowerCAmelCase__ : List[Any] = (TrOCRForCausalLM,) if is_torch_available() else ()
lowerCAmelCase__ : Optional[Any] = {"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
lowerCAmelCase__ : Optional[Any] = True
lowerCAmelCase__ : List[str] = False
def UpperCamelCase__ (self : Any ):
'''simple docstring'''
lowercase__ = TrOCRStandaloneDecoderModelTester(self , is_training=UpperCamelCase )
lowercase__ = ConfigTester(self , config_class=UpperCamelCase )
def UpperCamelCase__ (self : List[str] ):
'''simple docstring'''
pass
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
pass
def UpperCamelCase__ (self : Any ):
'''simple docstring'''
pass
def UpperCamelCase__ (self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase )
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
return
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def UpperCamelCase__ (self : List[str] ):
'''simple docstring'''
pass
| 2
| 0
|
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : int = {
'''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}
class __UpperCamelCase ( __lowercase ):
lowerCamelCase : Any ="""align_text_model"""
def __init__( self , lowerCAmelCase__=3_0522 , lowerCAmelCase__=768 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=3072 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=512 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-12 , lowerCAmelCase__=0 , lowerCAmelCase__="absolute" , lowerCAmelCase__=True , **lowerCAmelCase__ , ) -> List[str]:
super().__init__(**_a )
a : List[str] = vocab_size
a : Union[str, Any] = hidden_size
a : str = num_hidden_layers
a : Tuple = num_attention_heads
a : str = hidden_act
a : List[Any] = intermediate_size
a : List[str] = hidden_dropout_prob
a : Tuple = attention_probs_dropout_prob
a : Optional[int] = max_position_embeddings
a : str = type_vocab_size
a : Optional[Any] = initializer_range
a : List[str] = layer_norm_eps
a : Optional[int] = position_embedding_type
a : List[str] = use_cache
a : Optional[Any] = pad_token_id
@classmethod
def __a ( cls , lowerCAmelCase__ , **lowerCAmelCase__ ) -> "PretrainedConfig":
cls._set_token_in_kwargs(_a )
a, a : Optional[int] = cls.get_config_dict(_a , **_a )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
a : List[str] = 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 __UpperCamelCase ( __lowercase ):
lowerCamelCase : Optional[Any] ="""align_vision_model"""
def __init__( self , lowerCAmelCase__ = 3 , lowerCAmelCase__ = 600 , lowerCAmelCase__ = 2.0 , lowerCAmelCase__ = 3.1 , lowerCAmelCase__ = 8 , lowerCAmelCase__ = [3, 3, 5, 3, 5, 5, 3] , lowerCAmelCase__ = [32, 16, 24, 40, 80, 112, 192] , lowerCAmelCase__ = [16, 24, 40, 80, 112, 192, 320] , lowerCAmelCase__ = [] , lowerCAmelCase__ = [1, 2, 2, 2, 1, 2, 1] , lowerCAmelCase__ = [1, 2, 2, 3, 3, 4, 1] , lowerCAmelCase__ = [1, 6, 6, 6, 6, 6, 6] , lowerCAmelCase__ = 0.25 , lowerCAmelCase__ = "swish" , lowerCAmelCase__ = 2560 , lowerCAmelCase__ = "mean" , lowerCAmelCase__ = 0.02 , lowerCAmelCase__ = 0.001 , lowerCAmelCase__ = 0.99 , lowerCAmelCase__ = 0.2 , **lowerCAmelCase__ , ) -> int:
super().__init__(**_a )
a : Union[str, Any] = num_channels
a : List[str] = image_size
a : List[Any] = width_coefficient
a : Union[str, Any] = depth_coefficient
a : Dict = depth_divisor
a : Optional[Any] = kernel_sizes
a : List[str] = in_channels
a : Optional[int] = out_channels
a : List[str] = depthwise_padding
a : List[Any] = strides
a : Dict = num_block_repeats
a : Union[str, Any] = expand_ratios
a : str = squeeze_expansion_ratio
a : Optional[Any] = hidden_act
a : str = hidden_dim
a : str = pooling_type
a : Dict = initializer_range
a : int = batch_norm_eps
a : int = batch_norm_momentum
a : List[str] = drop_connect_rate
a : int = sum(_a ) * 4
@classmethod
def __a ( cls , lowerCAmelCase__ , **lowerCAmelCase__ ) -> "PretrainedConfig":
cls._set_token_in_kwargs(_a )
a, a : Optional[int] = cls.get_config_dict(_a , **_a )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
a : Tuple = 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 __UpperCamelCase ( __lowercase ):
lowerCamelCase : List[Any] ="""align"""
lowerCamelCase : Dict =True
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=640 , lowerCAmelCase__=1.0 , lowerCAmelCase__=0.02 , **lowerCAmelCase__ , ) -> Any:
super().__init__(**_a )
if text_config is None:
a : Union[str, Any] = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
a : Tuple = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
a : Union[str, Any] = AlignTextConfig(**_a )
a : Union[str, Any] = AlignVisionConfig(**_a )
a : Union[str, Any] = projection_dim
a : List[str] = temperature_init_value
a : Optional[Any] = initializer_range
@classmethod
def __a ( cls , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ) -> Optional[int]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_a )
def __a ( self ) -> Union[str, Any]:
a : Dict = copy.deepcopy(self.__dict__ )
a : Dict = self.text_config.to_dict()
a : Optional[int] = self.vision_config.to_dict()
a : str = self.__class__.model_type
return output
| 350
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[int] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
lowerCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 124
|
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
a : str = getLogger(__name__)
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 8 , __magic_name__ = 1024 , __magic_name__="val" , __magic_name__=None , __magic_name__=False , __magic_name__="summarization" , __magic_name__=None , __magic_name__=1 , __magic_name__ = None , __magic_name__="" , **__magic_name__ , ):
'''simple docstring'''
UpperCAmelCase : List[Any] = str(__magic_name__ )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=__magic_name__ )
UpperCAmelCase : List[str] = Path(__magic_name__ )
UpperCAmelCase : Dict = save_dir.joinpath(F"rank_{local_rank}_output.json" )
torch.cuda.set_device(__magic_name__ )
UpperCAmelCase : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained(__magic_name__ ).cuda()
if fpaa:
UpperCAmelCase : int = model.half()
# determine if we need to increase num_beams
use_task_specific_params(__magic_name__ , __magic_name__ ) # update config with task specific params
UpperCAmelCase : Dict = generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
UpperCAmelCase : Optional[Any] = num_return_sequences
UpperCAmelCase : Dict = AutoTokenizer.from_pretrained(__magic_name__ )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
if max_source_length is None:
UpperCAmelCase : Any = tokenizer.model_max_length
if prefix is None:
UpperCAmelCase : Tuple = prefix or getattr(model.config , "prefix" , "" ) or ""
UpperCAmelCase : Dict = SeqaSeqDataset(
__magic_name__ , __magic_name__ , __magic_name__ , max_target_length=1024 , type_path=__magic_name__ , n_obs=__magic_name__ , prefix=__magic_name__ , **__magic_name__ , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
UpperCAmelCase : int = ds.make_sortish_sampler(__magic_name__ , distributed=__magic_name__ , add_extra_examples=__magic_name__ , shuffle=__magic_name__ )
UpperCAmelCase : List[Any] = DataLoader(__magic_name__ , sampler=__magic_name__ , batch_size=__magic_name__ , collate_fn=ds.collate_fn )
UpperCAmelCase : Any = []
for batch in tqdm(__magic_name__ ):
UpperCAmelCase : List[Any] = model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=__magic_name__ , num_beams=__magic_name__ , **__magic_name__ , )
UpperCAmelCase : Optional[int] = tokenizer.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ , clean_up_tokenization_spaces=__magic_name__ )
UpperCAmelCase : int = batch["ids"]
if num_return_sequences > 1:
UpperCAmelCase : List[Any] = chunks(__magic_name__ , __magic_name__ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(__magic_name__ ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(__magic_name__ , __magic_name__ )
return results, sampler.num_replicas
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : str = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=__magic_name__ , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=__magic_name__ , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=__magic_name__ , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=__magic_name__ , default=__magic_name__ )
parser.add_argument(
"--type_path" , type=__magic_name__ , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=__magic_name__ , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=__magic_name__ , default=8 , required=__magic_name__ , help="batch size" )
parser.add_argument(
"--local_rank" , type=__magic_name__ , default=-1 , required=__magic_name__ , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=__magic_name__ , default=__magic_name__ , required=__magic_name__ , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=__magic_name__ , default=1 , required=__magic_name__ , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=__magic_name__ , default=600 , required=__magic_name__ , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=__magic_name__ , default=__magic_name__ , required=__magic_name__ )
parser.add_argument("--tgt_lang" , type=__magic_name__ , default=__magic_name__ , required=__magic_name__ )
parser.add_argument(
"--prefix" , type=__magic_name__ , required=__magic_name__ , default=__magic_name__ , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
UpperCAmelCase : Union[str, Any] = time.time()
UpperCAmelCase , UpperCAmelCase : Dict = parser.parse_known_args()
UpperCAmelCase : Tuple = parse_numeric_n_bool_cl_kwargs(__magic_name__ )
if generate_kwargs and args.local_rank <= 0:
print(F"parsed the following generate kwargs: {generate_kwargs}" )
UpperCAmelCase : Union[str, Any] = Path(args.save_dir + "_tmp" )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ ) # this handles locking.
UpperCAmelCase : List[Any] = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F"Found files at {json_save_dir} please move or remove them." )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
UpperCAmelCase : Optional[Any] = {}
if args.src_lang is not None:
UpperCAmelCase : List[str] = args.src_lang
if args.tgt_lang is not None:
UpperCAmelCase : Dict = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=__magic_name__ )
UpperCAmelCase , UpperCAmelCase : str = eval_data_dir(
args.data_dir , __magic_name__ , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=__magic_name__ , **__magic_name__ , )
if args.local_rank <= 0:
UpperCAmelCase : List[str] = Path(args.save_dir )
save_dir.mkdir(exist_ok=__magic_name__ )
UpperCAmelCase : str = gather_results_from_each_node(__magic_name__ , __magic_name__ , args.sync_timeout )
UpperCAmelCase : Dict = combine_partial_results(__magic_name__ )
if args.num_return_sequences > 1:
UpperCAmelCase : int = save_dir.joinpath("pseudolabel_results.json" )
print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/" )
save_json(__magic_name__ , __magic_name__ )
return
UpperCAmelCase : Dict = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(__magic_name__ ) as f:
UpperCAmelCase : Dict = [x.rstrip() for x in f.readlines()][: len(__magic_name__ )]
# Calculate metrics, save metrics, and save _generations.txt
UpperCAmelCase : Optional[int] = "translation" in args.task
UpperCAmelCase : str = calculate_bleu if calc_bleu else calculate_rouge
UpperCAmelCase : Tuple = "bleu" if calc_bleu else "rouge"
UpperCAmelCase : Dict = score_fn(__magic_name__ , __magic_name__ )
UpperCAmelCase : Any = len(__magic_name__ )
UpperCAmelCase : Union[str, Any] = time.time() - start_time
UpperCAmelCase : Dict = round(runtime / metrics["n_obs"] , 4 )
UpperCAmelCase : Optional[Any] = num_replicas
# TODO(@stas00): add whatever metadata to metrics
UpperCAmelCase : Dict = save_dir.joinpath(F"{args.type_path}_{metric_name}.json" )
save_json(__magic_name__ , __magic_name__ , indent=__magic_name__ )
print(__magic_name__ )
write_txt_file(__magic_name__ , save_dir.joinpath(F"{args.type_path}_generations.txt" ) )
if args.debug:
write_txt_file(__magic_name__ , save_dir.joinpath(F"{args.type_path}.target" ) )
else:
shutil.rmtree(__magic_name__ )
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Tuple = []
for partial_result in partial_results:
records.extend(__magic_name__ )
UpperCAmelCase : Optional[Any] = sorted(__magic_name__ , key=lambda __magic_name__ : x["id"] )
UpperCAmelCase : List[Any] = [x["pred"] for x in records]
return preds
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Dict = time.time()
logger.info("waiting for all nodes to finish" )
UpperCAmelCase : Union[str, Any] = None
while (time.time() - start_wait) < timeout:
UpperCAmelCase : Dict = list(save_dir.glob("rank_*.json" ) )
if len(__magic_name__ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
UpperCAmelCase : List[str] = lmap(__magic_name__ , __magic_name__ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 311
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenization_lxmert": ["LxmertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ["LxmertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"LxmertEncoder",
"LxmertForPreTraining",
"LxmertForQuestionAnswering",
"LxmertModel",
"LxmertPreTrainedModel",
"LxmertVisualFeatureEncoder",
"LxmertXLayer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLxmertForPreTraining",
"TFLxmertMainLayer",
"TFLxmertModel",
"TFLxmertPreTrainedModel",
"TFLxmertVisualFeatureEncoder",
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 354
|
"""simple docstring"""
__lowercase = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
__lowercase = frozenset(["""prompt""", """negative_prompt"""])
__lowercase = frozenset([])
__lowercase = frozenset(["""image"""])
__lowercase = frozenset(
[
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__lowercase = frozenset(["""image"""])
__lowercase = frozenset(
[
"""prompt""",
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
__lowercase = frozenset(["""prompt""", """image""", """negative_prompt"""])
__lowercase = frozenset(
[
# Text guided image variation with an image mask
"""prompt""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
__lowercase = frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])
__lowercase = frozenset(
[
# image variation with an image mask
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__lowercase = frozenset(["""image""", """mask_image"""])
__lowercase = frozenset(
[
"""example_image""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__lowercase = frozenset(["""example_image""", """image""", """mask_image"""])
__lowercase = frozenset(["""class_labels"""])
__lowercase = frozenset(["""class_labels"""])
__lowercase = frozenset(["""batch_size"""])
__lowercase = frozenset([])
__lowercase = frozenset(["""batch_size"""])
__lowercase = frozenset([])
__lowercase = frozenset(
[
"""prompt""",
"""audio_length_in_s""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
__lowercase = frozenset(["""prompt""", """negative_prompt"""])
__lowercase = frozenset(["""input_tokens"""])
__lowercase = frozenset(["""input_tokens"""])
| 226
| 0
|
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ = 9, 14 # noqa: F841
UpperCAmelCase__ = [
[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],
]
UpperCAmelCase__ = defaultdict(__A )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
UpperCAmelCase__ = mst(__A )
UpperCAmelCase__ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
UpperCAmelCase__ = tuple(answer[:2] )
UpperCAmelCase__ = tuple(edge[::-1] )
assert edge in result or reverse in result
| 65
|
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _lowerCAmelCase :
"""simple docstring"""
@staticmethod
def _lowercase ( *UpperCAmelCase__ : Tuple, **UpperCAmelCase__ : List[Any] ):
pass
def _A ( UpperCamelCase_ : Union[str, Any]) -> Any:
'''simple docstring'''
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
_a = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _lowercase ( self : Optional[Any], UpperCAmelCase__ : Optional[Any], UpperCAmelCase__ : Dict, UpperCAmelCase__ : Optional[Any] ):
__lowercase = pipeline(
"document-question-answering", model=UpperCAmelCase__, tokenizer=UpperCAmelCase__, image_processor=UpperCAmelCase__ )
__lowercase = INVOICE_URL
__lowercase = list(zip(*apply_tesseract(load_image(UpperCAmelCase__ ), UpperCAmelCase__, "" ) ) )
__lowercase = "What is the placebo?"
__lowercase = [
{
"image": load_image(UpperCAmelCase__ ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def _lowercase ( self : int, UpperCAmelCase__ : Tuple, UpperCAmelCase__ : Any ):
__lowercase = dqa_pipeline(UpperCAmelCase__, top_k=2 )
self.assertEqual(
UpperCAmelCase__, [
[
{"score": ANY(UpperCAmelCase__ ), "answer": ANY(UpperCAmelCase__ ), "start": ANY(UpperCAmelCase__ ), "end": ANY(UpperCAmelCase__ )},
{"score": ANY(UpperCAmelCase__ ), "answer": ANY(UpperCAmelCase__ ), "start": ANY(UpperCAmelCase__ ), "end": ANY(UpperCAmelCase__ )},
]
]
* 3, )
@require_torch
@require_detectrona
@require_pytesseract
def _lowercase ( self : Dict ):
__lowercase = pipeline("document-question-answering", model="hf-internal-testing/tiny-random-layoutlmv2" )
__lowercase = INVOICE_URL
__lowercase = "How many cats are there?"
__lowercase = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 3_8, "end": 3_9},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 3_8, "end": 4_0},
]
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, top_k=2 )
self.assertEqual(nested_simplify(UpperCAmelCase__, decimals=4 ), UpperCAmelCase__ )
__lowercase = dqa_pipeline({"image": image, "question": question}, top_k=2 )
self.assertEqual(nested_simplify(UpperCAmelCase__, decimals=4 ), UpperCAmelCase__ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowercase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, top_k=2 )
self.assertEqual(UpperCAmelCase__, [] )
# We can optionnally pass directly the words and bounding boxes
__lowercase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
__lowercase = []
__lowercase = []
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, words=UpperCAmelCase__, boxes=UpperCAmelCase__, top_k=2 )
self.assertEqual(UpperCAmelCase__, [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _lowercase ( self : List[str] ):
__lowercase = pipeline(
"document-question-answering", model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa", revision="9977165", )
__lowercase = INVOICE_URL
__lowercase = "What is the invoice number?"
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.9_944, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.0_009, "answer": "us-001", "start": 1_6, "end": 1_6},
], )
__lowercase = dqa_pipeline({"image": image, "question": question}, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.9_944, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.0_009, "answer": "us-001", "start": 1_6, "end": 1_6},
], )
__lowercase = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
[
{"score": 0.9_944, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.0_009, "answer": "us-001", "start": 1_6, "end": 1_6},
],
]
* 2, )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _lowercase ( self : Dict ):
__lowercase = pipeline(
"document-question-answering", model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa", revision="9977165", max_seq_len=5_0, )
__lowercase = INVOICE_URL
__lowercase = "What is the invoice number?"
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.9_974, "answer": "1110212019", "start": 2_3, "end": 2_3},
{"score": 0.9_948, "answer": "us-001", "start": 1_6, "end": 1_6},
], )
__lowercase = dqa_pipeline({"image": image, "question": question}, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.9_974, "answer": "1110212019", "start": 2_3, "end": 2_3},
{"score": 0.9_948, "answer": "us-001", "start": 1_6, "end": 1_6},
], )
__lowercase = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
[
{"score": 0.9_974, "answer": "1110212019", "start": 2_3, "end": 2_3},
{"score": 0.9_948, "answer": "us-001", "start": 1_6, "end": 1_6},
]
]
* 2, )
@slow
@require_torch
@require_pytesseract
@require_vision
def _lowercase ( self : Optional[Any] ):
__lowercase = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa", revision="3dc6de3", add_prefix_space=UpperCAmelCase__ )
__lowercase = pipeline(
"document-question-answering", model="impira/layoutlm-document-qa", tokenizer=UpperCAmelCase__, revision="3dc6de3", )
__lowercase = INVOICE_URL
__lowercase = "What is the invoice number?"
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
], )
__lowercase = dqa_pipeline({"image": image, "question": question}, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
], )
__lowercase = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
[
{"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
]
]
* 2, )
__lowercase = list(zip(*apply_tesseract(load_image(UpperCAmelCase__ ), UpperCAmelCase__, "" ) ) )
# This model should also work if `image` is set to None
__lowercase = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
], )
@slow
@require_torch
@require_pytesseract
@require_vision
def _lowercase ( self : Union[str, Any] ):
__lowercase = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa", revision="3dc6de3", add_prefix_space=UpperCAmelCase__ )
__lowercase = pipeline(
"document-question-answering", model="impira/layoutlm-document-qa", tokenizer=UpperCAmelCase__, revision="3dc6de3", max_seq_len=5_0, )
__lowercase = INVOICE_URL
__lowercase = "What is the invoice number?"
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.9_999, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.9_998, "answer": "us-001", "start": 1_6, "end": 1_6},
], )
__lowercase = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
[
{"score": 0.9_999, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.9_998, "answer": "us-001", "start": 1_6, "end": 1_6},
]
]
* 2, )
__lowercase = list(zip(*apply_tesseract(load_image(UpperCAmelCase__ ), UpperCAmelCase__, "" ) ) )
# This model should also work if `image` is set to None
__lowercase = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}, top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__, decimals=4 ), [
{"score": 0.9_999, "answer": "us-001", "start": 1_6, "end": 1_6},
{"score": 0.9_998, "answer": "us-001", "start": 1_6, "end": 1_6},
], )
@slow
@require_torch
def _lowercase ( self : Dict ):
__lowercase = pipeline(
"document-question-answering", model="naver-clova-ix/donut-base-finetuned-docvqa", tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ), feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa", )
__lowercase = INVOICE_URL
__lowercase = "What is the invoice number?"
__lowercase = dqa_pipeline(image=UpperCAmelCase__, question=UpperCAmelCase__, top_k=2 )
self.assertEqual(nested_simplify(UpperCAmelCase__, decimals=4 ), [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def _lowercase ( self : List[Any] ):
pass
| 17
| 0
|
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase = get_tests_dir("""fixtures/test_sentencepiece.model""")
lowerCamelCase = {"""target_lang""": """fi""", """source_lang""": """en"""}
lowerCamelCase = """>>zh<<"""
lowerCamelCase = """Helsinki-NLP/"""
if is_torch_available():
lowerCamelCase = """pt"""
elif is_tf_available():
lowerCamelCase = """tf"""
else:
lowerCamelCase = """jax"""
@require_sentencepiece
class _UpperCamelCase ( A , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = MarianTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def __lowerCamelCase ( self : int):
'''simple docstring'''
super().setUp()
__lowercase =['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
__lowercase =dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase))))
__lowercase =Path(self.tmpdirname)
save_json(_lowerCAmelCase , save_dir / VOCAB_FILES_NAMES['vocab'])
save_json(_lowerCAmelCase , save_dir / VOCAB_FILES_NAMES['tokenizer_config_file'])
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(_lowerCAmelCase , save_dir / VOCAB_FILES_NAMES['source_spm'])
copyfile(_lowerCAmelCase , save_dir / VOCAB_FILES_NAMES['target_spm'])
__lowercase =MarianTokenizer.from_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname)
def __lowerCamelCase ( self : Optional[int] , **_lowerCAmelCase : List[str]):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase)
def __lowerCamelCase ( self : List[str] , _lowerCAmelCase : str):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowerCamelCase ( self : Dict):
'''simple docstring'''
__lowercase ='</s>'
__lowercase =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase) , _lowerCAmelCase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase) , _lowerCAmelCase)
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
__lowercase =list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '</s>')
self.assertEqual(vocab_keys[1] , '<unk>')
self.assertEqual(vocab_keys[-1] , '<pad>')
self.assertEqual(len(_lowerCAmelCase) , 9)
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 9)
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase =MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de""")
__lowercase =en_de_tokenizer(['I am a small frog'] , return_tensors=_lowerCAmelCase)
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase)
__lowercase =[3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
self.assertListEqual(_lowerCAmelCase , batch.input_ids[0])
__lowercase =tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(_lowerCAmelCase)
__lowercase =[x.name for x in Path(_lowerCAmelCase).glob('*')]
self.assertIn('source.spm' , _lowerCAmelCase)
MarianTokenizer.from_pretrained(_lowerCAmelCase)
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =self.get_tokenizer()
__lowercase =tok(
['I am a small frog' * 1_0_0_0, 'I am a small frog'] , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors=_lowerCAmelCase)
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase)
self.assertEqual(batch.input_ids.shape , (2, 5_1_2))
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase =self.get_tokenizer()
__lowercase =tok(['I am a tiny frog', 'I am a small frog'] , padding=_lowerCAmelCase , return_tensors=_lowerCAmelCase)
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase)
self.assertEqual(batch_smaller.input_ids.shape , (2, 1_0))
@slow
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase ={'input_ids': [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_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, 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, 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, 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=_lowerCAmelCase , model_name='Helsinki-NLP/opus-mt-en-de' , revision='1a8c2263da11e68e50938f97e10cd57820bd504c' , decode_kwargs={'use_source_tokenizer': True} , )
def __lowerCamelCase ( self : int):
'''simple docstring'''
__lowercase =MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs')
__lowercase ='Tämä on testi'
__lowercase ='This is a test'
__lowercase =[7_6, 7, 2_0_4_7, 2]
__lowercase =[6_9, 1_2, 1_1, 9_4_0, 2]
__lowercase =tokenizer(_lowerCAmelCase).input_ids
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase)
__lowercase =tokenizer(text_target=_lowerCAmelCase).input_ids
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase)
__lowercase =tokenizer.decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase)
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase)
| 48
|
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
def _A ( _lowerCAmelCase = 10 ):
"""simple docstring"""
__lowercase ='https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
__lowercase =requests.get(_lowerCAmelCase ).json()[:max_stories]
return [get_hackernews_story(_lowerCAmelCase ) for story_id in story_ids]
def _A ( _lowerCAmelCase = 10 ):
"""simple docstring"""
__lowercase =hackernews_top_stories(_lowerCAmelCase )
return "\n".join('* [{title}]({url})'.format(**_lowerCAmelCase ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 48
| 1
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """WhisperFeatureExtractor"""
UpperCAmelCase = """WhisperTokenizer"""
def __init__( self ,a_ ,a_ ) -> int:
super().__init__(a_ ,a_ )
_UpperCAmelCase : Optional[Any] = self.feature_extractor
_UpperCAmelCase : Optional[int] = False
def _snake_case ( self ,a_=None ,a_=None ,a_=True ) -> int:
return self.tokenizer.get_decoder_prompt_ids(task=a_ ,language=a_ ,no_timestamps=a_ )
def __call__( self ,*a_ ,**a_ ) -> Optional[int]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*a_ ,**a_ )
_UpperCAmelCase : Union[str, Any] = kwargs.pop("""audio""" ,a_ )
_UpperCAmelCase : Optional[int] = kwargs.pop("""sampling_rate""" ,a_ )
_UpperCAmelCase : Tuple = kwargs.pop("""text""" ,a_ )
if len(a_ ) > 0:
_UpperCAmelCase : Union[str, Any] = args[0]
_UpperCAmelCase : Optional[int] = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
_UpperCAmelCase : Tuple = self.feature_extractor(a_ ,*a_ ,sampling_rate=a_ ,**a_ )
if text is not None:
_UpperCAmelCase : int = self.tokenizer(a_ ,**a_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase : Optional[int] = encodings["""input_ids"""]
return inputs
def _snake_case ( self ,*a_ ,**a_ ) -> List[str]:
return self.tokenizer.batch_decode(*a_ ,**a_ )
def _snake_case ( self ,*a_ ,**a_ ) -> Any:
return self.tokenizer.decode(*a_ ,**a_ )
def _snake_case ( self ,a_ ,a_="np" ) -> str:
return self.tokenizer.get_prompt_ids(a_ ,return_tensors=a_ )
| 215
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Optional[Any] = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
A_ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 215
| 1
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _a ( _lowerCAmelCase , unittest.TestCase ):
A = FlaxAutoencoderKL
@property
def __snake_case (self ) -> Tuple:
UpperCAmelCase_: Any = 4
UpperCAmelCase_: List[Any] = 3
UpperCAmelCase_: List[str] = (32, 32)
UpperCAmelCase_: Tuple = jax.random.PRNGKey(0 )
UpperCAmelCase_: str = jax.random.uniform(SCREAMING_SNAKE_CASE_, ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: Dict = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
UpperCAmelCase_: List[Any] = self.dummy_input
return init_dict, inputs_dict
| 82
|
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a : Tuple = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 82
| 1
|
import random
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list[Any]:
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
lowercase : List[Any] = random.randint(0 , len(SCREAMING_SNAKE_CASE__ ) - 1 )
lowercase : Any = random.randint(0 , len(SCREAMING_SNAKE_CASE__ ) - 1 )
lowercase , lowercase : int = data[b], data[a]
return data
if __name__ == "__main__":
lowercase : str = [0, 1, 2, 3, 4, 5, 6, 7]
lowercase : List[str] = ["""python""", """says""", """hello""", """!"""]
print("""Fisher-Yates Shuffle:""")
print("""List""", integers, strings)
print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 20
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 79
| 0
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _snake_case ( UpperCamelCase : Dict , UpperCamelCase : str , UpperCamelCase : Optional[Any] , UpperCamelCase : Any , ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = coefficient_matrix.shape
UpperCAmelCase , UpperCAmelCase : Tuple = constant_matrix.shape
if rowsa != colsa:
UpperCAmelCase : Union[str, Any] = F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase )
if colsa != 1:
UpperCAmelCase : Union[str, Any] = F"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase )
if rowsa != rowsa:
UpperCAmelCase : int = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
F"received {rowsa}x{colsa} and {rowsa}x{colsa}"
)
raise ValueError(UpperCamelCase )
if len(UpperCamelCase ) != rowsa:
UpperCAmelCase : Any = (
"""Number of initial values must be equal to number of rows in coefficient """
F"matrix but received {len(UpperCamelCase )} and {rowsa}"
)
raise ValueError(UpperCamelCase )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
UpperCAmelCase : int = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = table.shape
strictly_diagonally_dominant(UpperCamelCase )
# Iterates the whole matrix for given number of times
for _ in range(UpperCamelCase ):
UpperCAmelCase : Any = []
for row in range(UpperCamelCase ):
UpperCAmelCase : Dict = 0
for col in range(UpperCamelCase ):
if col == row:
UpperCAmelCase : str = table[row][col]
elif col == cols - 1:
UpperCAmelCase : Union[str, Any] = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
UpperCAmelCase : int = (temp + val) / denom
new_val.append(UpperCamelCase )
UpperCAmelCase : Union[str, Any] = new_val
return [float(UpperCamelCase ) for i in new_val]
def _snake_case ( UpperCamelCase : List[Any] ):
UpperCAmelCase , UpperCAmelCase : str = table.shape
UpperCAmelCase : Any = True
for i in range(0 , UpperCamelCase ):
UpperCAmelCase : Optional[int] = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359
|
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the same n-dimensional space""" )
return float(sum(abs(a - b ) for a, b in zip(UpperCamelCase , UpperCamelCase ) ) )
def _snake_case ( UpperCamelCase : list[float] ):
if point:
if isinstance(UpperCamelCase , UpperCamelCase ):
for item in point:
if not isinstance(UpperCamelCase , (int, float) ):
UpperCAmelCase : Any = (
"""Expected a list of numbers as input, found """
F"{type(UpperCamelCase ).__name__}"
)
raise TypeError(UpperCamelCase )
else:
UpperCAmelCase : int = F"Expected a list of numbers as input, found {type(UpperCamelCase ).__name__}"
raise TypeError(UpperCamelCase )
else:
raise ValueError("""Missing an input""" )
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the same n-dimensional space""" )
return float(sum(abs(x - y ) for x, y in zip(UpperCamelCase , UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 76
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|
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class snake_case_ ( __A ):
def __init__( self : Optional[Any] , lowercase_ : AutoencoderKL , lowercase_ : CLIPTextModel , lowercase_ : CLIPTokenizer , lowercase_ : UNetaDConditionModel , lowercase_ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowercase_ : StableDiffusionSafetyChecker , lowercase_ : CLIPImageProcessor , ) -> Optional[int]:
super().__init__()
self.register_modules(
vae=lowercase_ , text_encoder=lowercase_ , tokenizer=lowercase_ , unet=lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , feature_extractor=lowercase_ , )
def __UpperCamelCase ( self : Tuple , lowercase_ : Optional[Union[str, int]] = "auto" ) -> Optional[int]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowercase__ : str = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowercase_ )
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
self.enable_attention_slicing(lowercase_ )
@torch.no_grad()
def __call__( self : int , lowercase_ : Union[str, List[str]] , lowercase_ : int = 5_12 , lowercase_ : int = 5_12 , lowercase_ : int = 50 , lowercase_ : float = 7.5 , lowercase_ : Optional[Union[str, List[str]]] = None , lowercase_ : Optional[int] = 1 , lowercase_ : float = 0.0 , lowercase_ : Optional[torch.Generator] = None , lowercase_ : Optional[torch.FloatTensor] = None , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , lowercase_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowercase_ : int = 1 , lowercase_ : Optional[torch.FloatTensor] = None , **lowercase_ : int , ) -> List[Any]:
if isinstance(lowercase_ , lowercase_ ):
lowercase__ : Optional[int] = 1
elif isinstance(lowercase_ , lowercase_ ):
lowercase__ : List[Any] = len(lowercase_ )
else:
raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowercase_ )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowercase_ , lowercase_ ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(lowercase_ )}.''' )
# get prompt text embeddings
lowercase__ : List[str] = self.tokenizer(
lowercase_ , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
lowercase__ : str = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowercase__ : Any = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowercase__ : str = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowercase__ : str = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowercase__ , lowercase__ , lowercase__ : List[Any] = text_embeddings.shape
lowercase__ : Union[str, Any] = text_embeddings.repeat(1 , lowercase_ , 1 )
lowercase__ : Dict = text_embeddings.view(bs_embed * num_images_per_prompt , lowercase_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowercase__ : Tuple = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowercase__ : List[str]
if negative_prompt is None:
lowercase__ : int = [""]
elif type(lowercase_ ) is not type(lowercase_ ):
raise TypeError(
F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_ )} !='''
F''' {type(lowercase_ )}.''' )
elif isinstance(lowercase_ , lowercase_ ):
lowercase__ : str = [negative_prompt]
elif batch_size != len(lowercase_ ):
raise ValueError(
F'''`negative_prompt`: {negative_prompt} has batch size {len(lowercase_ )}, but `prompt`:'''
F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
" the batch size of `prompt`." )
else:
lowercase__ : Union[str, Any] = negative_prompt
lowercase__ : List[Any] = text_input_ids.shape[-1]
lowercase__ : Any = self.tokenizer(
lowercase_ , padding="max_length" , max_length=lowercase_ , truncation=lowercase_ , return_tensors="pt" , )
lowercase__ : Optional[int] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowercase__ : Union[str, Any] = uncond_embeddings.shape[1]
lowercase__ : str = uncond_embeddings.repeat(lowercase_ , lowercase_ , 1 )
lowercase__ : Optional[int] = uncond_embeddings.view(batch_size * num_images_per_prompt , lowercase_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowercase__ : Optional[Any] = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowercase__ : int = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowercase__ : Optional[Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowercase__ : Union[str, Any] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowercase__ : Tuple = torch.randn(
lowercase_ , generator=lowercase_ , device="cpu" , dtype=lowercase_ ).to(self.device )
lowercase__ : Union[str, Any] = torch.randn(lowercase_ , generator=lowercase_ , device="cpu" , dtype=lowercase_ ).to(
self.device )
else:
lowercase__ : Any = torch.randn(
lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
lowercase__ : Tuple = torch.randn(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowercase__ : Dict = latents_reference.to(self.device )
lowercase__ : str = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowercase__ : Union[str, Any] = (latents_shape[3] - latents_shape_reference[3]) // 2
lowercase__ : str = (latents_shape[2] - latents_shape_reference[2]) // 2
lowercase__ : List[Any] = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowercase__ : int = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowercase__ : Any = 0 if dx < 0 else dx
lowercase__ : Optional[Any] = 0 if dy < 0 else dy
lowercase__ : List[Any] = max(-dx , 0 )
lowercase__ : str = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowercase__ : Any = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(lowercase_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowercase__ : int = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowercase__ : Optional[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowercase__ : Tuple = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowercase__ : int = {}
if accepts_eta:
lowercase__ : List[Any] = eta
for i, t in enumerate(self.progress_bar(lowercase_ ) ):
# expand the latents if we are doing classifier free guidance
lowercase__ : Union[str, Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase__ : Any = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
# predict the noise residual
lowercase__ : Optional[int] = self.unet(lowercase_ , lowercase_ , encoder_hidden_states=lowercase_ ).sample
# perform guidance
if do_classifier_free_guidance:
lowercase__ , lowercase__ : List[str] = noise_pred.chunk(2 )
lowercase__ : Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowercase__ : Optional[int] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowercase_ , lowercase_ , lowercase_ )
lowercase__ : int = 1 / 0.1_82_15 * latents
lowercase__ : Dict = self.vae.decode(lowercase_ ).sample
lowercase__ : Tuple = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase__ : int = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowercase__ : List[str] = self.feature_extractor(self.numpy_to_pil(lowercase_ ) , return_tensors="pt" ).to(
self.device )
lowercase__ , lowercase__ : int = self.safety_checker(
images=lowercase_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowercase__ : List[str] = None
if output_type == "pil":
lowercase__ : List[str] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowercase_ , nsfw_content_detected=lowercase_ )
| 87
|
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = k_size // 2
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
__UpperCAmelCase : Any = 1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase ) + square(_UpperCAmelCase )) / (2 * square(_UpperCAmelCase )) )
return g
def a ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = image.shape[0], image.shape[1]
# dst image height and width
__UpperCAmelCase : str = height - k_size + 1
__UpperCAmelCase : Optional[int] = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
__UpperCAmelCase : str = zeros((dst_height * dst_width, k_size * k_size) )
__UpperCAmelCase : Optional[Any] = 0
for i, j in product(range(_UpperCAmelCase ) , range(_UpperCAmelCase ) ):
__UpperCAmelCase : int = ravel(image[i : i + k_size, j : j + k_size] )
__UpperCAmelCase : Union[str, Any] = window
row += 1
# turn the kernel into shape(k*k, 1)
__UpperCAmelCase : Tuple = gen_gaussian_kernel(_UpperCAmelCase , _UpperCAmelCase )
__UpperCAmelCase : List[Any] = ravel(_UpperCAmelCase )
# reshape and get the dst image
__UpperCAmelCase : Optional[Any] = dot(_UpperCAmelCase , _UpperCAmelCase ).reshape(_UpperCAmelCase , _UpperCAmelCase ).astype(_UpperCAmelCase )
return dst
if __name__ == "__main__":
# read original image
__A =imread(R"../image_data/lena.jpg")
# turn image in gray scale value
__A =cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
__A =gaussian_filter(gray, 3, sigma=1)
__A =gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("gaussian filter with 3x3 mask", gaussianaxa)
imshow("gaussian filter with 5x5 mask", gaussianaxa)
waitKey()
| 226
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"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class UpperCAmelCase_ ( _lowercase , _lowercase , _lowercase , unittest.TestCase):
snake_case__ = StableUnCLIPImgaImgPipeline
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case__ = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
snake_case__ = frozenset([])
def _UpperCamelCase ( self : Optional[int] ) -> str:
_UpperCamelCase = 32
_UpperCamelCase = embedder_hidden_size
# image encoding components
_UpperCamelCase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
_UpperCamelCase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=__UpperCamelCase , projection_dim=__UpperCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
_UpperCamelCase = StableUnCLIPImageNormalizer(embedding_dim=__UpperCamelCase )
_UpperCamelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__UpperCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__UpperCamelCase , layers_per_block=1 , upcast_attention=__UpperCamelCase , use_linear_projection=__UpperCamelCase , )
torch.manual_seed(0 )
_UpperCamelCase = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , prediction_type='''v_prediction''' , set_alpha_to_one=__UpperCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL()
_UpperCamelCase = {
# image encoding components
'''feature_extractor''': feature_extractor,
'''image_encoder''': image_encoder.eval(),
# image noising components
'''image_normalizer''': image_normalizer.eval(),
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder.eval(),
'''unet''': unet.eval(),
'''scheduler''': scheduler,
'''vae''': vae.eval(),
}
return components
def _UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any]=0 , __UpperCamelCase : Optional[int]=True ) -> Tuple:
if str(__UpperCamelCase ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
else:
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
_UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
if pil_image:
_UpperCamelCase = input_image * 0.5 + 0.5
_UpperCamelCase = input_image.clamp(0 , 1 )
_UpperCamelCase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
_UpperCamelCase = DiffusionPipeline.numpy_to_pil(__UpperCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _UpperCamelCase ( self : Tuple ) -> Dict:
_UpperCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = StableUnCLIPImgaImgPipeline(**__UpperCamelCase )
_UpperCamelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = self.get_dummy_inputs(__UpperCamelCase )
inputs.update({'''image_embeds''': None} )
_UpperCamelCase = sd_pipe(**__UpperCamelCase ).images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_UpperCamelCase = np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _UpperCamelCase ( self : Optional[Any] ) -> int:
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase )
def _UpperCamelCase ( self : Any ) -> List[str]:
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=__UpperCamelCase )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def _UpperCamelCase ( self : int ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__UpperCamelCase )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self : Dict ) -> int:
_UpperCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''' )
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe(__UpperCamelCase , '''anime turle''' , generator=__UpperCamelCase , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__UpperCamelCase , __UpperCamelCase )
def _UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
_UpperCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''' )
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe(__UpperCamelCase , '''anime turle''' , generator=__UpperCamelCase , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__UpperCamelCase , __UpperCamelCase )
def _UpperCamelCase ( self : Dict ) -> List[Any]:
_UpperCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''' )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa )
_UpperCamelCase = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = pipe(
__UpperCamelCase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 54
|
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase_ ( _lowercase):
snake_case__ = ['''image_processor''', '''tokenizer''']
snake_case__ = '''OwlViTImageProcessor'''
snake_case__ = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Any , __UpperCamelCase : int=None , __UpperCamelCase : Optional[Any]=None , **__UpperCamelCase : List[str] ) -> Union[str, Any]:
_UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCamelCase , )
_UpperCamelCase = kwargs.pop('''feature_extractor''' )
_UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__UpperCamelCase , __UpperCamelCase )
def __call__( self : List[str] , __UpperCamelCase : Dict=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Tuple=None , __UpperCamelCase : Optional[Any]="max_length" , __UpperCamelCase : List[Any]="np" , **__UpperCamelCase : Optional[int] ) -> Optional[int]:
if text is None and query_images is None and images is None:
raise ValueError(
'''You have to specify at least one text or query image or image. All three cannot be none.''' )
if text is not None:
if isinstance(__UpperCamelCase , __UpperCamelCase ) or (isinstance(__UpperCamelCase , __UpperCamelCase ) and not isinstance(text[0] , __UpperCamelCase )):
_UpperCamelCase = [self.tokenizer(__UpperCamelCase , padding=__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )]
elif isinstance(__UpperCamelCase , __UpperCamelCase ) and isinstance(text[0] , __UpperCamelCase ):
_UpperCamelCase = []
# Maximum number of queries across batch
_UpperCamelCase = max([len(__UpperCamelCase ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(__UpperCamelCase ) != max_num_queries:
_UpperCamelCase = t + [''' '''] * (max_num_queries - len(__UpperCamelCase ))
_UpperCamelCase = self.tokenizer(__UpperCamelCase , padding=__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
encodings.append(__UpperCamelCase )
else:
raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )
if return_tensors == "np":
_UpperCamelCase = np.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
_UpperCamelCase = np.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
_UpperCamelCase = jnp.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
_UpperCamelCase = jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
_UpperCamelCase = torch.cat([encoding['''input_ids'''] for encoding in encodings] , dim=0 )
_UpperCamelCase = torch.cat([encoding['''attention_mask'''] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
_UpperCamelCase = tf.stack([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
_UpperCamelCase = tf.stack([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
else:
raise ValueError('''Target return tensor type could not be returned''' )
_UpperCamelCase = BatchEncoding()
_UpperCamelCase = input_ids
_UpperCamelCase = attention_mask
if query_images is not None:
_UpperCamelCase = BatchEncoding()
_UpperCamelCase = self.image_processor(
__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase ).pixel_values
_UpperCamelCase = query_pixel_values
if images is not None:
_UpperCamelCase = self.image_processor(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
if text is not None and images is not None:
_UpperCamelCase = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
_UpperCamelCase = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCamelCase ) , tensor_type=__UpperCamelCase )
def _UpperCamelCase ( self : str , *__UpperCamelCase : str , **__UpperCamelCase : str ) -> List[Any]:
return self.image_processor.post_process(*__UpperCamelCase , **__UpperCamelCase )
def _UpperCamelCase ( self : str , *__UpperCamelCase : Optional[Any] , **__UpperCamelCase : Optional[Any] ) -> Optional[int]:
return self.image_processor.post_process_object_detection(*__UpperCamelCase , **__UpperCamelCase )
def _UpperCamelCase ( self : List[Any] , *__UpperCamelCase : List[str] , **__UpperCamelCase : Optional[Any] ) -> int:
return self.image_processor.post_process_image_guided_detection(*__UpperCamelCase , **__UpperCamelCase )
def _UpperCamelCase ( self : Tuple , *__UpperCamelCase : Optional[Any] , **__UpperCamelCase : Any ) -> str:
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def _UpperCamelCase ( self : Optional[int] , *__UpperCamelCase : Tuple , **__UpperCamelCase : List[Any] ) -> List[str]:
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@property
def _UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __UpperCamelCase , )
return self.image_processor_class
@property
def _UpperCamelCase ( self : List[str] ) -> Optional[Any]:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __UpperCamelCase , )
return self.image_processor
| 54
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# Algorithm for the pigeonhole sorting
def A ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
lowerCamelCase : Any = min(_SCREAMING_SNAKE_CASE ) # min() finds the minimum value
lowerCamelCase : Union[str, Any] = max(_SCREAMING_SNAKE_CASE ) # max() finds the maximum value
lowerCamelCase : int = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
lowerCamelCase : Any = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
lowerCamelCase : List[str] = 0
for count in range(_SCREAMING_SNAKE_CASE ):
while holes[count] > 0:
holes[count] -= 1
lowerCamelCase : int = count + min_val
i += 1
def A ( ) -> Optional[Any]:
lowerCamelCase : Optional[Any] = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(_SCREAMING_SNAKE_CASE )
print("Sorted order is:" ," ".join(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
main()
| 48
|
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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ) -> Any:
lowerCamelCase : Any = []
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'''deit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "deit.embeddings.cls_token"),
("dist_token", "deit.embeddings.distillation_token"),
("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "deit.embeddings.position_embeddings"),
] )
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 "deit" from all keys that start with "deit"
lowerCamelCase : Union[str, Any] = [(pair[0], pair[1][4:]) if pair[1].startswith("deit" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("norm.weight", "deit.layernorm.weight"),
("norm.bias", "deit.layernorm.bias"),
("head.weight", "cls_classifier.weight"),
("head.bias", "cls_classifier.bias"),
("head_dist.weight", "distillation_classifier.weight"),
("head_dist.bias", "distillation_classifier.bias"),
] )
return rename_keys
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ) -> str:
for i in range(config.num_hidden_layers ):
if base_model:
lowerCamelCase : Optional[int] = ""
else:
lowerCamelCase : List[str] = "deit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase : List[str] = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
lowerCamelCase : Optional[int] = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase : List[Any] = in_proj_weight[
: config.hidden_size, :
]
lowerCamelCase : Any = in_proj_bias[: config.hidden_size]
lowerCamelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase : List[Any] = in_proj_bias[-config.hidden_size :]
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> str:
lowerCamelCase : List[str] = dct.pop(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Any = val
def A ( ) -> List[str]:
lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase : str = Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowerCamelCase : Union[str, Any] = DeiTConfig()
# all deit models have fine-tuned heads
lowerCamelCase : Optional[int] = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
lowerCamelCase : Dict = 1000
lowerCamelCase : Tuple = "huggingface/label-files"
lowerCamelCase : List[str] = "imagenet-1k-id2label.json"
lowerCamelCase : List[Any] = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset" ) ,"r" ) )
lowerCamelCase : Optional[int] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowerCamelCase : Tuple = idalabel
lowerCamelCase : str = {v: k for k, v in idalabel.items()}
lowerCamelCase : Dict = int(deit_name[-6:-4] )
lowerCamelCase : Optional[Any] = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("tiny" ):
lowerCamelCase : Optional[Any] = 192
lowerCamelCase : List[str] = 768
lowerCamelCase : Tuple = 12
lowerCamelCase : Optional[Any] = 3
elif deit_name[9:].startswith("small" ):
lowerCamelCase : str = 384
lowerCamelCase : Optional[Any] = 1536
lowerCamelCase : Dict = 12
lowerCamelCase : Optional[int] = 6
if deit_name[9:].startswith("base" ):
pass
elif deit_name[4:].startswith("large" ):
lowerCamelCase : str = 1024
lowerCamelCase : List[str] = 4096
lowerCamelCase : Any = 24
lowerCamelCase : Dict = 16
# load original model from timm
lowerCamelCase : List[Any] = timm.create_model(_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowerCamelCase : Dict = timm_model.state_dict()
lowerCamelCase : Dict = create_rename_keys(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
read_in_q_k_v(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# load HuggingFace model
lowerCamelCase : Optional[Any] = DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE ).eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by DeiTImageProcessor
lowerCamelCase : Any = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE ,crop_size=config.image_size )
lowerCamelCase : str = image_processor(images=prepare_img() ,return_tensors="pt" )
lowerCamelCase : int = encoding["pixel_values"]
lowerCamelCase : Optional[Any] = model(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Union[str, Any] = timm_model(_SCREAMING_SNAKE_CASE )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_SCREAMING_SNAKE_CASE ,outputs.logits ,atol=1e-3 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--deit_name',
default='vit_deit_base_distilled_patch16_224',
type=str,
help='Name of the DeiT 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.'
)
SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args()
convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 48
| 1
|
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = "char"
lowerCAmelCase_ = "bpe"
lowerCAmelCase_ = "wp"
__lowerCamelCase : List[Any] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = ["image_processor", "char_tokenizer"]
lowerCAmelCase_ = "ViTImageProcessor"
lowerCAmelCase_ = "MgpstrTokenizer"
def __init__( self : List[Any] , _lowercase : List[Any]=None , _lowercase : Union[str, Any]=None , **_lowercase : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _lowercase , )
SCREAMING_SNAKE_CASE__ = kwargs.pop("""feature_extractor""" )
SCREAMING_SNAKE_CASE__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
SCREAMING_SNAKE_CASE__ = tokenizer
SCREAMING_SNAKE_CASE__ = AutoTokenizer.from_pretrained("""gpt2""" )
SCREAMING_SNAKE_CASE__ = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(_lowercase , _lowercase )
def __call__( self : Tuple , _lowercase : Tuple=None , _lowercase : List[Any]=None , _lowercase : Union[str, Any]=None , **_lowercase : List[str] ):
"""simple docstring"""
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
SCREAMING_SNAKE_CASE__ = self.image_processor(_lowercase , return_tensors=_lowercase , **_lowercase )
if text is not None:
SCREAMING_SNAKE_CASE__ = self.char_tokenizer(_lowercase , return_tensors=_lowercase , **_lowercase )
if text is None:
return inputs
elif images is None:
return encodings
else:
SCREAMING_SNAKE_CASE__ = encodings["""input_ids"""]
return inputs
def __a ( self : Optional[int] , _lowercase : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = sequences
SCREAMING_SNAKE_CASE__ = char_preds.size(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._decode_helper(_lowercase , """char""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._decode_helper(_lowercase , """bpe""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._decode_helper(_lowercase , """wp""" )
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
for i in range(_lowercase ):
SCREAMING_SNAKE_CASE__ = [char_scores[i], bpe_scores[i], wp_scores[i]]
SCREAMING_SNAKE_CASE__ = [char_strs[i], bpe_strs[i], wp_strs[i]]
SCREAMING_SNAKE_CASE__ = scores.index(max(_lowercase ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = final_strs
SCREAMING_SNAKE_CASE__ = final_scores
SCREAMING_SNAKE_CASE__ = char_strs
SCREAMING_SNAKE_CASE__ = bpe_strs
SCREAMING_SNAKE_CASE__ = wp_strs
return out
def __a ( self : Any , _lowercase : List[Any] , _lowercase : List[Any] ):
"""simple docstring"""
if format == DecodeType.CHARACTER:
SCREAMING_SNAKE_CASE__ = self.char_decode
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = """[s]"""
elif format == DecodeType.BPE:
SCREAMING_SNAKE_CASE__ = self.bpe_decode
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = """#"""
elif format == DecodeType.WORDPIECE:
SCREAMING_SNAKE_CASE__ = self.wp_decode
SCREAMING_SNAKE_CASE__ = 1_02
SCREAMING_SNAKE_CASE__ = """[SEP]"""
else:
raise ValueError(f"""Format {format} is not supported.""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = [], []
SCREAMING_SNAKE_CASE__ = pred_logits.size(0 )
SCREAMING_SNAKE_CASE__ = pred_logits.size(1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = pred_logits.topk(1 , dim=-1 , largest=_lowercase , sorted=_lowercase )
SCREAMING_SNAKE_CASE__ = preds_index.view(-1 , _lowercase )[:, 1:]
SCREAMING_SNAKE_CASE__ = decoder(_lowercase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = torch.nn.functional.softmax(_lowercase , dim=2 ).max(dim=2 )
SCREAMING_SNAKE_CASE__ = preds_max_prob[:, 1:]
for index in range(_lowercase ):
SCREAMING_SNAKE_CASE__ = preds_str[index].find(_lowercase )
SCREAMING_SNAKE_CASE__ = preds_str[index][:pred_eos]
SCREAMING_SNAKE_CASE__ = preds_index[index].cpu().tolist()
SCREAMING_SNAKE_CASE__ = pred_index.index(_lowercase ) if eos_token in pred_index else -1
SCREAMING_SNAKE_CASE__ = preds_max_prob[index][: pred_eos_index + 1]
SCREAMING_SNAKE_CASE__ = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(_lowercase )
conf_scores.append(_lowercase )
return dec_strs, conf_scores
def __a ( self : Any , _lowercase : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [seq.replace(""" """ , """""" ) for seq in self.char_tokenizer.batch_decode(_lowercase )]
return decode_strs
def __a ( self : Dict , _lowercase : int ):
"""simple docstring"""
return self.bpe_tokenizer.batch_decode(_lowercase )
def __a ( self : Union[str, Any] , _lowercase : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [seq.replace(""" """ , """""" ) for seq in self.wp_tokenizer.batch_decode(_lowercase )]
return decode_strs
| 204
|
from __future__ import annotations
__lowerCamelCase : Dict = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase : Union[str, Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : list[float] ) -> list[float]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = len(__UpperCamelCase )
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE__ = -1
for j in range(i + 1 , __UpperCamelCase ):
if arr[i] < arr[j]:
SCREAMING_SNAKE_CASE__ = arr[j]
break
result.append(__UpperCamelCase )
return result
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : list[float] ) -> list[float]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = []
for i, outer in enumerate(__UpperCamelCase ):
SCREAMING_SNAKE_CASE__ = -1
for inner in arr[i + 1 :]:
if outer < inner:
SCREAMING_SNAKE_CASE__ = inner
break
result.append(__UpperCamelCase )
return result
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : list[float] ) -> list[float]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = len(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = [-1] * arr_size
for index in reversed(range(__UpperCamelCase ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
SCREAMING_SNAKE_CASE__ = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__lowerCamelCase : List[Any] = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 204
| 1
|
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=snake_case , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=snake_case , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=snake_case )
return parser.parse_args()
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = parse_args()
# Import training_script as a module.
_lowerCAmelCase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_lowerCAmelCase = script_fpath.stem
_lowerCAmelCase = importlib.import_module(snake_case )
# Patch sys.argv
_lowerCAmelCase = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 82
|
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
while number > 0:
_lowerCAmelCase = number % 10
sum_of_digits += last_digit
_lowerCAmelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _UpperCAmelCase ( snake_case = 1_00 ):
"""simple docstring"""
_lowerCAmelCase = factorial(snake_case )
_lowerCAmelCase = split_and_add(snake_case )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 82
| 1
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
set_seed(7_7_0)
_A = {
"""c_attn""": """att_proj""",
"""c_proj""": """out_proj""",
"""c_fc""": """in_proj""",
"""transformer.""": """""",
"""h.""": """layers.""",
"""ln_1""": """layernorm_1""",
"""ln_2""": """layernorm_2""",
"""ln_f""": """layernorm_final""",
"""wpe""": """position_embeds_layer""",
"""wte""": """input_embeds_layer""",
}
_A = {
"""text_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """text.pt""",
},
"""coarse_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """coarse.pt""",
},
"""fine_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """fine.pt""",
},
"""text""": {
"""repo_id""": """suno/bark""",
"""file_name""": """text_2.pt""",
},
"""coarse""": {
"""repo_id""": """suno/bark""",
"""file_name""": """coarse_2.pt""",
},
"""fine""": {
"""repo_id""": """suno/bark""",
"""file_name""": """fine_2.pt""",
},
}
_A = os.path.dirname(os.path.abspath(__file__))
_A = os.path.join(os.path.expanduser("""~"""), """.cache""")
_A = os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase=False ) -> Dict:
lowerCAmelCase__ : int = model_type
if use_small:
key += "_small"
return os.path.join(__UpperCAmelCase , REMOTE_MODEL_PATHS[key]["""file_name"""] )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
hf_hub_download(repo_id=__UpperCAmelCase , filename=__UpperCAmelCase , local_dir=__UpperCAmelCase )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase="text" ) -> List[Any]:
if model_type == "text":
lowerCAmelCase__ : Dict = BarkSemanticModel
lowerCAmelCase__ : Optional[Any] = BarkSemanticConfig
lowerCAmelCase__ : int = BarkSemanticGenerationConfig
elif model_type == "coarse":
lowerCAmelCase__ : str = BarkCoarseModel
lowerCAmelCase__ : Optional[Any] = BarkCoarseConfig
lowerCAmelCase__ : List[str] = BarkCoarseGenerationConfig
elif model_type == "fine":
lowerCAmelCase__ : Dict = BarkFineModel
lowerCAmelCase__ : Any = BarkFineConfig
lowerCAmelCase__ : str = BarkFineGenerationConfig
else:
raise NotImplementedError()
lowerCAmelCase__ : Tuple = f"""{model_type}_small""" if use_small else model_type
lowerCAmelCase__ : int = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(__UpperCAmelCase ):
logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
_download(model_info["""repo_id"""] , model_info["""file_name"""] )
lowerCAmelCase__ : Union[str, Any] = torch.load(__UpperCAmelCase , map_location=__UpperCAmelCase )
# this is a hack
lowerCAmelCase__ : Optional[int] = checkpoint["""model_args"""]
if "input_vocab_size" not in model_args:
lowerCAmelCase__ : List[str] = model_args["""vocab_size"""]
lowerCAmelCase__ : str = model_args["""vocab_size"""]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
lowerCAmelCase__ : Dict = model_args.pop("""n_head""" )
lowerCAmelCase__ : Tuple = model_args.pop("""n_embd""" )
lowerCAmelCase__ : Any = model_args.pop("""n_layer""" )
lowerCAmelCase__ : Dict = ConfigClass(**checkpoint["""model_args"""] )
lowerCAmelCase__ : Optional[Any] = ModelClass(config=__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = GenerationConfigClass()
lowerCAmelCase__ : Tuple = model_generation_config
lowerCAmelCase__ : List[str] = checkpoint["""model"""]
# fixup checkpoint
lowerCAmelCase__ : int = """_orig_mod."""
for k, v in list(state_dict.items() ):
if k.startswith(__UpperCAmelCase ):
# replace part of the key with corresponding layer name in HF implementation
lowerCAmelCase__ : str = k[len(__UpperCAmelCase ) :]
for old_layer_name in new_layer_name_dict:
lowerCAmelCase__ : Tuple = new_k.replace(__UpperCAmelCase , new_layer_name_dict[old_layer_name] )
lowerCAmelCase__ : List[Any] = state_dict.pop(__UpperCAmelCase )
lowerCAmelCase__ : List[Any] = set(state_dict.keys() ) - set(model.state_dict().keys() )
lowerCAmelCase__ : Any = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
lowerCAmelCase__ : Dict = set(model.state_dict().keys() ) - set(state_dict.keys() )
lowerCAmelCase__ : Optional[int] = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
if len(__UpperCAmelCase ) != 0:
raise ValueError(f"""extra keys found: {extra_keys}""" )
if len(__UpperCAmelCase ) != 0:
raise ValueError(f"""missing keys: {missing_keys}""" )
model.load_state_dict(__UpperCAmelCase , strict=__UpperCAmelCase )
lowerCAmelCase__ : Dict = model.num_parameters(exclude_embeddings=__UpperCAmelCase )
lowerCAmelCase__ : List[str] = checkpoint["""best_val_loss"""].item()
logger.info(f"""model loaded: {round(n_params/1E6 , 1 )}M params, {round(__UpperCAmelCase , 3 )} loss""" )
model.eval()
model.to(__UpperCAmelCase )
del checkpoint, state_dict
return model
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase="text" ) -> Optional[Any]:
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
lowerCAmelCase__ : Optional[int] = """cpu""" # do conversion on cpu
lowerCAmelCase__ : List[Any] = _get_ckpt_path(__UpperCAmelCase , use_small=__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = _load_model(__UpperCAmelCase , __UpperCAmelCase , model_type=__UpperCAmelCase , use_small=__UpperCAmelCase )
# load bark initial model
lowerCAmelCase__ : Optional[Any] = _bark_load_model(__UpperCAmelCase , """cpu""" , model_type=__UpperCAmelCase , use_small=__UpperCAmelCase )
if model_type == "text":
lowerCAmelCase__ : Optional[Any] = bark_model["""model"""]
if model.num_parameters(exclude_embeddings=__UpperCAmelCase ) != bark_model.get_num_params():
raise ValueError("""initial and new models don't have the same number of parameters""" )
# check if same output as the bark model
lowerCAmelCase__ : Tuple = 5
lowerCAmelCase__ : Optional[Any] = 10
if model_type in ["text", "coarse"]:
lowerCAmelCase__ : List[str] = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int )
lowerCAmelCase__ : int = bark_model(__UpperCAmelCase )[0]
lowerCAmelCase__ : Dict = model(__UpperCAmelCase )
# take last logits
lowerCAmelCase__ : int = output_new_model_total.logits[:, [-1], :]
else:
lowerCAmelCase__ : Optional[Any] = 3
lowerCAmelCase__ : List[str] = 8
lowerCAmelCase__ : Dict = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
lowerCAmelCase__ : str = model(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : Tuple = bark_model(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("""initial and new outputs don't have the same shape""" )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError("""initial and new outputs are not equal""" )
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
model.save_pretrained(__UpperCAmelCase )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> str:
lowerCAmelCase__ : Optional[Any] = os.path.join(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = BarkSemanticConfig.from_pretrained(os.path.join(__UpperCAmelCase , """config.json""" ) )
lowerCAmelCase__ : Optional[Any] = BarkCoarseConfig.from_pretrained(os.path.join(__UpperCAmelCase , """config.json""" ) )
lowerCAmelCase__ : Union[str, Any] = BarkFineConfig.from_pretrained(os.path.join(__UpperCAmelCase , """config.json""" ) )
lowerCAmelCase__ : Optional[int] = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )
lowerCAmelCase__ : Any = BarkSemanticModel.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ : Dict = BarkCoarseModel.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = BarkFineModel.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ : List[Any] = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )
lowerCAmelCase__ : str = BarkConfig.from_sub_model_configs(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : Tuple = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
lowerCAmelCase__ : int = BarkModel(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = semantic
lowerCAmelCase__ : Union[str, Any] = coarseAcoustic
lowerCAmelCase__ : Tuple = fineAcoustic
lowerCAmelCase__ : Tuple = codec
lowerCAmelCase__ : List[Any] = bark_generation_config
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
bark.save_pretrained(__UpperCAmelCase , repo_id=__UpperCAmelCase , push_to_hub=__UpperCAmelCase )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")
_A = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 212
|
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class _lowerCamelCase ( a_ ):
def __init__( self : Tuple , UpperCamelCase : List[Any]="" , UpperCamelCase : List[str]="train" ) -> List[Any]:
"""simple docstring"""
assert os.path.isdir(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : List[Any] = os.listdir(UpperCamelCase )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
lowerCAmelCase__ : Any = os.path.join(UpperCamelCase , UpperCamelCase )
if not os.path.isfile(UpperCamelCase ):
continue
self.documents.append(UpperCamelCase )
def __len__( self : List[Any] ) -> int:
"""simple docstring"""
return len(self.documents )
def __getitem__( self : str , UpperCamelCase : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Any = self.documents[idx]
lowerCAmelCase__ : List[Any] = document_path.split("""/""" )[-1]
with open(UpperCamelCase , encoding="""utf-8""" ) as source:
lowerCAmelCase__ : List[str] = source.read()
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = process_story(UpperCamelCase )
return document_name, story_lines, summary_lines
def lowercase_ ( __UpperCAmelCase ) -> Union[str, Any]:
lowerCAmelCase__ : Tuple = list(filter(lambda __UpperCAmelCase : len(__UpperCAmelCase ) != 0 , [line.strip() for line in raw_story.split("""\n""" )] ) )
# for some unknown reason some lines miss a period, add it
lowerCAmelCase__ : List[str] = [_add_missing_period(__UpperCAmelCase ) for line in nonempty_lines]
# gather article lines
lowerCAmelCase__ : Optional[int] = []
lowerCAmelCase__ : Optional[int] = deque(__UpperCAmelCase )
while True:
try:
lowerCAmelCase__ : List[Any] = lines.popleft()
if element.startswith("""@highlight""" ):
break
story_lines.append(__UpperCAmelCase )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
lowerCAmelCase__ : Any = list(filter(lambda __UpperCAmelCase : not t.startswith("""@highlight""" ) , __UpperCAmelCase ) )
return story_lines, summary_lines
def lowercase_ ( __UpperCAmelCase ) -> Optional[int]:
lowerCAmelCase__ : List[str] = [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
if line.startswith("""@highlight""" ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
if len(__UpperCAmelCase ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(__UpperCAmelCase )) )
return sequence
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
lowerCAmelCase__ : List[str] = torch.ones_like(__UpperCAmelCase )
lowerCAmelCase__ : List[str] = sequence == pad_token_id
lowerCAmelCase__ : str = 0
return mask
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
lowerCAmelCase__ : Tuple = [tokenizer.encode(__UpperCAmelCase ) for line in story_lines]
lowerCAmelCase__ : List[str] = [token for sentence in story_lines_token_ids for token in sentence]
lowerCAmelCase__ : int = [tokenizer.encode(__UpperCAmelCase ) for line in summary_lines]
lowerCAmelCase__ : Union[str, Any] = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : Tuple = []
for sequence in batch:
lowerCAmelCase__ : Union[str, Any] = -1
lowerCAmelCase__ : List[str] = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(__UpperCAmelCase )
return torch.tensor(__UpperCAmelCase )
| 212
| 1
|
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected' , [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
({'num_shards': 10, 'max_num_jobs': 10}, [range(lowercase__ , i + 1 ) for i in range(10 )]),
({'num_shards': 1, 'max_num_jobs': 10}, [range(1 )]),
({'num_shards': 10, 'max_num_jobs': 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({'num_shards': 3, 'max_num_jobs': 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = _distribute_shards(**lowercase__ )
assert out == expected
@pytest.mark.parametrize(
'gen_kwargs, max_num_jobs, expected' , [
({'foo': 0}, 10, [{'foo': 0}]),
({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
] , )
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : int = _split_gen_kwargs(lowercase__ , lowercase__ )
assert out == expected
@pytest.mark.parametrize(
'gen_kwargs, expected' , [
({'foo': 0}, 1),
({'shards': [0]}, 1),
({'shards': [0, 1, 2, 3]}, 4),
({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
] , )
def _snake_case ( lowercase__ , lowercase__ ):
if expected is RuntimeError:
with pytest.raises(lowercase__ ):
_number_of_shards_in_gen_kwargs(lowercase__ )
else:
_lowerCamelCase : Tuple = _number_of_shards_in_gen_kwargs(lowercase__ )
assert out == expected
| 96
|
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , _a) or not isinstance(_a , _a):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
SCREAMING_SNAKE_CASE : int = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
SCREAMING_SNAKE_CASE : List[Any] = float(factorial(_a))
coefficient /= factorial(_a) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 76
| 0
|
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCamelCase__ = 10
UpperCamelCase__ = datasets.Features(
{
'''tokens''': datasets.Sequence(datasets.Value('''string''' ) ),
'''labels''': datasets.Sequence(datasets.ClassLabel(names=['''negative''', '''positive'''] ) ),
'''answers''': datasets.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
'''id''': datasets.Value('''int64''' ),
} )
UpperCamelCase__ = datasets.Dataset.from_dict(
{
'''tokens''': [['''foo'''] * 5] * n,
'''labels''': [[1] * 5] * n,
'''answers''': [{'''answer_start''': [97], '''text''': ['''1976''']}] * 10,
'''id''': list(range(UpperCamelCase__ ) ),
}, features=UpperCamelCase__, )
return dataset
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int], UpperCamelCase__ : str ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''file.arrow''' )
dataset.map(cache_file_name=UpperCamelCase__ )
return filename
# FILE_CONTENT + files
lowercase = """\
Text data.
Second line of data."""
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt'''
UpperCamelCase__ = FILE_CONTENT
with open(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__ )
return filename
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
import bza
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt.bz2'''
UpperCamelCase__ = bytes(UpperCamelCase__, '''utf-8''' )
with bza.open(UpperCamelCase__, '''wb''' ) as f:
f.write(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Dict ):
'''simple docstring'''
import gzip
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''file.txt.gz''' )
UpperCamelCase__ = bytes(UpperCamelCase__, '''utf-8''' )
with gzip.open(UpperCamelCase__, '''wb''' ) as f:
f.write(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ):
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt.lz4'''
UpperCamelCase__ = bytes(UpperCamelCase__, '''utf-8''' )
with lza.frame.open(UpperCamelCase__, '''wb''' ) as f:
f.write(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple, UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt.7z'''
with pyazr.SevenZipFile(UpperCamelCase__, '''w''' ) as archive:
archive.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : str, UpperCamelCase__ : Dict ):
'''simple docstring'''
import tarfile
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt.tar'''
with tarfile.TarFile(UpperCamelCase__, '''w''' ) as f:
f.add(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : str ):
'''simple docstring'''
import lzma
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt.xz'''
UpperCamelCase__ = bytes(UpperCamelCase__, '''utf-8''' )
with lzma.open(UpperCamelCase__, '''wb''' ) as f:
f.write(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int, UpperCamelCase__ : List[Any] ):
'''simple docstring'''
import zipfile
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ):
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zst'''
UpperCamelCase__ = bytes(UpperCamelCase__, '''utf-8''' )
with zstd.open(UpperCamelCase__, '''wb''' ) as f:
f.write(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''file.xml'''
UpperCamelCase__ = textwrap.dedent(
'''\
<?xml version="1.0" encoding="UTF-8" ?>
<tmx version="1.4">
<header segtype="sentence" srclang="ca" />
<body>
<tu>
<tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>
<tuv xml:lang="en"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>
<tuv xml:lang="en"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>
<tuv xml:lang="en"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>
<tuv xml:lang="en"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>
<tuv xml:lang="en"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>''' )
with open(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__ )
return filename
lowercase = [
{"""col_1""": """0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """3""", """col_2""": 3, """col_3""": 3.0},
]
lowercase = [
{"""col_1""": """4""", """col_2""": 4, """col_3""": 4.0},
{"""col_1""": """5""", """col_2""": 5, """col_3""": 5.0},
]
lowercase = {
"""col_1""": ["""0""", """1""", """2""", """3"""],
"""col_2""": [0, 1, 2, 3],
"""col_3""": [0.0, 1.0, 2.0, 3.0],
}
lowercase = [
{"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
{"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]
lowercase = [
{"""col_1""": """s0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """s1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """s2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """s3""", """col_2""": 3, """col_3""": 3.0},
]
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( ):
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ):
'''simple docstring'''
UpperCamelCase__ = datasets.Dataset.from_dict(UpperCamelCase__ )
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.arrow''' )
dataset.map(cache_file_name=UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.sqlite''' )
with contextlib.closing(sqlitea.connect(UpperCamelCase__ ) ) as con:
UpperCamelCase__ = con.cursor()
cur.execute('''CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)''' )
for item in DATA:
cur.execute('''INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)''', tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.csv''' )
with open(UpperCamelCase__, '''w''', newline='''''' ) as f:
UpperCamelCase__ = csv.DictWriter(UpperCamelCase__, fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.csv''' )
with open(UpperCamelCase__, '''w''', newline='''''' ) as f:
UpperCamelCase__ = csv.DictWriter(UpperCamelCase__, fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
import bza
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.bz2'''
with open(UpperCamelCase__, '''rb''' ) as f:
UpperCamelCase__ = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(UpperCamelCase__, '''wb''' ) as f:
f.write(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple, UpperCamelCase__ : Optional[Any], UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Any, UpperCamelCase__ : List[Any], UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.basename(csv_path.replace('''.csv''', '''.CSV''' ) ) )
f.write(UpperCamelCase__, arcname=os.path.basename(csva_path.replace('''.csv''', '''.CSV''' ) ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int], UpperCamelCase__ : Tuple, UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.csv.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.join('''main_dir''', os.path.basename(UpperCamelCase__ ) ) )
f.write(UpperCamelCase__, arcname=os.path.join('''main_dir''', os.path.basename(UpperCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.parquet''' )
UpperCamelCase__ = pa.schema(
{
'''col_1''': pa.string(),
'''col_2''': pa.intaa(),
'''col_3''': pa.floataa(),
} )
with open(UpperCamelCase__, '''wb''' ) as f:
UpperCamelCase__ = pq.ParquetWriter(UpperCamelCase__, schema=UpperCamelCase__ )
UpperCamelCase__ = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(UpperCamelCase__ ) )] for k in DATA[0]}, schema=UpperCamelCase__ )
writer.write_table(UpperCamelCase__ )
writer.close()
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
UpperCamelCase__ = {'''data''': DATA}
with open(UpperCamelCase__, '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
UpperCamelCase__ = {'''data''': DATA_DICT_OF_LISTS}
with open(UpperCamelCase__, '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Any ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl''' )
with open(UpperCamelCase__, '''w''' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.jsonl''' )
with open(UpperCamelCase__, '''w''' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Any ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_312.jsonl''' )
with open(UpperCamelCase__, '''w''' ) as f:
for item in DATA_312:
f.write(json.dumps(UpperCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset-str.jsonl''' )
with open(UpperCamelCase__, '''w''' ) as f:
for item in DATA_STR:
f.write(json.dumps(UpperCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int], UpperCamelCase__ : List[str] ):
'''simple docstring'''
import gzip
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt.gz''' )
with open(UpperCamelCase__, '''rb''' ) as orig_file:
with gzip.open(UpperCamelCase__, '''wb''' ) as zipped_file:
zipped_file.writelines(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple, UpperCamelCase__ : Tuple ):
'''simple docstring'''
import gzip
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.gz''' )
with open(UpperCamelCase__, '''rb''' ) as orig_file:
with gzip.open(UpperCamelCase__, '''wb''' ) as zipped_file:
zipped_file.writelines(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : str, UpperCamelCase__ : str, UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.join('''nested''', os.path.basename(UpperCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Dict ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.join('''main_dir''', os.path.basename(UpperCamelCase__ ) ) )
f.write(UpperCamelCase__, arcname=os.path.join('''main_dir''', os.path.basename(UpperCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any], UpperCamelCase__ : List[Any], UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.tar'''
with tarfile.TarFile(UpperCamelCase__, '''w''' ) as f:
f.add(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
f.add(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any], UpperCamelCase__ : List[Any], UpperCamelCase__ : int, UpperCamelCase__ : Any ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.tar'''
with tarfile.TarFile(UpperCamelCase__, '''w''' ) as f:
f.add(UpperCamelCase__, arcname=os.path.join('''nested''', os.path.basename(UpperCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ):
'''simple docstring'''
UpperCamelCase__ = ['''0''', '''1''', '''2''', '''3''']
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt''' )
with open(UpperCamelCase__, '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Dict ):
'''simple docstring'''
UpperCamelCase__ = ['''0''', '''1''', '''2''', '''3''']
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.txt''' )
with open(UpperCamelCase__, '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Dict ):
'''simple docstring'''
UpperCamelCase__ = ['''0''', '''1''', '''2''', '''3''']
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.abc'''
with open(UpperCamelCase__, '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : str ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.text.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int, UpperCamelCase__ : int, UpperCamelCase__ : List[str] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.text.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.join('''main_dir''', os.path.basename(UpperCamelCase__ ) ) )
f.write(UpperCamelCase__, arcname=os.path.join('''main_dir''', os.path.basename(UpperCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int, UpperCamelCase__ : Tuple, UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.ext.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.basename('''unsupported.ext''' ) )
f.write(UpperCamelCase__, arcname=os.path.basename('''unsupported_2.ext''' ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third'''] )
UpperCamelCase__ = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_with_unicode_new_lines.txt''' )
with open(UpperCamelCase__, '''w''', encoding='''utf-8''' ) as f:
f.write(UpperCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( ):
'''simple docstring'''
return os.path.join('''tests''', '''features''', '''data''', '''test_image_rgb.jpg''' )
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( ):
'''simple docstring'''
return os.path.join('''tests''', '''features''', '''data''', '''test_audio_44100.wav''' )
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : Any, UpperCamelCase__ : Any ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data''' ) / '''dataset.img.zip'''
with zipfile.ZipFile(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ) )
f.write(UpperCamelCase__, arcname=os.path.basename(UpperCamelCase__ ).replace('''.jpg''', '''2.jpg''' ) )
return path
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp('''data_dir''' )
(data_dir / "subdir").mkdir()
with open(data_dir / '''subdir''' / '''train.txt''', '''w''' ) as f:
f.write('''foo\n''' * 10 )
with open(data_dir / '''subdir''' / '''test.txt''', '''w''' ) as f:
f.write('''bar\n''' * 10 )
# hidden file
with open(data_dir / '''subdir''' / '''.test.txt''', '''w''' ) as f:
f.write('''bar\n''' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '''.subdir''' / '''train.txt''', '''w''' ) as f:
f.write('''foo\n''' * 10 )
with open(data_dir / '''.subdir''' / '''test.txt''', '''w''' ) as f:
f.write('''bar\n''' * 10 )
return data_dir
| 371
|
from __future__ import annotations
from collections import Counter
from random import random
class __lowercase :
'''simple docstring'''
def __init__( self : List[Any] ):
UpperCamelCase__ = {}
def A_ ( self : List[Any] , _a : str ):
UpperCamelCase__ = {}
def A_ ( self : List[Any] , _a : str , _a : str , _a : float ):
if nodea not in self.connections:
self.add_node(_a )
if nodea not in self.connections:
self.add_node(_a )
UpperCamelCase__ = probability
def A_ ( self : Optional[Any] ):
return list(self.connections )
def A_ ( self : Tuple , _a : str ):
UpperCamelCase__ = 0
UpperCamelCase__ = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def lowerCamelCase_ ( UpperCamelCase__ : str, UpperCamelCase__ : list[tuple[str, str, float]], UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
UpperCamelCase__ = Counter(graph.get_nodes() )
UpperCamelCase__ = start
for _ in range(UpperCamelCase__ ):
UpperCamelCase__ = graph.transition(UpperCamelCase__ )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 35
| 0
|
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
__SCREAMING_SNAKE_CASE = len(bin(lowerCAmelCase_ )[3:] )
__SCREAMING_SNAKE_CASE = bin(abs(lowerCAmelCase_ ) - (1 << binary_number_length) )[3:]
__SCREAMING_SNAKE_CASE = (
(
"1"
+ "0" * (binary_number_length - len(lowerCAmelCase_ ))
+ twos_complement_number
)
if number < 0
else "0"
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 54
|
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
# For consistency across different places the DisjunctiveConstraint is called,
# dc.token_ids is a list of integers. It is also initialized only by integers.
__SCREAMING_SNAKE_CASE = [[1, 2, 4], [1, 2, 3, 4]]
__SCREAMING_SNAKE_CASE = DisjunctiveConstraint(UpperCAmelCase__ )
self.assertTrue(isinstance(dc.token_ids , UpperCAmelCase__ ) )
with self.assertRaises(UpperCAmelCase__ ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(UpperCAmelCase__ ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def UpperCAmelCase_ ( self : Any ) -> int:
# We can't have constraints that are complete subsets of another. This leads to a preverse
# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
__SCREAMING_SNAKE_CASE = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(UpperCAmelCase__ ):
DisjunctiveConstraint(UpperCAmelCase__ ) # fails here
def UpperCAmelCase_ ( self : List[Any] ) -> Any:
__SCREAMING_SNAKE_CASE = [[1, 2, 3], [1, 2, 4]]
__SCREAMING_SNAKE_CASE = DisjunctiveConstraint(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(1 )
__SCREAMING_SNAKE_CASE = stepped is True and completed is False and reset is False
self.assertTrue(UpperCAmelCase__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(2 )
__SCREAMING_SNAKE_CASE = stepped is True and completed is False and reset is False
self.assertTrue(UpperCAmelCase__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(3 )
__SCREAMING_SNAKE_CASE = stepped is True and completed is True and reset is False
self.assertTrue(UpperCAmelCase__ )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def UpperCAmelCase_ ( self : str ) -> List[str]:
__SCREAMING_SNAKE_CASE = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
__SCREAMING_SNAKE_CASE = DisjunctiveConstraint(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 54
| 1
|
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=13 , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=33 , 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=0.02 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=None , ) -> Optional[Any]:
"""simple docstring"""
A : Optional[Any] = parent
A : str = batch_size
A : str = seq_length
A : List[str] = is_training
A : List[str] = use_input_mask
A : Tuple = use_token_type_ids
A : str = use_labels
A : List[Any] = vocab_size
A : Any = hidden_size
A : List[str] = num_hidden_layers
A : Optional[int] = num_attention_heads
A : Union[str, Any] = intermediate_size
A : Optional[int] = hidden_act
A : List[Any] = hidden_dropout_prob
A : str = attention_probs_dropout_prob
A : Tuple = max_position_embeddings
A : Union[str, Any] = type_vocab_size
A : List[str] = type_sequence_label_size
A : List[Any] = initializer_range
A : str = num_labels
A : Dict = num_choices
A : str = scope
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A : List[Any] = None
if self.use_input_mask:
A : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
A : str = None
A : Dict = None
A : Union[str, Any] = None
if self.use_labels:
A : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A : List[str] = ids_tensor([self.batch_size] , self.num_choices )
A : List[str] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , 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 , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
A : Optional[Any] = EsmModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A : Optional[int] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
A : List[str] = model(UpperCamelCase__ )
A : Dict = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
A : Tuple = EsmForMaskedLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A : Optional[int] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
A : Optional[int] = self.num_labels
A : Tuple = EsmForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A : int = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Tuple = self.prepare_config_and_inputs()
(
A
) : int = config_and_inputs
A : Dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A ( __snake_case , __snake_case , unittest.TestCase ):
__magic_name__ = False
__magic_name__ = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
__magic_name__ = ()
__magic_name__ = (
{
"""feature-extraction""": EsmModel,
"""fill-mask""": EsmForMaskedLM,
"""text-classification""": EsmForSequenceClassification,
"""token-classification""": EsmForTokenClassification,
"""zero-shot""": EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
__magic_name__ = True
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : str = EsmModelTester(self )
A : str = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Any = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A : Dict = type
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )
@slow
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : List[str] = EsmModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : Optional[Any] = self.model_tester.prepare_config_and_inputs()[0]
A : str = EsmEmbeddings(config=UpperCamelCase__ )
A : Union[str, Any] = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
A : int = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
A : Tuple = create_position_ids_from_input_ids(UpperCamelCase__ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(UpperCamelCase__ , UpperCamelCase__ ) ) )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : List[str] = self.model_tester.prepare_config_and_inputs()[0]
A : str = EsmEmbeddings(config=UpperCamelCase__ )
A : Optional[int] = torch.empty(2 , 4 , 30 )
A : Any = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
A : Any = torch.as_tensor([expected_single_positions, expected_single_positions] )
A : Tuple = embeddings.create_position_ids_from_inputs_embeds(UpperCamelCase__ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(UpperCamelCase__ , UpperCamelCase__ ) ) )
@unittest.skip('''Esm does not support embedding resizing''' )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
pass
@require_torch
class A ( __snake_case ):
@slow
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
with torch.no_grad():
A : Any = EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
model.eval()
A : Any = torch.tensor([[0, 1, 2, 3, 4, 5]] )
A : Optional[int] = model(UpperCamelCase__ )[0]
A : str = 33
A : Any = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
A : Optional[int] = torch.tensor(
[[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
A : str = EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
model.eval()
A : List[str] = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
A : List[Any] = model(UpperCamelCase__ )[0]
# compare the actual values for a slice.
A : Dict = torch.tensor(
[[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
| 370
|
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=sys.maxsize ) -> Union[str, Any]:
"""simple docstring"""
A : Tuple = '''bilinear'''
A : Optional[int] = max_size
A : Dict = short_edge_length
def __call__( self , SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
A : Tuple = []
for img in imgs:
A, A : str = img.shape[:2]
# later: provide list and randomly choose index for resize
A : Union[str, Any] = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
A : int = size * 1.0 / min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if h < w:
A, A : Tuple = size, scale * w
else:
A, A : str = scale * h, size
if max(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) > self.max_size:
A : List[str] = self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : Tuple = newh * scale
A : int = neww * scale
A : List[str] = int(neww + 0.5 )
A : int = int(newh + 0.5 )
if img.dtype == np.uinta:
A : Dict = Image.fromarray(SCREAMING_SNAKE_CASE )
A : Optional[Any] = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
A : str = np.asarray(SCREAMING_SNAKE_CASE )
else:
A : Dict = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
A : List[Any] = nn.functional.interpolate(
SCREAMING_SNAKE_CASE , (newh, neww) , mode=self.interp_method , align_corners=SCREAMING_SNAKE_CASE ).squeeze(0 )
img_augs.append(SCREAMING_SNAKE_CASE )
return img_augs
class A :
def __init__( self , SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
A : Any = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
A : str = cfg.INPUT.FORMAT
A : int = cfg.SIZE_DIVISIBILITY
A : Optional[int] = cfg.PAD_VALUE
A : Dict = cfg.INPUT.MAX_SIZE_TEST
A : Optional[Any] = cfg.MODEL.DEVICE
A : Dict = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
A : Tuple = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
A : str = lambda SCREAMING_SNAKE_CASE : (x - self.pixel_mean) / self.pixel_std
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
A : Union[str, Any] = tuple(max(SCREAMING_SNAKE_CASE ) for s in zip(*[img.shape for img in images] ) )
A : List[str] = [im.shape[-2:] for im in images]
A : Optional[Any] = [
nn.functional.pad(
SCREAMING_SNAKE_CASE , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
]
return torch.stack(SCREAMING_SNAKE_CASE ), torch.tensor(SCREAMING_SNAKE_CASE )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> Union[str, Any]:
"""simple docstring"""
with torch.no_grad():
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : str = [images]
if single_image:
assert len(SCREAMING_SNAKE_CASE ) == 1
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(SCREAMING_SNAKE_CASE , images.pop(SCREAMING_SNAKE_CASE ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
SCREAMING_SNAKE_CASE , torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
A : Tuple = torch.tensor([im.shape[:2] for im in images] )
A : Dict = self.aug(SCREAMING_SNAKE_CASE )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
A : Tuple = [self.normalizer(SCREAMING_SNAKE_CASE ) for x in images]
# now pad them to do the following operations
A, A : Optional[int] = self.pad(SCREAMING_SNAKE_CASE )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
A : Tuple = torch.true_divide(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
assert torch.isfinite(snake_case__ ).all(), "Box tensor contains infinite or NaN!"
A, A : str = box_size
tensor[:, 0].clamp_(min=0 , max=snake_case__ )
tensor[:, 1].clamp_(min=0 , max=snake_case__ )
tensor[:, 2].clamp_(min=0 , max=snake_case__ )
tensor[:, 3].clamp_(min=0 , max=snake_case__ )
| 311
| 0
|
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A( UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = PhobertTokenizer
UpperCamelCase = False
def a__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ = ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
lowerCamelCase_ = dict(zip(A_ , range(len(A_ ) ) ) )
lowerCamelCase_ = ['#version: 0.2', 'l à</w>']
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
for token in vocab_tokens:
fp.write(f"""{token} {vocab_tokens[token]}\n""" )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(A_ ) )
def a__ ( self : Dict , **A_ : Dict ) -> List[Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **A_ )
def a__ ( self : Dict , A_ : Optional[Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ = 'Tôi là VinAI Research'
lowerCamelCase_ = 'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
return input_text, output_text
def a__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCamelCase_ = 'Tôi là VinAI Research'
lowerCamelCase_ = 'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
lowerCamelCase_ = tokenizer.tokenize(A_ )
print(A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , A_ )
| 204
|
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
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 TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class A:
'''simple docstring'''
UpperCamelCase = BlenderbotConfig
UpperCamelCase = {}
UpperCamelCase = '''gelu'''
def __init__( self : int , A_ : Optional[int] , A_ : List[str]=13 , A_ : str=7 , A_ : Any=True , A_ : Any=False , A_ : Optional[Any]=99 , A_ : List[str]=32 , A_ : List[str]=2 , A_ : Dict=4 , A_ : List[str]=37 , A_ : List[str]=0.1 , A_ : Optional[int]=0.1 , A_ : str=20 , A_ : str=2 , A_ : Optional[Any]=1 , A_ : int=0 , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = eos_token_id
lowerCamelCase_ = pad_token_id
lowerCamelCase_ = bos_token_id
def a__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowerCamelCase_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowerCamelCase_ = tf.concat([input_ids, eos_tensor] , axis=1 )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = 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 , )
lowerCamelCase_ = prepare_blenderbot_inputs_dict(A_ , A_ , A_ )
return config, inputs_dict
def a__ ( self : Tuple , A_ : Union[str, Any] , A_ : List[str] ) -> int:
"""simple docstring"""
lowerCamelCase_ = TFBlenderbotModel(config=A_ ).get_decoder()
lowerCamelCase_ = inputs_dict['input_ids']
lowerCamelCase_ = input_ids[:1, :]
lowerCamelCase_ = inputs_dict['attention_mask'][:1, :]
lowerCamelCase_ = inputs_dict['head_mask']
lowerCamelCase_ = 1
# first forward pass
lowerCamelCase_ = model(A_ , attention_mask=A_ , head_mask=A_ , use_cache=A_ )
lowerCamelCase_ , lowerCamelCase_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCamelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCamelCase_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowerCamelCase_ = tf.concat([input_ids, next_tokens] , axis=-1 )
lowerCamelCase_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowerCamelCase_ = model(A_ , attention_mask=A_ )[0]
lowerCamelCase_ = model(A_ , attention_mask=A_ , past_key_values=A_ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowerCamelCase_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowerCamelCase_ = output_from_no_past[:, -3:, random_slice_idx]
lowerCamelCase_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(A_ , A_ , rtol=1E-3 )
def _SCREAMING_SNAKE_CASE ( lowercase : Union[str, Any] , lowercase : Any , lowercase : Tuple , lowercase : List[Any]=None , lowercase : List[str]=None , lowercase : List[Any]=None , lowercase : Tuple=None , lowercase : Union[str, Any]=None , ):
'''simple docstring'''
if attention_mask is None:
lowerCamelCase_ = tf.cast(tf.math.not_equal(lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowerCamelCase_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowerCamelCase_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCamelCase_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCamelCase_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
UpperCamelCase = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase = (
{
'''conversational''': TFBlenderbotForConditionalGeneration,
'''feature-extraction''': TFBlenderbotModel,
'''summarization''': TFBlenderbotForConditionalGeneration,
'''text2text-generation''': TFBlenderbotForConditionalGeneration,
'''translation''': TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = False
def a__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = TFBlenderbotModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=A_ )
def a__ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ ( self : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A_ )
@require_tokenizers
@require_tf
class A( unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = ['''My friends are cool but they eat too many carbs.''']
UpperCamelCase = '''facebook/blenderbot-400M-distill'''
@cached_property
def a__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def a__ ( self : List[Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def a__ ( self : str ) -> str:
"""simple docstring"""
lowerCamelCase_ = self.tokenizer(self.src_text , return_tensors='tf' )
lowerCamelCase_ = self.model.generate(
model_inputs.input_ids , )
lowerCamelCase_ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=A_ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
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|
def _lowercase ( UpperCamelCase_ = 200 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [1, 2, 5, 10, 20, 50, 100, 200]
SCREAMING_SNAKE_CASE__ = [0] * (pence + 1)
SCREAMING_SNAKE_CASE__ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(UpperCamelCase_ , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(2_00) == 7_36_82
| 169
|
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__snake_case = 50_00_00
__snake_case ,__snake_case = os.path.split(__file__)
__snake_case = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def _lowercase ( UpperCamelCase_ , **UpperCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = dataset.map(**UpperCamelCase_ )
@get_duration
def _lowercase ( UpperCamelCase_ , **UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = dataset.filter(**UpperCamelCase_ )
def _lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {'num examples': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE__ = datasets.Features({'text': datasets.Value('string' ), 'numbers': datasets.Value('float32' )} )
SCREAMING_SNAKE_CASE__ = generate_example_dataset(
os.path.join(UpperCamelCase_ , 'dataset.arrow' ) , UpperCamelCase_ , num_examples=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = transformers.AutoTokenizer.from_pretrained('bert-base-cased' , use_fast=UpperCamelCase_ )
def tokenize(UpperCamelCase_ ):
return tokenizer(examples['text'] )
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ , batched=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ , function=lambda UpperCamelCase_ : None , batched=UpperCamelCase_ )
with dataset.formatted_as(type='numpy' ):
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ , function=lambda UpperCamelCase_ : None , batched=UpperCamelCase_ )
with dataset.formatted_as(type='pandas' ):
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ , function=lambda UpperCamelCase_ : None , batched=UpperCamelCase_ )
with dataset.formatted_as(type='torch' , columns='numbers' ):
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ , function=lambda UpperCamelCase_ : None , batched=UpperCamelCase_ )
with dataset.formatted_as(type='tensorflow' , columns='numbers' ):
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ , function=lambda UpperCamelCase_ : None , batched=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = map(UpperCamelCase_ , function=UpperCamelCase_ , batched=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = filter(UpperCamelCase_ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(UpperCamelCase_ , 'wb' ) as f:
f.write(json.dumps(UpperCamelCase_ ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
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|
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCamelCase__ = logging.get_logger(__name__)
class A__ ( __magic_name__ ):
lowercase = ['input_values', 'padding_mask']
def __init__( self : Tuple , a : int = 1 , a : int = 24_000 , a : float = 0.0 , a : float = None , a : float = None , **a : List[str] , ):
'''simple docstring'''
super().__init__(feature_size=a , sampling_rate=a , padding_value=a , **a )
lowerCAmelCase__ : Optional[Any] = chunk_length_s
lowerCAmelCase__ : str = overlap
@property
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self : List[Any] , a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , a : Optional[Union[bool, str, PaddingStrategy]] = None , a : Optional[bool] = False , a : Optional[int] = None , a : Optional[Union[str, TensorType]] = None , a : Optional[int] = None , ):
'''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} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
f''' {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.' )
if padding and truncation:
raise ValueError('Both padding and truncation were set. Make sure you only set one.' )
elif padding is None:
# by default let's pad the inputs
lowerCAmelCase__ : List[Any] = True
lowerCAmelCase__ : Dict = bool(
isinstance(a , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
lowerCAmelCase__ : List[str] = [np.asarray(a , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(a , np.ndarray ):
lowerCAmelCase__ : Dict = np.asarray(a , dtype=np.floataa )
elif isinstance(a , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
lowerCAmelCase__ : Optional[Any] = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase__ : List[Any] = [np.asarray(a ).T]
# verify inputs are valid
for idx, example in enumerate(a ):
if example.ndim > 2:
raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}''' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels''' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels''' )
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Any = BatchFeature({'input_values': raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
lowerCAmelCase__ : Dict = min(array.shape[0] for array in raw_audio )
lowerCAmelCase__ : Dict = int(np.floor(max_length / self.chunk_stride ) )
lowerCAmelCase__ : Optional[Any] = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
lowerCAmelCase__ : Any = max(array.shape[0] for array in raw_audio )
lowerCAmelCase__ : Union[str, Any] = int(np.ceil(max_length / self.chunk_stride ) )
lowerCAmelCase__ : Optional[int] = (nb_step - 1) * self.chunk_stride + self.chunk_length
lowerCAmelCase__ : Optional[int] = 'max_length'
else:
lowerCAmelCase__ : Dict = input_values
# normal padding on batch
if padded_inputs is None:
lowerCAmelCase__ : Dict = self.pad(
a , max_length=a , truncation=a , padding=a , return_attention_mask=a , )
if padding:
lowerCAmelCase__ : Union[str, Any] = padded_inputs.pop('attention_mask' )
lowerCAmelCase__ : List[str] = []
for example in padded_inputs.pop('input_values' ):
if self.feature_size == 1:
lowerCAmelCase__ : Dict = example[..., None]
input_values.append(example.T )
lowerCAmelCase__ : Optional[Any] = input_values
if return_tensors is not None:
lowerCAmelCase__ : int = padded_inputs.convert_to_tensors(a )
return padded_inputs
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|
from __future__ import annotations
lowerCamelCase__ = list[list[int]]
# assigning initial values to the grid
lowerCamelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
lowerCamelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Matrix | None:
if location := find_empty_location(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ , lowerCAmelCase__ : Any = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : Optional[Any] = digit
if sudoku(SCREAMING_SNAKE_CASE_ ) is not None:
return grid
lowerCAmelCase__ : List[Any] = 0
return None
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> None:
for row in grid:
for cell in row:
print(SCREAMING_SNAKE_CASE_ , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("""\nExample grid:\n""" + """=""" * 20)
print_solution(example_grid)
print("""\nExample grid solution:""")
lowerCamelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("""Cannot find a solution.""")
| 212
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : List[Any] = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = [
'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMForMultipleChoice',
'XLMForQuestionAnswering',
'XLMForQuestionAnsweringSimple',
'XLMForSequenceClassification',
'XLMForTokenClassification',
'XLMModel',
'XLMPreTrainedModel',
'XLMWithLMHeadModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMForMultipleChoice',
'TFXLMForQuestionAnsweringSimple',
'TFXLMForSequenceClassification',
'TFXLMForTokenClassification',
'TFXLMMainLayer',
'TFXLMModel',
'TFXLMPreTrainedModel',
'TFXLMWithLMHeadModel',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 366
|
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
A__ : List[Any] = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
A__ : Optional[int] = {
'facebook/blenderbot_small-90M': 512,
}
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :Optional[int] = VOCAB_FILES_NAMES
_UpperCAmelCase :Tuple = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase :Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase :Tuple = BlenderbotSmallTokenizer
def __init__( self : Tuple , snake_case__ : Optional[Any]=None , snake_case__ : str=None , snake_case__ : Any="<|endoftext|>" , snake_case__ : Tuple="<|endoftext|>" , snake_case__ : Tuple="<|endoftext|>" , snake_case__ : str=False , snake_case__ : int=True , **snake_case__ : Tuple , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=snake_case__ , merges=snake_case__ , add_prefix_space=snake_case__ , trim_offsets=snake_case__ , ) , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , **snake_case__ , )
lowerCamelCase_ : Optional[int] =add_prefix_space
def UpperCAmelCase__ ( self : Tuple , snake_case__ : Optional[Any] , snake_case__ : List[str]=None ):
lowerCamelCase_ : Optional[Any] =[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 : Tuple , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
lowerCamelCase_ : int =[self.sep_token_id]
lowerCamelCase_ : List[Any] =[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]
| 209
| 0
|
'''simple docstring'''
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 (__A):
"""simple docstring"""
_a = VideoMAEConfig()
set_architecture_configs(_lowerCAmelCase , _lowerCAmelCase)
if "finetuned" not in model_name:
_a = False
if "finetuned" in model_name:
_a = """huggingface/label-files"""
if "kinetics" in model_name:
_a = 400
_a = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
_a = 174
_a = """something-something-v2-id2label.json"""
else:
raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.''')
_a = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type='''dataset''') , '''r'''))
_a = {int(_lowerCAmelCase): v for k, v in idalabel.items()}
_a = idalabel
_a = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if "small" in model_name:
_a = 384
_a = 1_536
_a = 12
_a = 16
_a = 12
_a = 3
_a = 192
_a = 768
elif "large" in model_name:
_a = 1_024
_a = 4_096
_a = 24
_a = 16
_a = 12
_a = 8
_a = 512
_a = 2_048
elif "huge" in model_name:
_a = 1_280
_a = 5_120
_a = 32
_a = 16
_a = 12
_a = 8
_a = 640
_a = 2_560
elif "base" not in model_name:
raise ValueError('''Model name should include either \"small\", \"base\", \"large\", or \"huge\"''')
def lowerCAmelCase (__A):
"""simple docstring"""
if "encoder." in name:
_a = name.replace('''encoder.''' , '''''')
if "cls_token" in name:
_a = name.replace('''cls_token''' , '''videomae.embeddings.cls_token''')
if "decoder_pos_embed" in name:
_a = name.replace('''decoder_pos_embed''' , '''decoder.decoder_pos_embed''')
if "pos_embed" in name and "decoder" not in name:
_a = name.replace('''pos_embed''' , '''videomae.embeddings.position_embeddings''')
if "patch_embed.proj" in name:
_a = name.replace('''patch_embed.proj''' , '''videomae.embeddings.patch_embeddings.projection''')
if "patch_embed.norm" in name:
_a = name.replace('''patch_embed.norm''' , '''videomae.embeddings.norm''')
if "decoder.blocks" in name:
_a = name.replace('''decoder.blocks''' , '''decoder.decoder_layers''')
if "blocks" in name:
_a = name.replace('''blocks''' , '''videomae.encoder.layer''')
if "attn.proj" in name:
_a = name.replace('''attn.proj''' , '''attention.output.dense''')
if "attn" in name and "bias" not in name:
_a = name.replace('''attn''' , '''attention.self''')
if "attn" in name:
_a = name.replace('''attn''' , '''attention.attention''')
if "norm1" in name:
_a = name.replace('''norm1''' , '''layernorm_before''')
if "norm2" in name:
_a = name.replace('''norm2''' , '''layernorm_after''')
if "mlp.fc1" in name:
_a = name.replace('''mlp.fc1''' , '''intermediate.dense''')
if "mlp.fc2" in name:
_a = name.replace('''mlp.fc2''' , '''output.dense''')
if "decoder_embed" in name:
_a = name.replace('''decoder_embed''' , '''decoder.decoder_embed''')
if "decoder_norm" in name:
_a = name.replace('''decoder_norm''' , '''decoder.decoder_norm''')
if "decoder_pred" in name:
_a = name.replace('''decoder_pred''' , '''decoder.decoder_pred''')
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
_a = name.replace('''norm.weight''' , '''videomae.layernorm.weight''')
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
_a = name.replace('''norm.bias''' , '''videomae.layernorm.bias''')
if "head" in name and "decoder" not in name:
_a = name.replace('''head''' , '''classifier''')
return name
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_a = orig_state_dict.pop(_lowerCAmelCase)
if key.startswith('''encoder.'''):
_a = key.replace('''encoder.''' , '''''')
if "qkv" in key:
_a = key.split('''.''')
if key.startswith('''decoder.blocks'''):
_a = config.decoder_hidden_size
_a = int(key_split[2])
_a = """decoder.decoder_layers."""
if "weight" in key:
_a = val[:dim, :]
_a = val[dim : dim * 2, :]
_a = val[-dim:, :]
else:
_a = config.hidden_size
_a = int(key_split[1])
_a = """videomae.encoder.layer."""
if "weight" in key:
_a = val[:dim, :]
_a = val[dim : dim * 2, :]
_a = val[-dim:, :]
else:
_a = val
return orig_state_dict
def lowerCAmelCase ():
"""simple docstring"""
_a = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''')
_a = np.load(_lowerCAmelCase)
return list(_lowerCAmelCase)
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = get_videomae_config(_lowerCAmelCase)
if "finetuned" in model_name:
_a = VideoMAEForVideoClassification(_lowerCAmelCase)
else:
_a = VideoMAEForPreTraining(_lowerCAmelCase)
# download original checkpoint, hosted on Google Drive
_a = """pytorch_model.bin"""
gdown.cached_download(_lowerCAmelCase , _lowerCAmelCase , quiet=_lowerCAmelCase)
_a = torch.load(_lowerCAmelCase , map_location='''cpu''')
if "model" in files:
_a = files["""model"""]
else:
_a = files["""module"""]
_a = convert_state_dict(_lowerCAmelCase , _lowerCAmelCase)
model.load_state_dict(_lowerCAmelCase)
model.eval()
# verify model on basic input
_a = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5])
_a = prepare_video()
_a = image_processor(_lowerCAmelCase , return_tensors='''pt''')
if "finetuned" not in model_name:
_a = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''')
_a = torch.load(_lowerCAmelCase)
_a = model(**_lowerCAmelCase)
_a = outputs.logits
_a = [
"""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":
_a = torch.Size([1, 400])
_a = torch.tensor([-0.92_91, -0.40_61, -0.93_07])
elif model_name == "videomae-small-finetuned-ssv2":
_a = torch.Size([1, 174])
_a = torch.tensor([0.26_71, -0.46_89, -0.82_35])
elif model_name == "videomae-base":
_a = torch.Size([1, 1_408, 1_536])
_a = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]])
elif model_name == "videomae-base-short":
_a = torch.Size([1, 1_408, 1_536])
_a = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]])
# we verified the loss both for normalized and unnormalized targets for this one
_a = torch.tensor([0.51_42]) if config.norm_pix_loss else torch.tensor([0.64_69])
elif model_name == "videomae-large":
_a = torch.Size([1, 1_408, 1_536])
_a = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]])
elif model_name == "videomae-large-finetuned-kinetics":
_a = torch.Size([1, 400])
_a = torch.tensor([0.07_71, 0.00_11, -0.36_25])
elif model_name == "videomae-huge-finetuned-kinetics":
_a = torch.Size([1, 400])
_a = torch.tensor([0.24_33, 0.16_32, -0.48_94])
elif model_name == "videomae-base-short-finetuned-kinetics":
_a = torch.Size([1, 400])
_a = torch.tensor([0.65_88, 0.09_90, -0.24_93])
elif model_name == "videomae-base-finetuned-kinetics":
_a = torch.Size([1, 400])
_a = torch.tensor([0.36_69, -0.06_88, -0.24_21])
elif model_name == "videomae-base-short-ssv2":
_a = torch.Size([1, 1_408, 1_536])
_a = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]])
elif model_name == "videomae-base-short-finetuned-ssv2":
_a = torch.Size([1, 174])
_a = torch.tensor([-0.05_37, -0.15_39, -0.32_66])
elif model_name == "videomae-base-ssv2":
_a = torch.Size([1, 1_408, 1_536])
_a = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]])
elif model_name == "videomae-base-finetuned-ssv2":
_a = torch.Size([1, 174])
_a = torch.tensor([0.19_61, -0.83_37, -0.63_89])
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] , _lowerCAmelCase , atol=1e-4)
else:
print('''Logits:''' , logits[0, :3, :3])
assert torch.allclose(logits[0, :3, :3] , _lowerCAmelCase , atol=1e-4)
print('''Logits ok!''')
# verify loss, if applicable
if model_name == "videomae-base-short":
_a = outputs.loss
assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase , 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(_lowerCAmelCase)
model.save_pretrained(_lowerCAmelCase)
if push_to_hub:
print('''Pushing to the hub...''')
model.push_to_hub(_lowerCAmelCase , organization='''nielsr''')
if __name__ == "__main__":
lowercase_ = 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."
)
lowercase_ = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 211
|
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
"""`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
f"{test_file} instead." )
snake_case__ : Dict = components[-1]
if not test_fn.endswith("""py""" ):
raise ValueError(f"`test_file` should be a python file. Got {test_fn} instead." )
if not test_fn.startswith("""test_modeling_""" ):
raise ValueError(
f"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead." )
snake_case__ : int = components[:-1] + [test_fn.replace(""".py""" , """""" )]
snake_case__ : int = """.""".join(_lowerCAmelCase )
return test_module_path
def __snake_case( _lowerCAmelCase ) -> List[str]:
snake_case__ : str = get_module_path(_lowerCAmelCase )
snake_case__ : Union[str, Any] = importlib.import_module(_lowerCAmelCase )
return test_module
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : List[Any] = []
snake_case__ : Optional[int] = get_test_module(_lowerCAmelCase )
for attr in dir(_lowerCAmelCase ):
if attr.endswith("""ModelTester""" ):
tester_classes.append(getattr(_lowerCAmelCase , _lowerCAmelCase ) )
# sort with class names
return sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : x.__name__ )
def __snake_case( _lowerCAmelCase ) -> Dict:
snake_case__ : List[str] = []
snake_case__ : Any = get_test_module(_lowerCAmelCase )
for attr in dir(_lowerCAmelCase ):
snake_case__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
snake_case__ : List[str] = getattr(_lowerCAmelCase , """all_model_classes""" , [] )
if len(_lowerCAmelCase ) > 0:
test_classes.append(_lowerCAmelCase )
# sort with class names
return sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : x.__name__ )
def __snake_case( _lowerCAmelCase ) -> Dict:
snake_case__ : Any = get_test_classes(_lowerCAmelCase )
snake_case__ : Optional[Any] = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : x.__name__ )
def __snake_case( _lowerCAmelCase ) -> Optional[Any]:
snake_case__ : Optional[int] = test_class()
if hasattr(_lowerCAmelCase , """setUp""" ):
test.setUp()
snake_case__ : Any = None
if hasattr(_lowerCAmelCase , """model_tester""" ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
snake_case__ : Tuple = test.model_tester.__class__
return model_tester
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
snake_case__ : Union[str, Any] = get_test_classes(_lowerCAmelCase )
snake_case__ : str = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(_lowerCAmelCase )
# sort with class names
return sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : x.__name__ )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
snake_case__ : Optional[Any] = get_test_classes_for_model(_lowerCAmelCase , _lowerCAmelCase )
snake_case__ : Union[str, Any] = []
for test_class in test_classes:
snake_case__ : Tuple = get_model_tester_from_test_class(_lowerCAmelCase )
if tester_class is not None:
tester_classes.append(_lowerCAmelCase )
# sort with class names
return sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : x.__name__ )
def __snake_case( _lowerCAmelCase ) -> Union[str, Any]:
snake_case__ : Optional[Any] = get_test_classes(_lowerCAmelCase )
snake_case__ : Union[str, Any] = {test_class: get_model_tester_from_test_class(_lowerCAmelCase ) for test_class in test_classes}
return test_tester_mapping
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Any = get_model_classes(_lowerCAmelCase )
snake_case__ : Any = {
model_class: get_test_classes_for_model(_lowerCAmelCase , _lowerCAmelCase ) for model_class in model_classes
}
return model_test_mapping
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Union[str, Any] = get_model_classes(_lowerCAmelCase )
snake_case__ : str = {
model_class: get_tester_classes_for_model(_lowerCAmelCase , _lowerCAmelCase ) for model_class in model_classes
}
return model_to_tester_mapping
def __snake_case( _lowerCAmelCase ) -> int:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return o
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return o.__name__
elif isinstance(_lowerCAmelCase , (list, tuple) ):
return [to_json(_lowerCAmelCase ) for x in o]
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return {to_json(_lowerCAmelCase ): to_json(_lowerCAmelCase ) for k, v in o.items()}
else:
return o
| 35
| 0
|
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase_ ( UpperCamelCase__ : List[str], UpperCamelCase__ : Any, UpperCamelCase__ : List[str], UpperCamelCase__ : Dict ):
'''simple docstring'''
UpperCamelCase__ = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
'''de''': '''Maschinelles Lernen ist großartig, nicht wahr?''',
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
UpperCamelCase__ = {
'''wmt16-en-de-dist-12-1''': [28.3, 27.52],
'''wmt16-en-de-dist-6-1''': [27.4, 27.11],
'''wmt16-en-de-12-1''': [26.9, 25.75],
}
UpperCamelCase__ = F"""{src_lang}-{tgt_lang}"""
UpperCamelCase__ = F"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 models are available:
* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
-------|---------|----------
{model_name} | {scores[model_name][0]} | {scores[model_name][1]}
The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
"""
model_card_dir.mkdir(parents=UpperCamelCase__, exist_ok=UpperCamelCase__ )
UpperCamelCase__ = os.path.join(UpperCamelCase__, '''README.md''' )
print(F"""Generating {path}""" )
with open(UpperCamelCase__, '''w''', encoding='''utf-8''' ) as f:
f.write(UpperCamelCase__ )
# make sure we are under the root of the project
lowercase = Path(__file__).resolve().parent.parent.parent
lowercase = repo_dir / """model_cards"""
for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
lowercase = model_cards_dir / """allenai""" / model_name
write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)
| 367
|
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : Any , *_a : Optional[Any] , **_a : Any ):
warnings.warn(
'''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use CLIPImageProcessor instead.''' , _a , )
super().__init__(*_a , **_a )
| 35
| 0
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[str] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Union[str, Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[int] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Tuple = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[str] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[int] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[str] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
def _lowerCAmelCase ( *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(UpperCamelCase_ , ["""torch"""] )
def _lowerCAmelCase ( *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(UpperCamelCase_ , ["""torch"""] )
def _lowerCAmelCase ( *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(UpperCamelCase_ , ["""torch"""] )
def _lowerCAmelCase ( *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(UpperCamelCase_ , ["""torch"""] )
def _lowerCAmelCase ( *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(UpperCamelCase_ , ["""torch"""] )
def _lowerCAmelCase ( *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(UpperCamelCase_ , ["""torch"""] )
def _lowerCAmelCase ( *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(UpperCamelCase_ , ["""torch"""] )
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : str = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Union[str, Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Union[str, Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : int = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Any = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[str] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[int] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Tuple = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Tuple = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[str] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Any = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : str = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Tuple = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Union[str, Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Any = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : str = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Any = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Dict = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[str] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : int = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : int = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : str = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : str = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[int] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : int = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[Any] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Optional[int] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Any = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : int = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(self , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
@classmethod
def snake_case_ ( cls , *lowerCAmelCase__ , **lowerCAmelCase__):
requires_backends(cls , ["""torch"""])
| 100
|
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a : Tuple = re.compile(R"([A-Z]+)([A-Z][a-z])")
a : Union[str, Any] = re.compile(R"([a-z\d])([A-Z])")
a : str = re.compile(R"(?<!_)_(?!_)")
a : List[Any] = re.compile(R"(_{2,})")
a : List[Any] = R"^\w+(\.\w+)*$"
a : Dict = R"<>:/\|?*"
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Dict = _uppercase_uppercase_re.sub(R"\1_\2" , __magic_name__ )
UpperCAmelCase : List[str] = _lowercase_uppercase_re.sub(R"\1_\2" , __magic_name__ )
return name.lower()
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Any = _single_underscore_re.split(__magic_name__ )
UpperCAmelCase : Union[str, Any] = [_multiple_underscores_re.split(__magic_name__ ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(__magic_name__ ) if n != "" )
def lowercase ( __magic_name__ ):
'''simple docstring'''
if os.path.basename(__magic_name__ ) != name:
raise ValueError(F"Should be a dataset name, not a path: {name}" )
return camelcase_to_snakecase(__magic_name__ )
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
if os.path.basename(__magic_name__ ) != name:
raise ValueError(F"Should be a dataset name, not a path: {name}" )
if not re.match(_split_re , __magic_name__ ):
raise ValueError(F"Split name should match '{_split_re}'' but got '{split}'." )
return F"{filename_prefix_for_name(__magic_name__ )}-{split}"
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ):
'''simple docstring'''
UpperCAmelCase : List[str] = filename_prefix_for_split(__magic_name__ , __magic_name__ )
if filetype_suffix:
prefix += F".{filetype_suffix}"
UpperCAmelCase : int = os.path.join(__magic_name__ , __magic_name__ )
return F"{filepath}*"
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__=None ):
'''simple docstring'''
UpperCAmelCase : List[str] = filename_prefix_for_split(__magic_name__ , __magic_name__ )
UpperCAmelCase : int = os.path.join(__magic_name__ , __magic_name__ )
if shard_lengths:
UpperCAmelCase : Tuple = len(__magic_name__ )
UpperCAmelCase : Optional[int] = [F"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(__magic_name__ )]
if filetype_suffix:
UpperCAmelCase : Optional[int] = [filename + F".{filetype_suffix}" for filename in filenames]
return filenames
else:
UpperCAmelCase : int = prefix
if filetype_suffix:
filename += F".{filetype_suffix}"
return [filename]
| 311
| 0
|
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 __a ( __UpperCamelCase ):
__lowercase : Union[List[PIL.Image.Image], np.ndarray]
__lowercase : Optional[List[bool]]
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 288
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '''▁'''
__lowerCAmelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}
__lowerCAmelCase = {
'''vocab_file''': {
'''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
},
'''monolingual_vocab_file''': {
'''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
},
}
__lowerCAmelCase = {'''vinai/bartpho-syllable''': 10_24}
class __a ( __UpperCamelCase ):
__lowercase : int = VOCAB_FILES_NAMES
__lowercase : str = PRETRAINED_VOCAB_FILES_MAP
__lowercase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : Optional[int] = ['input_ids', 'attention_mask']
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__="<s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> None:
'''simple docstring'''
# Mask token behave like a normal word, i.e. include the space before it
lowercase__: List[str] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
lowercase__: Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase__ , )
lowercase__: Dict = vocab_file
lowercase__: str = monolingual_vocab_file
lowercase__: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCAmelCase__ ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
lowercase__: List[Any] = {}
lowercase__: Optional[int] = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(lowerCAmelCase__ ) not in self.fairseq_tokens_to_ids:
lowercase__: str = cnt
cnt += 1
with open(lowerCAmelCase__ , 'r' , encoding='utf-8' ) as f:
for line in f.readlines():
lowercase__: Optional[Any] = line.strip().split()[0]
lowercase__: Optional[Any] = len(self.fairseq_tokens_to_ids )
if str(lowerCAmelCase__ ) not in self.fairseq_tokens_to_ids:
lowercase__: Optional[int] = len(self.fairseq_tokens_to_ids )
lowercase__: Optional[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Optional[int]:
'''simple docstring'''
lowercase__: Tuple = self.__dict__.copy()
lowercase__: Tuple = None
lowercase__: Optional[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
lowercase__: Union[str, Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
lowercase__: Union[str, Any] = {}
lowercase__: List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase__: Optional[int] = [self.cls_token_id]
lowercase__: Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = 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__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
'''simple docstring'''
lowercase__: Dict = [self.sep_token_id]
lowercase__: Dict = [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]
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
return len(self.fairseq_ids_to_tokens )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Union[str, Any] = {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
return self.fairseq_ids_to_tokens[index]
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
lowercase__: Optional[Any] = ''.join(lowerCAmelCase__ ).replace(lowerCAmelCase__ , ' ' ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase__: int = os.path.join(
lowerCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowercase__: List[str] = os.path.join(
lowerCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
lowercase__: Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
lowerCAmelCase__ ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , lowerCAmelCase__ )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(lowerCAmelCase__ , 'w' , encoding='utf-8' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(F'{str(lowerCAmelCase__ )} \n' )
return out_vocab_file, out_monolingual_vocab_file
| 288
| 1
|
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_lowerCAmelCase : Union[str, Any] = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n author = \"Lin, Chin-Yew and\n Och, Franz Josef\",\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n month = \"aug 23{--}aug 27\",\n year = \"2004\",\n address = \"Geneva, Switzerland\",\n publisher = \"COLING\",\n url = \"https://www.aclweb.org/anthology/C04-1072\",\n pages = \"501--507\",\n}\n"
_lowerCAmelCase : Optional[Any] = "\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"
_lowerCAmelCase : str = "\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n 'bleu': bleu score,\n 'precisions': geometric mean of n-gram precisions,\n 'brevity_penalty': brevity penalty,\n 'length_ratio': ratio of lengths,\n 'translation_length': translation_length,\n 'reference_length': reference_length\nExamples:\n\n >>> predictions = [\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric(\"bleu\")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results[\"bleu\"])\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
def UpperCAmelCase_ ( self :Tuple ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"] , reference_urls=[
"https://en.wikipedia.org/wiki/BLEU",
"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
] , )
def UpperCAmelCase_ ( self :Tuple , lowerCamelCase :Any , lowerCamelCase :List[str] , lowerCamelCase :int=4 , lowerCamelCase :Union[str, Any]=False ) -> Dict:
UpperCAmelCase__ = compute_bleu(
reference_corpus=lowerCamelCase , translation_corpus=lowerCamelCase , max_order=lowerCamelCase , smooth=lowerCamelCase )
((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 169
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCAmelCase : List[str] = 1_6
_lowerCAmelCase : List[Any] = 3_2
def lowerCAmelCase ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 ):
"""simple docstring"""
UpperCAmelCase__ = AutoTokenizer.from_pretrained("bert-base-cased" )
UpperCAmelCase__ = load_dataset("glue" , "mrpc" )
def tokenize_function(_lowerCAmelCase : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase__ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCAmelCase__ = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase__ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(_lowerCAmelCase : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCAmelCase__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCAmelCase__ = 16
elif accelerator.mixed_precision != "no":
UpperCAmelCase__ = 8
else:
UpperCAmelCase__ = None
return tokenizer.pad(
_lowerCAmelCase , padding="longest" , max_length=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_tensors="pt" , )
# Instantiate dataloaders.
UpperCAmelCase__ = DataLoader(
tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
UpperCAmelCase__ = DataLoader(
tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCAmelCase : int = mocked_dataloaders # noqa: F811
def lowerCAmelCase ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , _lowerCAmelCase ) == "1":
UpperCAmelCase__ = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
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"] )
set_seed(_lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase__ = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
UpperCAmelCase__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
UpperCAmelCase__ = batch_size // MAX_GPU_BATCH_SIZE
UpperCAmelCase__ = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase__ = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=_lowerCAmelCase )
# 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 )
# Instantiate optimizer
UpperCAmelCase__ = AdamW(params=model.parameters() , lr=_lowerCAmelCase )
# Instantiate scheduler
UpperCAmelCase__ = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps , )
# 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(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
UpperCAmelCase__ = os.path.split(_lowerCAmelCase )[-1].split("." )[0]
accelerator.init_trackers(_lowerCAmelCase , _lowerCAmelCase )
# Now we train the model
for epoch in range(_lowerCAmelCase ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
UpperCAmelCase__ = 0
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
UpperCAmelCase__ = model(**_lowerCAmelCase )
UpperCAmelCase__ = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
UpperCAmelCase__ = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase__ = model(**_lowerCAmelCase )
UpperCAmelCase__ = outputs.logits.argmax(dim=-1 )
UpperCAmelCase__ , UpperCAmelCase__ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=_lowerCAmelCase , references=_lowerCAmelCase , )
UpperCAmelCase__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , _lowerCAmelCase )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(_lowerCAmelCase ),
"epoch": epoch,
} , step=_lowerCAmelCase , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=_lowerCAmelCase , default=_lowerCAmelCase , 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(
"--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=_lowerCAmelCase , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 169
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : List[Any] = ShapEImgaImgPipeline
_UpperCamelCase : Any = ['''image''']
_UpperCamelCase : Dict = ['''image''']
_UpperCamelCase : Dict = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
_UpperCamelCase : Optional[Any] = False
@property
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
return 32
@property
def lowercase ( self: Any ) -> Tuple:
"""simple docstring"""
return 32
@property
def lowercase ( self: Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowercase ( self: List[str] ) -> Any:
"""simple docstring"""
return 8
@property
def lowercase ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
UpperCamelCase_ = CLIPVisionModel(_SCREAMING_SNAKE_CASE )
return model
@property
def lowercase ( self: str ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_SCREAMING_SNAKE_CASE , do_normalize=_SCREAMING_SNAKE_CASE , do_resize=_SCREAMING_SNAKE_CASE , image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , resample=3 , size=224 , )
return image_processor
@property
def lowercase ( self: Optional[int] ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"embedding_proj_norm_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
UpperCamelCase_ = PriorTransformer(**_SCREAMING_SNAKE_CASE )
return model
@property
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
UpperCamelCase_ = ShapERenderer(**_SCREAMING_SNAKE_CASE )
return model
def lowercase ( self: Dict ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.dummy_prior
UpperCamelCase_ = self.dummy_image_encoder
UpperCamelCase_ = self.dummy_image_processor
UpperCamelCase_ = self.dummy_renderer
UpperCamelCase_ = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=_SCREAMING_SNAKE_CASE , clip_sample=_SCREAMING_SNAKE_CASE , clip_sample_range=1.0 , )
UpperCamelCase_ = {
"prior": prior,
"image_encoder": image_encoder,
"image_processor": image_processor,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[Any]=0 ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"image": input_image,
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def lowercase ( self: List[Any] ) -> int:
"""simple docstring"""
UpperCamelCase_ = "cpu"
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = output.images[0]
UpperCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
UpperCamelCase_ = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self: List[Any] ) -> Optional[Any]:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase ( self: Any ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = torch_device == "cpu"
UpperCamelCase_ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_SCREAMING_SNAKE_CASE , relax_max_difference=_SCREAMING_SNAKE_CASE , )
def lowercase ( self: int ) -> str:
"""simple docstring"""
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = 1
UpperCamelCase_ = 2
UpperCamelCase_ = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
for key in inputs.keys():
if key in self.batch_params:
UpperCamelCase_ = batch_size * [inputs[key]]
UpperCamelCase_ = pipe(**_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: List[Any] ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self: Union[str, Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png" )
UpperCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_img2img_out.npy" )
UpperCamelCase_ = ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img" )
UpperCamelCase_ = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCamelCase_ = pipe(
_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 328
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = {
'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}
_UpperCAmelCase = {
'google/pegasus-xsum': 5_1_2,
}
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Optional[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : List[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self: str , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: str="<pad>" , _SCREAMING_SNAKE_CASE: Optional[Any]="</s>" , _SCREAMING_SNAKE_CASE: Any="<unk>" , _SCREAMING_SNAKE_CASE: int="<mask_2>" , _SCREAMING_SNAKE_CASE: List[Any]="<mask_1>" , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Optional[int]=103 , _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None , **_SCREAMING_SNAKE_CASE: Dict , ) -> None:
"""simple docstring"""
UpperCamelCase_ = offset
if additional_special_tokens is not None:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError(
f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE )}, but is'''
f''' {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE ) , self.offset - 1 )
]
if len(set(_SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
UpperCamelCase_ = additional_special_tokens_extended
else:
UpperCamelCase_ = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
UpperCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token_sent=_SCREAMING_SNAKE_CASE , offset=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = mask_token_sent
UpperCamelCase_ = vocab_file
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
# add special tokens to encoder dict
UpperCamelCase_ = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
UpperCamelCase_ = {v: k for k, v in self.encoder.items()}
@property
def lowercase ( self: Dict ) -> int:
"""simple docstring"""
return len(self.sp_model ) + self.offset
def lowercase ( self: int ) -> Dict[str, int]:
"""simple docstring"""
UpperCamelCase_ = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.__dict__.copy()
UpperCamelCase_ = None
return state
def __setstate__( self: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] ) -> Any:
"""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.Load(self.vocab_file )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: str ) -> int:
"""simple docstring"""
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
UpperCamelCase_ = self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE )
return sp_id + self.offset
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: int ) -> str:
"""simple docstring"""
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
UpperCamelCase_ = self.sp_model.IdToPiece(index - self.offset )
return token
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
UpperCamelCase_ = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string.strip()
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
return 1
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: str ) -> str:
"""simple docstring"""
UpperCamelCase_ = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: List , _SCREAMING_SNAKE_CASE: Optional[List] = None , _SCREAMING_SNAKE_CASE: bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(_SCREAMING_SNAKE_CASE )
elif token_ids_a is None:
return self._special_token_mask(_SCREAMING_SNAKE_CASE ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any]=None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , "wb" ) as fi:
UpperCamelCase_ = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 328
| 1
|
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : bool = False
SCREAMING_SNAKE_CASE_ : float = 3.0
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : List[str] ) -> List[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'''a''': 2} )
self.assertDictEqual(MockClass(a=2 , b=A ).to_kwargs() , {'''a''': 2, '''b''': True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {'''a''': 2, '''c''': 2.25} )
@require_cuda
def A ( self : Union[str, Any] ) -> Optional[int]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
lowercase_ : Optional[Any] = GradScalerKwargs(init_scale=10_24 , growth_factor=2 )
AcceleratorState._reset_state()
lowercase_ : Union[str, Any] = Accelerator(mixed_precision='''fp16''' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
lowercase_ : Dict = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1024.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 20_00 )
self.assertEqual(scaler._enabled , A )
@require_multi_gpu
def A ( self : Any ) -> Any:
lowercase_ : Any = ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
execute_subprocess_async(A , env=os.environ.copy() )
if __name__ == "__main__":
__A : Dict = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
__A : Tuple = Accelerator(kwargs_handlers=[ddp_scaler])
__A : Optional[Any] = torch.nn.Linear(100, 200)
__A : Tuple = accelerator.prepare(model)
# Check the values changed in kwargs
__A : List[Any] = ''''''
__A : Any = model.bucket_bytes_cap // (1_024 * 1_024)
if observed_bucket_cap_map != 15:
error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 33
|
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 = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """camembert"""
def __init__( self , __lowerCAmelCase=3_0_5_2_2 , __lowerCAmelCase=7_6_8 , __lowerCAmelCase=1_2 , __lowerCAmelCase=1_2 , __lowerCAmelCase=3_0_7_2 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=1 , __lowerCAmelCase=0 , __lowerCAmelCase=2 , __lowerCAmelCase="absolute" , __lowerCAmelCase=True , __lowerCAmelCase=None , **__lowerCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
lowerCamelCase__ = vocab_size
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = hidden_act
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = type_vocab_size
lowerCamelCase__ = initializer_range
lowerCamelCase__ = layer_norm_eps
lowerCamelCase__ = position_embedding_type
lowerCamelCase__ = use_cache
lowerCamelCase__ = classifier_dropout
class __A ( lowerCAmelCase ):
'''simple docstring'''
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
lowerCamelCase__ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCamelCase__ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 209
| 0
|
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE = 5_0 ) -> int:
__lowerCAmelCase: List[str] = [1] * (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 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 108
|
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
__A = get_logger(__name__)
class snake_case :
SCREAMING_SNAKE_CASE_ : List[Any] = """dummy_data"""
SCREAMING_SNAKE_CASE_ : List[Any] = """datasets"""
SCREAMING_SNAKE_CASE_ : Any = False
def __init__( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Union[Version, str] , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = True , UpperCamelCase__ : Optional[List[Callable]] = None , )-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: Optional[Any] = 0
__lowerCAmelCase: Tuple = dataset_name
__lowerCAmelCase: Optional[Any] = cache_dir
__lowerCAmelCase: Optional[int] = use_local_dummy_data
__lowerCAmelCase: Optional[Any] = config
# download_callbacks take a single url as input
__lowerCAmelCase: List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__lowerCAmelCase: Union[str, Any] = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__lowerCAmelCase: List[str] = str(UpperCamelCase__)
# to be downloaded
__lowerCAmelCase: Dict = None
__lowerCAmelCase: Dict = None
@property
def lowercase_ ( self : List[str])-> str:
'''simple docstring'''
if self._dummy_file is None:
__lowerCAmelCase: Tuple = self.download_dummy_data()
return self._dummy_file
@property
def lowercase_ ( self : Dict)-> Optional[Any]:
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" , self.config.name , self.version_name)
# structure is dummy / version_name
return os.path.join("dummy" , self.version_name)
@property
def lowercase_ ( self : List[str])-> Any:
'''simple docstring'''
return os.path.join(self.dummy_data_folder , "dummy_data.zip")
def lowercase_ ( self : Optional[Any])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: Dict = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__lowerCAmelCase: str = cached_path(
UpperCamelCase__ , cache_dir=self.cache_dir , extract_compressed_file=UpperCamelCase__ , force_extract=UpperCamelCase__)
return os.path.join(UpperCamelCase__ , self.dummy_file_name)
@property
def lowercase_ ( self : Dict)-> List[Any]:
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file)
@property
def lowercase_ ( self : Optional[Any])-> Tuple:
'''simple docstring'''
if self._bucket_url is None:
__lowerCAmelCase: int = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , "/"))
return self._bucket_url
@property
def lowercase_ ( self : str)-> Optional[int]:
'''simple docstring'''
if os.path.isdir(self.dummy_file):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , "/").split("/")[:-1])
def lowercase_ ( self : List[Any] , UpperCamelCase__ : int , *UpperCamelCase__ : List[str])-> Optional[int]:
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__lowerCAmelCase: List[Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__lowerCAmelCase: str = self.dummy_file_name
# special case when data_url is a dict
if isinstance(UpperCamelCase__ , UpperCamelCase__):
return self.create_dummy_data_dict(UpperCamelCase__ , UpperCamelCase__)
elif isinstance(UpperCamelCase__ , (list, tuple)):
return self.create_dummy_data_list(UpperCamelCase__ , UpperCamelCase__)
else:
return self.create_dummy_data_single(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Dict , UpperCamelCase__ : Dict , *UpperCamelCase__ : int)-> Dict:
'''simple docstring'''
return self.download_and_extract(UpperCamelCase__)
def lowercase_ ( self : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any])-> str:
'''simple docstring'''
return self.download_and_extract(UpperCamelCase__)
def lowercase_ ( self : Optional[Any] , UpperCamelCase__ : List[Any] , *UpperCamelCase__ : int , **UpperCamelCase__ : str)-> List[str]:
'''simple docstring'''
return path
def lowercase_ ( self : Optional[Any])-> Any:
'''simple docstring'''
return {}
def lowercase_ ( self : Any , UpperCamelCase__ : Tuple , UpperCamelCase__ : int)-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(UpperCamelCase__ , UpperCamelCase__):
for single_url in single_urls:
download_callback(UpperCamelCase__)
else:
__lowerCAmelCase: Union[str, Any] = single_urls
download_callback(UpperCamelCase__)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(UpperCamelCase__ , UpperCamelCase__):
__lowerCAmelCase: Dict = [os.path.join(UpperCamelCase__ , urllib.parse.quote_plus(Path(UpperCamelCase__).name)) for x in single_urls]
else:
__lowerCAmelCase: Any = single_urls
__lowerCAmelCase: Optional[int] = os.path.join(UpperCamelCase__ , urllib.parse.quote_plus(Path(UpperCamelCase__).name))
__lowerCAmelCase: Dict = value
# make sure that values are unique
if all(isinstance(UpperCamelCase__ , UpperCamelCase__) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
dummy_data_dict.values()):
# append key to value to make its name unique
__lowerCAmelCase: Any = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : List[Any])-> int:
'''simple docstring'''
__lowerCAmelCase: Tuple = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__lowerCAmelCase: Any = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" , UpperCamelCase__)) for url in data_url)
__lowerCAmelCase: str = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed") for url in data_url)
if data_url and (is_tf_records or is_pubmed_records):
__lowerCAmelCase: Optional[int] = [data_url[0]] * len(UpperCamelCase__)
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(UpperCamelCase__)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__lowerCAmelCase: Optional[Any] = os.path.join(UpperCamelCase__ , urllib.parse.quote_plus(single_url.split("/")[-1]))
dummy_data_list.append(UpperCamelCase__)
return dummy_data_list
def lowercase_ ( self : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any])-> Optional[int]:
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(UpperCamelCase__)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__lowerCAmelCase: List[Any] = os.path.join(UpperCamelCase__ , urllib.parse.quote_plus(data_url.split("/")[-1]))
if os.path.exists(UpperCamelCase__) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def lowercase_ ( self : List[str])-> Dict:
'''simple docstring'''
pass
def lowercase_ ( self : Union[str, Any])-> Tuple:
'''simple docstring'''
pass
def lowercase_ ( self : Dict , UpperCamelCase__ : str)-> int:
'''simple docstring'''
def _iter_archive_members(UpperCamelCase__ : str):
# this preserves the order of the members inside the ZIP archive
__lowerCAmelCase: Optional[Any] = Path(self.dummy_file).parent
__lowerCAmelCase: Optional[int] = path.relative_to(UpperCamelCase__)
with ZipFile(self.local_path_to_dummy_data) as zip_file:
__lowerCAmelCase: Optional[int] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix()):
yield dummy_parent_path.joinpath(UpperCamelCase__)
__lowerCAmelCase: str = Path(UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = _iter_archive_members(UpperCamelCase__) if self.use_local_dummy_data else path.rglob("*")
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__")):
yield file_path.relative_to(UpperCamelCase__).as_posix(), file_path.open("rb")
def lowercase_ ( self : str , UpperCamelCase__ : str)-> str:
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
__lowerCAmelCase: Dict = [paths]
for path in paths:
if os.path.isfile(UpperCamelCase__):
if os.path.basename(UpperCamelCase__).startswith((".", "__")):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(UpperCamelCase__):
if os.path.basename(UpperCamelCase__).startswith((".", "__")):
continue
dirnames.sort()
for filename in sorted(UpperCamelCase__):
if filename.startswith((".", "__")):
continue
yield os.path.join(UpperCamelCase__ , UpperCamelCase__)
| 108
| 1
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class UpperCAmelCase_ ( _a ):
'''simple docstring'''
a__ = """align_text_model"""
def __init__( self : Dict , UpperCamelCase__ : Any=3_0522 , UpperCamelCase__ : Tuple=768 , UpperCamelCase__ : Any=12 , UpperCamelCase__ : Union[str, Any]=12 , UpperCamelCase__ : List[Any]=3072 , UpperCamelCase__ : Dict="gelu" , UpperCamelCase__ : Optional[int]=0.1 , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : List[str]=512 , UpperCamelCase__ : str=2 , UpperCamelCase__ : Optional[Any]=0.02 , UpperCamelCase__ : Optional[Any]=1E-12 , UpperCamelCase__ : List[Any]=0 , UpperCamelCase__ : Union[str, Any]="absolute" , UpperCamelCase__ : List[Any]=True , **UpperCamelCase__ : List[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(**snake_case_ )
__magic_name__ = vocab_size
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = hidden_act
__magic_name__ = intermediate_size
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = max_position_embeddings
__magic_name__ = type_vocab_size
__magic_name__ = initializer_range
__magic_name__ = layer_norm_eps
__magic_name__ = position_embedding_type
__magic_name__ = use_cache
__magic_name__ = pad_token_id
@classmethod
def _lowercase ( cls : Union[str, Any] , UpperCamelCase__ : Union[str, os.PathLike] , **UpperCamelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
cls._set_token_in_kwargs(snake_case_ )
__magic_name__ = cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
__magic_name__ = 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(snake_case_ , **snake_case_ )
class UpperCAmelCase_ ( _a ):
'''simple docstring'''
a__ = """align_vision_model"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : int = 3 , UpperCamelCase__ : int = 600 , UpperCamelCase__ : float = 2.0 , UpperCamelCase__ : float = 3.1 , UpperCamelCase__ : int = 8 , UpperCamelCase__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , UpperCamelCase__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , UpperCamelCase__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , UpperCamelCase__ : List[int] = [] , UpperCamelCase__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , UpperCamelCase__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , UpperCamelCase__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , UpperCamelCase__ : float = 0.25 , UpperCamelCase__ : str = "swish" , UpperCamelCase__ : int = 2560 , UpperCamelCase__ : str = "mean" , UpperCamelCase__ : float = 0.02 , UpperCamelCase__ : float = 0.001 , UpperCamelCase__ : float = 0.99 , UpperCamelCase__ : float = 0.2 , **UpperCamelCase__ : Optional[int] , ) -> str:
"""simple docstring"""
super().__init__(**snake_case_ )
__magic_name__ = num_channels
__magic_name__ = image_size
__magic_name__ = width_coefficient
__magic_name__ = depth_coefficient
__magic_name__ = depth_divisor
__magic_name__ = kernel_sizes
__magic_name__ = in_channels
__magic_name__ = out_channels
__magic_name__ = depthwise_padding
__magic_name__ = strides
__magic_name__ = num_block_repeats
__magic_name__ = expand_ratios
__magic_name__ = squeeze_expansion_ratio
__magic_name__ = hidden_act
__magic_name__ = hidden_dim
__magic_name__ = pooling_type
__magic_name__ = initializer_range
__magic_name__ = batch_norm_eps
__magic_name__ = batch_norm_momentum
__magic_name__ = drop_connect_rate
__magic_name__ = sum(snake_case_ ) * 4
@classmethod
def _lowercase ( cls : str , UpperCamelCase__ : Union[str, os.PathLike] , **UpperCamelCase__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
cls._set_token_in_kwargs(snake_case_ )
__magic_name__ = cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
__magic_name__ = 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(snake_case_ , **snake_case_ )
class UpperCAmelCase_ ( _a ):
'''simple docstring'''
a__ = """align"""
a__ = True
def __init__( self : int , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : Tuple=640 , UpperCamelCase__ : List[Any]=1.0 , UpperCamelCase__ : List[str]=0.02 , **UpperCamelCase__ : Any , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**snake_case_ )
if text_config is None:
__magic_name__ = {}
logger.info("""text_config is None. Initializing the AlignTextConfig with default values.""" )
if vision_config is None:
__magic_name__ = {}
logger.info("""vision_config is None. Initializing the AlignVisionConfig with default values.""" )
__magic_name__ = AlignTextConfig(**snake_case_ )
__magic_name__ = AlignVisionConfig(**snake_case_ )
__magic_name__ = projection_dim
__magic_name__ = temperature_init_value
__magic_name__ = initializer_range
@classmethod
def _lowercase ( cls : Union[str, Any] , UpperCamelCase__ : AlignTextConfig , UpperCamelCase__ : AlignVisionConfig , **UpperCamelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _lowercase ( self : List[Any] ) -> Any:
"""simple docstring"""
__magic_name__ = copy.deepcopy(self.__dict__ )
__magic_name__ = self.text_config.to_dict()
__magic_name__ = self.vision_config.to_dict()
__magic_name__ = self.__class__.model_type
return output
| 88
|
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> 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(_lowerCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change brightness to 100
__a = change_brightness(img, 100)
brigt_img.save("image_data/lena_brightness.png", format="png")
| 35
| 0
|
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def _lowerCamelCase ( lowerCamelCase_ : Optional[Any] ):
"""simple docstring"""
return EnvironmentCommand()
def _lowerCamelCase ( lowerCamelCase_ : Tuple ):
"""simple docstring"""
return EnvironmentCommand(args.accelerate_config_file )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
@staticmethod
def _UpperCamelCase ( snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Any = parser.add_parser('env' )
download_parser.set_defaults(func=snake_case_ )
download_parser.add_argument(
'--accelerate-config_file' , default=snake_case_ , help='The accelerate config file to use for the default values in the launching script.' , )
download_parser.set_defaults(func=snake_case_ )
def __init__( self , snake_case_ , *snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : str = accelerate_config_file
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 'not installed'
if is_safetensors_available():
import safetensors
UpperCAmelCase_ : Dict = safetensors.__version__
elif importlib.util.find_spec('safetensors' ) is not None:
import safetensors
UpperCAmelCase_ : Optional[Any] = F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''
UpperCAmelCase_ : Any = 'not installed'
UpperCAmelCase_ : int = 'not found'
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
UpperCAmelCase_ : str = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(snake_case_ ):
UpperCAmelCase_ : List[Any] = load_config_from_file(self._accelerate_config_file ).to_dict()
UpperCAmelCase_ : Union[str, Any] = (
'\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(snake_case_ , snake_case_ )
else F'''\t{accelerate_config}'''
)
UpperCAmelCase_ : List[Any] = 'not installed'
UpperCAmelCase_ : Optional[Any] = 'NA'
if is_torch_available():
import torch
UpperCAmelCase_ : List[str] = torch.__version__
UpperCAmelCase_ : Optional[Any] = torch.cuda.is_available()
UpperCAmelCase_ : Union[str, Any] = 'not installed'
UpperCAmelCase_ : List[Any] = 'NA'
if is_tf_available():
import tensorflow as tf
UpperCAmelCase_ : Any = tf.__version__
try:
# deprecated in v2.1
UpperCAmelCase_ : str = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
UpperCAmelCase_ : Optional[Any] = bool(tf.config.list_physical_devices('GPU' ) )
UpperCAmelCase_ : str = 'not installed'
UpperCAmelCase_ : int = 'not installed'
UpperCAmelCase_ : Optional[Any] = 'not installed'
UpperCAmelCase_ : int = 'NA'
if is_flax_available():
import flax
import jax
import jaxlib
UpperCAmelCase_ : Tuple = flax.__version__
UpperCAmelCase_ : Union[str, Any] = jax.__version__
UpperCAmelCase_ : int = jaxlib.__version__
UpperCAmelCase_ : Optional[Any] = jax.lib.xla_bridge.get_backend().platform
UpperCAmelCase_ : Union[str, Any] = {
'`transformers` version': version,
'Platform': platform.platform(),
'Python version': platform.python_version(),
'Huggingface_hub version': huggingface_hub.__version__,
'Safetensors version': F'''{safetensors_version}''',
'Accelerate version': F'''{accelerate_version}''',
'Accelerate config': F'''{accelerate_config_str}''',
'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
'Tensorflow version (GPU?)': F'''{tf_version} ({tf_cuda_available})''',
'Flax version (CPU?/GPU?/TPU?)': F'''{flax_version} ({jax_backend})''',
'Jax version': F'''{jax_version}''',
'JaxLib version': F'''{jaxlib_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(snake_case_ ) )
return info
@staticmethod
def _UpperCamelCase ( snake_case_ ):
'''simple docstring'''
return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
| 274
|
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self , snake_case_ = 7_6_8 , ):
'''simple docstring'''
super().__init__()
UpperCAmelCase_ : int = nn.Parameter(torch.zeros(1 , snake_case_ ) )
UpperCAmelCase_ : str = nn.Parameter(torch.ones(1 , snake_case_ ) )
def _UpperCamelCase ( self , snake_case_ = None , snake_case_ = None , ):
'''simple docstring'''
UpperCAmelCase_ : int = nn.Parameter(self.mean.to(snake_case_ ).to(snake_case_ ) )
UpperCAmelCase_ : Tuple = nn.Parameter(self.std.to(snake_case_ ).to(snake_case_ ) )
return self
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = (embeds * self.std) + self.mean
return embeds
| 274
| 1
|
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : str , __lowerCamelCase : str ) -> Union[str, Any]:
def get_masked_lm_array(__lowerCamelCase : str ):
_snake_case = f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case = tf.train.load_variable(__lowerCamelCase , __lowerCamelCase )
if "kernel" in name:
_snake_case = array.transpose()
return torch.from_numpy(__lowerCamelCase )
def get_encoder_array(__lowerCamelCase : str ):
_snake_case = f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case = tf.train.load_variable(__lowerCamelCase , __lowerCamelCase )
if "kernel" in name:
_snake_case = array.transpose()
return torch.from_numpy(__lowerCamelCase )
def get_encoder_layer_array(__lowerCamelCase : int , __lowerCamelCase : str ):
_snake_case = f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case = tf.train.load_variable(__lowerCamelCase , __lowerCamelCase )
if "kernel" in name:
_snake_case = array.transpose()
return torch.from_numpy(__lowerCamelCase )
def get_encoder_attention_layer_array(__lowerCamelCase : int , __lowerCamelCase : str , __lowerCamelCase : List[str] ):
_snake_case = f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case = tf.train.load_variable(__lowerCamelCase , __lowerCamelCase )
_snake_case = array.reshape(__lowerCamelCase )
if "kernel" in name:
_snake_case = array.transpose()
return torch.from_numpy(__lowerCamelCase )
print(f'''Loading model based on config from {config_path}...''' )
_snake_case = BertConfig.from_json_file(__lowerCamelCase )
_snake_case = BertForMaskedLM(__lowerCamelCase )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
_snake_case = model.bert.encoder.layer[layer_index]
# Self-attention
_snake_case = layer.attention.self
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_query_dense/kernel''' , self_attn.query.weight.data.shape )
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_query_dense/bias''' , self_attn.query.bias.data.shape )
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_key_dense/kernel''' , self_attn.key.weight.data.shape )
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_key_dense/bias''' , self_attn.key.bias.data.shape )
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_value_dense/kernel''' , self_attn.value.weight.data.shape )
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_value_dense/bias''' , self_attn.value.bias.data.shape )
# Self-attention Output
_snake_case = layer.attention.output
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_output_dense/kernel''' , self_output.dense.weight.data.shape )
_snake_case = get_encoder_attention_layer_array(
__lowerCamelCase , '''_output_dense/bias''' , self_output.dense.bias.data.shape )
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_attention_layer_norm/gamma''' )
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_attention_layer_norm/beta''' )
# Intermediate
_snake_case = layer.intermediate
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_intermediate_dense/kernel''' )
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_intermediate_dense/bias''' )
# Output
_snake_case = layer.output
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_output_dense/kernel''' )
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_output_dense/bias''' )
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_output_layer_norm/gamma''' )
_snake_case = get_encoder_layer_array(__lowerCamelCase , '''_output_layer_norm/beta''' )
# Embeddings
_snake_case = get_encoder_array('''_position_embedding_layer/embeddings''' )
_snake_case = get_encoder_array('''_type_embedding_layer/embeddings''' )
_snake_case = get_encoder_array('''_embedding_norm_layer/gamma''' )
_snake_case = get_encoder_array('''_embedding_norm_layer/beta''' )
# LM Head
_snake_case = model.cls.predictions.transform
_snake_case = get_masked_lm_array('''dense/kernel''' )
_snake_case = get_masked_lm_array('''dense/bias''' )
_snake_case = get_masked_lm_array('''layer_norm/gamma''' )
_snake_case = get_masked_lm_array('''layer_norm/beta''' )
_snake_case = get_masked_lm_array('''embedding_table''' )
# Pooling
_snake_case = BertPooler(config=__lowerCamelCase )
_snake_case = get_encoder_array('''_pooler_layer/kernel''' )
_snake_case = get_encoder_array('''_pooler_layer/bias''' )
# Export final model
model.save_pretrained(__lowerCamelCase )
# Integration test - should load without any errors ;)
_snake_case = BertForMaskedLM.from_pretrained(__lowerCamelCase )
print(new_model.eval() )
print('''Model conversion was done sucessfully!''' )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
'--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
)
parser.add_argument(
'--bert_config_file',
type=str,
required=True,
help='The config json file corresponding to the BERT model. This specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path',
type=str,
required=True,
help='Path to the output PyTorch model.',
)
UpperCAmelCase__ = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 288
|
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str, Any] , _lowerCamelCase : Any=30522 , _lowerCamelCase : Union[str, Any]=768 , _lowerCamelCase : Tuple=12 , _lowerCamelCase : Any=12 , _lowerCamelCase : str=3072 , _lowerCamelCase : str="gelu" , _lowerCamelCase : int=0.1 , _lowerCamelCase : Optional[int]=0.1 , _lowerCamelCase : Dict=512 , _lowerCamelCase : List[Any]=2 , _lowerCamelCase : int=0.0_2 , _lowerCamelCase : Union[str, Any]=1e-12 , _lowerCamelCase : Union[str, Any]=0 , _lowerCamelCase : List[str]="topK" , _lowerCamelCase : Optional[Any]="constant" , _lowerCamelCase : Optional[Any]=0.0 , **_lowerCamelCase : str , ):
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = hidden_act
_snake_case = intermediate_size
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = initializer_range
_snake_case = layer_norm_eps
_snake_case = pruning_method
_snake_case = mask_init
_snake_case = mask_scale
| 288
| 1
|
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def UpperCAmelCase ( a_ , a_ ) -> int:
"""simple docstring"""
__A = old_name
if "patch_embed" in old_name:
__A = old_name.split("." )
if layer == "0":
__A = old_name.replace("0" , "convolution1" )
elif layer == "1":
__A = old_name.replace("1" , "batchnorm_before" )
elif layer == "3":
__A = old_name.replace("3" , "convolution2" )
else:
__A = old_name.replace("4" , "batchnorm_after" )
if "network" in old_name and re.search(r"\d\.\d" , lowerCAmelCase_ ):
__A = r'\b\d{2}\b'
if bool(re.search(lowerCAmelCase_ , lowerCAmelCase_ ) ):
__A = re.search(r"\d\.\d\d." , lowerCAmelCase_ ).group()
else:
__A = re.search(r"\d\.\d." , lowerCAmelCase_ ).group()
if int(match[0] ) < 6:
__A = old_name.replace(lowerCAmelCase_ , "" )
__A = trimmed_name.replace("network" , match[0] + ".meta4D_layers.blocks." + match[2:-1] )
__A = 'intermediate_stages.' + trimmed_name
else:
__A = old_name.replace(lowerCAmelCase_ , "" )
if int(match[2] ) < num_meta4D_last_stage:
__A = trimmed_name.replace("network" , "meta4D_layers.blocks." + match[2] )
else:
__A = str(int(match[2] ) - num_meta4D_last_stage )
__A = trimmed_name.replace("network" , "meta3D_layers.blocks." + layer_index )
if "norm1" in old_name:
__A = trimmed_name.replace("norm1" , "layernorm1" )
elif "norm2" in old_name:
__A = trimmed_name.replace("norm2" , "layernorm2" )
elif "fc1" in old_name:
__A = trimmed_name.replace("fc1" , "linear_in" )
elif "fc2" in old_name:
__A = trimmed_name.replace("fc2" , "linear_out" )
__A = 'last_stage.' + trimmed_name
elif "network" in old_name and re.search(r".\d." , lowerCAmelCase_ ):
__A = old_name.replace("network" , "intermediate_stages" )
if "fc" in new_name:
__A = new_name.replace("fc" , "convolution" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
__A = new_name.replace("norm1" , "batchnorm_before" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
__A = new_name.replace("norm2" , "batchnorm_after" )
if "proj" in new_name:
__A = new_name.replace("proj" , "projection" )
if "dist_head" in new_name:
__A = new_name.replace("dist_head" , "distillation_classifier" )
elif "head" in new_name:
__A = new_name.replace("head" , "classifier" )
elif "patch_embed" in new_name:
__A = 'efficientformer.' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
__A = new_name.replace("norm" , "layernorm" )
__A = 'efficientformer.' + new_name
else:
__A = 'efficientformer.encoder.' + new_name
return new_name
def UpperCAmelCase ( a_ , a_ ) -> Union[str, Any]:
"""simple docstring"""
for key in checkpoint.copy().keys():
__A = checkpoint.pop(lowerCAmelCase_ )
__A = val
return checkpoint
def UpperCAmelCase ( ) -> Any:
"""simple docstring"""
__A = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__A = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return image
def UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> List[Any]:
"""simple docstring"""
__A = torch.load(lowerCAmelCase_ , map_location="cpu" )['model']
__A = EfficientFormerConfig.from_json_file(lowerCAmelCase_ )
__A = EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase_ )
__A = '_'.join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )
__A = config.depths[-1] - config.num_metaad_blocks + 1
__A = convert_torch_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
model.eval()
__A = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
# prepare image
__A = prepare_img()
__A = 2_5_6
__A = 2_2_4
__A = EfficientFormerImageProcessor(
size={"shortest_edge": image_size} , crop_size={"height": crop_size, "width": crop_size} , resample=pillow_resamplings["bicubic"] , )
__A = processor(images=lowerCAmelCase_ , return_tensors="pt" ).pixel_values
# original processing pipeline
__A = Compose(
[
Resize(lowerCAmelCase_ , interpolation=pillow_resamplings["bicubic"] ),
CenterCrop(lowerCAmelCase_ ),
ToTensor(),
Normalize(lowerCAmelCase_ , lowerCAmelCase_ ),
] )
__A = image_transforms(lowerCAmelCase_ ).unsqueeze(0 )
assert torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ )
__A = model(lowerCAmelCase_ )
__A = outputs.logits
__A = (1, 1_0_0_0)
if "l1" in model_name:
__A = torch.Tensor(
[-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328] )
assert torch.allclose(logits[0, :1_0] , lowerCAmelCase_ , atol=1E-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
__A = torch.Tensor(
[-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127] )
assert torch.allclose(logits[0, :1_0] , lowerCAmelCase_ , atol=1E-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
__A = torch.Tensor(
[-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878] )
assert logits.shape == expected_shape
else:
raise ValueError(
F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7''' )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
processor.save_pretrained(lowerCAmelCase_ )
print(F'''Processor successfuly saved at {pytorch_dump_path}''' )
if push_to_hub:
print("Pushing model to the hub..." )
model.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' , commit_message="Add model" , use_temp_dir=lowerCAmelCase_ , )
processor.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' , commit_message="Add image processor" , use_temp_dir=lowerCAmelCase_ , )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path',
default=None,
type=str,
required=True,
help='Path to EfficientFormer pytorch checkpoint.',
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for EfficientFormer model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
parser.set_defaults(push_to_hub=True)
SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 369
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :List[Any] = [
'RagModel',
'RagPreTrainedModel',
'RagSequenceForGeneration',
'RagTokenForGeneration',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :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
SCREAMING_SNAKE_CASE :int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 124
| 0
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self )-> List[str]:
'''simple docstring'''
__UpperCamelCase = 10
def A__ ( self )-> List[str]:
'''simple docstring'''
__UpperCamelCase = [1, 2, 3, 4]
__UpperCamelCase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> Tuple:
'''simple docstring'''
__UpperCamelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
__UpperCamelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> List[Any]:
'''simple docstring'''
__UpperCamelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
__UpperCamelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> Dict:
'''simple docstring'''
__UpperCamelCase = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
__UpperCamelCase , __UpperCamelCase = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def A__ ( self )-> Dict:
'''simple docstring'''
__UpperCamelCase = ''''''
__UpperCamelCase , __UpperCamelCase = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def A__ ( self )-> Optional[Any]:
'''simple docstring'''
__UpperCamelCase = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
__UpperCamelCase , __UpperCamelCase = process_story(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = ['''It was the best of times.''']
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def A__ ( self )-> Any:
'''simple docstring'''
__UpperCamelCase = torch.tensor([1, 2, 3, 4] )
__UpperCamelCase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 0 ).numpy() , expected.numpy() )
def A__ ( self )-> str:
'''simple docstring'''
__UpperCamelCase = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
__UpperCamelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 23 ).numpy() , expected.numpy() )
def A__ ( self )-> str:
'''simple docstring'''
__UpperCamelCase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
__UpperCamelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 1 ).numpy() , expected.numpy() )
def A__ ( self )-> Optional[int]:
'''simple docstring'''
__UpperCamelCase = 101
__UpperCamelCase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
__UpperCamelCase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
__UpperCamelCase = compute_token_type_ids(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 328
|
from __future__ import annotations
from collections.abc import Callable
def A_ ( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake_case : int = 100 , ) -> float:
'''simple docstring'''
__UpperCamelCase = x_start
__UpperCamelCase = fnc(snake_case )
__UpperCamelCase = 0.0
for _ in range(snake_case ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
__UpperCamelCase = (x_end - x_start) / steps + xa
__UpperCamelCase = fnc(snake_case )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
__UpperCamelCase = xa
__UpperCamelCase = fxa
return area
if __name__ == "__main__":
def A_ ( snake_case : Tuple ) -> Optional[Any]:
'''simple docstring'''
return x**3 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
lowercase__ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(F"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 1_0
| 328
| 1
|
class A :
"""simple docstring"""
def __init__(self ):
__lowercase= """"""
__lowercase= """"""
__lowercase= []
def _A (self , lowerCAmelCase , lowerCAmelCase ):
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
__lowercase= self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
__lowercase= self.__min_dist_top_down_dp(snake_case_ , n - 1 )
__lowercase= self.__min_dist_top_down_dp(m - 1 , snake_case_ )
__lowercase= self.__min_dist_top_down_dp(m - 1 , n - 1 )
__lowercase= 1 + min(snake_case_ , snake_case_ , snake_case_ )
return self.dp[m][n]
def _A (self , lowerCAmelCase , lowerCAmelCase ):
__lowercase= worda
__lowercase= worda
__lowercase= [[-1 for _ in range(len(snake_case_ ) )] for _ in range(len(snake_case_ ) )]
return self.__min_dist_top_down_dp(len(snake_case_ ) - 1 , len(snake_case_ ) - 1 )
def _A (self , lowerCAmelCase , lowerCAmelCase ):
__lowercase= worda
__lowercase= worda
__lowercase= len(snake_case_ )
__lowercase= len(snake_case_ )
__lowercase= [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
__lowercase= j
elif j == 0: # second string is empty
__lowercase= i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
__lowercase= self.dp[i - 1][j - 1]
else:
__lowercase= self.dp[i][j - 1]
__lowercase= self.dp[i - 1][j]
__lowercase= self.dp[i - 1][j - 1]
__lowercase= 1 + min(snake_case_ , snake_case_ , snake_case_ )
return self.dp[m][n]
if __name__ == "__main__":
lowerCAmelCase = EditDistance()
print('''****************** Testing Edit Distance DP Algorithm ******************''')
print()
lowerCAmelCase = input('''Enter the first string: ''').strip()
lowerCAmelCase = input('''Enter the second string: ''').strip()
print()
print(F'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
print(F'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
print()
print('''*************** End of Testing Edit Distance DP Algorithm ***************''')
| 371
|
# Copyright 2023 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase = {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = [
'''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MraForMaskedLM''',
'''MraForMultipleChoice''',
'''MraForQuestionAnswering''',
'''MraForSequenceClassification''',
'''MraForTokenClassification''',
'''MraLayer''',
'''MraModel''',
'''MraPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 304
| 0
|
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
'''
lowerCAmelCase__ = '''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''
lowerCAmelCase__ = '''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the CUAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
\'aupr\': Area Under the Precision-Recall curve
\'prec_at_80_recall\': Precision at 80% recall
\'prec_at_90_recall\': Precision at 90% recall
Examples:
>>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> cuad_metric = datasets.load_metric("cuad")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
lowerCAmelCase : List[Any] = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
lowerCAmelCase : Any = evaluate(dataset=snake_case__ , predictions=snake_case__ )
return score
| 108
|
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ = "" , snake_case__ = False ):
"""simple docstring"""
lowerCAmelCase : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase : str = is_leaf
lowerCAmelCase : str = prefix
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Dict = 0
for q, w in zip(self.prefix , snake_case__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
for word in words:
self.insert(snake_case__ )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
if self.prefix == word:
lowerCAmelCase : Union[str, Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase : Optional[Any] = RadixNode(prefix=snake_case__ , is_leaf=snake_case__ )
else:
lowerCAmelCase : Tuple = self.nodes[word[0]]
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Optional[Any] = incoming_node.match(
snake_case__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(snake_case__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase : Optional[Any] = remaining_prefix
lowerCAmelCase : int = self.nodes[matching_string[0]]
lowerCAmelCase : List[Any] = RadixNode(snake_case__ , snake_case__ )
lowerCAmelCase : Optional[int] = aux_node
if remaining_word == "":
lowerCAmelCase : Optional[int] = True
else:
self.nodes[matching_string[0]].insert(snake_case__ )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : str = self.nodes.get(word[0] , snake_case__ )
if not incoming_node:
return False
else:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : int = incoming_node.match(
snake_case__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(snake_case__ )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : int = self.nodes.get(word[0] , snake_case__ )
if not incoming_node:
return False
else:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Union[str, Any] = incoming_node.match(
snake_case__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(snake_case__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase : List[str] = list(self.nodes.values() )[0]
lowerCAmelCase : List[str] = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase : Optional[int] = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase : Optional[int] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase : Optional[Any] = list(incoming_node.nodes.values() )[0]
lowerCAmelCase : int = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase : Tuple = merging_node.nodes
return True
def lowercase__ ( self , snake_case__ = 0 ):
"""simple docstring"""
if self.prefix != "":
print("-" * height , self.prefix , " (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = "banana bananas bandana band apple all beast".split()
lowerCAmelCase : List[str] = RadixNode()
root.insert_many(SCREAMING_SNAKE_CASE )
assert all(root.find(SCREAMING_SNAKE_CASE ) for word in words )
assert not root.find("bandanas" )
assert not root.find("apps" )
root.delete("all" )
assert not root.find("all" )
root.delete("banana" )
assert not root.find("banana" )
assert root.find("bananas" )
return True
def a__ ( ):
'''simple docstring'''
assert test_trie()
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Dict = RadixNode()
lowerCAmelCase : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(SCREAMING_SNAKE_CASE )
print("Words:" , SCREAMING_SNAKE_CASE )
print("Tree:" )
root.print_tree()
if __name__ == "__main__":
main()
| 108
| 1
|
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
assert column_title.isupper()
UpperCAmelCase : str =0
UpperCAmelCase : Optional[Any] =len(__lowerCAmelCase ) - 1
UpperCAmelCase : List[Any] =0
while index >= 0:
UpperCAmelCase : Union[str, Any] =(ord(column_title[index] ) - 64) * pow(26 , __lowerCAmelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 78
|
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
__snake_case = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def lowerCAmelCase_ ( )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , bootstrap_aggregation=__lowerCAmelCase , rouge_keys=['''rouge2''', '''rougeL'''] )
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : List[Any] =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , bootstrap_aggregation=__lowerCAmelCase , rouge_keys=['''rouge2'''] )
assert (
pd.DataFrame(no_aggregation['''rouge2'''] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra['''rouge2'''] ).fmeasure.mean()
)
def lowerCAmelCase_ ( )-> Dict:
'''simple docstring'''
UpperCAmelCase : Any ='''rougeLsum'''
UpperCAmelCase : Optional[Any] =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , newline_sep=__lowerCAmelCase , rouge_keys=[k] )[k]
UpperCAmelCase : List[Any] =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , newline_sep=__lowerCAmelCase , rouge_keys=[k] )[k]
assert score > score_no_sep
def lowerCAmelCase_ ( )-> Any:
'''simple docstring'''
UpperCAmelCase : str =['''rouge1''', '''rouge2''', '''rougeL''']
UpperCAmelCase : int =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , newline_sep=__lowerCAmelCase , rouge_keys=__lowerCAmelCase )
UpperCAmelCase : Tuple =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , newline_sep=__lowerCAmelCase , rouge_keys=__lowerCAmelCase )
assert score_sep == score_no_sep
def lowerCAmelCase_ ( )-> Dict:
'''simple docstring'''
UpperCAmelCase : int =[
'''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
]
UpperCAmelCase : Any =[
'''Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
''' the final seconds on board Flight 9525.''',
]
assert calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , newline_sep=__lowerCAmelCase ) == calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , newline_sep=__lowerCAmelCase )
def lowerCAmelCase_ ( )-> List[str]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =[
'''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
]
UpperCAmelCase : Optional[Any] =[
''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
]
UpperCAmelCase : Optional[int] =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , rouge_keys=['''rougeLsum'''] , newline_sep=__lowerCAmelCase )['''rougeLsum''']
UpperCAmelCase : int =calculate_rouge(__lowerCAmelCase , __lowerCAmelCase , rouge_keys=['''rougeLsum'''] )['''rougeLsum''']
assert new_score > prev_score
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[Any] =Path('''examples/seq2seq/test_data/wmt_en_ro''' )
UpperCAmelCase : Tuple =calculate_rouge_path(data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) )
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =calculate_rouge_path(
data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) , bootstrap_aggregation=__lowerCAmelCase )
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
| 78
| 1
|
import math
from collections.abc import Callable
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Any = xa
lowerCAmelCase__ : List[str] = xa
while True:
if x_n == x_na or function(UpperCamelCase__ ) == function(UpperCamelCase__ ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
lowerCAmelCase__ : Tuple = x_na - (
function(UpperCamelCase__ ) / ((function(UpperCamelCase__ ) - function(UpperCamelCase__ )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
lowerCAmelCase__ : Union[str, Any] = x_na
lowerCAmelCase__ : List[str] = x_na
def lowerCamelCase_ ( _a ):
"""simple docstring"""
return math.pow(UpperCamelCase__ , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 131
|
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=10))
| 273
| 0
|
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = {
"""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""": """--..""", """1""": """.----""",
"""2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
"""8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
""":""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
"""?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
"""(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ : Any = {value: key for key, value in MORSE_CODE_DICT.items()}
def _A (__a ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def _A (__a ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def _A () -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = '''Morse code here!'''
print(__a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = encrypt(__a )
print(__a )
SCREAMING_SNAKE_CASE_ : Any = decrypt(__a )
print(__a )
if __name__ == "__main__":
main()
| 352
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''的''',
'''价''',
'''格''',
'''是''',
'''15''',
'''便''',
'''alex''',
'''##andra''',
'''，''',
'''。''',
'''-''',
'''t''',
'''shirt''',
]
SCREAMING_SNAKE_CASE_ : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''])
with open(self.vocab_file , '''w''' , encoding='''utf-8''') as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))
SCREAMING_SNAKE_CASE_ : Dict = {
'''do_resize''': True,
'''size''': {'''height''': 224, '''width''': 224},
'''do_center_crop''': True,
'''crop_size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
'''do_convert_rgb''': True,
}
SCREAMING_SNAKE_CASE_ : int = os.path.join(self.tmpdirname , lowercase_)
with open(self.image_processor_file , '''w''' , encoding='''utf-8''') as fp:
json.dump(lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , **lowercase_ : str):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Tuple , **lowercase_ : List[Any]):
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_)
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **lowercase_ : str):
'''simple docstring'''
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def _SCREAMING_SNAKE_CASE ( self : str):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta)]
SCREAMING_SNAKE_CASE_ : Dict = [Image.fromarray(np.moveaxis(lowercase_ , 0 , -1)) for x in image_inputs]
return image_inputs
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE_ : int = ChineseCLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_)
processor_slow.save_pretrained(self.tmpdirname)
SCREAMING_SNAKE_CASE_ : Optional[int] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowercase_)
SCREAMING_SNAKE_CASE_ : Any = ChineseCLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_)
processor_fast.save_pretrained(self.tmpdirname)
SCREAMING_SNAKE_CASE_ : int = ChineseCLIPProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer , lowercase_)
self.assertIsInstance(processor_fast.tokenizer , lowercase_)
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor , lowercase_)
self.assertIsInstance(processor_fast.image_processor , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
SCREAMING_SNAKE_CASE_ : Optional[int] = self.get_tokenizer(cls_token='''(CLS)''' , sep_token='''(SEP)''')
SCREAMING_SNAKE_CASE_ : Optional[int] = self.get_image_processor(do_normalize=lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='''(CLS)''' , sep_token='''(SEP)''' , do_normalize=lowercase_)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer , lowercase_)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Dict):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE_ : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Tuple = ChineseCLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_)
SCREAMING_SNAKE_CASE_ : Optional[int] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ : Any = image_processor(lowercase_ , return_tensors='''np''')
SCREAMING_SNAKE_CASE_ : Union[str, Any] = processor(images=lowercase_ , return_tensors='''np''')
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE_ : Any = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : str = ChineseCLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_)
SCREAMING_SNAKE_CASE_ : Dict = '''Alexandra，T-shirt的价格是15便士。'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = processor(text=lowercase_)
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer(lowercase_)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key])
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : int = ChineseCLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = '''Alexandra，T-shirt的价格是15便士。'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ : int = processor(text=lowercase_ , images=lowercase_)
self.assertListEqual(list(inputs.keys()) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''])
# test if it raises when no input is passed
with pytest.raises(lowercase_):
processor()
def _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE_ : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Optional[int] = ChineseCLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_)
SCREAMING_SNAKE_CASE_ : Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ : Optional[int] = processor.batch_decode(lowercase_)
SCREAMING_SNAKE_CASE_ : Dict = tokenizer.batch_decode(lowercase_)
self.assertListEqual(lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE_ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Dict = ChineseCLIPProcessor(tokenizer=lowercase_ , image_processor=lowercase_)
SCREAMING_SNAKE_CASE_ : List[str] = '''Alexandra，T-shirt的价格是15便士。'''
SCREAMING_SNAKE_CASE_ : Dict = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ : Dict = processor(text=lowercase_ , images=lowercase_)
self.assertListEqual(list(inputs.keys()) , processor.model_input_names)
| 318
| 0
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE : List[Any] = 'AutoImageProcessor'
SCREAMING_SNAKE_CASE : Union[str, Any] = 'AutoTokenizer'
def __init__( self : Dict ,lowercase__ : int ,lowercase__ : List[Any] ):
super().__init__(lowercase__ ,lowercase__ )
__lowercase = self.image_processor
def __call__( self : int ,lowercase__ : Dict=None ,lowercase__ : int=None ,lowercase__ : str=None ,**lowercase__ : str ):
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
__lowercase = self.tokenizer(lowercase__ ,return_tensors=lowercase__ ,**lowercase__ )
if images is not None:
__lowercase = self.image_processor(lowercase__ ,return_tensors=lowercase__ ,**lowercase__ )
if text is not None and images is not None:
__lowercase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase__ ) ,tensor_type=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : str ,*lowercase__ : Union[str, Any] ,**lowercase__ : Optional[Any] ):
return self.tokenizer.batch_decode(*lowercase__ ,**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ,*lowercase__ : Optional[int] ,**lowercase__ : Tuple ):
return self.tokenizer.decode(*lowercase__ ,**lowercase__ )
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return ["input_ids", "attention_mask", "pixel_values"]
| 104
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : Tuple = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : str = [
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Tuple = [
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 124
| 0
|
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_SCREAMING_SNAKE_CASE = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_SCREAMING_SNAKE_CASE = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def _lowerCAmelCase ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[Any] ):
__lowercase = SavedModel()
__lowercase = []
with open(os.path.join(lowerCamelCase_ , '''utils''' , '''tf_ops''' , '''onnx.json''' ) ) as f:
__lowercase = json.load(lowerCamelCase_ )['''opsets''']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(lowerCamelCase_ )] )
with open(lowerCamelCase_ , '''rb''' ) as f:
saved_model.ParseFromString(f.read() )
__lowercase = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__lowercase = sorted(lowerCamelCase_ )
__lowercase = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(lowerCamelCase_ )
if strict and len(lowerCamelCase_ ) > 0:
raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops )
elif len(lowerCamelCase_ ) > 0:
print(f"Found the following incompatible ops for the opset {opset}:" )
print(*lowerCamelCase_ , sep='''\n''' )
else:
print(f"The saved model {saved_model_path} can properly be converted with ONNX." )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=1_2, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 217
|
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : Union[str, Any] ):
__lowercase = 1
__lowercase = 2
while i * i <= n:
__lowercase = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def _lowerCAmelCase ( ):
__lowercase = 1
__lowercase = 1
while True:
i += 1
t_num += i
if count_divisors(lowerCamelCase_ ) > 5_0_0:
break
return t_num
if __name__ == "__main__":
print(solution())
| 217
| 1
|
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = set()
# edges = list of graph's edges
_lowerCAmelCase : Dict = get_edges(_lowerCamelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
_lowerCAmelCase , _lowerCAmelCase : List[Any] = edges.pop()
chosen_vertices.add(_lowerCamelCase )
chosen_vertices.add(_lowerCamelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_lowerCamelCase )
return chosen_vertices
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 36
|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase)
class snake_case__ ( UpperCamelCase):
a_ = field(default="language-modeling" , metadata={"include_in_asdict_even_if_is_default": True})
a_ = Features({"text": Value("string")})
a_ = Features({})
a_ = "text"
@property
def A ( self : List[str] ) -> Dict[str, str]:
return {self.text_column: "text"}
| 304
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE : Dict = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[int] = [
'''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoCBertForCausalLM''',
'''RoCBertForMaskedLM''',
'''RoCBertForMultipleChoice''',
'''RoCBertForPreTraining''',
'''RoCBertForQuestionAnswering''',
'''RoCBertForSequenceClassification''',
'''RoCBertForTokenClassification''',
'''RoCBertLayer''',
'''RoCBertModel''',
'''RoCBertPreTrainedModel''',
'''load_tf_weights_in_roc_bert''',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
SCREAMING_SNAKE_CASE : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
|
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case_ , snake_case_ ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case_ ) if is_prime[i]]
def __UpperCAmelCase ( snake_case_ : int = 10**8 ) -> int:
"""simple docstring"""
_lowerCAmelCase = calculate_prime_numbers(max_number // 2 )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case_ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import unittest
from transformers import MPNetConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class A_ :
"""simple docstring"""
def __init__( self :str , lowercase_ :str , lowercase_ :int=13 , lowercase_ :Optional[Any]=7 , lowercase_ :List[Any]=True , lowercase_ :List[Any]=True , lowercase_ :List[str]=False , lowercase_ :Optional[Any]=True , lowercase_ :List[Any]=99 , lowercase_ :List[str]=64 , lowercase_ :int=5 , lowercase_ :List[str]=4 , lowercase_ :Any=64 , lowercase_ :int="gelu" , lowercase_ :Optional[int]=0.1 , lowercase_ :Union[str, Any]=0.1 , lowercase_ :Union[str, Any]=5_12 , lowercase_ :List[Any]=16 , lowercase_ :Optional[Any]=2 , lowercase_ :str=0.02 , lowercase_ :Any=3 , lowercase_ :Tuple=4 , lowercase_ :Optional[Any]=None , ) -> int:
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 = 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 = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Union[str, Any]:
return MPNetConfig.from_pretrained('microsoft/mpnet-base' )
def UpperCAmelCase__ ( self :Tuple ) -> 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 = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self :List[str] ) -> List[str]:
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[int] , lowercase_ :List[Any] , lowercase_ :Tuple , lowercase_ :Union[str, Any] , lowercase_ :str , lowercase_ :Any ) -> Any:
UpperCAmelCase = MPNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(lowercase_ , lowercase_ )
UpperCAmelCase = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase__ ( self :Dict , lowercase_ :str , lowercase_ :Any , lowercase_ :Any , lowercase_ :Union[str, Any] , lowercase_ :str , lowercase_ :int ) -> int:
UpperCAmelCase = MPNetForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 UpperCAmelCase__ ( self :Dict , lowercase_ :str , lowercase_ :List[Any] , lowercase_ :Optional[int] , lowercase_ :Dict , lowercase_ :List[Any] , lowercase_ :List[str] ) -> Optional[int]:
UpperCAmelCase = self.num_labels
UpperCAmelCase = MPNetForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self :List[str] , lowercase_ :Any , lowercase_ :Union[str, Any] , lowercase_ :Dict , lowercase_ :Optional[Any] , lowercase_ :Optional[int] , lowercase_ :Optional[int] ) -> Any:
UpperCAmelCase = self.num_choices
UpperCAmelCase = MPNetForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :Tuple , lowercase_ :str , lowercase_ :Tuple , lowercase_ :Any , lowercase_ :str , lowercase_ :Union[str, Any] ) -> Dict:
UpperCAmelCase = self.num_labels
UpperCAmelCase = MPNetForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase__ ( self :Any ) -> Dict:
UpperCAmelCase = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) = config_and_inputs
UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{
"""feature-extraction""": MPNetModel,
"""fill-mask""": MPNetForMaskedLM,
"""question-answering""": MPNetForQuestionAnswering,
"""text-classification""": MPNetForSequenceClassification,
"""token-classification""": MPNetForTokenClassification,
"""zero-shot""": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = True
def UpperCAmelCase__ ( self :Optional[Any] ) -> int:
UpperCAmelCase = MPNetModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def UpperCAmelCase__ ( self :Optional[int] ) -> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self :int ) -> Optional[int]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowercase_ )
def UpperCAmelCase__ ( self :Optional[Any] ) -> List[str]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_ )
def UpperCAmelCase__ ( self :Any ) -> int:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_ )
def UpperCAmelCase__ ( self :List[Any] ) -> List[str]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_ )
def UpperCAmelCase__ ( self :Dict ) -> List[str]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_ )
@require_torch
class A_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self :Dict ) -> List[str]:
UpperCAmelCase = MPNetModel.from_pretrained('microsoft/mpnet-base' )
UpperCAmelCase = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
UpperCAmelCase = model(lowercase_ )[0]
UpperCAmelCase = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , lowercase_ )
UpperCAmelCase = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase_ , atol=1E-4 ) )
| 78
|
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = list(range(len(lowercase_ ) ) )
UpperCAmelCase = [v / w for v, w in zip(lowercase_ , lowercase_ )]
index.sort(key=lambda lowercase_ : ratio[i] , reverse=lowercase_ )
UpperCAmelCase = 0
UpperCAmelCase = [0] * len(lowercase_ )
for i in index:
if weight[i] <= capacity:
UpperCAmelCase = 1
max_value += value[i]
capacity -= weight[i]
else:
UpperCAmelCase = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 78
| 1
|
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 __lowercase (unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=7 , lowerCAmelCase__=3 , lowerCAmelCase__=3_0 , lowerCAmelCase__=4_0_0 , lowerCAmelCase__=True , lowerCAmelCase__=None , lowerCAmelCase__=True , lowerCAmelCase__=[0.5, 0.5, 0.5] , lowerCAmelCase__=[0.5, 0.5, 0.5] , lowerCAmelCase__=True , lowerCAmelCase__=1 / 2_5_5 , lowerCAmelCase__=True , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
SCREAMING_SNAKE_CASE_ : Union[str, Any] = parent
SCREAMING_SNAKE_CASE_ : Dict = batch_size
SCREAMING_SNAKE_CASE_ : int = num_channels
SCREAMING_SNAKE_CASE_ : str = min_resolution
SCREAMING_SNAKE_CASE_ : Tuple = max_resolution
SCREAMING_SNAKE_CASE_ : Dict = do_resize
SCREAMING_SNAKE_CASE_ : Tuple = size
SCREAMING_SNAKE_CASE_ : str = do_normalize
SCREAMING_SNAKE_CASE_ : List[str] = image_mean
SCREAMING_SNAKE_CASE_ : str = image_std
SCREAMING_SNAKE_CASE_ : int = do_rescale
SCREAMING_SNAKE_CASE_ : Optional[Any] = rescale_factor
SCREAMING_SNAKE_CASE_ : Optional[Any] = do_pad
def UpperCamelCase__ ( self ):
"""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 UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__=False ):
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE_ : List[Any] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[int] = image.size
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE_ : Tuple = int(self.size['shortest_edge'] * h / w )
SCREAMING_SNAKE_CASE_ : List[str] = self.size['shortest_edge']
elif w > h:
SCREAMING_SNAKE_CASE_ : Any = self.size['shortest_edge']
SCREAMING_SNAKE_CASE_ : Tuple = int(self.size['shortest_edge'] * w / h )
else:
SCREAMING_SNAKE_CASE_ : int = self.size['shortest_edge']
SCREAMING_SNAKE_CASE_ : List[str] = self.size['shortest_edge']
else:
SCREAMING_SNAKE_CASE_ : List[str] = []
for image in image_inputs:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE_ : Dict = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowercase (__SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase = DeformableDetrImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = DeformableDetrImageProcessingTester(self )
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = 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 UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2, 'longest_edge': 8_4} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = 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
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = 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 UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE_ : int = 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
SCREAMING_SNAKE_CASE_ : str = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = 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
SCREAMING_SNAKE_CASE_ : Union[str, Any] = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] = 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 UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE_ : Dict = 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
SCREAMING_SNAKE_CASE_ : Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = 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
SCREAMING_SNAKE_CASE_ : Dict = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = 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 UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
SCREAMING_SNAKE_CASE_ : List[Any] = json.loads(f.read() )
SCREAMING_SNAKE_CASE_ : Dict = {'image_id': 3_9_7_6_9, 'annotations': target}
# encode them
SCREAMING_SNAKE_CASE_ : Dict = DeformableDetrImageProcessor()
SCREAMING_SNAKE_CASE_ : str = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors='pt' )
# verify pixel values
SCREAMING_SNAKE_CASE_ : int = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['pixel_values'].shape , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
SCREAMING_SNAKE_CASE_ : Optional[int] = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowerCAmelCase__ ) )
# verify boxes
SCREAMING_SNAKE_CASE_ : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowerCAmelCase__ ) )
# verify is_crowd
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowerCAmelCase__ ) )
# verify class_labels
SCREAMING_SNAKE_CASE_ : Tuple = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowerCAmelCase__ ) )
# verify orig_size
SCREAMING_SNAKE_CASE_ : Any = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowerCAmelCase__ ) )
# verify size
SCREAMING_SNAKE_CASE_ : Optional[int] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowerCAmelCase__ ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
SCREAMING_SNAKE_CASE_ : Tuple = json.loads(f.read() )
SCREAMING_SNAKE_CASE_ : List[str] = {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}
SCREAMING_SNAKE_CASE_ : Dict = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
SCREAMING_SNAKE_CASE_ : Dict = DeformableDetrImageProcessor(format='coco_panoptic' )
SCREAMING_SNAKE_CASE_ : Any = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors='pt' )
# verify pixel values
SCREAMING_SNAKE_CASE_ : Dict = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['pixel_values'].shape , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
SCREAMING_SNAKE_CASE_ : int = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowerCAmelCase__ ) )
# verify boxes
SCREAMING_SNAKE_CASE_ : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE_ : Tuple = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowerCAmelCase__ ) )
# verify is_crowd
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowerCAmelCase__ ) )
# verify class_labels
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowerCAmelCase__ ) )
# verify masks
SCREAMING_SNAKE_CASE_ : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , lowerCAmelCase__ )
# verify orig_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowerCAmelCase__ ) )
# verify size
SCREAMING_SNAKE_CASE_ : Optional[int] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowerCAmelCase__ ) )
| 162
|
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ : Union[str, Any] = len(A__ )
for i in range(n - 1 ):
for j in range(i + 1, A__ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def a__ ( A__ ):
if len(A__ ) <= 1:
return arr, 0
SCREAMING_SNAKE_CASE_ : Optional[int] = len(A__ ) // 2
SCREAMING_SNAKE_CASE_ : Union[str, Any] = arr[0:mid]
SCREAMING_SNAKE_CASE_ : List[str] = arr[mid:]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = count_inversions_recursive(A__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = count_inversions_recursive(A__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = _count_cross_inversions(A__, A__ )
SCREAMING_SNAKE_CASE_ : str = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : List[str] = []
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
while i < len(A__ ) and j < len(A__ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(A__ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(A__ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def a__ ( ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
SCREAMING_SNAKE_CASE_ : Optional[int] = count_inversions_bf(A__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = count_inversions_recursive(A__ )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ', A__ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
SCREAMING_SNAKE_CASE_ : int = count_inversions_bf(A__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] = count_inversions_recursive(A__ )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ', A__ )
# an empty list should also have zero inversions
SCREAMING_SNAKE_CASE_ : Optional[int] = []
SCREAMING_SNAKE_CASE_ : List[str] = count_inversions_bf(A__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = count_inversions_recursive(A__ )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ', A__ )
if __name__ == "__main__":
main()
| 162
| 1
|
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowercase_ = [
'''openmmlab/upernet-convnext-tiny''',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowercase_ = '''UperNetConfig'''
class __UpperCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , _A : List[Any] , _A : Dict , _A : str , _A : Dict = 0 , _A : Optional[int] = False , _A : Optional[Any] = 1 , ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.Convad(
in_channels=_A , out_channels=_A , kernel_size=_A , padding=_A , bias=_A , dilation=_A , )
__SCREAMING_SNAKE_CASE : Any = nn.BatchNormad(_A )
__SCREAMING_SNAKE_CASE : List[str] = nn.ReLU()
def UpperCAmelCase__ ( self : str , _A : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = self.conv(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.batch_norm(_A )
__SCREAMING_SNAKE_CASE : Tuple = self.activation(_A )
return output
class __UpperCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _A : Union[str, Any] , _A : str , _A : Union[str, Any] ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Any = [
nn.AdaptiveAvgPoolad(_A ),
UperNetConvModule(_A , _A , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_A ) , _A )
def UpperCAmelCase__ ( self : int , _A : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = input
for layer in self.layers:
__SCREAMING_SNAKE_CASE : Optional[int] = layer(_A )
return hidden_state
class __UpperCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Dict , _A : Tuple , _A : List[str] , _A : int , _A : Any ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Any = pool_scales
__SCREAMING_SNAKE_CASE : List[Any] = align_corners
__SCREAMING_SNAKE_CASE : str = in_channels
__SCREAMING_SNAKE_CASE : Tuple = channels
__SCREAMING_SNAKE_CASE : List[Any] = []
for i, pool_scale in enumerate(_A ):
__SCREAMING_SNAKE_CASE : List[Any] = UperNetPyramidPoolingBlock(pool_scale=_A , in_channels=_A , channels=_A )
self.blocks.append(_A )
self.add_module(str(_A ) , _A )
def UpperCAmelCase__ ( self : List[str] , _A : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = []
for ppm in self.blocks:
__SCREAMING_SNAKE_CASE : int = ppm(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.functional.interpolate(
_A , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(_A )
return ppm_outs
class __UpperCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , _A : str , _A : Any ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Tuple = config
__SCREAMING_SNAKE_CASE : str = config.pool_scales # e.g. (1, 2, 3, 6)
__SCREAMING_SNAKE_CASE : Optional[Any] = in_channels
__SCREAMING_SNAKE_CASE : str = config.hidden_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = False
__SCREAMING_SNAKE_CASE : List[Any] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
__SCREAMING_SNAKE_CASE : Any = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
__SCREAMING_SNAKE_CASE : Tuple = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
__SCREAMING_SNAKE_CASE : int = nn.ModuleList()
__SCREAMING_SNAKE_CASE : str = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
__SCREAMING_SNAKE_CASE : Optional[int] = UperNetConvModule(_A , self.channels , kernel_size=1 )
__SCREAMING_SNAKE_CASE : Any = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(_A )
self.fpn_convs.append(_A )
__SCREAMING_SNAKE_CASE : Tuple = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase__ ( self : List[str] , _A : Dict ):
"""simple docstring"""
if isinstance(_A , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self : List[str] , _A : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = inputs[-1]
__SCREAMING_SNAKE_CASE : Any = [x]
psp_outs.extend(self.psp_modules(_A ) )
__SCREAMING_SNAKE_CASE : int = torch.cat(_A , dim=1 )
__SCREAMING_SNAKE_CASE : int = self.bottleneck(_A )
return output
def UpperCAmelCase__ ( self : int , _A : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_A ) )
# build top-down path
__SCREAMING_SNAKE_CASE : Dict = len(_A )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
__SCREAMING_SNAKE_CASE : Tuple = laterals[i - 1].shape[2:]
__SCREAMING_SNAKE_CASE : Dict = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=_A , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
__SCREAMING_SNAKE_CASE : Tuple = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
__SCREAMING_SNAKE_CASE : int = torch.cat(_A , dim=1 )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.fpn_bottleneck(_A )
__SCREAMING_SNAKE_CASE : List[Any] = self.classifier(_A )
return output
class __UpperCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Dict , _A : str , _A : List[Any] = 2 , _A : Optional[int] = 3 , _A : Any = 1 ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[int] = config
__SCREAMING_SNAKE_CASE : List[str] = config.auxiliary_in_channels
__SCREAMING_SNAKE_CASE : Any = config.auxiliary_channels
__SCREAMING_SNAKE_CASE : Optional[int] = config.auxiliary_num_convs
__SCREAMING_SNAKE_CASE : Any = config.auxiliary_concat_input
__SCREAMING_SNAKE_CASE : Dict = in_index
__SCREAMING_SNAKE_CASE : Dict = (kernel_size // 2) * dilation
__SCREAMING_SNAKE_CASE : Tuple = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=_A , padding=_A , dilation=_A ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=_A , padding=_A , dilation=_A ) )
if self.num_convs == 0:
__SCREAMING_SNAKE_CASE : List[str] = nn.Identity()
else:
__SCREAMING_SNAKE_CASE : Tuple = nn.Sequential(*_A )
if self.concat_input:
__SCREAMING_SNAKE_CASE : int = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=_A , padding=kernel_size // 2 )
__SCREAMING_SNAKE_CASE : Any = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase__ ( self : List[Any] , _A : Tuple ):
"""simple docstring"""
if isinstance(_A , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self : List[Any] , _A : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = encoder_hidden_states[self.in_index]
__SCREAMING_SNAKE_CASE : Optional[int] = self.convs(_A )
if self.concat_input:
__SCREAMING_SNAKE_CASE : Dict = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
__SCREAMING_SNAKE_CASE : Any = self.classifier(_A )
return output
class __UpperCamelCase ( _lowercase ):
"""simple docstring"""
lowerCAmelCase_ = UperNetConfig
lowerCAmelCase_ = "pixel_values"
lowerCAmelCase_ = True
def UpperCAmelCase__ ( self : Optional[Any] , _A : int ):
"""simple docstring"""
if isinstance(_A , _A ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self : Tuple , _A : Any , _A : Any=False ):
"""simple docstring"""
if isinstance(_A , _A ):
__SCREAMING_SNAKE_CASE : Dict = value
lowercase_ = r'''
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): 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.
'''
lowercase_ = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , _lowercase , )
class __UpperCamelCase ( _lowercase ):
"""simple docstring"""
def __init__( self : List[Any] , _A : Union[str, Any] ):
"""simple docstring"""
super().__init__(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
__SCREAMING_SNAKE_CASE : List[Any] = UperNetHead(_A , in_channels=self.backbone.channels )
__SCREAMING_SNAKE_CASE : Dict = UperNetFCNHead(_A ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_A , config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase__ ( self : List[str] , _A : List[Any] = None , _A : Union[str, Any] = None , _A : str = None , _A : Optional[Any] = None , _A : Optional[Any] = None , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = return_dict if return_dict is not None else self.config.use_return_dict
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__SCREAMING_SNAKE_CASE : int = output_attentions if output_attentions is not None else self.config.output_attentions
__SCREAMING_SNAKE_CASE : Optional[int] = self.backbone.forward_with_filtered_kwargs(
_A , output_hidden_states=_A , output_attentions=_A )
__SCREAMING_SNAKE_CASE : str = outputs.feature_maps
__SCREAMING_SNAKE_CASE : int = self.decode_head(_A )
__SCREAMING_SNAKE_CASE : str = nn.functional.interpolate(_A , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.auxiliary_head is not None:
__SCREAMING_SNAKE_CASE : Dict = self.auxiliary_head(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = nn.functional.interpolate(
_A , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_A )
__SCREAMING_SNAKE_CASE : Any = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
__SCREAMING_SNAKE_CASE : List[Any] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
__SCREAMING_SNAKE_CASE : List[Any] = loss_fct(_A , _A )
__SCREAMING_SNAKE_CASE : List[str] = loss_fct(_A , _A )
__SCREAMING_SNAKE_CASE : int = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
__SCREAMING_SNAKE_CASE : Optional[int] = (logits,) + outputs[1:]
else:
__SCREAMING_SNAKE_CASE : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_A , logits=_A , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 303
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
__lowercase : Dict = logging.getLogger(__name__)
@dataclass
class __lowercase :
lowerCamelCase : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCamelCase : Optional[str] = field(
default=_lowercase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCamelCase : Optional[str] = field(
default=_lowercase , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCamelCase : Optional[str] = field(
default=_lowercase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowerCamelCase : bool = field(
default=_lowercase , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
lowerCamelCase : str = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
lowerCamelCase : bool = field(
default=_lowercase , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
@dataclass
class __lowercase :
lowerCamelCase : Optional[str] = field(default=_lowercase , metadata={"help": "The input training data file (a text file)."} )
lowerCamelCase : Optional[str] = field(
default=_lowercase , metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} , )
lowerCamelCase : bool = field(
default=_lowercase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
lowerCamelCase : Optional[int] = field(
default=_lowercase , metadata={"help": "The number of processes to use for the preprocessing."} , )
lowerCamelCase : Optional[int] = field(
default=_lowercase , metadata={
"help": (
"The maximum total input sequence length after tokenization. If passed, sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
lowerCamelCase : bool = field(
default=_lowercase , metadata={
"help": (
"Whether to pad all samples to the maximum sentence length. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
"efficient on GPU but very bad for TPU."
)
} , )
lowerCamelCase : Optional[int] = field(
default=_lowercase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
lowerCamelCase : Optional[int] = field(
default=_lowercase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def UpperCAmelCase__ (self ):
if self.train_file is not None:
lowerCamelCase_ : Optional[Any] = self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
lowerCamelCase_ : Optional[Any] = self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class __lowercase :
lowerCamelCase : PreTrainedTokenizerBase
lowerCamelCase : Union[bool, str, PaddingStrategy] = True
lowerCamelCase : Optional[int] = None
lowerCamelCase : Optional[int] = None
def __call__(self , A ):
lowerCamelCase_ : List[str] = '''label''' if '''label''' in features[0].keys() else '''labels'''
lowerCamelCase_ : str = [feature.pop(A ) for feature in features]
lowerCamelCase_ : Any = len(A )
lowerCamelCase_ : List[Any] = len(features[0]['''input_ids'''] )
lowerCamelCase_ : Union[str, Any] = [
[{k: v[i] for k, v in feature.items()} for i in range(A )] for feature in features
]
lowerCamelCase_ : str = list(chain(*A ) )
lowerCamelCase_ : Any = self.tokenizer.pad(
A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
# Un-flatten
lowerCamelCase_ : int = {k: v.view(A , A , -1 ) for k, v in batch.items()}
# Add back labels
lowerCamelCase_ : Tuple = torch.tensor(A , dtype=torch.intaa )
return batch
def lowercase_ ( ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ : int = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ : str = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_swag''' , _lowercase , _lowercase )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = training_args.get_process_log_level()
logger.setLevel(_lowercase )
datasets.utils.logging.set_verbosity(_lowercase )
transformers.utils.logging.set_verbosity(_lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowerCamelCase_ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase_ : str = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
lowerCamelCase_ : Optional[Any] = {}
if data_args.train_file is not None:
lowerCamelCase_ : Union[str, Any] = data_args.train_file
if data_args.validation_file is not None:
lowerCamelCase_ : Tuple = data_args.validation_file
lowerCamelCase_ : Optional[Any] = data_args.train_file.split('''.''' )[-1]
lowerCamelCase_ : Dict = load_dataset(
_lowercase , data_files=_lowercase , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
lowerCamelCase_ : Optional[Any] = load_dataset(
'''swag''' , '''regular''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase_ : str = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ : Any = 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 , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ : List[Any] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
lowerCamelCase_ : int = [F"""ending{i}""" for i in range(4 )]
lowerCamelCase_ : List[Any] = '''sent1'''
lowerCamelCase_ : Dict = '''sent2'''
if data_args.max_seq_length is None:
lowerCamelCase_ : str = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
'''The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'''
''' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'''
''' override this default with `--block_size xxx`.''' )
lowerCamelCase_ : Optional[int] = 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
lowerCamelCase_ : Optional[int] = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(_lowercase ):
lowerCamelCase_ : Tuple = [[context] * 4 for context in examples[context_name]]
lowerCamelCase_ : List[Any] = examples[question_header_name]
lowerCamelCase_ : Optional[Any] = [
[F"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(_lowercase )
]
# Flatten out
lowerCamelCase_ : Optional[Any] = list(chain(*_lowercase ) )
lowerCamelCase_ : List[Any] = list(chain(*_lowercase ) )
# Tokenize
lowerCamelCase_ : List[str] = tokenizer(
_lowercase , _lowercase , truncation=_lowercase , max_length=_lowercase , padding='''max_length''' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(_lowercase ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
lowerCamelCase_ : Union[str, Any] = raw_datasets['''train''']
if data_args.max_train_samples is not None:
lowerCamelCase_ : List[str] = min(len(_lowercase ) , data_args.max_train_samples )
lowerCamelCase_ : List[str] = train_dataset.select(range(_lowercase ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
lowerCamelCase_ : Dict = train_dataset.map(
_lowercase , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
lowerCamelCase_ : Optional[int] = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
lowerCamelCase_ : Optional[int] = min(len(_lowercase ) , data_args.max_eval_samples )
lowerCamelCase_ : Any = eval_dataset.select(range(_lowercase ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
lowerCamelCase_ : Tuple = eval_dataset.map(
_lowercase , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
lowerCamelCase_ : int = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=_lowercase , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(_lowercase ):
lowerCamelCase_, lowerCamelCase_ : Optional[Any] = eval_predictions
lowerCamelCase_ : Any = np.argmax(_lowercase , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
lowerCamelCase_ : Any = Trainer(
model=_lowercase , args=_lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=_lowercase , data_collator=_lowercase , compute_metrics=_lowercase , )
# Training
if training_args.do_train:
lowerCamelCase_ : int = None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase_ : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase_ : List[Any] = last_checkpoint
lowerCamelCase_ : Dict = trainer.train(resume_from_checkpoint=_lowercase )
trainer.save_model() # Saves the tokenizer too for easy upload
lowerCamelCase_ : Any = train_result.metrics
lowerCamelCase_ : Union[str, Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowercase )
)
lowerCamelCase_ : List[Any] = min(_lowercase , len(_lowercase ) )
trainer.log_metrics('''train''' , _lowercase )
trainer.save_metrics('''train''' , _lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowerCamelCase_ : str = trainer.evaluate()
lowerCamelCase_ : Dict = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowercase )
lowerCamelCase_ : Union[str, Any] = min(_lowercase , len(_lowercase ) )
trainer.log_metrics('''eval''' , _lowercase )
trainer.save_metrics('''eval''' , _lowercase )
lowerCamelCase_ : List[str] = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''multiple-choice''',
'''dataset_tags''': '''swag''',
'''dataset_args''': '''regular''',
'''dataset''': '''SWAG''',
'''language''': '''en''',
}
if training_args.push_to_hub:
trainer.push_to_hub(**_lowercase )
else:
trainer.create_model_card(**_lowercase )
def lowercase_ ( _lowercase ) -> Dict:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
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'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
A__ : str ='''docs/source/en/_toctree.yml'''
def UpperCamelCase__ ( lowerCAmelCase ) -> str:
"""simple docstring"""
_lowerCAmelCase = defaultdict(lowerCAmelCase )
_lowerCAmelCase = []
_lowerCAmelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(lowerCAmelCase )
_lowerCAmelCase = new_doc_list
_lowerCAmelCase = [key for key, value in counts.items() if value > 1]
_lowerCAmelCase = []
for duplicate_key in duplicates:
_lowerCAmelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(lowerCAmelCase ) > 1:
raise ValueError(
f"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
_lowerCAmelCase = sorted(lowerCAmelCase , key=lambda lowerCAmelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(lowerCAmelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(lowerCAmelCase )
# Sort
return overview_doc
def UpperCamelCase__ ( lowerCAmelCase=False ) -> List[str]:
"""simple docstring"""
with open(lowerCAmelCase , encoding="""utf-8""" ) as f:
_lowerCAmelCase = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase = content[api_idx]["""sections"""]
# Then to the model doc
_lowerCAmelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCAmelCase = api_doc[scheduler_idx]["""sections"""]
_lowerCAmelCase = clean_doc_toc(lowerCAmelCase )
_lowerCAmelCase = False
if new_scheduler_doc != scheduler_doc:
_lowerCAmelCase = True
if overwrite:
_lowerCAmelCase = new_scheduler_doc
if diff:
if overwrite:
_lowerCAmelCase = api_doc
with open(lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(lowerCAmelCase , allow_unicode=lowerCAmelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def UpperCamelCase__ ( lowerCAmelCase=False ) -> Union[str, Any]:
"""simple docstring"""
with open(lowerCAmelCase , encoding="""utf-8""" ) as f:
_lowerCAmelCase = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase = content[api_idx]["""sections"""]
# Then to the model doc
_lowerCAmelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCAmelCase = False
_lowerCAmelCase = api_doc[pipeline_idx]["""sections"""]
_lowerCAmelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCAmelCase = pipeline_doc["""section"""]
_lowerCAmelCase = clean_doc_toc(lowerCAmelCase )
if overwrite:
_lowerCAmelCase = new_sub_pipeline_doc
new_pipeline_docs.append(lowerCAmelCase )
# sort overall pipeline doc
_lowerCAmelCase = clean_doc_toc(lowerCAmelCase )
if new_pipeline_docs != pipeline_docs:
_lowerCAmelCase = True
if overwrite:
_lowerCAmelCase = new_pipeline_docs
if diff:
if overwrite:
_lowerCAmelCase = api_doc
with open(lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(lowerCAmelCase , allow_unicode=lowerCAmelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
A__ : str =argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A__ : Tuple =parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 370
|
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if not (isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
_lowerCAmelCase = len(lowerCAmelCase )
_lowerCAmelCase = len(lowerCAmelCase )
_lowerCAmelCase = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
_lowerCAmelCase = 0
_lowerCAmelCase = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
_lowerCAmelCase = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
_lowerCAmelCase = i
_lowerCAmelCase = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 220
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|
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> list[list[int]]:
__lowerCAmelCase: Any = []
if len(__SCREAMING_SNAKE_CASE ) == 1:
return [nums.copy()]
for _ in range(len(__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase: int = nums.pop(0 )
__lowerCAmelCase: Any = permute(__SCREAMING_SNAKE_CASE )
for perm in permutations:
perm.append(__SCREAMING_SNAKE_CASE )
result.extend(__SCREAMING_SNAKE_CASE )
nums.append(__SCREAMING_SNAKE_CASE )
return result
def a__ ( __SCREAMING_SNAKE_CASE ) -> Optional[int]:
def backtrack(__SCREAMING_SNAKE_CASE ):
if start == len(__SCREAMING_SNAKE_CASE ) - 1:
output.append(nums[:] )
else:
for i in range(__SCREAMING_SNAKE_CASE , len(__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = nums[i], nums[start]
backtrack(start + 1 )
__lowerCAmelCase , __lowerCAmelCase: Any = nums[i], nums[start] # backtrack
__lowerCAmelCase: str = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__A = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 217
|
"""simple docstring"""
__A = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> list[str]:
__lowerCAmelCase: Tuple = set()
# keep track of all the paths to be checked
__lowerCAmelCase: Optional[Any] = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowerCAmelCase: str = queue.pop(0 )
# get the last node from the path
__lowerCAmelCase: List[Any] = path[-1]
if node not in explored:
__lowerCAmelCase: str = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowerCAmelCase: Optional[int] = list(__SCREAMING_SNAKE_CASE )
new_path.append(__SCREAMING_SNAKE_CASE )
queue.append(__SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(__SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowerCAmelCase: Dict = [start]
__lowerCAmelCase: Any = set(__SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
__lowerCAmelCase: int = {start: 0, target: -1}
while queue:
__lowerCAmelCase: Optional[Any] = queue.pop(0 )
if node == target:
__lowerCAmelCase: Dict = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(__SCREAMING_SNAKE_CASE )
queue.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[str] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, "G", "D")) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, "G", "D")) # returns 4
| 217
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|
"""simple docstring"""
class UpperCamelCase_ :
def __init__( self : Union[str, Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[str] ) -> Tuple:
UpperCAmelCase_ : Optional[int] = None
UpperCAmelCase_ : Any = None
UpperCAmelCase_ : Any = graph
self._normalize_graph(a_ , a_ )
UpperCAmelCase_ : Dict = len(a_ )
UpperCAmelCase_ : Union[str, Any] = None
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any] ) -> Tuple:
if sources is int:
UpperCAmelCase_ : Optional[int] = [sources]
if sinks is int:
UpperCAmelCase_ : int = [sinks]
if len(a_ ) == 0 or len(a_ ) == 0:
return
UpperCAmelCase_ : int = sources[0]
UpperCAmelCase_ : Optional[int] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(a_ ) > 1 or len(a_ ) > 1:
UpperCAmelCase_ : Optional[Any] = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
UpperCAmelCase_ : Optional[int] = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
UpperCAmelCase_ : List[str] = max_input_flow
UpperCAmelCase_ : Tuple = 0
UpperCAmelCase_ : List[str] = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
UpperCAmelCase_ : Optional[int] = max_input_flow
UpperCAmelCase_ : Optional[int] = size - 1
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : List[str] ) -> Optional[Any]:
UpperCAmelCase_ : Optional[Any] = algorithm(self )
class UpperCamelCase_ :
def __init__( self : int , lowerCAmelCase_ : Optional[int] ) -> str:
UpperCAmelCase_ : List[Any] = flow_network
UpperCAmelCase_ : List[str] = flow_network.verticesCount
UpperCAmelCase_ : Union[str, Any] = flow_network.sourceIndex
UpperCAmelCase_ : Dict = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
UpperCAmelCase_ : str = flow_network.graph
UpperCAmelCase_ : int = False
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Any:
if not self.executed:
self._algorithm()
UpperCAmelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
pass
class UpperCamelCase_ (SCREAMING_SNAKE_CASE__ ):
def __init__( self : List[str] , lowerCAmelCase_ : Optional[int] ) -> List[Any]:
super().__init__(a_ )
# use this to save your result
UpperCAmelCase_ : int = -1
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class UpperCamelCase_ (SCREAMING_SNAKE_CASE__ ):
def __init__( self : List[Any] , lowerCAmelCase_ : Union[str, Any] ) -> Optional[int]:
super().__init__(a_ )
UpperCAmelCase_ : int = [[0] * self.verticies_count for i in range(self.verticies_count )]
UpperCAmelCase_ : List[Any] = [0] * self.verticies_count
UpperCAmelCase_ : Optional[Any] = [0] * self.verticies_count
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[str]:
UpperCAmelCase_ : Union[str, Any] = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
UpperCAmelCase_ : Optional[Any] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
UpperCAmelCase_ : Optional[int] = 0
while i < len(a_ ):
UpperCAmelCase_ : str = vertices_list[i]
UpperCAmelCase_ : Dict = self.heights[vertex_index]
self.process_vertex(a_ )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(a_ ) )
UpperCAmelCase_ : Any = 0
else:
i += 1
UpperCAmelCase_ : Tuple = sum(self.preflow[self.source_index] )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : Dict ) -> Union[str, Any]:
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(a_ , a_ )
self.relabel(a_ )
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] ) -> str:
UpperCAmelCase_ : Optional[Any] = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : List[str] ) -> Dict:
UpperCAmelCase_ : Tuple = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
UpperCAmelCase_ : int = self.heights[to_index]
if min_height is not None:
UpperCAmelCase_ : Union[str, Any] = min_height + 1
if __name__ == "__main__":
lowerCamelCase_ = [0]
lowerCamelCase_ = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
lowerCamelCase_ = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
lowerCamelCase_ = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
lowerCamelCase_ = flow_network.find_maximum_flow()
print(f'maximum flow is {maximum_flow}')
| 359
|
"""simple docstring"""
import re
import string
import numpy as np
import datasets
lowerCamelCase_ = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
lowerCamelCase_ = '''
Args:
predictions: List of predicted texts.
references: List of reference texts.
regexes_to_ignore: List, defaults to None. Regex expressions of characters to
ignore when calculating the exact matches. Note: these regexes are removed
from the input data before the changes based on the options below (e.g. ignore_case,
ignore_punctuation, ignore_numbers) are applied.
ignore_case: Boolean, defaults to False. If true, turns everything
to lowercase so that capitalization differences are ignored.
ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
Returns:
exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results["exact_match"], 1))
25.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results["exact_match"], 1))
50.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results["exact_match"], 1))
75.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
>>> print(round(results["exact_match"], 1))
100.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
>>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results["exact_match"], 1))
33.3
'''
lowerCamelCase_ = '''
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase_ (datasets.Metric ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
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" ),
} ) , reference_urls=[] , )
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : Optional[Any]=False , ) -> str:
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
UpperCAmelCase_ : str = np.array([re.sub(lowerCAmelCase_ , "" , lowerCAmelCase_ ) for x in predictions] )
UpperCAmelCase_ : Dict = np.array([re.sub(lowerCAmelCase_ , "" , lowerCAmelCase_ ) for x in references] )
else:
UpperCAmelCase_ : int = np.asarray(lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = np.asarray(lowerCAmelCase_ )
if ignore_case:
UpperCAmelCase_ : Optional[Any] = np.char.lower(lowerCAmelCase_ )
UpperCAmelCase_ : int = np.char.lower(lowerCAmelCase_ )
if ignore_punctuation:
UpperCAmelCase_ : Any = string.punctuation.maketrans("" , "" , string.punctuation )
UpperCAmelCase_ : Any = np.char.translate(lowerCAmelCase_ , table=lowerCAmelCase_ )
UpperCAmelCase_ : Any = np.char.translate(lowerCAmelCase_ , table=lowerCAmelCase_ )
if ignore_numbers:
UpperCAmelCase_ : Dict = string.digits.maketrans("" , "" , string.digits )
UpperCAmelCase_ : Optional[Any] = np.char.translate(lowerCAmelCase_ , table=lowerCAmelCase_ )
UpperCAmelCase_ : int = np.char.translate(lowerCAmelCase_ , table=lowerCAmelCase_ )
UpperCAmelCase_ : Optional[int] = predictions == references
return {"exact_match": np.mean(lowerCAmelCase_ ) * 100}
| 253
| 0
|
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__ = {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
"""xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
"""xlm-roberta-large-finetuned-conll02-dutch""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
),
"""xlm-roberta-large-finetuned-conll02-spanish""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
),
"""xlm-roberta-large-finetuned-conll03-english""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
),
"""xlm-roberta-large-finetuned-conll03-german""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
),
}
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Dict = """xlm-roberta"""
def __init__( self : Any , lowerCAmelCase : Tuple=3_0522 , lowerCAmelCase : Tuple=768 , lowerCAmelCase : Any=12 , lowerCAmelCase : str=12 , lowerCAmelCase : Any=3072 , lowerCAmelCase : int="gelu" , lowerCAmelCase : Union[str, Any]=0.1 , lowerCAmelCase : Dict=0.1 , lowerCAmelCase : List[str]=512 , lowerCAmelCase : Optional[int]=2 , lowerCAmelCase : Tuple=0.02 , lowerCAmelCase : int=1E-12 , lowerCAmelCase : Optional[Any]=1 , lowerCAmelCase : Optional[int]=0 , lowerCAmelCase : Any=2 , lowerCAmelCase : int="absolute" , lowerCAmelCase : Union[str, Any]=True , lowerCAmelCase : Dict=None , **lowerCAmelCase : Any , ) -> List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase)
_snake_case : List[Any] = vocab_size
_snake_case : Optional[Any] = hidden_size
_snake_case : Optional[Any] = num_hidden_layers
_snake_case : Union[str, Any] = num_attention_heads
_snake_case : List[Any] = hidden_act
_snake_case : Tuple = intermediate_size
_snake_case : Any = hidden_dropout_prob
_snake_case : List[str] = attention_probs_dropout_prob
_snake_case : List[Any] = max_position_embeddings
_snake_case : List[str] = type_vocab_size
_snake_case : Optional[int] = initializer_range
_snake_case : int = layer_norm_eps
_snake_case : Optional[Any] = position_embedding_type
_snake_case : Tuple = use_cache
_snake_case : Optional[Any] = classifier_dropout
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@property
def UpperCamelCase_ ( self : Dict) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_snake_case : List[str] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_snake_case : Optional[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
])
| 317
|
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class snake_case ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Optional[Any] = """swin"""
snake_case_ : Optional[Any] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : str , lowerCAmelCase : Optional[int]=224 , lowerCAmelCase : int=4 , lowerCAmelCase : Any=3 , lowerCAmelCase : int=96 , lowerCAmelCase : Optional[Any]=[2, 2, 6, 2] , lowerCAmelCase : Optional[Any]=[3, 6, 12, 24] , lowerCAmelCase : Tuple=7 , lowerCAmelCase : List[Any]=4.0 , lowerCAmelCase : Tuple=True , lowerCAmelCase : Optional[int]=0.0 , lowerCAmelCase : Union[str, Any]=0.0 , lowerCAmelCase : Optional[int]=0.1 , lowerCAmelCase : Tuple="gelu" , lowerCAmelCase : Any=False , lowerCAmelCase : Union[str, Any]=0.02 , lowerCAmelCase : int=1E-5 , lowerCAmelCase : Optional[Any]=32 , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : Dict=None , **lowerCAmelCase : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**lowerCAmelCase)
_snake_case : int = image_size
_snake_case : Any = patch_size
_snake_case : Union[str, Any] = num_channels
_snake_case : int = embed_dim
_snake_case : Dict = depths
_snake_case : Dict = len(lowerCAmelCase)
_snake_case : Optional[Any] = num_heads
_snake_case : Tuple = window_size
_snake_case : int = mlp_ratio
_snake_case : Any = qkv_bias
_snake_case : Union[str, Any] = hidden_dropout_prob
_snake_case : List[str] = attention_probs_dropout_prob
_snake_case : Optional[Any] = drop_path_rate
_snake_case : List[Any] = hidden_act
_snake_case : str = use_absolute_embeddings
_snake_case : Tuple = layer_norm_eps
_snake_case : Any = initializer_range
_snake_case : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_snake_case : Dict = int(embed_dim * 2 ** (len(lowerCAmelCase) - 1))
_snake_case : Optional[Any] = ["""stem"""] + [F'''stage{idx}''' for idx in range(1 , len(lowerCAmelCase) + 1)]
_snake_case , _snake_case : List[str] = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase , out_indices=lowerCAmelCase , stage_names=self.stage_names)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : int = version.parse("""1.11""" )
@property
def UpperCamelCase_ ( self : Dict) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
])
@property
def UpperCamelCase_ ( self : Dict) -> float:
"""simple docstring"""
return 1E-4
| 317
| 1
|
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'''The RoBERTa Model transformer with early exiting (DeeRoBERTa). ''' , __lowerCAmelCase , )
class UpperCAmelCase_ ( __lowerCAmelCase):
snake_case__ = RobertaConfig
snake_case__ = '''roberta'''
def __init__( self : Optional[int] , __UpperCamelCase : Union[str, Any] ) -> Union[str, Any]:
super().__init__(lowerCAmelCase_ )
_UpperCamelCase = RobertaEmbeddings(lowerCAmelCase_ )
self.init_weights()
@add_start_docstrings(
'''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
also takes care of multi-layer training. ''' , __lowerCAmelCase , )
class UpperCAmelCase_ ( __lowerCAmelCase):
snake_case__ = RobertaConfig
snake_case__ = '''roberta'''
def __init__( self : str , __UpperCamelCase : Union[str, Any] ) -> int:
super().__init__(lowerCAmelCase_ )
_UpperCamelCase = config.num_labels
_UpperCamelCase = config.num_hidden_layers
_UpperCamelCase = DeeRobertaModel(lowerCAmelCase_ )
_UpperCamelCase = nn.Dropout(config.hidden_dropout_prob )
_UpperCamelCase = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(lowerCAmelCase_ )
def _UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : Optional[int]=None , __UpperCamelCase : Any=None , __UpperCamelCase : Dict=None , __UpperCamelCase : Optional[int]=None , __UpperCamelCase : Optional[Any]=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Optional[Any]=None , __UpperCamelCase : Optional[int]=-1 , __UpperCamelCase : List[str]=False , ) -> Dict:
_UpperCamelCase = self.num_layers
try:
_UpperCamelCase = self.roberta(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , )
_UpperCamelCase = outputs[1]
_UpperCamelCase = self.dropout(lowerCAmelCase_ )
_UpperCamelCase = self.classifier(lowerCAmelCase_ )
_UpperCamelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_UpperCamelCase = e.message
_UpperCamelCase = e.exit_layer
_UpperCamelCase = outputs[0]
if not self.training:
_UpperCamelCase = entropy(lowerCAmelCase_ )
_UpperCamelCase = []
_UpperCamelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_UpperCamelCase = MSELoss()
_UpperCamelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_UpperCamelCase = CrossEntropyLoss()
_UpperCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_UpperCamelCase = []
for highway_exit in outputs[-1]:
_UpperCamelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCAmelCase_ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_UpperCamelCase = MSELoss()
_UpperCamelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_UpperCamelCase = CrossEntropyLoss()
_UpperCamelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(lowerCAmelCase_ )
if train_highway:
_UpperCamelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_UpperCamelCase = (loss,) + outputs
if not self.training:
_UpperCamelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_UpperCamelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 364
|
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCAmelCase = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
""">""": operator.gt,
}
def lowercase ( a__ : Union[str, Any] , a__ : int , a__ : List[Any] , a__ : Union[str, Any] , a__ : Tuple , a__ : List[Any] ) -> Optional[Any]:
if got_ver is None or want_ver is None:
raise ValueError(
F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
F''' reinstalling {pkg}.''' )
if not ops[op](version.parse(a__ ) , version.parse(a__ ) ):
raise ImportError(
F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''' )
def lowercase ( a__ : str , a__ : Optional[str] = None ) -> None:
_UpperCamelCase = F'''\n{hint}''' if hint is not None else ''''''
# non-versioned check
if re.match(R'''^[\w_\-\d]+$''' , a__ ):
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = requirement, None, None
else:
_UpperCamelCase = re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''' , a__ )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
F''' got {requirement}''' )
_UpperCamelCase , _UpperCamelCase = match[0]
_UpperCamelCase = want_full.split(''',''' ) # there could be multiple requirements
_UpperCamelCase = {}
for w in want_range:
_UpperCamelCase = re.findall(R'''^([\s!=<>]{1,2})(.+)''' , a__ )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
F''' but got {requirement}''' )
_UpperCamelCase , _UpperCamelCase = match[0]
_UpperCamelCase = want_ver
if op not in ops:
raise ValueError(F'''{requirement}: need one of {list(ops.keys() )}, but got {op}''' )
# special case
if pkg == "python":
_UpperCamelCase = '''.'''.join([str(a__ ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(a__ , a__ , a__ , a__ , a__ , a__ )
return
# check if any version is installed
try:
_UpperCamelCase = importlib.metadata.version(a__ )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''' )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(a__ , a__ , a__ , a__ , a__ , a__ )
def lowercase ( a__ : Tuple ) -> Any:
_UpperCamelCase = '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
return require_version(a__ , a__ )
| 54
| 0
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
# initialize config
if "resnet-50" in model_name:
A_ = ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
elif "resnet-101" in model_name:
A_ = ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
else:
raise ValueError("""Model name should include either resnet50 or resnet101""" )
A_ = DetrConfig(use_timm_backbone=UpperCAmelCase__, backbone_config=UpperCAmelCase__ )
# set label attributes
A_ = """panoptic""" in model_name
if is_panoptic:
A_ = 2_50
else:
A_ = 91
A_ = """huggingface/label-files"""
A_ = """coco-detection-id2label.json"""
A_ = json.load(open(hf_hub_download(UpperCAmelCase__, UpperCAmelCase__, repo_type="""dataset""" ), """r""" ) )
A_ = {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
A_ = idalabel
A_ = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> List[str]:
# here we list all keys to be renamed (original name on the left, our name on the right)
A_ = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
F'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
F'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
] )
return rename_keys
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Union[str, Any]:
A_ = state_dict.pop(UpperCAmelCase__ )
A_ = val
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=False ) -> Tuple:
A_ = """"""
if is_panoptic:
A_ = """detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A_ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
A_ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
A_ = in_proj_weight[:2_56, :]
A_ = in_proj_bias[:2_56]
A_ = in_proj_weight[2_56:5_12, :]
A_ = in_proj_bias[2_56:5_12]
A_ = in_proj_weight[-2_56:, :]
A_ = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A_ = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
A_ = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
A_ = in_proj_weight[:2_56, :]
A_ = in_proj_bias[:2_56]
A_ = in_proj_weight[2_56:5_12, :]
A_ = in_proj_bias[2_56:5_12]
A_ = in_proj_weight[-2_56:, :]
A_ = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
A_ = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
A_ = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A_ = in_proj_weight_cross_attn[:2_56, :]
A_ = in_proj_bias_cross_attn[:2_56]
A_ = in_proj_weight_cross_attn[2_56:5_12, :]
A_ = in_proj_bias_cross_attn[2_56:5_12]
A_ = in_proj_weight_cross_attn[-2_56:, :]
A_ = in_proj_bias_cross_attn[-2_56:]
def UpperCAmelCase__ ( ) -> Any:
A_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ = Image.open(requests.get(UpperCAmelCase__, stream=UpperCAmelCase__ ).raw )
return im
@torch.no_grad()
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None, UpperCAmelCase__=False ) -> Union[str, Any]:
A_ , A_ = get_detr_config(UpperCAmelCase__ )
# load original model from torch hub
A_ = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(F'''Converting model {model_name}...''' )
A_ = torch.hub.load("""facebookresearch/detr""", model_name_to_original_name[model_name], pretrained=UpperCAmelCase__ ).eval()
A_ = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(UpperCAmelCase__ ):
if is_panoptic:
A_ = """detr.""" + src
rename_key(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCAmelCase__, is_panoptic=UpperCAmelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A_ = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
A_ = state_dict.pop(UpperCAmelCase__ )
A_ = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
A_ = state_dict.pop(UpperCAmelCase__ )
A_ = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
A_ = state_dict.pop(UpperCAmelCase__ )
A_ = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
A_ = state_dict.pop(UpperCAmelCase__ )
A_ = val
# finally, create HuggingFace model and load state dict
A_ = DetrForSegmentation(UpperCAmelCase__ ) if is_panoptic else DetrForObjectDetection(UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
model.eval()
# verify our conversion on an image
A_ = """coco_panoptic""" if is_panoptic else """coco_detection"""
A_ = DetrImageProcessor(format=UpperCAmelCase__ )
A_ = processor(images=prepare_img(), return_tensors="""pt""" )
A_ = encoding["""pixel_values"""]
A_ = detr(UpperCAmelCase__ )
A_ = model(UpperCAmelCase__ )
assert torch.allclose(outputs.logits, original_outputs["""pred_logits"""], atol=1e-3 )
assert torch.allclose(outputs.pred_boxes, original_outputs["""pred_boxes"""], atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs["""pred_masks"""], atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
processor.save_pretrained(UpperCAmelCase__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("""Uploading PyTorch model and image processor to the hub...""" )
model.push_to_hub(F'''nielsr/{model_name}''' )
processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''detr-resnet-50''',
type=str,
choices=['''detr-resnet-50''', '''detr-resnet-101'''],
help='''Name of the DETR model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
__lowerCamelCase = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 162
|
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[list[float]]:
A_ = []
for data in source_data:
for i, el in enumerate(UpperCAmelCase__ ):
if len(UpperCAmelCase__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(UpperCAmelCase__ ) )
return data_lists
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> list[list[float]]:
A_ = []
for dlist, weight in zip(UpperCAmelCase__, UpperCAmelCase__ ):
A_ = min(UpperCAmelCase__ )
A_ = max(UpperCAmelCase__ )
A_ = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
A_ = F'''Invalid weight of {weight:f} provided'''
raise ValueError(UpperCAmelCase__ )
score_lists.append(UpperCAmelCase__ )
return score_lists
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[float]:
A_ = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(UpperCAmelCase__ ):
A_ = final_scores[j] + ele
return final_scores
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> list[list[float]]:
A_ = get_data(UpperCAmelCase__ )
A_ = calculate_each_score(UpperCAmelCase__, UpperCAmelCase__ )
A_ = generate_final_scores(UpperCAmelCase__ )
# append scores to source data
for i, ele in enumerate(UpperCAmelCase__ ):
source_data[i].append(UpperCAmelCase__ )
return source_data
| 162
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class A_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
__UpperCamelCase = "convnextv2"
def __init__( self :List[Any] , lowercase_ :Dict=3 , lowercase_ :Any=4 , lowercase_ :Union[str, Any]=4 , lowercase_ :Tuple=None , lowercase_ :str=None , lowercase_ :Tuple="gelu" , lowercase_ :Optional[Any]=0.02 , lowercase_ :int=1E-12 , lowercase_ :Tuple=0.0 , lowercase_ :Optional[int]=2_24 , lowercase_ :Dict=None , lowercase_ :Dict=None , **lowercase_ :int , ) -> str:
super().__init__(**a__ )
UpperCAmelCase = num_channels
UpperCAmelCase = patch_size
UpperCAmelCase = num_stages
UpperCAmelCase = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
UpperCAmelCase = [3, 3, 9, 3] if depths is None else depths
UpperCAmelCase = hidden_act
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = drop_path_rate
UpperCAmelCase = image_size
UpperCAmelCase = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
UpperCAmelCase , UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=a__ , out_indices=a__ , stage_names=self.stage_names )
| 364
|
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
snake_case_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :CLIPSegForImageSegmentation , lowercase_ :CLIPSegProcessor , lowercase_ :AutoencoderKL , lowercase_ :CLIPTextModel , lowercase_ :CLIPTokenizer , lowercase_ :UNetaDConditionModel , lowercase_ :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowercase_ :StableDiffusionSafetyChecker , lowercase_ :CLIPImageProcessor , ) -> List[str]:
super().__init__()
if hasattr(scheduler.config , 'steps_offset' ) and scheduler.config.steps_offset != 1:
UpperCAmelCase = (
f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
' file'
)
deprecate('steps_offset!=1' , '1.0.0' , lowercase_ , standard_warn=lowercase_ )
UpperCAmelCase = dict(scheduler.config )
UpperCAmelCase = 1
UpperCAmelCase = FrozenDict(lowercase_ )
if hasattr(scheduler.config , 'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
UpperCAmelCase = (
f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
' Hub, it would be very nice if you could open a Pull request for the'
' `scheduler/scheduler_config.json` file'
)
deprecate('skip_prk_steps not set' , '1.0.0' , lowercase_ , standard_warn=lowercase_ )
UpperCAmelCase = dict(scheduler.config )
UpperCAmelCase = True
UpperCAmelCase = FrozenDict(lowercase_ )
if safety_checker is None:
logger.warning(
f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
' results in services or applications open to the public. Both the diffusers team and Hugging Face'
' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )
self.register_modules(
segmentation_model=lowercase_ , segmentation_processor=lowercase_ , vae=lowercase_ , text_encoder=lowercase_ , tokenizer=lowercase_ , unet=lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , feature_extractor=lowercase_ , )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[Union[str, int]] = "auto" ) -> int:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowercase_ )
def UpperCAmelCase__ ( self :Optional[int] ) -> List[str]:
self.enable_attention_slicing(lowercase_ )
def UpperCAmelCase__ ( self :int ) -> int:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
UpperCAmelCase = torch.device('cuda' )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(lowercase_ , lowercase_ )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase__ ( self :Optional[Any] ) -> List[str]:
if self.device != torch.device('meta' ) or not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowercase_ , '_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()
def __call__( self :Optional[Any] , lowercase_ :Union[str, List[str]] , lowercase_ :Union[torch.FloatTensor, PIL.Image.Image] , lowercase_ :str , lowercase_ :int = 5_12 , lowercase_ :int = 5_12 , lowercase_ :int = 50 , lowercase_ :float = 7.5 , lowercase_ :Optional[Union[str, List[str]]] = None , lowercase_ :Optional[int] = 1 , lowercase_ :float = 0.0 , lowercase_ :Optional[torch.Generator] = None , lowercase_ :Optional[torch.FloatTensor] = None , lowercase_ :Optional[str] = "pil" , lowercase_ :bool = True , lowercase_ :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowercase_ :int = 1 , **lowercase_ :int , ) -> int:
UpperCAmelCase = self.segmentation_processor(
text=[text] , images=[image] , padding='max_length' , return_tensors='pt' ).to(self.device )
UpperCAmelCase = self.segmentation_model(**lowercase_ )
UpperCAmelCase = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
UpperCAmelCase = self.numpy_to_pil(lowercase_ )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
UpperCAmelCase = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , height=lowercase_ , width=lowercase_ , num_inference_steps=lowercase_ , guidance_scale=lowercase_ , negative_prompt=lowercase_ , num_images_per_prompt=lowercase_ , eta=lowercase_ , generator=lowercase_ , latents=lowercase_ , output_type=lowercase_ , return_dict=lowercase_ , callback=lowercase_ , callback_steps=lowercase_ , )
| 181
| 0
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(a_ ) , """Tatoeba directory does not exist.""" )
class a ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self : Tuple ) -> Optional[int]:
lowerCamelCase_ = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_lowerCamelCase )
@slow
def UpperCamelCase ( self : int ) -> Tuple:
self.resolver.convert_models(['heb-eng'] )
@slow
def UpperCamelCase ( self : Dict ) -> int:
lowerCamelCase_ , lowerCamelCase_ = self.resolver.write_model_card('opus-mt-he-en' , dry_run=_lowerCamelCase )
assert mmeta["long_pair"] == "heb-eng"
| 183
|
"""simple docstring"""
_UpperCamelCase : List[str] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_UpperCamelCase : str = {
'm': 0,
'km': 3,
'Mm': 6,
'Gm': 9,
'Tm': 1_2,
'Pm': 1_5,
'Em': 1_8,
'Zm': 2_1,
'Ym': 2_4,
}
def _SCREAMING_SNAKE_CASE ( __snake_case : float , __snake_case : str , __snake_case : str ):
'''simple docstring'''
lowercase = from_type.lower().strip('s' )
lowercase = to_type.lower().strip('s' )
lowercase = UNIT_SYMBOL.get(__snake_case , __snake_case )
lowercase = UNIT_SYMBOL.get(__snake_case , __snake_case )
if from_sanitized not in METRIC_CONVERSION:
lowercase = (
f'Invalid \'from_type\' value: {from_type!r}.\n'
f'Conversion abbreviations are: {", ".join(__snake_case )}'
)
raise ValueError(__snake_case )
if to_sanitized not in METRIC_CONVERSION:
lowercase = (
f'Invalid \'to_type\' value: {to_type!r}.\n'
f'Conversion abbreviations are: {", ".join(__snake_case )}'
)
raise ValueError(__snake_case )
lowercase = METRIC_CONVERSION[from_sanitized]
lowercase = METRIC_CONVERSION[to_sanitized]
lowercase = 1
if from_exponent > to_exponent:
lowercase = from_exponent - to_exponent
else:
lowercase = -(to_exponent - from_exponent)
return value * pow(10 , __snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 220
| 0
|
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--txt2img_unclip''',
default='''kakaobrain/karlo-v1-alpha''',
type=str,
required=False,
help='''The pretrained txt2img unclip.''',
)
_SCREAMING_SNAKE_CASE : str = parser.parse_args()
_SCREAMING_SNAKE_CASE : int = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
_SCREAMING_SNAKE_CASE : Optional[int] = CLIPImageProcessor()
_SCREAMING_SNAKE_CASE : Optional[Any] = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
_SCREAMING_SNAKE_CASE : Optional[Any] = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 218
|
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if not isinstance(_A , _A ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''' )
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = str(_A )
while len(_A ) != 1:
SCREAMING_SNAKE_CASE__ = [int(_A ) for i in num_string]
SCREAMING_SNAKE_CASE__ = 1
for i in range(0 , len(_A ) ):
total *= numbers[i]
SCREAMING_SNAKE_CASE__ = str(_A )
steps += 1
return steps
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if not isinstance(_A , _A ):
raise ValueError('''additive_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''' )
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = str(_A )
while len(_A ) != 1:
SCREAMING_SNAKE_CASE__ = [int(_A ) for i in num_string]
SCREAMING_SNAKE_CASE__ = 0
for i in range(0 , len(_A ) ):
total += numbers[i]
SCREAMING_SNAKE_CASE__ = str(_A )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 218
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ : Union[str, Any] = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecTextConfig',
'Data2VecTextOnnxConfig',
],
'configuration_data2vec_vision': [
'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecVisionConfig',
'Data2VecVisionOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ : Tuple = [
'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecAudioForAudioFrameClassification',
'Data2VecAudioForCTC',
'Data2VecAudioForSequenceClassification',
'Data2VecAudioForXVector',
'Data2VecAudioModel',
'Data2VecAudioPreTrainedModel',
]
UpperCAmelCase__ : Dict = [
'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecTextForCausalLM',
'Data2VecTextForMaskedLM',
'Data2VecTextForMultipleChoice',
'Data2VecTextForQuestionAnswering',
'Data2VecTextForSequenceClassification',
'Data2VecTextForTokenClassification',
'Data2VecTextModel',
'Data2VecTextPreTrainedModel',
]
UpperCAmelCase__ : List[Any] = [
'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecVisionForImageClassification',
'Data2VecVisionForMaskedImageModeling',
'Data2VecVisionForSemanticSegmentation',
'Data2VecVisionModel',
'Data2VecVisionPreTrainedModel',
]
if is_tf_available():
UpperCAmelCase__ : Optional[int] = [
'TFData2VecVisionForImageClassification',
'TFData2VecVisionForSemanticSegmentation',
'TFData2VecVisionModel',
'TFData2VecVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 25
|
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowerCAmelCase : List[str] = {
'169M': 12,
'430M': 24,
'1B5': 24,
'3B': 32,
'7B': 32,
'14B': 40,
}
lowerCAmelCase : Tuple = {
'169M': 7_68,
'430M': 10_24,
'1B5': 20_48,
'3B': 25_60,
'7B': 40_96,
'14B': 51_20,
}
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = list(state_dict.keys() )
for name in state_dict_keys:
SCREAMING_SNAKE_CASE_ : Optional[int] = state_dict.pop(a )
# emb -> embedding
if name.startswith('emb.' ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = name.replace('emb.' , 'embeddings.' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('blocks.0.ln0' ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = name.replace('blocks.0.ln0' , 'blocks.0.pre_ln' )
# att -> attention
SCREAMING_SNAKE_CASE_ : Optional[Any] = re.sub(R'blocks\.(\d+)\.att' , R'blocks.\1.attention' , a )
# ffn -> feed_forward
SCREAMING_SNAKE_CASE_ : Any = re.sub(R'blocks\.(\d+)\.ffn' , R'blocks.\1.feed_forward' , a )
# time_mix_k -> time_mix_key and reshape
if name.endswith('.time_mix_k' ):
SCREAMING_SNAKE_CASE_ : Any = name.replace('.time_mix_k' , '.time_mix_key' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('.time_mix_v' ):
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('.time_mix_v' , '.time_mix_value' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('.time_mix_r' ):
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('.time_mix_r' , '.time_mix_receptance' )
if name != "head.weight":
SCREAMING_SNAKE_CASE_ : Any = 'rwkv.' + name
SCREAMING_SNAKE_CASE_ : Dict = weight
return state_dict
def A_ ( a , a , a , a=None , a=None , a=False , a=None ):
"""simple docstring"""
if tokenizer_file is None:
print('No `--tokenizer_file` provided, we will use the default tokenizer.' )
SCREAMING_SNAKE_CASE_ : Optional[Any] = 5_0_2_7_7
SCREAMING_SNAKE_CASE_ : Union[str, Any] = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b' )
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = PreTrainedTokenizerFast(tokenizer_file=a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(a )
tokenizer.save_pretrained(a )
# 2. Build the config
SCREAMING_SNAKE_CASE_ : List[Any] = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
SCREAMING_SNAKE_CASE_ : str = candidate
break
if size is None:
raise ValueError('Could not infer the size, please provide it with the `--size` argument.' )
if size not in possible_sizes:
raise ValueError(f"`size` should be one of {possible_sizes}, got {size}." )
SCREAMING_SNAKE_CASE_ : str = RwkvConfig(
vocab_size=a , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(a )
# 3. Download model file then convert state_dict
SCREAMING_SNAKE_CASE_ : List[Any] = hf_hub_download(a , a )
SCREAMING_SNAKE_CASE_ : int = torch.load(a , map_location='cpu' )
SCREAMING_SNAKE_CASE_ : Optional[Any] = convert_state_dict(a )
# 4. Split in shards and save
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = shard_checkpoint(a )
for shard_file, shard in shards.items():
torch.save(a , os.path.join(a , a ) )
if index is not None:
SCREAMING_SNAKE_CASE_ : Any = os.path.join(a , a )
# Save the index as well
with open(a , 'w' , encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE_ : int = json.dumps(a , indent=2 , sort_keys=a ) + '\n'
f.write(a )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.' )
SCREAMING_SNAKE_CASE_ : List[Any] = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
SCREAMING_SNAKE_CASE_ : List[str] = torch.load(os.path.join(a , a ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(a , a ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('Please provide a `model_name` to push the model to the Hub.' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = AutoModelForCausalLM.from_pretrained(a )
model.push_to_hub(a , max_shard_size='2GB' )
tokenizer.push_to_hub(a )
if __name__ == "__main__":
lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
)
parser.add_argument(
'--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
)
parser.add_argument(
'--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
)
parser.add_argument(
'--tokenizer_file',
default=None,
type=str,
help='Path to the tokenizer file to use (if not provided, only the model is converted).',
)
parser.add_argument(
'--size',
default=None,
type=str,
help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Push to the Hub the converted model.',
)
parser.add_argument(
'--model_name',
default=None,
type=str,
help='Name of the pushed model on the Hub, including the username / organization.',
)
lowerCAmelCase : Optional[int] = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 253
| 0
|
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = ['input_ids', 'attention_mask']
def __init__( self , lowercase="</s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase=125 , lowercase=None , **lowercase , ) -> None:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowerCamelCase_ = [f'<extra_id_{i}>' for i in range(lowercase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
lowerCamelCase_ = len(set(filter(lambda lowercase : bool("extra_id" in str(lowercase ) ) , lowercase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens" )
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else pad_token
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else eos_token
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else unk_token
super().__init__(
eos_token=lowercase , unk_token=lowercase , pad_token=lowercase , extra_ids=lowercase , additional_special_tokens=lowercase , **lowercase , )
lowerCamelCase_ = extra_ids
lowerCamelCase_ = 2**8 # utf is 8 bits
# define special tokens dict
lowerCamelCase_ = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
lowerCamelCase_ = len(self.special_tokens_encoder )
lowerCamelCase_ = len(lowercase )
for i, token in enumerate(lowercase ):
lowerCamelCase_ = self.vocab_size + i - n
lowerCamelCase_ = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None , lowercase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(lowercase )) + [1]
return ([0] * len(lowercase )) + [1] + ([0] * len(lowercase )) + [1]
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> List[int]:
if len(lowercase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
lowerCamelCase_ = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
lowerCamelCase_ = self._add_eos_if_not_present(lowercase )
if token_ids_a is None:
return token_ids_a
else:
lowerCamelCase_ = self._add_eos_if_not_present(lowercase )
return token_ids_a + token_ids_a
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> List[str]:
lowerCamelCase_ = [chr(lowercase ) for i in text.encode("utf-8" )]
return tokens
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Dict:
if token in self.special_tokens_encoder:
lowerCamelCase_ = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
lowerCamelCase_ = self.added_tokens_encoder[token]
elif len(lowercase ) != 1:
lowerCamelCase_ = self.unk_token_id
else:
lowerCamelCase_ = ord(lowercase ) + self._num_special_tokens
return token_id
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Dict:
if index in self.special_tokens_decoder:
lowerCamelCase_ = self.special_tokens_decoder[index]
else:
lowerCamelCase_ = chr(index - self._num_special_tokens )
return token
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> int:
lowerCamelCase_ = b""
for token in tokens:
if token in self.special_tokens_decoder:
lowerCamelCase_ = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.added_tokens_decoder:
lowerCamelCase_ = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.special_tokens_encoder:
lowerCamelCase_ = token.encode("utf-8" )
elif token in self.added_tokens_encoder:
lowerCamelCase_ = token.encode("utf-8" )
else:
lowerCamelCase_ = bytes([ord(lowercase )] )
bstring += tok_string
lowerCamelCase_ = bstring.decode("utf-8" , errors="ignore" )
return string
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> Tuple[str]:
return ()
| 47
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using some other dataset input the target column
__A =df.iloc[:, 1:2]
__A =actual_data.values.reshape(len_data, 1)
__A =MinMaxScaler().fit_transform(actual_data)
__A =1_0
__A =5
__A =2_0
__A =len_data - periods * look_back
__A =actual_data[:division]
__A =actual_data[division - look_back :]
__A, __A =[], []
__A, __A =[], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
__A =np.array(train_x)
__A =np.array(test_x)
__A =np.array([list(i.ravel()) for i in train_y])
__A =np.array([list(i.ravel()) for i in test_y])
__A =Sequential()
model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
__A =model.fit(
x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
)
__A =model.predict(x_test)
| 47
| 1
|
'''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,
)
| 79
|
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
__SCREAMING_SNAKE_CASE = len(bin(lowerCAmelCase_ )[3:] )
__SCREAMING_SNAKE_CASE = bin(abs(lowerCAmelCase_ ) - (1 << binary_number_length) )[3:]
__SCREAMING_SNAKE_CASE = (
(
"1"
+ "0" * (binary_number_length - len(lowerCAmelCase_ ))
+ twos_complement_number
)
if number < 0
else "0"
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 54
| 0
|
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for ch in input_str:
__SCREAMING_SNAKE_CASE = ord(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = pow(2 , lowerCAmelCase_ )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
print(f"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(lowerCAmelCase_ ):
print(f"""{i}\t\t{d}""" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
for j in range(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
return True
return False
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [float("inf" )] * vertex_count
__SCREAMING_SNAKE_CASE = 0.0
for _ in range(vertex_count - 1 ):
for j in range(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
__SCREAMING_SNAKE_CASE = distance[u] + w
__SCREAMING_SNAKE_CASE = check_negative_cycle(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
if negative_cycle_exists:
raise Exception("Negative cycle found" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ : Union[str, Any] = int(input('''Enter number of vertices: ''').strip())
a__ : Any = int(input('''Enter number of edges: ''').strip())
a__ : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print('''Edge ''', i + 1)
a__ , a__ , a__ : str = (
int(x)
for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
)
a__ : str = {'''src''': src, '''dst''': dest, '''weight''': weight}
a__ : str = int(input('''\nEnter shortest path source:''').strip())
a__ : List[Any] = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 195
| 0
|
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
a_ = '\\n@inproceedings{lin-2004-rouge,\n title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n author = "Lin, Chin-Yew",\n booktitle = "Text Summarization Branches Out",\n month = jul,\n year = "2004",\n address = "Barcelona, Spain",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W04-1013",\n pages = "74--81",\n}\n'
a_ = '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'
a_ = '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n `"rougeL"`: Longest common subsequence based scoring.\n `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric(\'rouge\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n >>> print(results["rouge1"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results["rouge1"].mid.fmeasure)\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
'''simple docstring'''
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
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/google-research/google-research/tree/master/rouge"] , reference_urls=[
"https://en.wikipedia.org/wiki/ROUGE_(metric)",
"https://github.com/google-research/google-research/tree/master/rouge",
] , )
def __UpperCamelCase ( self : int , a : Any , a : Tuple , a : str=None , a : int=True , a : Optional[Any]=False ) -> List[str]:
"""simple docstring"""
if rouge_types is None:
SCREAMING_SNAKE_CASE : Any = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
SCREAMING_SNAKE_CASE : List[str] = rouge_scorer.RougeScorer(rouge_types=a , use_stemmer=a )
if use_aggregator:
SCREAMING_SNAKE_CASE : Union[str, Any] = scoring.BootstrapAggregator()
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for ref, pred in zip(a , a ):
SCREAMING_SNAKE_CASE : int = scorer.score(a , a )
if use_aggregator:
aggregator.add_scores(a )
else:
scores.append(a )
if use_aggregator:
SCREAMING_SNAKE_CASE : Optional[int] = aggregator.aggregate()
else:
SCREAMING_SNAKE_CASE : Tuple = {}
for key in scores[0]:
SCREAMING_SNAKE_CASE : List[Any] = [score[key] for score in scores]
return result
| 76
|
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = '''\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
'''
UpperCamelCase__ = '''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''
UpperCamelCase__ = '''
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting \'keep_singletons=False\', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
\'mentions\': mentions
\'muc\': MUC metric [Vilain et al, 1995]
\'bcub\': B-cubed [Bagga and Baldwin, 1998]
\'ceafe\': CEAFe [Luo et al., 2005]
\'lea\': LEA [Moosavi and Strube, 2016]
\'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric(\'coval\')
>>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',
... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',
... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',
... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',
... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',
... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__="dummy_doc" ) -> Dict:
UpperCAmelCase__ : List[str] = {doc: key_lines}
UpperCAmelCase__ : int = {doc: sys_lines}
UpperCAmelCase__ : Optional[Any] = {}
UpperCAmelCase__ : Optional[Any] = 0
UpperCAmelCase__ : Optional[Any] = 0
UpperCAmelCase__ : Union[str, Any] = 0
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : List[Any] = 0
UpperCAmelCase__ : Union[str, Any] = 0
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = reader.get_doc_mentions(lowerCAmelCase__ , key_doc_lines[doc] , lowerCAmelCase__ )
key_singletons_num += singletons_num
if NP_only or min_span:
UpperCAmelCase__ : int = reader.set_annotated_parse_trees(lowerCAmelCase__ , key_doc_lines[doc] , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ , UpperCAmelCase__ : str = reader.get_doc_mentions(lowerCAmelCase__ , sys_doc_lines[doc] , lowerCAmelCase__ )
sys_singletons_num += singletons_num
if NP_only or min_span:
UpperCAmelCase__ : str = reader.set_annotated_parse_trees(lowerCAmelCase__ , key_doc_lines[doc] , lowerCAmelCase__ , lowerCAmelCase__ )
if remove_nested:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = reader.remove_nested_coref_mentions(lowerCAmelCase__ , lowerCAmelCase__ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = reader.remove_nested_coref_mentions(lowerCAmelCase__ , lowerCAmelCase__ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
UpperCAmelCase__ : Dict = reader.get_mention_assignments(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : str = reader.get_mention_assignments(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : Optional[Any] = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
F"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}""" )
logger.info(
'''Number of resulting singleton clusters in the key '''
F"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}""" )
if not keep_singletons:
logger.info(
F"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
'''files, respectively''' )
return doc_coref_infos
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
UpperCAmelCase__ : str = get_coref_infos(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : Optional[Any] = {}
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : Optional[int] = 0
for name, metric in metrics:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Dict = evaluator.evaluate_documents(lowerCAmelCase__ , lowerCAmelCase__ , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({F"""{name}/recall""": recall, F"""{name}/precision""": precision, F"""{name}/f1""": fa} )
logger.info(
name.ljust(10 ) , F"""Recall: {recall * 1_00:.2f}""" , F""" Precision: {precision * 1_00:.2f}""" , F""" F1: {fa * 1_00:.2f}""" , )
if conll_subparts_num == 3:
UpperCAmelCase__ : Any = (conll / 3) * 1_00
logger.info(F"""CoNLL score: {conll:.2f}""" )
output_scores.update({'''conll_score''': conll} )
return output_scores
def a__ ( lowerCAmelCase__ ) -> str:
UpperCAmelCase__ : int = False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
UpperCAmelCase__ : str = line.split()[5]
if not parse_col == "-":
UpperCAmelCase__ : Tuple = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase_ ( datasets.Metric ):
def lowercase_ ( self : Any ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Sequence(datasets.Value('''string''' ) ),
} ) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def lowercase_ ( self : Tuple , _A : Union[str, Any] , _A : Tuple , _A : Dict=True , _A : Optional[int]=False , _A : str=False , _A : List[str]=False ):
'''simple docstring'''
UpperCAmelCase__ : Any = [
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
UpperCAmelCase__ : int = util.check_gold_parse_annotation(_A )
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''' )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
UpperCAmelCase__ : List[str] = evaluate(
key_lines=_A , sys_lines=_A , metrics=_A , NP_only=_A , remove_nested=_A , keep_singletons=_A , min_span=_A , )
return score
| 181
| 0
|
'''simple docstring'''
import heapq
def __magic_name__ ( A ) -> set[int]:
snake_case = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase__ , [-1 * len(lowerCamelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
snake_case = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
snake_case = heapq.heappop(lowerCamelCase__ )[1][0]
chosen_vertices.add(lowerCamelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
snake_case = elem[1][1].index(lowerCamelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase_ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(f"Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}")
| 354
|
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowerCAmelCase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCamelCase :
snake_case_ = field(
default=__lowerCAmelCase , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(__lowerCAmelCase )} )
snake_case_ = field(
default=__lowerCAmelCase , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
snake_case_ = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
snake_case_ = field(
default=128 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
snake_case_ = field(
default=64 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
snake_case_ = field(
default=30 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
snake_case_ = field(
default=__lowerCAmelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
snake_case_ = field(
default=__lowerCAmelCase , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
snake_case_ = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
snake_case_ = field(
default=20 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
snake_case_ = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
snake_case_ = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = '''train'''
snake_case_ = '''dev'''
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
def __init__( self, lowercase_, lowercase_, lowercase_ = None, lowercase_ = Split.train, lowercase_ = False, lowercase_ = None, lowercase_ = "pt", ) -> int:
snake_case = args
snake_case = is_language_sensitive
snake_case = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(lowercase_, lowercase_ ):
try:
snake_case = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
snake_case = mode
# Load data features from cache or dataset file
snake_case = 'v2' if args.version_2_with_negative else 'v1'
snake_case = os.path.join(
cache_dir if cache_dir is not None else args.data_dir, F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''', )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case = cached_features_file + '.lock'
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not args.overwrite_cache:
snake_case = time.time()
snake_case = torch.load(lowercase_ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case = self.old_features['features']
snake_case = self.old_features.get('dataset', lowercase_ )
snake_case = self.old_features.get('examples', lowercase_ )
logger.info(
F'''Loading features from cached file {cached_features_file} [took %.3f s]''', time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
' future run' )
else:
if mode == Split.dev:
snake_case = self.processor.get_dev_examples(args.data_dir )
else:
snake_case = self.processor.get_train_examples(args.data_dir )
snake_case , snake_case = squad_convert_examples_to_features(
examples=self.examples, tokenizer=lowercase_, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=mode == Split.train, threads=args.threads, return_dataset=lowercase_, )
snake_case = time.time()
torch.save(
{'features': self.features, 'dataset': self.dataset, 'examples': self.examples}, lowercase_, )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self ) -> Tuple:
return len(self.features )
def __getitem__( self, lowercase_ ) -> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
snake_case = self.features[i]
snake_case = torch.tensor(feature.input_ids, dtype=torch.long )
snake_case = torch.tensor(feature.attention_mask, dtype=torch.long )
snake_case = torch.tensor(feature.token_type_ids, dtype=torch.long )
snake_case = torch.tensor(feature.cls_index, dtype=torch.long )
snake_case = torch.tensor(feature.p_mask, dtype=torch.float )
snake_case = torch.tensor(feature.is_impossible, dtype=torch.float )
snake_case = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'cls_index': cls_index, 'p_mask': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'is_impossible': is_impossible} )
if self.is_language_sensitive:
inputs.update({'langs': (torch.ones(input_ids.shape, dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case = torch.tensor(feature.start_position, dtype=torch.long )
snake_case = torch.tensor(feature.end_position, dtype=torch.long )
inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )
return inputs
| 332
| 0
|
import argparse
import os
import re
_lowerCAmelCase : Tuple = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowerCAmelCase : Optional[int] = re.compile(r"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
_lowerCAmelCase : Any = re.compile(r"\s*\(\s*\"(\S[^\"]+)\"")
def UpperCamelCase_( _snake_case : Optional[Any] , _snake_case : bool = False ):
"""simple docstring"""
with open(_snake_case , 'r' , encoding='utf-8' ) as f:
__a =f.read()
__a =content.split('\n' )
__a =[]
__a =0
while line_idx < len(_snake_case ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
__a =len(re.search(r'^(\s*)\S' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(' ' * indent + '(' ):
new_lines.append(lines[line_idx] )
line_idx += 1
__a =[]
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
__a =line_idx
while not lines[line_idx].startswith(' ' * indent + ')' ):
line_idx += 1
blocks.append('\n'.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
__a =sorted(_snake_case , key=lambda _snake_case : _re_identifier.search(_snake_case ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.write('\n'.join(_snake_case ) )
elif "\n".join(_snake_case ) != content:
return True
def UpperCamelCase_( _snake_case : bool = False ):
"""simple docstring"""
__a =[os.path.join(_snake_case , _snake_case ) for f in os.listdir(_snake_case ) if f.endswith('.py' )]
__a =[sort_auto_mapping(_snake_case , overwrite=_snake_case ) for fname in fnames]
if not overwrite and any(_snake_case ):
__a =[f for f, d in zip(_snake_case , _snake_case ) if d]
raise ValueError(
F'The following files have auto mappings that need sorting: {", ".join(_snake_case )}. Run `make style` to fix'
' this.' )
if __name__ == "__main__":
_lowerCAmelCase : str = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
_lowerCAmelCase : Union[str, Any] = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 218
|
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def UpperCamelCase_( _snake_case : int , _snake_case : int ):
"""simple docstring"""
__a =old_name
if "patch_embed" in old_name:
__a , __a , __a =old_name.split('.' )
if layer == "0":
__a =old_name.replace('0' , 'convolution1' )
elif layer == "1":
__a =old_name.replace('1' , 'batchnorm_before' )
elif layer == "3":
__a =old_name.replace('3' , 'convolution2' )
else:
__a =old_name.replace('4' , 'batchnorm_after' )
if "network" in old_name and re.search(r'\d\.\d' , _snake_case ):
__a =r'\b\d{2}\b'
if bool(re.search(_snake_case , _snake_case ) ):
__a =re.search(r'\d\.\d\d.' , _snake_case ).group()
else:
__a =re.search(r'\d\.\d.' , _snake_case ).group()
if int(match[0] ) < 6:
__a =old_name.replace(_snake_case , '' )
__a =trimmed_name.replace('network' , match[0] + '.meta4D_layers.blocks.' + match[2:-1] )
__a ='intermediate_stages.' + trimmed_name
else:
__a =old_name.replace(_snake_case , '' )
if int(match[2] ) < num_meta4D_last_stage:
__a =trimmed_name.replace('network' , 'meta4D_layers.blocks.' + match[2] )
else:
__a =str(int(match[2] ) - num_meta4D_last_stage )
__a =trimmed_name.replace('network' , 'meta3D_layers.blocks.' + layer_index )
if "norm1" in old_name:
__a =trimmed_name.replace('norm1' , 'layernorm1' )
elif "norm2" in old_name:
__a =trimmed_name.replace('norm2' , 'layernorm2' )
elif "fc1" in old_name:
__a =trimmed_name.replace('fc1' , 'linear_in' )
elif "fc2" in old_name:
__a =trimmed_name.replace('fc2' , 'linear_out' )
__a ='last_stage.' + trimmed_name
elif "network" in old_name and re.search(r'.\d.' , _snake_case ):
__a =old_name.replace('network' , 'intermediate_stages' )
if "fc" in new_name:
__a =new_name.replace('fc' , 'convolution' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
__a =new_name.replace('norm1' , 'batchnorm_before' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
__a =new_name.replace('norm2' , 'batchnorm_after' )
if "proj" in new_name:
__a =new_name.replace('proj' , 'projection' )
if "dist_head" in new_name:
__a =new_name.replace('dist_head' , 'distillation_classifier' )
elif "head" in new_name:
__a =new_name.replace('head' , 'classifier' )
elif "patch_embed" in new_name:
__a ='efficientformer.' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
__a =new_name.replace('norm' , 'layernorm' )
__a ='efficientformer.' + new_name
else:
__a ='efficientformer.encoder.' + new_name
return new_name
def UpperCamelCase_( _snake_case : List[str] , _snake_case : Dict ):
"""simple docstring"""
for key in checkpoint.copy().keys():
__a =checkpoint.pop(_snake_case )
__a =val
return checkpoint
def UpperCamelCase_( ):
"""simple docstring"""
__a ='http://images.cocodataset.org/val2017/000000039769.jpg'
__a =Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return image
def UpperCamelCase_( _snake_case : Path , _snake_case : Path , _snake_case : Path , _snake_case : bool ):
"""simple docstring"""
__a =torch.load(_snake_case , map_location='cpu' )['model']
__a =EfficientFormerConfig.from_json_file(_snake_case )
__a =EfficientFormerForImageClassificationWithTeacher(_snake_case )
__a ='_'.join(checkpoint_path.split('/' )[-1].split('.' )[0].split('_' )[:-1] )
__a =config.depths[-1] - config.num_metaad_blocks + 1
__a =convert_torch_checkpoint(_snake_case , _snake_case )
model.load_state_dict(_snake_case )
model.eval()
__a ={
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
# prepare image
__a =prepare_img()
__a =256
__a =224
__a =EfficientFormerImageProcessor(
size={'shortest_edge': image_size} , crop_size={'height': crop_size, 'width': crop_size} , resample=pillow_resamplings['bicubic'] , )
__a =processor(images=_snake_case , return_tensors='pt' ).pixel_values
# original processing pipeline
__a =Compose(
[
Resize(_snake_case , interpolation=pillow_resamplings['bicubic'] ),
CenterCrop(_snake_case ),
ToTensor(),
Normalize(_snake_case , _snake_case ),
] )
__a =image_transforms(_snake_case ).unsqueeze(0 )
assert torch.allclose(_snake_case , _snake_case )
__a =model(_snake_case )
__a =outputs.logits
__a =(1, 1000)
if "l1" in model_name:
__a =torch.Tensor(
[-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328] )
assert torch.allclose(logits[0, :10] , _snake_case , atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
__a =torch.Tensor(
[-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127] )
assert torch.allclose(logits[0, :10] , _snake_case , atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
__a =torch.Tensor(
[-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878] )
assert logits.shape == expected_shape
else:
raise ValueError(
F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7' )
# Save Checkpoints
Path(_snake_case ).mkdir(exist_ok=_snake_case )
model.save_pretrained(_snake_case )
print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )
processor.save_pretrained(_snake_case )
print(F'Processor successfuly saved at {pytorch_dump_path}' )
if push_to_hub:
print('Pushing model to the hub...' )
model.push_to_hub(
repo_id=F'Bearnardd/{pytorch_dump_path}' , commit_message='Add model' , use_temp_dir=_snake_case , )
processor.push_to_hub(
repo_id=F'Bearnardd/{pytorch_dump_path}' , commit_message='Add image processor' , use_temp_dir=_snake_case , )
if __name__ == "__main__":
_lowerCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path",
default=None,
type=str,
required=True,
help="Path to EfficientFormer pytorch checkpoint.",
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for EfficientFormer model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
parser.set_defaults(push_to_hub=True)
_lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 218
| 1
|
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 lowerCAmelCase__( __lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = None
__snake_case = BloomTokenizerFast
__snake_case = BloomTokenizerFast
__snake_case = True
__snake_case = False
__snake_case = 'tokenizer_file'
__snake_case = {'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}
def UpperCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_SCREAMING_SNAKE_CASE : Optional[int] = BloomTokenizerFast.from_pretrained("bigscience/tokenizer" )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self , **__lowerCamelCase ) -> Any:
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE : List[Any] = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE : Tuple = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
_SCREAMING_SNAKE_CASE : List[str] = [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]
_SCREAMING_SNAKE_CASE : Dict = tokenizer.batch_encode_plus(__lowerCamelCase )["input_ids"]
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase=6 ) -> Optional[int]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_SCREAMING_SNAKE_CASE : Dict = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
_SCREAMING_SNAKE_CASE : Tuple = "This is a simple input"
_SCREAMING_SNAKE_CASE : str = ["This is a simple input 1", "This is a simple input 2"]
_SCREAMING_SNAKE_CASE : Dict = ("This is a simple input", "This is a pair")
_SCREAMING_SNAKE_CASE : Dict = [
("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(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.encode_plus(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.batch_encode_plus(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.encode(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.batch_encode_plus(__lowerCamelCase , max_length=__lowerCamelCase )
except ValueError:
self.fail("Bloom Tokenizer should be able to deal with padding" )
_SCREAMING_SNAKE_CASE : List[str] = None # Hotfixing padding = None
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
def UpperCamelCase_ ( self ) -> Any:
_SCREAMING_SNAKE_CASE : Any = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset("xnli" , "all_languages" , split="test" , streaming=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = next(iter(__lowerCamelCase ) )["premise"] # pick up one data
_SCREAMING_SNAKE_CASE : Optional[Any] = list(sample_data.values() )
_SCREAMING_SNAKE_CASE : Optional[int] = list(map(tokenizer.encode , __lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : Any = [tokenizer.decode(__lowerCamelCase , clean_up_tokenization_spaces=__lowerCamelCase ) for x in output_tokens]
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def UpperCamelCase_ ( self ) -> Tuple:
# 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 )
| 364
|
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# ===== initialization =====
_SCREAMING_SNAKE_CASE : List[Any] = Mock()
_SCREAMING_SNAKE_CASE : Optional[Any] = conn, Mock()
_SCREAMING_SNAKE_CASE : Dict = iter([1, None] )
_SCREAMING_SNAKE_CASE : Optional[Any] = lambda __lowerCamelCase : next(__lowerCamelCase )
# ===== invoke =====
send_file(filename="mytext.txt", testing=__lowerCamelCase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 325
| 0
|
'''simple docstring'''
class A__ : # Public class to implement a graph
def __init__( self : List[Any] , _a : int , _a : int , _a : list[list[bool]] ) -> None:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =row
_SCREAMING_SNAKE_CASE =col
_SCREAMING_SNAKE_CASE =graph
def A ( self : List[str] , _a : int , _a : int , _a : list[list[bool]] ) -> bool:
'''simple docstring'''
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def A ( self : Dict , _a : int , _a : int , _a : list[list[bool]] ) -> None:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
_SCREAMING_SNAKE_CASE =[-1, 0, 1, -1, 1, -1, 0, 1]
_SCREAMING_SNAKE_CASE =True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , _a ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , _a )
def A ( self : str ) -> int: # And finally, count all islands.
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[[False for j in range(self.COL )] for i in range(self.ROW )]
_SCREAMING_SNAKE_CASE =0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(_a , _a , _a )
count += 1
return count
| 47
|
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowerCamelCase : List[Any] = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n year={2020},\n booktitle={Findings of EMNLP},\n}\n"
lowerCamelCase : Optional[Any] = "\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"
lowerCamelCase : int = "\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n predictions: list of predictions to score (as int64),\n except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n references: list of ground truth labels corresponding to the predictions (as int64),\n except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"precision\": Precision@10\nExamples:\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'precision@10': 1.0}\n\n"
def _lowerCAmelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : Tuple ) -> List[Any]:
"""simple docstring"""
return float((preds == labels).mean() )
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =simple_accuracy(_UpperCamelCase , _UpperCamelCase )
_SCREAMING_SNAKE_CASE =float(fa_score(y_true=_UpperCamelCase , y_pred=_UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def _lowerCAmelCase ( _UpperCamelCase : Any , _UpperCamelCase : int ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =np.array(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =np.array(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =en_sentvecs.shape[0]
# mean centering
_SCREAMING_SNAKE_CASE =en_sentvecs - np.mean(_UpperCamelCase , axis=0 )
_SCREAMING_SNAKE_CASE =in_sentvecs - np.mean(_UpperCamelCase , axis=0 )
_SCREAMING_SNAKE_CASE =cdist(_UpperCamelCase , _UpperCamelCase , 'cosine' )
_SCREAMING_SNAKE_CASE =np.array(range(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =sim.argsort(axis=1 )[:, :10]
_SCREAMING_SNAKE_CASE =np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__ ( datasets.Metric ):
def A ( self : Any ) -> List[str]:
'''simple docstring'''
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int64' )
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32' ) ),
'references': datasets.Value('int64' )
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32' ) ),
} ) , codebase_urls=[] , reference_urls=[] , format='numpy' if self.config_name != 'cvit-mkb-clsr' else None , )
def A ( self : List[str] , _a : Tuple , _a : Optional[int] ) -> int:
'''simple docstring'''
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(_a , _a )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(_a , _a )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(_a , _a )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]' )
| 47
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : str = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = '''visual_bert'''
def __init__( self , _UpperCAmelCase=3_0522 , _UpperCAmelCase=768 , _UpperCAmelCase=512 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=False , _UpperCAmelCase=True , _UpperCAmelCase=1 , _UpperCAmelCase=0 , _UpperCAmelCase=2 , **_UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase)
__A : str = vocab_size
__A : Union[str, Any] = max_position_embeddings
__A : Dict = hidden_size
__A : Optional[Any] = visual_embedding_dim
__A : int = num_hidden_layers
__A : str = num_attention_heads
__A : int = intermediate_size
__A : List[str] = hidden_act
__A : str = hidden_dropout_prob
__A : int = attention_probs_dropout_prob
__A : str = initializer_range
__A : List[str] = type_vocab_size
__A : Tuple = layer_norm_eps
__A : Union[str, Any] = bypass_transformer
__A : int = special_visual_initialize
| 190
|
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowercase__ : Dict = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
'''bicubic''': PIL.Image.Resampling.BICUBIC,
'''lanczos''': PIL.Image.Resampling.LANCZOS,
'''nearest''': PIL.Image.Resampling.NEAREST,
}
else:
lowercase__ : Any = {
'''linear''': PIL.Image.LINEAR,
'''bilinear''': PIL.Image.BILINEAR,
'''bicubic''': PIL.Image.BICUBIC,
'''lanczos''': PIL.Image.LANCZOS,
'''nearest''': PIL.Image.NEAREST,
}
def _lowerCAmelCase ( __snake_case : Any ) -> Optional[Any]:
__A : Dict = (images / 2 + 0.5).clamp(0 , 1 )
__A : str = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__A : Dict = numpy_to_pil(__snake_case )
return images
def _lowerCAmelCase ( __snake_case : List[Any] ) -> Optional[Any]:
if images.ndim == 3:
__A : List[Any] = images[None, ...]
__A : List[str] = (images * 2_55).round().astype('uint8' )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__A : str = [Image.fromarray(image.squeeze() , mode='L' ) for image in images]
else:
__A : str = [Image.fromarray(__snake_case ) for image in images]
return pil_images
| 190
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 205
|
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def UpperCAmelCase_ ( ):
lowercase = Github(os.environ['GITHUB_TOKEN'] )
lowercase = g.get_repo('huggingface/diffusers' )
lowercase = repo.get_issues(state='open' )
for issue in open_issues:
lowercase = sorted(issue.get_comments() , key=lambda __SCREAMING_SNAKE_CASE : i.created_at , reverse=__SCREAMING_SNAKE_CASE )
lowercase = comments[0] if len(__SCREAMING_SNAKE_CASE ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='closed' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='open' )
issue.remove_from_labels('stale' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
issue.add_to_labels('stale' )
if __name__ == "__main__":
main()
| 195
| 0
|
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = SMALL_MODEL_IDENTIFIER
_lowerCAmelCase : Union[str, Any] = "pt"
_lowerCAmelCase : List[Any] = "tf"
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Any = AutoModel.from_pretrained(self.test_model)
model_pt.save_pretrained(__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = TFAutoModel.from_pretrained(self.test_model, from_pt=__a)
model_tf.save_pretrained(__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = "mock_framework"
# Framework provided - return whatever the user provides
_lowerCAmelCase : List[str] = FeaturesManager.determine_framework(self.test_model, __a)
self.assertEqual(__a, __a)
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a)
_lowerCAmelCase : Union[str, Any] = FeaturesManager.determine_framework(__a, __a)
self.assertEqual(__a, __a)
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a)
_lowerCAmelCase : int = FeaturesManager.determine_framework(__a, __a)
self.assertEqual(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a)
_lowerCAmelCase : Tuple = FeaturesManager.determine_framework(__a)
self.assertEqual(__a, self.framework_pt)
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a)
_lowerCAmelCase : List[Any] = FeaturesManager.determine_framework(__a)
self.assertEqual(__a, self.framework_tf)
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a):
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = MagicMock(return_value=__a)
with patch("transformers.onnx.features.is_tf_available", __a):
_lowerCAmelCase : str = FeaturesManager.determine_framework(self.test_model)
self.assertEqual(__a, self.framework_pt)
# PyTorch not in environment -> use TensorFlow
_lowerCAmelCase : Tuple = MagicMock(return_value=__a)
with patch("transformers.onnx.features.is_torch_available", __a):
_lowerCAmelCase : int = FeaturesManager.determine_framework(self.test_model)
self.assertEqual(__a, self.framework_tf)
# Both in environment -> use PyTorch
_lowerCAmelCase : Union[str, Any] = MagicMock(return_value=__a)
_lowerCAmelCase : str = MagicMock(return_value=__a)
with patch("transformers.onnx.features.is_tf_available", __a), patch(
"transformers.onnx.features.is_torch_available", __a):
_lowerCAmelCase : str = FeaturesManager.determine_framework(self.test_model)
self.assertEqual(__a, self.framework_pt)
# Both not in environment -> raise error
_lowerCAmelCase : Optional[int] = MagicMock(return_value=__a)
_lowerCAmelCase : str = MagicMock(return_value=__a)
with patch("transformers.onnx.features.is_tf_available", __a), patch(
"transformers.onnx.features.is_torch_available", __a):
with self.assertRaises(__a):
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(self.test_model)
| 300
|
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
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.
_snake_case = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCamelCase__ = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
lowerCamelCase__ = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
lowerCamelCase__ = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = ZeroShotClassificationPipeline(
model=__a, tokenizer=__a, candidate_labels=["polics", "health"])
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = classifier("Who are you voting for in 2020?", candidate_labels="politics")
self.assertEqual(__a, {"sequence": ANY(__a), "labels": [ANY(__a)], "scores": [ANY(__a)]})
# No kwarg
_lowerCAmelCase : int = classifier("Who are you voting for in 2020?", ["politics"])
self.assertEqual(__a, {"sequence": ANY(__a), "labels": [ANY(__a)], "scores": [ANY(__a)]})
_lowerCAmelCase : Tuple = classifier("Who are you voting for in 2020?", candidate_labels=["politics"])
self.assertEqual(__a, {"sequence": ANY(__a), "labels": [ANY(__a)], "scores": [ANY(__a)]})
_lowerCAmelCase : List[Any] = classifier("Who are you voting for in 2020?", candidate_labels="politics, public health")
self.assertEqual(
__a, {"sequence": ANY(__a), "labels": [ANY(__a), ANY(__a)], "scores": [ANY(__a), ANY(__a)]})
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"])), 1.0)
_lowerCAmelCase : List[str] = classifier("Who are you voting for in 2020?", candidate_labels=["politics", "public health"])
self.assertEqual(
__a, {"sequence": ANY(__a), "labels": [ANY(__a), ANY(__a)], "scores": [ANY(__a), ANY(__a)]})
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"])), 1.0)
_lowerCAmelCase : List[Any] = classifier(
"Who are you voting for in 2020?", candidate_labels="politics", hypothesis_template="This text is about {}")
self.assertEqual(__a, {"sequence": ANY(__a), "labels": [ANY(__a)], "scores": [ANY(__a)]})
# https://github.com/huggingface/transformers/issues/13846
_lowerCAmelCase : Optional[int] = classifier(["I am happy"], ["positive", "negative"])
self.assertEqual(
__a, [
{"sequence": ANY(__a), "labels": [ANY(__a), ANY(__a)], "scores": [ANY(__a), ANY(__a)]}
for i in range(1)
], )
_lowerCAmelCase : Any = classifier(["I am happy", "I am sad"], ["positive", "negative"])
self.assertEqual(
__a, [
{"sequence": ANY(__a), "labels": [ANY(__a), ANY(__a)], "scores": [ANY(__a), ANY(__a)]}
for i in range(2)
], )
with self.assertRaises(__a):
classifier("", candidate_labels="politics")
with self.assertRaises(__a):
classifier(__a, candidate_labels="politics")
with self.assertRaises(__a):
classifier("Who are you voting for in 2020?", candidate_labels="")
with self.assertRaises(__a):
classifier("Who are you voting for in 2020?", candidate_labels=__a)
with self.assertRaises(__a):
classifier(
"Who are you voting for in 2020?", candidate_labels="politics", hypothesis_template="Not formatting template", )
with self.assertRaises(__a):
classifier(
"Who are you voting for in 2020?", candidate_labels="politics", hypothesis_template=__a, )
self.run_entailment_id(__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = zero_shot_classifier.model.config
_lowerCAmelCase : Optional[Any] = config.labelaid
_lowerCAmelCase : Union[str, Any] = zero_shot_classifier.entailment_id
_lowerCAmelCase : Any = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
self.assertEqual(zero_shot_classifier.entailment_id, -1)
_lowerCAmelCase : Optional[int] = {"entailment": 0, "neutral": 1, "contradiction": 2}
self.assertEqual(zero_shot_classifier.entailment_id, 0)
_lowerCAmelCase : Optional[int] = {"ENTAIL": 0, "NON-ENTAIL": 1}
self.assertEqual(zero_shot_classifier.entailment_id, 0)
_lowerCAmelCase : Optional[Any] = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
self.assertEqual(zero_shot_classifier.entailment_id, 2)
_lowerCAmelCase : List[str] = original_labelaid
self.assertEqual(__a, zero_shot_classifier.entailment_id)
@require_torch
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = pipeline(
"zero-shot-classification", model="sshleifer/tiny-distilbert-base-cased-distilled-squad", framework="pt", )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"Who are you voting for in 2020?" * 100, candidate_labels=["politics", "public health", "science"])
@require_torch
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = pipeline(
"zero-shot-classification", model="sshleifer/tiny-distilbert-base-cased-distilled-squad", framework="pt", )
_lowerCAmelCase : List[Any] = zero_shot_classifier(
"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"])
self.assertEqual(
nested_simplify(__a), {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.333, 0.333, 0.333],
}, )
@require_tf
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = pipeline(
"zero-shot-classification", model="sshleifer/tiny-distilbert-base-cased-distilled-squad", framework="tf", )
_lowerCAmelCase : Union[str, Any] = zero_shot_classifier(
"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"])
self.assertEqual(
nested_simplify(__a), {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.333, 0.333, 0.333],
}, )
@slow
@require_torch
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = pipeline("zero-shot-classification", model="roberta-large-mnli", framework="pt")
_lowerCAmelCase : Optional[Any] = zero_shot_classifier(
"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"])
self.assertEqual(
nested_simplify(__a), {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.976, 0.015, 0.009],
}, )
_lowerCAmelCase : Union[str, Any] = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data.", candidate_labels=["machine learning", "statistics", "translation", "vision"], multi_label=__a, )
self.assertEqual(
nested_simplify(__a), {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.817, 0.713, 0.018, 0.018],
}, )
@slow
@require_tf
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = pipeline("zero-shot-classification", model="roberta-large-mnli", framework="tf")
_lowerCAmelCase : Dict = zero_shot_classifier(
"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"])
self.assertEqual(
nested_simplify(__a), {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.976, 0.015, 0.009],
}, )
_lowerCAmelCase : str = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data.", candidate_labels=["machine learning", "statistics", "translation", "vision"], multi_label=__a, )
self.assertEqual(
nested_simplify(__a), {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.817, 0.713, 0.018, 0.018],
}, )
| 300
| 1
|
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__snake_case ="""\
@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}
}
"""
__snake_case ="""\
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.
"""
__snake_case ="""
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 a_ ( lowerCamelCase : str , lowerCamelCase : Union[str, Any] ):
return float((preds == labels).mean() )
def a_ ( lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict , lowerCamelCase : str="binary" ):
lowerCAmelCase = simple_accuracy(lowerCamelCase , lowerCamelCase )
lowerCAmelCase = float(fa_score(y_true=lowerCamelCase , y_pred=lowerCamelCase , average=lowerCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def a_ ( lowerCamelCase : List[Any] , lowerCamelCase : List[Any] ):
lowerCAmelCase = {}
for id_pred, label in zip(lowerCamelCase , lowerCamelCase ):
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(*lowerCamelCase )
lowerCAmelCase = fa_score(y_true=lowerCamelCase , y_pred=lowerCamelCase , average='macro' )
fas.append(lowerCamelCase )
lowerCAmelCase = int(sum(pred == label for pred, label in preds_labels ) == len(lowerCamelCase ) )
ems.append(lowerCamelCase )
lowerCAmelCase = float(sum(lowerCamelCase ) / len(lowerCamelCase ) )
lowerCAmelCase = sum(lowerCamelCase ) / len(lowerCamelCase )
lowerCAmelCase = float(fa_score(y_true=lowerCamelCase , 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 UpperCAmelCase_ ( datasets.Metric ):
def __UpperCAmelCase ( self : List[str] ) -> List[Any]:
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 __UpperCAmelCase ( self : Union[str, Any] ) -> str:
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 __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Optional[Any] ) -> Any:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase__ , UpperCAmelCase__ )}
elif self.config_name == "cb":
return acc_and_fa(UpperCAmelCase__ , UpperCAmelCase__ , 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(UpperCAmelCase__ , UpperCAmelCase__ )[0]
elif self.config_name == "multirc":
return evaluate_multirc(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(UpperCAmelCase__ , UpperCAmelCase__ )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
| 4
|
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
def a ( self : Dict , _lowercase : Union[str, Any] ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
__UpperCAmelCase = model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(_lowercase )
def a ( self : str ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : List[str] ):
__UpperCAmelCase = '''sgugger/tiny-distilbert-classification'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , only_pretrain_model=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : str ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , torchscript=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def a ( self : Optional[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , fpaa=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : int ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
# set architectures equal to `None`
__UpperCAmelCase = None
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : Tuple ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Can\'t do half precision''' )
def a ( self : Optional[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=_lowercase , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a ( self : Any ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : str ):
__UpperCAmelCase = '''sshleifer/tinier_bart'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a ( self : int ):
__UpperCAmelCase = '''sshleifer/tinier_bart'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a ( self : Optional[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , save_to_csv=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(_lowercase , '''inf_time.csv''' ) , train_memory_csv_file=os.path.join(_lowercase , '''train_mem.csv''' ) , inference_memory_csv_file=os.path.join(_lowercase , '''inf_mem.csv''' ) , train_time_csv_file=os.path.join(_lowercase , '''train_time.csv''' ) , env_info_csv_file=os.path.join(_lowercase , '''env.csv''' ) , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
benchmark.run()
self.assertTrue(Path(os.path.join(_lowercase , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''train_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''train_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''env.csv''' ) ).exists() )
def a ( self : List[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(_lowercase : str ):
self.assertTrue(hasattr(_lowercase , '''sequential''' ) )
self.assertTrue(hasattr(_lowercase , '''cumulative''' ) )
self.assertTrue(hasattr(_lowercase , '''current''' ) )
self.assertTrue(hasattr(_lowercase , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(_lowercase , '''log.txt''' ) , log_print=_lowercase , trace_memory_line_by_line=_lowercase , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(_lowercase , '''log.txt''' ) ).exists() )
| 332
| 0
|
def A_ ( A__ , A__ , A__ ) -> float:
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
raise Exception('Years to repay must be an integer > 0' )
# Yearly rate is divided by 12 to get monthly rate
a__ : str = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
a__ : List[Any] = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 225
|
def A_ ( A__ , A__ , A__ ) -> float:
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
raise Exception('Years to repay must be an integer > 0' )
# Yearly rate is divided by 12 to get monthly rate
a__ : str = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
a__ : List[Any] = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 225
| 1
|
"""simple docstring"""
from math import pi
def _A ( UpperCamelCase_ : int, UpperCamelCase_ : int) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 17
|
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class A__ ( enum.Enum ):
lowerCAmelCase__ : Dict = "all_checks"
lowerCAmelCase__ : List[Any] = "basic_checks"
lowerCAmelCase__ : Dict = "no_checks"
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[dict] , SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : Optional[Any]=None ) -> Optional[Any]:
if expected_checksums is None:
logger.info('Unable to verify checksums.' )
return
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
__lowercase = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
__lowercase = ' for ' + verification_name if verification_name is not None else ''
if len(SCREAMING_SNAKE_CASE ) > 0:
raise NonMatchingChecksumError(
F"""Checksums didn't match{for_verification_name}:\n"""
F"""{bad_urls}\n"""
'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error' )
logger.info('All the checksums matched successfully' + for_verification_name )
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[dict] , SCREAMING_SNAKE_CASE : dict ) -> Optional[int]:
if expected_splits is None:
logger.info('Unable to verify splits sizes.' )
return
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
__lowercase = [
{'expected': expected_splits[name], 'recorded': recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(SCREAMING_SNAKE_CASE ) > 0:
raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE ) )
logger.info('All the splits matched successfully.' )
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : bool = True ) -> dict:
if record_checksum:
__lowercase = shaaaa()
with open(SCREAMING_SNAKE_CASE , 'rb' ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , b'' ):
m.update(SCREAMING_SNAKE_CASE )
__lowercase = m.hexdigest()
else:
__lowercase = None
return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE ), "checksum": checksum}
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Dict:
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 325
| 0
|
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
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 TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase :
def __init__( self :Union[str, Any] , lowercase_ :Tuple , lowercase_ :str=13 , lowercase_ :Tuple=30 , lowercase_ :List[Any]=2 , lowercase_ :Any=3 , lowercase_ :int=True , lowercase_ :List[Any]=True , lowercase_ :int=32 , lowercase_ :int=2 , lowercase_ :int=4 , lowercase_ :List[str]=37 , lowercase_ :Optional[Any]="gelu" , lowercase_ :List[str]=0.1 , lowercase_ :Tuple=0.1 , lowercase_ :Tuple=10 , lowercase_ :Optional[Any]=0.0_2 , lowercase_ :List[str]=3 , lowercase_ :Optional[Any]=0.6 , lowercase_ :Union[str, Any]=None , )-> Optional[int]:
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__ = mask_ratio
A__ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
A__ = (image_size // patch_size) ** 2
A__ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def UpperCAmelCase_ ( self :str )-> List[str]:
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 UpperCAmelCase_ ( self :Optional[int] )-> int:
return ViTMAEConfig(
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 , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_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=_lowerCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def UpperCAmelCase_ ( self :Optional[Any] , lowercase_ :Optional[int] , lowercase_ :List[Any] , lowercase_ :Tuple )-> Any:
A__ = TFViTMAEModel(config=_lowerCamelCase )
A__ = model(_lowerCamelCase , training=_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self :Optional[Any] , lowercase_ :str , lowercase_ :Optional[int] , lowercase_ :Union[str, Any] )-> Optional[Any]:
A__ = TFViTMAEForPreTraining(_lowerCamelCase )
A__ = model(_lowerCamelCase , training=_lowerCamelCase )
# expected sequence length = num_patches
A__ = (self.image_size // self.patch_size) ** 2
A__ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
A__ = 1
A__ = TFViTMAEForPreTraining(_lowerCamelCase )
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(_lowerCamelCase , training=_lowerCamelCase )
A__ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def UpperCAmelCase_ ( self :int )-> Union[str, Any]:
A__ = self.prepare_config_and_inputs()
(A__) = config_and_inputs
A__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class UpperCAmelCase ( a__ , a__ , unittest.TestCase ):
__lowercase = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
__lowercase = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
__lowercase = False
__lowercase = False
__lowercase = False
__lowercase = False
def UpperCAmelCase_ ( self :Dict )-> Any:
A__ = TFViTMAEModelTester(self )
A__ = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self :Tuple )-> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def UpperCAmelCase_ ( self :Any )-> Any:
pass
def UpperCAmelCase_ ( self :Optional[Any] )-> Optional[int]:
A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(_lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , tf.keras.layers.Layer ) )
def UpperCAmelCase_ ( self :Any )-> Union[str, Any]:
A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(_lowerCamelCase )
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] , _lowerCamelCase )
def UpperCAmelCase_ ( self :str )-> int:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def UpperCAmelCase_ ( self :Any )-> List[Any]:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase )
def UpperCAmelCase_ ( self :Union[str, Any] )-> str:
np.random.seed(2 )
A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = int((config.image_size // config.patch_size) ** 2 )
A__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
A__ = model_class(_lowerCamelCase )
A__ = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A__ = model(_lowerCamelCase , noise=_lowerCamelCase )
A__ = copy.deepcopy(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A__ = model(**_lowerCamelCase , noise=_lowerCamelCase )
A__ = outputs_dict[0].numpy()
A__ = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1E-6 )
def UpperCAmelCase_ ( self :List[Any] )-> Optional[Any]:
np.random.seed(2 )
A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = int((config.image_size // config.patch_size) ** 2 )
A__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(lowercase_ :Optional[Any] ):
A__ = {}
for k, v in inputs_dict.items():
if tf.is_tensor(_lowerCamelCase ):
A__ = v.numpy()
else:
A__ = np.array(_lowerCamelCase )
return inputs_np_dict
for model_class in self.all_model_classes:
A__ = model_class(_lowerCamelCase )
A__ = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A__ = prepare_numpy_arrays(_lowerCamelCase )
A__ = model(_lowerCamelCase , noise=_lowerCamelCase )
A__ = model(**_lowerCamelCase , noise=_lowerCamelCase )
self.assert_outputs_same(_lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase_ ( self :List[str] , lowercase_ :Any , lowercase_ :List[str] , lowercase_ :Optional[Any] )-> Optional[int]:
np.random.seed(2 )
A__ = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
A__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
A__ = tf.constant(_lowerCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
A__ = tf_noise
super().check_pt_tf_models(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase_ ( self :Dict )-> Tuple:
np.random.seed(2 )
A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(_lowerCamelCase )
if module_member_name.endswith("MainLayer" )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("MainLayer" )] == model_class.__name__[: -len("Model" )]
for module_member in (getattr(_lowerCamelCase , _lowerCamelCase ),)
if isinstance(_lowerCamelCase , _lowerCamelCase )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(_lowerCamelCase , "_keras_serializable" , _lowerCamelCase )
}
A__ = int((config.image_size // config.patch_size) ** 2 )
A__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
A__ = tf.convert_to_tensor(_lowerCamelCase )
inputs_dict.update({"noise": noise} )
for main_layer_class in tf_main_layer_classes:
A__ = main_layer_class(_lowerCamelCase )
A__ = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
A__ = tf.keras.Model(_lowerCamelCase , outputs=main_layer(_lowerCamelCase ) )
A__ = model(_lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
A__ = os.path.join(_lowerCamelCase , "keras_model.h5" )
model.save(_lowerCamelCase )
A__ = tf.keras.models.load_model(
_lowerCamelCase , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(_lowerCamelCase , tf.keras.Model )
A__ = model(_lowerCamelCase )
self.assert_outputs_same(_lowerCamelCase , _lowerCamelCase )
@slow
def UpperCAmelCase_ ( self :str )-> Optional[int]:
np.random.seed(2 )
A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = int((config.image_size // config.patch_size) ** 2 )
A__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
A__ = model_class(_lowerCamelCase )
A__ = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A__ = model(_lowerCamelCase , noise=_lowerCamelCase )
if model_class.__name__ == "TFViTMAEModel":
A__ = outputs.last_hidden_state.numpy()
A__ = 0
else:
A__ = outputs.logits.numpy()
A__ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCamelCase , saved_model=_lowerCamelCase )
A__ = model_class.from_pretrained(_lowerCamelCase )
A__ = model(_lowerCamelCase , noise=_lowerCamelCase )
if model_class.__name__ == "TFViTMAEModel":
A__ = after_outputs['''last_hidden_state'''].numpy()
A__ = 0
else:
A__ = after_outputs['''logits'''].numpy()
A__ = 0
A__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCamelCase , 1E-5 )
def UpperCAmelCase_ ( self :Optional[Any] )-> str:
np.random.seed(2 )
A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = int((config.image_size // config.patch_size) ** 2 )
A__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
A__ = model_class(_lowerCamelCase )
A__ = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A__ = model(_lowerCamelCase , noise=_lowerCamelCase )
A__ = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(_lowerCamelCase )
A__ = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
A__ = model_class.from_config(model.config )
A__ = new_model(_lowerCamelCase ) # Build model
new_model.set_weights(model.get_weights() )
A__ = new_model(_lowerCamelCase , noise=_lowerCamelCase )
self.assert_outputs_same(_lowerCamelCase , _lowerCamelCase )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def UpperCAmelCase_ ( self :List[str] )-> str:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def UpperCAmelCase_ ( self :List[Any] )-> List[str]:
pass
@slow
def UpperCAmelCase_ ( self :Optional[Any] )-> List[str]:
A__ = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(_lowerCamelCase )
def UpperCamelCase ( ):
A__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
@cached_property
def UpperCAmelCase_ ( self :int )-> Any:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def UpperCAmelCase_ ( self :Optional[Any] )-> Dict:
np.random.seed(2 )
A__ = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=_lowerCamelCase , return_tensors="tf" )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
A__ = ViTMAEConfig()
A__ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
A__ = np.random.uniform(size=(1, num_patches) )
# forward pass
A__ = model(**_lowerCamelCase , noise=_lowerCamelCase )
# verify the logits
A__ = tf.convert_to_tensor([1, 1_96, 7_68] )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A__ = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , _lowerCamelCase , atol=1E-4 )
| 354
|
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class UpperCAmelCase :
def __init__( self :Optional[Any] , lowercase_ :int , lowercase_ :Union[str, Any]=13 , lowercase_ :Union[str, Any]=10 , lowercase_ :Any=3 , lowercase_ :Tuple=2 , lowercase_ :List[Any]=2 , lowercase_ :int=True , lowercase_ :int=True , lowercase_ :List[str]=32 , lowercase_ :Dict=5 , lowercase_ :List[Any]=4 , lowercase_ :List[Any]=37 , lowercase_ :List[Any]="gelu" , lowercase_ :int=0.1 , lowercase_ :List[Any]=0.1 , lowercase_ :List[Any]=10 , lowercase_ :int=0.0_2 , lowercase_ :Union[str, Any]="divided_space_time" , lowercase_ :Tuple=None , )-> Tuple:
A__ = parent
A__ = batch_size
A__ = image_size
A__ = num_channels
A__ = patch_size
A__ = num_frames
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__ = attention_type
A__ = initializer_range
A__ = scope
A__ = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
A__ = (image_size // patch_size) ** 2
A__ = (num_frames) * self.num_patches_per_frame + 1
def UpperCAmelCase_ ( self :str )-> str:
A__ = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.num_labels )
A__ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self :int )-> Any:
A__ = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
A__ = self.num_labels
return config
def UpperCAmelCase_ ( self :Optional[Any] , lowercase_ :List[str] , lowercase_ :List[Any] , lowercase_ :Tuple )-> Optional[int]:
A__ = TimesformerModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self :List[str] , lowercase_ :Tuple , lowercase_ :Tuple , lowercase_ :Dict )-> Tuple:
A__ = TimesformerForVideoClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_ )
# verify the logits shape
A__ = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , lowercase_ )
def UpperCAmelCase_ ( self :Optional[Any] )-> str:
A__ = self.prepare_config_and_inputs()
A__, A__, A__ = config_and_inputs
A__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
__lowercase = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
__lowercase = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
__lowercase = False
__lowercase = False
__lowercase = False
__lowercase = False
def UpperCAmelCase_ ( self :Union[str, Any] )-> Optional[int]:
A__ = TimesformerModelTester(self )
A__ = ConfigTester(
self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 )
def UpperCAmelCase_ ( self :Union[str, Any] , lowercase_ :int , lowercase_ :Dict , lowercase_ :int=False )-> str:
A__ = copy.deepcopy(lowercase_ )
if return_labels:
if model_class in get_values(lowercase_ ):
A__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def UpperCAmelCase_ ( self :Union[str, Any] )-> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="TimeSformer does not use inputs_embeds" )
def UpperCAmelCase_ ( self :List[Any] )-> Tuple:
pass
def UpperCAmelCase_ ( self :Dict )-> Optional[Any]:
A__, A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(lowercase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase_ , nn.Linear ) )
def UpperCAmelCase_ ( self :Union[str, Any] )-> Dict:
A__, A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(lowercase_ )
A__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowercase_ )
def UpperCAmelCase_ ( self :Optional[Any] )-> Optional[int]:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def UpperCAmelCase_ ( self :Dict )-> Optional[int]:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*lowercase_ )
@slow
def UpperCAmelCase_ ( self :Any )-> List[Any]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = TimesformerModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def UpperCAmelCase_ ( self :List[str] )-> str:
if not self.has_attentions:
pass
else:
A__, A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
A__ = self.model_tester.seq_length
A__ = self.model_tester.num_frames
A__ = True
A__ = False
A__ = True
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
A__ = outputs.attentions
self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
A__ = outputs.attentions
self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
A__ = len(lowercase_ )
# Check attention is always last and order is fine
A__ = True
A__ = True
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
self.assertEqual(out_len + 1 , len(lowercase_ ) )
A__ = outputs.attentions
self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def UpperCAmelCase_ ( self :List[Any] )-> List[str]:
def check_hidden_states_output(lowercase_ :Dict , lowercase_ :int , lowercase_ :List[Any] ):
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
A__ = outputs.hidden_states
A__ = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowercase_ ) , lowercase_ )
A__ = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [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(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def UpperCamelCase ( ):
A__ = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename="eating_spaghetti.npy" , repo_type="dataset" )
A__ = np.load(_lowerCamelCase )
return list(_lowerCamelCase )
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
@cached_property
def UpperCAmelCase_ ( self :Optional[Any] )-> int:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self :int )-> Any:
A__ = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400" ).to(
lowercase_ )
A__ = self.default_image_processor
A__ = prepare_video()
A__ = image_processor(video[:8] , return_tensors="pt" ).to(lowercase_ )
# forward pass
with torch.no_grad():
A__ = model(**lowercase_ )
# verify the logits
A__ = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , lowercase_ )
A__ = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1E-4 ) )
| 123
| 0
|
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowercase__ : List[Any] = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
'''bicubic''': PIL.Image.Resampling.BICUBIC,
'''lanczos''': PIL.Image.Resampling.LANCZOS,
'''nearest''': PIL.Image.Resampling.NEAREST,
}
else:
lowercase__ : int = {
'''linear''': PIL.Image.LINEAR,
'''bilinear''': PIL.Image.BILINEAR,
'''bicubic''': PIL.Image.BICUBIC,
'''lanczos''': PIL.Image.LANCZOS,
'''nearest''': PIL.Image.NEAREST,
}
def _lowerCAmelCase ( __snake_case : Union[str, Any] ) -> Union[str, Any]:
__A : List[Any] = (images / 2 + 0.5).clamp(0 , 1 )
__A : int = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__A : Any = numpy_to_pil(__snake_case )
return images
def _lowerCAmelCase ( __snake_case : Dict ) -> Tuple:
if images.ndim == 3:
__A : str = images[None, ...]
__A : Union[str, Any] = (images * 2_55).round().astype('uint8' )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__A : List[Any] = [Image.fromarray(image.squeeze() , mode='L' ) for image in images]
else:
__A : Optional[int] = [Image.fromarray(__snake_case ) for image in images]
return pil_images
| 190
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class SCREAMING_SNAKE_CASE (datasets.BuilderConfig ):
lowerCAmelCase = None
class SCREAMING_SNAKE_CASE (datasets.ArrowBasedBuilder ):
lowerCAmelCase = PandasConfig
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
__A : Dict = dl_manager.download_and_extract(self.config.data_files)
if isinstance(_UpperCAmelCase , (str, list, tuple)):
__A : Union[str, Any] = data_files
if isinstance(_UpperCAmelCase , _UpperCAmelCase):
__A : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__A : Optional[Any] = [dl_manager.iter_files(_UpperCAmelCase) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files})]
__A : Tuple = []
for split_name, files in data_files.items():
if isinstance(_UpperCAmelCase , _UpperCAmelCase):
__A : Dict = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__A : Optional[Any] = [dl_manager.iter_files(_UpperCAmelCase) for file in files]
splits.append(datasets.SplitGenerator(name=_UpperCAmelCase , gen_kwargs={'files': files}))
return splits
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
__A : List[str] = table_cast(_UpperCAmelCase , self.config.features.arrow_schema)
return pa_table
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
for i, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase)):
with open(_UpperCAmelCase , 'rb') as f:
__A : Optional[int] = pa.Table.from_pandas(pd.read_pickle(_UpperCAmelCase))
yield i, self._cast_table(_UpperCAmelCase)
| 190
| 1
|
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
SCREAMING_SNAKE_CASE : Optional[Any] = logging.getLogger(__name__)
def UpperCamelCase ( _a ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ :List[Any] = git.Repo(search_parent_directories=lowercase_ )
lowercase_ :int = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , '''git_log.json''' ) , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def UpperCamelCase ( _a ) -> List[Any]:
'''simple docstring'''
if params.n_gpu <= 0:
lowercase_ :Any = 0
lowercase_ :Tuple = -1
lowercase_ :Optional[int] = True
lowercase_ :Union[str, Any] = False
return
assert torch.cuda.is_available()
logger.info('''Initializing GPUs''' )
if params.n_gpu > 1:
assert params.local_rank != -1
lowercase_ :Any = int(os.environ['''WORLD_SIZE'''] )
lowercase_ :Dict = int(os.environ['''N_GPU_NODE'''] )
lowercase_ :Dict = int(os.environ['''RANK'''] )
# number of nodes / node ID
lowercase_ :Optional[Any] = params.world_size // params.n_gpu_per_node
lowercase_ :Union[str, Any] = params.global_rank // params.n_gpu_per_node
lowercase_ :Dict = True
assert params.n_nodes == int(os.environ['''N_NODES'''] )
assert params.node_id == int(os.environ['''NODE_RANK'''] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowercase_ :str = 1
lowercase_ :Optional[Any] = 0
lowercase_ :Union[str, Any] = 0
lowercase_ :Optional[Any] = 0
lowercase_ :Optional[int] = 1
lowercase_ :Dict = 1
lowercase_ :Optional[Any] = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowercase_ :List[str] = params.node_id == 0 and params.local_rank == 0
lowercase_ :Union[str, Any] = params.n_nodes > 1
# summary
lowercase_ :Tuple = f"--- Global rank: {params.global_rank} - "
logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes )
logger.info(PREFIX + '''Node ID : %i''' % params.node_id )
logger.info(PREFIX + '''Local rank : %i''' % params.local_rank )
logger.info(PREFIX + '''World size : %i''' % params.world_size )
logger.info(PREFIX + '''GPUs per node : %i''' % params.n_gpu_per_node )
logger.info(PREFIX + '''Master : %s''' % str(params.is_master ) )
logger.info(PREFIX + '''Multi-node : %s''' % str(params.multi_node ) )
logger.info(PREFIX + '''Multi-GPU : %s''' % str(params.multi_gpu ) )
logger.info(PREFIX + '''Hostname : %s''' % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info('''Initializing PyTorch distributed''' )
torch.distributed.init_process_group(
init_method='''env://''' , backend='''nccl''' , )
def UpperCamelCase ( _a ) -> Tuple:
'''simple docstring'''
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 357
|
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE : Any = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
},
"tokenizer_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
},
}
# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE : Tuple = {
"t5-small": 512,
"t5-base": 512,
"t5-large": 512,
"t5-3b": 512,
"t5-11b": 512,
}
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
lowercase : Tuple =VOCAB_FILES_NAMES
lowercase : Dict =PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Optional[Any] =["""input_ids""", """attention_mask"""]
lowercase : str =TaTokenizer
lowercase : List[int] =[]
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="</s>" , UpperCamelCase_="<unk>" , UpperCamelCase_="<pad>" , UpperCamelCase_=100 , UpperCamelCase_=None , **UpperCamelCase_ , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowercase_ :Union[str, Any] = [f"<extra_id_{i}>" for i in range(UpperCamelCase_ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
lowercase_ :Tuple = len(set(filter(lambda UpperCamelCase_ : bool('''extra_id_''' in str(UpperCamelCase_ ) ) , UpperCamelCase_ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , extra_ids=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
lowercase_ :Union[str, Any] = vocab_file
lowercase_ :Optional[int] = False if not self.vocab_file else True
lowercase_ :Dict = extra_ids
@staticmethod
def UpperCamelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
lowercase_ :Optional[int] = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f" {pretrained_model_name_or_path} automatically truncating your input to"
f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , UpperCamelCase_ , )
return max_model_length
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(UpperCamelCase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
lowercase_ :Dict = os.path.join(
UpperCamelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
copyfile(self.vocab_file , UpperCamelCase_ )
logger.info(f"Copy vocab file to {out_vocab_file}" )
return (out_vocab_file,)
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
lowercase_ :Any = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
lowercase_ :str = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
lowercase_ :Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCamelCase ( self ):
return list(
set(filter(lambda UpperCamelCase_ : bool(re.search(R'''<extra_id_\d+>''' , UpperCamelCase_ ) ) is not None , self.additional_special_tokens ) ) )
def UpperCamelCase ( self ):
return [self.convert_tokens_to_ids(UpperCamelCase_ ) for token in self.get_sentinel_tokens()]
| 252
| 0
|
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