code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceCl... | 61 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase__ ( lowercase , lowercase=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = None
if token ... | 62 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : list ):
if len(__lowerCamelCase ) <= 1:
return lst
__UpperCAmelCase : Optional[Any] = 1
while i < len(__lowerCamelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCAmelCase ,... | 63 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _a (... | 23 | 0 |
def A__ ( snake_case_ : int ):
return str(snake_case_ ) == str(snake_case_ )[::-1]
def A__ ( snake_case_ : int ):
return int(snake_case_ ) + int(str(snake_case_ )[::-1] )
def A__ ( snake_case_ : int = 10_000 ):
SCREAMING_SNAKE_CASE__: Dict... | 64 |
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 i... | 23 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __lowercase :
snake_case_ = 42
snake_case_ = ... | 65 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( __snake_case ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ):
warnings.war... | 66 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 0 |
import re
def SCREAMING_SNAKE_CASE__ ( snake_case__ :str ) -> str:
if len(re.findall('[ATCG]' , snake_case__ ) ) != len(snake_case__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__ma... | 67 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin"... | 68 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 0 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make... | 69 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 70 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 0 |
'''simple docstring'''
from math import sqrt
def a__ ( _SCREAMING_SNAKE_CASE : int = 1_00_00_00 ) -> int:
"""simple docstring"""
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int
while num_cub... | 71 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
T... | 72 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 0 |
# 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 ap... | 73 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 74 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 0 |
'''simple docstring'''
from math import loga
def a__ ( lowerCAmelCase__ ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError('''Input value must b... | 75 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 0 |
"""simple docstring"""
import math
def __UpperCAmelCase ( __UpperCamelCase = 1_00 ):
__lowercase : List[Any] = sum(i * i for i in range(1 , n + 1 ) )
__lowercase : Any = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return sq... | 76 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.d... | 23 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = ""
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCase... | 77 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : Dict ) -> Union[str, Any]:
'''simple docstring'''
stooge(snake_case_ , 0 , len(snake_case_ ) - 1 )
return arr
def lowerCAmelCase_ ( snake_case_ : Optional[i... | 78 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Any = {"""configuration_plbart""": ["""PLBART_PRETRAINED_... | 79 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 0 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes... | 80 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
while second != 0:
__snake_case : List[str] = first & second
first ^= second
__snake_case : Tuple = c << 1
return first
if __name__ == "__ma... | 81 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 0 |
"""simple docstring"""
import os
import sys
import unittest
lowerCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: ... | 82 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@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={arXi... | 23 | 0 |
"""simple docstring"""
lowerCAmelCase__ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''... | 83 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
UpperCAmelCase = TypeVar('''U''')
class A_ ( Generic[T, U] ):
'''simple docstring'''
def __init__( self , snake_case ... | 84 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 0 |
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 85 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _a (... | 23 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __snake_case ( __UpperCamelCase : Optional[int] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
A_ ... | 86 |
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 i... | 23 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase_ ( UpperCAmelCase_... | 87 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCAmelCase = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
UpperCAmelCase = None
def _snake_case ( ):
"""simple docs... | 88 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 0 |
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
fro... | 89 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
f... | 90 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : list[int] , snake_case__ : list[int] , snake_case__ : list[int] , snake_case__ : list[list[str]] , snake_case__ : int , ):
A = len(snake_case__ )
# If row is equal ... | 91 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ = {
"""configuratio... | 92 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 0 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNA... | 93 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : List[str] ,... | 94 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 0 |
"""simple docstring"""
import numpy as np
lowerCamelCase_ = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', ''... | 95 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : int = 1_0_0_0 ) -> int:
__magic_name__: int = 2**power
__magic_name__: Any = 0
while n:
__magic_name__, __magic_name__: int = r + n % 1_0, n // 1_... | 96 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 0 |
def a ( snake_case__: int = 1_000_000 ):
'''simple docstring'''
lowercase_ = 1
lowercase_ = 1
lowercase_ = {1: 1}
for inputa in range(2 , snake_case__ ):
lowercase_ = 0
lowercase_ = inputa
while True:
... | 97 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 0 |
'''simple docstring'''
def a__ ( lowercase : int, lowercase : int ) -> str:
"""simple docstring"""
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(... | 98 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.d... | 23 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase ( __A ):
"""simple docstring"""
_lowerCamelCase = ["""image_processor""", """tokenizer"""]
_lowerCamelCase = ... | 99 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : List[str] = logging.get_logger(__name__)
_A : List[Any] = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c... | 100 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
b... | 101 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
__magic_name__ : List[Any] = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/re... | 102 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_te... | 103 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(_... | 104 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@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={arXi... | 23 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Dict = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 105 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
fr... | 106 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : str = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
... | 107 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _a (... | 23 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ..... | 108 |
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 i... | 23 | 0 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
#
#... | 109 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 0 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase ( _snake_case ,_snake_case=None ):
UpperCAmelCase__ : int = None
if token is not None:
... | 110 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 0 |
'''simple docstring'''
def A_( A : Optional[int]):
UpperCamelCase = int(__lowercase)
if decimal in (0, 1): # Exit cases for the recursion
return str(__lowercase)
UpperCamelCase , UpperCamelCase = divmod(__lowercase , 2)
return binar... | 3 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] ) -> List[str]:
return math.sqrt(sum(pow(a - b , 2 ... | 69 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Optional[int] = logging.get_logger(__name__)
A__ : Optional... | 171 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 0 |
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 _A ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
@register_to_config
... | 36 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 0 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
UpperCamelCase_ : Optional[i... | 331 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 0 |
from torch import nn
def A_ ( lowercase_ ) -> List[str]:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(... | 326 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto... | 169 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
__a : Optional[Any] = False
while is_sorted is False: # Until all the indices are traversed keep looping
__a : Tuple = True
for i in range(0 , len(__lowerca... | 597 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xforme... | 197 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : Optional[Any] , __a : Optional[int] , __a : Tuple , __a : Union[str, Any] , __a : str , ... | 278 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.d... | 23 | 0 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
... | 435 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def A_( A : Any , A : Optional[int]=() , A : int=None , A... | 3 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 0 |
'''simple docstring'''
import torch
from torch import nn
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Any , a_ : Dict , a_ : List[str] , a_ : List[str] , a_ : Any , a_ : Dict=1 ... | 69 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Dict = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotA... | 171 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( __A : Union[str, Any] , __A : Optional[Any] , __A : List[str] ) ... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : Dict = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig""... | 331 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@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={arXi... | 23 | 0 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {"""vocab_file""": """spiece.model"""... | 326 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 0 |
"""simple docstring"""
import numpy as np
def lowercase (snake_case__ : int ) -> Tuple:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase (snake_case__ : int ) -> Optional[int]:
'''simple docstring'''
return vecto... | 169 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ ... | 597 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _a (... | 23 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __A ( _A ):
"""simple docstring"""
__a = SwinConfig(image_size=192 )
if "base" in model_name:
__a =... | 197 |
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 i... | 23 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Any = {
"""kssteven/ibert-r... | 278 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 435 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 0 |
'''simple docstring'''
def A_( A : List[Any] , A : int , A : Any , A : Dict , A : str):
if index == number_of_items:
return 0
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = knapsa... | 3 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLogg... | 69 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 0 |
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,
AutoModelForMultipleChoic... | 171 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 36 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowerCAmelCase ():
"""simple docstring"""
a__ = HfArgumentParser(__lowercase )
a__ = parser.parse_args_into_dataclasses(... | 331 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 0 |
lowerCAmelCase_ = """Input must be a string of 8 numbers plus letter"""
lowerCAmelCase_ = """TRWAGMYFPDXBNJZSQVHLCKE"""
def A_ ( lowercase_ ) -> Optional[Any]:
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = f'''Expected string ... | 326 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase = [1]
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 0, 0, 0
lowerCAmelCase = ugly_nums[ia] * 2
lowerCAmelC... | 169 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ = tuple[float, float, float]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__a : Union[str, Any] = e... | 597 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 0 |
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, DDPMSchedul... | 197 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 0 |
import unittest
from transformers import DonutProcessor
snake_case__ : Union[str, Any] = """naver-clova-ix/donut-base"""
class SCREAMING_SNAKE_CASE_ (unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self : Union[str, Any] ... | 278 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.d... | 23 | 0 |
'''simple docstring'''
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> Dict:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = len(__lowercase )
_UpperCamelCase : Tuple = [[False] * (required_sum + 1) for _ in ... | 435 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_... | 3 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import ... | 69 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __snake_case ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ( self : Optional[int... | 171 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__lowercase : Dict = TypeVar('''T''')
class _A ( Generic[T] ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = 4_2 # Cache store of ... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 0 |
'''simple docstring'''
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
a__ = [0] * len(__lowercase )
a__ = []
a__ = [1] * len(__lowercase )
for values in graph.values():
for i in values:
... | 331 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@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={arXi... | 23 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
"""... | 326 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def lowercase (snake_case__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
def decorator(snake_case__ : Tuple ):
lowerCAmelCase = getattr(__lower... | 169 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_... | 597 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _a (... | 23 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : Dict = {"""configuration_xlnet""": ["""XLNET_PRETRAI... | 197 |
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 i... | 23 | 0 |
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
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__ : List[str] ... | 278 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCAmelCase_ : Tuple = {
"""configuration_trocr""": ["""TR... | 435 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 0 |
'''simple docstring'''
import os
def A_( ):
UpperCamelCase = os.path.dirname(os.path.realpath(__lowercase))
UpperCamelCase = os.path.join(__lowercase , 'triangle.txt')
with open(__lowercase) as f:
UpperCamelCase = f.readlin... | 3 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : Any ) -> Tuple:
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return... | 69 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCamelCase( __UpperCamelCase : int = 8 ):
lowerCAmelCase_ : Dict = ascii_letters + digits + punctuation
return "".join(secrets.choice(_... | 171 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Co... | 36 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase_ : Optional[int] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""... | 331 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 0 |
from math import factorial
def A_ ( lowercase_ = 20 ) -> Union[str, Any]:
_snake_case : List[Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_snake_case : Dict = n // 2
return int(factori... | 326 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 0 |
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