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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
... | 99 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __A (_SCREAMING_SNAKE_CASE ) ->An... | 93 | 0 |
from __future__ import annotations
def snake_case (UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Any , UpperCamelCase : str ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowerCamelCase__ = (l + r) // ... | 165 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_rober... | 93 | 0 |
'''simple docstring'''
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
if not nums:
raise ValueError("""List is empty""" )
return sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
... | 585 |
"""simple docstring"""
from __future__ import annotations
import math
def __A (_SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even nu... | 93 | 0 |
def lowercase__ ( A_: Optional[int] ) -> str:
"""simple docstring"""
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("""\'float\' object cannot be interpreted as an integer""" )
if isinstance(_SCREAMING_SNAK... | 68 |
"""simple docstring"""
import re
def __A (_SCREAMING_SNAKE_CASE ) ->list:
"""simple docstring"""
return [char.split() for char in re.split(r'[^ a-z A-Z 0-9 \s]' , str_ )]
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Op... | 93 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :Tuple , lowerCAmelCase__ :int ) -> List[str]:
'''simple docstring'''
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if n == 0:
return 0
... | 359 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
__A = """"""
__A = """"""
__A = """"""
__A = """"""
def __A (_SCREAMING_SNAKE_CASE ) ->None:
"""simple docstring"""
lowerCAmelCase__ :Any ... | 93 | 0 |
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def __SCREAMING_SNAKE_CASE ( *lowercase_ ) -> Optional[Any]:
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE , '''r''' ) as fh:
fcntl.flock(_SCRE... | 462 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditi... | 93 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class __magi... | 271 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig... | 93 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniza... | 407 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@... | 93 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
snake_case : Any = [0] * len(_SCREAMING_SNAKE_CASE )
snake_case : str = []
snake_case : Dict = []
snake_case :... | 638 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 93 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any =logging.get_logger(__name__)
_lowercase : Tuple ={
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960... | 136 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :List[Any] = int(_SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ ... | 93 | 0 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
__SCREAM... | 627 |
"""simple docstring"""
# 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.... | 93 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/reso... | 99 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedT... | 93 | 0 |
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, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class lowercase (... | 165 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",... | 93 | 0 |
'''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 tra... | 585 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pas... | 93 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__A ... | 68 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__A = """sshleifer/bart-... | 93 | 0 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__lowerCAmelCase : Optional[int] =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"... | 359 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
__A = version.parse(vers... | 93 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
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 check_copies # noqa: E402
# This is the reference ... | 462 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDep... | 93 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.uti... | 271 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_SCREAMING_SNAKE_CASE ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 93 | 0 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class _snake_case :
def __init__( self):
UpperCAmelCase__ : Tuple = [2, 1, 2, -1]
UpperCAmelCase__ : Tuple = [1, 2, 3, 4]
def snake_... | 407 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcesso... | 93 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, 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,
res... | 638 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__A = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
"... | 93 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( _lowercase : Optional[Any]) -> List[str]:
"""simple docstring"""
a__ : Optional[int] = [
'encoder.ve... | 136 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils impor... | 93 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def a__ ( a__ , a__ , a__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if... | 627 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import ... | 93 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['... | 99 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __A (_SCREAMING_SNAKE_CASE ) ->An... | 93 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
snake_case_ = ["""flax""", """transformers"""]
def __init__( self : List[str] , *a_ : Optional[Any] , **a... | 165 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_rober... | 93 | 0 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = [0] * no_of_processes
_snake_case = [0] * no_of_processes
# Copy the burs... | 585 |
"""simple docstring"""
from __future__ import annotations
import math
def __A (_SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even nu... | 93 | 0 |
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_CLASSIFI... | 68 |
"""simple docstring"""
import re
def __A (_SCREAMING_SNAKE_CASE ) ->list:
"""simple docstring"""
return [char.split() for char in re.split(r'[^ a-z A-Z 0-9 \s]' , str_ )]
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Op... | 93 | 0 |
"""simple docstring"""
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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCAmel... | 359 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
__A = """"""
__A = """"""
__A = """"""
__A = """"""
def __A (_SCREAMING_SNAKE_CASE ) ->None:
"""simple docstring"""
lowerCAmelCase__ :Any ... | 93 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""vocab_file""": """vocab.txt""",
"""merges_f... | 462 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditi... | 93 | 0 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vis... | 271 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig... | 93 | 0 |
'''simple docstring'''
import os
def _UpperCamelCase ( ):
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + """/p022_names.txt""" ) as file:
UpperCAmelCase__ : Tuple = str(file.readlines()[0] )
UpperCAmelCase__ : Optional[Any] ... | 407 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@... | 93 | 0 |
'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoC... | 638 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 93 | 0 |
from __future__ import annotations
import time
_lowercase : Optional[Any] =list[tuple[int, int]]
_lowercase : List[str] =[
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, ... | 136 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :List[Any] = int(_SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ ... | 93 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decode... | 627 |
"""simple docstring"""
# 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.... | 93 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class ... | 99 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedT... | 93 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def snake_case (UpperCamelCase : List[str] ):
'''... | 165 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",... | 93 | 0 |
'''simple docstring'''
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
__lowerCAmelCase = 'facebook/wmt19-en-de'
__lowerCAmelCase = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase = FSMTConfig.from_pretr... | 585 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pas... | 93 | 0 |
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def lowercase__ ( A_: List[Any] , A_: Optional[int] , A_: Optional[Any]=0 ) -> Dict:
"""simple docstring"""
if name is No... | 68 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__A = """sshleifer/bart-... | 93 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : List[Any] ={"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch... | 359 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
__A = version.parse(vers... | 93 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=_UpperCamelCase ):
_lowerCAmelCase : List[Any] = ["""torch""", """scipy"""]
def __init__( self , *lowercase__ , **lowercase__):
requires_backends(self , ['''torch'''... | 462 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDep... | 93 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
UpperCAmelCase_ = collections.namedtuple('''_Datasets''', ['''train''', ''... | 271 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_SCREAMING_SNAKE_CASE ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 93 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 407 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcesso... | 93 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""... | 638 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__A = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
"... | 93 | 0 |
def lowerCAmelCase_ ( _lowercase : str) -> bool:
"""simple docstring"""
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6"""))
def lowerCAmelCase_ ( _lowercase : Dict) -> bool:
"""simple... | 136 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils impor... | 93 | 0 |
'''simple docstring'''
def a__ ( a__ ):
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
__SCREAMING_SNAKE_CASE = 0
while number:
... | 627 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import ... | 93 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot... | 99 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __A (_SCREAMING_SNAKE_CASE ) ->An... | 93 | 0 |
from PIL import Image
def snake_case (UpperCamelCase : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ = image.size
lowerCamelCase__ = 0
lowerCamelCase__ = image.load()
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_... | 165 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_rober... | 93 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__lowerCAmelCase = False
class _lowerCAmelCase ( unittest.TestCa... | 585 |
"""simple docstring"""
from __future__ import annotations
import math
def __A (_SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even nu... | 93 | 0 |
import os
import sys
import unittest
__A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noq... | 68 |
"""simple docstring"""
import re
def __A (_SCREAMING_SNAKE_CASE ) ->list:
"""simple docstring"""
return [char.split() for char in re.split(r'[^ a-z A-Z 0-9 \s]' , str_ )]
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Op... | 93 | 0 |
"""simple docstring"""
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,
FlaxKarrasDiffus... | 359 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
__A = """"""
__A = """"""
__A = """"""
__A = """"""
def __A (_SCREAMING_SNAKE_CASE ) ->None:
"""simple docstring"""
lowerCAmelCase__ :Any ... | 93 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( lowercase_ ... | 462 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditi... | 93 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCAmelCase_ = ... | 271 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig... | 93 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuratio... | 407 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@... | 93 | 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
lowercase__ = logging.get_logger(__name__)
lowe... | 638 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 93 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCAmelCas... | 136 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :List[Any] = int(_SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ ... | 93 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 627 |
"""simple docstring"""
# 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.... | 93 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import to... | 99 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedT... | 93 | 0 |
def snake_case (UpperCamelCase : Union[str, Any] = 1000 ):
'''simple docstring'''
lowerCamelCase__ = 3
lowerCamelCase__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__... | 165 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",... | 93 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 585 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pas... | 93 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import... | 68 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__A = """sshleifer/bart-... | 93 | 0 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def UpperCAmelCase__ ( lowerCAmelCase__ :str ) -> Any:
'''simple docstring'''
lowercase ... | 359 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
__A = version.parse(vers... | 93 | 0 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('--du... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 1 |
'''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 imp... | 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 1 |
'''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... | 94 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
... | 94 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tran... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
assert (
isinstance(__A , __A ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
r... | 94 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
fro... | 94 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 | 1 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
SCREAMING_SNAKE_CASE = HfApi()
SCREAMING_SNAKE_CASE = {}
# fmt: off
SCREAMING_SNAKE_CASE = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.... | 94 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
f... | 94 | 1 |
'''simple docstring'''
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 UpperCAmelCase_ ( _... | 94 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 94 | 1 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCR... | 94 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 94 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CA... | 94 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test impo... | 94 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ... | 94 | 1 |
'''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():
from PIL import Imag... | 94 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 94 | 1 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE = logging.get_logger(... | 94 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_avail... | 94 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''flax''']
def __init__( self : Dict , *UpperCAmelCase : List[Any] , ... | 94 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
SCREAMING_SNAKE_CASE = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no co... | 94 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 | 1 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
SCREAMING_S... | 94 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def A__ ( self : Optional[int] ) -> List[str]:
'''simple docstr... | 94 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedCo... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 )... | 94 | 1 |
'''simple docstring'''
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Co... | 94 |
'''simple docstring'''
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_c... | 94 | 1 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
SCREAMING_SNAKE_CASE =... | 94 |
'''simple docstring'''
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 UpperCAmelCase_ ( _... | 94 | 1 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE... | 94 |
'''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_av... | 94 | 1 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
SCREAMING_SNAKE_CASE = 'src/transformers'
SCREA... | 94 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
SCREAMING_SNAKE_CASE =... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : list ) -> list:
"""simple docstring"""
for i in range(len(__A ) - 1 , 0 , -1 ):
lowercase : List[str] =False
for j in range(__A , 0 , -1 ):
if unsorted[j] <... | 94 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class... | 94 | 1 |
'''simple docstring'''
import numpy
# List of input, output pairs
SCREAMING_SNAKE_CASE = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE = (((515, 22, 13), 555), ((61, 35, 49), 150))
SCREAMING_SNAKE_C... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : float , __A : float ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"""{price_plus_tax(100, 0.25) = }""")
print(f"""{price_plus_tax(125.50, 0... | 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 1 |
'''simple docstring'''
from math import pow, sqrt
def lowercase_ ( *__A : float ) -> bool:
"""simple docstring"""
lowercase : Dict =len(__A ) > 0 and all(value > 0.0 for value in values )
return result
def lowercase_ ( __A : ... | 94 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 | 1 |
'''simple docstring'''
import os
from collections.abc import Iterator
def lowercase_ ( __A : str = "." ) -> Iterator[str]:
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(__A ):
lowercase : List[Any] =[d for d in dir_names if d... | 94 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tran... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mod... | 94 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 | 1 |
'''simple docstring'''
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : List[str] ) -> None:
'''simple docstring'''
lowercase : dict[str, TrieNode] ={} # Mapping from char to TrieNode
lowercase : Dict =False
... | 94 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
SCREAMING_SNAKE_CASE = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa:... | 94 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
f... | 94 | 1 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE = 'scheduler_config.json'
class UpperCAmelCase_ ( __A ):
... | 94 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 94 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILIm... | 94 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 94 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 94 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict , UpperCAmelCase : int | None = None ) -> Union[str, Any]:
'''simple docstring'''
lowercase... | 94 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ... | 94 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart i... | 94 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 94 | 1 |
'''simple docstring'''
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 = {
'configuration_rembert': ['... | 94 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ... | 94 | 1 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowercase_ ( __A : str , __A : str = "cpu" , __A : Union[str, None] = None ) -> None:
"""simple docstring"""
lowercase : str ... | 94 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''flax''']
def __init__( self : Dict , *UpperCAmelCase : List[Any] , ... | 94 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 94 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
... | 94 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def A__ ( self : Optional[int] ) -> List[str]:
'''simple docstr... | 94 | 1 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
Character... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 )... | 94 | 1 |
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase_ :
"""simple docstring"""
UpperCamelCase_ = None
def A__ ( self : List[str] ) -> s... | 94 |
'''simple docstring'''
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_c... | 94 | 1 |
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