code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def a__ ( __UpperCamelCase , __UpperCamelCase = "cpu" , __UpperCamelCase = None ):
SCREAMING_SNAKE_CASE_ = torch.load(__a , map_location=__a )
for k, v in ... | 350 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : Optional[int] = {
"face... | 351 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 0 |
def a__ ( __UpperCamelCase ):
if len(lowerCAmelCase__ ) <= 1:
return [tuple(lowerCAmelCase__ )]
SCREAMING_SNAKE_CASE_ = []
def generate(__UpperCamelCase , __UpperCamelCase ):
if k == 1:
res.append(tuple(arr[:] ) ... | 352 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 0 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers... | 353 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 0 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
A : Any = logging.getLogger()
def a__ ( ... | 354 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 0 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) + 1
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of inpu... | 355 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 0 |
"""simple docstring"""
import torch
from torch import nn
class lowerCamelCase (nn.Module ):
"""simple docstring"""
def __init__( self : Dict , __magic_name__ : Dict , __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_nam... | 356 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 0 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import... | 357 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 0 |
def a__ ( __UpperCamelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 358 | 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 AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 0 |
from collections.abc import Sequence
def a__ ( __UpperCamelCase = None ):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
SCREAMING_SNAKE_CASE_ = nums[0]
for i in range(1 , len(lowerCAmelCase__ ) ):
... | 359 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
A : List[Any] = logging.get_logger(__name... | 360 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowerCamelCase (unittest.TestCase , ... | 361 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 0 |
"""simple docstring"""
def a__ ( __UpperCamelCase ):
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE_ = False
for j in range(__snake_case , 0 , -1 ):
if unsorted[j] < unsorted[j ... | 362 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : Tuple = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
... | 363 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Dict = {
"configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_... | 364 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : st... | 365 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 0 |
def a__ ( __UpperCamelCase ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError("check_bouncy() accepts only integer arguments" )
SCREAMING_SNAKE_CASE_ = str(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ =... | 366 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 305 | 0 |
from __future__ import annotations
A : List[str] = 10
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = max(__UpperCamelCase )
while placement <= max_digit:
# declare and initialize emp... | 367 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 0 |
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = len(snake_case_ )
while cur > 1:
# Find the maximum number in arr
SCREAMING_SNAKE_CASE_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
SCREAMING_... | 368 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = [
'safety_checker... | 369 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 0 |
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = set({"(", "[", "{"} )
SCREAMING_SNAKE_CASE_ = set({")", "]", "}"} )
SCREAMING_SNAKE_CASE_ = {"{": "}", "[": "]", "(": ")"}
... | 370 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : O... | 305 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsMode... | 371 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = divmod(len(__UpperCamelCase ) ... | 350 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 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,
)
A : Optional[int] = {
"configuration_blenderbot_small": ... | 351 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBert... | 352 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 353 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A : Union[str, Any] ... | 354 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 0 |
from __future__ import annotations
import pandas as pd
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
# Copy the burst time in... | 355 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 0 |
"""simple docstring"""
import math
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCamelCase )
... | 356 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)... | 357 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 0 |
def a__ ( ):
return [
a * b * (1_0_0_0 - a - b)
for a in range(1 , 9_9_9 )
for b in range(__UpperCamelCase , 9_9_9 )
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"{solution() = }")
| 358 | 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 AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Union[str, Any] = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/conf... | 359 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 0 |
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,
CharacterTokenizer,
JumanppTokenizer,
... | 360 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
A : Dict = TypeVar("KT")
A : List[Any] = TypeVar("VT")
class lowerCamelCase (Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : List[A... | 361 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def ... | 362 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 0 |
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 363 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 0 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=1_0_2_4 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = ... | 364 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
A : List[... | 365 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 0 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):... | 366 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 305 | 0 |
from collections.abc import Callable
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , __magic_name__ : Callable | None = None ) -> None:
# Stores actual heap items.
SCREAMING_SNAKE_CASE_ = []
# Stores inde... | 367 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 0 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [1]
for i in range(2 , __UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
SCREAMING_SNAKE_CASE_ ... | 368 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : int , __magic_name__ : Union[str, Any] , __magic_name__ : List[str] ) ... | 369 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from... | 370 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : O... | 305 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Union[str, Any] = 1_00
A : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
... | 371 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 0 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
A : Any = {"UserAgent": UserAgent().random}
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = s... | 350 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 0 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn #... | 351 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
A : Any = logging.get_logger(__name__)
def a__ ( __UpperCamelCase ):
if isinstance(__UpperCamelCase , np.ndarray ):
return list(tensor.shape ... | 352 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntime... | 353 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 0 |
"""simple docstring"""
from __future__ import annotations
A : List[str] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class lowerCamelCase :
"""simple docstring"""
... | 354 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transform... | 355 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 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_common im... | 356 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 0 |
import math
def a__ ( __UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = int(math.sqrt(__UpperCamelCase ) ) # Size of every segment
SCREAMI... | 357 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : List[Any] = {
"roberta-base": "https://huggingface... | 358 | 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 AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 0 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , __magic_name__ : Optional[int] ) ->... | 359 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 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
A : Tuple = logging.get_logger(__name__)
A : List[str] = {
"go... | 360 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 0 |
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Return True if there is node that has not iterated.
SCREAMING_SNAKE_CASE_ = [False] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
que... | 361 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ... | 362 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 0 |
def a__ ( __UpperCamelCase ):
assert column_title.isupper()
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) - 1
SCREAMING_SNAKE_CASE_ = 0
while index >= 0:
SCREAMING_SNAKE_CASE_ ... | 363 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 0 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils i... | 364 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 0 |
"""simple docstring"""
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 lowerCamelCase (SCREA... | 365 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
lowerCamelCas... | 366 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 305 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from .... | 367 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_t... | 368 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Optional[An... | 369 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def a__ ( __UpperCamelCase ):
# vision encoder
if "img_encoder.pos_embed" in name:
SCREAMING_SNAKE_CASE_ = name.replace("img... | 370 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : O... | 305 | 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 required by applicabl... | 371 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Dict = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
... | 350 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 0 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 351 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSol... | 352 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 0 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
A : Dict = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "c... | 353 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : str = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
"XLMRo... | 354 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 0 |
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 a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CA... | 355 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 0 |
"""simple docstring"""
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... | 356 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 0 |
from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 357 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 0 |
A : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
... | 358 | 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 AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_tra... | 359 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
A : Any = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class se... | 360 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require... | 361 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 0 |
"""simple docstring"""
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = set({"(", "[", "{"} )
SCREAMING_SNAKE_CASE_ = set({")", "]", "}"} )
SCREAMING_SNAKE_CASE_ = {"{": "}",... | 362 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Un... | 363 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pyte... | 364 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 0 |
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ ... | 365 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 0 |
import cmath
import math
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = math.radians(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = math.radians(__UpperCamelCase )
... | 366 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 305 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : Optional[Any] = logging.get_logger(__name__)
A : int = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class ... | 367 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 0 |
from __future__ import annotations
import math
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if len(__UpperCamelCase ) != 2 or len(a[0] ) != 2 or len(__UpperCamelCase ) != 2 or len(b[0] ) != 2:
raise Exception("Matrices are not 2x2" )
SCREAMING_SNAKE_CASE_ ... | 368 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 0 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
fr... | 369 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 0 |
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("Input value must be an 'int' type" )
SCREAMING_SNAKE_CASE_ = 0
while number:
position += 1
number >>= 1
return po... | 370 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : O... | 305 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[Any] = logging.get_logger(__name__)
A : int = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
... | 371 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Dict = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pret... | 350 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_visio... | 351 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 0 |
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 352 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : ... | 353 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 0 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import versi... | 354 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenizati... | 355 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Tuple = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 356 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 357 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfi... | 358 | 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 AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 0 |
import requests
from bsa import BeautifulSoup
def a__ ( __UpperCamelCase = "AAPL" ):
SCREAMING_SNAKE_CASE_ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE_ = BeautifulSoup(requests.get(__UpperCamelCase ).text , "h... | 359 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 0 |
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = generate_pascal_triangle(__UpperCamelCase )
for row_idx in range(__UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
... | 360 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 361 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 0 |
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