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 |
|---|---|---|---|---|
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tran... | 305 | 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 | 1 |
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_camembert impor... | 305 | 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 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFI... | 305 | 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 | 1 |
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"""
def __init__( self... | 305 | 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 | 1 |
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=" " )
... | 305 | 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 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""... | 305 | 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 | 1 |
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__ ):
"""s... | 305 | # 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 | 1 |
import torch
from torch import nn
class lowerCamelCase (nn.Module ):
"""simple docstring"""
def __init__( self : str , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple , __magic_name__ : int ... | 305 | 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 | 1 |
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:
if not is_tokenizers_av... | 305 | 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 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_pro... | 305 | 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 | 1 |
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... | 305 | 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 | 1 |
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... | 305 | 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 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
lowerCamelCase__ = '''timm_backbone'''... | 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 __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A : Optional[Any] = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A : Tuple = [ord(letter) for l... | 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 TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : int = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available():
raise OptionalDe... | 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 |
A : Optional[int] = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_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"""
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def a__ ( __UpperCamelCase , __Upp... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[Any] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 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 numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__snake_case )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
... | 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 os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
A : Optional[int] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD 6S 9D TH AD",
... | 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 itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transfo... | 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 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
SCREAMING_SNAKE_CASE_ = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE_ ... | 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 ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_ = str(abs(__lowerCamelCase ) )
SCREAMING_SNAKE_CASE_ ... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Union[str, Any] = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m... | 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"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusion... | 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 pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common ... | 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 torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# load base model
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 warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
A : Tuple = logging.get_logger(__name__)
class lowerCamelCase (__lowercase ):
"""simple docstring"""
def __init__( self ... | 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 TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Optional[int] = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependenc... | 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
from math import pow, sqrt
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
... | 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 gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelin... | 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
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_in... | 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 math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a__ ( __UpperCame... | 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 os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A : Optional[int] = logging.get_logger(__name__)
def a__ ( __UpperCamelCase , __UpperC... | 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"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, re... | 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 __future__ import annotations
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_failure_array(__lowerCamelCase )
# 2) Step through text searching for pattern
SCREAMING_SNAKE_CASE_ = 0,... | 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 TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Dict = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDe... | 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 |
def a__ ( __UpperCamelCase ):
if not isinstance(_A , _A ):
raise TypeError("Input value must be an \'int\' type" )
SCREAMING_SNAKE_CASE_ = 0
while number:
position += 1
number >>= 1
return position
if __... | 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 gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils... | 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 ):
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 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 TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : List[str] = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "Deb... | 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 os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distr... | 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
import sys
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = ""
try:
with open(_UpperCamelCase , "rb" ) as binary_file:
SCREAMING_SNAKE_CASE_ = binary_file.read()
for dat in data:
... | 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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : str = logging.get_logger(__name__)
A : List[Any] = {
"""YituTech/conv-bert-base""": """ht... | 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 pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def a__ ( __UpperCamelCase ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def a__ ( __UpperCamelCase ):
c... | 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 os
# Precomputes a list of the 100 first triangular numbers
A : Optional[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def a__ ( ):
SCREAMING_SNAKE_CASE_ = os.path.dirname(os.path.realpath(lowercase_ ) )
SC... | 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 unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from ... | 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 ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalD... | 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 json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Dict = {
"vocab_file": "voca... | 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 json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = None
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CA... | 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 Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 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 ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(__lowerCamelCase ) * abs(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=... | 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 |
A : Optional[int] = {str(digit): digit**5 for digit in range(10)}
def a__ ( __UpperCamelCase ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_snake_case ) )
def a__ ( ):
return sum(
number
for number in range(1_0_0_0 ... | 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 logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
A : Optional[Any] = logging.getLogger(__name__)
class lowerCamelCase :
"""simple docstring"""
def __init__(... | 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 typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A : List[str] = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
"tokenization... | 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"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_ut... | 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
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowerCamelCase (unitt... | 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 collections import namedtuple
import requests
from lxml import html # type: ignore
A : int = namedtuple("covid_data", "cases deaths recovered")
def a__ ( __UpperCamelCase = "https://www.worldometers.info/coronavirus/" ):
SCREAMING_SNAKE_CASE_ = "//div... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/res... | 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
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Any = {
"configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAnt... | 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 collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 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 ..utils import DummyObject, requires_backends
class lowerCamelCase (metaclass=snake_case_ ):
"""simple docstring"""
lowerCamelCase__ = ['''torch''']
def __init__( self : Dict , *__magic_name__ : Union[str, Any] , **__m... | 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 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
_validate_point(__UpperCamelCase )
_validate_point(__UpperCamelCase )
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError("Both points must be in the same n... | 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 ):
SCREAMING_SNAKE_CASE_ = int(A__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(A__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = divmod(A__ , 2 )
return binary_recu... | 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 typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''EncodecFeatureExtractor'''
lowerCamelCase__ = ('''T5Token... | 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 , __UpperCamelCase ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
SCREAMING_SNAKE_CASE_ = (boundary[1] - boundary[0]) / steps
SCREAMING_SNAKE_CASE_ = boundary[0]
SCREAMING_SNAKE_CASE_... | 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 copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase (__lowercase ):
"""simple docstring"""
lowerCamelCase__ = '''encoder-decoder'''
lowerCamelCase__ ... | 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"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
fr... | 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 operator as op
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = lambda __UpperCamelCase , __UpperCamelCase : int(x / y ) # noqa: E731 integer division operation
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 |
def a__ ( __UpperCamelCase = 1_0_0_0 ):
SCREAMING_SNAKE_CASE_ = 1, 1
SCREAMING_SNAKE_CASE_ = []
for i in range(1 , n + 1 ):
SCREAMING_SNAKE_CASE_ = prev_numerator + 2 * prev_denominator
SCREAMING_SNAKE_CAS... | 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 unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase (__snake_case , unittest.TestCase ):
"""simple docstring"""
... | 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 unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
F... | 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
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict , __magic_name__ : int = 0 ) -> str:
SCREAMING_SNAKE_CASE_ = key
def __A ( self : List[str] , __magi... | 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 inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import ... | 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 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if digit_amount > 0:
return round(number - int(a__ ) , a__ )
return number - int(a__ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.3_45, 1))
print(decimal... | 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 os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME,... | 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 collections import deque
from .hash_table import HashTable
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : int , *__magic_name__ : Any , **__magic_name__ : Any ) -> str:
super().__init__(*_SCREAMIN... | 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 collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def a__ ( ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 9, 1_4 # noqa: F841
SCREAMING_SNAKE_CASE_ = [
[... | 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 os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase (__SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""... | 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
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ):
SCREAMING_SNAKE_CASE_ = [randint(-1_0_0_0 , 1_0_0_0 ) for i in range(1_0 )]
SCREAMING_SNAKE_CASE_ = randint(-5_0_... | 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 os
import torch
from transformers.utils import WEIGHTS_NAME
A : Tuple = ['small', 'medium', 'large']
A : Union[str, Any] = 'lm_head.decoder.weight'
A : Union[str, Any] = 'lm_head.weight'
def a__ ( __UpperCam... | 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 itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
A : Optional[int] = datasets.utils.logging.get_logger(__name__)
@dataclass
c... | 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
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils impo... | 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 importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A : Union[str, Any] = 'src/transformers'
# This is to make sure the... | 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 os
import unicodedata
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 SPIECE_UNDERLINE, logging
A : Optional[int] = logging.get_logger(__nam... | 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 os
import sys
import unittest
A : int = 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, r... | 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 warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
A : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : Any , *__magic... | 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 copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, lo... | 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 pytest
A : Dict = "__dummy_dataset1__"
A : Union[str, Any] = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"v... | 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__ ( ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 1
while len(__lowerCAmelCase ) < 1E6:
constant.append(str(__lowerCAmelCase ) )
i += 1
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 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 lowerCamelCase (a__ ):
"""simple docstring"""
... | 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 Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : Union[str, Any] = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
... | 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 json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
A : Optional[Any] = logging.get_logger(__name__)... | 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 unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
fr... | 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_ = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE_ = get_edges(lowerCamelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) a... | 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 |
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str ) -> List[str]:
SCREAMING_SNAKE_CASE_ = ""
SCREAMING_SNAKE_CASE_ = ""
SCREAMING_SNAKE_CASE_ = []
def __A ( self : ... | 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 gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism... | 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 json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_token... | 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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.