code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import os
def _snake_case ( ) -> Tuple:
with open(os.path.dirname(lowercase ) + """/p022_names.txt""" ) as file:
__a : Tuple = str(file.readlines()[0] )
__a : List[str] = names.replace("""\"""" , """... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''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 SCREAMING_SNAKE_CASE__ ... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus impo... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
) | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(lowercase ) == 0:
raise ValueError("""Input list must be a non ... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tok... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__SCREAMING_SNAKE_CASE : Tuple = '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally ... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import ... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=__UpperCamelCase ):
lowercase__ = ["flax", "transformers"]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
'''simple docstring'''
import math
import sys
def _snake_case ( lowercase ) -> int:
if number != int(lowercase ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of input must not be a ne... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE__ :
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ... | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
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... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
class SCREAM... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers i... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
import math
import qiskit
def _snake_case ( lowercase = 1 , lowercase = 1 , lowercase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(lowercase , lowercase )
or isinstance(lowercase , lowercase )
or... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase = 1_0_0_0 ) -> int:
__a : str = 2**power
__a : List[Any] = 0
while n:
__a , __a : Any = r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> list:
__a : List[Any] = len(lowercase )
for i in range(1 , lowercase ):
__a : Dict = collection[i]
__a : Any = 0
__a : str =... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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_configura... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_incr... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowercase ) -> List[Any]:
#... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _snake_case ( lowercase , lowercase , lowercase ) -> Optional[int]:
__a : int = AutoConfig.from_pretrained(lowe... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowercase ) -> ... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__SCREAMING_SNAKE_CASE : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotA... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
from math import sqrt
def _snake_case ( lowercase ) -> bool:
assert isinstance(lowercase , lowercase ) and (
number >= 0
), "'number' must been an int and positive"
__a : Union[str, Any] = True
# 0 and 1 a... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as im... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
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
__SCREAMING_SNAKE_CASE : ... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimen... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_comm... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _snake_case ( lowercase , lowercase , lowercase = "x" , lowercase = 1_0**-1_0 , lowercase = 1 , ) -> complex:
__a : int = ... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> str:
return "".join(chr(ord(lowercase ) - 3_2 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod() | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__SCREAMING_SNAKE_CASE : Tuple = HUGGINGFACE_HUB_CACHE
__SCREAMING_SNAKE_CASE : Dict = 'config.json'
__SCREAMING_SNAKE_CASE : str = '... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodu... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
import math
def _snake_case ( lowercase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are no... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def _snake_case ( lowercase ) -> Tuple:
# This defines a "chinese character" as anything in the CJK Unico... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__SCREAMING_SNAKE_CASE : str = importlib.util.find_spec('s3fs... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> float:
__a : int = 0
while len(lowercase ) > 1:
__a : Tuple = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
__a : ... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[Any... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''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
__SCREAMING_SNAKE_CASE : int = logging.get_lo... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE : Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE : str = False
if NLTK_AVAILABLE:
with Fil... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__SCREAMING_SNAKE_CASE : Optional[Any] ... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Dict = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise Option... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
__SCREAMING_SNAKE_CASE : Optional[Any] = yaml.safe_load(
'\\nname: ""\nal... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMSchedul... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
'''simple docstring'''
import sys
__SCREAMING_SNAKE_CASE : int = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> list:
__a : str = len(lowercase )
for _ in range(lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__a , __a : ... | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _snake_case ( lowercase ) -> Dict:
for i in range(0 , lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__SCREAMING_SNAKE_CASE : List[Any] = 'naver-clova-ix/donut-base'
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase , lowercase ) -> float:
def get_matched_characters(lowercase , lowercase ) -> str:
__a : Dict = []
__a : Tuple = min(len(_stra ) , len(_stra ) ) // 2
for... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fr... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def ... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
lowercase__ = ["image_processor", "tokenizer"]
lowercase__ = "AutoImageProcessor"... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase , lowercase ) -> list:
__a : Dict = len(lowercase )
__a : Optional[int] = []
for i in range(len(lowercase ) - pat_len + 1 ):
__a : Tuple = True
for ... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.it... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def _snake_case ( lowercase , lowercase = 0.0 , lowercase = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
@staticmethod
@abstractmethod
def __lowerCamelCase ( __UpperCamelCase ):
'''simple docstring'''
... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normaliz... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class SCREAMING_SNAKE_CASE__ :
def __init__( self , __UpperCamelCase = None ):
'''simple docstring'''
... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_token... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self ):
'''simple docstring'''
__a : List[str] = 0
__a : Tuple = 0
__a : List[str] = {}
def __lowerCamelCase ( self ... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ) -> List[Any]:
with offline(OfflineSimu... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any = {
... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_comman... | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE__ :
def __init__( self , __UpperCamelCase ):
'''simple docstring'''
__a : str = value
__a : No... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_i... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation i... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> list:
if len(lowercase ) < 2:
return collection
def circle_sort_util(lowercase , lowercase , lowercase ) -> bool:
__a : Optional[int] = False
if low == high:
... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__SCREAMING_SNAKE_CASE : ... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_input... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase , lowercase ) -> bool:
__a : int = len(lowercase ) + 1
__a : Any = len(lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Tuple = {
'... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Dict = {
'google/canine-s': 'https://huggingface.co/google/can... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def _snake_case ( lowercase , lowercase , lowercase ) -> list[int]:
__a : List[Any] = [0] * no_of_processes
__a : Dict = [0] * no_of_processe... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
import os
from math import logaa
def _snake_case ( lowercase = "base_exp.txt" ) -> int:
__a : float = 0
__a : Optional[int] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase ) , lower... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__S... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokeniza... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFI... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CAC... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_mo... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
fr... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = 42
lowercase__ = Non... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
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