code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 |
"""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
A_ = logging.get_logger... | 64 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available... | 64 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tok... | 64 | 1 |
"""simple docstring"""
# 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/... | 64 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import ded... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : float , snake_case__ : float ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def UpperCAmelCase__ (snake_case__ ... | 64 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if ... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
_snake_case : list[list[int]] = []
_snake_case : list[int] = []
_sn... | 64 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaV... | 64 | 1 |
"""simple docstring"""
A_ = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cook... | 64 |
"""simple docstring"""
import argparse
import os
# New Code #
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... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 1_00 ):
"""simple docstring"""
_snake_case : List[Any] = set()
_snake_case : Any = 0
_snake_case : List[str] = n + 1 # maximum limit
for a in rang... | 64 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCAmelCase__ (snake_case__ : Optional[int] , snake_case__ : Any=7 ):
"""simple docstring"""
_snake_case... | 64 | 1 |
"""simple docstring"""
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 Bat... | 64 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 | 1 |
"""simple docstring"""
import warnings
from typing import Any, Dict, 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_ext... | 64 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__... | 64 | 1 |
"""simple docstring"""
import cmath
import math
def UpperCAmelCase__ (snake_case__ : float , snake_case__ : float , snake_case__ : float , snake_case__ : float ):
"""simple docstring"""
_snake_case : Dict = math.r... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[float] ):
"""simple docstring"""
_snake_case : int = 0.00
_snake_case : int = 0
for resistor in resistors:
if resistor <= 0:
... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(snake_case__ , snake_case__ ):
raise TypeError("""Input value must be a... | 64 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 64 | 1 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers... | 64 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 64 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control th... | 64 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_S... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 10_00 ):
"""simple docstring"""
_snake_case : Tuple = -1
_snake_case : Optional[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2... | 64 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[Any] ... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/confi... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Union[str, Any] ):
"""simple docstring"""
stooge(snake_case__ , 0 , len(snake_case__ ) - 1 )
return arr
def UpperCAmelCase__ (snake_case__ : List[Any] , snake_case__ ... | 64 | 1 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A_ = '''src/transformers'''
A_ ... | 64 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase( metaclass=__a ):
'''simple docstring'''
lowercase__ = ["note_seq"]
def __init__( self: Dict, *a_: Union[str, Any], **a_: List[str] )... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 4_00_00_00 ):
"""simple docstring"""
_snake_case : Dict = [0, 1]
_snake_case : int = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 64 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowercase:
'''simple docstring'''
def __init__( self: List[Any], a_: List[str] ):
'''simple docstring'''
... | 64 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
... | 64 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control th... | 64 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_S... | 64 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
A_ = TypeVar('''T''')
A_ = Union[List[T], Tuple[T, ...]]
A_ = Union[T, List[T], Dict[str, T]]
A_ = Union[str, bytes, os.PathLike]
| 64 | 1 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class ... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
_snake_case : List[Any] = []
def generate(snake_case__ : int , snake_case__... | 64 | 1 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class lowercase:
'''simple docstring'''
def __init__( self: Dict, a_: Dict, a_: Any, a_: int, a_: Optional[Any]=None, a_: int=None ):
... | 64 |
"""simple docstring"""
from math import factorial
A_ = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def UpperCAmelCas... | 64 | 1 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCAmelCase__ (snake_case__ : Optional[int] , snake_case__ : Any=7 ):
"""simple docstring"""
_snake_case... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if len(snake_case__ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case ... | 64 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
A_ = TypeVar('''T''')
A_ = Union[List[T], Tuple[T, ...]]
A_ = Union[T, List[T], Dict[str, T]]
A_ = Union[str, bytes, os.PathLike]
| 64 |
"""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
A_ = logging.get_logger... | 64 | 1 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrain... | 64 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tok... | 64 | 1 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_... | 64 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import ded... | 64 | 1 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTIO... | 64 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if ... | 64 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except O... | 64 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaV... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 10_00 ):
"""simple docstring"""
return sum(e for e in range(3 , snake_case__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 64 |
"""simple docstring"""
import argparse
import os
# New Code #
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... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''token... | 64 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCAmelCase__ (snake_case__ : Optional[int] , snake_case__ : Any=7 ):
"""simple docstring"""
_snake_case... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : Union[str, Any] = [randint(-10_00 , 10_00 ) for... | 64 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def UpperCAmelCase__ (snake_case__ : np.ndarray ):
"""simple docstring"""
_snake_case , _snake_case : str = np.shape(snake_case__ )
if rows != columns:
_snak... | 64 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__... | 64 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from tran... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[float] ):
"""simple docstring"""
_snake_case : int = 0.00
_snake_case : int = 0
for resistor in resistors:
if resistor <= 0:
... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[float] ):
"""simple docstring"""
_snake_case : int = 0.00
_snake_case : int = 0
for resistor in resistors:
if resistor <= 0:
... | 64 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 64 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 64 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
fro... | 64 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_S... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
_snake_case : List[str] = 0
_snake_case : Optional[int] = len(snake_ca... | 64 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[Any] ... | 64 | 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
A_ = logging.get_logger(__name__)
A_ = ... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Union[str, Any] ):
"""simple docstring"""
stooge(snake_case__ , 0 , len(snake_case__ ) - 1 )
return arr
def UpperCAmelCase__ (snake_case__ : List[Any] , snake_case__ ... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[Any] ... | 64 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase( metaclass=__a ):
'''simple docstring'''
lowercase__ = ["note_seq"]
def __init__( self: Dict, *a_: Union[str, Any], **a_: List[str] )... | 64 | 1 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
fro... | 64 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowercase:
'''simple docstring'''
def __init__( self: List[Any], a_: List[str] ):
'''simple docstring'''
... | 64 | 1 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
... | 64 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control th... | 64 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
... | 64 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
A_ = TypeVar('''T''')
A_ = Union[List[T], Tuple[T, ...]]
A_ = Union[T, List[T], Dict[str, T]]
A_ = Union[str, bytes, os.PathLike]
| 64 | 1 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase( __a ):
'''simple docstring'''
lowercase__ = (DDIMParallelScheduler,)
lowercase__ = ... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
_snake_case : List[Any] = []
def generate(snake_case__ : int , snake_case__... | 64 | 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
A_ = logging.get_logger(__name__)
A_ = {
... | 64 |
"""simple docstring"""
from math import factorial
A_ = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def UpperCAmelCas... | 64 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if len(snake_case__ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case ... | 64 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | 64 |
"""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
A_ = logging.get_logger... | 64 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gp... | 64 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tok... | 64 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_... | 64 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import ded... | 64 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diff... | 64 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if ... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if len(snake_case__ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case ... | 64 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaV... | 64 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowercase( ... | 64 |
"""simple docstring"""
import argparse
import os
# New Code #
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... | 64 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasonin... | 64 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCAmelCase__ (snake_case__ : Optional[int] , snake_case__ : Any=7 ):
"""simple docstring"""
_snake_case... | 64 | 1 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelFo... | 64 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 | 1 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
A_ = '''scheduler_config.json'''
class lowercase( __a ):
... | 64 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__... | 64 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...tes... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[float] ):
"""simple docstring"""
_snake_case : int = 0.00
_snake_case : int = 0
for resistor in resistors:
if resistor <= 0:
... | 64 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
... | 64 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
_snake_case : List[Any] = []
def generate(snake_case__ : int , snake_case__... | 64 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerCo... | 64 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_S... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visual... | 64 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[Any] ... | 64 | 1 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common im... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Union[str, Any] ):
"""simple docstring"""
stooge(snake_case__ , 0 , len(snake_case__ ) - 1 )
return arr
def UpperCAmelCase__ (snake_case__ : List[Any] , snake_case__ ... | 64 | 1 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepende... | 64 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase( metaclass=__a ):
'''simple docstring'''
lowercase__ = ["note_seq"]
def __init__( self: Dict, *a_: Union[str, Any], **a_: List[str] )... | 64 | 1 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from ... | 64 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowercase:
'''simple docstring'''
def __init__( self: List[Any], a_: List[str] ):
'''simple docstring'''
... | 64 | 1 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
A_ = logging.get_logger(__name__)
class lowercase:
'''simple docstring'''
lowercase__ ... | 64 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control th... | 64 | 1 |
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowercase:
'''simple docstring'''
def UpperCamelCase_ ( self: Tuple, a_: Optiona... | 64 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
A_ = TypeVar('''T''')
A_ = Union[List[T], Tuple[T, ...]]
A_ = Union[T, List[T], Dict[str, T]]
A_ = Union[str, bytes, os.PathLike]
| 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : list[int] ):
"""simple docstring"""
if not len(snake_case__ ) == len(snake_case__ ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equatio... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
_snake_case : List[Any] = []
def generate(snake_case__ : int , snake_case__... | 64 | 1 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def UpperCAmelCase__ (snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : int , snake_case__ : List[str]=None ):
"""simple docstring"""
_snake_ca... | 64 |
"""simple docstring"""
from math import factorial
A_ = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def UpperCAmelCas... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {'''configura... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if len(snake_case__ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case ... | 64 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A_ = 50_00_00
A_ , A_ = os.path.split(__file__)
A_ = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS... | 64 |
"""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
A_ = logging.get_logger... | 64 | 1 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
A_ = logging.get_logger(__name__)
class lowercase( __a ):
'''simple docstring'''
def __init__( self: Optional[int], a_: int=None, ... | 64 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tok... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : list[str] ):
"""simple docstring"""
_snake_case : int = """"""
for word_or_phrase in separated:
if not isinstance(snake_case__ , snake_case__ ):
... | 64 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import ded... | 64 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForP... | 64 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if ... | 64 | 1 |
"""simple docstring"""
from string import ascii_lowercase, ascii_uppercase
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
if not sentence:
return ""
_snake_case : str = dict(zip(snake_case__ , snake_case__ ) )
ret... | 64 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaV... | 64 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import j... | 64 |
"""simple docstring"""
import argparse
import os
# New Code #
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... | 64 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 64 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCAmelCase__ (snake_case__ : Optional[int] , snake_case__ : Any=7 ):
"""simple docstring"""
_snake_case... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
... | 64 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str , snake_case__ : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = Path(snake_case__ )
... | 64 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__... | 64 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational impo... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[float] ):
"""simple docstring"""
_snake_case : int = 0.00
_snake_case : int = 0
for resistor in resistors:
if resistor <= 0:
... | 64 | 1 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
A_ = ''''''
A_ = ''''''
A_ = ''''''
A_ = ''''''
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
_snake_case : Tuple = ... | 64 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONF... | 64 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 64 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class lowercase( __a ):
'''simple docstring'''
lowercase_... | 64 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_S... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
_snake_case : Tuple = F"Input value of [number={number}] must be an integer"
raise TypeError(snake_case... | 64 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[Any] ... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
fro... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Union[str, Any] ):
"""simple docstring"""
stooge(snake_case__ , 0 , len(snake_case__ ) - 1 )
return arr
def UpperCAmelCase__ (snake_case__ : List[Any] , snake_case__ ... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[list] ):
"""simple docstring"""
_snake_case : Tuple = current_set.copy()
for row_index, row in enumerate(snake_case__ ):
_snake_case : Tuple = row[0]
for column_i... | 64 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase( metaclass=__a ):
'''simple docstring'''
lowercase__ = ["note_seq"]
def __init__( self: Dict, *a_: Union[str, Any], **a_: List[str] )... | 64 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def Uppe... | 64 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowercase:
'''simple docstring'''
def __init__( self: List[Any], a_: List[str] ):
'''simple docstring'''
... | 64 | 1 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase( nn.Module ):
'''simple docstring'''
def __init__( self: Any, a_: int = 16, ... | 64 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control th... | 64 | 1 |
"""simple docstring"""
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... | 64 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
A_ = TypeVar('''T''')
A_ = Union[List[T], Tuple[T, ...]]
A_ = Union[T, List[T], Dict[str, T]]
A_ = Union[str, bytes, os.PathLike]
| 64 | 1 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A_ = [ord(letter) for letter in string.ascii_lowercas... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
_snake_case : List[Any] = []
def generate(snake_case__ : int , snake_case__... | 64 | 1 |
"""simple docstring"""
import baseaa
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
return baseaa.baaencode(string.encode("""utf-8""" ) )
def UpperCAmelCase__ (snake_case__ : bytes ):
"""simple docstring"""
... | 64 |
"""simple docstring"""
from math import factorial
A_ = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def UpperCAmelCas... | 64 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lice... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if len(snake_case__ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case ... | 64 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerCon... | 64 |
"""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
A_ = logging.get_logger... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A_ = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
... | 64 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tok... | 64 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ =... | 64 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import ded... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at ... | 64 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if ... | 64 | 1 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowercase:
'''simple docstring'''
def __init__( self: Union[str, Any], a_: Optional[Any], a_: int, a_: int ):
'''sim... | 64 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaV... | 64 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Dict = len(snake_case__ )
_snake_case : Union[str, Any] = len(snake_case__ )
_snake_case ... | 64 |
"""simple docstring"""
import argparse
import os
# New Code #
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... | 64 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | 64 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCAmelCase__ (snake_case__ : Optional[int] , snake_case__ : Any=7 ):
"""simple docstring"""
_snake_case... | 64 | 1 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.d... | 64 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_availa... | 64 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__... | 64 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils ... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[float] ):
"""simple docstring"""
_snake_case : int = 0.00
_snake_case : int = 0
for resistor in resistors:
if resistor <= 0:
... | 64 | 1 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, ma... | 64 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 64 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__... | 64 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 64 | 1 |
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