code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
def _A (lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
_a = [], []
while len(__lowerCamelCase ) > 1:
_a = min(__lowerCamelCase ), max(__lowerCamelCase )
... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
... | 104 | 0 |
'''simple docstring'''
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
a_ : Union[str, Any] = [
"""EAGER""",
... | 352 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class a :
"""simple docstring"""
def __init__( self , __magic_name__ ) -> int:
_a = data
_a = None
_a = None
... | 353 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a_ : Optional[Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *__magic_name__ ... | 104 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common i... | 354 |
'''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 impor... | 104 | 0 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTest... | 355 |
'''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... | 104 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a_ : int = logging.get_logg... | 356 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> List[Any]:
'''simple docstring'''
if n == 1 or not isinstance(A__ , A__ ):
return 0
elif n == 2:
return 1
else:
_a = [0, 1]
... | 357 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> int:
_a = [
'safety_checker/pytorch_model.bin... | 104 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, re... | 358 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int | float | str , lowerCAmelCase__ :int | float | str ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
_a ... | 359 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 0 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
fro... | 360 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :Dict ) -> list[int]:
'''simple docstring'''
_a = [True] * limit
_a = False
_a = False
_a = True
... | 361 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 0 |
def _A (lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Optional[int] ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multi... | 362 |
'''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_QUE... | 104 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _A (lowerCAmelC... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 | 0 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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
# pyt... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : int = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "G... | 104 | 0 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a ( snake_case__ ):
def __init__( self , __... | 365 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstr... | 104 | 0 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a_ : Tuple = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirio... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CO... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a :
def __init__( self , __magic_name__ ) -> Any:
_a = data
_a = None
class a :
def... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a_ : List[str] ... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 368 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> float:
'''simple docstring'''
_a = 0
while len(lowerCAmelCase__ ) > 1:
_a = 0
# Consider two files with minimum cost to be merged
... | 104 | 0 |
'''simple docstring'''
from functools import lru_cache
def _A (lowerCAmelCase__ :int ) -> set:
'''simple docstring'''
_a = 2
_a = set()
while i * i <= n:
if n % i:
i += 1
... | 369 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ ) -> int:
'''simple docstring'''
_a = len(lowerCAmelCase__ ) // 2
# choose the middle 3 elements
_a = lst[m - 1 : m + 2]
... | 370 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 104 | 0 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def _A (lowerCAmelCase__ :Optional[int] ) -> str:
'''simple docstring'''
if not sentence:
return ""
_a = dict(zip(_lowerCAmelCase , _low... | 371 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("T")
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
r... | 104 | 0 |
'''simple docstring'''
a_ : Optional[int] = {
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the fa... | 350 |
'''simple docstring'''
from timeit import timeit
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
_a = 0
while nu... | 104 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
a_ :... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def _A (lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Any = False ) -> ... | 352 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 104 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Any = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/m... | 353 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a_ : Optional[Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *__magic_name__ ... | 104 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : List[str] = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
... | 354 |
'''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 impor... | 104 | 0 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_... | 355 |
'''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... | 104 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tens... | 356 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 0 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly f... | 357 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> int:
_a = [
'safety_checker/pytorch_model.bin... | 104 | 0 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
a_ : Tuple = logging.getLogger(__name__)
@dataclass
class a ( lowerCamelCase__ ):
... | 358 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 | 0 |
'''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_ : Any = 'src/transf... | 359 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.conf... | 360 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _A (lowerCAmelCase__ :Optional[int] ) -> List[Any]:
'''simple docstring'''
if "model" in orig_key:
_a = o... | 361 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 0 |
import re
def _A (lowerCAmelCase__ :List[str] ) -> bool:
'''simple docstring'''
_a = re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(__lowerCAmelCase , __lowerCAme... | 362 |
'''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_QUE... | 104 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _A (lowerCAmelCase__ :List[str] ,... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/mai... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : int = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "G... | 104 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import... | 365 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstr... | 104 | 0 |
'''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_inp... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CO... | 104 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a_ : List[str] ... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
a_ : int = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of... | 368 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> float:
'''simple docstring'''
_a = 0
while len(lowerCAmelCase__ ) > 1:
_a = 0
# Consider two files with minimum cost to be merged
... | 104 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : Any = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
... | 369 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeErro... | 370 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 104 | 0 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
fro... | 371 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("T")
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
r... | 104 | 0 |
'''simple docstring'''
from timeit import timeit
a_ : Union[str, Any] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama... | 350 |
'''simple docstring'''
from timeit import timeit
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
_a = 0
while nu... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int , lowerCAmelCase__ :int , lowerCAmelCase__ :list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
... | 104 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Tuple = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/m... | 352 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 104 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _A () -> List[Any]:
'''simple docstring'''
_a = HfArgumentParser(UpperCAmelCase_ )
_a = parser.par... | 353 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a_ : Optional[Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *__magic_name__ ... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
fr... | 354 |
'''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 impor... | 104 | 0 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,... | 355 |
'''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... | 104 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _A () -> int:
_a , _a = 9, 14 # noqa: F841
_a = [
[0, 1, 4],
[0, 7, 8],
... | 356 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def _A (lowerCAmelCase__ :Optional[int] ) -> int:
'''simple docstring'''
return choice(lowercase_ )
def _A (lowerCAmelCase__ :lis... | 357 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> int:
_a = [
'safety_checker/pytorch_model.bin... | 104 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class a :
def __init__( self ) -> Optional[Any]:
_a = {}
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__=1 ) ->... | 358 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 | 0 |
'''simple docstring'''
import os
def _A () -> Any:
'''simple docstring'''
_a = os.path.join(os.path.dirname(_lowercase ) , 'num.txt' )
with open(_lowercase ) as file_hand:
return str(sum(int(_lowercase ... | 359 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int , lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> Tuple:
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _A (lowerCAmelCase__ ... | 360 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 0 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
a_ : Union[str, Any] = "src/transformers"
# Matches is_xxx_available()
a_ : Optional[Any] = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import... | 361 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : List[Any] = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-... | 362 |
'''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_QUE... | 104 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def _A (lowerCAmelCase__ :dict ) -> int:
'''simple docstring'''
_a = {key: len(_lowercase ) for key, value in gen_kwargs.items() if isinstance(_lowercase ,... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a_ ... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : int = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "G... | 104 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_ava... | 365 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstr... | 104 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_config... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CO... | 104 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : List[Any] = logging.get_logger(__name__)
a_ : Any =... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a_ : List[str] ... | 104 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level... | 368 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> float:
'''simple docstring'''
_a = 0
while len(lowerCAmelCase__ ) > 1:
_a = 0
# Consider two files with minimum cost to be merged
... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -> Dict:
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def _A () -> str:
'''simple docstring'''
... | 369 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__... | 104 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
... | 370 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 104 | 0 |
'''simple docstring'''
from math import factorial, radians
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :int = 18 , lowerCAmelCase__ :int = 10 ) -> List[Any]:
'''simple docstring'''
_a = angle_in_degrees - ((ang... | 371 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("T")
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
r... | 104 | 0 |
'''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 ImageProcessin... | 350 |
'''simple docstring'''
from timeit import timeit
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
_a = 0
while nu... | 104 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : Tuple = {
"configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> List[str]:
'''simple docstring'''
if number > 0:
raise ValueError('input must be a negative integer' )
_a = len(bin(lowerCAmelCase__ )[3:] )
_a ... | 352 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 104 | 0 |
'''simple docstring'''
import random
from typing import Any
def _A (lowerCAmelCase__ :list ) -> Optional[int]:
'''simple docstring'''
for _ in range(len(lowerCAmelCase__ ) ):
_a = random.randint(0 , len(low... | 353 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a_ : Optional[Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *__magic_name__ ... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -> List[str]:
'''simple docstring'''
while b:
_a , _a = b, a % b
return a
def _A (lowerCAmelCase__ :int , lowe... | 354 |
'''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 impor... | 104 | 0 |
'''simple docstring'''
import math
import sys
def _A (lowerCAmelCase__ :Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
_a = ''
try:
with open(lowerCAmelCase__ , 'rb' ) as binary_file:
... | 355 |
'''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... | 104 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : Optional[int] = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_M... | 356 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
a_ : Optional[int] = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
a_ : int = (((5_1_5, 2_2, 1_3), 5_5_5), (... | 357 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> int:
_a = [
'safety_checker/pytorch_model.bin... | 104 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : List[str] = {
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'Tab... | 358 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 | 0 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class a ( SCREAMING_SNAKE_CASE__ ):
def __init__( self , *__magic_name__ , **__magic_name__ ) -> List[str]:
super().__init__(*A__ , **A__ )
def __UpperC... | 359 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 0 |
'''simple docstring'''
a_ : Optional[Any] = tuple[float, float, float]
a_ : List[Any] = tuple[float, float, float]
def _A (lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :int ) -> Vectorad:
'''simple docs... | 360 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils... | 361 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_... | 362 |
'''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_QUE... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _A () -> tuple[list[int], int]:
'''simple docstring'''
_a = [randint(-10_00 , 10_0... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 | 0 |
'''simple docstring'''
import math
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> str:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : int = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "G... | 104 | 0 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def _A (lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tuple=None , **lowerCAmelCase__ :Union[str, Any] ) -> Union[str, Any]:
'''si... | 365 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstr... | 104 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CO... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
a_ : int = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
a_ : Optional[Any] = typing.Union[np.floa... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a_ : List[str] ... | 104 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Li... | 368 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list ) -> float:
'''simple docstring'''
_a = 0
while len(lowerCAmelCase__ ) > 1:
_a = 0
# Consider two files with minimum cost to be merged
... | 104 | 0 |
'''simple docstring'''
import sys
from collections import defaultdict
class a :
def __init__( self ) -> int:
_a = []
def __UpperCAmelCase ( self , __magic_name__ ) -> List[str]:
return self.node_position[vertex]
def ... | 369 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> Any:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('... | 370 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 104 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : str = re.compile(R"\b(a|an|the)\b", re.UNICODE)
a_ : List[Any] = None
def _A () -> ... | 371 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("T")
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
r... | 104 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : int = {
"configuration_vision_encoder_decoder": ["Vision... | 350 |
'''simple docstring'''
from timeit import timeit
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
_a = 0
while nu... | 104 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : Tuple = {
"configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
... | 104 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
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_wa... | 352 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 104 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_model... | 353 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a_ : Optional[Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *__magic_name__ ... | 104 | 0 |
'''simple docstring'''
from math import loga
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(lowerCAmelCase__ , lo... | 354 |
'''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 impor... | 104 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :str , lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
_a = len(lowerCAmelCase__ )
_a = (
first_str_... | 355 |
'''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... | 104 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Optional[Any] ... | 356 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_prop... | 357 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> int:
_a = [
'safety_checker/pytorch_model.bin... | 104 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("T")
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (position - 1) // 2
... | 358 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 | 0 |
'''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 impor... | 359 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float:
'''simple docstring'''
_a = x
_a ... | 104 | 0 |
'''simple docstring'''
from math import ceil
def _A (lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] ) -> Tuple:
'''simple docstring'''
_a = list(range(0 , lowerCAmelCase__ ) )
_a = ... | 360 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__na... | 361 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = ["""image_processor""", """tokenizer"""]
_lowerCAmelCase = """AutoImageProcessor"""
_lowerCAmelCase = """AutoTokenize... | 362 |
'''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_QUE... | 104 | 0 |
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