code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import ... | 275 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = []
__lowerCAmelCase : List[Any] = []
__lowe... | 275 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 1000 ) -> int:
"""simple docstring"""
__lowerCamelCase = 2**power
__lowerCamelCase = 0
while n:
__lowerCamelCase , __lowerCamelCase = r + n % 10, n // 10
return r
if __name_... | 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring''... | 331 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : str = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextCon... | 264 | 0 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
... | 367 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowerCamelCase__ = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7"""):
... | 307 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=lowerCamelCase_ ):
lowerCAmelCase_ = ['''torch''', '''scipy''']
def __init__( self , *__SCREAMING_SNAKE_CASE , **__SCR... | 93 | def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> List[str]:
lowerCAmelCase = len(snake_case__ )
for i in range(length - 1 ):
lowerCAmelCase = i
for k in range(i + 1 , snake_case__ ):
if collection[k] < coll... | 338 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
snake_case : str = logging.get_logger(__name__)
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
... | 362 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 281 | 0 |
'''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/LICENSE-2.0
#
# U... | 331 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCamelCase__ = 6378137.0
UpperCamelCase__ = 6356752.314245
UpperCamelCase__ = 6378137
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_S... | 102 |
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size()
UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class a__ ( enum.Enum ):
_a : Any = 0
_... | 102 | 1 |
"""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
lowercase__ = logging.get_logger(__... | 96 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
... | 335 | 0 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _a ( ) -> Tuple:
... | 352 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=13_37 , num_examples=42 , dataset_n... | 191 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 299 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase__ ( __... | 299 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''shi-labs/nat-mini-in1k-224''':... | 333 | import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diff... | 333 | 1 |
from math import factorial, pi
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ = 30 ) -> float:
if not isinstance(lowerCamelCase_ , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(lowerCamelCase_... | 21 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 21 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 359 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__snake_case = False
try:
__snake_case = _is_packa... | 169 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def __a(SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE_ : Tuple ):
_lowerCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , "ha... | 158 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __a(SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple , S... | 158 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 342 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_M... | 342 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
_UpperCamelCase : Optional[int] = get_logger(__name__)
class snake_case__ :
def __init__( self : int , _A : str , _A : Optional[int]=None )... | 304 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
_UpperCamelCase : Any = logging.getLogger(__name__)
class snake_case__ ( UpperCamelCase):
a_ = "masked_bert"
def __init__( self : str , _A : ... | 304 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A ( snake_case :List[Any] ) -> Union[str, Any]:
__UpperCamelCase = [
'encoder.version',
'decoder.version',
... | 263 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import loa... | 263 | 1 |
'''simple docstring'''
from collections import defaultdict
def _A ( A__ ):
"""simple docstring"""
__lowercase = 1
__lowercase = True
for v in tree[start]:
if v not in visited:
ret += dfs(A__ )
if ret % 2 == 0:
cuts.append(A__ )
return ret
def _... | 104 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/... | 104 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case__ : str = '''\
'''
snake_case__ : Union[str, Any] = '''
Perplexit... | 274 | '''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''':... | 274 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
a__ : int =logging.get_logger(__name__)
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self : List[str... | 53 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
fr... | 53 | 1 |
"""simple docstring"""
from math import factorial
lowerCamelCase_ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def _A ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
... | 215 |
"""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,
... | 215 | 1 |
'''simple docstring'''
def _A (lowerCAmelCase__ :Any , lowerCAmelCase__ :Optional[Any] ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_a = str(bin(A_... | 168 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import P... | 99 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from trans... | 23 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import log... | 23 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 35 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
... | 78 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
lowerCamelCase__ = '''timm_backbone'''... | 350 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@py... | 101 |
from __future__ import annotations
lowercase__ :Any = 1.60_21E-19 # units = C
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You ... | 101 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
UpperCamelCase__ : Optional[int] = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_bl... | 362 |
'''simple docstring'''
UpperCamelCase__ : int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def Upper... | 164 | 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... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__: str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optional... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _a :
def __init__( self ,_SCREAMING_SNAKE_CASE = 6 ) -> None:
_snake_case = None
_snake_case = None
self.create_linked_list(_SCREAMING_SN... | 142 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkp... | 142 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCamelCase ( unittest.TestCase , lowerCamelCase__ ):
def lowercase__ ( self ):
"""simple docstring"""
... | 75 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
snake_case__ : str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 35 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 353 |
from math import ceil
def lowerCamelCase__ ( _lowercase = 1001 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
UpperCAmelCase_ : List[Any] = 2 * i +... | 235 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case__ (unittest.TestCase ):
... | 170 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_av... | 250 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DD... | 29 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tor... | 29 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.s... | 146 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__UpperCamelCase : Dict = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
def ... | 146 | 1 |
"""simple docstring"""
from math import sqrt
def __lowercase ( snake_case_ : int ) ->int:
'''simple docstring'''
__A : Any = 0
for i in range(1 ,int(sqrt(snake_case_ ) + 1 ) ):
if n % i == 0 and i != sqrt(snake_c... | 291 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 1 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def UpperCAmelCase_ ( __lowercase : str ) -> Any:
'''simple docstring'''
def decorator(__lowercase : List[Any] ):
_UpperCAmelCase = ... | 22 | """simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm m... | 213 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class lowerCamelCase__ ... | 365 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase : Any = logging.get_logger(__n... | 151 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_a = "\nimport os\n"
_a = "\ndef foo():\n import os\n return False\n"
_a = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return b... | 322 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case... | 281 | 0 |
import math
import unittest
def lowerCAmelCase__ ( _a : int ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 =... | 362 |
def lowerCAmelCase__ ( _a : int = 50 ):
snake_case_ : Union[str, Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
... | 36 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
... | 165 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( snake_case__ ):
... | 165 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbo... | 85 | """simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowercase = logging.getLogger(__name__)
def lowerCAmelCase ():
"""simple docstring"""
__UpperCamelCase =argparse.Arg... | 85 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list[list] ) -> list[list]:
'''simple docstring'''
_UpperCAmelCase = current_set.copy()
for row_index, row in enumerate(__lowercase ):
_UpperCAmelCase = r... | 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> ... | 22 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
snake_case__ : Union[str, Any] = 'src/diffusers'
# Matches is_xxx_available()
snake_case__ : ... | 250 |
from __future__ import annotations
from math import pi, sqrt
def _a ( lowerCamelCase: float , lowerCamelCase: float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be ... | 250 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmel... | 31 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipel... | 218 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import o... | 357 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __lowerCamelCase : str = "AAPL" ):
snake_case : List[Any] = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
snake_case : Tuple = BeautifulSoup(requests.get(__lower... | 10 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf ... | 200 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase_ : Optional[int] = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
UpperCAmelCase_ : Tuple... | 200 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 94 |
def lowerCAmelCase__ ( lowerCamelCase_ : str = "The quick brown fox jumps over the lazy dog" ,):
'''simple docstring'''
lowerCAmelCase__ : Any = set()
# Replace all the whitespace in our sentence
lowerCAmelCase__ : List[Any] = input_str.re... | 94 | 1 |
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> Union[str, Any]:
... | 77 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils i... | 77 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> str:
"""simple docstring"""
return "".join(chr(ord(lowercase_ ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 310 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCamelCase__ = {"UserAgent": UserAgent().random}
def lowercase__ ( lowercase_ ) -> dict:
"""simple docstrin... | 310 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_... | 235 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 1 |
def __lowerCAmelCase ( lowercase : int ) -> list[list[float]]:
"""simple docstring"""
snake_case : Optional[int] = []
for data in source_data:
for i, el in enumerate(__UpperCAmelCase ):
if len(__UpperCAmelCase ) < i + 1:
d... | 356 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
fro... | 112 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''_T''')
class _snake_case ( Generic[_T] ):
'''simple docstring'''
def __init__( self: Tuple ,lowerCamelCase_: Tuple = None ) -> None:
UpperCA... | 345 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __a(SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple , S... | 158 | 0 |
def lowerCAmelCase_ ( __a , __a ) -> List[str]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__a , int(b / 2 ) ) * actual_power(__a , int(b / 2 ) )
else:
return a * actual_power(__a , int(b / 2 ) ) * actua... | 369 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_envi... | 273 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
... | 132 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = x
SCREAMING_SNAKE_CASE__ : Unio... | 132 | 1 |
import inspect
import re
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_config_docstrings.py
lowerCamelCase_ : Union[str, Any] = """src/transformers"""
# This is to ma... | 223 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils... | 223 | 1 |
'''simple docstring'''
from math import pi, sqrt
def snake_case__ ( _A: float ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_A ) not in (0, 0.5):
r... | 272 | '''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_py... | 272 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
... | 370 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.... | 184 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class A_ :
"""simple docstring"""
__UpperCamelCase = 42 # [batch_size x 3]
__UpperCamelCase = 42 # [ba... | 78 | import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, ... | 182 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class UpperCAmelCase_ ( __a ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = field(defa... | 369 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : List[str] ={
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():... | 118 | 0 |
from math import factorial
class lowerCamelCase__:
def __init__( self: List[str] , UpperCamelCase_: str , UpperCamelCase_: int ):
__lowerCamelCase = real
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerC... | 12 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Dict =... | 12 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determi... | 94 |
def lowerCAmelCase__ ( lowerCamelCase_ : str ,lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = len(lowerCamelCase_)
lowerCAmelCase__ : str = len(lowerCamelCase_)
lowerCAmelCase__ : Union[str, Any] ... | 94 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvail... | 59 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbed... | 59 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCAmelCase_ : List[Any] = mf_knapsack(i - 1, __lowerCamelCase, __lowerCamelCa... | 23 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_a = logging.getLogger()
def ... | 23 | 1 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase__ ( __snake_case ) -> typing.Counter[int]:
"""simple docstring"""
_UpperCamelCase = Counter()
for base in range(1, ... | 194 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
f... | 194 | 1 |
"""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:... | 318 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
de... | 318 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int = 50 ):
'''simple docstring'''
_UpperCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_sta... | 260 |
"""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 : Tuple = logging.get_logger(__name__)
_... | 260 | 1 |
'''simple docstring'''
import warnings
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
_lowerCAmelCase = logging.get_logger(__name__)
_l... | 184 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 1000 ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = -1
lowerCAmelCase__ : Optional[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equa... | 184 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''... | 43 |
def __lowerCamelCase ( __a :str ) -> list:
"""simple docstring"""
A__ = [0] * len(__a )
for i in range(1 , len(__a ) ):
# use last results for better performance - dynamic programming
A__ = prefix_result[i - 1]
w... | 274 | 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 lowercase__ ( lowercase_ ) -> ... | 365 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCamelCase__ = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 310 | 0 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_... | 71 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word... | 71 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : int = 1_0_0_0 ):
'''simple docstring'''
_UpperCAmelCase : List[Any] =2**power
_UpperCAmelCase : List[str] =str(__lowerCamelCase )
_UpperCAmelCase :... | 242 |
'''simple docstring'''
import numpy as np
class __magic_name__ :
def __init__( self) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] =(0, 0)
_UpperCAmelCase : Any =None
... | 242 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
clas... | 34 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_S... | 102 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCamelCase ( __UpperCamelCase ) -> Dict:
"""simple docstring"""
lowerCAmelCase_ : Tuple = {}
lowerCAmelCase_ : Union... | 161 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHI... | 161 | 1 |
'''simple docstring'''
import itertools
import math
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Neg... | 3 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import ... | 3 | 1 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class UpperCamelCase ( lowerCAmelCase__ ):
SCR... | 354 | """simple docstring"""
import os
import numpy
import onnx
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> List[str]:
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake_case_ = a == b
snake_case_ = name_a
snake_... | 312 | 0 |
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_common import ModelTesterMixin, ids_tensor
from .... | 275 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAND... | 275 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 357 |
from __future__ import annotations
from collections.abc import Callable
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Callable[[int | float], int | float] , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_SNAKE_CASE_ : int = 100 ... | 216 | 0 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowerCamelCase__ = logging.getLogger(__name__)
@dataclass
class ... | 302 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
a__ : Union[str, Any] = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
... | 349 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase )
# We need to create solution object to save path.
_A = [[0 for _ in range(_lowercase )] for _ in range(_lowercase )]
... | 351 |
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',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
... | 75 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers... | 331 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 331 | 1 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.js... | 263 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
__UpperCamelCase = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
... | 263 | 1 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase... | 151 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowercase__ = logging.get_logger(__name__)
class A_ ( _snake_case ):
'''simple docstring'''
def __init__( se... | 151 | 1 |
# 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/LICENSE-2.0
#
# Unless req... | 221 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoder... | 221 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
UpperCamelCase__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesys... | 65 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface ... | 65 | 1 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
lowercase__ : List[str] = abs(_UpperCAmelCase )
lowercase__ : str = 0
while n > 0:
res += n % 10
n //= 10
return res
def snake_ca... | 365 |
import math
import sys
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE_ ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the valu... | 216 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import V... | 316 | """simple docstring"""
from __future__ import annotations
import numpy as np
def UpperCAmelCase__ ( lowerCAmelCase__ :list[float] ) -> Optional[Any]:
'''simple docstring'''
return np.maximum(0 , lowerCAmelCase__ )
if __name__ == "__main__":
p... | 197 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase__ = logging... | 83 |
'''simple docstring'''
from collections.abc import Generator
def _UpperCamelCase ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case ,snake_case : Tuple = 0, 1
while True:
snake_case ,snake_case : List[Any] = b, a + b
yie... | 83 | 1 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers impo... | 84 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
d... | 84 | 1 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int ) -> "list[int]":
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
__SCREAMING_SNAKE_CASE :Tuple = [0] * (upper_limit + 1)
... | 359 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCamelCase_ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow... | 239 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''_T''')
class __snake_case ( Generic[_T]):
def __init__( self : Dict , __lowerCAmelCase : Iterable[_T] | None = None ):
... | 72 |
from __future__ import annotations
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : Optional[int]=None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = data
... | 226 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ... | 57 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for test... | 57 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
'''conf... | 35 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if ... | 41 | 0 |
'''simple docstring'''
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Co... | 96 |
'''simple docstring'''
import math
class a__:
def __init__( self : int , __snake_case : Any=0 ): # a graph with Node 0,1,...,N-1
a : List[Any] = n
a : Optional[Any] = [
[math.inf for j in range(0 , ... | 96 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase ={
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Mo... | 67 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_I... | 294 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __SCREAMING_SNAKE_CASE ) -> Tuple:
SCREAMING_SNAKE_CASE: Dict = [
'''encoder.version''',
'''dec... | 371 |
"""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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImage... | 108 | 0 |
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
logging.set_verbosity_info()
__A =logging.... | 19 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
fro... | 200 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featu... | 357 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requi... | 101 | 0 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int = 4_00_00_00 ) -> int:
__SCREAMING_SNAKE_CASE :Any = [0, 1]
__SCREAMING_SNAKE_CASE :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 191 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _snake_case( SCREAMING_SNAKE... | 7 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE ( _... | 352 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
_lowerCamelCase : List[Any] = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sa... | 249 | 0 |
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