code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_... | 120 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _lowerCamelCase ( __A : bool = True , *__A : List[Any] , **__A : Tuple ) -> Optional[int]:
if not is_... | 186 |
from __future__ import annotations
def _lowerCamelCase ( __A : int ) -> list[int]:
_UpperCAmelCase : List[str] = [True] * limit
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : Dict = False
_UpperCAmel... | 186 | 1 |
from collections import Counter
from timeit import timeit
def __A ( _lowercase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def __A ( _lowercase = "" ):
... | 484 |
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 ( _lowercase ):
'''simple docstring'''
... | 484 | 1 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules ... | 635 | """simple docstring"""
from __future__ import annotations
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more o... | 635 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnn... | 79 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
... | 292 | 0 |
"""simple docstring"""
from math import sqrt
def lowercase__(A = 1_000_000 ) ->int:
"""simple docstring"""
lowercase__ : int= 0
lowercase__ : int= 0
lowercase__ : int
while num_cuboids <= limit:
... | 85 |
"""simple docstring"""
from ....utils import logging
a : List[str] = logging.get_logger(__name__)
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , snake_case__ , ... | 85 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ ... | 470 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_V... | 470 | 1 |
"""simple docstring"""
import enum
import shutil
import sys
__lowerCamelCase , __lowerCamelCase = shutil.get_terminal_size()
__lowerCamelCase = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class _lowercase ( enum.Enum ):
_lowerCamelCase = ... | 712 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowercase ( __UpperCAmelCase ):
_lowerCamelCase = (EulerDiscreteScheduler,)
_lowerCamelCase ... | 190 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
lowerCAmelCase__ : int = tuple[int, int]
class a :
"""simple docstring"""
def __init__( self : Any , snake_case_ : set[int] ... | 347 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
"""simple docstring"""
def __init__( self : Optional[Any] , snake_case_ : List[str]=2 ... | 347 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_log... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""Squ... | 582 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
UpperCamelCase = 'scheduler_config.json'
class _A ( UpperCAmelCas... | 269 |
def __lowerCamelCase ( __lowerCAmelCase : str ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
__UpperCamelCase : List[str] = sorted(string.lower() )
return ... | 269 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : Union[str, A... | 720 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> int:
"""simple docstring"""
while a != 0:
UpperCamelCase , UpperCamelCase = b % a, a
return b
def __lowerCamelCase ( A__ , A__ ... | 324 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention... | 263 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transforme... | 633 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
SCREAMING_SNAKE_CASE_:Tuple = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
""">""": operator.... | 520 |
from __future__ import annotations
import math
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
"""simple docstring"""
A : str = u
for i in range(1 , _lowerCAmelCase ):
A : str = temp * (u - i)
return temp
def ... | 520 | 1 |
import os
from pathlib import Path
def UpperCamelCase__ ( ):
from torch.utils.cpp_extension import load
lowercase = Path(a__ ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
lowercase = [
root / filename
for filename in [
... | 428 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _lowercase):
snake_case__ = '''encoder-decoder'''
snake_case__ = True
d... | 420 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
... | 507 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _lowerCame... | 507 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ ) -> list[int]: # This function is recursive
UpperCAmelCase__ : Union[str, Any] = len(UpperCamelCase_ )
# If the array contains only one element, we return it (it's t... | 75 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 339 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 337 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
... | 337 | 1 |
import os
import numpy
import onnx
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_A = a.name
_A = b.name
_A = ''
_A = ... | 27 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor... | 27 | 1 |
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 _UpperCAmelCase ( A__ ):
UpperCamelCase__ ... | 526 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# Unl... | 526 | 1 |
import os
import string
import sys
__A : Optional[Any] = 1 << 8
__A : List[str] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left"... | 27 |
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 must be c... | 27 | 1 |
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 367 |
from __future__ import annotations
import unittest
from transformers import 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_tensor, random_attention_mask
fro... | 367 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvisio... | 88 |
# 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 required by app... | 60 | 0 |
"""simple docstring"""
from collections import defaultdict
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Optional[int] , __lowercase :str , __lowercase :Dict ):
__lowerCamelCase : Dict =to... | 363 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, requir... | 363 | 1 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_fla... | 76 |
'''simple docstring'''
import numpy as np
def lowercase__ ( __UpperCamelCase : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 566 | 0 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_lis... | 705 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils imp... | 474 | 0 |
"""simple docstring"""
import os
from math import logaa
def _lowercase ( __lowerCAmelCase = "base_exp.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : float = 0
SCREAMING_SNAKE_CASE__ : Tuple = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(... | 680 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
a :str = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not ... | 680 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowercase__ ):
UpperCAmelCase : Union[str, Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self :List[str] ,*__UpperCAmelC... | 715 | """simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequence... | 121 | 0 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
depre... | 488 |
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
from transformers.models.b... | 488 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCAmelCa... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 54 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {... | 159 | """simple docstring"""
import math
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__UpperCAmelCase )
def SCREAMIN... | 159 | 1 |
def lowerCAmelCase_ ( A_):
UpperCamelCase__: Dict = len(_snake_case)
UpperCamelCase__: Optional[int] = len(matrix[0])
UpperCamelCase__: Optional[int] = min(_snake_case ,_snake_case)
for row in range(_snake_case):
# Check ... | 708 |
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''',
'''albert-lar... | 221 | 0 |
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
def lowerCAmelCas... | 635 | import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, req... | 635 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
... | 680 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = ... | 680 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
UpperCAmelCase = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_to... | 84 | 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 ( lowerCAmelCase : Lis... | 216 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniza... | 78 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_a = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_... | 78 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def UpperCAmelCase_ ( __lowerCamelCase : list ,__lowerCamelCase : list ,__lowerCamelCase : list ,__lowerCamelCase : int ):
lowercase_ :Optional[Any] = 0
# Numb... | 172 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class a_ ( _lowerCAmelCase ):
def lowercase__ ( self : Tuple , lowercase : float )... | 172 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureEx... | 714 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
... | 137 | 0 |
'''simple docstring'''
from math import sqrt
def A__ ( __lowerCAmelCase : int = 100_0000 ):
lowerCamelCase__ = 0
lowerCamelCase__ = 0
lowerCamelCase__ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_s... | 50 |
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, require_torch
@r... | 279 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase = """sshleifer/bart-tiny-random"""
lowerCAme... | 706 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, ... | 675 | 0 |
def a (lowerCAmelCase__ ):
__a = []
if len(lowerCAmelCase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowerCAmelCase__ ) ):
__a = nums.pop(0 )
__a = permute(lowerCAmelCase__ )
for perm in permutations:
perm.append(lowerCAmelCase... | 99 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.p... | 602 | 0 |
import os
import pytest
from attr import dataclass
_A : Any = 'us-east-1' # defaults region
@dataclass
class __SCREAMING_SNAKE_CASE :
_UpperCAmelCase : str
_UpperCAmelCase : Any = "arn:aws:iam::558105141721:role/sagemaker_execution_role"... | 130 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 130 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( SCREAMING_SNAKE_CASE_ : list[float] ) -> float:
"""simple docstring"""
UpperCAmelCase = 0.00
UpperCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
... | 51 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : Li... | 49 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from tran... | 461 | '''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase ( __lowerCamelCase ):
def __init__( self : str , __lowerCAmelCase : Callab... | 461 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def lowerCamelCase (a_ :Sequence[float] , a_ :float) -> float:
return sum(c * (x**i) for i, c in enumerate(a_))
def lowerCamelCase (a_ :Sequence[float] , a_ :float) -> float:
... | 677 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowerCamelCase (a_ :int)... | 677 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 252 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pip... | 252 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__a = (7_2_0, 1_2_8_0) # Height, Width
__a = (0.4, 0.6) # if height or width lower than this scale, drop it.
__a = 1 / 1_0_0
__a = ''
__a ... | 97 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See al... | 97 | 1 |
lowerCamelCase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def UpperCAmelCase_ ( __UpperCamelCase ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__UpperCamelCase, __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ ... | 588 |
import math
def UpperCAmelCase_ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ =[]
SCREAMING_SNAKE_CASE__ =2
SCREAMING_SNAKE_CASE__ =int(math.sqrt(__UpperCamelCase ) ) # Size of every segment
SCREAMING_SNAKE_CASE__ =[True] * (end + 1)
SCREAMI... | 588 | 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, l... | 20 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ) -> list:
_UpperCamelCase : Any = False
while is_sorted is False: # Until all the indices are traversed keep looping
_UpperCamelCase : List[str] = True
for i in range(0 ,len(UpperCamelCase ... | 683 | 0 |
def SCREAMING_SNAKE_CASE_ ( __A : list , __A : int = 0 ) -> list:
"""simple docstring"""
a_ : Tuple = length or len(__A )
a_ : Union[str, Any] = False
for i in range(length - 1 ):
if li... | 443 |
UpperCAmelCase_ : Optional[int] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
UpperCA... | 443 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowerCAmelCase_ = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noq... | 60 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 0 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowercase (__lowerCamelCase ):
_lowerCamelCase = ''''''
_lowerCam... | 6 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCAmelCase__ = 3
def __UpperCAmelCase ( lowerCamelCase_) -> int:
print('Generating primitive root o... | 6 | 1 |
"""simple docstring"""
import baseaa
def snake_case ( lowerCAmelCase_ ) -> bytes:
return baseaa.baaencode(string.encode('''utf-8''' ) )
def snake_case ( lowerCAmelCase_ ) -> str:
return baseaa.baadecode(lowerCAmelCase_ ).decode('''utf-8''' )
if... | 103 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ = 1_0_0_0 ):
_UpperCamelCase : List[str] = 3
_UpperCamelCase : Any = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
... | 195 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokenize... | 255 |
"""simple docstring"""
UpperCAmelCase =256
# Modulus to hash a string
UpperCAmelCase =1_000_003
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a )
A = len(_a )
if p... | 255 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Union[str, Any]:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError("The length of profit and weight... | 628 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertS... | 574 | 0 |
'''simple docstring'''
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
A : Tuple = """src/transformer... | 163 |
'''simple docstring'''
def snake_case_ ( a__ : int ):
"""simple docstring"""
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
__lowercase = gray_code_sequence_string(... | 163 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaT... | 216 | from __future__ import annotations
def _snake_case ( lowerCAmelCase : int | float | str , lowerCAmelCase : int | float | str ):
"""simple docstring"""
if nth_term == "":
return [""]
SCREAMING_SNAKE_CASE_ : Tuple = int(lowerCAmelCase )
SCREAMING_S... | 216 | 1 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
from sklearn.metrics import fa_score
import datasets
__a = '\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'
__a = '\nArgs:\n predictions (`list` of `int`): Predicted labe... | 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfi... | 706 |
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 AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import ... | 241 | 0 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.confi... | 299 |
"""simple docstring"""
from __future__ import annotations
import queue
class _lowerCamelCase :
def __init__( self : Optional[int] , UpperCamelCase : List[Any] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, A... | 299 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
_SCREAMING_SNAKE_CASE = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
... | 703 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
# Load... | 534 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=A_ ):
'''simple docstring'''
UpperCAmelCase_ = ['''torch''']
def __init__( self : Union[str, Any] , *UpperCamelCase : List[Any] , **UpperCamelCase : ... | 322 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from data... | 322 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( snake_case):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1, len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
__snak... | 93 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase : Dict = {
"configuration_vision_text_dual_encoder": ["Visi... | 93 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_com... | 270 |
'''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_ = logging.get_logger(__name__)
... | 270 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( lowercase: Union[str, Any] ) -> list[int]: # This function is recursive
'''simple docstring'''
_UpperCamelCase: int = len(lowercase )
# If the array contains only one element, we return it (it's the stop condition... | 719 | from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def lowerCAmelCase_ ( lowercase: Optional[Any] ... | 264 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, 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,
re... | 536 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE (__A ):
"""simple docstring"""
_a : List[Any] = ['''image_processor''', '''tokenizer''']
_a : L... | 536 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_p... | 701 | '''simple docstring'''
import argparse
_lowercase : Optional[int] = "docs/source/_static/js/custom.js"
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Dict:
with open(UpperCAmelCase__ , encoding="""utf-8""" , newline="""\n""" ) as f:
lower... | 30 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 64 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowercase__ =argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type... | 263 | 0 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
... | 693 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase : int ={
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
... | 693 | 1 |
"""simple docstring"""
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, Trai... | 65 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class a_ ( UpperCAmelCase__ ):
def __lt__( self : Optional[Any] , __lowerCAmelCase : List[Any] ):
retu... | 427 |
'''simple docstring'''
import argparse
import copy
def lowerCamelCase__ ( a ):
__snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__snake_case = []
... | 427 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( lowercase__ : int = 3 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if isinstance(lowercase__ , lo... | 600 |
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 __UpperCamelCase ( lowercase... | 600 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avai... | 709 |
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
from ...test_backbone_common ... | 102 | 0 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__lowerCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classif... | 684 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.uti... | 684 | 1 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return vector * sigmoid(1.7_0_2 * vector ... | 711 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return vector * sigmoid(1.7_0_2 * vector ... | 623 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
clas... | 464 |
import numpy as np
def a_ ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : float ):
'''simple docstring'''
return np.where(vector > 0 , SCREAMING_SNAKE_CASE__ , (alpha * (np.exp(SCREAMING_SNAKE_CASE__ ) - 1)) )
if __name__ == "__main... | 464 | 1 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class __a ( __a ):
"""simple docstring"""
_A : int = "facebook/bart-large-mnli"
_A : Optional[Any] = (
... | 719 |
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 import Huggin... | 588 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/... | 89 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if n == 1 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
return 0
elif n == 2:
return 1
else:
_lowercase : List[str] = [0, 1]
for i in range(2 , n + 1 ):
... | 89 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.num... | 710 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _lowerCAmelCase ( __lowerCamelCase:float , __lowerCamelCase:float , __lowerCamelCase:int ):
'''simple docstring'''
__magic_name__ = x
__magic_n... | 468 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
... | 105 |
"""simple docstring"""
import argparse
import struct
import unittest
class a :
def __init__( self : List[str] , lowerCAmelCase : bytes ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Tuple =data
# Initia... | 409 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'nielsr/canine-s': 2048,
}
# Unicode defines 1,114,112 total “code... | 112 |
"""simple docstring"""
from math import sqrt
def __lowercase ( lowerCamelCase_ : int ):
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , int(sqrt(lowerCamelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCamelCase_ ):
total += i + n // i
... | 112 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
A : Dict = {
'cola': 2,
... | 140 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ..... | 714 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@... | 517 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler... | 65 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCamelCase__ =logging.get_logger(__name__)
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
def __init__( self , *__lowerCamelCase , ... | 249 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerat... | 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = ... | 12 | 1 |
'''simple docstring'''
__lowercase : Dict = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def lowercase_ ( ) -> None:
'''simple docstring'''
lowerCamelCase_ : int = input('''Enter message: ''' )
lowerCamelCase_ : Optional[int] = input('''Enter key [alphanumeric]:... | 422 | from heapq import heappop, heappush
import numpy as np
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> tuple[float | int, list[tuple[int, int]]]:
'''simple docstring'''
__lowercase , __lowercase = grid.shape
__low... | 321 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__snake_case :Optional[Any] ={
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokeni... | 705 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def lowerCamelCase_ ( lowerCAmelCase__ : Optional[A... | 224 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCamelCase_ = []
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> bool:
'''simple docstring'''
for i in range(len(__lowercase ) ):
if board[row][i] == 1:
... | 330 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A_ = {
"debug": logging.DEBUG,
... | 393 | 0 |
"""simple docstring"""
class lowerCAmelCase_ : # Public class to implement a graph
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ ) -> None:
__lowerCAmelCase = row
... | 705 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
def __init__( self ... | 573 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAv... | 659 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class lowerCa... | 201 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a ( _a ... | 714 | '''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = JukeboxTokenizer
__lowerCAmelCase = {
"""artist""... | 466 | 0 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: int ):
"""simple docstring"""
if length <= 0 or not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError('Length must be a positive integer.' )
... | 580 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copie... | 580 | 1 |
__UpperCamelCase : Tuple = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CAS... | 709 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : Optional[int] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base... | 106 | 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
if is_torch_available():
import torch... | 91 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterToke... | 155 | 0 |
# 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 tests
from os.path import j... | 15 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that... | 431 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import... | 431 | 1 |
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
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Union[... | 720 |
import os
_UpperCAmelCase : int = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def A ( lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = 0
UpperCamelCase = 0
while index < len(lowercase ) - 1:
UpperCamelCase = SY... | 3 | 0 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase = logging.get_logger(__name__)
U... | 45 |
def A ( lowercase__ : int ) -> Optional[Any]:
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def A ( lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : str ) -> List[str]:
if i >= h:
return
# If first element is smaller than the last the... | 45 | 1 |
'''simple docstring'''
from math import sqrt
def A_ ( SCREAMING_SNAKE_CASE_ = 1_00_00_00 ) ->int:
lowercase_ = 0
lowercase_ = 0
lowercase_ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if ... | 603 | '''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__snake_case = """
import os
"""
__snake_case = """
def foo():
import os
return False
"""
__snake_case = """
def foo():
def bar():
if True:
import os
... | 603 | 1 |
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