code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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 logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAM... | 0 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('check_bouncy() accepts only integer arguments' )
_A = str(_S... | 27 | 0 |
from math import pow, sqrt
def _A ( *_lowercase ) -> bool:
"""simple docstring"""
__UpperCamelCase = len(_lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _lowercase , _lowercase ) -> float | ValueError:
... | 1 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.2_5) = }")
print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) ... | 27 | 0 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configurat... | 2 |
from collections.abc import Callable
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
_A = a
_A = b
if function(_SCREAMING_S... | 27 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : str = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONF... | 3 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 27 | 0 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = data
lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return F... | 4 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_blenderbot": [
"BLENDER... | 27 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def A (__lowerCamelCase :NDArray[floataa] , __lowerCamelCase :NDArray[floataa] , __lowerCamelCase :list[int] , __lowerCamelCase :int , ):
... | 5 |
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 | 0 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: List[str] ):
SCREAMING_SNAKE_CASE__ = len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = sum(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for... | 6 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCAmelCase( ) -> None:
"""simple docstring"""
... | 27 | 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
a = logging.get_logger(__name__)
a = {
'''goo... | 7 |
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 | 0 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, Test... | 8 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase( ... | 27 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A ( __UpperCamelCase ) -> Optional[Any]:
A__ = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.vers... | 9 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAM... | 27 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
f... | 10 |
# Copyright 2022 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 r... | 27 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return 10 - x * x
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if equation(__A) * equation(__A) >= 0:
raise ValueError('''Wrong space!''')
_a = a
... | 11 |
from ... import PretrainedConfig
__A : Optional[Any] = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = ... | 27 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtrac... | 12 |
from collections import defaultdict
from math import ceil, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = 1_000_000 , _SCREAMING_SNAKE_CASE = 10 ) -> int:
"""simple docstring"""
_A = defaultdict(_SCREAMING_SNAKE_CASE )
... | 27 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
A__ : Any = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
A__ : Union[str, Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
A__ : str = [2... | 13 |
from math import pi, sqrt, tan
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
... | 27 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a__ = logging.get_logger(__name__)
# TODO: upload to AWS
a__ = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/... | 14 |
import numpy as np
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> np.array:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
def UpperCamelCase ( __magic_name__ : Optional[Any] , __magic_name__ : Tuple ) -> int:
"""simple docstring"""
lowercase__ = """"""
for i in table:
res += inp[i - 1]
return res
def UpperCamelCase ( __magic_name__ : int... | 15 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 27 | 0 |
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_features_output_indices
... | 16 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A : List[Any] = "http://www.m... | 27 | 0 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __vers... | 17 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
_A = int(number**0.5 )
return number == sq * sq
... | 27 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
... | 18 |
from __future__ import annotations
import math
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
if num <= 0:
_A = F"{num}: Invalid input, please enter a positive integer."
raise ValueErro... | 27 | 0 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_a = None
try:
import msvcrt
except ImportError:
_a = None
try:
import fcntl
except ImportError:
_a = None... | 19 |
__A : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 27 | 0 |
def _lowercase( __a : str , __a : str ):
assert x is not None
assert y is not None
a__ =len(__a )
a__ =len(__a )
# declaring the array for storing the dp values
a__ =[[0] * (n + 1) for _ in range(m + 1 )] # noqa:... | 20 |
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 lowerCamelCase( un... | 27 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeniz... | 21 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 27 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from trans... | 22 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('check_bouncy() accepts only integer arguments' )
_A = str(_S... | 27 | 0 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase , __lowercase):
UpperCamelCase_ = cva.getAffineTransform(__lowercase , __low... | 23 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.2_5) = }")
print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) ... | 27 | 0 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch... | 24 |
from collections.abc import Callable
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
_A = a
_A = b
if function(_SCREAMING_S... | 27 | 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 (
... | 25 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 27 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _a ( _lowerCamelCase , _lowerCamelCase ) -> float:
"""simple docstring"""
__snake_case : Any = u
for i in range(1 , _lowerCamelCase... | 26 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_blenderbot": [
"BLENDER... | 27 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
... | 28 |
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 | 0 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pip... | 29 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCAmelCase( ) -> None:
"""simple docstring"""
... | 27 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
return len(set(_lowercase ) ) == len(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 30 |
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 | 0 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requi... | 31 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase( ... | 27 | 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 __UpperCamelCase ( ... | 32 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAM... | 27 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = "" ) -> dict[str, float]:
snake_case__ = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
snake_case__ = BeautifulSo... | 33 |
# Copyright 2022 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 r... | 27 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attenti... | 34 |
from ... import PretrainedConfig
__A : Optional[Any] = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = ... | 27 | 0 |
import argparse
import os
import re
import packaging.version
a_ :int = 'examples/'
a_ :Dict = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__ver... | 35 |
from collections import defaultdict
from math import ceil, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = 1_000_000 , _SCREAMING_SNAKE_CASE = 10 ) -> int:
"""simple docstring"""
_A = defaultdict(_SCREAMING_SNAKE_CASE )
... | 27 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[Any] = logging.get_logger(__name__)
__lowercase : Dict = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xl... | 36 |
from math import pi, sqrt, tan
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
... | 27 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase : Any = False
class A__ ( unittest.TestCase ):
"... | 37 |
import numpy as np
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> np.array:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __snake_case :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
snake_case__ : List[Any] = str(id_ )
snake_case__ : Dict... | 38 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 27 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.ut... | 39 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A : List[Any] = "http://www.m... | 27 | 0 |
from __future__ import annotations
import math
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> None:
UpperCamelCase : str = size
# approximate the overall size of segment tree with given value
UpperCamelC... | 40 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
_A = int(number**0.5 )
return number == sq * sq
... | 27 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfi... | 41 |
from __future__ import annotations
import math
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
if num <= 0:
_A = F"{num}: Invalid input, please enter a positive integer."
raise ValueErro... | 27 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
lowerCamelCase_ = str(__UpperCamelCase )
return len(__UpperCamelCase ) == 9 and set(__UpperCamelCase ) == set('123456789' )
def _UpperCamelCase ( ) -> ... | 42 |
__A : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 27 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def _a ( SCREAMING_SNA... | 43 |
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 lowerCamelCase( un... | 27 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = [
"encoder... | 44 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 27 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
... | 45 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('check_bouncy() accepts only integer arguments' )
_A = str(_S... | 27 | 0 |
"""simple docstring"""
from math import sqrt
def lowerCamelCase_( _lowerCamelCase = 1000000 ) -> int:
'''simple docstring'''
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 46 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.2_5) = }")
print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) ... | 27 | 0 |
import socket
def UpperCAmelCase__ ( ):
__a : int = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__a : Optional[Any] = socket.gethostname()
__a : int = 1_2_3_1_2
sock.connect((host, port) )
sock.sen... | 47 |
from collections.abc import Callable
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
_A = a
_A = b
if function(_SCREAMING_S... | 27 | 0 |
'''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_d... | 48 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 27 | 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 imp... | 49 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_blenderbot": [
"BLENDER... | 27 | 0 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate imp... | 50 |
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 | 0 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def __snake_case ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str]=None ) -> List[Any]:
"""simple docstring"""
... | 51 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCAmelCase( ) -> None:
"""simple docstring"""
... | 27 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask... | 52 |
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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case : List[str] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
... | 53 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase( ... | 27 | 0 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAM... | 27 | 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
@pyt... | 55 |
# Copyright 2022 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 r... | 27 | 0 |
'''simple docstring'''
def _a (lowercase__ : int = 3 , lowercase__ : int = 7 , lowercase__ : int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
__snake_case = 0
__snake_case = 1
for current_denominator in range(1 ... | 56 |
from ... import PretrainedConfig
__A : Optional[Any] = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = ... | 27 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class _lowerCAmelCase( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCa... | 57 |
from collections import defaultdict
from math import ceil, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = 1_000_000 , _SCREAMING_SNAKE_CASE = 10 ) -> int:
"""simple docstring"""
_A = defaultdict(_SCREAMING_SNAKE_CASE )
... | 27 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exce... | 58 |
from math import pi, sqrt, tan
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
... | 27 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : str) ->... | 59 |
import numpy as np
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> np.array:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
l... | 60 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 27 | 0 |
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 accelerate impor... | 61 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A : List[Any] = "http://www.m... | 27 | 0 |
snake_case = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
snake_case = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] ... | 62 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
_A = int(number**0.5 )
return number == sq * sq
... | 27 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 63 |
from __future__ import annotations
import math
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
if num <= 0:
_A = F"{num}: Invalid input, please enter a positive integer."
raise ValueErro... | 27 | 0 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCamelCase ( UpperCamelCase_ , unittest.TestCase ):
__a ... | 64 |
__A : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 27 | 0 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase ( __UpperCamelCase = 100 ):
'''simple docstring'''
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter t... | 65 |
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 lowerCamelCase( un... | 27 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is... | 66 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 27 | 0 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 67 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('check_bouncy() accepts only integer arguments' )
_A = str(_S... | 27 | 0 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart... | 68 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.2_5) = }")
print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) ... | 27 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 |
from collections.abc import Callable
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
_A = a
_A = b
if function(_SCREAMING_S... | 27 | 0 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase : Any = logging.get_logger(__name__)
class A( UpperCamelCase ):
'''simple docstring'''
def __init__( self : ... | 70 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 27 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 71 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_blenderbot": [
"BLENDER... | 27 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch... | 72 |
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 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTok... | 73 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCAmelCase( ) -> None:
"""simple docstring"""
... | 27 | 0 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("""fixtures/spiece.model""")
@... | 74 |
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 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def a__ ( lowerCAmelCase__ = "isbn/0140328726" ) -> dict:
UpperCAmelCase__ : str = olid.strip().strip('''/''' ) # Remove leadin... | 75 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase( ... | 27 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
__lowercase : Optional[int] = len(__UpperCamelCase )
__lowercase : Dict = len(__UpperCamelCase )
__lowercase : int = [[False for _ in range(m + ... | 76 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAM... | 27 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 |
# Copyright 2022 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 r... | 27 | 0 |
'''simple docstring'''
from PIL import Image
def lowerCAmelCase_ ( snake_case_ : Image ) -> Image:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ = image.size
UpperCAmelCase_ = 0
UpperCAmelCase_ = image.l... | 78 |
from ... import PretrainedConfig
__A : Optional[Any] = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = ... | 27 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase=None ) -> str:
'''simple docstring'''
UpperCAm... | 79 |
from collections import defaultdict
from math import ceil, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = 1_000_000 , _SCREAMING_SNAKE_CASE = 10 ) -> int:
"""simple docstring"""
_A = defaultdict(_SCREAMING_SNAKE_CASE )
... | 27 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __UpperCamelCase ( _lowerCAmelCase , _... | 80 |
from math import pi, sqrt, tan
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
... | 27 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...tes... | 81 |
import numpy as np
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> np.array:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus impor... | 82 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 27 | 0 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class __snake_case :
def __init__( self : Union[str, Any] , __lowerCAmelCase : list[tuple[float, float]] ):
"""simple docstring"""
_lowerCamelC... | 83 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A : List[Any] = "http://www.m... | 27 | 0 |
from __future__ import annotations
UpperCAmelCase = 8.988e9 # units = N * m^s * C^-2
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = abs(chargea * chargea )
if (f... | 84 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
_A = int(number**0.5 )
return number == sq * sq
... | 27 | 0 |
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_SNAKE_CASE__ ... | 85 |
from __future__ import annotations
import math
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
if num <= 0:
_A = F"{num}: Invalid input, please enter a positive integer."
raise ValueErro... | 27 | 0 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__a :Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https://huggingfa... | 86 |
__A : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 27 | 0 |
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybr... | 87 |
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 lowerCamelCase( un... | 27 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers... | 88 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 27 | 0 |
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, random_attentio... | 89 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('check_bouncy() accepts only integer arguments' )
_A = str(_S... | 27 | 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__ ( a__ ):
'''simple docstring'''
def __lt__( self ... | 90 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.2_5) = }")
print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) ... | 27 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''],
}
try:
if not is_torch_availa... | 91 |
from collections.abc import Callable
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
_A = a
_A = b
if function(_SCREAMING_S... | 27 | 0 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMix... | 92 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 27 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A = datasets.load_iris()
__A = np.array(data["""data"""])
__A = np.array(data["""target"""])
__A ... | 93 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_blenderbot": [
"BLENDER... | 27 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''flax''']
def __init__( self : Dict , *UpperCAmelCase : List[Any] , ... | 94 |
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 | 0 |
"""simple docstring"""
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
lowerCamelCase_ ... | 95 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCAmelCase( ) -> None:
"""simple docstring"""
... | 27 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
def a ( __UpperCAmel... | 96 |
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 | 0 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a ( snake_case__: bytes ):
'''simple docstring'''
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case__ , snake_case__ ):
lowercase_ = F'''... | 97 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase( ... | 27 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_availab... | 98 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAM... | 27 | 0 |
# 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.configuration_utils ... | 99 |
# Copyright 2022 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 r... | 27 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_A : str = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S 9S AC""",
"""KD 6S... | 100 |
from ... import PretrainedConfig
__A : Optional[Any] = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = ... | 27 | 0 |
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