code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_tor... | 21 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former... | 325 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteSchedule... | 350 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https:... | 43 | 0 |
_lowercase : Dict ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowercase : str =["a", "b", "c", "d", "e"]
def lowerCAmelCase_ ( _lowercase : Optional[Any] , _lowercase : Tuple , _lowercase : int) -> Optional[Any]:
... | 170 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretr... | 169 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = collection[i]
__lowerCAmelCase = 0
__lowerCAmelCa... | 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
return [
a * b * (1000 - a - b)
for a in range(1 ,999 )
for b in range(lowercase_ ,999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution(... | 44 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCAmelCase ( lowercase_ = "isbn/0140328726" ):
UpperCAmelCase = olid.strip().strip('/' ) # Remove leading/trailing whitespace & ... | 78 | 0 |
from math import ceil
def snake_case_ ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] ):
__lowercase : List[Any] = list(range(0 , lowerCAmelCase_ ) )
__lowercase : Dict = [item for sublist in list(device_map.values() )... | 306 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase : Dict = get_tests_di... | 47 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =ArgumentPar... | 47 | 1 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = JukeboxTokenizer
UpperCamelCase__ = {
'''artist''': '''Zac Brown Band''',
'''g... | 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__... | 21 | 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 impo... | 36 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''... | 43 | 0 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('Coefficient \'a\' must ... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
... | 0 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _snake_case( ) -> int:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__... | 7 |
"""simple docstring"""
def lowercase ( _snake_case : int , _snake_case : int ) ->str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__snake_case : Tuple = str(bin(_snake_case ) ... | 102 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE :List[Any] = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
... | 359 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : int ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = [False] * l... | 156 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_im... | 306 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ )
_SCREAMING_SNAKE_CASE = [[0] * n for i in range(snake_case__ )]
for i... | 306 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffus... | 289 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : Dict = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconCon... | 289 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pa... | 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 | 1 |
from math import factorial, pi
def __a ( lowerCAmelCase_ : float ,lowerCAmelCase_ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ ,(int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or... | 354 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schu... | 277 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> -... | 205 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxCo... | 0 | 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 = {
"micro... | 2 | """simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx... | 2 | 1 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : Optional[Any] = ... | 269 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : str = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise... | 156 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( UpperCamelCase__ ):
a : Any = (DDIMParallelScheduler,)
a : Dict = (("""eta""", 0.0), ("""num_inference_steps""", 50))
def lowerCAm... | 350 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : Dict = "▁"
lowercase__... | 180 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ , lowerCA... | 289 | """simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class a :
def __init__( self : Union[str, Any] ):
_UpperCAmelCase = {}
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase ... | 289 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from... | 357 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ... | 320 | 0 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
... | 229 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
a_ ... | 277 | 0 |
import math
def A(__a: int = 100 ):
lowerCAmelCase_ = sum(i * i for i in range(1 , n + 1 ) )
lowerCAmelCase_ = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F'''{solutio... | 362 |
def A():
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCamelCase__ = generate_large_matrix()
lowerCamelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7... | 22 | 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
lowerCamelCase : List[str] = logging.get_logger(__name__)
lowerCamel... | 2 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
lowerCamelCase : Tuple = 'naver-clova-ix/donut-base'
class __lowerCAmelCase (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ (self : int ):
'... | 2 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
... | 16 |
'''simple docstring'''
from statistics import mean
import numpy as np
def lowercase_ ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAm... | 16 | 1 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
A: Optional[Any] = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_im... | 109 | from math import isqrt, loga
def snake_case ( snake_case__ :int) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , snake_case__ , snake_case__):
... | 180 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available... | 238 |
# 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 r... | 238 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Optional[Any] = logging.get_logger(__name__)
snake_case_ : Tuple = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/re... | 51 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization... | 320 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 212 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fr... | 212 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowercase_ , int(b / 2 ) ) * actual_power(lowercase_ , int(b / 2 ) )
else:
return ... | 78 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmel... | 22 | 0 |
import numpy as np
def UpperCamelCase ( snake_case__ : List[str] ) -> List[Any]:
return 1 / (1 + np.exp(-vector ))
def UpperCamelCase ( snake_case__ : List[str] ) -> Union[str, Any]:
return vector * sigmoid(snake_case__ )
if __name__ == "__main__":... | 355 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/data2vec-text-base''': '''https://hugg... | 103 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplic... | 16 |
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase_ = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
f... | 16 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
_lowerCamelCase : int = TypeVar("_T")
class __snake_case (Generic[_T] ):
def __init__( self : Dict , _UpperCAmelCase : Iterable[_T] | None = None ) -> List[Any]:
'''simple docs... | 368 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _UpperCAmelCase (UpperCamelCase_ : Sequence[float] , UpperCamelCase_ : int , UpperCamelCase_ : int ):
'''simple docstring'''
... | 159 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, ... | 238 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy... | 238 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_lowercase: Optional[int] = logging.get_logger(__name__)
def ... | 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
... | 71 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 212 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerCamelCase__ ... | 212 | 1 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int = 10_00 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 8 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 1 |
__A = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, Arraya... | 90 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replica... | 103 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : List[str] = {
"configuration_electra": ["ELECTRA... | 21 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ ... | 21 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 59 |
def _lowerCAmelCase ( lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Tuple , lowerCAmelCase_ :Any )->List[Any]:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowerCAmelCase_ , ... | 159 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
A_ : str = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgeto... | 292 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 292 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = [int(a_ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(a_ ) == 4 and all(0 <= int(a_ ) <= 254 for octet in octets )
if _... | 346 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransforme... | 267 |
import numpy as np
import datasets
UpperCAmelCase = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mah... | 267 | 1 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 8 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return [ord(SCREAMING_SNAKE_CASE__ ) - 96 for elem in plain]
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return "".join(chr(elem + 96 ) for elem in encoded ... | 8 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, 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_common import Model... | 354 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCamelCase : Tuple = 3_0_0 # TEMPERATURE (unit = K)
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , ) -> f... | 99 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_tor... | 21 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater tha... | 21 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, ... | 206 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
''... | 206 | 1 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unorder... | 292 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperC... | 292 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torc... | 367 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __init__( self , ... | 288 | 0 |
'''simple docstring'''
import os
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(a__ ) , """num.txt""" )
with open(a__ ) as file_hand:
return str(sum(int(a__ ) for line in file_hand )... | 267 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_... | 267 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def snake_case ( lowerCamelCase : ArgumentParser )-> int:
raise NotImplemented... | 353 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def snake_case__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any] ):
... | 272 | 0 |
'''simple docstring'''
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 TensorFlo... | 93 |
def A_ ( A__ ) -> int:
stooge(A__ , 0 , len(A__ ) - 1 )
return arr
def A_ ( A__ , A__ , A__ ) -> List[Any]:
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
... | 99 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 10_00 ):
"""simple docstring"""
_snake_case , _snake_case : List[Any] = 1, 1
_snake_case : str = []
for i in range(1 , n + 1 ):
_snake_case : Any = prev_num... | 132 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Optional[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = []
_snake_case : Dict = set({"""(""", """[""", """{"""} )
_snake_case : Union[str, Any] = set({""")""",... | 132 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCamelCase :Optional[int] = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConf... | 206 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowerCamelCase :str = TypeVar('''T''')
class _lowerCAmelCase (... | 206 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( __snake_case ):
lowercase__: Union[str, Any] = ['''image_processor''', '''tokenizer''']
lowercase__: i... | 351 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, s... | 13 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenize... | 10 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 198 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( _A : List[Any] , _A : List[str]=7 )-> Optional[Any]:
"""simple docstring"""
A__ = None
if token is ... | 198 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokeniz... | 267 | '''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : str = '''EncodecFeatureExtractor'''
Upper... | 272 | 0 |
"""simple docstring"""
class lowercase__ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict ) -> int:
... | 241 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCL... | 241 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a :Dict = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Bli... | 132 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.ut... | 132 | 1 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https:/... | 362 |
"""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_copies.py
A_ ... | 296 | 0 |
def lowerCamelCase__ ( snake_case_ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
__snake_case = set()
# Replace all the whitespace in our sentence
__snake_case = input_str.replace(''' ''' , '''''' ... | 24 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
... | 13 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : int = {
"""facebook/wav2vec2-base-960h""": """h... | 9 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassifi... | 9 | 1 |
'''simple docstring'''
from torch import nn
def __UpperCamelCase ( UpperCAmelCase ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {act_fn}""" )
... | 198 | '''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__a: Optional[int] = 4
__a: Optional[Any] = 3
class UpperCAmelCase (... | 198 | 1 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowercase__ = 299792458
# Symbols
lowercase__ , lowercase__ , lowercase__ , lowercase__ = symbols("""ct x y z""")
def __lowerCamelCase ( _... | 161 |
"""simple docstring"""
from __future__ import annotations
import math
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Dict , a_ : int ):
lowerCAmelCase_ : Union[str, Any] = size
# approximate the ove... | 161 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowercase__ = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 241 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
lowercase__ = TypeVar("""_T""")
class __lowerCamelCase ( Generic[_T] ):
'''simple docstring'''
def __init__( self : Optional[int] , a_ : Iterable[_T] | None = No... | 241 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 183 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number... | 183 | 1 |
from __future__ import annotations
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = str(_SCREAMING_SNAKE_CASE )
return len(_SCREAMING_SNAKE_CASE ) == 9 and set(_SCREAMING_SNAKE_CASE ) == set('''123456789''' )
def _lowercase ( ):
... | 275 |
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... | 296 | 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 ... | 337 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_lowerCamelCase : Any = {
# 1536-bit
5: {
... | 337 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
__lowerCAmelCase : List[str] ={
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve... | 9 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import to... | 9 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __magic_name__ :
__A : int
... | 172 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = '... | 172 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a__ : Tuple = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfi... | 161 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings... | 161 | 1 |
from random import randint, random
def A ( a_ ,a_ ,a_ ,a_ = False ,a_ = False ,a_ = 5 ,) -> list:
__UpperCamelCase : Dict =[[-1] * number_of_cells] # Create a highway without any car
__UpperCamelCase : Union[str, Any... | 359 |
def A ( a_ ) -> bool:
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 245 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : Optional[int] , _lowerCamelCase ... | 183 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
... | 183 | 1 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTes... | 149 | """simple docstring"""
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 transform... | 149 | 1 |
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
if is_tf_available():
... | 337 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
... | 337 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowerCAmelCase = logging.ge... | 367 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, 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
if is_f... | 172 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int= logging.get_logger(__name__)
_a : Optional[Any]= {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/reso... | 172 | 1 |
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 TOKEN, USER, g... | 71 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A ... | 71 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class a_ ( snak... | 58 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quie... | 245 | 0 |
"""simple docstring"""
__UpperCamelCase : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __SCREAMING_SNAKE_CASE ( A_ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(A_ , A_ ):
lowerCAmelCase__ : ... | 74 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__UpperCamelCase : Union[str, Any] = namedtuple(
... | 74 | 1 |
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 imp... | 149 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _a :
"""simple docstring"""
def __init__( self: ... | 149 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
d... | 128 |
"""simple docstring"""
from __future__ import annotations
import math
def a_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
... | 128 | 1 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ) -> Tuple:
return getitem, k
def SCREAMING_SNAKE_CASE ( _... | 44 | """simple docstring"""
import requests
from bsa import BeautifulSoup
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = BeautifulSoup(requests.get(lowerCamelCase , params=lowerCamelCase ).content , 'html.parser' )
UpperCAmelCa... | 98 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 19 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterM... | 19 | 1 |
def A ( a_ ,a_ ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def A ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or_gate(1 ,0 ) ==... | 71 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word... | 71 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class SCREAMING_SNAKE_CASE__ ( datasets.BeamBasedBuilder ):
"""simple docs... | 360 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow hav... | 74 |
"""simple docstring"""
from string import ascii_uppercase
_lowercase = {char: i for i, char in enumerate(ascii_uppercase)}
_lowercase = dict(enumerate(ascii_uppercase))
def _snake_case ( snake_case__ : str , snake_case__ : str ):
A = len(snak... | 74 | 1 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100 ):
"""simple docstring"""
a :Union[str, Any] = n * (n + 1) * (2 * n + 1) / 6
a :List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
... | 358 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : List[str] = logging.get_logger(__name__)
snake_case : Optional[Any] = {
'''vocab_file''': '''vocab... | 281 | 0 |
UpperCAmelCase : List[str] ={0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase : Any ={0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
UpperCamelCase_ ... | 128 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase : Dict =TypeVar("""T""")
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case__ ):
... | 128 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
snake_case_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE__ ... | 238 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class SCR... | 238 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__A ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A ={
'''vocab_fil... | 19 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optional... | 19 | 1 |
import math
_A = 10
_A = 7
_A = BALLS_PER_COLOUR * NUM_COLOURS
def __UpperCamelCase ( _A = 20 ):
lowerCAmelCase_ = math.comb(_A , _A )
lowerCAmelCase_ = math.comb(NUM_BALLS - BALLS_PER_COLOUR , _A )
lowerCAmelCase_ ... | 167 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 167 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils impor... | 98 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_lowerCAmelCase = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems... | 37 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A = True
except (ImportError, ModuleNotFoundError):
A = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def ... | 188 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A = '''src/transformers'''... | 188 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowerCamelCase : int = pytest.mark.integration
@pytest.mark.parametrize("""path"... | 219 |
import math
def lowerCAmelCase_ ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return math.pow(_snake_case , 2 ) - a
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstri... | 281 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
snake_case = False
snake_case = True
snake_case = False
if __name__ == "__main__":
snake_case = argp... | 319 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 319 | 1 |
"""simple docstring"""
# Imports
import numpy as np
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Any, lowerCamelCase : List[Any]=None, lowerCamelCase : Union[str, Any]=None, lowerCamelCase : Dict=None, lowerCamelCase ... | 238 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTo... | 238 | 1 |
"""simple docstring"""
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = [False] * len(__lowerCAmelCase )
UpperCAmelCase = [-1] * len(__lowerCAmelCase )
def dfs(_snake_case , _snake_case ):
Upper... | 362 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case , _snake_case = None , _snake_case = None ):
"""simple docstring"""
if start is None:
UpperCAmelCase = 0
if end is None:
UpperCAmelCase =... | 234 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.