code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
A_ : Tuple = datasets.logging.get_logger(__name__)
A_ : Any = '''\
@InProceedings{moosavi2019... | 215 |
# flake8: noqa
# Lint as: python3
lowerCamelCase : Optional[Any] = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .log... | 233 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common imp... | 366 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
return base * power(__UpperCAmelCase, (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion... | 56 |
'''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 ... | 56 | 1 |
from math import factorial
def A(__a: int , __a: int , __a: float ):
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers" )
if not isinstan... | 22 |
import logging
from transformers import PretrainedConfig
lowerCamelCase__ = logging.getLogger(__name__)
lowerCamelCase__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
cl... | 22 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 264 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowercase ( _a , _a ):
# Load checkpoint... | 264 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
fr... | 368 |
'''simple docstring'''
from __future__ import annotations
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> None:
lowerCAmelCase__ = order
# a_{0} ... a_{k}
... | 228 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[Any] ) -> bool:
__lowercase = 0
for ch in input_str:
__lowercase = ord(__snake_case )
__lowercase = pow(2 , __snake_case )
# If we already turned o... | 325 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__snake_case : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluati... | 269 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 361 |
from __future__ import annotations
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : Any , __magic_name__ : List[Any] , __magic_name__ : str ): # noqa: E741
while r - l > 1:
a__: Any =(l + r) // 2
if v[m] >... | 42 | 0 |
"""simple docstring"""
def lowercase ( )-> int:
'''simple docstring'''
return 1
def lowercase ( A_ )-> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def lowercase ... | 40 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowercase ( A_ , A_ , A_ , A_ )-> Union[str, Any]:
'''simple docstring'''
a : Any = sorted(zip(A_ , A_ ) , key=lambda A_ ... | 40 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
... | 88 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informe... | 88 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"fea... | 311 | 1 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
def __a ( UpperCAmelCase=None , Upp... | 337 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
... | 337 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( UpperC... | 257 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 257 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class a__( unittest.TestCase , lowerCamelCase__ ):
def lowercase_ ( self : Dict ):
a : Optional[Any] = load_tool('text-... | 367 |
'''simple docstring'''
import torch
from transformers import AutoModel
class a__( torch.nn.Module ):
def __init__( self : Any , __snake_case : str="sayef/fsner-bert-base-uncased" ):
super(__snake_case , self ).__init__()
a : List[s... | 96 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
fro... | 15 | """simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threade... | 44 | 0 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import ... | 353 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from ... | 227 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeniza... | 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" ... | 22 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import A... | 359 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .t... | 286 | 0 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase__ = 6_3_7_8_1_3_7.0
lowerCAmelCase__ = 6_3_5_6_7_5_2.3_1_4_2_4_5
lowerCAmelCase__ = 6378137
def snake_case_ ( A_ : float, A_ : float, A_ : floa... | 72 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> np.ndarray:
lowercase : List[Any] = int(np.ceil((x_end -... | 350 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
fro... | 53 | 0 |
__snake_case = "Input must be a string of 8 numbers plus letter"
__snake_case = "TRWAGMYFPDXBNJZSQVHLCKE"
def _A ( _lowercase ) -> bool:
"""simple docstring"""
if not isinstance(__A , __A ):
__UpperCamelCase = ... | 310 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 42 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = ['image_processor', 'feature_extractor']
lowerCAmelCase__ = 'TvltImageProcessor'
lowerCAmelCase__ = 'TvltFeatureEx... | 355 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.... | 299 | 0 |
def a__ ( A_, A_ ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def a__ ( ):
'''simple docstring'''
assert xnor_gate(0, 0 ) == 1
assert xnor_gate(0, 1 ) == 0
assert xnor_gate(1, 0 ) == 0... | 88 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data im... | 88 | 1 |
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 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 71 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase=None, _UpperCamelCase=None ) ->List[Any]:
"""si... | 337 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowercase ( _UpperCamelCase = 8 ) ->str:
"""simple docstring"""
lowercase : List[str] = ascii_letters + digits + punctuation
... | 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_A = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if ... | 358 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"nvidia/segformer-b0-fine... | 137 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 15 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid... | 96 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",... | 316 |
"""simple docstring"""
def lowerCamelCase_ ( ):
lowerCamelCase__ : Optional[Any] = []
lowerCamelCase__ : List[Any] = 1
while len(_lowerCamelCase ) < 1e6:
constant.append(str(_lowerCamelCase ) )
i += 1
lowerCamelCase__ : str = ... | 316 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import ... | 330 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modelin... | 330 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {'''configuration_plbart''': ['''PLBART_PRETRAI... | 364 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
... | 334 | 0 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> list[int]:
lowerCamelCase__ : Tuple = []
lowerCamelCase__ : int = 2
lowerCamelCase__ : str = int(... | 41 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
# flake8: noqa
# Lint as: python3
__a :int = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is... | 329 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_c... | 329 | 1 |
'''simple docstring'''
import os
lowercase : Optional[int] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def SCREAMING_SNAKE_CASE__ ( __A ) -> int:
_snake_case = 0
_snake_case = 0
while index < len(__A ) - 1:
_snake_case ... | 42 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise Option... | 53 | 0 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torc... | 220 |
'''simple docstring'''
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_u... | 220 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( __a : dict ,__a : str ) -> set[str]:
"""simple docstring"""
_a , _a : List[str] = set(__a ), [start]
while stack:
_a : Optional[Any] = stack.pop()
e... | 235 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTrans... | 366 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 167 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from tran... | 71 |
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_m... | 71 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : List[Any] = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Sque... | 46 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxrun... | 46 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 8 , _SCREAMING_SNAKE_CASE : int | None = None ):
__a : int = np.random.default_rng(seed=_SCREAMING_SNAKE_CASE )
# Roughly 25% of the qubit... | 27 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import Au... | 137 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( __a ) -> list[int]:
"""simple docstring"""
if num <= 0:
lowerCamelCase__: Any =F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(__a )
lowerCamelCase__: Lis... | 273 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = "▁"
__A = {"vocab_... | 273 | 1 |
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedL... | 220 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : List[str] , __snake_case : List[str] ):
'''simple docstring'''
lowercase = ''
for i in table:
res += inp[i - 1]
return res
def _SCREAMING_SNAKE_CASE ( __snake_case : str ... | 220 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
... | 359 |
from __future__ import annotations
def lowerCAmelCase_ ( __a , __a ) -> List[Any]:
"""simple docstring"""
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(__a ):
print(F"""{i}\t\t{d}""" )
def lowerCAmelCase_ ( __a , ... | 273 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RE... | 53 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 334 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWit... | 137 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=A_ ):
UpperCAmelCase__ : Union[str, Any] = ["onnx"]
def __init__(self : Tuple , *_A : Optional[int] , **_A : Any ) -> Dict:
... | 137 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( A__ ):
UpperCamelCase__ = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE ( self , **_a ):
__magic_name__ : Tuple = {
... | 281 |
import numpy as np
from PIL import Image
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = np.array(_A )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
SCR... | 314 | 0 |
"""simple docstring"""
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipelin... | 354 |
"""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 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
'''simple docstring'''
assert column_title.isupper()
lowercase = 0
lowercase = len(__snake_case ) - 1
lowercase = 0
while index >= 0:
lowercase ... | 220 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=__snake_case , defaul... | 220 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface... | 62 |
import string
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
UpperCamelCase :List[str] = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
UpperC... | 62 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase__ ( A__ : Dict ):
'''simple docstring'''
... | 12 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase ( __UpperCA... | 167 | 0 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 143 | import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.... | 143 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_t... | 46 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torc... | 46 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_confi... | 118 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGE... | 118 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__A : Union[str, Any] = logging.get_logger(__name__)
class A_ (a_ ):
UpperCAmelCase__ = '''upernet'''
def __init__( self , ... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 1 |
def A ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
stooge(__UpperCAmelCase , 0 , len(__UpperCAmelCase ) - 1 )
return arr
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
'''si... | 344 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase_ = [ord(letter) for letter in string.ascii_lowercase]
UpperCam... | 344 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__A = logging.get_logger(__name__)
class snake_case ( a_ ):
def __init__( self : str , *UpperCamelCase__ : Tuple , *... | 217 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 0 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCamelCase : Any = '''src/transformers''... | 309 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets b... | 309 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Optional[int] = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependency... | 137 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _snake_case ( A__ ):
_lowercase : int = '''Speech2TextFeatureExtractor'''
_lowercase : List[Any] = '''Speech2TextTokenizer'''
def __init__( ... | 137 | 1 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> bool:
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
... | 111 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
... | 111 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | 45 |
'''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 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
'''simple docstring'''
snake_case : Any = [
... | 83 |
'''simple docstring'''
from collections.abc import Generator
def _UpperCamelCase ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case ,snake_case : Tuple = 0, 1
while True:
snake_case ,snake_case : List[Any] = b, a + b
yie... | 83 | 1 |
import math
import sys
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
__UpperCamelCase =''
try:
with open(SCREAMING_SNAKE_CASE__ , 'rb' ) as binary_file:
__UpperCamelCase =binary_file.read()
for dat in data:
... | 62 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAm... | 62 | 1 |
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
pass
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
pass
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : ... | 366 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCAmelCase__ : Tuple = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['export',... | 301 | 0 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelT... | 143 | 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()
lowerCAmelCase__ : int = [
'''word_embeddings_l... | 143 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.uti... | 184 |
'''simple docstring'''
import unittest
import numpy as np
import requests
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_inpu... | 184 | 1 |
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) <= 1:
return [tuple(__UpperCamelCase )]
SCREAMING_SNAKE_CASE_ = []
def generate(__UpperCamelCase , __UpperCamelCase ):
if k == 1:
res.append(tuple(arr[:] ) )
... | 118 | import math
from datetime import datetime, timedelta
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = year % 1_9
SCREAMING_SNAKE_CASE_ = year % 4
SCREAMING_SNAKE_CASE_ = year % 7
SCREAMING_SNAKE_CASE_ = math.floor(year / 1_0_0 )
SCRE... | 118 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : Dict, UpperCamelCase__ : Any ):
'''simple docstring'''
UpperCamelCase__ = ''''''
for i in table:
res += inp[i - 1]
return res
def lowerCamelCase_ ( UpperCamelCase__ ... | 365 | import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase = {
"""facebook/maskformer-swin-base-ade""": (
... | 35 | 0 |
'''simple docstring'''
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
stooge(a_ , 0 , len(a_ ) - 1 )
return arr
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[Any]:
"""simple do... | 344 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCAmelCase ( a_ , a_ ) -> tuple:
"""simple docstring"""
if inductance <= 0:
raise ValueError("""Inductance cannot be 0 or negative""" )
... | 344 | 1 |
'''simple docstring'''
import random
def _UpperCAmelCase ( _UpperCamelCase : int ) -> bool:
A_ = num - 1
A_ = 0
while s % 2 == 0:
A_ = s // 2
t += 1
for _ in range(5 ):
A_ = r... | 18 | '''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_availabl... | 18 | 1 |
'''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
UpperCamelCase_ = """src/transformers"""
UpperCamelCase_ ... | 309 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_... | 309 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a n... | 368 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 201 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tok... | 111 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
f... | 111 | 1 |
from __future__ import annotations
from math import pi
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 218 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/viv... | 218 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...... | 83 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowercase__ ( lowercase ):
def __init__( self : Any ,lowerCamelCase__ : str ,lowerCamelCase__ : Tuple ,lowerCame... | 83 | 1 |
from ...configuration_utils import PretrainedConfig
snake_case_ : Optional[Any] = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.... | 7 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ... | 7 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( A__ ):
lowerCamelCase : Optional[Any] =["image_processor", "tokenizer"]
lowerCamelCase : Optional[Any] ="ViTImageProc... | 67 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImagePr... | 301 | 0 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 356 |
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
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentencepi... | 47 | 0 |
A : Optional[int] = 65521
def lowercase_ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : Dict = 1
lowerCamelCase__ : List[Any] = 0
for plain_chr in plain_text:
lowerCamelCase__ : str = (a + ord(_A ... | 184 |
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_inpu... | 184 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowerCAmelCase :
'''simple docstring'''
_A : List[str] = None
def lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
... | 306 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 306 | 1 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : Optional[Any] ):
'''simple docstring'''
def brightness(lowercase : Optional[int] ) -> float:
return 1_28 + level + (c - 1_28)
if not -255.... | 204 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1_000 ) -> int:
return sum(e for e in range(3 , _lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 35 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table im... | 17 | '''simple docstring'''
import unittest
import numpy as np
import requests
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
... | 17 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__lowerCamelCase : List[Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__lowerCamelCase : List[Any] = typing.Union[np.floataa, int, float] # noqa: UP007
... | 18 | import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : Union[str, ... | 18 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase: Optional[int] = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFI... | 96 |
'''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, add_start... | 96 | 1 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # s... | 14 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResamplin... | 201 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : float ,__lowerCamelCase : float ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(100, 0.25) = }''')
print(F'''{price_plus_tax(125.50, 0.05) = }''')
| 147 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a_ ... | 147 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_ut... | 218 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( lowerCAmelCase_ ):
S... | 218 | 1 |
import numpy as np
from PIL import Image
def _a ( lowerCamelCase: np.ndarray , lowerCamelCase: int , lowerCamelCase: int ) -> np.ndarray:
'''simple docstring'''
__A = np.array(lowerCamelCase )
if arr.shape[0] != arr.shape[1]:
... | 364 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : ... | 250 | 0 |
from ...configuration_utils import PretrainedConfig
lowercase_ = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-f... | 7 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Toke... | 7 | 1 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attent... | 369 | '''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)... | 18 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class snake_case_:
__UpperCamelCase = 42
__UpperCamelCase = None
__UpperCamelCas... | 60 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase : Dict = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
"ut/deta": "https://huggingface.co/ut/deta/resol... | 47 | 0 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A_ ( _lowerCAmelCase : Any, _lowerCAmelCas... | 153 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def A_ ( _lowerCAmelCase : str="ro", _lowerCAmelCase : Optional[Any]="en", _lowerCAmelCase : Union[str, Any]="wmt16", _lowerCAmelCase : int=None ):
"""simple... | 153 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCamelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any , ... | 237 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if num < 0:
return False
snake_case_ = num
snake_case_ = 0
while num > 0:
snake_case_ = rev_num * 10 + (num % 10)
num //= 10
return ... | 347 | 0 |
"""simple docstring"""
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = {}
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
... | 356 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "feature_extractor"]
__UpperCamelCase = "TvltImageProcessor"
_... | 318 | 0 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 17 |
"""simple docstring"""
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 (
Autoenco... | 17 | 1 |
_UpperCAmelCase = 8.314_4598
def UpperCamelCase ( __lowercase : float ,__lowercase : float ):
'''simple docstring'''
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
if molar_mass <= 0:
raise Exception('Molar mass cannot be... | 361 | 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 = namedtuple(
"""_TestCommandArgs""",
[
... | 192 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = 42
l... | 96 |
"""simple docstring"""
lowercase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": ""... | 96 | 1 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(m... | 360 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
de... | 349 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase_ (lowerCAmelCase__: Optional[Any] ):
"""simple docstring"""
def wrapper(*lowerCAmelCase_... | 147 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD... | 147 | 1 |
'''simple docstring'''
import math
__a = 10
__a = 7
__a = BALLS_PER_COLOUR * NUM_COLOURS
def __UpperCAmelCase ( a_: int = 20 ):
_UpperCAmelCase : int = math.comb(a_, a_ )
_UpperCAmelCase : Tuple = math.com... | 17 | '''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""roberta-... | 4 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : int | float | str ):
try:
A__ = float(_lowerCamelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
A__ = decimal - int(_lowerCamelCase )
... | 237 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> str:
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed to the function""" ... | 368 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case( ) -> tuple[list[int], int]:
lowercase : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
lowercase : ... | 285 | 0 |
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