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 |
|---|---|---|---|---|
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Optional[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
if i >= h:
... | 156 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_... | 296 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_availabl... | 48 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Args:
predictions ... | 48 | 1 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _UpperCAmelCase :
'''simple docstring'''
pass
| 102 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def snake_case_ ( A_ : Any ):
'''simple docstring'''
_lowerCamelCase : Any = [
'''encoder.ve... | 72 | 0 |
from typing import Any
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , __lowerCamelCase : Any ) -> Any:
A : Union[str, Any] = data
A : Any = None
... | 356 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_ava... | 256 | 0 |
import random
class A__ :
"""simple docstring"""
@staticmethod
def __lowercase ( lowercase) -> tuple[list[int], list[int]]:
'''simple docstring'''
a__ : Union[str, Any] = [ord(lowercase) for i in text]
a__ : int = []... | 99 |
"""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[Any] = {
'''configuration_distilbert''': [
... | 264 | 0 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None ):
if version.parse(hfh.__version__ ).rele... | 352 |
'''simple docstring'''
import functools
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
# Validation
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not all(isinstance(UpperCamelCase__ , UpperCamelCase__ ) for day in days ):
raise V... | 283 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : float , lowercase : int ) -> float:
if digit_amount > 0:
return round(number - int(lowercase ) , lowercase )
return number - int(lowercase )
if __name__ == "__main__":
... | 63 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked befor... | 370 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class _snake_case ... | 342 | 0 |
UpperCAmelCase_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = input("""Enter message: """ )
__lowerCamelCase = input("""Enter key [alphanumeric]: """ )
__lowerCamelCase = input(... | 12 |
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 onnxruntime as ort
UpperCAmelC... | 12 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__SCREAMING_SNAKE_CASE : List[Any] = {'''vocab_file''': '''vocab.txt''... | 73 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : bool = False ) -> list[fl... | 73 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a):
lowercase__ : Dict = value
lowercase__ : Node | None = None
lowercase__ : Node | None =... | 214 |
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_ = {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-... | 214 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A = logging.get_logger(__name__)
__A = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
cl... | 64 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( a__ ):
def __init__( self : Option... | 64 | 1 |
from __future__ import annotations
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return len(set(UpperCamelCase__ ) ) == len(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 285 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
... | 78 | import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 78 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowerCAmelCase__ : str = set()
# Replace all the whitespace in our sentence
lowerCAmelCase__ : Tuple ... | 37 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__na... | 37 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowerCamelCase_( _lowerCamelCase ) -> Optional[int]:
'''simple docstring'''
_lowerCamelCase : Di... | 369 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 | 0 |
import datasets
from .evaluate import evaluate
a_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
a_ ... | 175 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : str , __snake_case : str ) -> str:
__A : int = len(__snake_case )
__A : int = len(__snake_case )
__A : int = (
first_str_leng... | 190 |
'''simple docstring'''
import itertools
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 190 | 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_distilbert import DistilBertTokenizer
__a = logging.get_logger(__name__)
__a ... | 66 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 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 require... | 366 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress... | 252 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
'''configuration_re... | 181 |
'''simple docstring'''
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 AutoProc... | 181 | 1 |
from timeit import timeit
snake_case : Tuple = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure our test dat... | 353 |
from ...configuration_utils import PretrainedConfig
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'bert-generation'
def __init__( self , _lowerCamelCase=5_0358 , _lowerCamelCase=1024 , _lowerCamelCase=24 , _lowerCamelCase=16 , _lowerCamelCase=4096 , _lowerCamelCase="g... | 281 | 0 |
"""simple docstring"""
import os
from math import logaa
def _A ( UpperCamelCase_ : str = "base_exp.txt") -> int:
'''simple docstring'''
__lowercase = 0
__lowercase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCamelCase_), Upper... | 17 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggin... | 17 | 1 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not number >= 1:
raise ValueError(
... | 182 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 182 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full... | 205 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase = logging.get_logger(__name__)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = r'\w+[.]\d+'
... | 195 | 0 |
import numpy as np
def __snake_case ( _UpperCAmelCase ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 131 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case :Tuple = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not... | 131 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A_ ( unittest.TestCase ):
def lowercase ( self ... | 22 |
'''simple docstring'''
class a__ :
def __init__( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Tuple = n
_lowercase : Any = [None] * self.n
_lowercase : Tuple = 0 # index of the first element
_lower... | 250 | 0 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
return 1 if input_a == input_a else 0
def a__ ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 163 |
"""simple docstring"""
from collections.abc import Generator
from math import sin
def a__ ( __lowercase ) -> bytes:
if len(__lowercase ) != 32:
raise ValueError("Input must be of length 32" )
_A = B""
for i in [3, 2, 1, 0]:
littl... | 163 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase_ = '''\
'''
lowerCamelCase_ = '''
Perplexity (PPL) is one of the most common... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A : Optional[Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and... | 363 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 323 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> List[Any]:
if len(_lowerCAmelCase ) <= 1:
return lst
__lowerCamelCase = 1
while i < len(_lowerCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__lowerCamelCase , _... | 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 |
"""simple docstring"""
__snake_case : Any = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66... | 58 |
"""simple docstring"""
from ....utils import logging
__snake_case : Optional[Any] = logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: str , _SCREAMING_SNAKE_CA... | 58 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int , lowercase : int ) -> str:
return base * power(_a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
... | 63 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
... | 34 | 0 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import Mar... | 9 |
'''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_utils import requ... | 9 | 1 |
"""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
__lowercase = logging.get_logger(__name__)
... | 40 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
... | 183 | 0 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
def merge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 357 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_... | 60 | 0 |
class __a :
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[int]=None , SCREAMING_SNAKE_CASE : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase... | 189 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 189 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_... | 360 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase : Any = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
... | 36 | 0 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowerCAmelCase__ = datasets.logging.get_logger(__name__)
lowerCAmelCase__ = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for ... | 108 |
import doctest
from collections import deque
import numpy as np
class _a :
"""simple docstring"""
def __init__( self: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: int = [2, 1, 2, -1]
UpperCa... | 149 | 0 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : str = "" , UpperCamelCase__ : bool = False ):
"""simple docstring"""
UpperCamelCase ... | 357 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bu... | 249 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torc... | 289 | """simple docstring"""
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
)
| 289 | 1 |
import argparse
import copy
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> List[Any]:
__lowerCamelCase : Optional[Any] = {}
with open(a__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__lowerCamelCase : int = []
... | 361 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a ="""src/transformers"""
a ="""docs/source/en"""
... | 113 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : int = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_torch_available():
raise Optio... | 143 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert... | 143 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( _Upper... | 310 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCamelCase__ = {"UserAgent": UserAgent().random}
def lowercase__ ( lowercase_ ) -> dict:
"""simple docstrin... | 310 | 1 |
'''simple docstring'''
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 ... | 47 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : List[Any] = logging.get_logger(__na... | 165 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''p... | 0 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKIN... | 92 | from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...t... | 348 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemant... | 125 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
'''configuration_layoutlmv3''': [
'''L... | 125 | 1 |
def _A ( _lowercase , _lowercase ) -> List[str]:
"""simple docstring"""
return number | (1 << position)
def _A ( _lowercase , _lowercase ) -> Union[str, Any]:
"""simple docstring"""
return number & ~(1 << ... | 310 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''xlm-mlm-en-2048''': '''https://hugg... | 361 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
r... | 22 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INP... | 103 |
from pathlib import Path
import fire
def UpperCamelCase( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : int ):
lowerCAmelCase_ : List[str] = Path(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = Path(__UpperCamelCase )
d... | 103 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.j... | 206 | # 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 : Union[str, Any] = TypeVar('''T''')
class lowercase ( Generic[T]... | 206 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowercase( ... | 64 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A : Any = logging.get_logger(__name__)
__A : ... | 154 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def snake_case__ ( SCREAMING_SNAKE_CASE_ : BertModel , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
... | 359 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
snake_case_ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
snake_case_ = [file for file in filepaths if file != ... | 216 | 0 |
class a__ :
def __init__( self : Optional[Any],_A : int,_A : List[str]=None,_A : Tuple=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = data
SCREAMING_SNAKE_CASE_ : int = previous
... | 18 | import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : Tuple ... | 18 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers... | 354 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 0 |
from timeit import timeit
def __snake_case ( _UpperCAmelCase ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__a = 0
while number:
number &= number - 1
result += 1
return result
def __snake_case ( ... | 49 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Tuple = (DDPMParallelScheduler,)
def _lowerCamelCase ( self : int , **__SCREAMING_SNAKE_... | 49 | 1 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 209 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[Any] = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise... | 209 | 1 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
UpperCAmelCase_ :int = CustomTokenizer
pass
| 84 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_t... | 130 | 0 |
def __magic_name__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int] ) -> List[Any]:
if index == r... | 339 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCAmelCase_ = 'examples/'
UpperCAmelCase_ = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=... | 346 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCAm... | 346 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : Union[str, Any] = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalD... | 41 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
snake_case : List[str] = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}... | 41 | 1 |
from typing import Any
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ... | 9 |
'''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 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
__snake_c... | 371 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VAR... | 112 | 0 |
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
... | 290 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__SCREAMING_SNAKE_CASE :Tuple = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any m... | 371 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__SCREAMING_SNAKE_CASE :Tuple = '''\
'''
__SCREAMING_SNAKE_CASE :Union[str, Any] = ... | 156 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 1_0_0 * 2**2_0, 9_0_0 ... | 265 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : List[str] = {
"""huggingface/infor... | 224 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __A ( unittest.TestCa... | 262 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( a , unittest.TestCase ):
__A = ... | 262 | 1 |
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
lowercase : str = logging.get_logger(__name__)
low... | 232 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging im... | 88 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
if not isinstance(snake_case_,snake_case_ ):
_A : Any = f'''Input value of [number={number}] must be an integer'''
raise TypeError(snake_case_ )
if number < 0:
return False
_A ... | 343 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_snake_case = logging.getLogger()
def lowerCAmelCase_ ... | 343 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_devic... | 95 |
from dataclasses import dataclass
from typing import 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 .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, ... | 122 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,... | 52 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_toke... | 52 | 1 |
'''simple docstring'''
import math
def __snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
return math.pow(UpperCAmelCase_ , 2 ) - a
def __snake_case ( UpperCAmelCase_ : float ):
return 2 * x
d... | 55 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''EncodecFeatureExtractor'''
... | 90 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_ma... | 361 |
import torch
from diffusers import StableDiffusionPipeline
__lowerCAmelCase = '''path-to-your-trained-model'''
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
__lowerCAmelCase = '''A photo of sks dog in a bucket'''
... | 288 | 0 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Any = 0
if start ... | 37 |
'''simple docstring'''
from pathlib import Path
import fire
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : List[str] = Path(lowerCAmelCase... | 254 | 0 |
import argparse
import os
import re
_UpperCamelCase = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
_UpperCamelCase = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
_UpperCamelCase = re.compile(R'''^\s*"([... | 335 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfo... | 335 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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 Tokenize... | 97 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_... | 102 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ ... | 356 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE models at http... | 59 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transfor... | 59 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def a__ ( SCREAMING_SNAKE_CASE : Dict[str, torch.Tensor] ):
'''simple docstring'''
lowerCAmelCase : st... | 365 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( lowercase ... | 133 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Any =logging.get_logger(__name__)
__lowerCAmelCase : Tuple ={
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-... | 9 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCAmelCase : List[Any] =datasets.load_iris()
__lowerCAmelCase : Tuple =np.array(data['data'])
__lowerCAmelCase : Dict =np.array(data['target'])
__lowerCAmelCase... | 9 | 1 |
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Optional[int]:
if height >= 1:
move_tower(height - 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
move_disk(SCREAMING_SNAKE_CASE__ , SCREAM... | 293 |
import inspect
import unittest
from transformers import MobileViTConfig
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 ...t... | 293 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Dict = pd.read_csv("sample_data.csv", header=None)
SCREAMING_... | 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 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
... | 229 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusion... | 229 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowercase__ ( __snake_case : Tuple , __snake_case : Tuple=None ):
'''simple docstring'''... | 29 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Any = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIV... | 95 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
_UpperCamelCase = Mapping[str, np.ndarray]
_UpperCamelCase = Mapping[str, Any] # Is a nested dict.
_Up... | 335 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfo... | 335 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from tra... | 11 |
'''simple docstring'''
from __future__ import annotations
import queue
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> str:
_UpperCAmelCase : Optional[Any] = data
_UpperCAmelCase : Optional[int] = None
... | 215 | 0 |
from collections import deque
from .hash_table import HashTable
class lowercase ( UpperCamelCase__ ):
def __init__( self , *_a , **_a ) -> Optional[int]:
super().__init__(*_a , **_a )
def a__ ( self , _a , _a ) ... | 343 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 343 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = '▁'
_A = {'vocab_fil... | 62 | """simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 98 | 0 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_... | 354 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
'''c... | 132 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available(... | 76 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : str = logging.get_logger(__name__)
A_ : str = OrderedDict(
[
... | 333 | 0 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
'''simple docstring'''
while second != 0:
lowercase : Any = first & second
first ^= second
lowercase : Any... | 53 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils impo... | 53 | 1 |
"""simple docstring"""
def A ( snake_case :list , snake_case :list , snake_case :int ) -> int:
if len(snake_case ) != len(snake_case ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must gr... | 316 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't b... | 316 | 1 |
snake_case_ : int = {str(digit): digit**5 for digit in range(10)}
def A (__A : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__A ) )
def A () -> int:
... | 7 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Dict):
"""simple... | 7 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_UpperCamelCase = 2048
_UpperCamelCase = 4096
_UpperCamelCase = 42
_UpperCamelCase = os.environ.pop("PROCESS_TRAIN", "false")
_UpperCamelCase = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def _lowercase ( ... | 275 |
def _lowercase ( lowercase__ = 2_0_0 ):
__lowerCAmelCase : Union[str, Any] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
__lowerCAmelCase : Dict = [0] * (pence + 1)
__lowerCAmelCase : Optional[int] = 1 # base case: 1 way to make 0 pence
... | 275 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _lowerCAmelCase ( lowerCamelCase_ : int ):
__lowercase = int(number**0.5 )
return number == sq * sq
def _lowerCAmelCas... | 217 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'''Intel/dpt-large''': '''https://huggingface.... | 217 | 1 |
import mpmath # for roots of unity
import numpy as np
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : str=None ):
"""simple docstring"""
_A: Optional[int] ... | 121 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_UpperCAmelCase = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMN... | 173 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def a_ ( __snake_case : List[str] ) -> Dict:
"""simple docstring"""
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitt... | 6 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def a_ ( __snake_case ... | 6 | 1 |
'''simple docstring'''
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,... | 162 |
'''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 A__ :
lowercase = 42
lowercase = None
lowercase = None
def... | 162 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase =[int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def __lowerCAmelCase ( ) -> Optional[Any]:
__lowerCamelCase = os.path.dirname(os.path.realpath(UpperCamelCase__ ... | 237 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase ={
"configuration_whisper": ["WHISPER_PRETRAINED_C... | 237 | 1 |
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