code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.u... | 252 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe ... | 252 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( _snake_case : int ): # picklable ... | 355 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 314 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
... | 294 |
'''simple docstring'''
import os
import sys
import unittest
lowercase : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E40... | 3 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
defa... | 62 |
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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Opt... | 62 | 1 |
class _UpperCamelCase :
def __init__( self :int ) -> int:
UpperCAmelCase__ = {}
def UpperCAmelCase_ ( self :str ) -> Optional[Any]:
print(self.vertex )
for i in self.vertex:
print(__lowerCamelCase , " -> " , ... | 169 |
from __future__ import annotations
import math
def UpperCamelCase__( UpperCamelCase__ : 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, ... | 193 | 0 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowerCAmelCase_ = 2_9_9_7_9_2_4_5_8
# Symbols
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = symbols('''ct x y z''')
def lowerCamelCase_ ( _UpperCam... | 279 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCAmelCase_ = TypeVar('''KEY''')
lowerCAmelCase_ = TypeVar('''VAL''')
@dataclass(frozen=_a, slots=_a )
class __lowerCAmelCase ( Generic[KEY, VAL] ):
l... | 279 | 1 |
from math import ceil
def __UpperCAmelCase ( __a : int ,__a : Any ) -> Union[str, Any]:
"""simple docstring"""
_a : List[Any] = list(range(0 ,__a ) )
_a : int = [item for sublist in list(device_map.values() ) for item in sublist]
... | 235 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
... | 350 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft... | 232 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A =logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lo... | 19 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''and... | 314 | 0 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowercase : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership functi... | 294 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Union[str, Any] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'to... | 294 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMi... | 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str = "" ):
__UpperCamelCase =url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__UpperCamelCase =BeautifulSoup(request... | 62 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_av... | 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[Any] = ArgumentParser(
... | 279 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> tuple[float, list[float]]:
"""simple docstring"""
snake_case_ : Dict = list(range(len(_UpperCamelCase ) ) )
snake_case_ : Dict ... | 279 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase( __a , unittest.TestCase ):
'''sim... | 132 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Optional[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = []
_snake_case : Dict = set({"""(""", """[""", """{"""} )
_snake_case : Union[str, Any] = set({""")""",... | 132 | 1 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampl... | 123 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 0 |
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,
)
| 218 |
import argparse
import copy
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {}
with open(_A ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
SCREAMING_SNAKE_CASE__ = []
... | 218 | 1 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : str ) -> Optional[int]:
_a : List[Any] = 0
_a : Any ... | 294 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_s... | 294 | 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.prepro... | 370 |
"""simple docstring"""
import qiskit
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : int )-> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase__ : str = qiskit.Aer.get_backend("aer_simulator" )
UpperCAme... | 298 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.... | 10 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 328 | 0 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __a (tf.keras.optimizers.schedules.LearningRateSchedule):
... | 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> list:
if len(__lowerCAmelCase ) == 0:
return []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = min(__lowerCAmelCase ... | 132 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.ut... | 132 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 191 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : complex , SCREAMING_SNAKE_CASE__ : str = "x" , SCREAMING_SNAKE_CASE__ : float = 10**-10 , SCREAMING_SN... | 191 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowerCAmelCase : Optional[Any] = Lock()
def UpperCamelCase_( _snake_case : List[str] , _snake_case : Optional[int] , _s... | 218 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say th... | 218 | 1 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from tra... | 226 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
... | 226 | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils i... | 41 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowerCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for... | 298 | 0 |
'''simple docstring'''
import math
import sys
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
if number != int(__lowercase ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a ne... | 366 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
fro... | 334 | 0 |
'''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 require_vision
fro... | 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 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(__SCREAMING_SNAKE_CASE ) - ngram_size + 1 )]
if __na... | 264 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__SCREAMING_SNAKE_CASE ) )
if txt[a].isalpha()
... | 264 | 1 |
"""simple docstring"""
from math import isqrt, loga
def __lowerCamelCase ( a_ : int ) -> list[int]:
__SCREAMING_SNAKE_CASE :str = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 191 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( a_ : ... | 191 | 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_bert import BertTokenizer
__lowerCamelCase = logging.get_logger(__name_... | 367 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if not postfix_notation:
return 0
A_ = {"""+""", """-""", """*""", """/"""}
A_ = []
for token in postfix_notati... | 101 | 0 |
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 check... | 226 |
from __future__ import annotations
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : Optional[int]=None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = data
... | 226 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ = "isbn/0140328726" ) -> dict:
"""simple docstring"""
A__ = olid.strip().strip('''/''' ) # Remove leading/trailing whit... | 351 |
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 0) ->None:
'''simple d... | 231 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from tran... | 198 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase =2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 334 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] = HfArgumentParser(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = pars... | 56 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a (metaclass=UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *_a , **_a ) ... | 56 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : Any = ["""image_processor""", """tokenizer"""]
_lowerCAmelCase : Optional[Any]... | 264 |
"""simple docstring"""
import sys
lowercase__ : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''... | 264 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 368 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase__ : str = "#"
class lowerCAmelCase_ :
"""simple docstring"""
def __init__(self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CA... | 25 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = ('''dense.weight''', '''attention.self.qu... | 101 | 0 |
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
f... | 351 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ ):
if not isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(lowercase_ )
if number < 1:
... | 181 | 0 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
if num <= 0:
lowerCAmelCase_ = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(__lowerCAmelCase )
lowerCAmelCase... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_A = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Spee... | 231 | 1 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> Tuple:
__lowerCAmelCase: List[Any] = []
__lowerCAmelCase: int = set({"(", "[", "{"} )
__lowerCAmelCase: Optional[Any] = set({")", "]", "}"} )
__lowerCAmelCase: Optional[int] = ... | 108 |
"""simple docstring"""
from math import ceil
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> str:
__lowerCAmelCase: Tuple = list(range(0 , __SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase: Optional[Any] = [item for sublist in list(de... | 108 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : Optional[Any] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
... | 56 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 56 | 1 |
import math
def _SCREAMING_SNAKE_CASE ( lowercase : int ):
'''simple docstring'''
lowerCamelCase_ = 0
lowerCamelCase_ = 0
while num > 0:
lowerCamelCase_ = num % 8
lowerCamelCase_ = octal... | 208 |
from ..utils import DummyObject, requires_backends
class A( metaclass=UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : Any , *A_ : Any , **A... | 208 | 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing im... | 290 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 280 | 0 |
from __future__ import annotations
def lowercase__ ( __snake_case : Any ):
'''simple docstring'''
UpperCAmelCase_ : Any = str(__lowerCamelCase )
return len(__lowerCamelCase ) == 9 and set(__lowerCamelCase ) == set('123456789' )
... | 351 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested... | 145 | 0 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = (DDPMParallelScheduler,)
def __UpperCAm... | 107 |
'''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 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available... | 64 | """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_trust_r... | 64 | 1 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from .... | 108 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 108 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a__ = datasets.load_iris()
a__ = np.array(data['''data'''])
a__ = np.array(data['''target'''])
a__ = data['''target_names''']
a__ , a__ , a... | 15 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : Dict = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : int = 2 * i + 1
_a : ... | 15 | 1 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCamelCase_ ( pl.LightningModule ):
"""simple docstring"""
def _... | 208 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils... | 208 | 1 |
import math
from datetime import datetime, timedelta
def lowercase( UpperCamelCase_ ) -> Dict:
'''simple docstring'''
UpperCamelCase = year % 19
UpperCamelCase = year % 4
UpperCamelCase = year % 7
UpperCamelCase = math.floor(year / 100 )
Up... | 369 | def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
UpperCamelCase = len(UpperCamelCase_ )
UpperCamelCase = len(matrix[0] )
UpperCamelCase = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase_ ):
# Check if dia... | 165 | 0 |
import os
import sys
import unittest
lowercase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_te... | 20 | '''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__... | 145 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : List[str] = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise Optional... | 367 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_snake_case : Union[str, Any] = ["small", "medium", "large"]
_snake_case : List[Any] = "lm_head.decoder.weight"
_snake_case : Optional[Any] = "lm_head.weight"
def lowerCAmelCase_ ... | 134 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
A_ = TypeVar('''T''')
A_ = Union[List[T], Tuple[T, ...]]
A_ = Union[T, List[T], Dict[str, T]]
A_ = Union[str, bytes, os.PathLike]
| 64 |
"""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_retribert import RetriBertTokenizer
A_ = logging.get_logger... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__lowerCamelCase = TypeVar("T")
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
return (position... | 154 | """simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> Optional[int]:
... | 154 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
SCREAMING_SNAKE_CASE :str = datasets.load_iris()
SCREAMING_SNAKE_CASE :Dict = np.array(data['data'])
SCREAMING_SNAKE_CASE :Union[str, Any] ... | 15 |
# Copyright 2021 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
#
# Unless required by app... | 15 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_prop... | 76 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 1 |
from PIL import Image
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = (2_59 * (level + 2_55)) / (2_55 * (2_59 - level))
def contrast(snake_case ) -> int:
return int(1_28 + factor * (c - 1_28) )
return img.... | 82 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : List[str] = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
t... | 165 | 0 |
'''simple docstring'''
def __magic_name__ ( A ) -> Optional[Any]:
snake_case = len(A )
while cur > 1:
# Find the maximum number in arr
snake_case = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
snake_case = arr[mi::-1] + arr[mi + 1 ... | 332 |
'''simple docstring'''
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_ = {
"roberta-base": "https://h... | 332 | 1 |
"""simple docstring"""
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
from .embeddings import GaussianFourierProjection, Timeste... | 81 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Optional[Any] = {'con... | 134 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"laye... | 351 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, Fl... | 339 | 0 |
# Function to print upper half of diamond (pyramid)
def __UpperCamelCase ( _A : Union[str, Any] ) ->List[Any]:
"""simple docstring"""
for i in range(0 , _A ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , ... | 154 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : int = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
if not is_tor... | 154 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
UpperCamelCase = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
UpperCamelCase = reque... | 369 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 221 | 0 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : int , *a : L... | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
a : Tuple = logging.get_logger(__name_... | 82 |
def lowerCAmelCase_ (lowerCAmelCase__: int , lowerCAmelCase__: float , lowerCAmelCase__: float ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCAmelCase_ (lowerCAmelCase__: f... | 82 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :str ):
__UpperCAmelCase = len(snake_case_ )
while cur > 1:
# Find the maximum number in arr
__UpperCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
__UpperCAmelCase = arr[mi::-... | 332 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , _lowercase : Optional[Any] ):
__UpperCAmelCase = str(id_ )
__UpperCAmelCase = None
... | 332 | 1 |
import argparse
from collections import defaultdict
import yaml
__lowerCAmelCase = '''docs/source/en/_toctree.yml'''
def snake_case_ ( snake_case ) -> Optional[int]:
lowercase__: List[Any] = defaultdict(a_ )
lowercase__: Union[st... | 364 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 288 | 0 |
'''simple docstring'''
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
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAK... | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import ... | 339 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
a : Dict = HfArgumentParser(InitializationArguments)
a : Union[str, Any] = parser.parse_args()
# Load codeparrot tokeniz... | 368 |
"""simple docstring"""
from __future__ import annotations
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__=None ):
'''simple docstring'''
lowercase__ : Union[str, Any]= data
... | 150 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : List[Any] = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'conf... | 80 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Chinese... | 221 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
'configuration_blenderbot': [
... | 187 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as tr... | 187 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
A__ = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def _UpperCAmelCase ( snake_case = "mumbai" ):
"""simple docstring"""
_lowerCAmelC... | 82 |
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_availabl... | 82 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
_lowercase ... | 91 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCamelCase__ ( A : int , A : int , A : int , A : int , A : int , A ... | 91 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray ) -> float:
'''simple docstring'''
return math.sqrt(sum... | 7 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 288 | 0 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
UpperCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for row in range... | 352 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
... | 60 | 0 |
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
from t... | 121 | """simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
... | 150 | 0 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_pat... | 362 |
'''simple docstring'''
from collections.abc import Sequence
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
return sum(c * (x**i) for i, c in enumerate(__lowerCAmelCase ) )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase ... | 322 | 0 |
lowercase__ : Optional[Any] = 0 # The first color of the flag.
lowercase__ : List[Any] = 1 # The second color of the flag.
lowercase__ : Dict = 2 # The third color of the flag.
lowercase__ : Optional[Any] = (red, white, blue)
def lowerCa... | 187 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
impor... | 187 | 1 |
import string
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> str:
lowercase__ = ''
for i in sequence:
lowercase__ = ord(_SCREAMING_SNAKE_CASE )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
... | 269 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __UpperCamelCase () -> str:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as or... | 269 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timeste... | 91 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class ... | 91 | 1 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return [ord(SCREAMING_SNAKE_CASE_ ) - 9_6 for elem in plain]
def a ( SCREAMING_SNAKE_CASE_ : list[int] ):
... | 315 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Union[str, Any] = ""
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Any = 1 # (0 is vertical, 1 is horizontal)
de... | 315 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
if not isinstance(_snake_case, _snake_case):
raise TypeError('''only integers accepted as input''')
else:
__lowerCAmelCase = str(abs(_snake_case))
__lowerCAmelCase = [l... | 174 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : Dict = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is_torch_available()... | 370 |
from scipy.stats import pearsonr
import datasets
snake_case : Tuple = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that eac... | 41 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 48 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, ... | 352 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''WhisperFeatureExtractor'''
a__ ='''WhisperTokenizer'''
def __init__( self , A , A ) -> Any:
super().__i... | 68 | 0 |
"""simple docstring"""
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
_... | 269 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 269 | 1 |
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_VARS_TRUE_VALUES,
FEA... | 319 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import loggin... | 319 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configura... | 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def ... | 315 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_avail... | 246 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common ... | 246 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowercase_ = False
class __A ( unittest.TestCase ):
'''simple docstring... | 211 |
'''simple docstring'''
class _lowercase :
def __init__( self: Optional[Any] ):
lowerCamelCase__ : dict[str, TrieNode] = {} # Mapping from char to TrieNode
lowerCamelCase__ : List[str] = False
... | 41 | 0 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
lowerCAmelCase : str = {name: getattr(... | 353 |
'''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
lowerCAmelCase : Optional[Any] = logging.get_logger(__nam... | 251 | 0 |
"""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_docst... | 44 |
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
A__ = 1
A__ ... | 68 | 0 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : bool = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
... | 69 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE_ : List[str] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n a... | 69 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 319 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPP... | 319 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''vocab_file''': ''... | 350 |
'''simple docstring'''
import requests
UpperCamelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def SCREAMING_SNAKE_CASE( __lowercase ) -> None:
# fetching a list of articles in json format
A: Tuple = requests.get(_NE... | 334 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace... | 246 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_UpperCAmelCase : Optional[Any] = 1
_UpperCAmelCase : List[str] = 1
while repunit:
_UpperCAmelCase : Tuple = ... | 246 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as... | 327 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 | 1 |
def _A ( SCREAMING_SNAKE_CASE__ : List[Any] ):
UpperCamelCase :Union[str, Any] = 1
UpperCamelCase :Any = 2
while i * i <= n:
UpperCamelCase :str = 0
while n % i == 0:
n //= i
multiplicity += 1
n... | 259 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
... | 251 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acc... | 369 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int , snake_case : int ):
'''simple docstring'''
while b:
snake_case_ , snake_case_ = b, a % b
return a
def UpperCamelCase_( snake_case : int ,... | 92 | 0 |
"""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/LICENSE-2.0
#
# Unl... | 69 | """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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__Up... | 69 | 1 |
"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to... | 80 |
"""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, ... | 80 | 1 |
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