code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
... | 87 |
'''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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae impor... | 125 | 0 |
import torch
def _lowerCamelCase ( ):
"""simple docstring"""
if torch.cuda.is_available():
_lowerCamelCase = torch.cuda.device_count()
else:
_lowerCamelCase = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 713 |
from __future__ import annotations
from typing import Any
def _lowerCamelCase ( _a ):
"""simple docstring"""
if not postfix_notation:
return 0
_lowerCamelCase = {'''+''', '''-''', '''*''', '''/'''}
_lowerCamelCase = []
for token in postfix_notation:
if toke... | 297 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE... | 234 |
import numpy as np
__SCREAMING_SNAKE_CASE =[
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z""... | 234 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 ,len(grid[0] ) ):
grid[0][cel... | 51 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that g... | 51 | 1 |
import argparse
import os
import re
import packaging.version
__a = """examples/"""
__a = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s... | 377 |
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
if n == 1 or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return 0
elif n == 2:
return 1
else:
UpperCAmelCase = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + s... | 377 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, Hf... | 169 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
snake_case_ : Tuple = False
class __snake_case ( unittest.Test... | 169 | 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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from tr... | 320 |
'''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 = logging.get_logger(__name__)
A ... | 320 | 1 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_UpperCamelCase , n - 1 , _UpperCamelCase ) * a) % mod
else:
__lowerCAmelCase : Dict = binar... | 549 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase = False ):
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : str = F"Expected string as input, found {type(_UpperCamelCase )}"
raise ValueError(_UpperCamelCase )
if not i... | 549 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
def __A (_SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ :str = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
lowerCAmelCase__ :L... | 93 |
'''simple docstring'''
from math import factorial
def _a ( __lowerCAmelCase : int = 20 ):
"""simple docstring"""
snake_case__ : Union[str, Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case__ : Lis... | 347 | 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 = {
"""bert-base-uncased""": """https://huggingface.co/b... | 207 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderO... | 207 | 1 |
from __future__ import annotations
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,):
"""simple docstring"""
snake_case = len(UpperCamelCase_ )
# If row is equal to the size... | 550 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=snake_case__ ):
"""simple docstring"""
__magic_name__ = ['flax', 'transformers']
def __init__( self , *__snake_case , **__snake_case ):
requires_ba... | 550 | 1 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"nvi... | 297 |
import heapq
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, s... | 297 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=__a ):
__SCREAMING_SNAKE_CASE :str = ["note_seq"]
def __init__( self : int , *a__ : Optional[int] , **a__ : Dict ... | 432 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm ... | 147 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 315 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__lowercase : Union[str, Any] = logging.get_logger(__name__)
__lowercase : Optional[Any] = r'''
Args:
i... | 315 | 1 |
"""simple docstring"""
class _snake_case :
'''simple docstring'''
def __init__( self : Optional[int] , snake_case : list[int] ):
UpperCAmelCase_ :List[Any] = len(snake_case )
UpperCAmelCase_ :Dict = [0] * len_array
if l... | 608 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 608 | 1 |
"""simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def UpperCAmelCase__ ( lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tuple ) -> Dict:
'''simp... | 718 | """simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, On... | 197 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Dict =logging.get_logger(__name__)
__lowerCAmelCase : ... | 696 |
import os
import sys
import unittest
__lowerCAmelCase : List[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 check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_obj... | 696 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...... | 705 | """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... | 342 | 0 |
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__ ... | 598 |
def a_ ( __magic_name__ = 1_000 ) -> int:
"""simple docstring"""
snake_case , snake_case : Optional[Any] = 1, 1
snake_case : Optional[Any] = 2
while True:
snake_case : Dict = 0
... | 598 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeech-large-1500h... | 452 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 452 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import hugg... | 44 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCamelCase = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCamelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowerCamelCase :
"""simple docs... | 590 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_... | 721 |
'''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_tokenizati... | 568 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowerCamelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
cla... | 70 |
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, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import ... | 70 | 1 |
"""simple docstring"""
def a__ ( __lowercase ) -> list:
if any(not isinstance(__lowercase , __lowercase ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__lowercase ) ):
for i, (rod_uppe... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTester... | 134 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ :Union[str, Any] = argparse.ArgumentParser('Stable Diffusion script with intel optimizat... | 522 | 0 |
'''simple docstring'''
def lowercase_ ( _lowercase ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ : Optional[int] = 0
while len(_UpperCAmelCase ) > 1:
lowerCamelCase_ : Tuple = 0
# Consider two files with minimum cost to be... | 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : str = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': [''... | 357 | 0 |
import warnings
from .generation import TFGenerationMixin
class lowercase_ ( _UpperCAmelCase ):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in T... | 443 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartToke... | 339 | 0 |
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.co/Intel/dpt-large/resolve/main/config.... | 717 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( lowerCAmelCase__ ):
lowerCAme... | 688 | 0 |
# 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
#
# Unless required by app... | 55 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/m... | 508 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .p... | 176 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_com... | 176 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHea... | 60 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=2_81_23 ) -> str:
__snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 69 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def lowercase ( lowerCAmelCase_ : ArgumentParser ) -> int:
raise NotImplementedError... | 421 |
_snake_case : List[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,
44,
22,
0,
]
_snake_case : Lis... | 421 | 1 |
__UpperCamelCase : Tuple = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" ... | 80 |
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> bool:
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> bool:
UpperCamelCase__ : Tuple ... | 253 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
f... | 702 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[int] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Tuple = np.a... | 126 | 0 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
a = "naver-clova-ix/donut-base"
class __a ( unittest.TestCase ):
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = Donut... | 109 |
"""simple docstring"""
A_ = 2_56
# Modulus to hash a string
A_ = 1_00_00_03
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Any = len(snake_case__ )
_snak... | 609 | 0 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
__lowerCamelCase : Any = parse(importlib.metadata.version("""torch"""))
def A__ ( _a : Union[str, Version] , _a : str , _... | 448 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_... | 448 | 1 |
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,
cached_file,
get_file... | 113 |
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
_lowerCAmelCase : int ="""src/transformers"""
# This is to make sure the ... | 113 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase : str = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_x... | 516 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
_lowerCamelCase : Optional[int] = TypeVar('''T''')
class lowerCAmelCase__ ( Generic[T] ):
'''simple docstring'''
def __init__( self , ... | 516 | 1 |
"""simple docstring"""
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_tenso... | 65 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
tr... | 3 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 708 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
"""microsoft/markuplm-large""": """htt... | 488 | 0 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
lowerCAmelCase__ = '''1'''
lowerCAmelCase__ = '''0'''
lowerCAmelCase__ = '''1'''
lowerCAmelCase__ = ort.SessionOptions()
lowerCAmelCase__ ... | 83 |
"""simple docstring"""
from collections import namedtuple
lowerCAmelCase__ = namedtuple('''from_to''', '''from_ to''')
lowerCAmelCase__ = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_0_1, 1000),
'''kilolitre''': from_to(1, 1),
'''gallon'''... | 83 | 1 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Optional[Any] = number
while duplicate > 0:
... | 230 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class A_ ( __UpperCamelCase , unittest.TestCase... | 230 | 1 |
'''simple docstring'''
import json
import sys
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : List[str],_SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE,encoding='utf-8' ) as f:
__A= json.load(_SCREAMING_SNAKE_CASE )
__A= ['<det... | 186 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCAmelCase__ = 1_0_0
UpperCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not... | 186 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImagePro... | 524 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = 'T5Config'
class ... | 524 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : List[Any] = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
... | 542 |
import string
from math import logaa
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
A : List[str] = document.translate(
str.maketrans('''''' , '''''' , string.punctuation ) ).replace('''\n''' , '''''' )
... | 542 | 1 |
"""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... | 487 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class a__ :
lowercase_ = None
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[An... | 487 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowercase__ : Any = False
class __lowerCAmelCase ( unitte... | 98 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_av... | 542 | 0 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
req... | 103 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __A( UpperCAmelCase ):
@staticmethod
@abstractmethod
def lowercase__ ( __UpperCamelCase : ArgumentParser ):
raise NotImplementedError()
@abstractmetho... | 103 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softm... | 101 |
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... | 297 | 0 |
from collections.abc import Callable
import numpy as np
def __UpperCamelCase ( A , A , A , A , A ):
UpperCamelCase__ = int(np.ceil((x_end - xa) / step_size ) )
UpperCamelCase__ = np.zeros((n + 1,) )
... | 469 | import math
import tensorflow as tf
from packaging import version
def __UpperCamelCase ( A ):
UpperCamelCase__ = tf.convert_to_tensor(A )
UpperCamelCase__ = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ... | 469 | 1 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,... | 611 | '''simple docstring'''
from __future__ import annotations
def __UpperCamelCase( _A : list[int] , _A : int , _A : int , _A : int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[in... | 614 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase : int = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_avail... | 714 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_t... | 501 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import Ro... | 79 |
'''simple docstring'''
from __future__ import annotations
__UpperCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCas... | 26 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _SCREAMING_SNAKE_CASE( snake_case_ : int , snake_case_ : int , snake_case_ : float = 1 / sqrt(2 ) ) ->IIRFilter:
... | 411 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
... | 411 | 1 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_av... | 341 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 341 | 1 |
def _SCREAMING_SNAKE_CASE ( __lowercase : str ) -> bool:
"""simple docstring"""
__A = 0
for ch in input_str:
__A = ord(__lowercase )
__A = pow(2 , __lowercase )
# If we already turned on bit for current charact... | 199 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__a : Optional[int] = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__a : int = _LazyModule(__name__, globa... | 199 | 1 |
from __future__ import annotations
class lowercase :
def __init__( self , snake_case=None ):
snake_case_ = data
snake_case_ = None
def __repr__( self ):
snake_case_ = []
snake_case... | 362 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAm... | 362 | 1 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowercase__ ( ) -> Optional[Any]:
'''simple docstring'''
a__ : Any = 9
a__ : List[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, ... | 251 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase__ : int ) -> bool:
'''simple docstring'''
if num < 0:
return False
a__ : int = num
a__ : int = 0
while num > 0:
a__ : Dict = rev_num * 1_0 + (num % 1_0)
num //= 1_0
... | 251 | 1 |
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowerCamelCase (__UpperCamelCase ):
... | 1 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return abs(lowerCamelCase__ ) if a == 0 else greatest_common_divisor(b % a , lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCa... | 644 | 0 |
import heapq
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : dict ) -> set[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq modul... | 379 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : List[Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''In... | 379 | 1 |
import numpy as np
import datasets
SCREAMING_SNAKE_CASE__ = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. ... | 9 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, ... | 315 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ : Any = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 714 | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase__ : List[Any] = datasets.utils.logging.get_logger(__name__)
class __snake_case ( folder_based_builder.FolderBasedBuild... | 699 | 0 |
'''simple docstring'''
def lowercase__( _UpperCamelCase : Tuple = 1000 )-> Dict:
"""simple docstring"""
return sum(e for e in range(3 , _UpperCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 138 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common i... | 681 | 0 |
from __future__ import annotations
def A ( _lowercase , _lowercase = None , _lowercase = None , _lowercase = False , ) -> Tuple:
SCREAMING_SNAKE_CASE : List[str] = cipher_alphabet or [chr(UpperCAmelCase__ ) for i in range(97 , 123 )]
#... | 713 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker... | 34 | 0 |
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,
)
fr... | 383 |
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self ) -> List[str]:
__UpperCamelCase = {}
def __lowercase( self , _SCREAMING_SNAKE_CASE ) ->... | 383 | 1 |
'''simple docstring'''
def _A ( snake_case__ : Any = 1_00 ):
snake_case__ : Any = (n * (n + 1) // 2) ** 2
snake_case__ : Dict = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 711 |
'''simple docstring'''
def _A ( snake_case__ : float ):
return 10 - x * x
def _A ( snake_case__ : float , snake_case__ : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(snake_case__ ) * equation(snake_case__ ) >= 0:
... | 694 | 0 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class UpperCamelCase ( _SCREAMING_SNAKE_CASE ):
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
'''b... | 389 |
"""simple docstring"""
from math import ceil
def __a ( a, a ):
"""simple docstring"""
_a = list(range(0, a ) )
_a = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
_a ... | 388 | 0 |
def __lowercase ( lowerCamelCase_ : list , lowerCamelCase_ : list , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int ):
if index == number_of_items:
return 0
SCREAMING_SNAKE_CASE__ = 0
S... | 707 |
"""simple docstring"""
from math import factorial
def __lowercase ( lowerCamelCase_ : int = 100 ):
return sum(int(lowerCamelCase_ ) for x in str(factorial(lowerCamelCase_ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 112 | 0 |
'''simple docstring'''
from math import loga
def UpperCamelCase__ ( __magic_name__ : int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__magic_name__ , __magic_name__ ):
ra... | 38 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessi... | 149 | 0 |
'''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,
TRANSF... | 715 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 | 0 |
import argparse
import datetime
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a ={
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
... | 242 |
from jiwer import compute_measures
import datasets
_lowerCAmelCase : Union[str, Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and W... | 242 | 1 |
'''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,
)
_UpperCamelCase : Any ={"conf... | 703 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : List[str] ={
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusC... | 575 | 0 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowerCAmelCase__: Dict = logging.get_logger(__name__)
class s... | 345 |
import sys
import turtle
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_, snake_case_, snake_case_, ) -... | 416 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[str] = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
if not is_torch_available():
... | 75 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
... | 75 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dumm... | 348 | 0 |
'''simple docstring'''
import re
import subprocess
import sys
snake_case : str = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
snake_case : Optional[int] = (
subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split... | 711 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBa... | 339 | 0 |
import re
from filelock import FileLock
try:
import nltk
a__ : Optional[int] = True
except (ImportError, ModuleNotFoundError):
a__ : str = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def ... | 188 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 217 | 0 |
"""simple docstring"""
def __a ( _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def __a ( _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
... | 720 | """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 __a ( _lowercase , _lowercase , _lowerc... | 121 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCAmelCase ( *A , A = None , A=True , A=2 ):
'''simple docstring'''
from .. import __version__
UpperCAmelCase_... | 625 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class snake_case_ ( a ):
'''simple docstring'''
__UpperCamelCase = 'EncodecFeatureExtractor'
__UpperCamelCase ... | 625 | 1 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.... | 715 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP... | 595 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_... | 42 |
'''simple docstring'''
import argparse
import json
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
... | 42 | 1 |
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 ImageProcessingSavingTestMixin, prepare_i... | 715 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ):
warnings.warn(
... | 565 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : Optional[int] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if ... | 31 |
def UpperCAmelCase_ ( ) -> list[list[int]]:
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
lowerCamelCase__ : List[Any] = generate_large_matrix()
lowerCamelCase__ : List[Any] = (
[[4, 3, 2, -1], [3,... | 31 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=lowercase__ ):
"""simple docstring"""
a : Any =["torch", "scipy"]
def __init__( self , *snake_case__ , **snake_case__ ):
... | 700 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fro... | 681 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ = 1_000 ) -> int:
a_ : List[Any] = 2**power
a_ : Dict = 0
while n:
a_ , a_ : Union[str, Any] = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(i... | 237 |
"""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
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING... | 237 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ): # noqa: E741
__UpperCAmelCase : Any = len(_UpperCAmelCase )
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : List[Any] = [0] * n
__UpperCAmelCase : Any = ... | 711 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ : int = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if ... | 329 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
f... | 13 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
'''simple docstring'''
from math import sqrt
def _SCREAMING_SNAKE_CASE ( snake_case_ = 1000000 ):
'''simple docstring'''
_lowercase = 0
_lowercase = 0
_lowercase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , ... | 714 |
'''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
_lowerCamelCase = logging.get_logger(_... | 572 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = arg... | 671 |
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,
PILImageResampli... | 671 | 1 |
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:List[Any] = str(_lowercase )
return n == n[::-1]
def A_ ( snake_case = 1000000 ):
SCREAMING_SNAKE_CASE:int = 0
for i in range(1 , _lowercase ):
... | 712 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=_a ):
_A : Any = ['''torch''', '''torchsde''']
def __init__( self : Any ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CASE__ ... | 465 | 0 |
"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
_UpperCamelCase = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
... | 453 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.util... | 453 | 1 |
"""simple docstring"""
import numpy as np
class UpperCAmelCase__ :
def __init__( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
A = (0, 0)
A = None
A = 0
A = 0
... | 109 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTest... | 109 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase: Optional[Any] ="https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def __snake_case ( __A = "mumbai"... | 607 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_convbert": ["CONVBERT_PRETRAINE... | 607 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokeniz... | 701 |
def __lowerCamelCase ( _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = [0] * len(_lowerCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowerCAmelCas... | 129 | 0 |
class __A :
def __init__( self : Dict , UpperCAmelCase_ : Any , UpperCAmelCase_ : int ):
lowerCAmelCase : Optional[Any] = name
lowerCAmelCase : int = val
def __str__( self :... | 343 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
lowerCAmelCase : Any = []
lowerCAmelCase : Dict = []
lowerCAmelCase : int = {
'^': 3,
'*': 2,
'/': 2,
... | 343 | 1 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow... | 350 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if... | 350 | 1 |
"""simple docstring"""
from functools import lru_cache
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> set:
_SCREAMING_SNAKE_CASE : Union[str, Any] = 2
_SCREAMING_SNAKE_CASE : Dict = set()
while i * i <= n:
if n % i:
i += 1... | 338 | """simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for T... | 338 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __lowercase ... | 713 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__magic_name__ = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def _lowerCAmelCase ( UpperCamelCase_ = "mumbai" ):
__SCREAMI... | 155 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config... | 155 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
lowerCAmelCase = 42
lowerCAmelCase ... | 713 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
lowercase : Union[str, Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground tru... | 159 | 0 |
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