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 |
|---|---|---|---|---|
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def snake_case (__lowercase , __lowercase , __lowercase=1_024 , __lowercase=1_024 , __lowercase=False , **__lowercase ) -> L... | 368 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autofor... | 284 | 0 |
import os
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_pegasus import PegasusTokenizer
else:
__SCREAMING_S... | 369 | import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sent... | 284 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=__snake_case ):
_lowerCamelCase = ['onnx']
def __init__( self , *lowercase_ , **lowercase_ ):
requires_backends(self , ["onnx"] )
@classmethod
... | 370 | import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 284 | 0 |
"""simple docstring"""
def snake_case (__lowercase ) -> list:
'''simple docstring'''
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
_snake_case : List[Any] = gray_code_sequence_string(... | 371 | import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def snake_case (__lowercase ) -> str:
'''simple docstring'''
_snake_case : int = args.pruning_method
_snake_case : List[Any] ... | 284 | 0 |
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 ..auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__na... | 350 | from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 42
class lowercase_ :
def __init__( self , ... | 284 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class lowercase_ ( unittest.TestCase ):
def UpperCamelCa... | 351 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_distilbert': [
'DISTIL... | 284 | 0 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things ... | 352 | from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__SCREAMING_SNAKE_CASE : str = logging.get_logge... | 284 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from trans... | 353 | from __future__ import annotations
import requests
__SCREAMING_SNAKE_CASE : Tuple = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categori... | 284 | 0 |
def snake_case (__lowercase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
_snake_case : Any = [True] * (num + 1)
_snake_case : str = 2
while p * p <= num:
i... | 354 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 284 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import P... | 355 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case ... | 284 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):
... | 356 | import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__SCREAMING_SNAKE_CASE : Any = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem... | 284 | 0 |
"""simple docstring"""
import functools
from typing import Any
def snake_case (__lowercase , __lowercase ) -> bool:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or len(__lowercase ) == 0:
raise ValueError("the string should be not em... | 357 | import os
import pytest
from attr import dataclass
__SCREAMING_SNAKE_CASE : int = 'us-east-1' # defaults region
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_lowerCamelCase... | 284 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 42
class lowercase_ :
def ... | 358 | def snake_case (__lowercase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
_snake_case : Any = [True] * (num + 1)
_snake_case : str = 2
while p * p <= num:
i... | 284 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__SCREAMING_SNAKE_CASE : int = logging... | 359 | from __future__ import annotations
def snake_case (__lowercase , __lowercase ) -> float:
'''simple docstring'''
_snake_case : Any = sorted(numsa + numsa )
_snake_case ,_snake_case : Any = divmod(len(__lowercase ) , 2 )
if mod... | 284 | 0 |
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
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : T... | 360 | def snake_case (__lowercase ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__lowercase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod() | 284 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transf... | 361 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for le... | 284 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__snake_case )
class lowercase_ ( __snake_case ):
# `task` is not a ClassVar since we want it to be part of the `asdict` ... | 362 | def snake_case () -> Dict:
'''simple docstring'''
_snake_case : List[str] = 0
for i in range(1 , 1_001 ):
total += i**i
return str(__lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 284 | 0 |
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE : Any = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE : str = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True... | 363 | from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, id... | 284 | 0 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def snake_case (__lowercase ) -> int:
'''simple docstring'''
_snake_case : List[Any] = test... | 364 | def snake_case (__lowercase , __lowercase ) -> str:
'''simple docstring'''
_snake_case : Tuple = ""
for word_or_phrase in separated:
if not isinstance(__lowercase , __lowercase ):
raise Exception("join() accepts only strings to ... | 284 | 0 |
import numpy as np
def snake_case (__lowercase ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 365 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_... | 284 | 0 |
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 | def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : Dict = 0
for ch in input_str:
_snake_case : int = ord(__lowercase )
_snake_case : List[Any] = pow(2 , __lowercase )
#... | 284 | 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():
import torch
... | 367 | import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing... | 284 | 0 |
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
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
__SCREAMING_SNAKE... | 368 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autofor... | 284 | 0 |
import re
def snake_case (__lowercase ) -> list:
'''simple docstring'''
return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]" , str_ )]
def snake_case (__lowercase ) -> str:
'''simple docstring'''
_snake_case : List[str] = sp... | 369 | import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sent... | 284 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
... | 370 | import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 284 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( __snake_case , unitte... | 371 | import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def snake_case (__lowercase ) -> str:
'''simple docstring'''
_snake_case : int = args.pruning_method
_snake_case : List[Any] ... | 284 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__SCREAMING_SNAKE_CASE : Dict = datasets.logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = '\\n@InPr... | 350 | from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 42
class lowercase_ :
def __init__( self , ... | 284 | 0 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Backbon... | 351 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_distilbert': [
'DISTIL... | 284 | 0 |
import os
def snake_case (__lowercase = "input.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(__lowercase ) , __lowercase ) ) as input_file:
_snake_case : List[Any] = [
[int(__lowercase ) for elem... | 352 | from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__SCREAMING_SNAKE_CASE : str = logging.get_logge... | 284 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTest... | 353 | from __future__ import annotations
import requests
__SCREAMING_SNAKE_CASE : Tuple = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categori... | 284 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterM... | 354 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 284 | 0 |
import datasets
__SCREAMING_SNAKE_CASE : Dict = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, ... | 355 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case ... | 284 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFI... | 356 | import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__SCREAMING_SNAKE_CASE : Any = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem... | 284 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from tr... | 357 | import os
import pytest
from attr import dataclass
__SCREAMING_SNAKE_CASE : int = 'us-east-1' # defaults region
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_lowerCamelCase... | 284 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE : List[Any] = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'... | 358 | def snake_case (__lowercase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
_snake_case : Any = [True] * (num + 1)
_snake_case : str = 2
while p * p <= num:
i... | 284 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __snake_case ):
_lowerCamelCase = (DDIMParallelScheduler,)
_lowerCamelCase = (('eta', 0.0), ('num_inference_steps', 50))
def UpperCamelCase... | 359 | from __future__ import annotations
def snake_case (__lowercase , __lowercase ) -> float:
'''simple docstring'''
_snake_case : Any = sorted(numsa + numsa )
_snake_case ,_snake_case : Any = divmod(len(__lowercase ) , 2 )
if mod... | 284 | 0 |
def snake_case (__lowercase , __lowercase ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(__lowercase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod() | 360 | def snake_case (__lowercase ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__lowercase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod() | 284 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MA... | 361 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for le... | 284 | 0 |
def snake_case (__lowercase , __lowercase = False ) -> str:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : str = F"""Expected string as input, found {type(__lowercase )}"""
raise ValueError(__lowerc... | 362 | def snake_case () -> Dict:
'''simple docstring'''
_snake_case : List[str] = 0
for i in range(1 , 1_001 ):
total += i**i
return str(__lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 284 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
... | 363 | from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, id... | 284 | 0 |
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
_snake_case : Tuple = 4
_snake_case : Dict = (1 << p) - 1
... | 364 | def snake_case (__lowercase , __lowercase ) -> str:
'''simple docstring'''
_snake_case : Tuple = ""
for word_or_phrase in separated:
if not isinstance(__lowercase , __lowercase ):
raise Exception("join() accepts only strings to ... | 284 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def snake_case () -> Dict:
'''simple docstring'''
_snake_case : Union[str, Any] = 9
_snake_case : Union[str, Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 365 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_... | 284 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVec... | 366 | def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : Dict = 0
for ch in input_str:
_snake_case : int = ord(__lowercase )
_snake_case : List[Any] = pow(2 , __lowercase )
#... | 284 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__SCREAMING_SNAKE_CASE : Union[str, Any] = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title =... | 367 | import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing... | 284 | 0 |
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_timesteps,
smartaa_timesteps,
smart... | 368 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autofor... | 284 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case : Any = {}
_snake_case : Union[str, Any] = job["started_at"]
_snake_ca... | 369 | import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sent... | 284 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureE... | 370 | import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 284 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepar... | 371 | import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def snake_case (__lowercase ) -> str:
'''simple docstring'''
_snake_case : int = args.pruning_method
_snake_case : List[Any] ... | 284 | 0 |
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
return 1 if input_a == input_a else 0
def snake_case () -> None:
'''simple docstring'''
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
asser... | 350 | from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 42
class lowercase_ :
def __init__( self , ... | 284 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingE... | 351 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_distilbert': [
'DISTIL... | 284 | 0 |
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 transformers.utils import lo... | 352 | from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__SCREAMING_SNAKE_CASE : str = logging.get_logge... | 284 | 0 |
import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 353 | from __future__ import annotations
import requests
__SCREAMING_SNAKE_CASE : Tuple = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categori... | 284 | 0 |
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Optional[int] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowercase_ :
def __init__( self , ... | 354 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 284 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableU... | 355 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case ... | 284 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoToke... | 356 | import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__SCREAMING_SNAKE_CASE : Any = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem... | 284 | 0 |
"""simple docstring"""
import os
import sys
__SCREAMING_SNAKE_CASE : int = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 357 | import os
import pytest
from attr import dataclass
__SCREAMING_SNAKE_CASE : int = 'us-east-1' # defaults region
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_lowerCamelCase... | 284 | 0 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn #... | 358 | def snake_case (__lowercase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
_snake_case : Any = [True] * (num + 1)
_snake_case : str = 2
while p * p <= num:
i... | 284 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def snake_case (__lowercase = "" ) -> dict[str, float]:
'''simple docstring'''
_snake_case : str = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
_snake_case : O... | 359 | from __future__ import annotations
def snake_case (__lowercase , __lowercase ) -> float:
'''simple docstring'''
_snake_case : Any = sorted(numsa + numsa )
_snake_case ,_snake_case : Any = divmod(len(__lowercase ) , 2 )
if mod... | 284 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def snake_case (__lowercase ) -> Optional[int]:... | 360 | def snake_case (__lowercase ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__lowercase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod() | 284 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for le... | 361 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for le... | 284 | 0 |
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
from .dataclasses import BnbQua... | 362 | def snake_case () -> Dict:
'''simple docstring'''
_snake_case : List[str] = 0
for i in range(1 , 1_001 ):
total += i**i
return str(__lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 284 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDCondi... | 363 | from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, id... | 284 | 0 |
import os
import re
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 : Tuple = logging.get_logger(__name__)
... | 364 | def snake_case (__lowercase , __lowercase ) -> str:
'''simple docstring'''
_snake_case : Tuple = ""
for word_or_phrase in separated:
if not isinstance(__lowercase , __lowercase ):
raise Exception("join() accepts only strings to ... | 284 | 0 |
from __future__ import annotations
from math import pi, sqrt
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise ValueErr... | 365 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_... | 284 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
Co... | 366 | def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : Dict = 0
for ch in input_str:
_snake_case : int = ord(__lowercase )
_snake_case : List[Any] = pow(2 , __lowercase )
#... | 284 | 0 |
def snake_case (__lowercase = 2_000_000 ) -> int:
'''simple docstring'''
_snake_case : Tuple = [0 for i in range(n + 1 )]
_snake_case : Optional[int] = 1
_snake_case : List[Any] = 1
for i in range(2 , int(n**0.5 ) + 1... | 367 | import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing... | 284 | 0 |
import datasets
from .evaluate import evaluate
__SCREAMING_SNAKE_CASE : List[Any] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv pre... | 368 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autofor... | 284 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__SCREAMING_SNAKE_CASE : Any = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__SCREAM... | 369 | import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sent... | 284 | 0 |
def snake_case (__lowercase ) -> int:
'''simple docstring'''
_snake_case : str = []
_snake_case : Optional[int] = []
_snake_case : List[str] = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
... | 370 | import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 284 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : List[Any] = TypeVar('_T')
class lowercase_ ( Generic[_T] ):
def __init__( self , lowercase_ = None ):
_snake_case : ... | 371 | import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def snake_case (__lowercase ) -> str:
'''simple docstring'''
_snake_case : int = args.pruning_method
_snake_case : List[Any] ... | 284 | 0 |
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_im... | 350 | from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 42
class lowercase_ :
def __init__( self , ... | 284 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_ve... | 351 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_distilbert': [
'DISTIL... | 284 | 0 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class lowercase_ ( nn.Module ):
... | 352 | from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__SCREAMING_SNAKE_CASE : str = logging.get_logge... | 284 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
... | 353 | from __future__ import annotations
import requests
__SCREAMING_SNAKE_CASE : Tuple = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categori... | 284 | 0 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstan... | 354 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 284 | 0 |
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
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__... | 355 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case ... | 284 | 0 |
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 im... | 356 | import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__SCREAMING_SNAKE_CASE : Any = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem... | 284 | 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,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] =... | 357 | import os
import pytest
from attr import dataclass
__SCREAMING_SNAKE_CASE : int = 'us-east-1' # defaults region
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_lowerCamelCase... | 284 | 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
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
... | 358 | def snake_case (__lowercase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
_snake_case : Any = [True] * (num + 1)
_snake_case : str = 2
while p * p <= num:
i... | 284 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Dict = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionC... | 359 | from __future__ import annotations
def snake_case (__lowercase , __lowercase ) -> float:
'''simple docstring'''
_snake_case : Any = sorted(numsa + numsa )
_snake_case ,_snake_case : Any = divmod(len(__lowercase ) , 2 )
if mod... | 284 | 0 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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 appl... | 360 | def snake_case (__lowercase ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__lowercase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod() | 284 | 0 |
from __future__ import annotations
import requests
__SCREAMING_SNAKE_CASE : Tuple = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categori... | 361 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for le... | 284 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__SCR... | 362 | def snake_case () -> Dict:
'''simple docstring'''
_snake_case : List[str] = 0
for i in range(1 , 1_001 ):
total += i**i
return str(__lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 284 | 0 |
def snake_case (__lowercase , __lowercase ):
'''simple docstring'''
_snake_case : str = (boundary[1] - boundary[0]) / steps
_snake_case : Dict = boundary[0]
_snake_case : Dict = boundary[1]
_snake_case : Optional[int] ... | 363 | from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, id... | 284 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def snake_case (__lowercase ) -> int:
'''simple docstring... | 364 | def snake_case (__lowercase , __lowercase ) -> str:
'''simple docstring'''
_snake_case : Tuple = ""
for word_or_phrase in separated:
if not isinstance(__lowercase , __lowercase ):
raise Exception("join() accepts only strings to ... | 284 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Array... | 365 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_... | 284 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
__SCREAMING_SNAKE_CASE : Dict = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
__SCR... | 366 | def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : Dict = 0
for ch in input_str:
_snake_case : int = ord(__lowercase )
_snake_case : List[Any] = pow(2 , __lowercase )
#... | 284 | 0 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute... | 367 | import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing... | 284 | 0 |
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_resiz... | 368 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autofor... | 284 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def snake_case (__lowercase , __lowercase , __lowercase ) -> int:
'''simple docstring'''
_snake_case : Optional[int] = ("dense.weight", "attent... | 369 | import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sent... | 284 | 0 |
def snake_case (__lowercase , __lowercase ) -> str:
'''simple docstring'''
_snake_case : Tuple = ""
for word_or_phrase in separated:
if not isinstance(__lowercase , __lowercase ):
raise Exception("join() accepts only strings to ... | 370 | import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 284 | 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_common impor... | 371 | import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def snake_case (__lowercase ) -> str:
'''simple docstring'''
_snake_case : int = args.pruning_method
_snake_case : List[Any] ... | 284 | 0 |
from __future__ import annotations
from math import pow, sqrt
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one... | 285 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Tuple = {
"""google/umt5-small""": """https://huggingface.co/g... | 285 | 1 |
from PIL import Image
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Image:
lowercase : Optional[int] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(SCREAMING_SNAKE_CASE__ ) -> int:
return int(128 + factor *... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 | 1 |
from __future__ import annotations
from statistics import mean
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> list[int]:
lowercase : Optional[Any] = [0] * no_of_processes
lowercase : Tuple = ... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
re... | 285 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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_attention_mask
from ... | 285 |
from ...processing_utils import ProcessorMixin
class __snake_case ( lowerCAmelCase ):
_a : Union[str, Any]= "WhisperFeatureExtractor"
_a : int= "WhisperTokenizer"
def __init__( self ,snake_case ,snake_case ):
'''simple docstring'''
super().__... | 285 | 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,
OpenAIGPTDoubleHeadsMod... | 285 |
from bisect import bisect
from itertools import accumulate
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : Dict = sorted(zip(SCREAMING_SNAKE_CASE__ , SCREAMIN... | 285 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1... | 285 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase : Union[str, Any] = logging.get_logger(_... | 285 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...... | 285 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1... | 285 | 1 |
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