'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__a										=					{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a										=					[
         'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Data2VecAudioForAudioFrameClassification',
         'Data2VecAudioForCTC',
         'Data2VecAudioForSequenceClassification',
         'Data2VecAudioForXVector',
         'Data2VecAudioModel',
         'Data2VecAudioPreTrainedModel',
     ]
     __a										=					[
         'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Data2VecTextForCausalLM',
         'Data2VecTextForMaskedLM',
         'Data2VecTextForMultipleChoice',
         'Data2VecTextForQuestionAnswering',
         'Data2VecTextForSequenceClassification',
         'Data2VecTextForTokenClassification',
         'Data2VecTextModel',
         'Data2VecTextPreTrainedModel',
     ]
     __a										=					[
         'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Data2VecVisionForImageClassification',
         'Data2VecVisionForMaskedImageModeling',
         'Data2VecVisionForSemanticSegmentation',
         'Data2VecVisionModel',
         'Data2VecVisionPreTrainedModel',
     ]

if is_tf_available():
     __a										=					[
         'TFData2VecVisionForImageClassification',
         'TFData2VecVisionForSemanticSegmentation',
         'TFData2VecVisionModel',
         'TFData2VecVisionPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
     from .configuration_dataavec_text import (
         DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DataaVecTextConfig,
         DataaVecTextOnnxConfig,
     )
     from .configuration_dataavec_vision import (
         DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DataaVecVisionConfig,
         DataaVecVisionOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dataavec_audio import (
              DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecAudioForAudioFrameClassification,
              DataaVecAudioForCTC,
              DataaVecAudioForSequenceClassification,
              DataaVecAudioForXVector,
              DataaVecAudioModel,
              DataaVecAudioPreTrainedModel,
          )
          from .modeling_dataavec_text import (
              DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecTextForCausalLM,
              DataaVecTextForMaskedLM,
              DataaVecTextForMultipleChoice,
              DataaVecTextForQuestionAnswering,
              DataaVecTextForSequenceClassification,
              DataaVecTextForTokenClassification,
              DataaVecTextModel,
              DataaVecTextPreTrainedModel,
          )
          from .modeling_dataavec_vision import (
              DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecVisionForImageClassification,
              DataaVecVisionForMaskedImageModeling,
              DataaVecVisionForSemanticSegmentation,
              DataaVecVisionModel,
              DataaVecVisionPreTrainedModel,
          )
     if is_tf_available():
          from .modeling_tf_dataavec_vision import (
              TFDataaVecVisionForImageClassification,
              TFDataaVecVisionForSemanticSegmentation,
              TFDataaVecVisionModel,
              TFDataaVecVisionPreTrainedModel,
          )

else:
     import sys

     __a										=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""
from __future__ import annotations

__a      =				10
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			1
						snake_case_ :List[str]						=			max(_lowercase	)
						while placement <= max_digit:
												# declare and initialize empty buckets
												snake_case_ :list[list]						=			[[] for _ in range(_lowercase	)]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		snake_case_ :Any						=			int((i / placement) % RADIX	)
																		buckets[tmp].append(_lowercase	)
												# put each buckets' contents into list_of_ints
												snake_case_ :Optional[Any]						=			0
												for b in range(_lowercase	):
																		for i in buckets[b]:
																								snake_case_ :Union[str, Any]						=			i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()
