


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__						:   Tuple				=    {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Union[str, Any]				=    ["""ConvBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    [
          """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ConvBertForMaskedLM""",
          """ConvBertForMultipleChoice""",
          """ConvBertForQuestionAnswering""",
          """ConvBertForSequenceClassification""",
          """ConvBertForTokenClassification""",
          """ConvBertLayer""",
          """ConvBertModel""",
          """ConvBertPreTrainedModel""",
          """load_tf_weights_in_convbert""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFConvBertForMaskedLM""",
          """TFConvBertForMultipleChoice""",
          """TFConvBertForQuestionAnswering""",
          """TFConvBertForSequenceClassification""",
          """TFConvBertForTokenClassification""",
          """TFConvBertLayer""",
          """TFConvBertModel""",
          """TFConvBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
      from .tokenization_convbert import ConvBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_convbert_fast import ConvBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_convbert import (
                CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                ConvBertForMaskedLM,
                ConvBertForMultipleChoice,
                ConvBertForQuestionAnswering,
                ConvBertForSequenceClassification,
                ConvBertForTokenClassification,
                ConvBertLayer,
                ConvBertModel,
                ConvBertPreTrainedModel,
                load_tf_weights_in_convbert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_convbert import (
                TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFConvBertForMaskedLM,
                TFConvBertForMultipleChoice,
                TFConvBertForQuestionAnswering,
                TFConvBertForSequenceClassification,
                TFConvBertForTokenClassification,
                TFConvBertLayer,
                TFConvBertModel,
                TFConvBertPreTrainedModel,
            )


else:
      import sys

      A__						:   Tuple				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










'''simple docstring'''



from __future__ import annotations

A__						:   int				=    10



def      UpperCAmelCase__   (      UpperCAmelCase_       :							list[int]				)	->						list[int]:
     __lowerCamelCase  :      List[Any]														=						1
     __lowerCamelCase  :      Any														=						max(UpperCAmelCase_				)
     while placement <= max_digit:
          # declare and initialize empty buckets
          __lowerCamelCase  :      list[list]														=						[[] for _ in range(UpperCAmelCase_				)]
          # split list_of_ints between the buckets
          for i in list_of_ints:
               __lowerCamelCase  :      List[Any]														=						int((i / placement) % RADIX				)
               buckets[tmp].append(UpperCAmelCase_				)
          # put each buckets' contents into list_of_ints
          __lowerCamelCase  :      Tuple														=						0
          for b in range(UpperCAmelCase_				):
               for i in buckets[b]:
                    __lowerCamelCase  :      List[Any]														=						i
                    a += 1
        # move to next
          placement *= RADIX
     return list_of_ints


if __name__ == "__main__":
      import doctest

      doctest.testmod()






