


'''simple docstring'''



def     UpperCAmelCase__      (			UpperCAmelCase_					:       list							)  ->	list:
     __lowerCamelCase     :			Dict              =	len(UpperCAmelCase_							)

     # Set the gap value to be decreased by a factor of 1.3
     # after each iteration
     __lowerCamelCase     :			int              =	1.3

     # Continue sorting until the gap is 1
     while gap > 1:
          # Decrease the gap value
          __lowerCamelCase     :			str              =	int(gap / shrink							)

          # Sort the elements using insertion sort
          for i in range(UpperCAmelCase_   ,					len(UpperCAmelCase_							)							):
               __lowerCamelCase     :			str              =	collection[i]
               __lowerCamelCase     :			Optional[int]              =	i
               while j >= gap and collection[j - gap] > temp:
                    __lowerCamelCase     :			Any              =	collection[j - gap]
                    j -= gap
               __lowerCamelCase     :			Any              =	temp

     return collection


if __name__ == "__main__":
      import doctest

      doctest.testmod()










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__	:      List[str]				=						{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      int				=						[
          """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """FlaubertForMultipleChoice""",
          """FlaubertForQuestionAnswering""",
          """FlaubertForQuestionAnsweringSimple""",
          """FlaubertForSequenceClassification""",
          """FlaubertForTokenClassification""",
          """FlaubertModel""",
          """FlaubertWithLMHeadModel""",
          """FlaubertPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      int				=						[
          """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFFlaubertForMultipleChoice""",
          """TFFlaubertForQuestionAnsweringSimple""",
          """TFFlaubertForSequenceClassification""",
          """TFFlaubertForTokenClassification""",
          """TFFlaubertModel""",
          """TFFlaubertPreTrainedModel""",
          """TFFlaubertWithLMHeadModel""",
      ]


if TYPE_CHECKING:
      from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
      from .tokenization_flaubert import FlaubertTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flaubert import (
                FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaubertForMultipleChoice,
                FlaubertForQuestionAnswering,
                FlaubertForQuestionAnsweringSimple,
                FlaubertForSequenceClassification,
                FlaubertForTokenClassification,
                FlaubertModel,
                FlaubertPreTrainedModel,
                FlaubertWithLMHeadModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_flaubert import (
                TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFFlaubertForMultipleChoice,
                TFFlaubertForQuestionAnsweringSimple,
                TFFlaubertForSequenceClassification,
                TFFlaubertForTokenClassification,
                TFFlaubertModel,
                TFFlaubertPreTrainedModel,
                TFFlaubertWithLMHeadModel,
            )

else:
      import sys

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






