from typing import TYPE_CHECKING

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


__snake_case			      =    {
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    ['''LayoutLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''LayoutLMForMaskedLM''',
         '''LayoutLMForSequenceClassification''',
         '''LayoutLMForTokenClassification''',
         '''LayoutLMForQuestionAnswering''',
         '''LayoutLMModel''',
         '''LayoutLMPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFLayoutLMForMaskedLM''',
         '''TFLayoutLMForSequenceClassification''',
         '''TFLayoutLMForTokenClassification''',
         '''TFLayoutLMForQuestionAnswering''',
         '''TFLayoutLMMainLayer''',
         '''TFLayoutLMModel''',
         '''TFLayoutLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
     from .tokenization_layoutlm import LayoutLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlm import (
              LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMForMaskedLM,
              LayoutLMForQuestionAnswering,
              LayoutLMForSequenceClassification,
              LayoutLMForTokenClassification,
              LayoutLMModel,
              LayoutLMPreTrainedModel,
          )
     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlm import (
              TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForQuestionAnswering,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForTokenClassification,
              TFLayoutLMMainLayer,
              TFLayoutLMModel,
              TFLayoutLMPreTrainedModel,
          )

else:
     import sys

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

import math





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  list[int]:






      '''simple docstring'''





      if not isinstance(__lowerCAmelCase		,					__lowerCAmelCase						):
            UpperCAmelCase    : Union[str, Any]      =f'''Input value of [number={number}] must be an integer'''
            raise TypeError(__lowerCAmelCase						)
      if number < 1:
            UpperCAmelCase    : Dict      =f'''Input value of [number={number}] must be a positive integer'''
            raise ValueError(__lowerCAmelCase						)
      UpperCAmelCase    : Dict      =[number]
      while number != 1:
            if number % 2 == 0:
                  UpperCAmelCase    : Optional[Any]      =math.floor(math.sqrt(__lowerCAmelCase						)						)
            else:
                  UpperCAmelCase    : List[str]      =math.floor(
                      math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						)						)
            sequence.append(__lowerCAmelCase						)
      return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()
