



"""simple docstring"""







from typing import TYPE_CHECKING

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


lowercase__						:							int        =      {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__						:							Optional[int]        =      ["""LayoutLMTokenizerFast"""]

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

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__						:							Tuple        =      [
         """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

     lowercase__						:							int        =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""







def 							UpperCamelCase_       (       lowerCAmelCase__ :							int = 400_0000		)		->  int:

  """simple docstring"""




  if n <= 1:
    return 0
  lowerCAmelCase_  :							Union[str, Any]								=					0
  lowerCAmelCase_  :							Tuple								=					2
  lowerCAmelCase_  :							List[Any]								=					0
  while 4 * b + a <= n:
    lowerCAmelCase_		,lowerCAmelCase_  :							str								=					b, 4 * b + a
    count += a
  return count + b


if __name__ == "__main__":
     print(f'{solution() = }')





