






'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							=						{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=						[
          'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TapasForMaskedLM',
          'TapasForQuestionAnswering',
          'TapasForSequenceClassification',
          'TapasModel',
          'TapasPreTrainedModel',
          'load_tf_weights_in_tapas',
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=						[
          'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFTapasForMaskedLM',
          'TFTapasForQuestionAnswering',
          'TFTapasForSequenceClassification',
          'TFTapasModel',
          'TFTapasPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

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











'''simple docstring'''



def 				lowercase_					(     __A			:							int      )							->   int:



 """simple docstring"""




 if n == 1 or not isinstance(__A       ,   __A      ):
  return 0
 elif n == 2:
  return 1
 else:
  lowercase       : Tuple						=[0, 1]
  for i in range(2       ,   n + 1      ):
   sequence.append(sequence[i - 1] + sequence[i - 2]      )

  return sequence[n]


def 				lowercase_					(     __A			:							int      )							->   int:



 """simple docstring"""




 lowercase       : List[str]						=0
 lowercase       : str						=2

 while digits < n:
  index += 1
  lowercase       : int						=len(str(fibonacci(__A      )      )      )

 return index


def 				lowercase_					(     __A			:							int = 1_0_0_0      )							->   int:



 """simple docstring"""




 return fibonacci_digits_index(__A      )


if __name__ == "__main__":
      print(solution(int(str(input()).strip())))



