from typing import TYPE_CHECKING

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


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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__    							= [
          '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:
      UpperCamelCase__    							= [
          '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

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




from __future__ import annotations




def        lowerCAmelCase_			(					__A							)					->    bool:







      '''simple docstring'''


      UpperCAmelCase__								=  str(__A							)
      return n == n[::-1]




def        lowerCAmelCase_			(					__A = 1_000_000							)					->    Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  0

      for i in range(1,  __A							):
            if is_palindrome(__A							) and is_palindrome(bin(__A							).split("b"							)[1]							):
                  total += i
      return total


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



