




from __future__ import annotations
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      str(lowercase_				)
  return n == n[::-1]
def 		a_							( __lowerCAmelCase = 1_00_00_00				):
  lowerCAmelCase__										=      0

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


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




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



