






"""simple docstring"""


from math import sqrt



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= 0
  for i in range(1		,       int(sqrt(snake_case__ ) + 1 ) ):
    if n % i == 0 and i != sqrt(snake_case__ ):
      total += i + n // i
    elif i == sqrt(snake_case__ ):
      total += i
  return total - n



def 					lowerCAmelCase				(    __UpperCamelCase = 1_0_0_0_0 ):




  """simple docstring"""







  __A									= sum(
      i
      for i in range(1		,       snake_case__ )
      if sum_of_divisors(sum_of_divisors(snake_case__ ) ) == i and sum_of_divisors(snake_case__ ) != i )
  return total


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










"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def     a__ (				)						->					Union[str, Any]:
			lowerCamelCase			      =					ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch """
			        """helper utility that will spawn up """
			        """multiple distributed processes"""
			    )				)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""       , type=snake_case__       , default=1       , help="""Number of TPU cores to use (1 or 8)."""				)

			# positional
			parser.add_argument(
			    """training_script"""       , type=snake_case__       , help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )       , )

			# rest from the training program
			parser.add_argument("""training_script_args"""       , nargs=snake_case__				)

			return parser.parse_args()



def     a__ (				)						->					List[str]:
			lowerCamelCase			      =					parse_args()

			# Import training_script as a module.
			lowerCamelCase			      =					Path(args.training_script				)
			sys.path.append(str(script_fpath.parent.resolve()				)				)
			lowerCamelCase			      =					script_fpath.stem
			lowerCamelCase			      =					importlib.import_module(snake_case__				)

			# Patch sys.argv
			lowerCamelCase			      =					[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores				)]
			xmp.spawn(mod._mp_fn       , args=()       , nprocs=args.num_cores				)


if __name__ == "__main__":
					main()



