





from math import sqrt



def 	A_       (  _lowerCAmelCase  )		->				int:
 UpperCamelCase			:      Optional[int] 			=     0
 for i in range(1							,     int(sqrt(_lowerCAmelCase  ) + 1  )  ):
  if n % i == 0 and i != sqrt(_lowerCAmelCase  ):
   total += i + n // i
  elif i == sqrt(_lowerCAmelCase  ):
   total += i
 return total - n



def 	A_       (  _lowerCAmelCase = 1_0000  )		->				int:
 UpperCamelCase			:      Dict 			=     sum(
     i
     for i in range(1							,     _lowerCAmelCase  )
     if sum_of_divisors(sum_of_divisors(_lowerCAmelCase  )  ) == i and sum_of_divisors(_lowerCAmelCase  ) != i  )
 return total


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







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

import torch_xla.distributed.xla_multiprocessing as xmp



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      Tuple 			=     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=_lowerCAmelCase							,     default=1							,     help="Number of TPU cores to use (1 or 8)."  )

 # positional
 parser.add_argument(
     "training_script"							,     type=_lowerCAmelCase							,     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=_lowerCAmelCase  )

 return parser.parse_args()



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     parse_args()

 # Import training_script as a module.
 UpperCamelCase			:      Union[str, Any] 			=     Path(args.training_script  )
 sys.path.append(str(script_fpath.parent.resolve()  )  )
 UpperCamelCase			:      List[Any] 			=     script_fpath.stem
 UpperCamelCase			:      Optional[Any] 			=     importlib.import_module(_lowerCAmelCase  )

 # Patch sys.argv
 UpperCamelCase			:      List[Any] 			=     [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()
