





from math import sqrt


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Optional[Any]											=	0
	for i in range(1   , int(sqrt(UpperCamelCase__   ) + 1   )   ):
		if n % i == 0 and i != sqrt(UpperCamelCase__   ):
			total += i + n // i
		elif i == sqrt(UpperCamelCase__   ):
			total += i
	return total - n


def 							SCREAMING_SNAKE_CASE__						(    __a = 1_00_00   ):
	snake_case_		:			Tuple											=	sum(
	    i
	    for i in range(1   , UpperCamelCase__   )
	    if sum_of_divisors(sum_of_divisors(UpperCamelCase__   )   ) == i and sum_of_divisors(UpperCamelCase__   ) != i   )
	return total


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








import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	# Load checkpoint
	snake_case_		:			Union[str, Any]											=	torch.load(__a   , map_location='cpu'   )

	snake_case_		:			Union[str, Any]											=	chkpt['model']

	# We have the base model one level deeper than the original XLM repository
	snake_case_		:			str											=	{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			snake_case_		:			Tuple											=	v
		else:
			snake_case_		:			Dict											=	v

	snake_case_		:			Tuple											=	chkpt['params']
	snake_case_		:			List[Any]											=	{n: v for n, v in config.items() if not isinstance(__a   , (torch.FloatTensor, numpy.ndarray)   )}

	snake_case_		:			Optional[int]											=	chkpt['dico_word2id']
	snake_case_		:			List[str]											=	{s + '</w>' if s.find('@@'   ) == -1 and i > 13 else s.replace('@@'   , ''   ): i for s, i in vocab.items()}

	# Save pytorch-model
	snake_case_		:			List[str]											=	pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	snake_case_		:			Dict											=	pytorch_dump_folder_path + '/' + CONFIG_NAME
	snake_case_		:			Any											=	pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
	torch.save(__a   , __a   )

	print(f"""Save configuration file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )

	print(f"""Save vocab file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

