"""simple docstring"""







from math import sqrt
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	__snake_case    :							str   	=				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 						__UpperCAmelCase       (					UpperCAmelCase_					:       int = 1_00_00						)							->    int:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				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())))






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





