





'''simple docstring'''




from math import sqrt



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			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_00_00	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			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())))










'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



