
"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					'ClapFeatureExtractor'
				_lowerCAmelCase      :      Union[str, Any]      							=					('RobertaTokenizer', 'RobertaTokenizerFast')



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								super().__init__(UpperCAmelCase__       ,		UpperCAmelCase__     )



				def __call__(						self       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		**UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("sampling_rate"       ,		UpperCAmelCase__     )

								if text is None and audios is None:
												raise ValueError("You have to specify either text or audios. Both cannot be none."     )

								if text is not None:
												SCREAMING_SNAKE_CASE__     							=				self.tokenizer(UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__       ,		**UpperCAmelCase__     )

								if audios is not None:
												SCREAMING_SNAKE_CASE__     							=				self.feature_extractor(
												    UpperCAmelCase__       ,		sampling_rate=UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__       ,		**UpperCAmelCase__     )

								if text is not None and audios is not None:
												SCREAMING_SNAKE_CASE__     							=				audio_features.input_features
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**UpperCAmelCase__     )       ,		tensor_type=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		*UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								return self.tokenizer.batch_decode(*UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		*UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								return self.tokenizer.decode(*UpperCAmelCase__       ,		**UpperCAmelCase__     )






				@property
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.tokenizer.model_input_names
								SCREAMING_SNAKE_CASE__     							=				self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names     )     )




"""simple docstring"""






from math import sqrt


def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      int = 10000  ):
				SCREAMING_SNAKE_CASE__     							=				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())))
