





'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE__				=	'''BlipImageProcessor'''
					SCREAMING_SNAKE_CASE__				=	('''BertTokenizer''', '''BertTokenizerFast''')


					def __init__(						self				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			False
							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			self.image_processor


					def __call__(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			ImageInput = None      ,						lowerCamelCase_				:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,						lowerCamelCase_				:			bool = True      ,						lowerCamelCase_				:			Union[bool, str, PaddingStrategy] = False      ,						lowerCamelCase_				:			Union[bool, str, TruncationStrategy] = None      ,						lowerCamelCase_				:			Optional[int] = None      ,						lowerCamelCase_				:			int = 0      ,						lowerCamelCase_				:			Optional[int] = None      ,						lowerCamelCase_				:			Optional[bool] = None      ,						lowerCamelCase_				:			bool = False      ,						lowerCamelCase_				:			bool = False      ,						lowerCamelCase_				:			bool = False      ,						lowerCamelCase_				:			bool = False      ,						lowerCamelCase_				:			bool = False      ,						lowerCamelCase_				:			bool = True      ,						lowerCamelCase_				:			Optional[Union[str, TensorType]] = None      ,						**lowerCamelCase_				:			Optional[Any]      ,						):

							'''simple docstring'''



							if images is None and text is None:
									raise ValueError("""You have to specify either images or text."""						)

							# Get only text
							if images is None:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			self.tokenizer
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer(
									    text=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_      ,						padding=lowerCamelCase_      ,						truncation=lowerCamelCase_      ,						max_length=lowerCamelCase_      ,						stride=lowerCamelCase_      ,						pad_to_multiple_of=lowerCamelCase_      ,						return_attention_mask=lowerCamelCase_      ,						return_overflowing_tokens=lowerCamelCase_      ,						return_special_tokens_mask=lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						return_token_type_ids=lowerCamelCase_      ,						return_length=lowerCamelCase_      ,						verbose=lowerCamelCase_      ,						return_tensors=lowerCamelCase_      ,						**lowerCamelCase_      ,						)
									return text_encoding

							# add pixel_values
							SCREAMING_SNAKE_CASE :				Dict		=			self.image_processor(lowerCamelCase_      ,						return_tensors=lowerCamelCase_						)

							if text is not None:
									SCREAMING_SNAKE_CASE :				List[str]		=			self.tokenizer(
									    text=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_      ,						padding=lowerCamelCase_      ,						truncation=lowerCamelCase_      ,						max_length=lowerCamelCase_      ,						stride=lowerCamelCase_      ,						pad_to_multiple_of=lowerCamelCase_      ,						return_attention_mask=lowerCamelCase_      ,						return_overflowing_tokens=lowerCamelCase_      ,						return_special_tokens_mask=lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						return_token_type_ids=lowerCamelCase_      ,						return_length=lowerCamelCase_      ,						verbose=lowerCamelCase_      ,						return_tensors=lowerCamelCase_      ,						**lowerCamelCase_      ,						)
							else:
									SCREAMING_SNAKE_CASE :				Any		=			None

							if text_encoding is not None:
									encoding_image_processor.update(lowerCamelCase_						)

							return encoding_image_processor


					def lowerCamelCase_			(						self				:			List[Any]      ,						*lowerCamelCase_				:			Dict      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							return self.tokenizer.batch_decode(*lowerCamelCase_      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Any      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							return self.tokenizer.decode(*lowerCamelCase_      ,						**lowerCamelCase_						)



					@property
					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.tokenizer.model_input_names
							SCREAMING_SNAKE_CASE :				Any		=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)










'''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())))



