

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}


class        a__  (							SCREAMING_SNAKE_CASE__  ):
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							'swinv2'

   _SCREAMING_SNAKE_CASE   :					str     	=							{
       'num_attention_heads': 'num_heads',
       'num_hidden_layers': 'num_layers',
   }


   def __init__(			self      ,    _UpperCamelCase=224      ,    _UpperCamelCase=4      ,    _UpperCamelCase=3      ,    _UpperCamelCase=96      ,    _UpperCamelCase=[2, 2, 6, 2]      ,    _UpperCamelCase=[3, 6, 12, 24]      ,    _UpperCamelCase=7      ,    _UpperCamelCase=4.0      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0.0      ,    _UpperCamelCase=0.0      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=False      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-5      ,    _UpperCamelCase=32      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_SCREAMING_SNAKE_CASE  )

      _lowercase      :				Any      					=       image_size
      _lowercase      :				int      					=       patch_size
      _lowercase      :				List[str]      					=       num_channels
      _lowercase      :				List[Any]      					=       embed_dim
      _lowercase      :				str      					=       depths
      _lowercase      :				Dict      					=       len(_SCREAMING_SNAKE_CASE  )
      _lowercase      :				Optional[int]      					=       num_heads
      _lowercase      :				List[str]      					=       window_size
      _lowercase      :				Optional[Any]      					=       mlp_ratio
      _lowercase      :				List[Any]      					=       qkv_bias
      _lowercase      :				str      					=       hidden_dropout_prob
      _lowercase      :				Optional[Any]      					=       attention_probs_dropout_prob
      _lowercase      :				List[Any]      					=       drop_path_rate
      _lowercase      :				Union[str, Any]      					=       hidden_act
      _lowercase      :				List[str]      					=       use_absolute_embeddings
      _lowercase      :				Optional[Any]      					=       layer_norm_eps
      _lowercase      :				Tuple      					=       initializer_range
      _lowercase      :				Optional[int]      					=       encoder_stride
      # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _lowercase      :				Tuple      					=       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE  ) - 1)  )
      _lowercase      :				Union[str, Any]      					=       (0, 0, 0, 0)









'''simple docstring'''
from math import sqrt







def  _A		(     snake_case					)      ->     int:
   _lowercase      :				str      					=       0
   for i in range(1		,							int(sqrt(snake_case					) + 1					)					):
      if n % i == 0 and i != sqrt(snake_case					):
         total += i + n // i
      elif i == sqrt(snake_case					):
         total += i
   return total - n







def  _A		(     snake_case = 1_00_00					)      ->     int:
   _lowercase      :				List[Any]      					=       sum(
       i
       for i in range(1		,							snake_case					)
       if sum_of_divisors(sum_of_divisors(snake_case					)					) == i and sum_of_divisors(snake_case					) != i					)
   return total


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






