





"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str							)      ->				bool:

      if not isinstance(__lowerCAmelCase			,       __lowerCAmelCase							):
            raise ValueError('''check_bouncy() accepts only integer arguments'''							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  str(__lowerCAmelCase							)
      __SCREAMING_SNAKE_CASE       :Dict		    =  ''''''.join(sorted(__lowerCAmelCase							)							)
      return sorted_str_n != str_n and sorted_str_n[::-1] != str_n



def 					__lowerCamelCase       (							a_					:		str = 99							)      ->				int:

      if not 0 < percent < 1_00:
            raise ValueError('''solution() only accepts values from 0 to 100'''							)
      __SCREAMING_SNAKE_CASE       :int		    =  0
      __SCREAMING_SNAKE_CASE       :Any		    =  1

      while True:
            if check_bouncy(__lowerCAmelCase							):
                  bouncy_num += 1
            if (bouncy_num / num) * 1_00 >= percent:
                  return num
            num += 1


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print(f'{solution(9_9)}')






"""simple docstring"""
from torch import nn






class  _SCREAMING_SNAKE_CASE(       nn.Module  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  class_size
             __SCREAMING_SNAKE_CASE       :str		    =  embed_size
             # self.mlp1 = nn.Linear(embed_size, embed_size)
             # self.mlp2 = (nn.Linear(embed_size, class_size))
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  nn.Linear(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.mlp(SCREAMING_SNAKE_CASE__							)
             return logits