




import torch
from torch import autograd

class 			_lowerCamelCase    (      autograd.Function ):

      """simple docstring"""

      @staticmethod
      def    snake_case						(  snake_case       :			Optional[Any]			,							snake_case       :			torch.tensor			,							snake_case       :			float			,							snake_case       :			bool					):
          __UpperCamelCase			=    inputs.numel()
          __UpperCamelCase			=    int(0.0_05 * nb_elems					) + 1
          if sigmoid:
              __UpperCamelCase			=    (torch.sigmoid(snake_case					) > threshold).type(inputs.type()					)
          else:
              __UpperCamelCase			=    (inputs > threshold).type(inputs.type()					)
          if mask.sum() < nb_min:
              # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
              __UpperCamelCase			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,							1					)					).values
              __UpperCamelCase			=    (inputs > k_threshold).type(inputs.type()					)
          return mask







      @staticmethod
      def    snake_case						(  snake_case       :			Optional[Any]			,							snake_case       :			List[Any]					):
          return gradOutput, None, None







class 			_lowerCamelCase    (      autograd.Function ):

      """simple docstring"""

      @staticmethod
      def    snake_case						(  snake_case       :			Union[str, Any]			,							snake_case       :			torch.tensor			,							snake_case       :			float					):
          __UpperCamelCase			=    inputs.clone()
          __UpperCamelCase		,   __UpperCamelCase			=    inputs.flatten().sort(descending=snake_case					)
          __UpperCamelCase			=    int(threshold * inputs.numel()					)

          # flat_out and mask access the same memory.
          __UpperCamelCase			=    mask.flatten()
          __UpperCamelCase			=    0
          __UpperCamelCase			=    1
          return mask







      @staticmethod
      def    snake_case						(  snake_case       :			List[Any]			,							snake_case       :			Optional[int]					):
          return gradOutput, None







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      @staticmethod
      def    snake_case						(  snake_case       :			torch.tensor			,							snake_case       :			float					):
          __UpperCamelCase			=    inputs.clone()
          __UpperCamelCase		,   __UpperCamelCase			=    inputs.abs().flatten().sort(descending=snake_case					)
          __UpperCamelCase			=    int(threshold * inputs.numel()					)

          # flat_out and mask access the same memory.
          __UpperCamelCase			=    mask.flatten()
          __UpperCamelCase			=    0
          __UpperCamelCase			=    1
          return mask












def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					if not isinstance(__A				,__A					):
										__magic_name__				:						Dict     				=				F'''Input value of [number={number}] must be an integer'''
										raise TypeError(__A					)
					if number < 0 or number % 2 == 1:
										return False
					__magic_name__				:						Any     				=				int(number**0.5					)
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()



