
"""simple docstring"""

import torch
from torch import autograd
class    _UpperCAmelCase							(						autograd.Function							):




      @staticmethod
      def 			a_					(							lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_							)  ->    List[Any]:
          UpperCAmelCase  	=    inputs.numel()
          UpperCAmelCase  	=    int(0.0_0_5 * nb_elems							) + 1
          if sigmoid:
              UpperCAmelCase  	=    (torch.sigmoid(lowerCAmelCase__							) > 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 			a_					(							lowercase_      ,				lowercase_							)  ->    Union[str, Any]:
          return gradOutput, None, None

class    _UpperCAmelCase							(						autograd.Function							):




      @staticmethod
      def 			a_					(							lowercase_      ,				lowercase_      ,				lowercase_							)  ->    Dict:
          UpperCAmelCase  	=    inputs.clone()
          UpperCAmelCase  	=    inputs.flatten().sort(descending=lowerCAmelCase__							)
          UpperCAmelCase  	=    int(threshold * inputs.numel()							)

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




      @staticmethod
      def 			a_					(							lowercase_      ,				lowercase_							)  ->    int:
          return gradOutput, None

class    _UpperCAmelCase							(						_lowerCAmelCase							):




      @staticmethod
      def 			a_					(							lowercase_      ,				lowercase_							)  ->    Dict:
          UpperCAmelCase  	=    inputs.clone()
          UpperCAmelCase  	=    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
          UpperCAmelCase  	=    int(threshold * inputs.numel()							)

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










"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



