






import torch
from torch import autograd




class 	lowerCamelCase__				(							autograd.Function ):






      @staticmethod
      def      UpperCAmelCase_     (_snake_case					:    Optional[Any]		,		_snake_case					:    torch.tensor		,		_snake_case					:    float		,		_snake_case					:    bool		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    int							=     inputs.numel()
       lowerCamelCase_							:    int							=     int(0.005 * nb_elems		) + 1
       if sigmoid:
        lowerCamelCase_							:    List[str]							=     (torch.sigmoid(_snake_case		) > threshold).type(inputs.type()		)
       else:
        lowerCamelCase_							:    List[Any]							=     (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
        lowerCamelCase_							:    Optional[Any]							=     inputs.flatten().kthvalue(max(nb_elems - nb_min		,		1		)		).values
        lowerCamelCase_							:    Union[str, Any]							=     (inputs > k_threshold).type(inputs.type()		)
       return mask






      @staticmethod
      def      UpperCAmelCase_     (_snake_case					:    int		,		_snake_case					:    Union[str, Any]		)       ->   Any:


       """simple docstring"""






       return gradOutput, None, None




class 	lowerCamelCase__				(							autograd.Function ):






      @staticmethod
      def      UpperCAmelCase_     (_snake_case					:    Any		,		_snake_case					:    torch.tensor		,		_snake_case					:    float		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    str							=     inputs.clone()
       lowerCamelCase_		,      lowerCamelCase_							:    List[str]							=     inputs.flatten().sort(descending=_snake_case		)
       lowerCamelCase_							:    int							=     int(threshold * inputs.numel()		)

       # flat_out and mask access the same memory.
       lowerCamelCase_							:    str							=     mask.flatten()
       lowerCamelCase_							:    Tuple							=     0
       lowerCamelCase_							:    str							=     1
       return mask






      @staticmethod
      def      UpperCAmelCase_     (_snake_case					:    int		,		_snake_case					:    Dict		)       ->   str:


       """simple docstring"""






       return gradOutput, None




class 	lowerCamelCase__				(							UpperCAmelCase ):






      @staticmethod
      def      UpperCAmelCase_     (_snake_case					:    torch.tensor		,		_snake_case					:    float		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Tuple							=     inputs.clone()
       lowerCamelCase_		,      lowerCamelCase_							:    Dict							=     inputs.abs().flatten().sort(descending=_snake_case		)
       lowerCamelCase_							:    Dict							=     int(threshold * inputs.numel()		)

       # flat_out and mask access the same memory.
       lowerCamelCase_							:    Optional[Any]							=     mask.flatten()
       lowerCamelCase_							:    Optional[int]							=     0
       lowerCamelCase_							:    Any							=     1
       return mask








def       _a   (							lowerCamelCase__				)  ->      int:
 return int(lowerCamelCase__				) if x - int(lowerCamelCase__				) >= 0 else int(lowerCamelCase__				) - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod()
