'''simple docstring'''




import torch
from torch import autograd




class 	__UpperCAmelCase				(				autograd.Function     ):





       '''simple docstring'''



       @staticmethod
       def 					__A       (      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           A_     =   inputs.numel()
           A_     =   int(0.005 * nb_elems ) + 1
           if sigmoid:
               A_     =   (torch.sigmoid(lowerCamelCase_ ) > threshold).type(inputs.type() )
           else:
               A_     =   (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
               A_     =   inputs.flatten().kthvalue(max(nb_elems - nb_min    ,     1 ) ).values
               A_     =   (inputs > k_threshold).type(inputs.type() )
           return mask

       @staticmethod
       def 					__A       (      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Any:
           return gradOutput, None, None




class 	__UpperCAmelCase				(				autograd.Function     ):





       '''simple docstring'''



       @staticmethod
       def 					__A       (      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     str:
           A_     =   inputs.clone()
           A_     =   inputs.flatten().sort(descending=lowerCamelCase_ )
           A_     =   int(threshold * inputs.numel() )

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

       @staticmethod
       def 					__A       (      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     List[Any]:
           return gradOutput, None




class 	__UpperCAmelCase				(				snake_case_     ):





       '''simple docstring'''



       @staticmethod
       def 					__A       (      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Optional[Any]:
           A_     =   inputs.clone()
           A_     =   inputs.abs().flatten().sort(descending=lowerCamelCase_ )
           A_     =   int(threshold * inputs.numel() )

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














"""simple docstring"""
def        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			int     				=						5381
 for x in s:
  lowerCamelCase__     :			Union[str, Any]     				=						((hash_value << 5) + hash_value) + ord(_lowerCamelCase				)
 return hash_value & 0Xffffffff

