

import torch
from torch import autograd



class 						UpperCAmelCase					(						autograd.Function  ):



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







      @staticmethod
      def 		UpperCamelCase_	(    A__   :       List[str]    ,      A__   :       Optional[Any]	)					->  List[Any]:
        return gradOutput, None, None



class 						UpperCAmelCase					(						autograd.Function  ):



      @staticmethod
      def 		UpperCamelCase_	(    A__   :       str    ,      A__   :       str    ,      A__   :       List[str]	)					->  str:
        _snake_case		=							inputs.clone()
        _snake_case,  _snake_case		=							inputs.flatten().sort(descending=A__	)
        _snake_case		=							int(threshold * inputs.numel()	)

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







      @staticmethod
      def 		UpperCamelCase_	(    A__   :       Any    ,      A__   :       str	)					->  Optional[Any]:
        return gradOutput, None



class 						UpperCAmelCase					(						A_  ):



      @staticmethod
      def 		UpperCamelCase_	(    A__   :       Union[str, Any]    ,      A__   :       Dict	)					->  int:
        _snake_case		=							inputs.clone()
        _snake_case,  _snake_case		=							inputs.abs().flatten().sort(descending=A__	)
        _snake_case		=							int(threshold * inputs.numel()	)

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




from __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

