






import torch
from torch import autograd






class 	lowercase_      (			autograd.Function     ):






      '''simple docstring'''




      @staticmethod
      def    __lowerCAmelCase      (   __UpperCAmelCase   :		Tuple				,      __UpperCAmelCase   :		torch.tensor				,      __UpperCAmelCase   :		float				,      __UpperCAmelCase   :		bool						)					->int:




       """simple docstring"""


       a							=   inputs.numel()
       a							=   int(0.005 * nb_elems						) + 1
       if sigmoid:
        a							=   (torch.sigmoid(__UpperCAmelCase						) > 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    __lowerCAmelCase      (   __UpperCAmelCase   :		List[Any]				,      __UpperCAmelCase   :		str						)					->Optional[int]:




       """simple docstring"""


       return gradOutput, None, None






class 	lowercase_      (			autograd.Function     ):






      '''simple docstring'''




      @staticmethod
      def    __lowerCAmelCase      (   __UpperCAmelCase   :		Optional[int]				,      __UpperCAmelCase   :		torch.tensor				,      __UpperCAmelCase   :		float						)					->int:




       """simple docstring"""


       a							=   inputs.clone()
       a						,  a							=   inputs.flatten().sort(descending=__UpperCAmelCase						)
       a							=   int(threshold * inputs.numel()						)

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







      @staticmethod
      def    __lowerCAmelCase      (   __UpperCAmelCase   :		Union[str, Any]				,      __UpperCAmelCase   :		List[Any]						)					->List[Any]:




       """simple docstring"""


       return gradOutput, None






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      @staticmethod
      def    __lowerCAmelCase      (   __UpperCAmelCase   :		torch.tensor				,      __UpperCAmelCase   :		float						)					->List[str]:




       """simple docstring"""


       a							=   inputs.clone()
       a						,  a							=   inputs.abs().flatten().sort(descending=__UpperCAmelCase						)
       a							=   int(threshold * inputs.numel()						)

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










def 		_a					(   a				:float				)							->    float:
 return -num if num < 0 else num
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_min() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs_val(a				) < abs_val(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs(a				) > abs(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max_sort() arg is an empty sequence'''				)
 return sorted(a			,							key=a				)[-1]
def 		_a					(   )							->    int:
 assert abs_val(0				) == 0
 assert abs_val(34				) == 34
 assert abs_val(-100_000_000_000				) == 100_000_000_000

 a							=   [-3, -1, 2, -11]
 assert abs_max(a				) == -11
 assert abs_max_sort(a				) == -11
 assert abs_min(a				) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34


