






def 						A_					(						lowercase_			)				->list[int]:







 """simple docstring"""

 if len(lowercase_			) > 1:
  SCREAMING_SNAKE_CASE				    =		len(lowercase_			) // 2  # Finds the middle of the array
  SCREAMING_SNAKE_CASE				    =		arr[
      :middle_length
  ]  # Creates an array of the elements in the first half.
  SCREAMING_SNAKE_CASE				    =		arr[
      middle_length:
  ]  # Creates an array of the elements in the second half.
  SCREAMING_SNAKE_CASE				    =		len(lowercase_			)
  SCREAMING_SNAKE_CASE				    =		len(lowercase_			)
  merge(lowercase_			)  # Starts sorting the left.
  merge(lowercase_			)  # Starts sorting the right
  SCREAMING_SNAKE_CASE				    =		0  # Left Counter
  SCREAMING_SNAKE_CASE				    =		0  # Right Counter
  SCREAMING_SNAKE_CASE				    =		0  # Position Counter
  while (
      left_index < left_size and right_index < right_size
  ):  # Runs until the lowers size of the left and right are sorted.
   if left_array[left_index] < right_array[right_index]:
    SCREAMING_SNAKE_CASE				    =		left_array[left_index]
    left_index += 1
   else:
    SCREAMING_SNAKE_CASE				    =		right_array[right_index]
    right_index += 1
   index += 1
  while (
      left_index < left_size
  ):  # Adds the left over elements in the left half of the array
   SCREAMING_SNAKE_CASE				    =		left_array[left_index]
   left_index += 1
   index += 1
  while (
      right_index < right_size
  ):  # Adds the left over elements in the right half of the array
   SCREAMING_SNAKE_CASE				    =		right_array[right_index]
   right_index += 1
   index += 1
 return arr


if __name__ == "__main__":
      import doctest

      doctest.testmod()












"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(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						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

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



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





