

import torch
from torch import autograd



class     lowercase_       (    autograd.Function	):



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							List[Any]     ,   A__ :							torch.tensor     ,   A__ :							float     ,   A__ :							bool						)       ->	Optional[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__ :							Any     ,   A__ :							Union[str, Any]						)       ->	Optional[int]:
        return gradOutput, None, None



class     lowercase_       (    autograd.Function	):



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							Tuple     ,   A__ :							torch.tensor     ,   A__ :							float						)       ->	Any:
        _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__ :							List[str]     ,   A__ :							str						)       ->	Optional[int]:
        return gradOutput, None



class     lowercase_       (    __lowercase	):



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							torch.tensor     ,   A__ :							float						)       ->	Any:
        _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




def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				index
  _snake_case		=				2 * index + 1
  _snake_case		=				2 * index + 2
  if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
    _snake_case		=				left_index

  if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
    _snake_case		=				right_index

  if largest != index:
    _snake_case,	_snake_case		=				unsorted[index], unsorted[largest]
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)



def  snake_case_(_UpperCamelCase	)				->      Any:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  for i in range(n // 2 - 1					,			-1					,			-1	):
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)
  for i in range(n - 1					,			0					,			-1	):
    _snake_case,	_snake_case		=				unsorted[i], unsorted[0]
    heapify(_UpperCamelCase					,			0					,			_UpperCamelCase	)
  return unsorted


if __name__ == "__main__":
 __A     						=      input('''Enter numbers separated by a comma:\n''').strip()
 __A     						=      [int(item) for item in user_input.split(''',''')]
 print(heap_sort(unsorted))

