



def        a( A			:       str					,	A			:       str					,	A			:       List[Any]			)						->			Optional[Any]:
					"""simple docstring"""





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

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

					if largest != index:
										a   	=  unsorted[index], unsorted[largest]
										heapify(lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_			)






def        a( A			:       int			)						->			List[Any]:
					"""simple docstring"""





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


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

import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (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	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

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


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

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