

import torch
from torch import autograd





class    __UpperCAmelCase	(			autograd.Function		):



   @staticmethod
   def 			__magic_name__       (				__A				:	Dict,						__A				:	Any,						__A				:	List[Any],						__A				:	Dict		):
     UpperCAmelCase			:				Any										=					inputs.numel()
     UpperCAmelCase			:				List[Any]										=					int(0.0_0_5 * nb_elems		) + 1
     if sigmoid:
       UpperCAmelCase			:				Any										=					(torch.sigmoid(__A		) > threshold).type(inputs.type()		)
     else:
       UpperCAmelCase			:				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
       UpperCAmelCase			:				List[Any]										=					inputs.flatten().kthvalue(max(nb_elems - nb_min,						1		)		).values
       UpperCAmelCase			:				List[str]										=					(inputs > k_threshold).type(inputs.type()		)
     return mask





   @staticmethod
   def 			__magic_name__       (				__A				:	Dict,						__A				:	Tuple		):
     return gradOutput, None, None






class    __UpperCAmelCase	(			autograd.Function		):



   @staticmethod
   def 			__magic_name__       (				__A				:	Union[str, Any],						__A				:	Optional[Any],						__A				:	str		):
     UpperCAmelCase			:				List[Any]										=					inputs.clone()
     UpperCAmelCase					,		UpperCAmelCase			:				str										=					inputs.flatten().sort(descending=__A		)
     UpperCAmelCase			:				int										=					int(threshold * inputs.numel()		)

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





   @staticmethod
   def 			__magic_name__       (				__A				:	Tuple,						__A				:	str		):
     return gradOutput, None






class    __UpperCAmelCase	(			snake_case_		):



   @staticmethod
   def 			__magic_name__       (				__A				:	List[Any],						__A				:	Optional[Any]		):
     UpperCAmelCase			:				int										=					inputs.clone()
     UpperCAmelCase					,		UpperCAmelCase			:				str										=					inputs.abs().flatten().sort(descending=__A		)
     UpperCAmelCase			:				str										=					int(threshold * inputs.numel()		)

     # flat_out and mask access the same memory.
     UpperCAmelCase			:				Union[str, Any]										=					mask.flatten()
     UpperCAmelCase			:				Optional[int]										=					0
     UpperCAmelCase			:				Tuple										=					1
     return mask









from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	return [*quick_sort(__lowerCamelCase  ), pivot, *quick_sort(__lowerCamelCase  )]


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

