





"""simple docstring"""


def 							a__	(     _UpperCAmelCase		)			->     list:

 '''simple docstring'''






 snake_case__										= len(_UpperCAmelCase		)
 for cycle_start in range(0		,   array_len - 1		):
  snake_case__										= array[cycle_start]

  snake_case__										= cycle_start
  for i in range(cycle_start + 1		,   _UpperCAmelCase		):
   if array[i] < item:
    pos += 1

  if pos == cycle_start:
   continue

  while item == array[pos]:
   pos += 1

  snake_case__							,      snake_case__										= item, array[pos]
  while pos != cycle_start:
   snake_case__										= cycle_start
   for i in range(cycle_start + 1		,   _UpperCAmelCase		):
    if array[i] < item:
     pos += 1

   while item == array[pos]:
    pos += 1

   snake_case__							,      snake_case__										= item, array[pos]

 return array


if __name__ == "__main__":
   assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
   assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]








'''simple docstring'''







import torch
from torch import autograd





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.numel()
					__lowerCAmelCase											=				int(0.005 * nb_elems			) + 1
					if sigmoid:
								__lowerCAmelCase											=				(torch.sigmoid(__SCREAMING_SNAKE_CASE			) > threshold).type(inputs.type()			)
					else:
								__lowerCAmelCase											=				(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
								__lowerCAmelCase											=				inputs.flatten().kthvalue(max(nb_elems - nb_min,1			)			).values
								__lowerCAmelCase											=				(inputs > k_threshold).type(inputs.type()			)
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None, None





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

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







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

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

