
'''simple docstring'''







import torch
from torch import autograd







class 		snake_case_       (						autograd.Function						):



   """simple docstring"""





   @staticmethod
   def     __UpperCAmelCase      ( UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      inputs.numel()
          lowerCamelCase__				   =      int(0.0_0_5 * nb_elems) + 1
          if sigmoid:
                 lowerCamelCase__				   =      (torch.sigmoid(UpperCAmelCase__) > 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     __UpperCAmelCase      ( UpperCamelCase			,   UpperCamelCase):
          return gradOutput, None, None







class 		snake_case_       (						autograd.Function						):



   """simple docstring"""





   @staticmethod
   def     __UpperCAmelCase      ( UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      inputs.clone()
          lowerCamelCase__   ,		lowerCamelCase__				   =      inputs.flatten().sort(descending=UpperCAmelCase__)
          lowerCamelCase__				   =      int(threshold * inputs.numel())

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







   @staticmethod
   def     __UpperCAmelCase      ( UpperCamelCase			,   UpperCamelCase):
          return gradOutput, None







class 		snake_case_       (						A__						):



   """simple docstring"""





   @staticmethod
   def     __UpperCAmelCase      ( UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      inputs.clone()
          lowerCamelCase__   ,		lowerCamelCase__				   =      inputs.abs().flatten().sort(descending=UpperCAmelCase__)
          lowerCamelCase__				   =      int(threshold * inputs.numel())

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





"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				if len(lowerCamelCase_	) == 1:
								return True
				try:
								SCREAMING_SNAKE_CASE__  				=					series[1] / series[0]
								for index in range(len(lowerCamelCase_	) - 1	):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True


def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				SCREAMING_SNAKE_CASE__  				=					1
				for value in series:
								answer *= value
				return pow(lowerCamelCase_    ,       1 / len(lowerCamelCase_	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
