



"""simple docstring"""


import math

def 				_lowerCAmelCase   (     lowercase_		):
      if not isinstance(lowercase_	,			lowercase_		):
            UpperCAmelCase    =				F"""Input value of [number={number}] must be an integer"""
            raise TypeError(lowercase_		)
      if number < 1:
            UpperCAmelCase    =				F"""Input value of [number={number}] must be a positive integer"""
            raise ValueError(lowercase_		)
      UpperCAmelCase    =				[number]
      while number != 1:
            if number % 2 == 0:
                  UpperCAmelCase    =				math.floor(math.sqrt(lowercase_		)		)
            else:
                  UpperCAmelCase    =				math.floor(
                      math.sqrt(lowercase_		) * math.sqrt(lowercase_		) * math.sqrt(lowercase_		)		)
            sequence.append(lowercase_		)
      return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()










"""simple docstring"""


import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class    A_    (  nn.Linear       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
												UpperCAmelCase    =				pruning_method

												if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		UpperCAmelCase    =				mask_scale
																		UpperCAmelCase    =				mask_init
																		UpperCAmelCase    =				nn.Parameter(torch.empty(self.weight.size()      )      )
																		self.init_mask()






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												if self.mask_init == "constant":
																		init.constant_(self.mask_scores     ,						val=self.mask_scale      )
												elif self.mask_init == "uniform":
																		init.uniform_(self.mask_scores     ,						a=-self.mask_scale     ,						b=self.mask_scale      )
												elif self.mask_init == "kaiming":
																		init.kaiming_uniform_(self.mask_scores     ,						a=math.sqrt(5      )      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												elif self.pruning_method == "l0":
																		UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				-0.1, 1.1, 2 / 3
																		if self.training:
																								UpperCAmelCase    =				torch.zeros_like(self.mask_scores      ).uniform_().clamp(0.0001     ,						0.9999      )
																								UpperCAmelCase    =				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b      )
																		else:
																								UpperCAmelCase    =				torch.sigmoid(self.mask_scores      )
																		UpperCAmelCase    =				s * (r - l) + l
																		UpperCAmelCase    =				s_bar.clamp(min=0.0     ,						max=1.0      )
												# Mask weights with computed mask
												UpperCAmelCase    =				mask * self.weight
												# Compute output (linear layer) with masked weights
												return nn.functional.linear(lowercase_     ,						lowercase_     ,						self.bias      )





