



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class 			__snake_case   ( nn.Linear):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_ = True	,	a_ = "constant"	,	a_ = 0.0	,	a_ = "topK"	,	):
											super(a_	,	self		).__init__(in_features=a_	,	out_features=a_	,	bias=a_		)
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											a__					   =		pruning_method

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


							def _a   (				self		):
											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 _a   (				self	,	a_	,	a_		):
											# Get the mask
											if self.pruning_method == "topK":
															a__					   =		TopKBinarizer.apply(self.mask_scores	,	a_		)
											elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
															a__					   =		'''sigmoied''' in self.pruning_method
															a__					   =		ThresholdBinarizer.apply(self.mask_scores	,	a_	,	a_		)
											elif self.pruning_method == "magnitude":
															a__					   =		MagnitudeBinarizer.apply(self.weight	,	a_		)
											elif self.pruning_method == "l0":
															a__					   =		-0.1, 1.1, 2 / 3
															if self.training:
																			a__					   =		torch.zeros_like(self.mask_scores		).uniform_().clamp(0.0_001	,	0.9_999		)
																			a__					   =		torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b		)
															else:
																			a__					   =		torch.sigmoid(self.mask_scores		)
															a__					   =		s * (r - l) + l
															a__					   =		s_bar.clamp(min=0.0	,	max=1.0		)
											# Mask weights with computed mask
											a__					   =		mask * self.weight
											# Compute output (linear layer) with masked weights
											return nn.functional.linear(a_	,	a_	,	self.bias		)





'''simple docstring'''



def       _a      (		_lowercase   :      list    ):





     '''simple docstring'''






     if len(_lowercase    ) <= 1:
          return data
     else:
          return [
              *quick_sort([e for e in data[1:] if e <= data[0]]    ),
              data[0],
              *quick_sort([e for e in data[1:] if e > data[0]]    ),
          ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()