



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"		,      )		->							List[str]:




   """simple docstring"""







   super(lowerCAmelCase__		,      self   ).__init__(in_features=lowerCAmelCase__		,      out_features=lowerCAmelCase__		,      bias=lowerCAmelCase__   )
   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 lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   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 lowercase_						(  self		,      a		,      a   )		->							Any:




   """simple docstring"""







   if self.pruning_method == "topK":
     _A          =      TopKBinarizer.apply(self.mask_scores		,      lowerCAmelCase__   )
   elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
     _A          =      '''sigmoied''' in self.pruning_method
     _A          =      ThresholdBinarizer.apply(self.mask_scores		,      lowerCAmelCase__		,      lowerCAmelCase__   )
   elif self.pruning_method == "magnitude":
     _A          =      MagnitudeBinarizer.apply(self.weight		,      lowerCAmelCase__   )
   elif self.pruning_method == "l0":
     _A      ,				_A      ,				_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(lowerCAmelCase__		,      lowerCAmelCase__		,      self.bias   )




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	int:

  # Base Case
  if counter == 0 or capacity == 0:
    return 0

  # If weight of the nth item is more than Knapsack of capacity,
  #   then this item cannot be included in the optimal solution,
  # else return the maximum of two cases:
  #   (1) nth item included
  #   (2) not included
  if weights[counter - 1] > capacity:
    return knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
  else:
    _A          =      capacity - weights[counter - 1]
    _A          =      values[counter - 1] + knapsack(
        __snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    _A          =      knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    return max(__snake_case  ,			__snake_case							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()