



'''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			,       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  a__  (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  a__  (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(0.0001			,       0.9999	)
                _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	)








'''simple docstring'''





from math import gcd




def lowerCAmelCase  (__A = 12_000):



     """simple docstring"""



     _a     =  0
     for d in range(max_d + 1):
          for n in range(d // 3 + 1  ,     (d + 1) // 2):
               if gcd(__A  ,     __A) == 1:
                    fractions_number += 1
     return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")



