






"""simple docstring"""



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class 	_UpperCamelCase ( nn.Linear							):



    '''simple docstring'''


    def __init__(      self   ,			__lowercase   ,			__lowercase   ,			__lowercase = True   ,			__lowercase = "constant"   ,			__lowercase = 0.0   ,			__lowercase = "topK"   ,			):
     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 		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   ,			__lowercase   ,			__lowercase			):
     # 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(0.0_001   ,			0.9_999			)
       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			)











"""simple docstring"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



