






"""simple docstring"""





import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
class  __lowerCamelCase     (	nn.Linear	):

   def __init__(       self			,					snake_case_			,					snake_case_			,					snake_case_ = True			,					snake_case_ = "constant"			,					snake_case_ = 0.0			,					snake_case_ = "topK"			,					)		->							Tuple:
       super(_lowerCAmelCase			,					self   ).__init__(in_features=_lowerCAmelCase			,					out_features=_lowerCAmelCase			,					bias=_lowerCAmelCase   )
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       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     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							Union[str, Any]:
       # Get the mask
       if self.pruning_method == "topK":
           UpperCamelCase__								=     TopKBinarizer.apply(self.mask_scores			,					_lowerCAmelCase   )
       elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
           UpperCamelCase__								=     'sigmoied' in self.pruning_method
           UpperCamelCase__								=     ThresholdBinarizer.apply(self.mask_scores			,					_lowerCAmelCase			,					_lowerCAmelCase   )
       elif self.pruning_method == "magnitude":
           UpperCamelCase__								=     MagnitudeBinarizer.apply(self.weight			,					_lowerCAmelCase   )
       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(_lowerCAmelCase			,					_lowerCAmelCase			,					self.bias   )









def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						float  ,					_SCREAMING_SNAKE_CASE :						float					)->float:
      _lowerCAmelCase			      =	float()
      try:
            _lowerCAmelCase			      =	mass * acceleration
      except Exception:
            return -0.0
      return force


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo
    UpperCAmelCase_          =				12.5
    UpperCAmelCase_          =				1_0
    UpperCAmelCase_          =				newtons_second_law_of_motion(mass, acceleration)
    print("The force is ", force, "N")