

"""simple docstring"""




import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class   __lowercase				(nn.Linear			):
 def __init__(							self				,							A_				,							A_				,							A_ = True				,							A_ = "constant"				,							A_ = 0.0				,							A_ = "topK"				,							) ->Optional[int]:



       '''simple docstring'''





       super(lowerCAmelCase__				,							self ).__init__(in_features=lowerCAmelCase__				,							out_features=lowerCAmelCase__				,							bias=lowerCAmelCase__ )
       assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
       __lowerCAmelCase :						Optional[Any] = pruning_method

       if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
             __lowerCAmelCase :						List[Any] = mask_scale
             __lowerCAmelCase :						Union[str, Any] = mask_init
             __lowerCAmelCase :						Dict = nn.Parameter(torch.empty(self.weight.size() ) )
             self.init_mask()
 def      UpperCamelCase__  (							self ) ->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      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Union[str, Any]:



       '''simple docstring'''





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





def 		_lowercase			(					lowercase__						):
      return "".join(chr(ord(lowercase__						) + 3_2						) if '''A''' <= char <= '''Z''' else char for char in word						)


if __name__ == "__main__":
 from doctest import testmod

 testmod()


