


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



class 							lowercase__(						nn.Linear			):






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = True   ,			__SCREAMING_SNAKE_CASE = "constant"   ,			__SCREAMING_SNAKE_CASE = 0.0   ,			__SCREAMING_SNAKE_CASE = "topK"   ,			)					->						List[Any]:







         """simple docstring"""







         super(__SCREAMING_SNAKE_CASE   ,			self).__init__(in_features=__SCREAMING_SNAKE_CASE   ,			out_features=__SCREAMING_SNAKE_CASE   ,			bias=__SCREAMING_SNAKE_CASE)
         assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
         UpperCamelCase__ :			int 						=pruning_method

         if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
            UpperCamelCase__ :			Dict 						=mask_scale
            UpperCamelCase__ :			Optional[int] 						=mask_init
            UpperCamelCase__ :			Tuple 						=nn.Parameter(torch.empty(self.weight.size()))
            self.init_mask()




      def    UpperCAmelCase							(		self)					->						Optional[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   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         if self.pruning_method == "topK":
            UpperCamelCase__ :			int 						=TopKBinarizer.apply(self.mask_scores   ,			__SCREAMING_SNAKE_CASE)
         elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
            UpperCamelCase__ :			int 						="sigmoied" in self.pruning_method
            UpperCamelCase__ :			Union[str, Any] 						=ThresholdBinarizer.apply(self.mask_scores   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
         elif self.pruning_method == "magnitude":
            UpperCamelCase__ :			int 						=MagnitudeBinarizer.apply(self.weight   ,			__SCREAMING_SNAKE_CASE)
         elif self.pruning_method == "l0":
            UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ :			List[str] 						=-0.1, 1.1, 2 / 3
            if self.training:
               UpperCamelCase__ :			List[Any] 						=torch.zeros_like(self.mask_scores).uniform_().clamp(0.00_01   ,			0.99_99)
               UpperCamelCase__ :			Tuple 						=torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b)
            else:
               UpperCamelCase__ :			List[str] 						=torch.sigmoid(self.mask_scores)
            UpperCamelCase__ :			Tuple 						=s * (r - l) + l
            UpperCamelCase__ :			Dict 						=s_bar.clamp(min=0.0   ,			max=1.0)
         # Mask weights with computed mask
         UpperCamelCase__ :			List[str] 						=mask * self.weight
         # Compute output (linear layer) with masked weights
         return nn.functional.linear(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			self.bias)





def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Optional[int]      )			->			Dict:

   '''simple docstring'''






   if len(str(A_      )      ) == 1 or len(str(A_      )      ) == 1:
      return a * b

   UpperCamelCase__ :			Any 						=max(len(str(A_      )      )							,  len(str(A_      )      )      )
   UpperCamelCase__ :			Optional[int] 						=ma // 2

   UpperCamelCase__						,	UpperCamelCase__ :			Dict 						=divmod(A_							,  1_0**ma      )
   UpperCamelCase__						,	UpperCamelCase__ :			Union[str, Any] 						=divmod(A_							,  1_0**ma      )

   UpperCamelCase__ :			List[Any] 						=karatsuba(A_							,  A_      )
   UpperCamelCase__ :			Dict 						=karatsuba((aa + aa)							,  (ba + ba)      )
   UpperCamelCase__ :			Optional[int] 						=karatsuba(A_							,  A_      )

   return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)
def 	_lowerCamelCase  (		)			->			Any:

   '''simple docstring'''






   print(karatsuba(1_5_4_6_3							,  2_3_4_8_9      )      )


if __name__ == "__main__":
     main()

