


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   , __snake_case   , __snake_case   , __snake_case = True   , __snake_case = "constant"   , __snake_case = 0.0   , __snake_case = "topK"   , ):
											super(lowerCAmelCase__   , self  ).__init__(in_features=lowerCAmelCase__   , out_features=lowerCAmelCase__   , bias=lowerCAmelCase__  )
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											_SCREAMING_SNAKE_CASE			:	str							=				pruning_method

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






							def UpperCAmelCase_     (   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 UpperCAmelCase_     (   self   , __snake_case   , __snake_case  ):


											if self.pruning_method == "topK":
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				TopKBinarizer.apply(self.mask_scores   , lowerCAmelCase__  )
											elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				"""sigmoied""" in self.pruning_method
															_SCREAMING_SNAKE_CASE			:	Any							=				ThresholdBinarizer.apply(self.mask_scores   , lowerCAmelCase__   , lowerCAmelCase__  )
											elif self.pruning_method == "magnitude":
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				MagnitudeBinarizer.apply(self.weight   , lowerCAmelCase__  )
											elif self.pruning_method == "l0":
															_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Tuple							=				-0.1, 1.1, 2 / 3
															if self.training:
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				torch.zeros_like(self.mask_scores  ).uniform_().clamp(0.0001   , 0.9999  )
																			_SCREAMING_SNAKE_CASE			:	Dict							=				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b  )
															else:
																			_SCREAMING_SNAKE_CASE			:	Any							=				torch.sigmoid(self.mask_scores  )
															_SCREAMING_SNAKE_CASE			:	str							=				s * (r - l) + l
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				s_bar.clamp(min=0.0   , max=1.0  )
											# Mask weights with computed mask
											_SCREAMING_SNAKE_CASE			:	str							=				mask * self.weight
											# Compute output (linear layer) with masked weights
											return nn.functional.linear(lowerCAmelCase__   , lowerCAmelCase__   , self.bias  )








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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

				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				max(len(str(SCREAMING_SNAKE_CASE__					)					)							,				len(str(SCREAMING_SNAKE_CASE__					)					)					)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				ma // 2

				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba((aa + aa)							,				(ba + ba)					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)







def       snake_case_       (  ):







				"""simple docstring"""





				print(karatsuba(1_5463							,				2_3489					)					)


if __name__ == "__main__":
				main()




