






"""simple docstring"""





import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 		_a       (						nn.Linear   ):





			'''simple docstring'''






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




										'''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"]
										lowercase__:    List[Any]        =      pruning_method

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





			def __lowercase    (   self)	-> Dict:




										'''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 __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




										'''simple docstring'''




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














"""simple docstring"""





def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       assert isinstance(snake_case_						,       snake_case_		), F"""The input value of [set_a={set_a}] is not a set"""
       assert isinstance(snake_case_						,       snake_case_		), F"""The input value of [set_b={set_b}] is not a set"""

       return {a + b for a in set_a for b in set_b}


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

  testmod()






