






'''simple docstring'''





import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class 				snake_case__     (			nn.Linear):




					def __init__(       self       :						Optional[Any]							,				_A       :						List[Any]							,				_A       :						Tuple							,				_A       :						str = True							,				_A       :						Optional[Any] = "constant"							,				_A       :						int = 0.0							,				_A       :						List[Any] = "topK"							,				)  ->    Tuple:
								super(lowercase_							,				self     ).__init__(in_features=lowercase_							,				out_features=lowercase_							,				bias=lowercase_     )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								UpperCAmelCase_     :   Union[str, Any]       =   pruning_method

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




					def 						A     (       self       :						Any     )  ->    List[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 						A     (       self       :						Any							,				_A       :						Optional[int]							,				_A       :						str     )  ->    Optional[int]:






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










'''simple docstring'''





def __UpperCAmelCase (			)  -> Optional[Any]:
			UpperCAmelCase_     :   Union[str, Any]       =   1

			while True:
						if (
						    sorted(str(A						)						)
						    == sorted(str(2 * i						)						)
						    == sorted(str(3 * i						)						)
						    == sorted(str(4 * i						)						)
						    == sorted(str(5 * i						)						)
						    == sorted(str(6 * i						)						)
						):
									return i

						i += 1


if __name__ == "__main__":
						print(solution())


