

import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 			__magic_name__					(    nn.Linear   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case = True      ,						__snake_case = "constant"      ,						__snake_case = 0.0      ,						__snake_case = "topK"      ,						)     ->				Dict:




						'''simple docstring'''
						super(__snake_case      ,						self      ).__init__(in_features=__snake_case      ,						out_features=__snake_case      ,						bias=__snake_case      )
						assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
						__a						   =pruning_method

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





			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      )     ->				Optional[int]:




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









import os
import sys
import time

from . import transposition_cipher as trans_cipher






def  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   ='Prehistoric Men.txt'
			__a						   ='Output.txt'
			__a						   =int(input('Enter key: '			)			)
			__a						   =input('Encrypt/Decrypt [e/d]: '			)

			if not os.path.exists(_snake_case			):
						print(F'File {input_file} does not exist. Quitting...'			)
						sys.exit()
			if os.path.exists(_snake_case			):
						print(F'Overwrite {output_file}? [y/n]'			)
						__a						   =input('> '			)
						if not response.lower().startswith('y'			):
									sys.exit()

			__a						   =time.time()
			if mode.lower().startswith('e'			):
						with open(_snake_case			) as f:
									__a						   =f.read()
						__a						   =trans_cipher.encrypt_message(_snake_case ,  _snake_case			)
			elif mode.lower().startswith('d'			):
						with open(_snake_case			) as f:
									__a						   =f.read()
						__a						   =trans_cipher.decrypt_message(_snake_case ,  _snake_case			)

			with open(_snake_case ,  'w'			) as output_obj:
						output_obj.write(_snake_case			)

			__a						   =round(time.time() - start_time ,  2			)
			print(('Done (', total_time, 'seconds )')			)


if __name__ == "__main__":
						main()






