


import os
import sys
import time

from . import transposition_cipher as trans_cipher
def 							UpperCAmelCase_   (				)	->     None:







 """simple docstring"""


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

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

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

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

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


if __name__ == "__main__":
  main()









"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(inputs > threshold).type(inputs.type() )
   if mask.sum() < nb_min:
    # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





