


import random
import sys

__UpperCAmelCase      			=  """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def 	_lowerCamelCase  (		)			->			None:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=input("Enter message: "      )
   UpperCamelCase__ :			Optional[int] 						="LFWOAYUISVKMNXPBDCRJTQEGHZ"
   UpperCamelCase__ :			Union[str, Any] 						=input("Encrypt/Decrypt [e/d]: "      )

   check_valid_key(A_      )

   if resp.lower().startswith("e"      ):
      UpperCamelCase__ :			Any 						="encrypt"
      UpperCamelCase__ :			Dict 						=encrypt_message(A_							,  A_      )
   elif resp.lower().startswith("d"      ):
      UpperCamelCase__ :			Union[str, Any] 						="decrypt"
      UpperCamelCase__ :			Tuple 						=decrypt_message(A_							,  A_      )

   print(f'''\n{mode.title()}ion: \n{translated}'''      )
def 	_lowerCamelCase  (		A_  :	str      )			->			None:

   '''simple docstring'''






   UpperCamelCase__ :			Tuple 						=list(A_      )
   UpperCamelCase__ :			Tuple 						=list(A_      )
   key_list.sort()
   letters_list.sort()

   if key_list != letters_list:
      sys.exit("Error in the key or symbol set."      )
def 	_lowerCamelCase  (		A_  :	str							,  A_  :	str      )			->			str:

   '''simple docstring'''






   return translate_message(A_							,  A_							,  "encrypt"      )
def 	_lowerCamelCase  (		A_  :	str							,  A_  :	str      )			->			str:

   '''simple docstring'''






   return translate_message(A_							,  A_							,  "decrypt"      )
def 	_lowerCamelCase  (		A_  :	str							,  A_  :	str							,  A_  :	str      )			->			str:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=""
   UpperCamelCase__ :			Tuple 						=LETTERS
   UpperCamelCase__ :			Tuple 						=key

   if mode == "decrypt":
      UpperCamelCase__						,	UpperCamelCase__ :			List[Any] 						=chars_b, chars_a

   for symbol in message:
      if symbol.upper() in chars_a:
         UpperCamelCase__ :			List[str] 						=chars_a.find(symbol.upper()      )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         translated += symbol

   return translated
def 	_lowerCamelCase  (		)			->			str:

   '''simple docstring'''






   UpperCamelCase__ :			str 						=list(A_      )
   random.shuffle(A_      )
   return "".join(A_      )


if __name__ == "__main__":
     main()





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	List[Any]=2_5_6      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=min(img.size      )

   if s < target_image_size:
      raise ValueError(f'''min dim for image {s} < {target_image_size}'''      )

   UpperCamelCase__ :			Optional[Any] 						=target_image_size / s
   UpperCamelCase__ :			Optional[Any] 						=(round(r * img.size[1]      ), round(r * img.size[0]      ))
   UpperCamelCase__ :			List[Any] 						=TF.resize(A_							,  A_							,  interpolation=PIL.Image.LANCZOS      )
   UpperCamelCase__ :			Optional[int] 						=TF.center_crop(A_							,  output_size=2 * [target_image_size]      )
   UpperCamelCase__ :			Union[str, Any] 						=torch.unsqueeze(T.ToTensor()(A_      )							,  0      )
   return img
def 	_lowerCamelCase  (		A_  :	Optional[int]      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=2.0 * x - 1.0
   return x
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Any=True							,  A_  :	int="RGB"      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=x.detach().cpu()
   if process:
      UpperCamelCase__ :			Optional[int] 						=post_process_tensor(A_      )
   UpperCamelCase__ :			Optional[int] 						=x.numpy()
   if process:
      UpperCamelCase__ :			Dict 						=(2_5_5 * x).astype(np.uinta      )
   UpperCamelCase__ :			int 						=Image.fromarray(A_      )
   if not x.mode == mode:
      UpperCamelCase__ :			Tuple 						=x.convert(A_      )
   return x
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.clamp(A_							,  -1.0							,  1.0      )
   UpperCamelCase__ :			Union[str, Any] 						=(x + 1.0) / 2.0
   UpperCamelCase__ :			Union[str, Any] 						=x.permute(1							,  2							,  0      )
   return x
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=post_process_tensor(x.squeeze()      )
   return x.permute(2							,  0							,  1      ).unsqueeze(0      )

