
'''simple docstring'''



from __future__ import annotations

from string import ascii_letters
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = None  )   ->   str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = alphabet or ascii_letters

   # The final result string
   _SCREAMING_SNAKE_CASE          = """"""

   for character in input_string:
      if character not in alpha:
         # Append without encryption if character is not in the alphabet
         result += character
      else:
         # Get the index of the new key and make sure it isn't too large
         _SCREAMING_SNAKE_CASE          = (alpha.index(SCREAMING_SNAKE_CASE_  ) + key) % len(SCREAMING_SNAKE_CASE_  )

         # Append the encoded character to the alphabet
         result += alpha[new_key]

   return result
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = None  )   ->   str:




   """simple docstring"""
   key *= -1

   return encrypt(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = None  )   ->   dict[int, str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = alphabet or ascii_letters

   # To store data on all the combinations
   _SCREAMING_SNAKE_CASE          = {}

   # Cycle through each combination
   for key in range(1			,					len(SCREAMING_SNAKE_CASE_  ) + 1  ):
      # Decrypt the message and store the result in the data
      _SCREAMING_SNAKE_CASE          = decrypt(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )

   return brute_force_data


if __name__ == "__main__":
     while True:
          print(f"""\n{"-" * 10}\n Menu\n{"-" * 10}""")
          print(*["1.Encrypt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n")

          # get user input
          UpperCamelCase__					:						int											=     input("\nWhat would you like to do?: ").strip() or "4"

          # run functions based on what the user chose
          if choice not in ("1", "2", "3", "4"):
               print("Invalid choice, please enter a valid choice")
          elif choice == "1":
               UpperCamelCase__					:						Any											=     input("Please enter the string to be encrypted: ")
               UpperCamelCase__					:						Optional[Any]											=     int(input("Please enter off-set: ").strip())

               print(encrypt(input_string, key))
          elif choice == "2":
               UpperCamelCase__					:						List[Any]											=     input("Please enter the string to be decrypted: ")
               UpperCamelCase__					:						Any											=     int(input("Please enter off-set: ").strip())

               print(decrypt(input_string, key))
          elif choice == "3":
               UpperCamelCase__					:						List[Any]											=     input("Please enter the string to be decrypted: ")
               UpperCamelCase__					:						int											=     brute_force(input_string)

               for key, value in brute_force_data.items():
                    print(f"""Key: {key} | Message: {value}""")

          elif choice == "4":
               print("Goodbye.")
               break








'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__					)			->	Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__				,							scheduler=A__					)






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

          # set step values
          self.scheduler.set_timesteps(A__					)

          for t in self.progress_bar(self.scheduler.timesteps					):
             # 1. predict noise model_output
             _SCREAMING_SNAKE_CASE          = self.unet(A__				,							A__					).sample

             # 2. compute previous image: x_t -> x_t-1
             _SCREAMING_SNAKE_CASE          = self.scheduler.step(A__				,							A__				,							A__				,							generator=A__					).prev_sample

          _SCREAMING_SNAKE_CASE          = (image / 2 + 0.5).clamp(0				,							1					)
          _SCREAMING_SNAKE_CASE          = image.cpu().permute(0				,							2				,							3				,							1					).numpy()
          if output_type == "pil":
             _SCREAMING_SNAKE_CASE          = self.numpy_to_pil(A__					)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






