'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_ = 13				):
     lowercase_       :							List[Any]  					=       ''
     for c in s:
          if "A" <= c <= "Z":
               out += chr(ord('A'				) + (ord(SCREAMING_SNAKE_CASE_				) - ord('A'				) + n) % 26				)
          elif "a" <= c <= "z":
               out += chr(ord('a'				) + (ord(SCREAMING_SNAKE_CASE_				) - ord('a'				) + n) % 26				)
          else:
               out += c
     return out

def _UpperCamelCase (							):
     lowercase_       :							str  					=       input('Enter message: '				)

     lowercase_       :							Tuple  					=       dencrypt(SCREAMING_SNAKE_CASE_	,							13				)
     print('Encryption:'	,							SCREAMING_SNAKE_CASE_				)

     lowercase_       :							Dict  					=       dencrypt(SCREAMING_SNAKE_CASE_	,							13				)
     print('Decryption: '	,							SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




