

import string
def 	a (     A__      :  str	)       -> str:





      """simple docstring"""



      _lowercase      			=''
      for i in sequence:
            _lowercase      			=ord(A__	)
            if 65 <= extract <= 90:
                  output += chr(155 - extract	)
            elif 97 <= extract <= 122:
                  output += chr(219 - extract	)
            else:
                  output += i
      return output
def 	a (     A__      :  str	)       -> str:





      """simple docstring"""



      _lowercase      			=string.ascii_letters
      _lowercase      			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
      return "".join(
          letters_reversed[letters.index(A__	)] if c in letters else c for c in sequence	)
def 	a (     )       -> None:





      """simple docstring"""



      from timeit import timeit

      print('Running performance benchmarks...'	)
      _lowercase      			='from string import printable ; from __main__ import atbash, atbash_slow'
      print(F'''> atbash_slow(): {timeit("atbash_slow(printable)" ,  setup=A__	)} seconds'''	)
      print(F'''>      atbash(): {timeit("atbash(printable)" ,  setup=A__	)} seconds'''	)


if __name__ == "__main__":
  for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
    print(f"{example} encrypted in atbash: {atbash(example)}")
  benchmark()





from dataclasses import dataclass
from enum import Enum
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
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


