code: 
  
  
  from typing import Literal
  
  lowerCAmelCase					 =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			str:
  		__lowercase=    LETTERS if mode == 'decrypt' else key
  		__lowercase=    key if mode == 'decrypt' else LETTERS
  		__lowercase=    ''
  		# loop through each symbol in the message
  		for symbol in message:
  				if symbol.upper() in chars_a:
  						# encrypt/decrypt the symbol
  						__lowercase=    chars_a.find(symbol.upper()			)
  						if symbol.isupper():
  								translated += chars_b[sym_index].upper()
  						else:
  								translated += chars_b[sym_index].lower()
  				else:
  						# symbol is not in LETTERS, just add it
  						translated += symbol
  		return translated
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			str:
  		return translate_message(lowercase__     ,				lowercase__     ,				'encrypt'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			str:
  		return translate_message(lowercase__     ,				lowercase__     ,				'decrypt'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			None:
  		__lowercase=    'Hello World'
  		__lowercase=    'QWERTYUIOPASDFGHJKLZXCVBNM'
  		__lowercase=    'decrypt'  # set to 'encrypt' or 'decrypt'
  
  		if mode == "encrypt":
  				__lowercase=    encrypt_message(lowercase__     ,				lowercase__			)
  		elif mode == "decrypt":
  				__lowercase=    decrypt_message(lowercase__     ,				lowercase__			)
  		print(F'Using the key {key}, the {mode}ed message is: {translated}'			)
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  	main()
  
  
  
  
  
  
  
style_context: 
  
  
  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    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  