code: 
  
  
  
  
  
  
  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__ ):
   _lowerCAmelCase : Union[str, Any] 				=				{
       '''sld_warmup_steps''': 1_5,
       '''sld_guidance_scale''': 2_0,
       '''sld_threshold''': 0.0,
       '''sld_momentum_scale''': 0.0,
       '''sld_mom_beta''': 0.0,
   }
   _lowerCAmelCase : int 				=				{
       '''sld_warmup_steps''': 1_0,
       '''sld_guidance_scale''': 1_0_0_0,
       '''sld_threshold''': 0.0_1,
       '''sld_momentum_scale''': 0.3,
       '''sld_mom_beta''': 0.4,
   }
   _lowerCAmelCase : Any 				=				{
       '''sld_warmup_steps''': 7,
       '''sld_guidance_scale''': 2_0_0_0,
       '''sld_threshold''': 0.0_2_5,
       '''sld_momentum_scale''': 0.5,
       '''sld_mom_beta''': 0.7,
   }
   _lowerCAmelCase : int 				=				{
       '''sld_warmup_steps''': 0,
       '''sld_guidance_scale''': 5_0_0_0,
       '''sld_threshold''': 1.0,
       '''sld_momentum_scale''': 0.5,
       '''sld_mom_beta''': 0.7,
   }
  
  
  
  
  
  
  
  @dataclass
  class     __a						(					A__ ):
   _lowerCAmelCase : Union[List[PIL.Image.Image], np.ndarray]
   _lowerCAmelCase : Optional[List[bool]]
   _lowerCAmelCase : Optional[Union[List[PIL.Image.Image], np.ndarray]]
   _lowerCAmelCase : Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
   from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
   from .safety_checker import SafeStableDiffusionSafetyChecker
style_context: 
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			str:
       return "".join(chr(ord(__lowerCAmelCase    ) + 32    ) if "A" <= char <= "Z" else char for char in word    )
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()