
'''simple docstring'''

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 							_snake_case	(    UpperCAmelCase__  ):
   _A    :       List[str]      				=   {
       'sld_warmup_steps': 1_5,
       'sld_guidance_scale': 2_0,
       'sld_threshold': 0.0,
       'sld_momentum_scale': 0.0,
       'sld_mom_beta': 0.0,
   }
   _A    :       Union[str, Any]      				=   {
       'sld_warmup_steps': 1_0,
       'sld_guidance_scale': 1_0_0_0,
       'sld_threshold': 0.01,
       'sld_momentum_scale': 0.3,
       'sld_mom_beta': 0.4,
   }
   _A    :       Union[str, Any]      				=   {
       'sld_warmup_steps': 7,
       'sld_guidance_scale': 2_0_0_0,
       'sld_threshold': 0.0_25,
       'sld_momentum_scale': 0.5,
       'sld_mom_beta': 0.7,
   }
   _A    :       Tuple      				=   {
       '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 							_snake_case	(    UpperCAmelCase__  ):
   _A    :       Union[List[PIL.Image.Image], np.ndarray]
   _A    :       Optional[List[bool]]
   _A    :       Optional[Union[List[PIL.Image.Image], np.ndarray]]
   _A    :       Optional[str]


if is_transformers_available() and is_torch_available():
    from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
    from .safety_checker import SafeStableDiffusionSafetyChecker









'''simple docstring'''

def 				A_					(	snake_case	):
  return "".join(chr(ord(snake_case	) + 32	) if "A" <= char <= "Z" else char for char in word	)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






