


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










def  _lowerCamelCase(			lowercase__			)		->			str:

		'''simple docstring'''







		return " ".join([word[::-1] for word in input_str.split()]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






