code: 
  '''simple docstring'''
  from typing import Any, Callable, Dict, List, Optional, Union
  
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionImgaImgPipeline,
      StableDiffusionInpaintPipelineLegacy,
      StableDiffusionPipeline,
      UNetaDConditionModel,
  )
  from diffusers.configuration_utils import FrozenDict
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.utils import deprecate, logging
  
  
  lowercase__						=			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	["""safety_checker""", """feature_extractor"""]
  
  
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: AutoencoderKL     ,  UpperCamelCase__							: CLIPTextModel     ,  UpperCamelCase__							: CLIPTokenizer     ,  UpperCamelCase__							: UNetaDConditionModel     ,  UpperCamelCase__							: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,  UpperCamelCase__							: StableDiffusionSafetyChecker     ,  UpperCamelCase__							: CLIPImageProcessor     ,  UpperCamelCase__							: bool = True     ,  )	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__()
  				if hasattr(scheduler.config     ,  '''steps_offset'''			) and scheduler.config.steps_offset != 1:
  						snake_case     :					str     =	(
  						    f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
  						    f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
  						    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
  						    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
  						    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
  						    ''' file'''
  						)
  						deprecate('''steps_offset!=1'''     ,  '''1.0.0'''     ,  UpperCamelCase__     ,  standard_warn=UpperCamelCase__			)
  						snake_case     :					List[str]     =	dict(scheduler.config			)
  						snake_case     :					int     =	1
  						snake_case     :					Union[str, Any]     =	FrozenDict(UpperCamelCase__			)
  
  				self.register_modules(
  				    vae=UpperCamelCase__     ,  text_encoder=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     ,  unet=UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  safety_checker=UpperCamelCase__     ,  feature_extractor=UpperCamelCase__     ,  )
  				self.register_to_config(requires_safety_checker=UpperCamelCase__			)
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Dict[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return {k: getattr(self     ,  UpperCamelCase__			) for k in self.config.keys() if not k.startswith('''_'''			)}
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Optional[Union[str, int]] = "auto"			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if slice_size == "auto":
  						# half the attention head size is usually a good trade-off between
  						# speed and memory
  						snake_case     :					Any     =	self.unet.config.attention_head_dim // 2
  				self.unet.set_attention_slice(UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self.enable_attention_slicing(UpperCamelCase__			)
  
  
  
  		@torch.no_grad()
  		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: Union[str, List[str]]     ,  UpperCamelCase__							: Union[torch.FloatTensor, PIL.Image.Image]     ,  UpperCamelCase__							: Union[torch.FloatTensor, PIL.Image.Image]     ,  UpperCamelCase__							: float = 0.8     ,  UpperCamelCase__							: Optional[int] = 50     ,  UpperCamelCase__							: Optional[float] = 7.5     ,  UpperCamelCase__							: Optional[Union[str, List[str]]] = None     ,  UpperCamelCase__							: Optional[int] = 1     ,  UpperCamelCase__							: Optional[float] = 0.0     ,  UpperCamelCase__							: Optional[torch.Generator] = None     ,  UpperCamelCase__							: Optional[str] = "pil"     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,  UpperCamelCase__							: int = 1     ,  )	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return StableDiffusionInpaintPipelineLegacy(**self.components			)(
  				    prompt=UpperCamelCase__     ,  image=UpperCamelCase__     ,  mask_image=UpperCamelCase__     ,  strength=UpperCamelCase__     ,  num_inference_steps=UpperCamelCase__     ,  guidance_scale=UpperCamelCase__     ,  negative_prompt=UpperCamelCase__     ,  num_images_per_prompt=UpperCamelCase__     ,  eta=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  output_type=UpperCamelCase__     ,  return_dict=UpperCamelCase__     ,  callback=UpperCamelCase__     ,  )
  
  
  
  		@torch.no_grad()
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Union[str, List[str]]     ,  UpperCamelCase__							: Union[torch.FloatTensor, PIL.Image.Image]     ,  UpperCamelCase__							: float = 0.8     ,  UpperCamelCase__							: Optional[int] = 50     ,  UpperCamelCase__							: Optional[float] = 7.5     ,  UpperCamelCase__							: Optional[Union[str, List[str]]] = None     ,  UpperCamelCase__							: Optional[int] = 1     ,  UpperCamelCase__							: Optional[float] = 0.0     ,  UpperCamelCase__							: Optional[torch.Generator] = None     ,  UpperCamelCase__							: Optional[str] = "pil"     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,  UpperCamelCase__							: int = 1     ,  **UpperCamelCase__							: Tuple     ,  )	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return StableDiffusionImgaImgPipeline(**self.components			)(
  				    prompt=UpperCamelCase__     ,  image=UpperCamelCase__     ,  strength=UpperCamelCase__     ,  num_inference_steps=UpperCamelCase__     ,  guidance_scale=UpperCamelCase__     ,  negative_prompt=UpperCamelCase__     ,  num_images_per_prompt=UpperCamelCase__     ,  eta=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  output_type=UpperCamelCase__     ,  return_dict=UpperCamelCase__     ,  callback=UpperCamelCase__     ,  callback_steps=UpperCamelCase__     ,  )
  
  
  
  		@torch.no_grad()
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Union[str, List[str]]     ,  UpperCamelCase__							: int = 512     ,  UpperCamelCase__							: int = 512     ,  UpperCamelCase__							: int = 50     ,  UpperCamelCase__							: float = 7.5     ,  UpperCamelCase__							: Optional[Union[str, List[str]]] = None     ,  UpperCamelCase__							: Optional[int] = 1     ,  UpperCamelCase__							: float = 0.0     ,  UpperCamelCase__							: Optional[torch.Generator] = None     ,  UpperCamelCase__							: Optional[torch.FloatTensor] = None     ,  UpperCamelCase__							: Optional[str] = "pil"     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,  UpperCamelCase__							: int = 1     ,  )	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return StableDiffusionPipeline(**self.components			)(
  				    prompt=UpperCamelCase__     ,  height=UpperCamelCase__     ,  width=UpperCamelCase__     ,  num_inference_steps=UpperCamelCase__     ,  guidance_scale=UpperCamelCase__     ,  negative_prompt=UpperCamelCase__     ,  num_images_per_prompt=UpperCamelCase__     ,  eta=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  latents=UpperCamelCase__     ,  output_type=UpperCamelCase__     ,  return_dict=UpperCamelCase__     ,  callback=UpperCamelCase__     ,  callback_steps=UpperCamelCase__     ,  )
  
  
  
style_context: 
  '''simple docstring'''
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	0
  		for ele in arr:
  				total_sum += product_sum(SCREAMING_SNAKE_CASE__   ,					depth + 1 ) if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) else ele
  		return total_sum * depth
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		return product_sum(SCREAMING_SNAKE_CASE__   ,					1 )
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  