code: 
  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
  
  
  UpperCamelCase_	:    str  					=       logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  class      __lowercase	(	snake_case_					):
  	_A   			=      ["safety_checker", "feature_extractor"]
  
  
  
  
  
  
  	def __init__(self							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   int			,  snake_case							:   int			,  snake_case							:   List[Any]			,  snake_case							:   List[Any]			,  snake_case							:   Tuple			,  snake_case							:   int			,  snake_case							:   str = True			,  )   -> List[Any]:
  							super().__init__()
  							if hasattr(scheduler.config			,  "steps_offset"						) and scheduler.config.steps_offset != 1:
  													_lowercase					:  Tuple    			=      (
  													    F"""The configuration file of this scheduler: {scheduler} is outdated.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"			,  snake_case			,  standard_warn=snake_case						)
  													_lowercase					:  Any    			=      dict(scheduler.config						)
  													_lowercase					:  int    			=      1
  													_lowercase					:  List[Any]    			=      FrozenDict(snake_case						)
  
  							self.register_modules(
  							    vae=snake_case			,  text_encoder=snake_case			,  tokenizer=snake_case			,  unet=snake_case			,  scheduler=snake_case			,  safety_checker=snake_case			,  feature_extractor=snake_case			,  )
  							self.register_to_config(requires_safety_checker=snake_case						)
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   str						)   -> Dict[str, Any]:
  							return {k: getattr(self			,  snake_case						) for k in self.config.keys() if not k.startswith("_"						)}
  
  
  
  
  
  
  	def 			_a(self							:   Tuple			,  snake_case							:   Optional[Any] = "auto"						)   -> Union[str, Any]:
  							if slice_size == "auto":
  													# half the attention head size is usually a good trade-off between
  													# speed and memory
  													_lowercase					:  Optional[int]    			=      self.unet.config.attention_head_dim // 2
  							self.unet.set_attention_slice(snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> Any:
  							self.enable_attention_slicing(snake_case						)
  
  
  
  
  
  
  	@torch.no_grad()
  	def 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   List[str]			,  snake_case							:   Optional[int]			,  snake_case							:   Optional[Any] = 0.8			,  snake_case							:   Any = 50			,  snake_case							:   List[Any] = 7.5			,  snake_case							:   List[Any] = None			,  snake_case							:   List[Any] = 1			,  snake_case							:   Tuple = 0.0			,  snake_case							:   Any = None			,  snake_case							:   Tuple = "pil"			,  snake_case							:   Union[str, Any] = True			,  snake_case							:   Union[str, Any] = None			,  snake_case							:   Optional[int] = 1			,  )   -> Union[str, Any]:
  							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
  							return StableDiffusionInpaintPipelineLegacy(**self.components						)(
  							    prompt=snake_case			,  image=snake_case			,  mask_image=snake_case			,  strength=snake_case			,  num_inference_steps=snake_case			,  guidance_scale=snake_case			,  negative_prompt=snake_case			,  num_images_per_prompt=snake_case			,  eta=snake_case			,  generator=snake_case			,  output_type=snake_case			,  return_dict=snake_case			,  callback=snake_case			,  )
  
  
  
  
  
  
  	@torch.no_grad()
  	def 			_a(self							:   Any			,  snake_case							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int] = 0.8			,  snake_case							:   Tuple = 50			,  snake_case							:   Optional[Any] = 7.5			,  snake_case							:   List[str] = None			,  snake_case							:   Dict = 1			,  snake_case							:   str = 0.0			,  snake_case							:   List[str] = None			,  snake_case							:   List[str] = "pil"			,  snake_case							:   Tuple = True			,  snake_case							:   int = None			,  snake_case							:   Union[str, Any] = 1			,  **snake_case							:   Tuple			,  )   -> Optional[Any]:
  							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
  							return StableDiffusionImgaImgPipeline(**self.components						)(
  							    prompt=snake_case			,  image=snake_case			,  strength=snake_case			,  num_inference_steps=snake_case			,  guidance_scale=snake_case			,  negative_prompt=snake_case			,  num_images_per_prompt=snake_case			,  eta=snake_case			,  generator=snake_case			,  output_type=snake_case			,  return_dict=snake_case			,  callback=snake_case			,  callback_steps=snake_case			,  )
  
  
  
  
  
  
  	@torch.no_grad()
  	def 			_a(self							:   str			,  snake_case							:   List[str]			,  snake_case							:   Optional[int] = 512			,  snake_case							:   Dict = 512			,  snake_case							:   List[Any] = 50			,  snake_case							:   Union[str, Any] = 7.5			,  snake_case							:   List[str] = None			,  snake_case							:   List[Any] = 1			,  snake_case							:   Tuple = 0.0			,  snake_case							:   Tuple = None			,  snake_case							:   Optional[Any] = None			,  snake_case							:   List[str] = "pil"			,  snake_case							:   List[Any] = True			,  snake_case							:   List[str] = None			,  snake_case							:   Any = 1			,  )   -> Any:
  							# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
  							return StableDiffusionPipeline(**self.components						)(
  							    prompt=snake_case			,  height=snake_case			,  width=snake_case			,  num_inference_steps=snake_case			,  guidance_scale=snake_case			,  negative_prompt=snake_case			,  num_images_per_prompt=snake_case			,  eta=snake_case			,  generator=snake_case			,  latents=snake_case			,  output_type=snake_case			,  return_dict=snake_case			,  callback=snake_case			,  callback_steps=snake_case			,  )
  
  
  
  
  
style_context: 
  from __future__ import annotations
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
  						while start_ind <= end_ind:
  												if array[start_ind] == search_item:
  																		return start_ind
  												elif array[end_ind] == search_item:
  																		return end_ind
  												else:
  																		start_ind += 1
  																		end_ind -= 1
      # returns -1 if search_item is not found in array
  						return -1
  
  
  if __name__ == "__main__":
  							print(double_linear_search(list(range(100)), 40))
  
  
  
  
  