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
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						["safety_checker", "feature_extractor"]
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A = True				,					)      ->  int:
       super().__init__()
       if hasattr(scheduler.config				,					'''steps_offset'''		) and scheduler.config.steps_offset != 1:
           a					=(
               f'''The configuration file of this scheduler: {scheduler} is outdated.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'''				,					__A				,					standard_warn=__A		)
           a					=dict(scheduler.config		)
           a					=1
           a					=FrozenDict(__A		)
  
       self.register_modules(
           vae=__A				,					text_encoder=__A				,					tokenizer=__A				,					unet=__A				,					scheduler=__A				,					safety_checker=__A				,					feature_extractor=__A				,					)
       self.register_to_config(requires_safety_checker=__A		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict[str, Any]:
       return {k: getattr(self				,					__A		) for k in self.config.keys() if not k.startswith('''_'''		)}
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A = "auto"		)      ->  int:
       if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           a					=self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       self.enable_attention_slicing(__A		)
  
  
   @torch.no_grad()
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A = 0.8				,					__A = 50				,					__A = 7.5				,					__A = None				,					__A = 1				,					__A = 0.0				,					__A = None				,					__A = "pil"				,					__A = True				,					__A = None				,					__A = 1				,					)      ->  str:
       # 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=__A				,					image=__A				,					mask_image=__A				,					strength=__A				,					num_inference_steps=__A				,					guidance_scale=__A				,					negative_prompt=__A				,					num_images_per_prompt=__A				,					eta=__A				,					generator=__A				,					output_type=__A				,					return_dict=__A				,					callback=__A				,					)
  
  
   @torch.no_grad()
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A = 0.8				,					__A = 50				,					__A = 7.5				,					__A = None				,					__A = 1				,					__A = 0.0				,					__A = None				,					__A = "pil"				,					__A = True				,					__A = None				,					__A = 1				,					**__A				,					)      ->  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=__A				,					image=__A				,					strength=__A				,					num_inference_steps=__A				,					guidance_scale=__A				,					negative_prompt=__A				,					num_images_per_prompt=__A				,					eta=__A				,					generator=__A				,					output_type=__A				,					return_dict=__A				,					callback=__A				,					callback_steps=__A				,					)
  
  
  
  
  
   @torch.no_grad()
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = 512				,					__A = 512				,					__A = 50				,					__A = 7.5				,					__A = None				,					__A = 1				,					__A = 0.0				,					__A = None				,					__A = None				,					__A = "pil"				,					__A = True				,					__A = None				,					__A = 1				,					)      ->  Tuple:
       # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
       return StableDiffusionPipeline(**self.components		)(
           prompt=__A				,					height=__A				,					width=__A				,					num_inference_steps=__A				,					guidance_scale=__A				,					negative_prompt=__A				,					num_images_per_prompt=__A				,					eta=__A				,					generator=__A				,					latents=__A				,					output_type=__A				,					return_dict=__A				,					callback=__A				,					callback_steps=__A				,					)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from random import randrange
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if len(lowercase	) < 2:
          return collection
      a					=randrange(len(lowercase	)	)  # Use random element as pivot
      a					=collection[pivot_index]
      a					=[]  # All elements greater than pivot
      a					=[]  # All elements less than or equal to pivot
  
      for element in collection[:pivot_index]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      for element in collection[pivot_index + 1 :]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      return [*quick_sort(lowercase	), pivot, *quick_sort(lowercase	)]
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
         lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
         print(quick_sort(unsorted))