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     =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase					):
  					__A          =   ["safety_checker", "feature_extractor"]
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = True    ,      )						->  Union[str, Any]:
  								super().__init__()
  								if hasattr(scheduler.config    ,      """steps_offset"""       ) and scheduler.config.steps_offset != 1:
  											UpperCamelCase__         =	(
  											    F"""The configuration file of this scheduler: {scheduler} is outdated.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"""    ,      __lowerCAmelCase    ,      standard_warn=__lowerCAmelCase       )
  											UpperCamelCase__         =	dict(scheduler.config       )
  											UpperCamelCase__         =	1
  											UpperCamelCase__         =	FrozenDict(__lowerCAmelCase       )
  
  								self.register_modules(
  								    vae=__lowerCAmelCase    ,      text_encoder=__lowerCAmelCase    ,      tokenizer=__lowerCAmelCase    ,      unet=__lowerCAmelCase    ,      scheduler=__lowerCAmelCase    ,      safety_checker=__lowerCAmelCase    ,      feature_extractor=__lowerCAmelCase    ,      )
  								self.register_to_config(requires_safety_checker=__lowerCAmelCase       )
  
  
  
  
  					@property
  					def 	snake_case_		(						self       )						->  Dict[str, Any]:
  								return {k: getattr(self    ,      __lowerCAmelCase       ) for k in self.config.keys() if not k.startswith("""_"""       )}
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase = "auto"       )						->  Tuple:
  								if slice_size == "auto":
  											# half the attention head size is usually a good trade-off between
  											# speed and memory
  											UpperCamelCase__         =	self.unet.config.attention_head_dim // 2
  								self.unet.set_attention_slice(__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  List[Any]:
  								self.enable_attention_slicing(__lowerCAmelCase       )
  
  
  
  
  					@torch.no_grad()
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = 0.8    ,      __lowerCAmelCase = 50    ,      __lowerCAmelCase = 7.5    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = 1    ,      __lowerCAmelCase = 0.0    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = "pil"    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = 1    ,      )						->  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=__lowerCAmelCase    ,      image=__lowerCAmelCase    ,      mask_image=__lowerCAmelCase    ,      strength=__lowerCAmelCase    ,      num_inference_steps=__lowerCAmelCase    ,      guidance_scale=__lowerCAmelCase    ,      negative_prompt=__lowerCAmelCase    ,      num_images_per_prompt=__lowerCAmelCase    ,      eta=__lowerCAmelCase    ,      generator=__lowerCAmelCase    ,      output_type=__lowerCAmelCase    ,      return_dict=__lowerCAmelCase    ,      callback=__lowerCAmelCase    ,      )
  
  
  
  
  					@torch.no_grad()
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = 0.8    ,      __lowerCAmelCase = 50    ,      __lowerCAmelCase = 7.5    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = 1    ,      __lowerCAmelCase = 0.0    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = "pil"    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = 1    ,      **__lowerCAmelCase    ,      )						->  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=__lowerCAmelCase    ,      image=__lowerCAmelCase    ,      strength=__lowerCAmelCase    ,      num_inference_steps=__lowerCAmelCase    ,      guidance_scale=__lowerCAmelCase    ,      negative_prompt=__lowerCAmelCase    ,      num_images_per_prompt=__lowerCAmelCase    ,      eta=__lowerCAmelCase    ,      generator=__lowerCAmelCase    ,      output_type=__lowerCAmelCase    ,      return_dict=__lowerCAmelCase    ,      callback=__lowerCAmelCase    ,      callback_steps=__lowerCAmelCase    ,      )
  
  
  
  
  					@torch.no_grad()
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase = 512    ,      __lowerCAmelCase = 512    ,      __lowerCAmelCase = 50    ,      __lowerCAmelCase = 7.5    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = 1    ,      __lowerCAmelCase = 0.0    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = "pil"    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = 1    ,      )						->  List[Any]:
  								# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
  								return StableDiffusionPipeline(**self.components       )(
  								    prompt=__lowerCAmelCase    ,      height=__lowerCAmelCase    ,      width=__lowerCAmelCase    ,      num_inference_steps=__lowerCAmelCase    ,      guidance_scale=__lowerCAmelCase    ,      negative_prompt=__lowerCAmelCase    ,      num_images_per_prompt=__lowerCAmelCase    ,      eta=__lowerCAmelCase    ,      generator=__lowerCAmelCase    ,      latents=__lowerCAmelCase    ,      output_type=__lowerCAmelCase    ,      return_dict=__lowerCAmelCase    ,      callback=__lowerCAmelCase    ,      callback_steps=__lowerCAmelCase    ,      )
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def     a						(      _lowercase   :     Optional[int]       )     ->List[str]:
  		UpperCamelCase__         =	len(_lowercase       )
  		for i in range(length - 1       ):
  				UpperCamelCase__         =	False
  				for j in range(length - 1 - i       ):
  						if collection[j] > collection[j + 1]:
  								UpperCamelCase__         =	True
  								UpperCamelCase__  ,			UpperCamelCase__         =	collection[j + 1], collection[j]
  				if not swapped:
  						break  # Stop iteration if the collection is sorted.
  		return collection
  
  
  if __name__ == "__main__":
  				import doctest
  				import time
  
  				doctest.testmod()
  
  				__lowerCAmelCase     =			input("Enter numbers separated by a comma:").strip()
  				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
  				__lowerCAmelCase     =			time.process_time()
  				print(*bubble_sort(unsorted), sep=",")
  				print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  
  
  