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_  =	logging.get_logger(__name__)  # pylint: disable=invalid-name

class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     ['safety_checker', 'feature_extractor']





				def __init__(		self :		Union[str, Any]					,      snake_case_ :		AutoencoderKL					,      snake_case_ :		CLIPTextModel					,      snake_case_ :		CLIPTokenizer					,      snake_case_ :		UNetaDConditionModel					,      snake_case_ :		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,      snake_case_ :		StableDiffusionSafetyChecker					,      snake_case_ :		CLIPImageProcessor					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								super().__init__()
								if hasattr(scheduler.config					,      '''steps_offset'''							) and scheduler.config.steps_offset != 1:
												A     :  Dict   =				(
												    f"""The configuration file of this scheduler: {scheduler} is outdated.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_							)
												A     :  Dict   =				dict(scheduler.config							)
												A     :  List[str]   =				1
												A     :  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        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								return {k: getattr(self					,      snake_case_							) for k in self.config.keys() if not k.startswith('''_'''							)}





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		Optional[Union[str, int]] = "auto"							):


								"""simple docstring"""

								if slice_size == "auto":
												# half the attention head size is usually a good trade-off between
												# speed and memory
												A     :  str   =				self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(snake_case_							)





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								self.enable_attention_slicing(snake_case_							)





				@torch.no_grad()
				def        _UpperCAmelCase						(		self :		str					,      snake_case_ :		Union[str, List[str]]					,      snake_case_ :		Union[torch.FloatTensor, PIL.Image.Image]					,      snake_case_ :		Union[torch.FloatTensor, PIL.Image.Image]					,      snake_case_ :		float = 0.8					,      snake_case_ :		Optional[int] = 50					,      snake_case_ :		Optional[float] = 7.5					,      snake_case_ :		Optional[Union[str, List[str]]] = None					,      snake_case_ :		Optional[int] = 1					,      snake_case_ :		Optional[float] = 0.0					,      snake_case_ :		Optional[torch.Generator] = None					,      snake_case_ :		Optional[str] = "pil"					,      snake_case_ :		bool = True					,      snake_case_ :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      snake_case_ :		int = 1					,      ):


								"""simple docstring"""

								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        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		Union[str, List[str]]					,      snake_case_ :		Union[torch.FloatTensor, PIL.Image.Image]					,      snake_case_ :		float = 0.8					,      snake_case_ :		Optional[int] = 50					,      snake_case_ :		Optional[float] = 7.5					,      snake_case_ :		Optional[Union[str, List[str]]] = None					,      snake_case_ :		Optional[int] = 1					,      snake_case_ :		Optional[float] = 0.0					,      snake_case_ :		Optional[torch.Generator] = None					,      snake_case_ :		Optional[str] = "pil"					,      snake_case_ :		bool = True					,      snake_case_ :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      snake_case_ :		int = 1					,      **snake_case_ :		Dict					,      ):


								"""simple docstring"""

								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        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		Union[str, List[str]]					,      snake_case_ :		int = 512					,      snake_case_ :		int = 512					,      snake_case_ :		int = 50					,      snake_case_ :		float = 7.5					,      snake_case_ :		Optional[Union[str, List[str]]] = None					,      snake_case_ :		Optional[int] = 1					,      snake_case_ :		float = 0.0					,      snake_case_ :		Optional[torch.Generator] = None					,      snake_case_ :		Optional[torch.FloatTensor] = None					,      snake_case_ :		Optional[str] = "pil"					,      snake_case_ :		bool = True					,      snake_case_ :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      snake_case_ :		int = 1					,      ):


								"""simple docstring"""

								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_					,      )

def 							_lowerCamelCase			(					lowerCamelCase_:  list		):



				'''simple docstring'''







				A     :  Optional[int]   =				1.3
				A     :  List[str]   =				len(lowerCamelCase_		)
				A     :  Optional[Any]   =				False

				while not completed:
								# Update the gap value for a next comb
								A     :  str   =				int(gap / shrink_factor		)
								if gap <= 1:
												A     :  Tuple   =				True

								A     :  List[Any]   =				0
								while index + gap < len(lowerCamelCase_		):
												if data[index] > data[index + gap]:
																# Swap values
																A ,       A     :  int   =				data[index + gap], data[index]
																A     :  Dict   =				False
												index += 1

				return data


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase_  =	input("Enter numbers separated by a comma:\n").strip()
					UpperCamelCase_  =	[int(item) for item in user_input.split(",")]
					print(comb_sort(unsorted))