

"""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


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






class       _UpperCAmelCase     (						SCREAMING_SNAKE_CASE__):
			__a	:    List[str]			   =							["""safety_checker""", """feature_extractor"""]
			def __init__(						self ,						_A ,						_A ,						_A ,						_A ,						_A ,						_A ,						_A ,						_A = True ,						)      ->      List[str]:






					'''simple docstring'''







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							    f'''The configuration file of this scheduler: {scheduler} is outdated.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






					'''simple docstring'''







					return {k: getattr(self ,						_UpperCAmelCase       ) for k in self.config.keys() if not k.startswith("""_"""       )}
			def 							__snake_case	(						self ,						_A = "auto"       )      ->      List[str]:






					'''simple docstring'''







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






					'''simple docstring'''







					self.enable_attention_slicing(_UpperCAmelCase       )
			@torch.no_grad()
			def 							__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 ,						)      ->      List[str]:






					'''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 							__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]:






					'''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 							__snake_case	(						self ,						_A ,						_A = 5_12 ,						_A = 5_12 ,						_A = 50 ,						_A = 7.5 ,						_A = None ,						_A = 1 ,						_A = 0.0 ,						_A = None ,						_A = None ,						_A = "pil" ,						_A = True ,						_A = None ,						_A = 1 ,						)      ->      Union[str, 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 ,						)








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     str:
	_UpperCAmelCase      :							Any       =   len(_lowerCAmelCase	)
	for _ in range(
	    0,					int(((no_of_elements - 1) / 2) + 1	)	):  # we don't need to traverse to end of list as
		for j in range(0,					no_of_elements - 1	):
			if (
			    lst[j + 1] < lst[j]
			):  # applying bubble sort algorithm from left to right (or forwards)
				_UpperCAmelCase      :							List[str]       =   lst[j + 1]
				_UpperCAmelCase      :							Optional[int]       =   lst[j]
				_UpperCAmelCase      :							Optional[int]       =   temp
			if (
			    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
			):  # applying bubble sort algorithm from right to left (or backwards)
				_UpperCAmelCase      :							Any       =   lst[no_of_elements - 1 - j]
				_UpperCAmelCase      :							Optional[Any]       =   lst[no_of_elements - 2 - j]
				_UpperCAmelCase      :							Optional[Any]       =   temp
	return lst


if __name__ == "__main__":
						print('''enter the list to be sorted''')
						lowerCamelCase__			:					List[str]							  =					[int(x) for x in input().split()]  # inputing elements of the list in one line
						lowerCamelCase__			:					int							  =					double_sort(lst)
						print('''the sorted list is''')
						print(sorted_lst)





