


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase:						Optional[int]   				=       logging.get_logger(__name__)  # pylint: disable=invalid-name






def   _lowercase(					__a				:						Union[List, PIL.Image.Image, torch.Tensor]     ):
							warnings.warn(
							    'The preprocess method is deprecated and will be removed in a future version. Please'
							    ' use VaeImageProcessor.preprocess instead'    ,     __a    ,     )
							if isinstance(__a    ,     torch.Tensor     ):
														return image
							elif isinstance(__a    ,     PIL.Image.Image     ):
														a__      =[image]

							if isinstance(image[0]    ,     PIL.Image.Image     ):
														a__    ,     a__      =image[0].size
														a__    ,     a__      =(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														a__      =[np.array(i.resize((w, h)    ,     resample=PIL_INTERPOLATION['lanczos']     )     )[None, :] for i in image]
														a__      =np.concatenate(__a    ,     axis=0     )
														a__      =np.array(__a     ).astype(np.floataa     ) / 2_55.0
														a__      =image.transpose(0    ,     3    ,     1    ,     2     )
														a__      =2.0 * image - 1.0
														a__      =torch.from_numpy(__a     )
							elif isinstance(image[0]    ,     torch.Tensor     ):
														a__      =torch.cat(__a    ,     dim=0     )
							return image






def   _lowercase(					__a				:						Union[List, PIL.Image.Image, torch.Tensor]     ):
							if isinstance(__a    ,     torch.Tensor     ):
														return mask
							elif isinstance(__a    ,     PIL.Image.Image     ):
														a__      =[mask]

							if isinstance(mask[0]    ,     PIL.Image.Image     ):
														a__    ,     a__      =mask[0].size
														a__    ,     a__      =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														a__      =[np.array(m.convert('L'     ).resize((w, h)    ,     resample=PIL_INTERPOLATION['nearest']     )     )[None, :] for m in mask]
														a__      =np.concatenate(__a    ,     axis=0     )
														a__      =mask.astype(np.floataa     ) / 2_55.0
														a__      =0
														a__      =1
														a__      =torch.from_numpy(__a     )
							elif isinstance(mask[0]    ,     torch.Tensor     ):
														a__      =torch.cat(__a    ,     dim=0     )
							return mask







class       lowercase_					(lowercase__   ):
				snake_case	    =42
				snake_case	    =42





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> Tuple:
											super().__init__()
											self.register_modules(unet=lowercase_       ,  scheduler=lowercase_)





				@torch.no_grad()
				def __call__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = 250       ,  lowercase_ = 0.0       ,  lowercase_ = 10       ,  lowercase_ = 10       ,  lowercase_ = None       ,  lowercase_ = "pil"       ,  lowercase_ = True       ,  )  -> Union[ImagePipelineOutput, Tuple]:
											a__      =image

											a__      =_preprocess_image(lowercase_)
											a__      =original_image.to(device=self.device       ,  dtype=self.unet.dtype)
											a__      =_preprocess_mask(lowercase_)
											a__      =mask_image.to(device=self.device       ,  dtype=self.unet.dtype)

											a__      =original_image.shape[0]

											# sample gaussian noise to begin the loop
											if isinstance(lowercase_       ,  lowercase_) and len(lowercase_) != batch_size:
																		raise ValueError(
																		    F"""You have passed a list of generators of length {len(lowercase_)}, but requested an effective batch"""
																		    F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

											a__      =original_image.shape
											a__      =randn_tensor(lowercase_       ,  generator=lowercase_       ,  device=self.device       ,  dtype=self.unet.dtype)

											# set step values
											self.scheduler.set_timesteps(lowercase_       ,  lowercase_       ,  lowercase_       ,  self.device)
											a__      =eta

											a__      =self.scheduler.timesteps[0] + 1
											a__      =generator[0] if isinstance(lowercase_       ,  lowercase_) else generator
											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
																		if t < t_last:
																									# predict the noise residual
																									a__      =self.unet(lowercase_       ,  lowercase_).sample
																									# compute previous image: x_t -> x_t-1
																									a__      =self.scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_).prev_sample

																		else:
																									# compute the reverse: x_t-1 -> x_t
																									a__      =self.scheduler.undo_step(lowercase_       ,  lowercase_       ,  lowercase_)
																		a__      =t

											a__      =(image / 2 + 0.5).clamp(0       ,  1)
											a__      =image.cpu().permute(0       ,  2       ,  3       ,  1).numpy()
											if output_type == "pil":
																		a__      =self.numpy_to_pil(lowercase_)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=lowercase_)





def   _lowercase(					__a				:						list[int]     ):
							a__      =len(__a     )
							for i in range(__a     ):
														for j in range(i + 1    ,     __a     ):
																					if numbers[j] < numbers[i]:
																												a__    ,     a__      =numbers[j], numbers[i]
							return numbers


if __name__ == "__main__":
	_lowerCAmelCase:						Tuple   				=       input('Enter numbers separated by a comma:\n').strip()
	_lowerCAmelCase:						int   				=       [int(item) for item in user_input.split(',')]
	print(exchange_sort(unsorted))

