"""simple docstring"""







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


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


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





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

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

		_lowercase :    Dict							=							[np.array(i.resize((w, h)     ,resample=PIL_INTERPOLATION['lanczos']			)			)[None, :] for i in image]
		_lowercase :    Tuple							=							np.concatenate(__UpperCAmelCase     ,axis=0			)
		_lowercase :    str							=							np.array(__UpperCAmelCase			).astype(np.floataa			) / 2_5_5.0
		_lowercase :    Optional[int]							=							image.transpose(0     ,3     ,1     ,2			)
		_lowercase :    Optional[Any]							=							2.0 * image - 1.0
		_lowercase :    List[Any]							=							torch.from_numpy(__UpperCAmelCase			)
	elif isinstance(image[0]     ,torch.Tensor			):
		_lowercase :    Tuple							=							torch.cat(__UpperCAmelCase     ,dim=0			)
	return image


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if isinstance(__UpperCAmelCase     ,torch.Tensor			):
		return mask
	elif isinstance(__UpperCAmelCase     ,PIL.Image.Image			):
		_lowercase :    str							=							[mask]

	if isinstance(mask[0]     ,PIL.Image.Image			):
		_lowercase :    int							=							mask[0].size
		_lowercase :    Any							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		_lowercase :    List[Any]							=							[np.array(m.convert('L'			).resize((w, h)     ,resample=PIL_INTERPOLATION['nearest']			)			)[None, :] for m in mask]
		_lowercase :    Dict							=							np.concatenate(__UpperCAmelCase     ,axis=0			)
		_lowercase :    Optional[Any]							=							mask.astype(np.floataa			) / 2_5_5.0
		_lowercase :    Tuple							=							0
		_lowercase :    Optional[Any]							=							1
		_lowercase :    Any							=							torch.from_numpy(__UpperCAmelCase			)
	elif isinstance(mask[0]     ,torch.Tensor			):
		_lowercase :    Optional[int]							=							torch.cat(__UpperCAmelCase     ,dim=0			)
	return mask







class  _lowerCamelCase			(snake_case__     ):
				_snake_case     =  42
				_snake_case     =  42







				def __init__( self    :       List[str]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					super().__init__()
					self.register_modules(unet=_A			,					scheduler=_A						)







				@torch.no_grad()
				def __call__( self    :       Union[str, Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       Union[str, Any] = 2_5_0			,					lowerCamelCase_    :       Tuple = 0.0			,					lowerCamelCase_    :       Tuple = 1_0			,					lowerCamelCase_    :       List[str] = 1_0			,					lowerCamelCase_    :       str = None			,					lowerCamelCase_    :       Tuple = "pil"			,					lowerCamelCase_    :       Dict = True			,					):

					"""simple docstring"""






					_lowercase :    Tuple							=							image

					_lowercase :    Tuple							=							_preprocess_image(_A						)
					_lowercase :    Tuple							=							original_image.to(device=self.device			,					dtype=self.unet.dtype						)
					_lowercase :    List[Any]							=							_preprocess_mask(_A						)
					_lowercase :    Optional[int]							=							mask_image.to(device=self.device			,					dtype=self.unet.dtype						)

					_lowercase :    Optional[Any]							=							original_image.shape[0]

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

					_lowercase :    Optional[int]							=							original_image.shape
					_lowercase :    Dict							=							randn_tensor(_A			,					generator=_A			,					device=self.device			,					dtype=self.unet.dtype						)

					# set step values
					self.scheduler.set_timesteps(_A			,					_A			,					_A			,					self.device						)
					_lowercase :    Tuple							=							eta

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

						else:
							# compute the reverse: x_t-1 -> x_t
							_lowercase :    Union[str, Any]							=							self.scheduler.undo_step(_A			,					_A			,					_A						)
						_lowercase :    Any							=							t

					_lowercase :    Union[str, Any]							=							(image / 2 + 0.5).clamp(0			,					1						)
					_lowercase :    List[str]							=							image.cpu().permute(0			,					2			,					3			,					1						).numpy()
					if output_type == "pil":
						_lowercase :    Tuple							=							self.numpy_to_pil(_A						)

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=_A						)







"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	stooge(__UpperCAmelCase     ,0     ,len(__UpperCAmelCase			) - 1			)
	return arr


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		_lowercase				,      _lowercase :    List[str]							=							arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		_lowercase :    Optional[int]							=							(int)((h - i + 1) / 3			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)

		# Recursively sort last 2/3 elements
		stooge(__UpperCAmelCase     ,i + t     ,(__UpperCAmelCase)			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)


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



