



"""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


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




def 	UpperCAmelCase     (		A	:						Union[List, PIL.Image.Image, torch.Tensor]       ):

		'''simple docstring'''


		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       ):
				_UpperCAmelCase							 =		[image]

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

				_UpperCAmelCase							 =		[np.array(i.resize((w, h)     ,				resample=PIL_INTERPOLATION['lanczos']       )       )[None, :] for i in image]
				_UpperCAmelCase							 =		np.concatenate(A     ,				axis=0       )
				_UpperCAmelCase							 =		np.array(A       ).astype(np.floataa       ) / 255.0
				_UpperCAmelCase							 =		image.transpose(0     ,				3     ,				1     ,				2       )
				_UpperCAmelCase							 =		2.0 * image - 1.0
				_UpperCAmelCase							 =		torch.from_numpy(A       )
		elif isinstance(image[0]     ,				torch.Tensor       ):
				_UpperCAmelCase							 =		torch.cat(A     ,				dim=0       )
		return image




def 	UpperCAmelCase     (		A	:						Union[List, PIL.Image.Image, torch.Tensor]       ):

		'''simple docstring'''


		if isinstance(A     ,				torch.Tensor       ):
				return mask
		elif isinstance(A     ,				PIL.Image.Image       ):
				_UpperCAmelCase							 =		[mask]

		if isinstance(mask[0]     ,				PIL.Image.Image       ):
				_UpperCAmelCase ,					_UpperCAmelCase							 =		mask[0].size
				_UpperCAmelCase ,					_UpperCAmelCase							 =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				_UpperCAmelCase							 =		[np.array(m.convert('L'       ).resize((w, h)     ,				resample=PIL_INTERPOLATION['nearest']       )       )[None, :] for m in mask]
				_UpperCAmelCase							 =		np.concatenate(A     ,				axis=0       )
				_UpperCAmelCase							 =		mask.astype(np.floataa       ) / 255.0
				_UpperCAmelCase							 =		0
				_UpperCAmelCase							 =		1
				_UpperCAmelCase							 =		torch.from_numpy(A       )
		elif isinstance(mask[0]     ,				torch.Tensor       ):
				_UpperCAmelCase							 =		torch.cat(A     ,				dim=0       )
		return mask






class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					42
							_UpperCAmelCase      				=					42






							def __init__(  self	,       snake_case	,       snake_case						)			->							Union[str, Any]:
									super().__init__()
									self.register_modules(unet=snake_case	,       scheduler=snake_case						)






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

									_UpperCAmelCase							 =		_preprocess_image(snake_case						)
									_UpperCAmelCase							 =		original_image.to(device=self.device	,       dtype=self.unet.dtype						)
									_UpperCAmelCase							 =		_preprocess_mask(snake_case						)
									_UpperCAmelCase							 =		mask_image.to(device=self.device	,       dtype=self.unet.dtype						)

									_UpperCAmelCase							 =		original_image.shape[0]

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

									_UpperCAmelCase							 =		original_image.shape
									_UpperCAmelCase							 =		randn_tensor(snake_case	,       generator=snake_case	,       device=self.device	,       dtype=self.unet.dtype						)

									# set step values
									self.scheduler.set_timesteps(snake_case	,       snake_case	,       snake_case	,       self.device						)
									_UpperCAmelCase							 =		eta

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

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

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=snake_case						)







"""simple docstring"""





from __future__ import annotations




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[]
		create_all_state(1     ,				A     ,				A     ,				[]     ,				A       )
		return result




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int     ,				A	:						int     ,				A	:						list[int]     ,				A	:						list[list[int]]     ,				):

		'''simple docstring'''


		if level == 0:
				total_list.append(current_list[:]       )
				return

		for i in range(A     ,				total_number - level + 2       ):
				current_list.append(A       )
				create_all_state(i + 1     ,				A     ,				level - 1     ,				A     ,				A       )
				current_list.pop()




def 	UpperCAmelCase     (		A	:						list[list[int]]       ):

		'''simple docstring'''


		for i in total_list:
				print(*A       )


if __name__ == "__main__":
					lowercase  							=					4
					lowercase  							=					2
					lowercase  							=					generate_all_combinations(n, k)
					print_all_state(total_list)


