


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





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[List, PIL.Image.Image, torch.Tensor]						) ->   Union[str, Any]:





	"""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						):
		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(UpperCamelCase__							,					axis=0						)
		A								=				np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
		A								=				image.transpose(0							,					3							,					1							,					2						)
		A								=				2.0 * image - 1.0
		A								=				torch.from_numpy(UpperCamelCase__						)
	elif isinstance(image[0]							,					torch.Tensor						):
		A								=				torch.cat(UpperCamelCase__							,					dim=0						)
	return image





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[List, PIL.Image.Image, torch.Tensor]						) ->   Dict:





	"""simple docstring"""







	if isinstance(UpperCamelCase__							,					torch.Tensor						):
		return mask
	elif isinstance(UpperCamelCase__							,					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(UpperCamelCase__							,					axis=0						)
		A								=				mask.astype(np.floataa						) / 2_55.0
		A								=				0
		A								=				1
		A								=				torch.from_numpy(UpperCamelCase__						)
	elif isinstance(mask[0]							,					torch.Tensor						):
		A								=				torch.cat(UpperCamelCase__							,					dim=0						)
	return mask






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 42
				lowerCAmelCase 		= 42

				def __init__(						self				,   a__				,   a__   )	->				List[Any]:
					super().__init__()
					self.register_modules(unet=a__				,   scheduler=a__   )

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

					A								=				_preprocess_image(a__   )
					A								=				original_image.to(device=self.device				,   dtype=self.unet.dtype   )
					A								=				_preprocess_mask(a__   )
					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(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.'   )

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

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

					A								=				self.scheduler.timesteps[0] + 1
					A								=				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
							A								=				self.unet(a__				,   a__   ).sample
							# compute previous image: x_t -> x_t-1
							A								=				self.scheduler.step(a__				,   a__				,   a__				,   a__				,   a__				,   a__   ).prev_sample

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

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=a__   )







def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



