


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


A	:		Union[str, Any]    =       logging.get_logger(__name__)  # pylint: disable=invalid-name






def 			UpperCamelCase (     __magic_name__					:							Union[List, PIL.Image.Image, torch.Tensor]					)       ->  List[str]:
		"""simple docstring"""




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

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

				lowercase__					      =		[np.array(i.resize((w, h)   ,   resample=PIL_INTERPOLATION["""lanczos"""]					)					)[None, :] for i in image]
				lowercase__					      =		np.concatenate(__magic_name__   ,   axis=0					)
				lowercase__					      =		np.array(__magic_name__					).astype(np.floataa					) / 2_5_5.0
				lowercase__					      =		image.transpose(0   ,   3   ,   1   ,   2					)
				lowercase__					      =		2.0 * image - 1.0
				lowercase__					      =		torch.from_numpy(__magic_name__					)
		elif isinstance(image[0]   ,   torch.Tensor					):
				lowercase__					      =		torch.cat(__magic_name__   ,   dim=0					)
		return image






def 			UpperCamelCase (     __magic_name__					:							Union[List, PIL.Image.Image, torch.Tensor]					)       ->  Union[str, Any]:
		"""simple docstring"""




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

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







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






					A__      =			42
					A__      =			42

					def __init__(self   :						Tuple				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						List[Any]		)						->       Tuple:




							"""simple docstring"""


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


					@torch.no_grad()
					def __call__(self   :						Union[str, Any]				, _UpperCAmelCase   :						Union[torch.Tensor, PIL.Image.Image]				, _UpperCAmelCase   :						Union[torch.Tensor, PIL.Image.Image]				, _UpperCAmelCase   :						int = 250				, _UpperCAmelCase   :						float = 0.0				, _UpperCAmelCase   :						int = 10				, _UpperCAmelCase   :						int = 10				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		image

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

							lowercase__					      =		original_image.shape[0]

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

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

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

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

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

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

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



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






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


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


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

