




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


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







def 		_A(       UpperCamelCase__							:			Union[List, PIL.Image.Image, torch.Tensor]				)    -> List[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				):
														__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(UpperCamelCase__				,       axis=0				)
														__lowercase      =    np.array(UpperCamelCase__				).astype(np.floataa				) / 255.0
														__lowercase      =    image.transpose(0				,       3				,       1				,       2				)
														__lowercase      =    2.0 * image - 1.0
														__lowercase      =    torch.from_numpy(UpperCamelCase__				)
							elif isinstance(image[0]				,       torch.Tensor				):
														__lowercase      =    torch.cat(UpperCamelCase__				,       dim=0				)
							return image







def 		_A(       UpperCamelCase__							:			Union[List, PIL.Image.Image, torch.Tensor]				)    -> List[Any]:
							'''simple docstring'''


							if isinstance(UpperCamelCase__				,       torch.Tensor				):
														return mask
							elif isinstance(UpperCamelCase__				,       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(UpperCamelCase__				,       axis=0				)
														__lowercase      =    mask.astype(np.floataa				) / 255.0
														__lowercase      =    0
														__lowercase      =    1
														__lowercase      =    torch.from_numpy(UpperCamelCase__				)
							elif isinstance(mask[0]				,       torch.Tensor				):
														__lowercase      =    torch.cat(UpperCamelCase__				,       dim=0				)
							return mask







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       RePaintScheduler






						def __init__( self :     Union[str, Any]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Optional[int]				)    ->						Dict:
													"""simple docstring"""


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






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


													__lowercase      =    image

													__lowercase      =    _preprocess_image(lowerCamelCase__				)
													__lowercase      =    original_image.to(device=self.device				,							dtype=self.unet.dtype				)
													__lowercase      =    _preprocess_mask(lowerCamelCase__				)
													__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(lowerCamelCase__				,							lowerCamelCase__				) and len(lowerCamelCase__				) != batch_size:
																				raise ValueError(
																				    f'You have passed a list of generators of length {len(lowerCamelCase__				)}, 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(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				,							dtype=self.unet.dtype				)

													# set step values
													self.scheduler.set_timesteps(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							self.device				)
													__lowercase      =    eta

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

																				else:
																											# compute the reverse: x_t-1 -> x_t
																											__lowercase      =    self.scheduler.undo_step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)
																				__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(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)












from typing import List, Optional, Tuple, Union

import torch

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







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       KarrasVeScheduler






						def __init__( self :     Tuple				,							lowerCamelCase__ :     UNetaDModel				,							lowerCamelCase__ :     KarrasVeScheduler				)    ->						Optional[Any]:
													"""simple docstring"""


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






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							**lowerCamelCase__ :     int				,							)    ->						Union[Tuple, ImagePipelineOutput]:
													"""simple docstring"""


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

													__lowercase      =    self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# here sigma_t == t_i from the paper
																				__lowercase      =    self.scheduler.schedule[t]
																				__lowercase      =    self.scheduler.schedule[t - 1] if t > 0 else 0

																				# 1. Select temporarily increased noise level sigma_hat
																				# 2. Add new noise to move from sample_i to sample_hat
																				__lowercase     ,     __lowercase      =    self.scheduler.add_noise_to_input(lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				)

																				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																				# The model inputs and output are adjusted by following eq. (213) in [1].
																				__lowercase      =    (sigma_hat / 2) * model((sample_hat + 1) / 2				,							sigma_hat / 2				).sample

																				# 4. Evaluate dx/dt at sigma_hat
																				# 5. Take Euler step from sigma to sigma_prev
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																				if sigma_prev != 0:
																											# 6. Apply 2nd order correction
																											# The model inputs and output are adjusted by following eq. (213) in [1].
																											__lowercase      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,							sigma_prev / 2				).sample
																											__lowercase      =    self.scheduler.step_correct(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							step_output.prev_sample				,							step_output['''derivative''']				,							)
																				__lowercase      =    step_output.prev_sample

													__lowercase      =    (sample / 2 + 0.5).clamp(0				,							1				)
													__lowercase      =    sample.cpu().permute(0				,							2				,							3				,							1				).numpy()
													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






