




'''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


_snake_case				:				int       							=		logging.get_logger(__name__)  # pylint: disable=invalid-name
def 		snake_case_   (UpperCamelCase	:  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'''			,						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				) / 255.0
														_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 		snake_case_   (UpperCamelCase	:  Union[List, PIL.Image.Image, torch.Tensor]				):





							'''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				) / 255.0
														_a     					=    0
														_a     					=    1
														_a     					=    torch.from_numpy(UpperCamelCase				)
							elif isinstance(mask[0]			,						torch.Tensor				):
														_a     					=    torch.cat(UpperCamelCase			,						dim=0				)
							return mask

class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    42







							def __init__(  self	:  Any							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


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







							@torch.no_grad()
							def __call__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Union[torch.Tensor, PIL.Image.Image]							,      lowerCAmelCase_	:  Union[torch.Tensor, PIL.Image.Image]							,      lowerCAmelCase_	:  int = 2_50							,      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"""


														_a     					=    image

														_a     					=    _preprocess_image(lowerCAmelCase_ )
														_a     					=    original_image.to(device=self.device							,      dtype=self.unet.dtype )
														_a     					=    _preprocess_mask(lowerCAmelCase_ )
														_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(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.' )

														_a     					=    original_image.shape
														_a     					=    randn_tensor(lowerCAmelCase_							,      generator=lowerCAmelCase_							,      device=self.device							,      dtype=self.unet.dtype )

														# set step values
														self.scheduler.set_timesteps(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      self.device )
														_a     					=    eta

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

																					else:
																												# compute the reverse: x_t-1 -> x_t
																												_a     					=    self.scheduler.undo_step(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_ )
																					_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(lowerCAmelCase_ )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowerCAmelCase_ )







'''simple docstring'''


from ..utils import DummyObject, requires_backends

class       A   (		metaclass=_a   ):
							lowercase_          =    ['torch', 'scipy']







							def __init__(  self	:  Tuple							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Any )      ->							Tuple:


														"""simple docstring"""


														requires_backends(self							,      ['''torch''', '''scipy'''] )







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Dict							,      *lowerCAmelCase_	:  Dict							,      **lowerCAmelCase_	:  str )      ->							Tuple:


														"""simple docstring"""


														requires_backends(cls							,      ['''torch''', '''scipy'''] )







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Optional[int]							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  List[str] )      ->							str:


														"""simple docstring"""


														requires_backends(cls							,      ['''torch''', '''scipy'''] )

