

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


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


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

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

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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						if isinstance(SCREAMING_SNAKE_CASE__    ,						torch.Tensor			):
												return mask
						elif isinstance(SCREAMING_SNAKE_CASE__    ,						PIL.Image.Image			):
												snake_case_  =      [mask]

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





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  UNetaDModel
			SCREAMING_SNAKE_CASE	:  RePaintScheduler







			def __init__(	self					:							str				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Any				) ->Tuple:
									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 = 2_5_0				,  _UpperCamelCase					:							float = 0.0				,  _UpperCamelCase					:							int = 1_0				,  _UpperCamelCase					:							int = 1_0				,  _UpperCamelCase					:							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,  _UpperCamelCase					:							Optional[str] = "pil"				,  _UpperCamelCase					:							bool = True				,  ) ->Union[ImagePipelineOutput, Tuple]:
									snake_case_  =      image

									snake_case_  =      _preprocess_image(_UpperCamelCase				)
									snake_case_  =      original_image.to(device=self.device				,  dtype=self.unet.dtype				)
									snake_case_  =      _preprocess_mask(_UpperCamelCase				)
									snake_case_  =      mask_image.to(device=self.device				,  dtype=self.unet.dtype				)

									snake_case_  =      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.'''				)

									snake_case_  =      original_image.shape
									snake_case_  =      randn_tensor(_UpperCamelCase				,  generator=_UpperCamelCase				,  device=self.device				,  dtype=self.unet.dtype				)

									# set step values
									self.scheduler.set_timesteps(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  self.device				)
									snake_case_  =      eta

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_UpperCamelCase				)


'''simple docstring'''




from ..utils import DummyObject, requires_backends



class 							lowercase_						(		metaclass=a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= ["keras_nlp"]
  def __init__(  self ,			*a_ ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    requires_backends(self ,			['keras_nlp']  )


