

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_             =     logging.get_logger(__name__)  # pylint: disable=invalid-name
def   a   (    A__			:     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'   ,     A__   ,     )
      if isinstance(A__   ,     torch.Tensor  ):
            return image
      elif isinstance(A__   ,     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(A__   ,     axis=0  )
            _lowercase			 =np.array(A__  ).astype(np.floataa  ) / 255.0
            _lowercase			 =image.transpose(0   ,     3   ,     1   ,     2  )
            _lowercase			 =2.0 * image - 1.0
            _lowercase			 =torch.from_numpy(A__  )
      elif isinstance(image[0]   ,     torch.Tensor  ):
            _lowercase			 =torch.cat(A__   ,     dim=0  )
      return image
def   a   (    A__			:     Union[List, PIL.Image.Image, torch.Tensor]  )					->   List[Any]:





      """simple docstring"""



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







class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						42
  _a		    =						42




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				List[Any]:
        '''simple docstring'''






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




  @torch.no_grad()
  def __call__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = 250							,     lowerCAmelCase = 0.0							,     lowerCAmelCase = 10							,     lowerCAmelCase = 10							,     lowerCAmelCase = None							,     lowerCAmelCase = "pil"							,     lowerCAmelCase = 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      )





def   a   (    A__			:     str   ,     A__			:     int  )					->   list[str]:





      """simple docstring"""



      return [sentence[i : i + ngram_size] for i in range(len(A__  ) - ngram_size + 1  )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()


