


"""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


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


def      lowercase			(				A_       )->      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       ):
    a						:		Optional[Any]      							=			[image]

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

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


def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  if isinstance(A_  ,   torch.Tensor       ):
    return mask
  elif isinstance(A_  ,   PIL.Image.Image       ):
    a						:		Dict      							=			[mask]

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







class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							UNetaDModel
    UpperCAmelCase       :							RePaintScheduler






    def __init__(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Dict):
      super().__init__()
      self.register_modules(unet=__UpperCAmelCase				, scheduler=__UpperCAmelCase)






    @torch.no_grad()
    def __call__(					self				:    List[str]				, __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				, ):
      a						:		str      							=			image

      a						:		List[Any]      							=			_preprocess_image(__UpperCAmelCase)
      a						:		Tuple      							=			original_image.to(device=self.device				, dtype=self.unet.dtype)
      a						:		Optional[Any]      							=			_preprocess_mask(__UpperCAmelCase)
      a						:		Union[str, Any]      							=			mask_image.to(device=self.device				, dtype=self.unet.dtype)

      a						:		List[str]      							=			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.''')

      a						:		Any      							=			original_image.shape
      a						:		List[str]      							=			randn_tensor(__UpperCAmelCase				, generator=__UpperCAmelCase				, device=self.device				, dtype=self.unet.dtype)

      # set step values
      self.scheduler.set_timesteps(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, self.device)
      a						:		List[str]      							=			eta

      a						:		List[Any]      							=			self.scheduler.timesteps[0] + 1
      a						:		Union[str, Any]      							=			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
          a						:		Tuple      							=			self.unet(__UpperCAmelCase				, __UpperCAmelCase).sample
          # compute previous image: x_t -> x_t-1
          a						:		Dict      							=			self.scheduler.step(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase).prev_sample

        else:
          # compute the reverse: x_t-1 -> x_t
          a						:		str      							=			self.scheduler.undo_step(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
        a						:		Any      							=			t

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

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase)










"""simple docstring"""







class 						_A  :







    """simple docstring"""




    def __init__(					self				:    int				, __UpperCAmelCase				:    int):
      a						:		Tuple      							=			size
      a						:		Dict      							=			[0] * size
      a						:		Optional[int]      							=			[0] * size






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    int):
      return index | (index + 1)






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    int):
      return (index & (index + 1)) - 1






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int):
      a						:		Union[str, Any]      							=			value
      while index < self.size:
        a						:		Dict      							=			self.get_prev(__UpperCAmelCase) + 1
        if current_left_border == index:
          a						:		Optional[int]      							=			value
        else:
          a						:		Any      							=			max(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
        a						:		Optional[int]      							=			self.get_next(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int):
      right -= 1  # Because of right is exclusive
      a						:		List[str]      							=			0
      while left <= right:
        a						:		Dict      							=			self.get_prev(__UpperCAmelCase)
        if left <= current_left:
          a						:		Optional[int]      							=			max(__UpperCAmelCase				, self.tree[right])
          a						:		Optional[Any]      							=			current_left
        else:
          a						:		List[str]      							=			max(__UpperCAmelCase				, self.arr[right])
          right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()






