"""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


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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  warnings.warn(
      """The preprocess method is deprecated and will be removed in a future version. Please"""
      """ use VaeImageProcessor.preprocess instead"""   ,lowercase   ,)
  if isinstance(lowercase   ,torch.Tensor		):
    return image
  elif isinstance(lowercase   ,PIL.Image.Image		):
    _UpperCAmelCase						 =      [image]

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

    _UpperCAmelCase						 =      [np.array(i.resize((w, h)   ,resample=PIL_INTERPOLATION["""lanczos"""]		)		)[None, :] for i in image]
    _UpperCAmelCase						 =      np.concatenate(lowercase   ,axis=0		)
    _UpperCAmelCase						 =      np.array(lowercase		).astype(np.floataa		) / 2_55.0
    _UpperCAmelCase						 =      image.transpose(0   ,3   ,1   ,2		)
    _UpperCAmelCase						 =      2.0 * image - 1.0
    _UpperCAmelCase						 =      torch.from_numpy(lowercase		)
  elif isinstance(image[0]   ,torch.Tensor		):
    _UpperCAmelCase						 =      torch.cat(lowercase   ,dim=0		)
  return image






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  if isinstance(lowercase   ,torch.Tensor		):
    return mask
  elif isinstance(lowercase   ,PIL.Image.Image		):
    _UpperCAmelCase						 =      [mask]

  if isinstance(mask[0]   ,PIL.Image.Image		):
    _UpperCAmelCase ,				_UpperCAmelCase						 =      mask[0].size
    _UpperCAmelCase ,				_UpperCAmelCase						 =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    _UpperCAmelCase						 =      [np.array(m.convert("""L"""		).resize((w, h)   ,resample=PIL_INTERPOLATION["""nearest"""]		)		)[None, :] for m in mask]
    _UpperCAmelCase						 =      np.concatenate(lowercase   ,axis=0		)
    _UpperCAmelCase						 =      mask.astype(np.floataa		) / 2_55.0
    _UpperCAmelCase						 =      0
    _UpperCAmelCase						 =      1
    _UpperCAmelCase						 =      torch.from_numpy(lowercase		)
  elif isinstance(mask[0]   ,torch.Tensor		):
    _UpperCAmelCase						 =      torch.cat(lowercase   ,dim=0		)
  return mask


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  UNetaDModel
    _snake_case     :  RePaintScheduler


    def __init__(					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  str					):
      super().__init__()
      self.register_modules(unet=__lowerCAmelCase   ,				scheduler=__lowerCAmelCase					)


    @torch.no_grad()
    def __call__(					self	:  Union[str, Any]   ,				__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   ,				):
      _UpperCAmelCase						 =      image

      _UpperCAmelCase						 =      _preprocess_image(__lowerCAmelCase					)
      _UpperCAmelCase						 =      original_image.to(device=self.device   ,				dtype=self.unet.dtype					)
      _UpperCAmelCase						 =      _preprocess_mask(__lowerCAmelCase					)
      _UpperCAmelCase						 =      mask_image.to(device=self.device   ,				dtype=self.unet.dtype					)

      _UpperCAmelCase						 =      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.'''					)

      _UpperCAmelCase						 =      original_image.shape
      _UpperCAmelCase						 =      randn_tensor(__lowerCAmelCase   ,				generator=__lowerCAmelCase   ,				device=self.device   ,				dtype=self.unet.dtype					)

      # set step values
      self.scheduler.set_timesteps(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				self.device					)
      _UpperCAmelCase						 =      eta

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

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

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

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__lowerCAmelCase					)

"""simple docstring"""


from __future__ import annotations






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return [ord(lowercase		) - 96 for elem in plain]






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return "".join(chr(elem + 96		) for elem in encoded		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      encode(input("""-> """		).strip().lower()		)
  print("""Encoded: """   ,lowercase		)
  print("""Decoded:"""   ,decode(lowercase		)		)


if __name__ == "__main__":
    main()
