

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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)  # pylint: disable=invalid-name
def     UpperCAmelCase_       (						_A    ):





     '''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    ):
          SCREAMING_SNAKE_CASE__   =   [image]

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

          SCREAMING_SNAKE_CASE__   =   [np.array(i.resize((w, h)				,		resample=PIL_INTERPOLATION['''lanczos''']    )    )[None, :] for i in image]
          SCREAMING_SNAKE_CASE__   =   np.concatenate(_A				,		axis=0    )
          SCREAMING_SNAKE_CASE__   =   np.array(_A    ).astype(np.floataa    ) / 2_5_5.0
          SCREAMING_SNAKE_CASE__   =   image.transpose(0				,		3				,		1				,		2    )
          SCREAMING_SNAKE_CASE__   =   2.0 * image - 1.0
          SCREAMING_SNAKE_CASE__   =   torch.from_numpy(_A    )
     elif isinstance(image[0]				,		torch.Tensor    ):
          SCREAMING_SNAKE_CASE__   =   torch.cat(_A				,		dim=0    )
     return image
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




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

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







class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= 42
  a									= 42






  def __init__(							self						:		List[Any]     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		Optional[int] )		->							List[Any]:
       super().__init__()
       self.register_modules(unet=__lowerCamelCase     ,       scheduler=__lowerCamelCase )







  @torch.no_grad()
  def __call__(							self						:		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     ,       )		->							Union[ImagePipelineOutput, Tuple]:
       SCREAMING_SNAKE_CASE__   =   image

       SCREAMING_SNAKE_CASE__   =   _preprocess_image(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   original_image.to(device=self.device     ,       dtype=self.unet.dtype )
       SCREAMING_SNAKE_CASE__   =   _preprocess_mask(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   mask_image.to(device=self.device     ,       dtype=self.unet.dtype )

       SCREAMING_SNAKE_CASE__   =   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.''' )

       SCREAMING_SNAKE_CASE__   =   original_image.shape
       SCREAMING_SNAKE_CASE__   =   randn_tensor(__lowerCamelCase     ,       generator=__lowerCamelCase     ,       device=self.device     ,       dtype=self.unet.dtype )

       # set step values
       self.scheduler.set_timesteps(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       self.device )
       SCREAMING_SNAKE_CASE__   =   eta

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

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

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=__lowerCamelCase )





from __future__ import annotations
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


