





'''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


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


def 						__a(SCREAMING_SNAKE_CASE_	:       Union[List, PIL.Image.Image, torch.Tensor]     ):



    '''simple docstring'''





    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     ):
        _lowerCAmelCase						      =   [image]

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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       Union[List, PIL.Image.Image, torch.Tensor]     ):



    '''simple docstring'''





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

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





class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				UNetaDModel
 __lowerCamelCase    :				RePaintScheduler




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     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]:
     _lowerCAmelCase						      =   image

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

     _lowerCAmelCase						      =   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.'''		)

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

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

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

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

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

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase		)









'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       list     ):



    '''simple docstring'''





    if not nums:
        raise ValueError("List is empty"     )
    return sum(SCREAMING_SNAKE_CASE_     ) / len(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


