




'''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 						SCREAMING_SNAKE_CASE       (      a_	:							Union[List, PIL.Image.Image, torch.Tensor]			):
  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			=					[image]

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

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[List, PIL.Image.Image, torch.Tensor]			):
  if isinstance(a_				,					torch.Tensor			):
    return mask
  elif isinstance(a_				,					PIL.Image.Image			):
    __a			=					[mask]

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





class    __lowercase						(  __magic_name__ ):
   _a							=      42
   _a							=      42







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    int:
     super().__init__()
     self.register_modules(unet=UpperCamelCase      ,							scheduler=UpperCamelCase      )







   @torch.no_grad()
   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = 250      ,							UpperCamelCase = 0.0      ,							UpperCamelCase = 10      ,							UpperCamelCase = 10      ,							UpperCamelCase = None      ,							UpperCamelCase = "pil"      ,							UpperCamelCase = True      ,							)				->    Union[ImagePipelineOutput, Tuple]:
     __a			=					image

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

     __a			=					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			=					original_image.shape
     __a			=					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			=					eta

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

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

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

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=UpperCamelCase      )









'''simple docstring'''


from ..utils import DummyObject, requires_backends





class    __lowercase						(  metaclass=__magic_name__ ):
   _a							=      ["""onnx"""]







   def __init__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     requires_backends(self      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )



