
"""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      lowerCAmelCase__			(		__magic_name__      )						->Any:
			warnings.warn(
			    "The preprocess method is deprecated and will be removed in a future version. Please"
			    " use VaeImageProcessor.preprocess instead"			,   __magic_name__			,   )
			if isinstance(__magic_name__			,   torch.Tensor      ):
						return image
			elif isinstance(__magic_name__			,   PIL.Image.Image      ):
						__lowercase       =       [image]

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

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



def      lowerCAmelCase__			(		__magic_name__      )						->Optional[int]:
			if isinstance(__magic_name__			,   torch.Tensor      ):
						return mask
			elif isinstance(__magic_name__			,   PIL.Image.Image      ):
						__lowercase       =       [mask]

			if isinstance(mask[0]			,   PIL.Image.Image      ):
						__lowercase   ,					__lowercase       =       mask[0].size
						__lowercase   ,					__lowercase       =       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
						__lowercase       =       [np.array(m.convert("L"      ).resize((w, h)			,   resample=PIL_INTERPOLATION["nearest"]      )      )[None, :] for m in mask]
						__lowercase       =       np.concatenate(__magic_name__			,   axis=0      )
						__lowercase       =       mask.astype(np.floataa      ) / 255.0
						__lowercase       =       0
						__lowercase       =       1
						__lowercase       =       torch.from_numpy(__magic_name__      )
			elif isinstance(mask[0]			,   torch.Tensor      ):
						__lowercase       =       torch.cat(__magic_name__			,   dim=0      )
			return mask







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			UNetaDModel
							_lowerCamelCase					:			RePaintScheduler
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										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]:




										'''simple docstring'''
										__lowercase       =       image

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

										__lowercase       =       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.'''       )

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

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

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

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

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

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=_lowerCamelCase       )







"""simple docstring"""






def      lowerCAmelCase__			(		__magic_name__ = 1_0      )						->str:
			if not isinstance(__magic_name__			,   __magic_name__      ) or n < 0:
						raise ValueError("Invalid input"      )
			__lowercase       =       1_0**n
			__lowercase       =       2_8_4_3_3 * (pow(2			,   7_8_3_0_4_5_7			,   __magic_name__      )) + 1
			return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"{solution(10) = }")





