"""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 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[List, PIL.Image.Image, torch.Tensor]   )   ->			str:





						"""simple docstring"""






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

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

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[List, PIL.Image.Image, torch.Tensor]   )   ->			Tuple:





						"""simple docstring"""






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

						if isinstance(mask[0]	,     PIL.Image.Image   ):
												snake_case   ,snake_case        =     mask[0].size
												snake_case   ,snake_case        =     (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
												snake_case        =     [np.array(m.convert('L'   ).resize((w, h)	,     resample=PIL_INTERPOLATION['nearest']   )   )[None, :] for m in mask]
												snake_case        =     np.concatenate(_UpperCamelCase	,     axis=0   )
												snake_case        =     mask.astype(np.floataa   ) / 2_55.0
												snake_case        =     0
												snake_case        =     1
												snake_case        =     torch.from_numpy(_UpperCamelCase   )
						elif isinstance(mask[0]	,     torch.Tensor   ):
												snake_case        =     torch.cat(_UpperCamelCase	,     dim=0   )
						return mask



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      UNetaDModel
				_lowerCAmelCase       :      RePaintScheduler

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										super().__init__()
										self.register_modules(unet=lowerCAmelCase						,      scheduler=lowerCAmelCase      )

				@torch.no_grad()
				def __call__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = 2_50						,      lowerCAmelCase = 0.0						,      lowerCAmelCase = 10						,      lowerCAmelCase = 10						,      lowerCAmelCase = None						,      lowerCAmelCase = "pil"						,      lowerCAmelCase = True						,      ):






										"""simple docstring"""

										snake_case        =     image

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

										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."""      )

										snake_case        =     original_image.shape
										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      )
										snake_case        =     eta

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

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

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

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase      )



"""simple docstring"""
SCREAMING_SNAKE_CASE__   =					{str(digit): digit**5 for digit in range(10)}





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_UpperCamelCase   )   )





def 				lowerCAmelCase__      (     )   ->			int:





						"""simple docstring"""






						return sum(
						    number
						    for number in range(1_0_0_0	,     1_0_0_0_0_0_0   )
						    if number == digits_fifth_powers_sum(_UpperCamelCase   )   )


if __name__ == "__main__":
					print(solution())


