



'''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__:   List[str]								=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def  snake_case_     (					_lowerCAmelCase  :						Union[List, PIL.Image.Image, torch.Tensor]  )      ->   List[str]:
							warnings.warn(
							    '''The preprocess method is deprecated and will be removed in a future version. Please'''
							    ''' use VaeImageProcessor.preprocess instead'''   ,      _lowerCAmelCase   ,      )
							if isinstance(_lowerCAmelCase   ,      torch.Tensor  ):
														return image
							elif isinstance(_lowerCAmelCase   ,      PIL.Image.Image  ):
														UpperCAmelCase :       Optional[int] 							=		[image]

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

														UpperCAmelCase :       Optional[int] 							=		[np.array(i.resize((w, h)   ,      resample=PIL_INTERPOLATION['''lanczos''']  )  )[None, :] for i in image]
														UpperCAmelCase :       Union[str, Any] 							=		np.concatenate(_lowerCAmelCase   ,      axis=0  )
														UpperCAmelCase :       str 							=		np.array(_lowerCAmelCase  ).astype(np.floataa  ) / 2_5_5.0
														UpperCAmelCase :       Dict 							=		image.transpose(0   ,      3   ,      1   ,      2  )
														UpperCAmelCase :       Union[str, Any] 							=		2.0 * image - 1.0
														UpperCAmelCase :       Any 							=		torch.from_numpy(_lowerCAmelCase  )
							elif isinstance(image[0]   ,      torch.Tensor  ):
														UpperCAmelCase :       List[Any] 							=		torch.cat(_lowerCAmelCase   ,      dim=0  )
							return image


def  snake_case_     (					_lowerCAmelCase  :						Union[List, PIL.Image.Image, torch.Tensor]  )      ->   Tuple:
							if isinstance(_lowerCAmelCase   ,      torch.Tensor  ):
														return mask
							elif isinstance(_lowerCAmelCase   ,      PIL.Image.Image  ):
														UpperCAmelCase :       Optional[Any] 							=		[mask]

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




class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     str		)			->		Union[str, Any]:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Union[str, Any]		,			__snake_case			:     Union[torch.Tensor, PIL.Image.Image]		,			__snake_case			:     Union[torch.Tensor, PIL.Image.Image]		,			__snake_case			:     int = 250		,			__snake_case			:     float = 0.0		,			__snake_case			:     int = 10		,			__snake_case			:     int = 10		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       int 							=		image

								UpperCAmelCase :       int 							=		_preprocess_image(__snake_case		)
								UpperCAmelCase :       int 							=		original_image.to(device=self.device		,			dtype=self.unet.dtype		)
								UpperCAmelCase :       Tuple 							=		_preprocess_mask(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		mask_image.to(device=self.device		,			dtype=self.unet.dtype		)

								UpperCAmelCase :       Dict 							=		original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(__snake_case		,			__snake_case		) and len(__snake_case		) != batch_size:
															raise ValueError(
															    F"""You have passed a list of generators of length {len(__snake_case		)}, but requested an effective batch"""
															    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

								UpperCAmelCase :       Union[str, Any] 							=		original_image.shape
								UpperCAmelCase :       Optional[Any] 							=		randn_tensor(__snake_case		,			generator=__snake_case		,			device=self.device		,			dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(__snake_case		,			__snake_case		,			__snake_case		,			self.device		)
								UpperCAmelCase :       Any 							=		eta

								UpperCAmelCase :       Optional[int] 							=		self.scheduler.timesteps[0] + 1
								UpperCAmelCase :       Any 							=		generator[0] if isinstance(__snake_case		,			__snake_case		) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															if t < t_last:
																						# predict the noise residual
																						UpperCAmelCase :       Optional[int] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						# compute previous image: x_t -> x_t-1
																						UpperCAmelCase :       Union[str, Any] 							=		self.scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		).prev_sample

															else:
																						# compute the reverse: x_t-1 -> x_t
																						UpperCAmelCase :       Tuple 							=		self.scheduler.undo_step(__snake_case		,			__snake_case		,			__snake_case		)
															UpperCAmelCase :       int 							=		t

								UpperCAmelCase :       Optional[int] 							=		(image / 2 + 0.5).clamp(0		,			1		)
								UpperCAmelCase :       str 							=		image.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Union[str, Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=__snake_case		)












from heapq import heappop, heappush

import numpy as np







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      tuple[float | int, list[tuple[int, int]]]:
					__A    ,				__A	:						int       =				grid.shape
					__A	:						Any       =				[-1, 1, 0, 0]
					__A	:						Optional[Any]       =				[0, 0, -1, 1]
					if allow_diagonal:
										dx += [-1, -1, 1, 1]
										dy += [-1, 1, -1, 1]

					__A    ,				__A	:						Optional[int]       =				[(0, source)], set()
					__A	:						Any       =				np.full((rows, cols)					,						np.inf  )
					__A	:						Any       =				0
					__A	:						Any       =				np.empty((rows, cols)					,						dtype=a  )
					__A	:						Optional[Any]       =				None

					while queue:
										((__A)    ,				(__A))	:						List[str]       =				heappop(a  )
										if (x, y) in visited:
															continue
										visited.add((x, y)  )

										if (x, y) == destination:
															__A	:						int       =				[]
															while (x, y) != source:
																				path.append((x, y)  )
																				__A    ,				__A	:						Optional[int]       =				predecessors[x, y]
															path.append(a  )  # add the source manually
															path.reverse()
															return matrix[destination], path

										for i in range(len(a  )  ):
															__A    ,				__A	:						Union[str, Any]       =				x + dx[i], y + dy[i]
															if 0 <= nx < rows and 0 <= ny < cols:
																				__A	:						Optional[int]       =				grid[nx][ny]
																				if next_node == 1 and matrix[nx, ny] > dist + 1:
																									heappush(a					,						(dist + 1, (nx, ny))  )
																									__A	:						List[Any]       =				dist + 1
																									__A	:						Union[str, Any]       =				(x, y)

					return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()




