





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									super().__init__()
									self.register_modules(vqvae=_A     ,					unet=_A     ,					scheduler=_A     )





				@torch.no_grad()
				def __call__(			self     ,					_A = 1     ,					_A = None     ,					_A = 0.0     ,					_A = 50     ,					_A = "pil"     ,					_A = True     ,					**_A     ,					):
									__A	:						Optional[int]       =				randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,					generator=_A     ,					)
									__A	:						Any       =				latents.to(self.device     )

									# scale the initial noise by the standard deviation required by the scheduler
									__A	:						int       =				latents * self.scheduler.init_noise_sigma

									self.scheduler.set_timesteps(_A     )

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									__A	:						Optional[int]       =				'eta' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )

									__A	:						Any       =				{}
									if accepts_eta:
														__A	:						List[str]       =				eta

									for t in self.progress_bar(self.scheduler.timesteps     ):
														__A	:						Dict       =				self.scheduler.scale_model_input(_A     ,					_A     )
														# predict the noise residual
														__A	:						int       =				self.unet(_A     ,					_A     ).sample
														# compute the previous noisy sample x_t -> x_t-1
														__A	:						int       =				self.scheduler.step(_A     ,					_A     ,					_A     ,					**_A     ).prev_sample

									# decode the image latents with the VAE
									__A	:						Tuple       =				self.vqvae.decode(_A     ).sample

									__A	:						Dict       =				(image / 2 + 0.5).clamp(0     ,					1     )
									__A	:						Any       =				image.cpu().permute(0     ,					2     ,					3     ,					1     ).numpy()
									if output_type == "pil":
														__A	:						List[str]       =				self.numpy_to_pil(_A     )

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=_A     )











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()




