





"""simple docstring"""


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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									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 = 5_0						,   _A = "pil"						,   _A = True						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,   generator=_A						,   )
									UpperCamelCase				:		Optional[int]				   =			latents.to(self.device    )

									# scale the initial noise by the standard deviation required by the scheduler
									UpperCamelCase				:		Union[str, Any]				   =			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
									UpperCamelCase				:		Dict				   =			"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )

									UpperCamelCase				:		int				   =			{}
									if accepts_eta:
															UpperCamelCase				:		Any				   =			eta

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_A    )





import numpy as np





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[Any]     )  ->Any:
										lowercase         =      (0, 0)
										lowercase         =      None
										lowercase         =      0
										lowercase         =      0
										lowercase         =      0





						def __eq__(			self							:Optional[int]		,    lowerCAmelCase__							:Any     )  ->Optional[int]:
										return self.position == cell.position





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[Any]:
										print(self.position     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Optional[Any]		,    lowerCAmelCase__							:Tuple=(5, 5)     )  ->str:
										lowercase         =      np.zeros(lowerCAmelCase__     )
										lowercase         =      world_size[0]
										lowercase         =      world_size[1]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										print(self.w     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Optional[Any]     )  ->Optional[Any]:
										lowercase         =      [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase         =      cell.position[0]
										lowercase         =      cell.position[1]
										lowercase         =      []
										for n in neughbour_cord:
														lowercase         =      current_x + n[0]
														lowercase         =      current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase         =      Cell()
																		lowercase         =      (x, y)
																		lowercase         =      cell
																		neighbours.append(lowerCAmelCase__     )
										return neighbours




def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      []
				lowercase         =      []
				_open.append(__magic_name__						)

				while _open:
								lowercase         =      np.argmin([n.f for n in _open]						)
								lowercase         =      _open[min_f]
								_closed.append(_open.pop(__magic_name__						)						)
								if current == goal:
												break
								for n in world.get_neigbours(__magic_name__						):
												for c in _closed:
																if c == n:
																				continue
												lowercase         =      current.g + 1
												lowercase     ,	lowercase         =      n.position
												lowercase     ,	lowercase         =      goal.position
												lowercase         =      (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase         =      n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(__magic_name__						)
				lowercase         =      []
				while current.parent is not None:
								path.append(current.position						)
								lowercase         =      current.parent
				path.append(current.position						)
				return path[::-1]


if __name__ == "__main__":
						_snake_case      :							Optional[int]     	=    Gridworld()
						# Start position and goal
						_snake_case      :							Dict     	=    Cell()
						_snake_case      :							List[str]     	=    (0, 0)
						_snake_case      :							Union[str, Any]     	=    Cell()
						_snake_case      :							List[Any]     	=    (4, 4)
						print(F"path from {start.position} to {goal.position}")
						_snake_case      :							str     	=    astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												_snake_case      :							List[Any]     	=    1
						print(world.w)
