




"""simple docstring"""






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      ):
							super().__init__()
							_lowerCamelCase     :			Any    =    value_function
							_lowerCamelCase     :			str    =    unet
							_lowerCamelCase     :			Union[str, Any]    =    scheduler
							_lowerCamelCase     :			Optional[Any]    =    env
							_lowerCamelCase     :			Tuple    =    env.get_dataset()
							_lowerCamelCase     :			str    =    {}
							for key in self.data.keys():
											try:
															_lowerCamelCase     :			List[str]    =    self.data[key].mean()
											except:  # noqa: E722
															pass
							_lowerCamelCase     :			List[Any]    =    {}
							for key in self.data.keys():
											try:
															_lowerCamelCase     :			List[str]    =    self.data[key].std()
											except:  # noqa: E722
															pass
							_lowerCamelCase     :			int    =    env.observation_space.shape[0]
							_lowerCamelCase     :			str    =    env.action_space.shape[0]
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return (x_in - self.means[key]) / self.stds[key]
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return x_in * self.stds[key] + self.means[key]
			def 					A_    (  self  ,      lowercase  ):
							if type(lowercase  ) is dict:
											return {k: self.to_torch(lowercase  ) for k, v in x_in.items()}
							elif torch.is_tensor(lowercase  ):
											return x_in.to(self.unet.device  )
							return torch.tensor(lowercase  ,      device=self.unet.device  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							for key, val in cond.items():
											_lowerCamelCase     :			Dict    =    val.clone()
							return x_in
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[Any]    =    x.shape[0]
							_lowerCamelCase     :			Any    =    None
							for i in tqdm.tqdm(self.scheduler.timesteps  ):
											# create batch of timesteps to pass into model
											_lowerCamelCase     :			Tuple    =    torch.full((batch_size,)  ,      lowercase  ,      device=self.unet.device  ,      dtype=torch.long  )
											for _ in range(lowercase  ):
															with torch.enable_grad():
																			x.requires_grad_()

																			# permute to match dimension for pre-trained models
																			_lowerCamelCase     :			str    =    self.value_function(x.permute(0  ,      2  ,      1  )  ,      lowercase  ).sample
																			_lowerCamelCase     :			Optional[int]    =    torch.autograd.grad([y.sum()]  ,      [x]  )[0]

																			_lowerCamelCase     :			Optional[int]    =    self.scheduler._get_variance(lowercase  )
																			_lowerCamelCase     :			List[Any]    =    torch.exp(0.5 * posterior_variance  )
																			_lowerCamelCase     :			Optional[Any]    =    model_std * grad

															_lowerCamelCase     :			Tuple    =    0
															_lowerCamelCase     :			int    =    x.detach()
															_lowerCamelCase     :			List[str]    =    x + scale * grad
															_lowerCamelCase     :			Tuple    =    self.reset_xa(lowercase  ,      lowercase  ,      self.action_dim  )

											_lowerCamelCase     :			Tuple    =    self.unet(x.permute(0  ,      2  ,      1  )  ,      lowercase  ).sample.permute(0  ,      2  ,      1  )

											# TODO: verify deprecation of this kwarg
											_lowerCamelCase     :			List[str]    =    self.scheduler.step(lowercase  ,      lowercase  ,      lowercase  ,      predict_epsilon=lowercase  )['prev_sample']

											# apply conditions to the trajectory (set the initial state)
											_lowerCamelCase     :			Any    =    self.reset_xa(lowercase  ,      lowercase  ,      self.action_dim  )
											_lowerCamelCase     :			Union[str, Any]    =    self.to_torch(lowercase  )
							return x, y






			def __call__(  self  ,      lowercase  ,      lowercase=64  ,      lowercase=32  ,      lowercase=2  ,      lowercase=0.1  ):
							# normalize the observations and create  batch dimension
							_lowerCamelCase     :			List[str]    =    self.normalize(lowercase  ,      'observations'  )
							_lowerCamelCase     :			Dict    =    obs[None].repeat(lowercase  ,      axis=0  )

							_lowerCamelCase     :			Dict    =    {0: self.to_torch(lowercase  )}
							_lowerCamelCase     :			Any    =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

							# generate initial noise and apply our conditions (to make the trajectories start at current state)
							_lowerCamelCase     :			Tuple    =    randn_tensor(lowercase  ,      device=self.unet.device  )
							_lowerCamelCase     :			int    =    self.reset_xa(lowercase  ,      lowercase  ,      self.action_dim  )
							_lowerCamelCase     :			Dict    =    self.to_torch(lowercase  )

							# run the diffusion process
							_lowerCamelCase, _lowerCamelCase     :			Any    =    self.run_diffusion(lowercase  ,      lowercase  ,      lowercase  ,      lowercase  )

							# sort output trajectories by value
							_lowerCamelCase     :			List[Any]    =    y.argsort(0  ,      descending=lowercase  ).squeeze()
							_lowerCamelCase     :			Union[str, Any]    =    x[sorted_idx]
							_lowerCamelCase     :			Optional[int]    =    sorted_values[:, :, : self.action_dim]
							_lowerCamelCase     :			int    =    actions.detach().cpu().numpy()
							_lowerCamelCase     :			Union[str, Any]    =    self.de_normalize(lowercase  ,      key='actions'  )

							# select the action with the highest value
							if y is not None:
											_lowerCamelCase     :			int    =    0
							else:
											# if we didn't run value guiding, select a random action
											_lowerCamelCase     :			List[str]    =    np.random.randint(0  ,      lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    denorm_actions[selected_index, 0]
							return denorm_actions






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A    :					str =      0

A    :					Any =      [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

A    :					Dict =      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

A    :					Union[str, Any] =      tuple[int, int]


class   A			:
			'''simple docstring'''




			def __init__(			self   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Node | None							,	)	->	None:







					"""simple docstring"""






					A__        =   pos_x
					A__        =   pos_y
					A__        =   (pos_y, pos_x)
					A__        =   goal_x
					A__        =   goal_y
					A__        =   g_cost
					A__        =   parent
					A__        =   self.calculate_heuristic()
					A__        =   self.g_cost + self.h_cost






			def    a_       (			self   :    Dict   )	->	float:







					"""simple docstring"""






					A__        =   self.pos_x - self.goal_x
					A__        =   self.pos_y - self.goal_y
					if HEURISTIC == 1:
							return abs(__lowerCAmelCase   ) + abs(__lowerCAmelCase   )
					else:
							return sqrt(dy**2 + dx**2   )







			def __lt__(			self   :    int							,	__lowerCAmelCase   :    Node   )	->	bool:







					"""simple docstring"""






					return self.f_cost < other.f_cost


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Union[str, Any]							,	__lowerCAmelCase   :    TPosition							,	__lowerCAmelCase   :    TPosition   )	->	Tuple:







					"""simple docstring"""






					A__        =   Node(start[1]							,	start[0]							,	goal[1]							,	goal[0]							,	0							,	__lowerCAmelCase   )
					A__        =   Node(goal[1]							,	goal[0]							,	goal[1]							,	goal[0]							,	9_99_99							,	__lowerCAmelCase   )

					A__        =   [self.start]
					A__        =   []

					A__        =   False






			def    a_       (			self   :    List[str]   )	->	list[TPosition]:







					"""simple docstring"""






					while self.open_nodes:
							# Open Nodes are sorted using __lt__
							self.open_nodes.sort()
							A__        =   self.open_nodes.pop(0   )

							if current_node.pos == self.target.pos:
									return self.retrace_path(__lowerCAmelCase   )

							self.closed_nodes.append(__lowerCAmelCase   )
							A__        =   self.get_successors(__lowerCAmelCase   )

							for child_node in successors:
									if child_node in self.closed_nodes:
											continue

									if child_node not in self.open_nodes:
											self.open_nodes.append(__lowerCAmelCase   )
									else:
											# retrieve the best current path
											A__        =   self.open_nodes.pop(self.open_nodes.index(__lowerCAmelCase   )   )

											if child_node.g_cost < better_node.g_cost:
													self.open_nodes.append(__lowerCAmelCase   )
											else:
													self.open_nodes.append(__lowerCAmelCase   )

					return [self.start.pos]






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Node   )	->	list[Node]:







					"""simple docstring"""






					A__        =   []
					for action in delta:
							A__        =   parent.pos_x + action[1]
							A__        =   parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__lowerCAmelCase   ) - 1):
									continue

							if grid[pos_y][pos_x] != 0:
									continue

							successors.append(
							    Node(
							        __lowerCAmelCase							,	__lowerCAmelCase							,	self.target.pos_y							,	self.target.pos_x							,	parent.g_cost + 1							,	__lowerCAmelCase							,	)   )
					return successors







			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Node | None   )	->	list[TPosition]:







					"""simple docstring"""






					A__        =   node
					A__        =   []
					while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)   )
							A__        =   current_node.parent
					path.reverse()
					return path


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    TPosition							,	__lowerCAmelCase   :    TPosition   )	->	None:







					"""simple docstring"""






					A__        =   AStar(__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   AStar(__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   False






			def    a_       (			self   :    int   )	->	list[TPosition]:







					"""simple docstring"""






					while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
							self.fwd_astar.open_nodes.sort()
							self.bwd_astar.open_nodes.sort()
							A__        =   self.fwd_astar.open_nodes.pop(0   )
							A__        =   self.bwd_astar.open_nodes.pop(0   )

							if current_bwd_node.pos == current_fwd_node.pos:
									return self.retrace_bidirectional_path(
									    __lowerCAmelCase							,	__lowerCAmelCase   )

							self.fwd_astar.closed_nodes.append(__lowerCAmelCase   )
							self.bwd_astar.closed_nodes.append(__lowerCAmelCase   )

							A__        =   current_bwd_node
							A__        =   current_fwd_node

							A__        =   {
							    self.fwd_astar: self.fwd_astar.get_successors(__lowerCAmelCase   ),
							    self.bwd_astar: self.bwd_astar.get_successors(__lowerCAmelCase   ),
							}

							for astar in [self.fwd_astar, self.bwd_astar]:
									for child_node in successors[astar]:
											if child_node in astar.closed_nodes:
													continue

											if child_node not in astar.open_nodes:
													astar.open_nodes.append(__lowerCAmelCase   )
											else:
													# retrieve the best current path
													A__        =   astar.open_nodes.pop(
													    astar.open_nodes.index(__lowerCAmelCase   )   )

													if child_node.g_cost < better_node.g_cost:
															astar.open_nodes.append(__lowerCAmelCase   )
													else:
															astar.open_nodes.append(__lowerCAmelCase   )

					return [self.fwd_astar.start.pos]







			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    Node							,	__lowerCAmelCase   :    Node   )	->	list[TPosition]:







					"""simple docstring"""






					A__        =   self.fwd_astar.retrace_path(__lowerCAmelCase   )
					A__        =   self.bwd_astar.retrace_path(__lowerCAmelCase   )
					bwd_path.pop()
					bwd_path.reverse()
					A__        =   fwd_path + bwd_path
					return path


if __name__ == "__main__":
					# all coordinates are given in format [y,x]
					A    :					Optional[int] =      (0, 0)
					A    :					int =      (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					A    :					Dict =      time.time()
					A    :					Optional[Any] =      AStar(init, goal)
					A    :					Optional[int] =      a_star.search()
					A    :					Optional[int] =      time.time() - start_time
					print(F'''AStar execution time = {end_time:f} seconds''')

					A    :					Dict =      time.time()
					A    :					Tuple =      BidirectionalAStar(init, goal)
					A    :					List[Any] =      time.time() - bd_start_time
					print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')



