



'''simple docstring'''




from __future__ import annotations

import time

UpperCamelCase__ :			Optional[int]												=				list[tuple[int, int]]

UpperCamelCase__ :			Tuple												=				[
    [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],
]

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






class 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Node | None		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	pos_x
					__SCREAMING_SNAKE_CASE :	List[str]					 =	pos_y
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	(pos_y, pos_x)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	goal_x
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	goal_y
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	parent






class 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				str							,  lowerCAmelCase__  :				tuple[int, int]							,  lowerCAmelCase__  :				tuple[int, int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	Node(start[1]							,  start[0]							,  goal[1]							,  goal[0]							,  A_		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	Node(goal[1]							,  goal[0]							,  goal[1]							,  goal[0]							,  A_		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[self.start]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	False

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					while self.node_queue:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	self.node_queue.pop(0		)

							if current_node.pos == self.target.pos:
									__SCREAMING_SNAKE_CASE :	List[str]					 =	True
									return self.retrace_path(A_		)

							__SCREAMING_SNAKE_CASE :	int					 =	self.get_successors(A_		)

							for node in successors:
									self.node_queue.append(A_		)

					if not self.reached:
							return [self.start.pos]
					return None

			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Node		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[]
					for action in delta:
							__SCREAMING_SNAKE_CASE :	Any					 =	parent.pos_x + action[1]
							__SCREAMING_SNAKE_CASE :	List[str]					 =	parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(A_		) - 1):
									continue

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

							successors.append(
							    Node(A_							,  A_							,  self.target.pos_y							,  self.target.pos_x							,  A_		)		)
					return successors



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Node | None		):





					"""simple docstring"""






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






class 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	BreadthFirstSearch(A_							,  A_		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	BreadthFirstSearch(A_							,  A_		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	False

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							__SCREAMING_SNAKE_CASE :	int					 =	self.fwd_bfs.node_queue.pop(0		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.bwd_bfs.node_queue.pop(0		)

							if current_bwd_node.pos == current_fwd_node.pos:
									__SCREAMING_SNAKE_CASE :	Dict					 =	True
									return self.retrace_bidirectional_path(
									    A_							,  A_		)

							__SCREAMING_SNAKE_CASE :	Any					 =	current_bwd_node
							__SCREAMING_SNAKE_CASE :	str					 =	current_fwd_node

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	{
							    self.fwd_bfs: self.fwd_bfs.get_successors(A_		),
							    self.bwd_bfs: self.bwd_bfs.get_successors(A_		),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
									for node in successors[bfs]:
											bfs.node_queue.append(A_		)

					if not self.reached:
							return [self.fwd_bfs.start.pos]
					return None



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Node							,  lowerCAmelCase__  :				Node		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.fwd_bfs.retrace_path(A_		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.bwd_bfs.retrace_path(A_		)
					bwd_path.pop()
					bwd_path.reverse()
					__SCREAMING_SNAKE_CASE :	Any					 =	fwd_path + bwd_path
					return path


if __name__ == "__main__":
						# all coordinates are given in format [y,x]
						import doctest

						doctest.testmod()
						UpperCamelCase__ :			Tuple												=				(0, 0)
						UpperCamelCase__ :			Optional[Any]												=				(len(grid) - 1, len(grid[0]) - 1)
						for elem in grid:
												print(elem)

						UpperCamelCase__ :			Optional[int]												=				time.time()
						UpperCamelCase__ :			List[Any]												=				BreadthFirstSearch(init, goal)
						UpperCamelCase__ :			Dict												=				bfs.search()
						UpperCamelCase__ :			str												=				time.time() - start_bfs_time

						print('''Unidirectional BFS computation time : ''', bfs_time)

						UpperCamelCase__ :			Any												=				time.time()
						UpperCamelCase__ :			List[str]												=				BidirectionalBreadthFirstSearch(init, goal)
						UpperCamelCase__ :			int												=				bd_bfs.search()
						UpperCamelCase__ :			Any												=				time.time() - start_bd_bfs_time

						print('''Bidirectional BFS computation time : ''', bd_bfs_time)






'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	(KDPMaDiscreteScheduler,)
			_A   :	Dict    					=	10

			def UpperCamelCase__      (							self  :				Optional[int]							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	{
					    """num_train_timesteps""": 1_1_0_0,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**lowerCAmelCase__		)
					return config

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,  [0.00_02, 0.0_02, 0.02]		):
							self.check_over_configs(beta_start=lowerCAmelCase__							,  beta_end=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_scheduler_config(prediction_type="""v_prediction"""		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.69_34E-07		) < 1E-2
							assert abs(result_mean.item() - 6.11_12E-10		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07		) < 1E-2
							assert abs(result_mean.item() - 0.00_02		) < 1E-3

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	str					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	str					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	int					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3



			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps							,  device=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter.to(lowerCAmelCase__		) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	str					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if str(lowerCAmelCase__		).startswith("""cpu"""		):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3