
'''simple docstring'''






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A			      =      0

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

A			      =      tuple[int, int]




class     __snake_case		:
					def __init__(     self,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= pos_x
									lowerCamelCase					:			Optional[int]									= pos_y
									lowerCamelCase					:			Any									= (pos_y, pos_x)
									lowerCamelCase					:			Optional[Any]									= goal_x
									lowerCamelCase					:			str									= goal_y
									lowerCamelCase					:			Optional[int]									= g_cost
									lowerCamelCase					:			str									= parent
									lowerCamelCase					:			Dict									= self.calculate_heuristic()
									lowerCamelCase					:			str									= self.g_cost + self.h_cost
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.pos_x - self.goal_x
									lowerCamelCase					:			Tuple									= self.pos_y - self.goal_y
									if HEURISTIC == 1:
													return abs(A      ) + abs(A      )
									else:
													return sqrt(dy**2 + dx**2      )




					def __lt__(     self,    A      ):

									"""simple docstring"""


									return self.f_cost < other.f_cost





class     __snake_case		:
					def __init__(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= Node(start[1],    start[0],    goal[1],    goal[0],    0,    A      )
									lowerCamelCase					:			Optional[Any]									= Node(goal[1],    goal[0],    goal[1],    goal[0],    9_9999,    A      )

									lowerCamelCase					:			int									= [self.start]
									lowerCamelCase					:			list[Node]									= []

									lowerCamelCase					:			int									= False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													lowerCamelCase					:			Optional[int]									= self.open_nodes.pop(0      )

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

													self.closed_nodes.append(A      )
													lowerCamelCase					:			Dict									= self.get_successors(A      )

													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(A      )
																	else:
																					# retrieve the best current path
																					lowerCamelCase					:			List[Any]									= self.open_nodes.pop(self.open_nodes.index(A      )      )

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

									return [self.start.pos]
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= []
									for action in delta:
													lowerCamelCase					:			Dict									= parent.pos_x + action[1]
													lowerCamelCase					:			List[Any]									= 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,    parent.g_cost + 1,    A,    )      )
									return successors




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= node
									lowerCamelCase					:			List[str]									= []
									while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)      )
													lowerCamelCase					:			Union[str, Any]									= current_node.parent
									path.reverse()
									return path





class     __snake_case		:
					def __init__(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= AStar(A,    A      )
									lowerCamelCase					:			Optional[Any]									= AStar(A,    A      )
									lowerCamelCase					:			List[str]									= False
					def     UpperCAmelCase_							(     self      ):

									"""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()
													lowerCamelCase					:			Union[str, Any]									= self.fwd_astar.open_nodes.pop(0      )
													lowerCamelCase					:			Optional[Any]									= self.bwd_astar.open_nodes.pop(0      )

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

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

													lowerCamelCase					:			int									= current_bwd_node
													lowerCamelCase					:			Any									= current_fwd_node

													lowerCamelCase					:			Tuple									= {
													    self.fwd_astar: self.fwd_astar.get_successors(A      ),
													    self.bwd_astar: self.bwd_astar.get_successors(A      ),
													}

													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(A      )
																					else:
																									# retrieve the best current path
																									lowerCamelCase					:			Any									= astar.open_nodes.pop(
																									    astar.open_nodes.index(A      )      )

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

									return [self.fwd_astar.start.pos]




					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.fwd_astar.retrace_path(A      )
									lowerCamelCase					:			List[Any]									= self.bwd_astar.retrace_path(A      )
									bwd_path.pop()
									bwd_path.reverse()
									lowerCamelCase					:			Union[str, Any]									= fwd_path + bwd_path
									return path


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

					A			      =      time.time()
					A			      =      AStar(init, goal)
					A			      =      a_star.search()
					A			      =      time.time() - start_time
					print(f"""AStar execution time = {end_time:f} seconds""")

					A			      =      time.time()
					A			      =      BidirectionalAStar(init, goal)
					A			      =      time.time() - bd_start_time
					print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")


'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	(DPMSolverSinglestepScheduler,)
					_lowerCAmelCase			     =	(('''num_inference_steps''', 25),)
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lambda_min_clipped': -float('inf'      ),
									    'variance_type': None,
									}

									config.update(**A      )
									return config
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			Optional[Any]									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			Dict									= 0.1 * sample
									lowerCamelCase					:			Dict									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Dict									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )
													# copy over dummy past residuals
													lowerCamelCase					:			str									= dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			List[Any]									= scheduler_class.from_pretrained(A      )
																	new_scheduler.set_timesteps(A      )
																	# copy over dummy past residuals
																	lowerCamelCase					:			Optional[int]									= dummy_past_residuals[: new_scheduler.config.solver_order]

													lowerCamelCase			, lowerCamelCase					:			Optional[int]									= sample, sample
													for t in range(A,    time_step + scheduler.config.solver_order + 1      ):
																	lowerCamelCase					:			Dict									= scheduler.step(A,    A,    A,    **A      ).prev_sample
																	lowerCamelCase					:			Optional[int]									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

																	assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			str									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			List[str]									= 0.1 * sample
									lowerCamelCase					:			List[str]									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Tuple									= self.get_scheduler_config()
													lowerCamelCase					:			Optional[Any]									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )

													# copy over dummy past residuals (must be after setting timesteps)
													lowerCamelCase					:			Any									= dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			Tuple									= scheduler_class.from_pretrained(A      )
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(A      )

																	# copy over dummy past residual (must be after setting timesteps)
																	lowerCamelCase					:			List[Any]									= dummy_past_residuals[: new_scheduler.config.solver_order]

													lowerCamelCase					:			int									= scheduler.step(A,    A,    A,    **A      ).prev_sample
													lowerCamelCase					:			Dict									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

													assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
					def     UpperCAmelCase_							(     self,    A=None,    **A      ):

									"""simple docstring"""


									if scheduler is None:
													lowerCamelCase					:			Any									= self.scheduler_classes[0]
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
									lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			Any									= 10
									lowerCamelCase					:			Dict									= self.dummy_model()
									lowerCamelCase					:			Any									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			Dict									= model(A,    A      )
													lowerCamelCase					:			List[str]									= scheduler.step(A,    A,    A      ).prev_sample

									return sample
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			Dict									= 50
									lowerCamelCase					:			Tuple									= self.dummy_model()
									lowerCamelCase					:			Optional[int]									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]      ):
													lowerCamelCase					:			Any									= model(A,    A      )
													lowerCamelCase					:			Optional[int]									= scheduler.step(A,    A,    A      ).prev_sample

									lowerCamelCase					:			Any									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2574      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3

									lowerCamelCase					:			Dict									= DEISMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[int]									= DPMSolverMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Any									= UniPCMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[Any]									= DPMSolverSinglestepScheduler.from_config(scheduler.config      )

									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(thresholding=A      )
									for order in [1, 2, 3]:
													for solver_type in ["midpoint", "heun"]:
																	for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    thresholding=A,    prediction_type=A,    sample_max_value=A,    algorithm_type='dpmsolver++',    solver_order=A,    solver_type=A,    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									for algorithm_type in ["dpmsolver", "dpmsolver++"]:
													for solver_type in ["midpoint", "heun"]:
																	for order in [1, 2, 3]:
																					for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									lowerCamelCase					:			Optional[Any]									= self.full_loop(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									assert not torch.isnan(A      ).any(), "Samples have nan numbers"
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lower_order_final=A      )
									self.check_over_configs(lower_order_final=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lambda_min_clipped=-float('inf'      )      )
									self.check_over_configs(lambda_min_clipped=-5.1      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(variance_type=A      )
									self.check_over_configs(variance_type='learned_range'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=A,    time_step=0      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop()
									lowerCamelCase					:			str									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop(use_karras_sigmas=A      )
									lowerCamelCase					:			Tuple									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2248      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction'      )
									lowerCamelCase					:			Dict									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.1453      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction',    use_karras_sigmas=A      )
									lowerCamelCase					:			Optional[Any]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.0649      ) < 1e-3




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Dict									= self.get_scheduler_config(thresholding=A,    dynamic_thresholding_ratio=0      )
									lowerCamelCase					:			str									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= 10
									lowerCamelCase					:			List[str]									= self.dummy_model()
									lowerCamelCase					:			int									= self.dummy_sample_deter.half()
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			str									= model(A,    A      )
													lowerCamelCase					:			Tuple									= scheduler.step(A,    A,    A      ).prev_sample

									assert sample.dtype == torch.floataa
