





'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class       lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							Union[str, Any]				     =    (DEISMultistepScheduler,)
 __lowerCamelCase  :							Union[str, Any]				     =    (('''num_inference_steps''', 25),)




 def 							_snake_case      (		self	,   **_lowerCAmelCase						)			->							int:
     _lowerCAmelCase			=					{
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "solver_order": 2,
     }

     config.update(**__lowerCAmelCase						)
     return config




 def 							_snake_case      (		self	,   _lowerCAmelCase=0	,   **_lowerCAmelCase						)			->							int:
     _lowerCAmelCase			=					dict(self.forward_default_kwargs						)
     _lowerCAmelCase			=					kwargs.pop("num_inference_steps"	,   __lowerCAmelCase						)
     _lowerCAmelCase			=					self.dummy_sample
     _lowerCAmelCase			=					0.1 * sample
     _lowerCAmelCase			=					[residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase			=					self.get_scheduler_config(**__lowerCAmelCase						)
         _lowerCAmelCase			=					scheduler_class(**__lowerCAmelCase						)
         scheduler.set_timesteps(__lowerCAmelCase						)
         # copy over dummy past residuals
         _lowerCAmelCase			=					dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__lowerCAmelCase						)
             _lowerCAmelCase			=					scheduler_class.from_pretrained(__lowerCAmelCase						)
             new_scheduler.set_timesteps(__lowerCAmelCase						)
             # copy over dummy past residuals
             _lowerCAmelCase			=					dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase	,	_lowerCAmelCase			=					sample, sample
         for t in range(__lowerCAmelCase	,   time_step + scheduler.config.solver_order + 1						):
             _lowerCAmelCase			=					scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase						).prev_sample
             _lowerCAmelCase			=					new_scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase						).prev_sample

             assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"




 def 							_snake_case      (		self						)			->							List[Any]:
     pass




 def 							_snake_case      (		self	,   _lowerCAmelCase=0	,   **_lowerCAmelCase						)			->							Dict:
     _lowerCAmelCase			=					dict(self.forward_default_kwargs						)
     _lowerCAmelCase			=					kwargs.pop("num_inference_steps"	,   __lowerCAmelCase						)
     _lowerCAmelCase			=					self.dummy_sample
     _lowerCAmelCase			=					0.1 * sample
     _lowerCAmelCase			=					[residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase			=					self.get_scheduler_config()
         _lowerCAmelCase			=					scheduler_class(**__lowerCAmelCase						)
         scheduler.set_timesteps(__lowerCAmelCase						)

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

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

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

         _lowerCAmelCase			=					scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase						).prev_sample
         _lowerCAmelCase			=					new_scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase						).prev_sample

         assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"




 def 							_snake_case      (		self	,   _lowerCAmelCase=None	,   **_lowerCAmelCase						)			->							Any:






     if scheduler is None:
         _lowerCAmelCase			=					self.scheduler_classes[0]
         _lowerCAmelCase			=					self.get_scheduler_config(**__lowerCAmelCase						)
         _lowerCAmelCase			=					scheduler_class(**__lowerCAmelCase						)

     _lowerCAmelCase			=					self.scheduler_classes[0]
     _lowerCAmelCase			=					self.get_scheduler_config(**__lowerCAmelCase						)
     _lowerCAmelCase			=					scheduler_class(**__lowerCAmelCase						)

     _lowerCAmelCase			=					10
     _lowerCAmelCase			=					self.dummy_model()
     _lowerCAmelCase			=					self.dummy_sample_deter
     scheduler.set_timesteps(__lowerCAmelCase						)

     for i, t in enumerate(scheduler.timesteps						):
         _lowerCAmelCase			=					model(__lowerCAmelCase	,   __lowerCAmelCase						)
         _lowerCAmelCase			=					scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase						).prev_sample

     return sample




 def 							_snake_case      (		self						)			->							Any:
     _lowerCAmelCase			=					dict(self.forward_default_kwargs						)

     _lowerCAmelCase			=					kwargs.pop("num_inference_steps"	,   __lowerCAmelCase						)

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase			=					self.get_scheduler_config()
         _lowerCAmelCase			=					scheduler_class(**__lowerCAmelCase						)

         _lowerCAmelCase			=					self.dummy_sample
         _lowerCAmelCase			=					0.1 * sample

         if num_inference_steps is not None and hasattr(__lowerCAmelCase	,   "set_timesteps"						):
             scheduler.set_timesteps(__lowerCAmelCase						)
         elif num_inference_steps is not None and not hasattr(__lowerCAmelCase	,   "set_timesteps"						):
             _lowerCAmelCase			=					num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         _lowerCAmelCase			=					[residual + 0.2, residual + 0.15, residual + 0.10]
         _lowerCAmelCase			=					dummy_past_residuals[: scheduler.config.solver_order]

         _lowerCAmelCase			=					scheduler.timesteps[5]
         _lowerCAmelCase			=					scheduler.timesteps[6]

         _lowerCAmelCase			=					scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase						).prev_sample
         _lowerCAmelCase			=					scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase						).prev_sample

         self.assertEqual(output_a.shape	,   sample.shape						)
         self.assertEqual(output_a.shape	,   output_a.shape						)




 def 							_snake_case      (		self						)			->							Tuple:
     _lowerCAmelCase			=					DEISMultistepScheduler(**self.get_scheduler_config()						)
     _lowerCAmelCase			=					self.full_loop(scheduler=__lowerCAmelCase						)
     _lowerCAmelCase			=					torch.mean(torch.abs(__lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.23916						) < 1E-3

     _lowerCAmelCase			=					DPMSolverSinglestepScheduler.from_config(scheduler.config						)
     _lowerCAmelCase			=					DPMSolverMultistepScheduler.from_config(scheduler.config						)
     _lowerCAmelCase			=					UniPCMultistepScheduler.from_config(scheduler.config						)
     _lowerCAmelCase			=					DEISMultistepScheduler.from_config(scheduler.config						)

     _lowerCAmelCase			=					self.full_loop(scheduler=__lowerCAmelCase						)
     _lowerCAmelCase			=					torch.mean(torch.abs(__lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.23916						) < 1E-3




 def 							_snake_case      (		self						)			->							List[Any]:






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




 def 							_snake_case      (		self						)			->							int:
     self.check_over_configs(thresholding=__lowerCAmelCase						)
     for order in [1, 2, 3]:
         for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=__lowerCAmelCase	,   prediction_type=__lowerCAmelCase	,   sample_max_value=__lowerCAmelCase	,   algorithm_type="deis"	,   solver_order=__lowerCAmelCase	,   solver_type=__lowerCAmelCase	,   )




 def 							_snake_case      (		self						)			->							Optional[Any]:






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




 def 							_snake_case      (		self						)			->							Any:






     for algorithm_type in ["deis"]:
         for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=__lowerCAmelCase	,   solver_type=__lowerCAmelCase	,   prediction_type=__lowerCAmelCase	,   algorithm_type=__lowerCAmelCase	,   )
                     _lowerCAmelCase			=					self.full_loop(
                         solver_order=__lowerCAmelCase	,   solver_type=__lowerCAmelCase	,   prediction_type=__lowerCAmelCase	,   algorithm_type=__lowerCAmelCase	,   )
                     assert not torch.isnan(__lowerCAmelCase						).any(), "Samples have nan numbers"




 def 							_snake_case      (		self						)			->							Any:
     self.check_over_configs(lower_order_final=__lowerCAmelCase						)
     self.check_over_configs(lower_order_final=__lowerCAmelCase						)




 def 							_snake_case      (		self						)			->							Union[str, Any]:






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




 def 							_snake_case      (		self						)			->							Optional[Any]:
     _lowerCAmelCase			=					self.full_loop()
     _lowerCAmelCase			=					torch.mean(torch.abs(__lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.23916						) < 1E-3




 def 							_snake_case      (		self						)			->							Dict:
     _lowerCAmelCase			=					self.full_loop(prediction_type="v_prediction"						)
     _lowerCAmelCase			=					torch.mean(torch.abs(__lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.091						) < 1E-3





 def 							_snake_case      (		self						)			->							int:
     _lowerCAmelCase			=					self.scheduler_classes[0]
     _lowerCAmelCase			=					self.get_scheduler_config(thresholding=__lowerCAmelCase	,   dynamic_thresholding_ratio=0						)
     _lowerCAmelCase			=					scheduler_class(**__lowerCAmelCase						)

     _lowerCAmelCase			=					10
     _lowerCAmelCase			=					self.dummy_model()
     _lowerCAmelCase			=					self.dummy_sample_deter.half()
     scheduler.set_timesteps(__lowerCAmelCase						)

     for i, t in enumerate(scheduler.timesteps						):
         _lowerCAmelCase			=					model(__lowerCAmelCase	,   __lowerCAmelCase						)
         _lowerCAmelCase			=					scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase						).prev_sample

     assert sample.dtype == torch.floataa









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''')



