'''simple docstring'''

from __future__ import annotations

import time

__a:					Tuple			  =  list[tuple[int, int]]

__a:					int			  =  [
    [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:					Tuple			  =  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right



class      UpperCAmelCase    :





       '''simple docstring'''



       def __init__(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    Union[str, Any]:
        lowercase__							:       Union[str, Any]										=   pos_x
        lowercase__							:       List[str]										=   pos_y
        lowercase__							:       int										=   (pos_y, pos_x)
        lowercase__							:       Any										=   goal_x
        lowercase__							:       Optional[int]										=   goal_y
        lowercase__							:       int										=   parent



class      UpperCAmelCase    :





       '''simple docstring'''



       def __init__(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    Optional[int]:
        lowercase__							:       Optional[Any]										=   Node(start[1]      ,  start[0]      ,  goal[1]      ,  goal[0]      ,  __lowercase					)
        lowercase__							:       Any										=   Node(goal[1]      ,  goal[0]      ,  goal[1]      ,  goal[0]      ,  __lowercase					)

        lowercase__							:       Optional[int]										=   [self.start]
        lowercase__							:       Any										=   False

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






        while self.node_queue:
         lowercase__							:       Tuple										=   self.node_queue.pop(0					)

         if current_node.pos == self.target.pos:
          lowercase__							:       Tuple										=   True
          return self.retrace_path(__lowercase					)

         lowercase__							:       Any										=   self.get_successors(__lowercase					)

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

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

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase					)    ->    Dict:
        lowercase__							:       List[str]										=   []
        for action in delta:
         lowercase__							:       List[str]										=   parent.pos_x + action[1]
         lowercase__							:       List[Any]										=   parent.pos_y + action[0]
         if not (0 <= pos_x <= len(grid[0]					) - 1 and 0 <= pos_y <= len(__lowercase					) - 1):
          continue

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

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


       def _lowerCAmelCase(		self      ,  __lowerCAmelCase					)    ->    List[str]:
        lowercase__							:       List[str]										=   node
        lowercase__							:       Dict										=   []
        while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)					)
         lowercase__							:       Optional[Any]										=   current_node.parent
        path.reverse()
        return path



class      UpperCAmelCase    :





       '''simple docstring'''



       def __init__(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    str:
        lowercase__							:       int										=   BreadthFirstSearch(__lowercase      ,  __lowercase					)
        lowercase__							:       str										=   BreadthFirstSearch(__lowercase      ,  __lowercase					)
        lowercase__							:       Tuple										=   False

       def _lowerCAmelCase(		self					)    ->    Tuple:






        while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
         lowercase__							:       str										=   self.fwd_bfs.node_queue.pop(0					)
         lowercase__							:       List[Any]										=   self.bwd_bfs.node_queue.pop(0					)

         if current_bwd_node.pos == current_fwd_node.pos:
          lowercase__							:       Any										=   True
          return self.retrace_bidirectional_path(
              __lowercase      ,  __lowercase					)

         lowercase__							:       Any										=   current_bwd_node
         lowercase__							:       str										=   current_fwd_node

         lowercase__							:       Optional[Any]										=   {
             self.fwd_bfs: self.fwd_bfs.get_successors(__lowercase					),
             self.bwd_bfs: self.bwd_bfs.get_successors(__lowercase					),
         }

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

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


       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    List[Any]:
        lowercase__							:       Tuple										=   self.fwd_bfs.retrace_path(__lowercase					)
        lowercase__							:       Union[str, Any]										=   self.bwd_bfs.retrace_path(__lowercase					)
        bwd_path.pop()
        bwd_path.reverse()
        lowercase__							:       Any										=   fwd_path + bwd_path
        return path


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

  doctest.testmod()
  __a:					str			  =  (0, 0)
  __a:					List[str]			  =  (len(grid) - 1, len(grid[0]) - 1)
  for elem in grid:
    print(elem)

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

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

  __a:					List[str]			  =  time.time()
  __a:					Tuple			  =  BidirectionalBreadthFirstSearch(init, goal)
  __a:					int			  =  bd_bfs.search()
  __a:					Dict			  =  time.time() - start_bd_bfs_time

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







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (DPMSolverSinglestepScheduler,)
						lowerCAmelCase_							=  (('''num_inference_steps''', 25),)






						def 			snake_case__		(		self				:	str			,      **__lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	{
							    "num_train_timesteps": 10_00,
							    "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(**__lowercase		)
							return config






						def 			snake_case__		(		self				:	str			,      __lowercase				:	int=0			,      **__lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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






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






							"""simple docstring"""






							pass






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Optional[int]=0			,      **__lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								snake_case_						       =	self.get_scheduler_config()
								snake_case_						       =	scheduler_class(**__lowercase		)
								scheduler.set_timesteps(__lowercase		)

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

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

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

								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample
								snake_case_						       =	new_scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample

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






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Any=None			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






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

							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(**__lowercase		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

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

							return sample






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	50
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]		):
								snake_case_						       =	model(__lowercase			,      __lowercase		)
								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase		).prev_sample

							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2574		) < 1E-3






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase		)






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

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

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

							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

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






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							self.check_over_configs(thresholding=__lowercase		)
							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=__lowercase			,      prediction_type=__lowercase			,      sample_max_value=__lowercase			,      algorithm_type="dpmsolver++"			,      solver_order=__lowercase			,      solver_type=__lowercase			,      )






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






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""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=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											snake_case_						       =	self.full_loop(
											    solver_order=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											assert not torch.isnan(__lowercase		).any(), "Samples have nan numbers"






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(lower_order_final=__lowercase		)
							self.check_over_configs(lower_order_final=__lowercase		)






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






							"""simple docstring"""






							self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
							self.check_over_configs(lambda_min_clipped=-5.1		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(variance_type=__lowercase		)
							self.check_over_configs(variance_type="learned_range"		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
								self.check_over_forward(num_inference_steps=__lowercase			,      time_step=0		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop()
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

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






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2248		) < 1E-3






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.1453		) < 1E-3






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"			,      use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

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






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(thresholding=__lowercase			,      dynamic_thresholding_ratio=0		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter.half()
							scheduler.set_timesteps(__lowercase		)

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

							assert sample.dtype == torch.floataa
