"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  (DPMSolverSinglestepScheduler,)
				_snake_case     =  (("num_inference_steps", 25),)







				def 							__UpperCAmelCase					( self    :       str			,					**lowerCamelCase_    :       int						):

					"""simple docstring"""






					_lowercase :    str							=							{
					    'num_train_timesteps': 1_0_0_0,
					    '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(**lowerCamelCase_						)
					return config







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       int=0			,					**lowerCamelCase_    :       str						):

					"""simple docstring"""






					_lowercase :    str							=							dict(self.forward_default_kwargs						)
					_lowercase :    Tuple							=							kwargs.pop('num_inference_steps'			,					lowerCamelCase_						)
					_lowercase :    List[Any]							=							self.dummy_sample
					_lowercase :    int							=							0.1 * sample
					_lowercase :    int							=							[residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
						_lowercase :    List[str]							=							self.get_scheduler_config(**lowerCamelCase_						)
						_lowercase :    Dict							=							scheduler_class(**lowerCamelCase_						)
						scheduler.set_timesteps(lowerCamelCase_						)
						# copy over dummy past residuals
						_lowercase :    Tuple							=							dummy_past_residuals[: scheduler.config.solver_order]

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

						_lowercase :    str							=							sample, sample
						for t in range(lowerCamelCase_			,					time_step + scheduler.config.solver_order + 1						):
							_lowercase :    Optional[Any]							=							scheduler.step(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					**lowerCamelCase_						).prev_sample
							_lowercase :    List[Any]							=							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 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					pass







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       Dict=0			,					**lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






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

					for scheduler_class in self.scheduler_classes:
						_lowercase :    Any							=							self.get_scheduler_config()
						_lowercase :    Dict							=							scheduler_class(**lowerCamelCase_						)
						scheduler.set_timesteps(lowerCamelCase_						)

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

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(lowerCamelCase_						)
							_lowercase :    Optional[int]							=							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)
							_lowercase :    Union[str, Any]							=							dummy_past_residuals[: new_scheduler.config.solver_order]

						_lowercase :    Union[str, Any]							=							scheduler.step(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					**lowerCamelCase_						).prev_sample
						_lowercase :    Any							=							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 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       List[Any]=None			,					**lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






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

					_lowercase :    Optional[Any]							=							self.scheduler_classes[0]
					_lowercase :    Any							=							self.get_scheduler_config(**lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							scheduler_class(**lowerCamelCase_						)

					_lowercase :    Tuple							=							1_0
					_lowercase :    Union[str, Any]							=							self.dummy_model()
					_lowercase :    Tuple							=							self.dummy_sample_deter
					scheduler.set_timesteps(lowerCamelCase_						)

					for i, t in enumerate(scheduler.timesteps						):
						_lowercase :    Dict							=							model(lowerCamelCase_			,					lowerCamelCase_						)
						_lowercase :    Any							=							scheduler.step(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						).prev_sample

					return sample







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
					_lowercase :    List[str]							=							5_0
					_lowercase :    Optional[int]							=							self.dummy_model()
					_lowercase :    str							=							self.dummy_sample_deter
					scheduler.set_timesteps(lowerCamelCase_						)

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

					_lowercase :    str							=							torch.mean(torch.abs(lowerCamelCase_						)						)

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







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
						self.check_over_configs(num_train_timesteps=lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					_lowercase :    str							=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
					_lowercase :    List[Any]							=							self.full_loop(scheduler=lowerCamelCase_						)
					_lowercase :    Optional[int]							=							torch.mean(torch.abs(lowerCamelCase_						)						)

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

					_lowercase :    Tuple							=							DEISMultistepScheduler.from_config(scheduler.config						)
					_lowercase :    Dict							=							DPMSolverMultistepScheduler.from_config(scheduler.config						)
					_lowercase :    Union[str, Any]							=							UniPCMultistepScheduler.from_config(scheduler.config						)
					_lowercase :    Tuple							=							DPMSolverSinglestepScheduler.from_config(scheduler.config						)

					_lowercase :    Tuple							=							self.full_loop(scheduler=lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							torch.mean(torch.abs(lowerCamelCase_						)						)

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







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					self.check_over_configs(thresholding=lowerCamelCase_						)
					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=lowerCamelCase_			,					prediction_type=lowerCamelCase_			,					sample_max_value=lowerCamelCase_			,					algorithm_type='dpmsolver++'			,					solver_order=lowerCamelCase_			,					solver_type=lowerCamelCase_			,					)







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

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""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=lowerCamelCase_			,					solver_type=lowerCamelCase_			,					prediction_type=lowerCamelCase_			,					algorithm_type=lowerCamelCase_			,					)
									_lowercase :    Tuple							=							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 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					self.check_over_configs(lower_order_final=lowerCamelCase_						)
					self.check_over_configs(lower_order_final=lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					self.check_over_configs(variance_type=lowerCamelCase_						)
					self.check_over_configs(variance_type='learned_range'						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
						self.check_over_forward(num_inference_steps=lowerCamelCase_			,					time_step=0						)







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






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

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







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Dict							=							self.full_loop(use_karras_sigmas=lowerCamelCase_						)
					_lowercase :    Any							=							torch.mean(torch.abs(lowerCamelCase_						)						)

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







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							self.full_loop(prediction_type='v_prediction'						)
					_lowercase :    List[str]							=							torch.mean(torch.abs(lowerCamelCase_						)						)

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







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    int							=							self.full_loop(prediction_type='v_prediction'			,					use_karras_sigmas=lowerCamelCase_						)
					_lowercase :    Any							=							torch.mean(torch.abs(lowerCamelCase_						)						)

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







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

					"""simple docstring"""






					_lowercase :    List[Any]							=							self.scheduler_classes[0]
					_lowercase :    Optional[Any]							=							self.get_scheduler_config(thresholding=lowerCamelCase_			,					dynamic_thresholding_ratio=0						)
					_lowercase :    Union[str, Any]							=							scheduler_class(**lowerCamelCase_						)

					_lowercase :    Optional[Any]							=							1_0
					_lowercase :    str							=							self.dummy_model()
					_lowercase :    Optional[int]							=							self.dummy_sample_deter.half()
					scheduler.set_timesteps(lowerCamelCase_						)

					for i, t in enumerate(scheduler.timesteps						):
						_lowercase :    Dict							=							model(lowerCamelCase_			,					lowerCamelCase_						)
						_lowercase :    List[Any]							=							scheduler.step(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						).prev_sample

					assert sample.dtype == torch.floataa




"""simple docstring"""







from __future__ import annotations

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,):






	"""simple docstring"""





	_lowercase :    List[str]							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the reference grid
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Any							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the action grid

	_lowercase :    List[Any]							=							init[0]
	_lowercase :    Optional[Any]							=							init[1]
	_lowercase :    Union[str, Any]							=							0
	_lowercase :    Union[str, Any]							=							g + heuristic[x][y]  # cost from starting cell to destination cell
	_lowercase :    Dict							=							[[f, g, x, y]]

	_lowercase :    Tuple							=							False  # flag that is set when search is complete
	_lowercase :    Tuple							=							False  # flag set if we can't find expand

	while not found and not resign:
		if len(__UpperCAmelCase			) == 0:
			raise ValueError('Algorithm is unable to find solution'			)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			_lowercase :    List[Any]							=							cell.pop()
			_lowercase :    Optional[Any]							=							next_cell[2]
			_lowercase :    List[str]							=							next_cell[3]
			_lowercase :    Optional[int]							=							next_cell[1]

			if x == goal[0] and y == goal[1]:
				_lowercase :    Optional[Any]							=							True
			else:
				for i in range(len(__UpperCAmelCase			)			):  # to try out different valid actions
					_lowercase :    Any							=							x + DIRECTIONS[i][0]
					_lowercase :    Union[str, Any]							=							y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(__UpperCAmelCase			) and ya >= 0 and ya < len(grid[0]			):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							_lowercase :    int							=							g + cost
							_lowercase :    List[str]							=							ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]			)
							_lowercase :    List[Any]							=							1
							_lowercase :    Dict							=							i
	_lowercase :    Union[str, Any]							=							[]
	_lowercase :    Optional[int]							=							goal[0]
	_lowercase :    Any							=							goal[1]
	invpath.append([x, y]			)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		_lowercase :    str							=							x - DIRECTIONS[action[x][y]][0]
		_lowercase :    Any							=							y - DIRECTIONS[action[x][y]][1]
		_lowercase :    Dict							=							xa
		_lowercase :    Tuple							=							ya
		invpath.append([x, y]			)

	_lowercase :    List[str]							=							[]
	for i in range(len(__UpperCAmelCase			)			):
		path.append(invpath[len(__UpperCAmelCase			) - 1 - i]			)
	return path, action


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						[
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 1, 0],
						    [0, 0, 0, 0, 1, 0],
						]

						SCREAMING_SNAKE_CASE									=						[0, 0]
						# all coordinates are given in format [y,x]
						SCREAMING_SNAKE_CASE									=						[len(grid) - 1, len(grid[0]) - 1]
						SCREAMING_SNAKE_CASE									=						1

						# the cost map which pushes the path closer to the goal
						SCREAMING_SNAKE_CASE									=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
						for i in range(len(grid)):
												for j in range(len(grid[0])):
																		SCREAMING_SNAKE_CASE									=						abs(i - goal[0]) + abs(j - goal[1])
																		if grid[i][j] == 1:
																								# added extra penalty in the heuristic map
																								SCREAMING_SNAKE_CASE									=						99

						SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE									=						search(grid, init, goal, cost, heuristic)

						print('ACTION MAP')
						for i in range(len(action)):
												print(action[i])

						for i in range(len(path)):
												print(path[i])



