
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Dict   							=  (DPMSolverSinglestepScheduler,)
							lowercase_		:						Optional[Any]   							=  (('num_inference_steps', 25),)







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "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(**snake_case__ )
								return config







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

									# 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(snake_case__ )
										__lowerCAmelCase         =							scheduler_class.from_pretrained(snake_case__ )
										# copy over dummy past residuals
										new_scheduler.set_timesteps(snake_case__ )

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

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

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







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Optional[Any]=None      ,				**snake_case__  :  Optional[int] ):
								"""simple docstring"""







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

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

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

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

								return sample







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							50
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

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

								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_5_7_4 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								for timesteps in [25, 50, 100, 999, 1_000]:
									self.check_over_configs(num_train_timesteps=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3

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

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

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str ):
								"""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=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												__lowerCAmelCase         =							self.full_loop(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												assert not torch.isnan(snake_case__ ).any(), "Samples have nan numbers"







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								self.check_over_configs(lower_order_final=snake_case__ )
								self.check_over_configs(lower_order_final=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""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=snake_case__ )
								self.check_over_configs(variance_type="learned_range" )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
									self.check_over_forward(num_inference_steps=snake_case__      ,				time_step=0 )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop()
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_2_4_8 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction" )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.1_4_5_3 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction"      ,				use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.0_6_4_9 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(thresholding=snake_case__      ,				dynamic_thresholding_ratio=0 )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

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

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

								assert sample.dtype == torch.floataa








from __future__ import annotations

UpperCamelCase_   	=	list[tuple[int, int]]

UpperCamelCase_   	=	[
    [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_   	=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class       a		:







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  float      ,				snake_case__  :  Node | None      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							pos_x
								__lowerCAmelCase         =							pos_y
								__lowerCAmelCase         =							(pos_y, pos_x)
								__lowerCAmelCase         =							goal_x
								__lowerCAmelCase         =							goal_y
								__lowerCAmelCase         =							g_cost
								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							self.calculate_heuristic()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							abs(self.pos_x - self.goal_x )
								__lowerCAmelCase         =							abs(self.pos_y - self.goal_y )
								return dx + dy







							def __lt__(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								return self.f_cost < other.f_cost

class       a		:







							def __init__(						self  :  Dict      ,				snake_case__  :  tuple[int, int]      ,				snake_case__  :  tuple[int, int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Node(start[1]      ,				start[0]      ,				goal[1]      ,				goal[0]      ,				0      ,				snake_case__ )
								__lowerCAmelCase         =							Node(goal[1]      ,				goal[0]      ,				goal[1]      ,				goal[0]      ,				99_999      ,				snake_case__ )

								__lowerCAmelCase         =							[self.start]
								__lowerCAmelCase         =							[]

								__lowerCAmelCase         =							False







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__lowerCAmelCase         =							self.open_nodes.pop(0 )

									if current_node.pos == self.target.pos:
										__lowerCAmelCase         =							True
										return self.retrace_path(snake_case__ )

									self.closed_nodes.append(snake_case__ )
									__lowerCAmelCase         =							self.get_successors(snake_case__ )

									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(snake_case__ )
										else:
											# retrieve the best current path
											__lowerCAmelCase         =							self.open_nodes.pop(self.open_nodes.index(snake_case__ ) )

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

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







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Node ):
								"""simple docstring"""







								__lowerCAmelCase         =							[]
								for action in delta:
									__lowerCAmelCase         =							parent.pos_x + action[1]
									__lowerCAmelCase         =							parent.pos_y + action[0]

									if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case__ ) - 1):
										continue

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

									successors.append(
									    Node(
									        snake_case__      ,				snake_case__      ,				self.target.pos_y      ,				self.target.pos_x      ,				parent.g_cost + 1      ,				snake_case__      ,				) )
								return successors







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Node | None ):
								"""simple docstring"""







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


if __name__ == "__main__":
					UpperCamelCase_   	=	(0, 0)
					UpperCamelCase_   	=	(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("------")

					UpperCamelCase_   	=	GreedyBestFirst(init, goal)
					UpperCamelCase_   	=	greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															UpperCamelCase_   	=	2

										for elem in grid:
															print(elem)






