



'''simple docstring'''



from __future__ import annotations

lowerCAmelCase	:		List[Any]				=   list[tuple[int, int]]

lowerCAmelCase	:		str				=   [
    [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],
]

lowerCAmelCase	:		Optional[Any]				=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				)     ->			int:
						__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     )     ->			float:
						__lowerCAmelCase												=  abs(self.pos_x - self.goal_x     )
						__lowerCAmelCase												=  abs(self.pos_y - self.goal_y     )
						return dx + dy



					def __lt__(    self			,				UpperCamelCase     )     ->			bool:
						return self.f_cost < other.f_cost






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[int]:
						__lowerCAmelCase												=  Node(start[1]			,				start[0]			,				goal[1]			,				goal[0]			,				0			,				UpperCamelCase     )
						__lowerCAmelCase												=  Node(goal[1]			,				goal[0]			,				goal[1]			,				goal[0]			,				9_9999			,				UpperCamelCase     )

						__lowerCAmelCase												=  [self.start]
						__lowerCAmelCase												=  []

						__lowerCAmelCase												=  False



					def        UpperCAmelCase_ (    self     )     ->			Path | None:
						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(UpperCamelCase     )

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

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

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

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



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			list[Node]:
						__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(UpperCamelCase     ) - 1):
								continue

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

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



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Path:
						__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__":
	lowerCAmelCase	:		Dict				=   (0, 0)
	lowerCAmelCase	:		Optional[int]				=   (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print('''------''')

	lowerCAmelCase	:		List[str]				=   GreedyBestFirst(init, goal)
	lowerCAmelCase	:		Any				=   greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			lowerCAmelCase	:		str				=   2

		for elem in grid:
			print(elem)




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

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

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

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



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )