






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   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							Dict:
							UpperCAmelCase_   :				int						=						pos_x
							UpperCAmelCase_   :				Union[str, Any]						=						pos_y
							UpperCAmelCase_   :				Any						=						(pos_y, pos_x)
							UpperCAmelCase_   :				List[str]						=						goal_x
							UpperCAmelCase_   :				Union[str, Any]						=						goal_y
							UpperCAmelCase_   :				Optional[int]						=						g_cost
							UpperCAmelCase_   :				Union[str, Any]						=						parent
							UpperCAmelCase_   :				Any						=						self.calculate_heuristic()



		def  __UpperCAmelCase      (							self       )     ->							float:
							UpperCAmelCase_   :				Union[str, Any]						=						abs(self.pos_x - self.goal_x       )
							UpperCAmelCase_   :				Dict						=						abs(self.pos_y - self.goal_y       )
							return dx + dy



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





class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							int:
							UpperCAmelCase_   :				Union[str, Any]						=						Node(start[1]    ,		start[0]    ,		goal[1]    ,		goal[0]    ,		0    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						Node(goal[1]    ,		goal[0]    ,		goal[1]    ,		goal[0]    ,		9_9_9_9_9    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						[self.start]
							UpperCAmelCase_   :				list[Node]						=						[]

							UpperCAmelCase_   :				Optional[int]						=						False



		def  __UpperCAmelCase      (							self       )     ->							Path | None:
							while self.open_nodes:
												# Open Nodes are sorted using __lt__
												self.open_nodes.sort()
												UpperCAmelCase_   :				Union[str, Any]						=						self.open_nodes.pop(0       )

												if current_node.pos == self.target.pos:
																	UpperCAmelCase_   :				Dict						=						True
																	return self.retrace_path(_UpperCamelCase       )

												self.closed_nodes.append(_UpperCamelCase       )
												UpperCAmelCase_   :				List[Any]						=						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
																						UpperCAmelCase_   :				Optional[int]						=						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]:
							UpperCAmelCase_   :				Dict						=						[]
							for action in delta:
												UpperCAmelCase_   :				Optional[int]						=						parent.pos_x + action[1]
												UpperCAmelCase_   :				List[Any]						=						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:
							UpperCAmelCase_   :				Any						=						node
							UpperCAmelCase_   :				Dict						=						[]
							while current_node is not None:
												path.append((current_node.pos_y, current_node.pos_x)       )
												UpperCAmelCase_   :				Optional[int]						=						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)














import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Any							=  (DDPMParallelScheduler,)



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						{
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_UpperCamelCase       )
							return config



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,		[0.0_02, 0.02, 0.2, 2]       ):
												self.check_over_configs(beta_start=_UpperCamelCase    ,		beta_end=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							self.check_over_configs(thresholding=_UpperCamelCase       )
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=_UpperCamelCase    ,		prediction_type=_UpperCamelCase    ,		sample_max_value=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							for t in [0, 5_0_0, 9_9_9]:
												self.check_over_forward(time_step=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				int						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_09_79       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1E-5



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						self.dummy_model()
							UpperCAmelCase_   :				List[Any]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						self.dummy_sample_deter + 0.1
							UpperCAmelCase_   :				str						=						self.dummy_sample_deter - 0.1

							UpperCAmelCase_   :				Tuple						=						samplea.shape[0]
							UpperCAmelCase_   :				Tuple						=						torch.stack([samplea, samplea, samplea]    ,		dim=0       )
							UpperCAmelCase_   :				str						=						torch.arange(_UpperCamelCase       )[0:3, None].repeat(1    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						model(samples.flatten(0    ,		1       )    ,		timesteps.flatten(0    ,		1       )       )
							UpperCAmelCase_   :				Dict						=						scheduler.batch_step_no_noise(_UpperCamelCase    ,		timesteps.flatten(0    ,		1       )    ,		samples.flatten(0    ,		1       )       )

							UpperCAmelCase_   :				List[Any]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 11_53.18_33       ) < 1E-2
							assert abs(result_mean.item() - 0.50_05       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Any						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						self.dummy_model()
							UpperCAmelCase_   :				Optional[int]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Union[str, Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				str						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				str						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Optional[int]						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_58.96_06       ) < 1E-2
							assert abs(result_mean.item() - 0.33_72       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[int]						=						self.get_scheduler_config(prediction_type='v_prediction'       )
							UpperCAmelCase_   :				Tuple						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						self.dummy_model()
							UpperCAmelCase_   :				List[str]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Any						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Optional[Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				Any						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				List[str]						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_02.02_96       ) < 1E-2
							assert abs(result_mean.item() - 0.26_31       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				str						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 1, 0]

							scheduler.set_timesteps(timesteps=_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						scheduler.timesteps

							for i, timestep in enumerate(_UpperCamelCase       ):
												if i == len(_UpperCamelCase       ) - 1:
																	UpperCAmelCase_   :				List[str]						=						-1
												else:
																	UpperCAmelCase_   :				int						=						timesteps[i + 1]

												UpperCAmelCase_   :				int						=						scheduler.previous_timestep(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						prev_t.item()

												self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Tuple						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 5_1, 0]

							with self.assertRaises(_UpperCamelCase    ,		msg='`custom_timesteps` must be in descending order.'       ):
												scheduler.set_timesteps(timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[str]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				List[str]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						[1_0_0, 8_7, 5_0, 1, 0]
							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

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



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Optional[Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						[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       )






