
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 				lowerCamelCase__							(       __lowerCamelCase):




	'''simple docstring'''




	_A			=  (DDIMParallelScheduler,)
	_A			=  (('eta', 0.0), ('num_inference_steps', 5_0))







	def      _lowerCamelCase       (      self	:Optional[int]  ,     **a	:List[str]		)	->				Any:
								__UpperCamelCase			:     List[str]         =      {
								    'num_train_timesteps': 1_0_0_0,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'clip_sample': True,
								}

								config.update(**UpperCAmelCase_		)
								return config







	def      _lowerCamelCase       (      self	:Optional[int]  ,     **a	:Optional[Any]		)	->				Any:
								__UpperCamelCase			:     Optional[int]         =      self.scheduler_classes[0]
								__UpperCamelCase			:     List[Any]         =      self.get_scheduler_config(**UpperCAmelCase_		)
								__UpperCamelCase			:     List[Any]         =      scheduler_class(**UpperCAmelCase_		)

								__UpperCamelCase			:     int         =      1_0, 0.0

								__UpperCamelCase			:     Optional[int]         =      self.dummy_model()
								__UpperCamelCase			:     Dict         =      self.dummy_sample_deter

								scheduler.set_timesteps(UpperCAmelCase_		)

								for t in scheduler.timesteps:
															__UpperCamelCase			:     str         =      model(UpperCAmelCase_  ,     UpperCAmelCase_		)
															__UpperCamelCase			:     Optional[int]         =      scheduler.step(UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_		).prev_sample

								return sample







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Dict:
								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:str		)	->				Tuple:
								for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=UpperCAmelCase_		)

								__UpperCamelCase			:     int         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Union[str, Any]         =      self.get_scheduler_config(steps_offset=1		)
								__UpperCamelCase			:     List[Any]         =      scheduler_class(**UpperCAmelCase_		)
								scheduler.set_timesteps(5		)
								assert torch.equal(scheduler.timesteps  ,     torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]		)		)







	def      _lowerCamelCase       (      self	:List[str]		)	->				Union[str, Any]:
								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,     [0.002, 0.02, 0.2, 2]		):
															self.check_over_configs(beta_start=UpperCAmelCase_  ,     beta_end=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Optional[int]:
								for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:List[str]		)	->				Tuple:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:int		)	->				int:
								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:int		)	->				List[Any]:
								for timestep_spacing in ["trailing", "leading"]:
															self.check_over_configs(timestep_spacing=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				int:
								for rescale_betas_zero_snr in [True, False]:
															self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[Any]:
								self.check_over_configs(thresholding=UpperCAmelCase_		)
								for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=UpperCAmelCase_  ,     prediction_type=UpperCAmelCase_  ,     sample_max_value=UpperCAmelCase_  ,     )







	def      _lowerCamelCase       (      self	:int		)	->				List[str]:
								for t in [1, 1_0, 4_9]:
															self.check_over_forward(time_step=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:int		)	->				Dict:
								for t, num_inference_steps in zip([1, 1_0, 5_0]  ,     [1_0, 5_0, 5_0_0]		):
															self.check_over_forward(time_step=UpperCAmelCase_  ,     num_inference_steps=UpperCAmelCase_		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Union[str, Any]:
								for t, eta in zip([1, 1_0, 4_9]  ,     [0.0, 0.5, 1.0]		):
															self.check_over_forward(time_step=UpperCAmelCase_  ,     eta=UpperCAmelCase_		)







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

								assert torch.sum(torch.abs(scheduler._get_variance(0  ,     0		) - 0.0		)		) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,     4_0_0		) - 0.14771		)		) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,     9_6_0		) - 0.32460		)		) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0  ,     0		) - 0.0		)		) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ,     4_8_6		) - 0.00979		)		) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,     9_9_8		) - 0.02		)		) < 1E-5







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

								__UpperCamelCase			:     Any         =      1_0, 0.0
								scheduler.set_timesteps(UpperCAmelCase_		)

								__UpperCamelCase			:     Optional[int]         =      self.dummy_model()
								__UpperCamelCase			:     Any         =      self.dummy_sample_deter
								__UpperCamelCase			:     Any         =      self.dummy_sample_deter + 0.1
								__UpperCamelCase			:     List[str]         =      self.dummy_sample_deter - 0.1

								__UpperCamelCase			:     int         =      samplea.shape[0]
								__UpperCamelCase			:     Any         =      torch.stack([samplea, samplea, samplea]  ,     dim=0		)
								__UpperCamelCase			:     Any         =      torch.arange(UpperCAmelCase_		)[0:3, None].repeat(1  ,     UpperCAmelCase_		)

								__UpperCamelCase			:     Tuple         =      model(samples.flatten(0  ,     1		)  ,     timesteps.flatten(0  ,     1		)		)
								__UpperCamelCase			:     int         =      scheduler.batch_step_no_noise(UpperCAmelCase_  ,     timesteps.flatten(0  ,     1		)  ,     samples.flatten(0  ,     1		)  ,     UpperCAmelCase_		)

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

								assert abs(result_sum.item() - 1_1_4_7.7_9_0_4		) < 1E-2
								assert abs(result_mean.item() - 0.4982		) < 1E-3







	def      _lowerCamelCase       (      self	:int		)	->				Dict:
								__UpperCamelCase			:     Tuple         =      self.full_loop()

								__UpperCamelCase			:     Optional[Any]         =      torch.sum(torch.abs(UpperCAmelCase_		)		)
								__UpperCamelCase			:     List[str]         =      torch.mean(torch.abs(UpperCAmelCase_		)		)

								assert abs(result_sum.item() - 1_7_2.0_0_6_7		) < 1E-2
								assert abs(result_mean.item() - 0.223967		) < 1E-3







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				int:
								__UpperCamelCase			:     str         =      self.full_loop(prediction_type="v_prediction"		)

								__UpperCamelCase			:     str         =      torch.sum(torch.abs(UpperCAmelCase_		)		)
								__UpperCamelCase			:     Any         =      torch.mean(torch.abs(UpperCAmelCase_		)		)

								assert abs(result_sum.item() - 52.5302		) < 1E-2
								assert abs(result_mean.item() - 0.0684		) < 1E-3







	def      _lowerCamelCase       (      self	:List[str]		)	->				Tuple:
								# We specify different beta, so that the first alpha is 0.99
								__UpperCamelCase			:     Any         =      self.full_loop(set_alpha_to_one=UpperCAmelCase_  ,     beta_start=0.01		)
								__UpperCamelCase			:     List[Any]         =      torch.sum(torch.abs(UpperCAmelCase_		)		)
								__UpperCamelCase			:     Any         =      torch.mean(torch.abs(UpperCAmelCase_		)		)

								assert abs(result_sum.item() - 1_4_9.8_2_9_5		) < 1E-2
								assert abs(result_mean.item() - 0.1951		) < 1E-3







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Union[str, Any]:
								# We specify different beta, so that the first alpha is 0.99
								__UpperCamelCase			:     Any         =      self.full_loop(set_alpha_to_one=UpperCAmelCase_  ,     beta_start=0.01		)
								__UpperCamelCase			:     Any         =      torch.sum(torch.abs(UpperCAmelCase_		)		)
								__UpperCamelCase			:     Optional[int]         =      torch.mean(torch.abs(UpperCAmelCase_		)		)

								assert abs(result_sum.item() - 1_4_9.0_7_8_4		) < 1E-2
								assert abs(result_mean.item() - 0.1941		) < 1E-3

from __future__ import annotations

lowercase						:      str									= list[tuple[int, int]]

lowercase						:      Optional[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],
]

lowercase						:      int									= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:float  ,     a	:Node | None  ,     )	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      pos_x
								__UpperCamelCase			:     List[str]         =      pos_y
								__UpperCamelCase			:     str         =      (pos_y, pos_x)
								__UpperCamelCase			:     Optional[int]         =      goal_x
								__UpperCamelCase			:     str         =      goal_y
								__UpperCamelCase			:     int         =      g_cost
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     str         =      self.calculate_heuristic()







	def      _lowerCamelCase       (      self	:List[Any]		)	->				float:
								__UpperCamelCase			:     Any         =      abs(self.pos_x - self.goal_x		)
								__UpperCamelCase			:     Tuple         =      abs(self.pos_y - self.goal_y		)
								return dx + dy







	def __lt__(      self	:Union[str, Any]  ,     a	:Dict		)	->				bool:
								return self.f_cost < other.f_cost




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:tuple[int, int]  ,     a	:tuple[int, int]		)	->				List[str]:
								__UpperCamelCase			:     Any         =      Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     a		)
								__UpperCamelCase			:     List[str]         =      Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     9_9_9_9_9  ,     a		)

								__UpperCamelCase			:     Optional[int]         =      [self.start]
								__UpperCamelCase			:     list[Node]         =      []

								__UpperCamelCase			:     Any         =      False







	def      _lowerCamelCase       (      self	:List[str]		)	->				Path | None:
								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															__UpperCamelCase			:     Optional[int]         =      self.open_nodes.pop(0		)

															if current_node.pos == self.target.pos:
																						__UpperCamelCase			:     Dict         =      True
																						return self.retrace_path(a		)

															self.closed_nodes.append(a		)
															__UpperCamelCase			:     Union[str, Any]         =      self.get_successors(a		)

															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(a		)
																						else:
																													# retrieve the best current path
																													__UpperCamelCase			:     Union[str, Any]         =      self.open_nodes.pop(self.open_nodes.index(a		)		)

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

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







	def      _lowerCamelCase       (      self	:str  ,     a	:Node		)	->				list[Node]:
								__UpperCamelCase			:     List[Any]         =      []
								for action in delta:
															__UpperCamelCase			:     Optional[Any]         =      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(a		) - 1):
																						continue

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

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







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Node | None		)	->				Path:
								__UpperCamelCase			:     str         =      node
								__UpperCamelCase			:     int         =      []
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															__UpperCamelCase			:     str         =      current_node.parent
								path.reverse()
								return path


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

		print('------')

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

				for elem in grid:
						print(elem)