






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 		UpperCAmelCase_						(	UpperCamelCase_					):


						'''simple docstring'''




						A     :   Union[str, Any]						=					(DDIMParallelScheduler,)
						A     :   Tuple						=					(('eta', 0.0), ('num_inference_steps', 50))






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Tuple:
								snake_case_				:							str						 =       {
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**_SCREAMING_SNAKE_CASE				)
								return config






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							List[Any]						 =       self.scheduler_classes[0]
								snake_case_				:							int						 =       self.get_scheduler_config(**_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       scheduler_class(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							str						 =       10, 0.0

								snake_case_				:							Tuple						 =       self.dummy_model()
								snake_case_				:							int						 =       self.dummy_sample_deter

								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE				)

								for t in scheduler.timesteps:
										snake_case_				:							Optional[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[str]						 =       scheduler.step(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				).prev_sample

								return sample






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								for timesteps in [100, 500, 1000]:
										self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       self.scheduler_classes[0]
								snake_case_				:							Optional[int]						 =       self.get_scheduler_config(steps_offset=1				)
								snake_case_				:							Optional[int]						 =       scheduler_class(**_SCREAMING_SNAKE_CASE				)
								scheduler.set_timesteps(5				)
								assert torch.equal(scheduler.timesteps		,				torch.LongTensor([801, 601, 401, 201, 1]				)				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								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=_SCREAMING_SNAKE_CASE		,				beta_end=_SCREAMING_SNAKE_CASE				)






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






						def       _lowerCAmelCase       (	self				) ->				int:
								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE				)






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






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								for timestep_spacing in ["trailing", "leading"]:
										self.check_over_configs(timestep_spacing=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				str:
								for rescale_betas_zero_snr in [True, False]:
										self.check_over_configs(rescale_betas_zero_snr=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE				)
								for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(
												    thresholding=_SCREAMING_SNAKE_CASE		,				prediction_type=_SCREAMING_SNAKE_CASE		,				sample_max_value=_SCREAMING_SNAKE_CASE		,				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								for t in [1, 10, 49]:
										self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								for t, num_inference_steps in zip([1, 10, 50]		,				[10, 50, 500]				):
										self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		,				num_inference_steps=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				str:
								for t, eta in zip([1, 10, 49]		,				[0.0, 0.5, 1.0]				):
										self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		,				eta=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Tuple						 =       self.scheduler_classes[0]
								snake_case_				:							str						 =       self.get_scheduler_config()
								snake_case_				:							List[str]						 =       scheduler_class(**_SCREAMING_SNAKE_CASE				)

								assert torch.sum(torch.abs(scheduler._get_variance(0		,				0				) - 0.0				)				) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(420		,				400				) - 0.1_4771				)				) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(980		,				960				) - 0.3_2460				)				) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(0		,				0				) - 0.0				)				) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487		,				486				) - 0.0_0979				)				) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999		,				998				) - 0.02				)				) < 1e-5






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Optional[Any]						 =       self.scheduler_classes[0]
								snake_case_				:							List[Any]						 =       self.get_scheduler_config()
								snake_case_				:							List[str]						 =       scheduler_class(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Dict						 =       10, 0.0
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       self.dummy_model()
								snake_case_				:							Tuple						 =       self.dummy_sample_deter
								snake_case_				:							Tuple						 =       self.dummy_sample_deter + 0.1
								snake_case_				:							str						 =       self.dummy_sample_deter - 0.1

								snake_case_				:							List[Any]						 =       samplea.shape[0]
								snake_case_				:							Any						 =       torch.stack([samplea, samplea, samplea]		,				dim=0				)
								snake_case_				:							List[str]						 =       torch.arange(_SCREAMING_SNAKE_CASE				)[0:3, None].repeat(1		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       model(samples.flatten(0		,				1				)		,				timesteps.flatten(0		,				1				)				)
								snake_case_				:							List[Any]						 =       scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE		,				timesteps.flatten(0		,				1				)		,				samples.flatten(0		,				1				)		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
								snake_case_				:							int						 =       torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

								assert abs(result_sum.item() - 1147.7904				) < 1e-2
								assert abs(result_mean.item() - 0.4982				) < 1e-3






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							Any						 =       self.full_loop()

								snake_case_				:							Any						 =       torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
								snake_case_				:							List[str]						 =       torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

								assert abs(result_sum.item() - 172.0067				) < 1e-2
								assert abs(result_mean.item() - 0.22_3967				) < 1e-3






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Any						 =       self.full_loop(prediction_type="v_prediction"				)

								snake_case_				:							str						 =       torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
								snake_case_				:							Union[str, Any]						 =       torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

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






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

								assert abs(result_sum.item() - 149.8295				) < 1e-2
								assert abs(result_mean.item() - 0.1951				) < 1e-3






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

								assert abs(result_sum.item() - 149.0784				) < 1e-2
								assert abs(result_mean.item() - 0.1941				) < 1e-3








import heapq
import sys

import numpy as np

lowercase  :							str			  =    tuple[int, int]




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self				) ->				Optional[int]:
								snake_case_				:							int						 =       []
								snake_case_				:							int						 =       set()






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								if not self.empty():
										return self.elements[0][0]
								else:
										return float("inf"				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return len(self.elements				) == 0






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				str:
								if item not in self.set:
										heapq.heappush(self.elements		,				(priority, item)				)
										self.set.add(_SCREAMING_SNAKE_CASE				)
								else:
										# update
										# print("update", item)
										snake_case_				:							Any						 =       []
										((snake_case_) ,       (snake_case_))				:							Optional[Any]						 =       heapq.heappop(self.elements				)
										while x != item:
												temp.append((pri, x)				)
												((snake_case_) ,       (snake_case_))				:							Optional[int]						 =       heapq.heappop(self.elements				)
										temp.append((priority, item)				)
										for pro, xxx in temp:
												heapq.heappush(self.elements		,				(pro, xxx)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								if item in self.set:
										self.set.remove(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       []
										((snake_case_) ,       (snake_case_))				:							Tuple						 =       heapq.heappop(self.elements				)
										while x != item:
												temp.append((pro, x)				)
												((snake_case_) ,       (snake_case_))				:							Dict						 =       heapq.heappop(self.elements				)
										for prito, yyy in temp:
												heapq.heappush(self.elements		,				(prito, yyy)				)






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								return self.elements[0][1]






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								((snake_case_) ,       (snake_case_))				:							Any						 =       heapq.heappop(self.elements				)
								self.set.remove(_SCREAMING_SNAKE_CASE				)
								return (priority, item)




def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# euclidean distance
		snake_case_				:							Optional[Any]						 =       np.array(_a    )
		snake_case_				:							Dict						 =       np.array(_a    )
		return np.linalg.norm(a - b    )

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# integer division by time variable
		return consistent_heuristic(_a  ,      _a    ) // t

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# manhattan distance
		return abs(p[0] - goal[0]    ) + abs(p[1] - goal[1]    )

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			int  ,      _a       :			TPos  ,      _a       :			dict[TPos, float]    ):
		snake_case_				:							List[Any]						 =       g_function[start] + Wa * heuristics[i](_a  ,      _a    )
		return ans

def      lowerCAmelCase__	(    _a       :			Tuple  ,      _a       :			Union[str, Any]  ,      _a       :			List[Any]    ):
		snake_case_				:							Dict						 =       np.chararray((n, n)    )
		for i in range(_a    ):
				for j in range(_a    ):
						snake_case_				:							List[str]						 =       "*"

		for i in range(_a    ):
				for j in range(_a    ):
						if (j, (n - 1) - i) in blocks:
								snake_case_				:							Optional[Any]						 =       "#"

		snake_case_				:							Optional[int]						 =       "-"
		snake_case_				:							Tuple						 =       back_pointer[goal]
		while x != start:
				((snake_case_) ,       (snake_case_))				:							Any						 =       x
				# print(x)
				snake_case_				:							List[str]						 =       "-"
				snake_case_				:							Dict						 =       back_pointer[x]
		snake_case_				:							str						 =       "-"

		for i in range(_a    ):
				for j in range(_a    ):
						if (i, j) == (0, n - 1):
								print(grid[i][j]  ,      end=" "    )
								print("<-- End position"  ,      end=" "    )
						else:
								print(grid[i][j]  ,      end=" "    )
				print()
		print("^"    )
		print("Start position"    )
		print()
		print("# is an obstacle"    )
		print("- is the path taken by algorithm"    )
		print("PATH TAKEN BY THE ALGORITHM IS:-"    )
		snake_case_				:							List[Any]						 =       back_pointer[goal]
		while x != start:
				print(_a  ,      end=" "    )
				snake_case_				:							int						 =       back_pointer[x]
		print(_a    )
		sys.exit()

def      lowerCAmelCase__	(    _a       :			TPos    ):
		if p[0] < 0 or p[0] > n - 1:
				return False
		if p[1] < 0 or p[1] > n - 1:
				return False
		return True

def      lowerCAmelCase__	(    _a       :			int  ,      _a       :			str  ,      _a       :			Optional[int]  ,      _a       :			List[str]  ,      _a       :			Optional[Any]  ,      _a       :			Dict  ,      _a       :			Tuple  ,      _a       :			Any  ,      ):
		for itera in range(_a    ):
				open_list[itera].remove_element(_a    )
		# print("s", s)
		# print("j", j)
		((snake_case_) ,       (snake_case_))				:							Tuple						 =       s
		snake_case_				:							Dict						 =       (x - 1, y)
		snake_case_				:							Union[str, Any]						 =       (x + 1, y)
		snake_case_				:							List[str]						 =       (x, y + 1)
		snake_case_				:							int						 =       (x, y - 1)

		for neighbours in [left, right, up, down]:
				if neighbours not in blocks:
						if valid(_a    ) and neighbours not in visited:
								# print("neighbour", neighbours)
								visited.add(_a    )
								snake_case_				:							Optional[int]						 =       -1
								snake_case_				:							int						 =       float("inf"    )

						if valid(_a    ) and g_function[neighbours] > g_function[s] + 1:
								snake_case_				:							int						 =       g_function[s] + 1
								snake_case_				:							Any						 =       s
								if neighbours not in close_list_anchor:
										open_list[0].put(_a  ,      key(_a  ,      0  ,      _a  ,      _a    )    )
										if neighbours not in close_list_inad:
												for var in range(1  ,      _a    ):
														if key(_a  ,      _a  ,      _a  ,      _a    ) <= Wa * key(
														    _a  ,      0  ,      _a  ,      _a    ):
																open_list[j].put(
																    _a  ,      key(_a  ,      _a  ,      _a  ,      _a    )    )

def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       []
		for x in range(1  ,      5    ):
				for y in range(1  ,      6    ):
						some_list.append((x, y)    )

		for x in range(15  ,      20    ):
				some_list.append((x, 17)    )

		for x in range(10  ,      19    ):
				for y in range(1  ,      15    ):
						some_list.append((x, y)    )

    # L block
		for x in range(1  ,      4    ):
				for y in range(12  ,      19    ):
						some_list.append((x, y)    )
		for x in range(3  ,      13    ):
				for y in range(16  ,      19    ):
						some_list.append((x, y)    )
		return some_list


lowercase  :							Union[str, Any]			  =    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowercase  :							Union[str, Any]			  =    [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowercase  :							int			  =    make_common_ground()


lowercase  :							Optional[Any]			  =    blocks_blk
# hyper parameters
lowercase  :							Optional[Any]			  =    1
lowercase  :							str			  =    1
lowercase  :							Any			  =    20
lowercase  :							str			  =    3  # one consistent and two other inconsistent

# start and end destination
lowercase  :							Any			  =    (0, 0)
lowercase  :							int			  =    (n - 1, n - 1)

lowercase  :							str			  =    1

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos  ,      _a       :			int    ):
		snake_case_				:							List[str]						 =       {start: 0, goal: float("inf"    )}
		snake_case_				:							List[Any]						 =       {start: -1, goal: -1}
		snake_case_				:							Optional[Any]						 =       []
		snake_case_				:							Dict						 =       set()

		for i in range(_a    ):
				open_list.append(PriorityQueue()    )
				open_list[i].put(_a  ,      key(_a  ,      _a  ,      _a  ,      _a    )    )

		snake_case_				:							list[int]						 =       []
		snake_case_				:							list[int]						 =       []
		while open_list[0].minkey() < float("inf"    ):
				for i in range(1  ,      _a    ):
						# print(open_list[0].minkey(), open_list[i].minkey())
						if open_list[i].minkey() <= Wa * open_list[0].minkey():
								global t
								t += 1
								if g_function[goal] <= open_list[i].minkey():
										if g_function[goal] < float("inf"    ):
												do_something(_a  ,      _a  ,      _a    )
								else:
										snake_case_ ,       snake_case_				:							Optional[Any]						 =       open_list[i].top_show()
										visited.add(_a    )
										expand_state(
										    _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      )
										close_list_inad.append(_a    )
						else:
								if g_function[goal] <= open_list[0].minkey():
										if g_function[goal] < float("inf"    ):
												do_something(_a  ,      _a  ,      _a    )
								else:
										snake_case_				:							Tuple						 =       open_list[0].top_show()
										visited.add(_a    )
										expand_state(
										    _a  ,      0  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      )
										close_list_anchor.append(_a    )
		print("No path found to goal"    )
		print()
		for i in range(n - 1  ,      -1  ,      -1    ):
				for j in range(_a    ):
						if (j, i) in blocks:
								print("#"  ,      end=" "    )
						elif (j, i) in back_pointer:
								if (j, i) == (n - 1, n - 1):
										print("*"  ,      end=" "    )
								else:
										print("-"  ,      end=" "    )
						else:
								print("*"  ,      end=" "    )
						if (j, i) == (n - 1, n - 1):
								print("<-- End position"  ,      end=" "    )
				print()
		print("^"    )
		print("Start position"    )
		print()
		print("# is an obstacle"    )
		print("- is the path taken by algorithm"    )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)
