

import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							Dict    							=  (EulerAncestralDiscreteScheduler,)
			_UpperCAmelCase     :							int    							=  10



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		**lowerCAmelCase__						:  int):
									SCREAMING_SNAKE_CASE_:				List[Any]							= {
									    "num_train_timesteps": 1100,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**lowerCAmelCase__)
									return config



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]     ,		[0.0002, 0.002, 0.02]):
															self.check_over_configs(beta_start=lowerCAmelCase__     ,		beta_end=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_:				Any							= self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_:				Optional[int]							= scheduler_class(**lowerCAmelCase__)

									scheduler.set_timesteps(self.num_inference_steps)

									SCREAMING_SNAKE_CASE_:				Tuple							= torch.manual_seed(0)

									SCREAMING_SNAKE_CASE_:				Tuple							= self.dummy_model()
									SCREAMING_SNAKE_CASE_:				int							= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE_:				Any							= sample.to(lowerCAmelCase__)

									for i, t in enumerate(scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Optional[int]							= scheduler.scale_model_input(lowerCAmelCase__     ,		lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:				Optional[Any]							= model(lowerCAmelCase__     ,		lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:				List[Any]							= scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		generator=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				List[str]							= output.prev_sample

									SCREAMING_SNAKE_CASE_:				str							= torch.sum(torch.abs(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= torch.mean(torch.abs(lowerCAmelCase__))

									assert abs(result_sum.item() - 152.3192) < 1E-2
									assert abs(result_mean.item() - 0.1983) < 1E-3



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_:				Any							= self.get_scheduler_config(prediction_type="v_prediction")
									SCREAMING_SNAKE_CASE_:				Tuple							= scheduler_class(**lowerCAmelCase__)

									scheduler.set_timesteps(self.num_inference_steps)

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= torch.manual_seed(0)

									SCREAMING_SNAKE_CASE_:				str							= self.dummy_model()
									SCREAMING_SNAKE_CASE_:				str							= self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE_:				List[str]							= sample.to(lowerCAmelCase__)

									for i, t in enumerate(scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Dict							= scheduler.scale_model_input(lowerCAmelCase__     ,		lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:				Any							= model(lowerCAmelCase__     ,		lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:				Tuple							= scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		generator=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				int							= output.prev_sample

									SCREAMING_SNAKE_CASE_:				List[str]							= torch.sum(torch.abs(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:				int							= torch.mean(torch.abs(lowerCAmelCase__))

									assert abs(result_sum.item() - 108.4439) < 1E-2
									assert abs(result_mean.item() - 0.1412) < 1E-3




			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_:				Dict							= scheduler_class(**lowerCAmelCase__)

									scheduler.set_timesteps(self.num_inference_steps     ,		device=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				str							= torch.manual_seed(0)

									SCREAMING_SNAKE_CASE_:				Tuple							= self.dummy_model()
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE_:				List[Any]							= sample.to(lowerCAmelCase__)

									for t in scheduler.timesteps:
															SCREAMING_SNAKE_CASE_:				Any							= scheduler.scale_model_input(lowerCAmelCase__     ,		lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:				Tuple							= model(lowerCAmelCase__     ,		lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:				str							= scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		generator=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				List[str]							= output.prev_sample

									SCREAMING_SNAKE_CASE_:				Tuple							= torch.sum(torch.abs(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:				Dict							= torch.mean(torch.abs(lowerCAmelCase__))

									assert abs(result_sum.item() - 152.3192) < 1E-2
									assert abs(result_mean.item() - 0.1983) < 1E-3









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






