




def       snake_case      (							A		,     A    )							->							Optional[Any]:




	'''simple docstring'''


	print('\nVertex Distance'    )
	for i in range(_A    ):
		if dist[i] != float('inf'    ):
			print(_A		,     '\t'		,     int(dist[i]    )		,     end='\t'    )
		else:
			print(_A		,     '\t'		,     'INF'		,     end='\t'    )
		print()






def       snake_case      (							A		,     A		,     A    )							->							Tuple:




	'''simple docstring'''


	lowerCamelCase_							:	int							     =							float('inf'    )
	lowerCamelCase_							:	Union[str, Any]							     =							-1
	for i in range(_A    ):
		if (not vset[i]) and mdist[i] < min_val:
			lowerCamelCase_							:	Dict							     =							i
			lowerCamelCase_							:	Any							     =							mdist[i]
	return min_ind






def       snake_case      (							A		,     A		,     A    )							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:	Any							     =							[float('inf'    ) for _ in range(_A    )]
	lowerCamelCase_							:	List[str]							     =							[False for _ in range(_A    )]
	lowerCamelCase_							:	str							     =							0.0

	for _ in range(v - 1    ):
		lowerCamelCase_							:	Tuple							     =							min_dist(_A		,     _A		,     _A    )
		lowerCamelCase_							:	List[Any]							     =							True

		for i in range(_A    ):
			if (
			    (not vset[i])
			    and graph[u][i] != float('inf'    )
			    and mdist[u] + graph[u][i] < mdist[i]
			):
				lowerCamelCase_							:	Tuple							     =							mdist[u] + graph[u][i]

	print_dist(_A		,     _A    )


if __name__ == "__main__":
							UpperCamelCase__  =		int(input('Enter number of vertices: ').strip())
							UpperCamelCase__  =		int(input('Enter number of edges: ').strip())

							UpperCamelCase__  =		[[float('inf') for i in range(V)] for j in range(V)]

							for i in range(V):
														UpperCamelCase__  =		0.0

							for i in range(E):
														print('\nEdge ', i + 1)
														UpperCamelCase__  =		int(input('Enter source:').strip())
														UpperCamelCase__  =		int(input('Enter destination:').strip())
														UpperCamelCase__  =		float(input('Enter weight:').strip())
														UpperCamelCase__  =		weight

							UpperCamelCase__  =		int(input('\nEnter shortest path source:').strip())
							dijkstra(graph, V, gsrc)






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				(DDIMScheduler,)
					_lowerCamelCase							=				(("""eta""", 0.0), ("""num_inference_steps""", 50))

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							{
						    'num_train_timesteps': 1_000,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**__lowercase      )
						return config

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	List[Any]							     =							self.get_scheduler_config(**__lowercase      )
						lowerCamelCase_							:	Tuple							     =							scheduler_class(**__lowercase      )

						lowerCamelCase_     ,    lowerCamelCase_							:	Dict							     =							10, 0.0

						lowerCamelCase_							:	Optional[int]							     =							self.dummy_model()
						lowerCamelCase_							:	Union[str, Any]							     =							self.dummy_sample_deter

						scheduler.set_timesteps(__lowercase      )

						for t in scheduler.timesteps:
							lowerCamelCase_							:	Any							     =							model(__lowercase				, __lowercase      )
							lowerCamelCase_							:	Optional[Any]							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase				, __lowercase      ).prev_sample

						return sample

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timesteps in [100, 500, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=__lowercase      )

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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						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=__lowercase				, beta_end=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self.check_over_configs(thresholding=__lowercase      )
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=__lowercase				, prediction_type=__lowercase				, sample_max_value=__lowercase				, )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t in [1, 10, 49]:
							self.check_over_forward(time_step=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, num_inference_steps in zip([1, 10, 50]				, [10, 50, 500]      ):
							self.check_over_forward(time_step=__lowercase				, num_inference_steps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, eta in zip([1, 10, 49]				, [0.0, 0.5, 1.0]      ):
							self.check_over_forward(time_step=__lowercase				, eta=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	Dict							     =							self.get_scheduler_config()
						lowerCamelCase_							:	List[str]							     =							scheduler_class(**__lowercase      )

						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_47_71      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980				, 960      ) - 0.3_24_60      )      ) < 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_09_79      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999				, 998      ) - 0.02      )      ) < 1E-5

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							self.full_loop()

						lowerCamelCase_							:	Tuple							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	str							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
						assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.full_loop(prediction_type='v_prediction'      )

						lowerCamelCase_							:	Tuple							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	List[str]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 52.53_02      ) < 1E-2
						assert abs(result_mean.item() - 0.06_84      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[Any]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Dict							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
						assert abs(result_mean.item() - 0.19_51      ) < 1E-3





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[int]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Optional[int]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
						assert abs(result_mean.item() - 0.19_41      ) < 1E-3
