


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  _a							(    _lowercase):
	_a  :  List[str]				   =							(DDIMParallelScheduler,)
	_a  :  str				   =							(('''eta''', 0.0), ('''num_inference_steps''', 50))




	def 							UpperCAmelCase__( self						:		str		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      Optional[Any]   					= {
			    '''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 							UpperCAmelCase__( self						:		Optional[Any]		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	List[Any]:
			lowerCAmelCase__       :      str   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      str   					= self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Tuple   					= 10, 0.0

			lowerCAmelCase__       :      List[Any]   					= self.dummy_model()
			lowerCAmelCase__       :      Any   					= self.dummy_sample_deter

			scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

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

			return sample




	def 							UpperCAmelCase__( self						:		str		)->	List[Any]:
			for timesteps in [100, 500, 1000]:
					self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Optional[Any]:
			for steps_offset in [0, 1]:
					self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE		)

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




	def 							UpperCAmelCase__( self						:		Dict		)->	Optional[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=_SCREAMING_SNAKE_CASE		, beta_end=_SCREAMING_SNAKE_CASE		)




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




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Dict:
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Tuple:
			for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE		)




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




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




	def 							UpperCAmelCase__( self						:		Tuple		)->	Any:
			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 							UpperCAmelCase__( self						:		Dict		)->	Optional[int]:
			for t in [1, 10, 49]:
					self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Any		)->	Tuple:
			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 							UpperCAmelCase__( self						:		List[str]		)->	Any:
			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 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      str   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      str   					= self.get_scheduler_config()
			lowerCAmelCase__       :      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 							UpperCAmelCase__( self						:		Dict		)->	List[Any]:
			lowerCAmelCase__       :      Tuple   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      Optional[int]   					= self.get_scheduler_config()
			lowerCAmelCase__       :      Dict   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Union[str, Any]   					= 10, 0.0
			scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

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

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

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

			lowerCAmelCase__       :      Optional[int]   					= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
			lowerCAmelCase__       :      Any   					= 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 							UpperCAmelCase__( self						:		List[Any]		)->	Tuple:
			lowerCAmelCase__       :      Any   					= self.full_loop()

			lowerCAmelCase__       :      Optional[Any]   					= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
			lowerCAmelCase__       :      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 							UpperCAmelCase__( self						:		Optional[Any]		)->	str:
			lowerCAmelCase__       :      List[str]   					= self.full_loop(prediction_type='''v_prediction'''		)

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





class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Any=0		)->	Optional[Any]:  # a graph with Node 0,1,...,N-1
			lowerCAmelCase__       :      Optional[int]   					= n
			lowerCAmelCase__       :      List[Any]   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # adjacency matrix for weight
			lowerCAmelCase__       :      str   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # dp[i][j] stores minimum distance from i to j




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			lowerCAmelCase__       :      Optional[int]   					= w




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			for k in range(0		, self.n		):
					for i in range(0		, self.n		):
							for j in range(0		, self.n		):
									lowerCAmelCase__       :      Dict   					= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.dp[u][v]


if __name__ == "__main__":
		lowerCamelCase						=		Graph(5)
		graph.add_edge(0, 2, 9)
		graph.add_edge(0, 4, 10)
		graph.add_edge(1, 3, 5)
		graph.add_edge(2, 3, 7)
		graph.add_edge(3, 0, 10)
		graph.add_edge(3, 1, 2)
		graph.add_edge(3, 2, 1)
		graph.add_edge(3, 4, 6)
		graph.add_edge(4, 1, 3)
		graph.add_edge(4, 2, 4)
		graph.add_edge(4, 3, 9)
		graph.floyd_warshall()
		graph.show_min(1, 4)
		graph.show_min(0, 3)




