

'''simple docstring'''

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   (KDPMaAncestralDiscreteScheduler,)
				snake_case_           =   10

				def 				_lowerCamelCase						(					self,	**lowercase_   )						->       Tuple:
					snake_case							 =				{
					    'num_train_timesteps': 1100,
					    'beta_start': 0.0_001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					}

					config.update(**UpperCAmelCase_   )
					return config

				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					for timesteps in [10, 50, 100, 1000]:
						self.check_over_configs(num_train_timesteps=UpperCAmelCase_   )

				def 				_lowerCamelCase						(					self   )						->       int:
					for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001],	[0.0_002, 0.002, 0.02]   ):
						self.check_over_configs(beta_start=UpperCAmelCase_,	beta_end=UpperCAmelCase_   )

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

				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					if torch_device == "mps":
						return
					snake_case							 =				self.scheduler_classes[0]
					snake_case							 =				self.get_scheduler_config()
					snake_case							 =				scheduler_class(**UpperCAmelCase_   )

					scheduler.set_timesteps(self.num_inference_steps   )

					snake_case							 =				torch.manual_seed(0   )

					snake_case							 =				self.dummy_model()
					snake_case							 =				self.dummy_sample_deter * scheduler.init_noise_sigma
					snake_case							 =				sample.to(UpperCAmelCase_   )

					for i, t in enumerate(scheduler.timesteps   ):
						snake_case							 =				scheduler.scale_model_input(UpperCAmelCase_,	UpperCAmelCase_   )

						snake_case							 =				model(UpperCAmelCase_,	UpperCAmelCase_   )

						snake_case							 =				scheduler.step(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	generator=UpperCAmelCase_   )
						snake_case							 =				output.prev_sample

					snake_case							 =				torch.sum(torch.abs(UpperCAmelCase_   )   )
					snake_case							 =				torch.mean(torch.abs(UpperCAmelCase_   )   )

					assert abs(result_sum.item() - 13849.3877   ) < 1E-2
					assert abs(result_mean.item() - 18.0_331   ) < 5E-3

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

				def 				_lowerCamelCase						(					self   )						->       Dict:
					if torch_device == "mps":
						return
					snake_case							 =				self.scheduler_classes[0]
					snake_case							 =				self.get_scheduler_config(prediction_type='v_prediction'   )
					snake_case							 =				scheduler_class(**UpperCAmelCase_   )

					scheduler.set_timesteps(self.num_inference_steps   )

					snake_case							 =				self.dummy_model()
					snake_case							 =				self.dummy_sample_deter * scheduler.init_noise_sigma
					snake_case							 =				sample.to(UpperCAmelCase_   )

					snake_case							 =				torch.manual_seed(0   )

					for i, t in enumerate(scheduler.timesteps   ):
						snake_case							 =				scheduler.scale_model_input(UpperCAmelCase_,	UpperCAmelCase_   )

						snake_case							 =				model(UpperCAmelCase_,	UpperCAmelCase_   )

						snake_case							 =				scheduler.step(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	generator=UpperCAmelCase_   )
						snake_case							 =				output.prev_sample

					snake_case							 =				torch.sum(torch.abs(UpperCAmelCase_   )   )
					snake_case							 =				torch.mean(torch.abs(UpperCAmelCase_   )   )

					assert abs(result_sum.item() - 328.9970   ) < 1E-2
					assert abs(result_mean.item() - 0.4_284   ) < 1E-3






				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					if torch_device == "mps":
						return
					snake_case							 =				self.scheduler_classes[0]
					snake_case							 =				self.get_scheduler_config()
					snake_case							 =				scheduler_class(**UpperCAmelCase_   )

					scheduler.set_timesteps(self.num_inference_steps,	device=UpperCAmelCase_   )
					snake_case							 =				torch.manual_seed(0   )

					snake_case							 =				self.dummy_model()
					snake_case							 =				self.dummy_sample_deter.to(UpperCAmelCase_   ) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
						snake_case							 =				scheduler.scale_model_input(UpperCAmelCase_,	UpperCAmelCase_   )

						snake_case							 =				model(UpperCAmelCase_,	UpperCAmelCase_   )

						snake_case							 =				scheduler.step(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	generator=UpperCAmelCase_   )
						snake_case							 =				output.prev_sample

					snake_case							 =				torch.sum(torch.abs(UpperCAmelCase_   )   )
					snake_case							 =				torch.mean(torch.abs(UpperCAmelCase_   )   )

					assert abs(result_sum.item() - 13849.3818   ) < 1E-1
					assert abs(result_mean.item() - 18.0_331   ) < 1E-3








'''simple docstring'''

lowerCAmelCase_						   =			{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCAmelCase_						   =			{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}

def     __magic_name__       ( A					,   A					,   A							)			->		list:
	snake_case							 =				True
	snake_case							 =				[]

	for neighbour in graph[vert]:
		if not visited[neighbour]:
			connected_verts += dfs(A					,   A					,   A							)

	return [vert, *connected_verts]

def     __magic_name__       ( A							)			->		list:
	snake_case							 =				len(A							)
	snake_case							 =				graph_size * [False]
	snake_case							 =				[]

	for i in range(A							):
		if not visited[i]:
			snake_case							 =				dfs(A					,   A					,   A							)
			components_list.append(A							)

	return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()





