"""simple docstring"""







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: List[Any]       =						(EulerAncestralDiscreteScheduler,)
				UpperCAmelCase							: Dict       =						10



				def    _lowercase	(self							: List[str]      ,    **_A							: int)       ->      Union[str, Any]:
					__snake_case     :				List[Any] 						=					{
					    'num_train_timesteps': 11_00,
					    'beta_start': 0.0_001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					}

					config.update(**_A)
					return config



				def    _lowercase	(self							: Optional[int])       ->      List[str]:
					for timesteps in [10, 50, 1_00, 10_00]:
						self.check_over_configs(num_train_timesteps=_A)



				def    _lowercase	(self							: Any)       ->      List[Any]:
					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=_A      ,    beta_end=_A)



				def    _lowercase	(self							: Tuple)       ->      List[str]:
					for schedule in ["linear", "scaled_linear"]:
						self.check_over_configs(beta_schedule=_A)



				def    _lowercase	(self							: Union[str, Any])       ->      int:
					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=_A)



				def    _lowercase	(self							: Optional[int])       ->      Tuple:
					__snake_case     :				Optional[int] 						=					self.scheduler_classes[0]
					__snake_case     :				List[str] 						=					self.get_scheduler_config()
					__snake_case     :				List[Any] 						=					scheduler_class(**_A)

					scheduler.set_timesteps(self.num_inference_steps)

					__snake_case     :				List[Any] 						=					torch.manual_seed(0)

					__snake_case     :				Dict 						=					self.dummy_model()
					__snake_case     :				List[str] 						=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__snake_case     :				Any 						=					sample.to(_A)

					for i, t in enumerate(scheduler.timesteps):
						__snake_case     :				Dict 						=					scheduler.scale_model_input(_A      ,    _A)

						__snake_case     :				Tuple 						=					model(_A      ,    _A)

						__snake_case     :				Dict 						=					scheduler.step(_A      ,    _A      ,    _A      ,    generator=_A)
						__snake_case     :				Optional[Any] 						=					output.prev_sample

					__snake_case     :				int 						=					torch.sum(torch.abs(_A))
					__snake_case     :				Tuple 						=					torch.mean(torch.abs(_A))

					assert abs(result_sum.item() - 152.3_192) < 1E-2
					assert abs(result_mean.item() - 0.1_983) < 1E-3



				def    _lowercase	(self							: Tuple)       ->      Optional[int]:
					__snake_case     :				Optional[int] 						=					self.scheduler_classes[0]
					__snake_case     :				Optional[int] 						=					self.get_scheduler_config(prediction_type='v_prediction')
					__snake_case     :				List[str] 						=					scheduler_class(**_A)

					scheduler.set_timesteps(self.num_inference_steps)

					__snake_case     :				int 						=					torch.manual_seed(0)

					__snake_case     :				Dict 						=					self.dummy_model()
					__snake_case     :				Optional[Any] 						=					self.dummy_sample_deter * scheduler.init_noise_sigma
					__snake_case     :				int 						=					sample.to(_A)

					for i, t in enumerate(scheduler.timesteps):
						__snake_case     :				Any 						=					scheduler.scale_model_input(_A      ,    _A)

						__snake_case     :				Tuple 						=					model(_A      ,    _A)

						__snake_case     :				Any 						=					scheduler.step(_A      ,    _A      ,    _A      ,    generator=_A)
						__snake_case     :				Dict 						=					output.prev_sample

					__snake_case     :				List[str] 						=					torch.sum(torch.abs(_A))
					__snake_case     :				Any 						=					torch.mean(torch.abs(_A))

					assert abs(result_sum.item() - 108.4_439) < 1E-2
					assert abs(result_mean.item() - 0.1_412) < 1E-3






				def    _lowercase	(self							: str)       ->      Dict:
					__snake_case     :				str 						=					self.scheduler_classes[0]
					__snake_case     :				List[Any] 						=					self.get_scheduler_config()
					__snake_case     :				Union[str, Any] 						=					scheduler_class(**_A)

					scheduler.set_timesteps(self.num_inference_steps      ,    device=_A)
					__snake_case     :				Optional[int] 						=					torch.manual_seed(0)

					__snake_case     :				Any 						=					self.dummy_model()
					__snake_case     :				Union[str, Any] 						=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__snake_case     :				Optional[Any] 						=					sample.to(_A)

					for t in scheduler.timesteps:
						__snake_case     :				List[Any] 						=					scheduler.scale_model_input(_A      ,    _A)

						__snake_case     :				Dict 						=					model(_A      ,    _A)

						__snake_case     :				str 						=					scheduler.step(_A      ,    _A      ,    _A      ,    generator=_A)
						__snake_case     :				List[str] 						=					output.prev_sample

					__snake_case     :				Any 						=					torch.sum(torch.abs(_A))
					__snake_case     :				List[Any] 						=					torch.mean(torch.abs(_A))

					assert abs(result_sum.item() - 152.3_192) < 1E-2
					assert abs(result_mean.item() - 0.1_983) < 1E-3






"""simple docstring"""







from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a	:	List[Any]=					TypeVar("DatasetType", Dataset, IterableDataset)
def     __UpperCAmelCase							(       UpperCAmelCase_       :							DatasetType     ,				UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							int    )				->     DatasetType:

	'''simple docstring'''





	if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		return _split_by_node_map_style_dataset(UpperCAmelCase_     ,				rank=UpperCAmelCase_     ,				world_size=UpperCAmelCase_    )
	else:
		return _split_by_node_iterable_dataset(UpperCAmelCase_     ,				rank=UpperCAmelCase_     ,				world_size=UpperCAmelCase_    )





