
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      A							(    SCREAMING_SNAKE_CASE_			):
			lowerCamelCase							:							Union[str, Any]    						=					(EulerAncestralDiscreteScheduler,)
			lowerCamelCase							:							str    						=					10



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    {
									    """num_train_timesteps""": 1_100,
									    """beta_start""": 0.00_01,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**lowercase_    )
									return config



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									for timesteps in [10, 50, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=lowercase_    )



			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,       [0.00_02, 0.0_02, 0.02]    ):
															self.check_over_configs(beta_start=lowercase_    ,       beta_end=lowercase_    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



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



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



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



			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    self.scheduler_classes[0]
									lowercase__								=    self.get_scheduler_config()
									lowercase__								=    scheduler_class(**lowercase_    )

									scheduler.set_timesteps(self.num_inference_steps    )

									lowercase__								=    torch.manual_seed(0    )

									lowercase__								=    self.dummy_model()
									lowercase__								=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									lowercase__								=    sample.to(lowercase_    )

									for i, t in enumerate(scheduler.timesteps    ):
															lowercase__								=    scheduler.scale_model_input(lowercase_    ,       lowercase_    )

															lowercase__								=    model(lowercase_    ,       lowercase_    )

															lowercase__								=    scheduler.step(lowercase_    ,       lowercase_    ,       lowercase_    ,       generator=lowercase_    )
															lowercase__								=    output.prev_sample

									lowercase__								=    torch.sum(torch.abs(lowercase_    )    )
									lowercase__								=    torch.mean(torch.abs(lowercase_    )    )

									assert abs(result_sum.item() - 152.3_192    ) < 1e-2
									assert abs(result_mean.item() - 0.19_83    ) < 1e-3



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.scheduler_classes[0]
									lowercase__								=    self.get_scheduler_config(prediction_type="""v_prediction"""    )
									lowercase__								=    scheduler_class(**lowercase_    )

									scheduler.set_timesteps(self.num_inference_steps    )

									lowercase__								=    torch.manual_seed(0    )

									lowercase__								=    self.dummy_model()
									lowercase__								=    self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase__								=    sample.to(lowercase_    )

									for i, t in enumerate(scheduler.timesteps    ):
															lowercase__								=    scheduler.scale_model_input(lowercase_    ,       lowercase_    )

															lowercase__								=    model(lowercase_    ,       lowercase_    )

															lowercase__								=    scheduler.step(lowercase_    ,       lowercase_    ,       lowercase_    ,       generator=lowercase_    )
															lowercase__								=    output.prev_sample

									lowercase__								=    torch.sum(torch.abs(lowercase_    )    )
									lowercase__								=    torch.mean(torch.abs(lowercase_    )    )

									assert abs(result_sum.item() - 108.4_439    ) < 1e-2
									assert abs(result_mean.item() - 0.14_12    ) < 1e-3



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    self.scheduler_classes[0]
									lowercase__								=    self.get_scheduler_config()
									lowercase__								=    scheduler_class(**lowercase_    )

									scheduler.set_timesteps(self.num_inference_steps    ,       device=lowercase_    )
									lowercase__								=    torch.manual_seed(0    )

									lowercase__								=    self.dummy_model()
									lowercase__								=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									lowercase__								=    sample.to(lowercase_    )

									for t in scheduler.timesteps:
															lowercase__								=    scheduler.scale_model_input(lowercase_    ,       lowercase_    )

															lowercase__								=    model(lowercase_    ,       lowercase_    )

															lowercase__								=    scheduler.step(lowercase_    ,       lowercase_    ,       lowercase_    ,       generator=lowercase_    )
															lowercase__								=    output.prev_sample

									lowercase__								=    torch.sum(torch.abs(lowercase_    )    )
									lowercase__								=    torch.mean(torch.abs(lowercase_    )    )

									assert abs(result_sum.item() - 152.3_192    ) < 1e-2
									assert abs(result_mean.item() - 0.19_83    ) < 1e-3








'''simple docstring'''



def 	_A						(							lowercase__		):
						return " ".join(sorted(set(sentence.split()		)		)		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






