
"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  lowercase				(   lowercase__		):
			lowercase			     =    (KDPMaAncestralDiscreteScheduler,)
			lowercase			     =    10






			def 				UpperCAmelCase       (self     :  Optional[int]			,**SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     {
				    '''num_train_timesteps''': 1_100,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**SCREAMING_SNAKE_CASE_ )
				return config






			def 				UpperCAmelCase       (self     :  Any )					->  str:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  int:





				"""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=SCREAMING_SNAKE_CASE_			,beta_end=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Optional[int]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Dict:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 13_849.3_877 ) < 1e-2
				assert abs(result_mean.item() - 18.03_31 ) < 5e-3






			def 				UpperCAmelCase       (self     :  str )					->  List[Any]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  List[Any] )					->  List[str]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config(prediction_type='''v_prediction''' )
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 328.9_970 ) < 1e-2
				assert abs(result_mean.item() - 0.42_84 ) < 1e-3






			def 				UpperCAmelCase       (self     :  Dict )					->  Union[str, Any]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps			,device=SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_ ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 13_849.3_818 ) < 1e-1
				assert abs(result_mean.item() - 18.03_31 ) < 1e-3




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		list[int] )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     len(_lowerCamelCase )
	if n in (0, 1):
		return 0
	return sum(query * (n - i - 1) for i, query in enumerate(sorted(_lowerCamelCase ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


