"""simple docstring"""



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	__UpperCAmelCase						(snake_case__    ):







			'''simple docstring'''
			_UpperCamelCase     :	List[str]				    =							(KDPMaAncestralDiscreteScheduler,)
			_UpperCamelCase     :	str				    =							10





			def 		lowerCamelCase				( self      ,			**snake_case_       ):







										'''simple docstring'''


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

										config.update(**__snake_case       )
										return config





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''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=__snake_case      ,			beta_end=__snake_case       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if torch_device == "mps":
																	return
										A__					:    Dict														= self.scheduler_classes[0]
										A__					:    List[str]														= self.get_scheduler_config()
										A__					:    Any														= scheduler_class(**__snake_case       )

										scheduler.set_timesteps(self.num_inference_steps       )

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

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

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    Optional[Any]														= scheduler.scale_model_input(__snake_case      ,			__snake_case       )

																	A__					:    Union[str, Any]														= model(__snake_case      ,			__snake_case       )

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

										A__					:    str														= torch.sum(torch.abs(__snake_case       )       )
										A__					:    Any														= torch.mean(torch.abs(__snake_case       )       )

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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if torch_device == "mps":
																	return
										A__					:    str														= self.scheduler_classes[0]
										A__					:    Any														= self.get_scheduler_config(prediction_type="""v_prediction"""       )
										A__					:    str														= scheduler_class(**__snake_case       )

										scheduler.set_timesteps(self.num_inference_steps       )

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

										A__					:    Union[str, Any]														= torch.manual_seed(0       )

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    int														= scheduler.scale_model_input(__snake_case      ,			__snake_case       )

																	A__					:    List[str]														= model(__snake_case      ,			__snake_case       )

																	A__					:    str														= scheduler.step(__snake_case      ,			__snake_case      ,			__snake_case      ,			generator=__snake_case       )
																	A__					:    Any														= output.prev_sample

										A__					:    Optional[Any]														= torch.sum(torch.abs(__snake_case       )       )
										A__					:    Dict														= torch.mean(torch.abs(__snake_case       )       )

										assert abs(result_sum.item() - 3_28.99_70       ) < 1E-2
										assert abs(result_mean.item() - 0.42_84       ) < 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if torch_device == "mps":
																	return
										A__					:    Tuple														= self.scheduler_classes[0]
										A__					:    List[str]														= self.get_scheduler_config()
										A__					:    Optional[Any]														= scheduler_class(**__snake_case       )

										scheduler.set_timesteps(self.num_inference_steps      ,			device=__snake_case       )
										A__					:    int														= torch.manual_seed(0       )

										A__					:    str														= self.dummy_model()
										A__					:    Optional[int]														= self.dummy_sample_deter.to(__snake_case       ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
																	A__					:    Optional[int]														= scheduler.scale_model_input(__snake_case      ,			__snake_case       )

																	A__					:    Any														= model(__snake_case      ,			__snake_case       )

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

										A__					:    List[Any]														= torch.sum(torch.abs(__snake_case       )       )
										A__					:    Optional[int]														= torch.mean(torch.abs(__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    _A(		lowerCAmelCase       ):
							if not isinstance(lowerCAmelCase ,     lowerCAmelCase       ):
														A__					:    int														= F'''Input value of [number={number}] must be an integer'''
														raise TypeError(lowerCAmelCase       )
							if number < 0 or number % 2 == 1:
														return False
							A__					:    List[Any]														= int(number**0.5       )
							return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





