import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		a__						( A__ ):
					A     	=							(KDPMaAncestralDiscreteScheduler,)
					A     	=							10

					def     __UpperCamelCase       (   self				:	List[Any],**_A				:	Tuple   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   {
							    "num_train_timesteps": 1100,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**_A   )
							return config

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=_A   )

					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							for beta_start, beta_end in zip([0.00001, 0.0001, 0.001],[0.0002, 0.002, 0.02]   ):
									self.check_over_configs(beta_start=_A,beta_end=_A   )

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






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

							scheduler.set_timesteps(self.num_inference_steps   )

							SCREAMING_SNAKE_CASE_  :		Any    				=   torch.manual_seed(0   )

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

							for i, t in enumerate(scheduler.timesteps   ):
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   scheduler.scale_model_input(_A,_A   )

									SCREAMING_SNAKE_CASE_  :		Dict    				=   model(_A,_A   )

									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   scheduler.step(_A,_A,_A,generator=_A   )
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   output.prev_sample

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.sum(torch.abs(_A   )   )
							SCREAMING_SNAKE_CASE_  :		int    				=   torch.mean(torch.abs(_A   )   )

							assert abs(result_sum.item() - 13849.3877   ) < 1E-2
							assert abs(result_mean.item() - 18.0331   ) < 5E-3

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






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

							scheduler.set_timesteps(self.num_inference_steps   )

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.dummy_model()
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.dummy_sample_deter * scheduler.init_noise_sigma
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   sample.to(_A   )

							SCREAMING_SNAKE_CASE_  :		int    				=   torch.manual_seed(0   )

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

									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   model(_A,_A   )

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

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.sum(torch.abs(_A   )   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.mean(torch.abs(_A   )   )

							assert abs(result_sum.item() - 328.9970   ) < 1E-2
							assert abs(result_mean.item() - 0.4284   ) < 1E-3






					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






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

							scheduler.set_timesteps(self.num_inference_steps,device=_A   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.manual_seed(0   )

							SCREAMING_SNAKE_CASE_  :		Any    				=   self.dummy_model()
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.dummy_sample_deter.to(_A   ) * scheduler.init_noise_sigma

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

									SCREAMING_SNAKE_CASE_  :		Dict    				=   model(_A,_A   )

									SCREAMING_SNAKE_CASE_  :		Any    				=   scheduler.step(_A,_A,_A,generator=_A   )
									SCREAMING_SNAKE_CASE_  :		int    				=   output.prev_sample

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

							assert abs(result_sum.item() - 13849.3818   ) < 1E-1
							assert abs(result_mean.item() - 18.0331   ) < 1E-3

from ..utils import DummyObject, requires_backends






class 		a__						( metaclass=A__ ):
					A     	=							['tensorflow_text']

					def __init__(   self				:	List[str],*_A				:	Optional[Any],**_A				:	int   ):







							"""simple docstring"""






							requires_backends(self,["tensorflow_text"]   )
