





'''simple docstring'''






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     (KDPMaAncestralDiscreteScheduler,)
					_SCREAMING_SNAKE_CASE				 =     10


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  **__snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Dict	       =		{
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0_001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**lowercase__							)
											return config


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											for timesteps in [10, 50, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											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=lowercase__							,  beta_end=lowercase__							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





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

											scheduler.set_timesteps(self.num_inference_steps							)

											_snake_case: Optional[Any]	       =		torch.manual_seed(0							)

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

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

																	_snake_case: Optional[Any]	       =		model(lowercase__							,  lowercase__							)

																	_snake_case: Dict	       =		scheduler.step(lowercase__							,  lowercase__							,  lowercase__							,  generator=lowercase__							)
																	_snake_case: List[str]	       =		output.prev_sample

											_snake_case: Optional[int]	       =		torch.sum(torch.abs(lowercase__							)							)
											_snake_case: List[str]	       =		torch.mean(torch.abs(lowercase__							)							)

											assert abs(result_sum.item() - 1_38_49.38_77							) < 1e-2
											assert abs(result_mean.item() - 18.0_331							) < 5e-3


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											if torch_device == "mps":
																	return
											_snake_case: Optional[int]	       =		self.scheduler_classes[0]
											_snake_case: str	       =		self.get_scheduler_config(prediction_type='v_prediction'							)
											_snake_case: int	       =		scheduler_class(**lowercase__							)

											scheduler.set_timesteps(self.num_inference_steps							)

											_snake_case: Tuple	       =		self.dummy_model()
											_snake_case: Union[str, Any]	       =		self.dummy_sample_deter * scheduler.init_noise_sigma
											_snake_case: List[Any]	       =		sample.to(lowercase__							)

											_snake_case: str	       =		torch.manual_seed(0							)

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

																	_snake_case: Union[str, Any]	       =		model(lowercase__							,  lowercase__							)

																	_snake_case: List[str]	       =		scheduler.step(lowercase__							,  lowercase__							,  lowercase__							,  generator=lowercase__							)
																	_snake_case: Tuple	       =		output.prev_sample

											_snake_case: List[Any]	       =		torch.sum(torch.abs(lowercase__							)							)
											_snake_case: List[str]	       =		torch.mean(torch.abs(lowercase__							)							)

											assert abs(result_sum.item() - 3_28.99_70							) < 1e-2
											assert abs(result_mean.item() - 0.4_284							) < 1e-3




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											if torch_device == "mps":
																	return
											_snake_case: Optional[Any]	       =		self.scheduler_classes[0]
											_snake_case: str	       =		self.get_scheduler_config()
											_snake_case: Union[str, Any]	       =		scheduler_class(**lowercase__							)

											scheduler.set_timesteps(self.num_inference_steps							,  device=lowercase__							)
											_snake_case: Dict	       =		torch.manual_seed(0							)

											_snake_case: str	       =		self.dummy_model()
											_snake_case: List[str]	       =		self.dummy_sample_deter.to(lowercase__							) * scheduler.init_noise_sigma

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

																	_snake_case: Any	       =		model(lowercase__							,  lowercase__							)

																	_snake_case: int	       =		scheduler.step(lowercase__							,  lowercase__							,  lowercase__							,  generator=lowercase__							)
																	_snake_case: Optional[int]	       =		output.prev_sample

											_snake_case: Dict	       =		torch.sum(torch.abs(lowercase__							)							)
											_snake_case: Union[str, Any]	       =		torch.mean(torch.abs(lowercase__							)							)

											assert abs(result_sum.item() - 1_38_49.38_18							) < 1e-1
											assert abs(result_mean.item() - 18.0_331							) < 1e-3













'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           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      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   torch.manual_seed(0       )

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

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

                 __A		 =   model(lowercase__					,					lowercase__       )

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

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

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

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

           __A		 =   torch.manual_seed(0       )

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

                 __A		 =   model(lowercase__					,					lowercase__       )

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

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

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






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

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

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

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

                 __A		 =   model(lowercase__					,					lowercase__       )

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

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

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


