






"""simple docstring"""


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	__UpperCamelCase    (    UpperCamelCase	):

					'''simple docstring'''





					lowercase_						:							Tuple			=     (KDPMaAncestralDiscreteScheduler,)
					lowercase_						:							Optional[int]			=     10
					def 					UpperCAmelCase__   (       self       :  int    ,   **UpperCAmelCase       :  Dict      )  ->					List[Any]:
											lowerCAmelCase      :Union[str, Any]				  = {
											    'num_train_timesteps': 1100,
											    'beta_start': 0.0_0_0_1,
											    'beta_end': 0.0_2,
											    'beta_schedule': 'linear',
											}

											config.update(**UpperCAmelCase      )
											return config
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					List[Any]:
											for timesteps in [10, 50, 100, 1000]:
																	self.check_over_configs(num_train_timesteps=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					List[str]:
											for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,   [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																	self.check_over_configs(beta_start=UpperCAmelCase    ,   beta_end=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					int:
											for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					List[str]:
											if torch_device == "mps":
																	return
											lowerCAmelCase      :Any				  = self.scheduler_classes[0]
											lowerCAmelCase      :Optional[Any]				  = self.get_scheduler_config()
											lowerCAmelCase      :int				  = scheduler_class(**UpperCAmelCase      )

											scheduler.set_timesteps(self.num_inference_steps      )

											lowerCAmelCase      :int				  = torch.manual_seed(0      )

											lowerCAmelCase      :Tuple				  = self.dummy_model()
											lowerCAmelCase      :Optional[Any]				  = self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase      :List[str]				  = sample.to(UpperCAmelCase      )

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

																	lowerCAmelCase      :int				  = model(UpperCAmelCase    ,   UpperCAmelCase      )

																	lowerCAmelCase      :List[Any]				  = scheduler.step(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   generator=UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = output.prev_sample

											lowerCAmelCase      :Tuple				  = torch.sum(torch.abs(UpperCAmelCase      )      )
											lowerCAmelCase      :Union[str, Any]				  = torch.mean(torch.abs(UpperCAmelCase      )      )

											assert abs(result_sum.item() - 1_3849.3877      ) < 1e-2
											assert abs(result_mean.item() - 18.0331      ) < 5e-3
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[int]:
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					Optional[Any]:
											if torch_device == "mps":
																	return
											lowerCAmelCase      :List[Any]				  = self.scheduler_classes[0]
											lowerCAmelCase      :Any				  = self.get_scheduler_config(prediction_type='v_prediction'      )
											lowerCAmelCase      :Tuple				  = scheduler_class(**UpperCAmelCase      )

											scheduler.set_timesteps(self.num_inference_steps      )

											lowerCAmelCase      :str				  = self.dummy_model()
											lowerCAmelCase      :int				  = self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase      :Any				  = sample.to(UpperCAmelCase      )

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

											for i, t in enumerate(scheduler.timesteps      ):
																	lowerCAmelCase      :List[Any]				  = scheduler.scale_model_input(UpperCAmelCase    ,   UpperCAmelCase      )

																	lowerCAmelCase      :Optional[Any]				  = model(UpperCAmelCase    ,   UpperCAmelCase      )

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

											lowerCAmelCase      :Optional[Any]				  = torch.sum(torch.abs(UpperCAmelCase      )      )
											lowerCAmelCase      :List[Any]				  = torch.mean(torch.abs(UpperCAmelCase      )      )

											assert abs(result_sum.item() - 328.9970      ) < 1e-2
											assert abs(result_mean.item() - 0.4_2_8_4      ) < 1e-3
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					int:
											if torch_device == "mps":
																	return
											lowerCAmelCase      :Optional[int]				  = self.scheduler_classes[0]
											lowerCAmelCase      :Optional[Any]				  = self.get_scheduler_config()
											lowerCAmelCase      :Any				  = scheduler_class(**UpperCAmelCase      )

											scheduler.set_timesteps(self.num_inference_steps    ,   device=UpperCAmelCase      )
											lowerCAmelCase      :Union[str, Any]				  = torch.manual_seed(0      )

											lowerCAmelCase      :Optional[Any]				  = self.dummy_model()
											lowerCAmelCase      :Tuple				  = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma

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

																	lowerCAmelCase      :Optional[Any]				  = model(UpperCAmelCase    ,   UpperCAmelCase      )

																	lowerCAmelCase      :List[Any]				  = scheduler.step(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   generator=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = output.prev_sample

											lowerCAmelCase      :Optional[Any]				  = torch.sum(torch.abs(UpperCAmelCase      )      )
											lowerCAmelCase      :List[str]				  = torch.mean(torch.abs(UpperCAmelCase      )      )

											assert abs(result_sum.item() - 1_3849.3818      ) < 1e-1
											assert abs(result_mean.item() - 18.0331      ) < 1e-3







"""simple docstring"""


import safs




class 	__UpperCamelCase    (    safs.SaFileSystem	):
					pass