






"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class        __lowercase(       _UpperCamelCase  ):
						lowerCAmelCase_		:							str=       (LMSDiscreteScheduler,)
						lowerCAmelCase_		:							List[Any]=       10






						def 		lowerCAmelCase   (					self    ,						**_SCREAMING_SNAKE_CASE)			->  Any:




											"""simple docstring"""





											lowerCamelCase__				={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase   (					self)			->  Dict:




											"""simple docstring"""





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






						def 		lowerCAmelCase   (					self)			->  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=_SCREAMING_SNAKE_CASE    ,						beta_end=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





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






						def 		lowerCAmelCase   (					self)			->  int:




											"""simple docstring"""





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






						def 		lowerCAmelCase   (					self)			->  List[str]:




											"""simple docstring"""





											for t in [0, 5_00, 8_00]:
																self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__				=self.scheduler_classes[0]
											lowerCamelCase__				=self.get_scheduler_config()
											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

											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)
																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() - 1006.388) < 1E-2
											assert abs(result_mean.item() - 1.31) < 1E-3






						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											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

											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)
																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() - 0.0017) < 1E-2
											assert abs(result_mean.item() - 2.2676E-06) < 1E-3






						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											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__				=self.dummy_model()
											lowerCamelCase__				=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											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)
																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() - 1006.388) < 1E-2
											assert abs(result_mean.item() - 1.31) < 1E-3






						def 		lowerCAmelCase   (					self)			->  List[str]:




											"""simple docstring"""





											lowerCamelCase__				=self.scheduler_classes[0]
											lowerCamelCase__				=self.get_scheduler_config()
											lowerCamelCase__				=scheduler_class(**_SCREAMING_SNAKE_CASE    ,						use_karras_sigmas=_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps    ,						device=_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=self.dummy_model()
											lowerCamelCase__				=self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE) * scheduler.init_noise_sigma
											lowerCamelCase__				=sample.to(_SCREAMING_SNAKE_CASE)

											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)
																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() - 3812.9927) < 2E-2
											assert abs(result_mean.item() - 4.9648) < 1E-3







"""simple docstring"""



_snake_case       :      Dict       =					[0.485, 0.456, 0.406]
_snake_case       :      int       =					[0.229, 0.224, 0.225]
_snake_case       :      Optional[int]       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.4814_5466, 0.457_8275, 0.4082_1073]
_snake_case       :      str       =					[0.2686_2954, 0.2613_0258, 0.2757_7711]