
"""simple docstring"""




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	UpperCamelCase      (			_UpperCAmelCase ):

				"""simple docstring"""



				UpperCAmelCase_							: Optional[int]		    =							(LMSDiscreteScheduler,)
				UpperCAmelCase_							: Any		    =							10


				def 				A   (							self						,      **lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**_lowerCAmelCase     )
									return config


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







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


				def 				A   (							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=_lowerCAmelCase						,      beta_end=_lowerCAmelCase     )


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									for t in [0, 500, 800]:
														self.check_over_forward(time_step=_lowerCAmelCase     )


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =      self.get_scheduler_config()
									SCREAMING_SNAKE_CASE           =      scheduler_class(**_lowerCAmelCase     )

									scheduler.set_timesteps(self.num_inference_steps     )

									SCREAMING_SNAKE_CASE           =      self.dummy_model()
									SCREAMING_SNAKE_CASE           =      self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(scheduler.timesteps     ):
														SCREAMING_SNAKE_CASE           =      scheduler.scale_model_input(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      model(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      scheduler.step(_lowerCAmelCase						,      _lowerCAmelCase						,      _lowerCAmelCase     )
														SCREAMING_SNAKE_CASE           =      output.prev_sample

									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(_lowerCAmelCase     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(_lowerCAmelCase     )     )

									assert abs(result_sum.item() - 1_006.388     ) < 1E-2
									assert abs(result_mean.item() - 1.31     ) < 1E-3


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =      self.get_scheduler_config(prediction_type='v_prediction'     )
									SCREAMING_SNAKE_CASE           =      scheduler_class(**_lowerCAmelCase     )

									scheduler.set_timesteps(self.num_inference_steps     )

									SCREAMING_SNAKE_CASE           =      self.dummy_model()
									SCREAMING_SNAKE_CASE           =      self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(scheduler.timesteps     ):
														SCREAMING_SNAKE_CASE           =      scheduler.scale_model_input(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      model(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      scheduler.step(_lowerCAmelCase						,      _lowerCAmelCase						,      _lowerCAmelCase     )
														SCREAMING_SNAKE_CASE           =      output.prev_sample

									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(_lowerCAmelCase     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(_lowerCAmelCase     )     )

									assert abs(result_sum.item() - 0.0_017     ) < 1E-2
									assert abs(result_mean.item() - 2.2_676E-06     ) < 1E-3


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =      self.get_scheduler_config()
									SCREAMING_SNAKE_CASE           =      scheduler_class(**_lowerCAmelCase     )

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

									SCREAMING_SNAKE_CASE           =      self.dummy_model()
									SCREAMING_SNAKE_CASE           =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE           =      sample.to(_lowerCAmelCase     )

									for i, t in enumerate(scheduler.timesteps     ):
														SCREAMING_SNAKE_CASE           =      scheduler.scale_model_input(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      model(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      scheduler.step(_lowerCAmelCase						,      _lowerCAmelCase						,      _lowerCAmelCase     )
														SCREAMING_SNAKE_CASE           =      output.prev_sample

									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(_lowerCAmelCase     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(_lowerCAmelCase     )     )

									assert abs(result_sum.item() - 1_006.388     ) < 1E-2
									assert abs(result_mean.item() - 1.31     ) < 1E-3




				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =      self.get_scheduler_config()
									SCREAMING_SNAKE_CASE           =      scheduler_class(**_lowerCAmelCase						,      use_karras_sigmas=_lowerCAmelCase     )

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

									SCREAMING_SNAKE_CASE           =      self.dummy_model()
									SCREAMING_SNAKE_CASE           =      self.dummy_sample_deter.to(_lowerCAmelCase     ) * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE           =      sample.to(_lowerCAmelCase     )

									for t in scheduler.timesteps:
														SCREAMING_SNAKE_CASE           =      scheduler.scale_model_input(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      model(_lowerCAmelCase						,      _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE           =      scheduler.step(_lowerCAmelCase						,      _lowerCAmelCase						,      _lowerCAmelCase     )
														SCREAMING_SNAKE_CASE           =      output.prev_sample

									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(_lowerCAmelCase     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(_lowerCAmelCase     )     )

									assert abs(result_sum.item() - 3_812.9_927     ) < 2E-2
									assert abs(result_mean.item() - 4.9_648     ) < 1E-3




def 				UpperCamelCase_	(					__snake_case      :    set						,      __snake_case      :    set  )->   set:




						'''simple docstring'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_a={set_a}] is not a set'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_b={set_b}] is not a set'''

						return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()

