
'''simple docstring'''





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase__	(     lowerCAmelCase__):
 UpperCAmelCase__     :     Any    					=       (LMSDiscreteScheduler,)
 UpperCAmelCase__     :     Dict    					=       1_0






 def 						lowercase_    (      self	:Dict						,					**_A	:Union[str, Any]      )					->      Any:







       '''simple docstring'''



       __A													=      {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0_001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_A      )
       return config






 def 						lowercase_    (      self	:List[Any]      )					->      Dict:







       '''simple docstring'''



       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_A      )






 def 						lowercase_    (      self	:int      )					->      Dict:







       '''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=_A						,					beta_end=_A      )






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:List[str]      )					->      List[Any]:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:str      )					->      Union[str, Any]:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      self.scheduler_classes[0]
       __A													=      self.get_scheduler_config()
       __A													=      scheduler_class(**_A      )

       scheduler.set_timesteps(self.num_inference_steps      )

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

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

             __A													=      model(_A						,					_A      )

             __A													=      scheduler.step(_A						,					_A						,					_A      )
             __A													=      output.prev_sample

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

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






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       __A													=      self.scheduler_classes[0]
       __A													=      self.get_scheduler_config(prediction_type='v_prediction'      )
       __A													=      scheduler_class(**_A      )

       scheduler.set_timesteps(self.num_inference_steps      )

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

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

             __A													=      model(_A						,					_A      )

             __A													=      scheduler.step(_A						,					_A						,					_A      )
             __A													=      output.prev_sample

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

       assert abs(result_sum.item() - 0.0_017      ) < 1E-2
       assert abs(result_mean.item() - 2.26_76E-06      ) < 1E-3






 def 						lowercase_    (      self	:Dict      )					->      Tuple:







       '''simple docstring'''



       __A													=      self.scheduler_classes[0]
       __A													=      self.get_scheduler_config()
       __A													=      scheduler_class(**_A      )

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

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

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

             __A													=      model(_A						,					_A      )

             __A													=      scheduler.step(_A						,					_A						,					_A      )
             __A													=      output.prev_sample

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

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






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







       '''simple docstring'''



       __A													=      self.scheduler_classes[0]
       __A													=      self.get_scheduler_config()
       __A													=      scheduler_class(**_A						,					use_karras_sigmas=_A      )

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

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

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

             __A													=      model(_A						,					_A      )

             __A													=      scheduler.step(_A						,					_A						,					_A      )
             __A													=      output.prev_sample

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

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











'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE = 1000     ):
		return sum(i for i in range(SCREAMING_SNAKE_CASE     ) if i % 3 == 0 or i % 5 == 0     )


if __name__ == "__main__":
							print(f"""{solution() = }""")
