

'''simple docstring'''



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   (LMSDiscreteScheduler,)
 lowerCAmelCase__						      =   10
 def    UpperCAmelCase__	(	self      : List[str]      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """num_train_timesteps""": 1_100,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**__SCREAMING_SNAKE_CASE    )
      return config
 def    UpperCAmelCase__	(	self      : Tuple    )						->				Optional[Any]:


      """simple docstring"""




      for timesteps in [10, 50, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : str    )						->				Optional[int]:


      """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    UpperCAmelCase__	(	self      : int    )						->				List[Any]:


      """simple docstring"""




      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				List[str]:


      """simple docstring"""




      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Tuple    )						->				Any:


      """simple docstring"""




      for t in [0, 500, 800]:
           self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				str:


      """simple docstring"""




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

      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(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

           __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

           __SCREAMING_SNAKE_CASE						     =   scheduler.step(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   output.prev_sample

      __SCREAMING_SNAKE_CASE						     =   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE    )    )
      __SCREAMING_SNAKE_CASE						     =   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    UpperCAmelCase__	(	self      : int    )						->				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(**__SCREAMING_SNAKE_CASE    )

      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(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

           __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

           __SCREAMING_SNAKE_CASE						     =   scheduler.step(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   output.prev_sample

      __SCREAMING_SNAKE_CASE						     =   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE    )    )
      __SCREAMING_SNAKE_CASE						     =   torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

      assert abs(result_sum.item() - 0.0017    ) < 1E-2
      assert abs(result_mean.item() - 2.2_676E-06    ) < 1E-3
 def    UpperCAmelCase__	(	self      : str    )						->				Optional[Any]:


      """simple docstring"""




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

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

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

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

           __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

           __SCREAMING_SNAKE_CASE						     =   scheduler.step(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   output.prev_sample

      __SCREAMING_SNAKE_CASE						     =   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE    )    )
      __SCREAMING_SNAKE_CASE						     =   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    UpperCAmelCase__	(	self      : Any    )						->				List[Any]:


      """simple docstring"""




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

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

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

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

           __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

           __SCREAMING_SNAKE_CASE						     =   scheduler.step(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   output.prev_sample

      __SCREAMING_SNAKE_CASE						     =   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE    )    )
      __SCREAMING_SNAKE_CASE						     =   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'''



def      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   1

     while True:
          if (
              sorted(str(a__		)		)
              == sorted(str(2 * i		)		)
              == sorted(str(3 * i		)		)
              == sorted(str(4 * i		)		)
              == sorted(str(5 * i		)		)
              == sorted(str(6 * i		)		)
          ):
               return i

          i += 1


if __name__ == "__main__":
  print(solution())






