






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class  SCREAMING_SNAKE_CASE			(			_UpperCamelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Tuple			=	(CMStochasticIterativeScheduler,)
     UpperCamelCase_							:		Dict			=	1_0


     def 				_A       (				self       :	Tuple					, **UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Any							=    {
           "num_train_timesteps": 201,
           "sigma_min": 0.002,
           "sigma_max": 80.0,
       }

       config.update(**UpperCAmelCase_      )
       return config


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    10

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.scheduler_classes[0](**UpperCAmelCase_      )

       scheduler.set_timesteps(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    scheduler.timesteps[0]
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    scheduler.timesteps[1]

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.dummy_sample
       SCREAMING_SNAKE_CASE       :      Tuple							=    0.1 * sample

       SCREAMING_SNAKE_CASE       :      Tuple							=    scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      ).prev_sample
       SCREAMING_SNAKE_CASE       :      Any							=    scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      ).prev_sample

       self.assertEqual(output_a.shape					, sample.shape      )
       self.assertEqual(output_a.shape					, output_a.shape      )


     def 				_A       (				self       :	Dict      ):
       for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=UpperCAmelCase_      )


     def 				_A       (				self       :	str      ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=UpperCAmelCase_      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Dict							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    1
       scheduler.set_timesteps(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    scheduler.timesteps

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    self.dummy_model()
       SCREAMING_SNAKE_CASE       :      Any							=    self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(UpperCAmelCase_      ):
         # 1. scale model input
         SCREAMING_SNAKE_CASE       :      Tuple							=    scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_      )

         # 2. predict noise residual
         SCREAMING_SNAKE_CASE       :      Dict							=    model(UpperCAmelCase_					, UpperCAmelCase_      )

         # 3. predict previous sample x_t-1
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_      ).prev_sample

         SCREAMING_SNAKE_CASE       :      Dict							=    pred_prev_sample

       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.sum(torch.abs(UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.mean(torch.abs(UpperCAmelCase_      )      )

       assert abs(result_sum.item() - 192.7_614      ) < 1E-2
       assert abs(result_mean.item() - 0.2_510      ) < 1E-3


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Tuple							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    [106, 0]
       scheduler.set_timesteps(timesteps=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    scheduler.timesteps

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.dummy_model()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_      )

         # 2. predict noise residual
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    model(UpperCAmelCase_					, UpperCAmelCase_      )

         # 3. predict previous sample x_t-1
         SCREAMING_SNAKE_CASE       :      Tuple							=    scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_      ).prev_sample

         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    pred_prev_sample

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.sum(torch.abs(UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.mean(torch.abs(UpperCAmelCase_      )      )

       assert abs(result_sum.item() - 347.6_357      ) < 1E-2
       assert abs(result_mean.item() - 0.4_527      ) < 1E-3


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      str							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      List[Any]							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    [39, 30, 12, 15, 0]

       with self.assertRaises(UpperCAmelCase_					, msg="`timesteps` must be in descending order."      ):
         scheduler.set_timesteps(timesteps=UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      Dict							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [39, 30, 12, 1, 0]
       SCREAMING_SNAKE_CASE       :      Tuple							=    len(UpperCAmelCase_      )

       with self.assertRaises(UpperCAmelCase_					, msg="Can only pass one of `num_inference_steps` or `timesteps`."      ):
         scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_					, timesteps=UpperCAmelCase_      )






     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           UpperCAmelCase_					, msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"					, ):
         scheduler.set_timesteps(timesteps=UpperCAmelCase_      )














def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  return numa ^ numa < 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()






