
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				(CMStochasticIterativeScheduler,)
    _a		:						Optional[Any]=				10
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      {
               "num_train_timesteps": 201,
               "sigma_min": 0.002,
               "sigma_max": 80.0,
           }

           config.update(**__a      )
           return config
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      10

           lowercase							:     int       	=      self.get_scheduler_config()
           lowercase							:     Optional[int]       	=      self.scheduler_classes[0](**__a      )

           scheduler.set_timesteps(__a      )

           lowercase							:     str       	=      scheduler.timesteps[0]
           lowercase							:     Optional[Any]       	=      scheduler.timesteps[1]

           lowercase							:     Optional[Any]       	=      self.dummy_sample
           lowercase							:     Optional[int]       	=      0.1 * sample

           lowercase							:     Union[str, Any]       	=      scheduler.step(__a		,__a		,__a      ).prev_sample
           lowercase							:     Any       	=      scheduler.step(__a		,__a		,__a      ).prev_sample

           self.assertEqual(output_a.shape		,sample.shape      )
           self.assertEqual(output_a.shape		,output_a.shape      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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



           '''simple docstring'''







           for clip_denoised in [True, False]:
                  self.check_over_configs(clip_denoised=__a      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.scheduler_classes[0]
           lowercase							:     Optional[Any]       	=      self.get_scheduler_config()
           lowercase							:     List[Any]       	=      scheduler_class(**__a      )

           lowercase							:     Union[str, Any]       	=      1
           scheduler.set_timesteps(__a      )
           lowercase							:     Any       	=      scheduler.timesteps

           lowercase							:     List[str]       	=      torch.manual_seed(0      )

           lowercase							:     str       	=      self.dummy_model()
           lowercase							:     str       	=      self.dummy_sample_deter * scheduler.init_noise_sigma

           for i, t in enumerate(__a      ):
                  # 1. scale model input
                  lowercase							:     Dict       	=      scheduler.scale_model_input(__a		,__a      )

                  # 2. predict noise residual
                  lowercase							:     Union[str, Any]       	=      model(__a		,__a      )

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

                  lowercase							:     List[Any]       	=      pred_prev_sample

           lowercase							:     Optional[Any]       	=      torch.sum(torch.abs(__a      )      )
           lowercase							:     List[str]       	=      torch.mean(torch.abs(__a      )      )

           assert abs(result_sum.item() - 192.7_614      ) < 1e-2
           assert abs(result_mean.item() - 0.2_510      ) < 1e-3
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.scheduler_classes[0]
           lowercase							:     Dict       	=      self.get_scheduler_config()
           lowercase							:     Tuple       	=      scheduler_class(**__a      )

           lowercase							:     Dict       	=      [106, 0]
           scheduler.set_timesteps(timesteps=__a      )
           lowercase							:     Dict       	=      scheduler.timesteps

           lowercase							:     str       	=      torch.manual_seed(0      )

           lowercase							:     Tuple       	=      self.dummy_model()
           lowercase							:     str       	=      self.dummy_sample_deter * scheduler.init_noise_sigma

           for t in timesteps:
                  # 1. scale model input
                  lowercase							:     Tuple       	=      scheduler.scale_model_input(__a		,__a      )

                  # 2. predict noise residual
                  lowercase							:     List[str]       	=      model(__a		,__a      )

                  # 3. predict previous sample x_t-1
                  lowercase							:     str       	=      scheduler.step(__a		,__a		,__a		,generator=__a      ).prev_sample

                  lowercase							:     Tuple       	=      pred_prev_sample

           lowercase							:     List[Any]       	=      torch.sum(torch.abs(__a      )      )
           lowercase							:     Tuple       	=      torch.mean(torch.abs(__a      )      )

           assert abs(result_sum.item() - 347.6_357      ) < 1e-2
           assert abs(result_mean.item() - 0.4_527      ) < 1e-3
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.scheduler_classes[0]
           lowercase							:     List[str]       	=      self.get_scheduler_config()
           lowercase							:     Union[str, Any]       	=      scheduler_class(**__a      )

           lowercase							:     Tuple       	=      [39, 30, 12, 15, 0]

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



           '''simple docstring'''







           lowercase							:     str       	=      self.scheduler_classes[0]
           lowercase							:     Optional[int]       	=      self.get_scheduler_config()
           lowercase							:     List[str]       	=      scheduler_class(**__a      )

           lowercase							:     Optional[int]       	=      [39, 30, 12, 1, 0]
           lowercase							:     Dict       	=      len(__a      )

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



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.scheduler_classes[0]
           lowercase							:     Optional[Any]       	=      self.get_scheduler_config()
           lowercase							:     Dict       	=      scheduler_class(**__a      )

           lowercase							:     Any       	=      [scheduler.config.num_train_timesteps]

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





def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if density <= 0:
														raise ValueError("Impossible fluid density"  )
							if bulk_modulus <= 0:
														raise ValueError("Impossible bulk modulus"  )

							return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()
