



'''simple docstring'''





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :str						   =					(DDIMParallelScheduler,)
       lowerCAmelCase__  :List[Any]						   =					(("eta", 0.0), ("num_inference_steps", 50))



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							int:
           lowercase__		       =						{
               "num_train_timesteps": 1_000,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
               "clip_sample": True,
           }

           config.update(**UpperCAmelCase_      )
           return config



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config(**UpperCAmelCase_      )
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						10, 0.0

           lowercase__		       =						self.dummy_model()
           lowercase__		       =						self.dummy_sample_deter

           scheduler.set_timesteps(UpperCAmelCase_      )

           for t in scheduler.timesteps:
               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						scheduler.step(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      ).prev_sample

           return sample



       def 							_a      (				self      )  ->							Tuple:
           for timesteps in [100, 500, 1_000]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[Any]:
           for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=UpperCAmelCase_      )

           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config(steps_offset=1      )
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )
           scheduler.set_timesteps(5      )
           assert torch.equal(scheduler.timesteps     ,torch.LongTensor([801, 601, 401, 201, 1]      )      )



       def 							_a      (				self      )  ->							Any:
           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,[0.0_02, 0.02, 0.2, 2]      ):
               self.check_over_configs(beta_start=UpperCAmelCase_     ,beta_end=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Dict:
           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[Any]:
           for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           self.check_over_configs(thresholding=UpperCAmelCase_      )
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=UpperCAmelCase_     ,prediction_type=UpperCAmelCase_     ,sample_max_value=UpperCAmelCase_     ,)



       def 							_a      (				self      )  ->							List[str]:
           for t in [1, 10, 49]:
               self.check_over_forward(time_step=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           for t, num_inference_steps in zip([1, 10, 50]     ,[10, 50, 500]      ):
               self.check_over_forward(time_step=UpperCAmelCase_     ,num_inference_steps=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           for t, eta in zip([1, 10, 49]     ,[0.0, 0.5, 1.0]      ):
               self.check_over_forward(time_step=UpperCAmelCase_     ,eta=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config()
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           assert torch.sum(torch.abs(scheduler._get_variance(0     ,0      ) - 0.0      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(420     ,400      ) - 0.1_47_71      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(980     ,960      ) - 0.3_24_60      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(0     ,0      ) - 0.0      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487     ,486      ) - 0.0_09_79      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999     ,998      ) - 0.02      )      ) < 1E-5



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config()
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						10, 0.0
           scheduler.set_timesteps(UpperCAmelCase_      )

           lowercase__		       =						self.dummy_model()
           lowercase__		       =						self.dummy_sample_deter
           lowercase__		       =						self.dummy_sample_deter + 0.1
           lowercase__		       =						self.dummy_sample_deter - 0.1

           lowercase__		       =						samplea.shape[0]
           lowercase__		       =						torch.stack([samplea, samplea, samplea]     ,dim=0      )
           lowercase__		       =						torch.arange(UpperCAmelCase_      )[0:3, None].repeat(1     ,UpperCAmelCase_      )

           lowercase__		       =						model(samples.flatten(0     ,1      )     ,timesteps.flatten(0     ,1      )      )
           lowercase__		       =						scheduler.batch_step_no_noise(UpperCAmelCase_     ,timesteps.flatten(0     ,1      )     ,samples.flatten(0     ,1      )     ,UpperCAmelCase_      )

           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 1_147.7_904      ) < 1E-2
           assert abs(result_mean.item() - 0.49_82      ) < 1E-3



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.full_loop()

           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 172.0_067      ) < 1E-2
           assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.full_loop(prediction_type="v_prediction"      )

           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 52.53_02      ) < 1E-2
           assert abs(result_mean.item() - 0.06_84      ) < 1E-3



       def 							_a      (				self      )  ->							Dict:
           # We specify different beta, so that the first alpha is 0.99
           lowercase__		       =						self.full_loop(set_alpha_to_one=UpperCAmelCase_     ,beta_start=0.01      )
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 149.8_295      ) < 1E-2
           assert abs(result_mean.item() - 0.19_51      ) < 1E-3







       def 							_a      (				self      )  ->							int:
           # We specify different beta, so that the first alpha is 0.99
           lowercase__		       =						self.full_loop(set_alpha_to_one=UpperCAmelCase_     ,beta_start=0.01      )
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 149.0_784      ) < 1E-2
           assert abs(result_mean.item() - 0.19_41      ) < 1E-3









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float(moles / volume       ) * nfactor       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((moles * 0.0_821 * temperature) / (volume)       )       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((moles * 0.0_821 * temperature) / (pressure)       )       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((pressure * volume) / (0.0_821 * moles)       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




