



'''simple docstring'''





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					(DPMSolverSDEScheduler,)
       lowerCAmelCase__  :Union[str, Any]						   =					10



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						{
               "num_train_timesteps": 1_100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
               "noise_sampler_seed": 0,
           }

           config.update(**UpperCAmelCase_      )
           return config



       def 							_a      (				self      )  ->							Optional[Any]:
           for timesteps in [10, 50, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[str]:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,[0.00_02, 0.0_02, 0.02]      ):
               self.check_over_configs(beta_start=UpperCAmelCase_     ,beta_end=UpperCAmelCase_      )



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



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



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

           scheduler.set_timesteps(self.num_inference_steps      )

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

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

               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )

               lowercase__		       =						scheduler.step(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						output.prev_sample

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

           if torch_device in ["mps"]:
               assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75      ) < 1E-2
               assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77      ) < 1E-3
           elif torch_device in ["cuda"]:
               assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06      ) < 1E-2
               assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52      ) < 1E-3
           else:
               assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62      ) < 1E-2
               assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26      ) < 1E-3



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config(prediction_type="v_prediction"      )
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           scheduler.set_timesteps(self.num_inference_steps      )

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

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

               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )

               lowercase__		       =						scheduler.step(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						output.prev_sample

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

           if torch_device in ["mps"]:
               assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53      ) < 1E-2
               assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84      ) < 1E-3
           elif torch_device in ["cuda"]:
               assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03      ) < 1E-2
               assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97      ) < 1E-3
           else:
               assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25      ) < 1E-2
               assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21      ) < 1E-3



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

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

           lowercase__		       =						self.dummy_model()
           lowercase__		       =						self.dummy_sample_deter.to(UpperCAmelCase_      ) * scheduler.init_noise_sigma

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

               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )

               lowercase__		       =						scheduler.step(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						output.prev_sample

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

           if torch_device in ["mps"]:
               assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38      ) < 1E-2
               assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35      ) < 1E-3
           elif torch_device in ["cuda"]:
               assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12      ) < 1E-2
               assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71      ) < 1E-3
           else:
               assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62      ) < 1E-2
               assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26      ) < 1E-3







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

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

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

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

               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )

               lowercase__		       =						scheduler.step(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						output.prev_sample

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

           if torch_device in ["mps"]:
               assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88      ) < 1E-2
               assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11      ) < 1E-2
           elif torch_device in ["cuda"]:
               assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25      ) < 1E-2
               assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11      ) < 1E-2
           else:
               assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72      ) < 1E-2
               assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11      ) < 1E-2









'''simple docstring'''





print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))




