





'''simple docstring'''






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class  UpperCAmelCase     (			_UpperCamelCase   ):
      _lowercase:			Optional[Any]						     =   (DDPMParallelScheduler,)




      def 				lowercase__    (			self			: str			,			**__snake_case			: Dict  )				->      List[str]:
            _lowerCAmelCase							     =							{
                """num_train_timesteps""": 10_00,
                """beta_start""": 0.00_01,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
                """variance_type""": """fixed_small""",
                """clip_sample""": True,
            }

            config.update(**__lowerCamelCase  )
            return config




      def 				lowercase__    (			self			: Tuple  )				->      Any:
            for timesteps in [1, 5, 1_00, 10_00]:
                  self.check_over_configs(num_train_timesteps=__lowerCamelCase  )




      def 				lowercase__    (			self			: List[Any]  )				->      Dict:
            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=__lowerCamelCase			,			beta_end=__lowerCamelCase  )




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




      def 				lowercase__    (			self			: Dict  )				->      int:
            for variance in ["fixed_small", "fixed_large", "other"]:
                  self.check_over_configs(variance_type=__lowerCamelCase  )




      def 				lowercase__    (			self			: Tuple  )				->      Optional[Any]:
            for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=__lowerCamelCase  )




      def 				lowercase__    (			self			: str  )				->      List[str]:
            self.check_over_configs(thresholding=__lowerCamelCase  )
            for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=__lowerCamelCase			,			prediction_type=__lowerCamelCase			,			sample_max_value=__lowerCamelCase			,			)




      def 				lowercase__    (			self			: Any  )				->      List[str]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(prediction_type=__lowerCamelCase  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      int:
            for t in [0, 5_00, 9_99]:
                  self.check_over_forward(time_step=__lowerCamelCase  )




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

            assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_87  ) - 0.0_09_79  )  ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_99  ) - 0.02  )  ) < 1E-5




      def 				lowercase__    (			self			: List[Any]  )				->      Dict:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__lowerCamelCase  )

            _lowerCAmelCase							     =							len(__lowerCamelCase  )

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

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

            _lowerCAmelCase							     =							model(samples.flatten(0			,			1  )			,			timesteps.flatten(0			,			1  )  )
            _lowerCAmelCase							     =							scheduler.batch_step_no_noise(__lowerCamelCase			,			timesteps.flatten(0			,			1  )			,			samples.flatten(0			,			1  )  )

            _lowerCAmelCase							     =							torch.sum(torch.abs(__lowerCamelCase  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(__lowerCamelCase  )  )

            assert abs(result_sum.item() - 11_53.18_33  ) < 1E-2
            assert abs(result_mean.item() - 0.50_05  ) < 1E-3




      def 				lowercase__    (			self			: Dict  )				->      List[str]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__lowerCamelCase  )

            _lowerCAmelCase							     =							len(__lowerCamelCase  )

            _lowerCAmelCase							     =							self.dummy_model()
            _lowerCAmelCase							     =							self.dummy_sample_deter
            _lowerCAmelCase							     =							torch.manual_seed(0  )

            for t in reversed(range(__lowerCamelCase  )  ):
                  # 1. predict noise residual
                  _lowerCAmelCase							     =							model(__lowerCamelCase			,			__lowerCamelCase  )

                  # 2. predict previous mean of sample x_t-1
                  _lowerCAmelCase							     =							scheduler.step(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			generator=__lowerCamelCase  ).prev_sample

                  _lowerCAmelCase							     =							pred_prev_sample

            _lowerCAmelCase							     =							torch.sum(torch.abs(__lowerCamelCase  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(__lowerCamelCase  )  )

            assert abs(result_sum.item() - 2_58.96_06  ) < 1E-2
            assert abs(result_mean.item() - 0.33_72  ) < 1E-3




      def 				lowercase__    (			self			: List[Any]  )				->      List[str]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config(prediction_type="""v_prediction"""  )
            _lowerCAmelCase							     =							scheduler_class(**__lowerCamelCase  )

            _lowerCAmelCase							     =							len(__lowerCamelCase  )

            _lowerCAmelCase							     =							self.dummy_model()
            _lowerCAmelCase							     =							self.dummy_sample_deter
            _lowerCAmelCase							     =							torch.manual_seed(0  )

            for t in reversed(range(__lowerCamelCase  )  ):
                  # 1. predict noise residual
                  _lowerCAmelCase							     =							model(__lowerCamelCase			,			__lowerCamelCase  )

                  # 2. predict previous mean of sample x_t-1
                  _lowerCAmelCase							     =							scheduler.step(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			generator=__lowerCamelCase  ).prev_sample

                  _lowerCAmelCase							     =							pred_prev_sample

            _lowerCAmelCase							     =							torch.sum(torch.abs(__lowerCamelCase  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(__lowerCamelCase  )  )

            assert abs(result_sum.item() - 2_02.02_96  ) < 1E-2
            assert abs(result_mean.item() - 0.26_31  ) < 1E-3




      def 				lowercase__    (			self			: Tuple  )				->      List[str]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__lowerCamelCase  )

            _lowerCAmelCase							     =							[1_00, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=__lowerCamelCase  )

            _lowerCAmelCase							     =							scheduler.timesteps

            for i, timestep in enumerate(__lowerCamelCase  ):
                  if i == len(__lowerCamelCase  ) - 1:
                        _lowerCAmelCase							     =							-1
                  else:
                        _lowerCAmelCase							     =							timesteps[i + 1]

                  _lowerCAmelCase							     =							scheduler.previous_timestep(__lowerCamelCase  )
                  _lowerCAmelCase							     =							prev_t.item()

                  self.assertEqual(__lowerCamelCase			,			__lowerCamelCase  )




      def 				lowercase__    (			self			: List[Any]  )				->      List[Any]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__lowerCamelCase  )

            _lowerCAmelCase							     =							[1_00, 87, 50, 51, 0]

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




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

            _lowerCAmelCase							     =							[1_00, 87, 50, 1, 0]
            _lowerCAmelCase							     =							len(__lowerCamelCase  )

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




      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**__lowerCamelCase  )

            _lowerCAmelCase							     =							[scheduler.config.num_train_timesteps]

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












import numpy as np




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return 1 / (1 + np.exp(-vector   ))




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return vector * sigmoid(1.7_02 * vector   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






