





"""simple docstring"""





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 		lowercase ( A__ ):
      _SCREAMING_SNAKE_CASE         =				(DDIMScheduler,)
      _SCREAMING_SNAKE_CASE         =				(('eta', 0.0), ('num_inference_steps', 50))





      def    _snake_case      (	self				,						**lowercase			)		->					Optional[Any]:
            lowerCAmelCase  						=	{
                """num_train_timesteps""": 1_000,
                """beta_start""": 0.0_001,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
                """clip_sample""": True,
            }

            config.update(**lowercase			)
            return config





      def    _snake_case      (	self				,						**lowercase			)		->					int:
            lowerCAmelCase  						=	self.scheduler_classes[0]
            lowerCAmelCase  						=	self.get_scheduler_config(**lowercase			)
            lowerCAmelCase  						=	scheduler_class(**lowercase			)

            lowerCAmelCase						,		lowerCAmelCase  						=	10, 0.0

            lowerCAmelCase  						=	self.dummy_model()
            lowerCAmelCase  						=	self.dummy_sample_deter

            scheduler.set_timesteps(lowercase			)

            for t in scheduler.timesteps:
                  lowerCAmelCase  						=	model(lowercase				,						lowercase			)
                  lowerCAmelCase  						=	scheduler.step(lowercase				,						lowercase				,						lowercase				,						lowercase			).prev_sample

            return sample





      def    _snake_case      (	self			)		->					Optional[int]:
            for timesteps in [100, 500, 1_000]:
                  self.check_over_configs(num_train_timesteps=lowercase			)





      def    _snake_case      (	self			)		->					Union[str, Any]:
            for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=lowercase			)

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





      def    _snake_case      (	self			)		->					int:
            for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,						[0.002, 0.02, 0.2, 2]			):
                  self.check_over_configs(beta_start=lowercase				,						beta_end=lowercase			)





      def    _snake_case      (	self			)		->					str:
            for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=lowercase			)





      def    _snake_case      (	self			)		->					Any:
            for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=lowercase			)





      def    _snake_case      (	self			)		->					Tuple:
            for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=lowercase			)





      def    _snake_case      (	self			)		->					List[Any]:
            for timestep_spacing in ["trailing", "leading"]:
                  self.check_over_configs(timestep_spacing=lowercase			)





      def    _snake_case      (	self			)		->					List[Any]:
            for rescale_betas_zero_snr in [True, False]:
                  self.check_over_configs(rescale_betas_zero_snr=lowercase			)





      def    _snake_case      (	self			)		->					List[Any]:
            self.check_over_configs(thresholding=lowercase			)
            for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=lowercase				,						prediction_type=lowercase				,						sample_max_value=lowercase				,						)





      def    _snake_case      (	self			)		->					Optional[Any]:
            for t in [1, 10, 49]:
                  self.check_over_forward(time_step=lowercase			)





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





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





      def    _snake_case      (	self			)		->					Union[str, Any]:
            lowerCAmelCase  						=	self.scheduler_classes[0]
            lowerCAmelCase  						=	self.get_scheduler_config()
            lowerCAmelCase  						=	scheduler_class(**lowercase			)

            assert torch.sum(torch.abs(scheduler._get_variance(0				,						0			) - 0.0			)			) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(420				,						400			) - 0.14_771			)			) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(980				,						960			) - 0.32_460			)			) < 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			) - 0.00_979			)			) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(999				,						998			) - 0.02			)			) < 1e-5





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	self.full_loop()

            lowerCAmelCase  						=	torch.sum(torch.abs(lowercase			)			)
            lowerCAmelCase  						=	torch.mean(torch.abs(lowercase			)			)

            assert abs(result_sum.item() - 172.0_067			) < 1e-2
            assert abs(result_mean.item() - 0.223_967			) < 1e-3





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.full_loop(prediction_type="""v_prediction"""			)

            lowerCAmelCase  						=	torch.sum(torch.abs(lowercase			)			)
            lowerCAmelCase  						=	torch.mean(torch.abs(lowercase			)			)

            assert abs(result_sum.item() - 52.5_302			) < 1e-2
            assert abs(result_mean.item() - 0.0_684			) < 1e-3





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	self.full_loop(set_alpha_to_one=lowercase				,						beta_start=0.01			)
            lowerCAmelCase  						=	torch.sum(torch.abs(lowercase			)			)
            lowerCAmelCase  						=	torch.mean(torch.abs(lowercase			)			)

            assert abs(result_sum.item() - 149.8_295			) < 1e-2
            assert abs(result_mean.item() - 0.1_951			) < 1e-3





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	self.full_loop(set_alpha_to_one=lowercase				,						beta_start=0.01			)
            lowerCAmelCase  						=	torch.sum(torch.abs(lowercase			)			)
            lowerCAmelCase  						=	torch.mean(torch.abs(lowercase			)			)

            assert abs(result_sum.item() - 149.0_784			) < 1e-2
            assert abs(result_mean.item() - 0.1_941			) < 1e-3













"""simple docstring"""





from scipy.constants import g


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						float							,						SCREAMING_SNAKE_CASE					:						float      ):







      '''simple docstring'''




      if mass < 0:
            # handling of negative values of mass
            raise ValueError("""The mass of a body cannot be negative"""      )
      if height < 0:
            # handling of negative values of height
            raise ValueError("""The height above the ground cannot be negative"""      )
      return mass * g * height


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="potential_energy")






