"""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   lowerCAmelCase_    (			_snake_case					):



    """simple docstring"""
    _lowerCAmelCase     :    Optional[int]			  =						(DPMSolverSDEScheduler,)
    _lowerCAmelCase     :    int			  =						10

    def 		snake_case (				self		,    **lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			{
              'num_train_timesteps': 11_00,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'noise_sampler_seed': 0,
          }

          config.update(**lowerCAmelCase__    )
          return config

    def 		snake_case (				self    ):






          """simple docstring"""

          for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=lowerCAmelCase__    )

    def 		snake_case (				self    ):






          """simple docstring"""

          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=lowerCAmelCase__		,    beta_end=lowerCAmelCase__    )

    def 		snake_case (				self    ):






          """simple docstring"""

          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=lowerCAmelCase__    )

    def 		snake_case (				self    ):






          """simple docstring"""

          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=lowerCAmelCase__    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.scheduler_classes[0]
          snake_case								=			self.get_scheduler_config()
          snake_case								=			scheduler_class(**lowerCAmelCase__    )

          scheduler.set_timesteps(self.num_inference_steps    )

          snake_case								=			self.dummy_model()
          snake_case								=			self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case								=			sample.to(lowerCAmelCase__    )

          for i, t in enumerate(scheduler.timesteps    ):
                snake_case								=			scheduler.scale_model_input(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			model(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__    )
                snake_case								=			output.prev_sample

          snake_case								=			torch.sum(torch.abs(lowerCAmelCase__    )    )
          snake_case								=			torch.mean(torch.abs(lowerCAmelCase__    )    )

          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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.scheduler_classes[0]
          snake_case								=			self.get_scheduler_config(prediction_type='v_prediction'    )
          snake_case								=			scheduler_class(**lowerCAmelCase__    )

          scheduler.set_timesteps(self.num_inference_steps    )

          snake_case								=			self.dummy_model()
          snake_case								=			self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case								=			sample.to(lowerCAmelCase__    )

          for i, t in enumerate(scheduler.timesteps    ):
                snake_case								=			scheduler.scale_model_input(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			model(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__    )
                snake_case								=			output.prev_sample

          snake_case								=			torch.sum(torch.abs(lowerCAmelCase__    )    )
          snake_case								=			torch.mean(torch.abs(lowerCAmelCase__    )    )

          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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.scheduler_classes[0]
          snake_case								=			self.get_scheduler_config()
          snake_case								=			scheduler_class(**lowerCAmelCase__    )

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

          snake_case								=			self.dummy_model()
          snake_case								=			self.dummy_sample_deter.to(lowerCAmelCase__    ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                snake_case								=			scheduler.scale_model_input(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			model(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__    )
                snake_case								=			output.prev_sample

          snake_case								=			torch.sum(torch.abs(lowerCAmelCase__    )    )
          snake_case								=			torch.mean(torch.abs(lowerCAmelCase__    )    )

          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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.scheduler_classes[0]
          snake_case								=			self.get_scheduler_config()
          snake_case								=			scheduler_class(**lowerCAmelCase__		,    use_karras_sigmas=lowerCAmelCase__    )

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

          snake_case								=			self.dummy_model()
          snake_case								=			self.dummy_sample_deter.to(lowerCAmelCase__    ) * scheduler.init_noise_sigma
          snake_case								=			sample.to(lowerCAmelCase__    )

          for t in scheduler.timesteps:
                snake_case								=			scheduler.scale_model_input(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			model(lowerCAmelCase__		,    lowerCAmelCase__    )

                snake_case								=			scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__    )
                snake_case								=			output.prev_sample

          snake_case								=			torch.sum(torch.abs(lowerCAmelCase__    )    )
          snake_case								=			torch.mean(torch.abs(lowerCAmelCase__    )    )

          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"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					)					->				float:





						"""simple docstring"""






						if initial_intensity < 0:
												raise ValueError('The value of intensity cannot be negative'					)
												# handling of negative values of initial intensity
						if angle < 0 or angle > 3_6_0:
												raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'					)
												# handling of values out of allowed range
						return initial_intensity * (math.cos(math.radians(_UpperCamelCase					)					) ** 2)


if __name__ == "__main__":
					import doctest

					doctest.testmod(name="malus_law")


