

"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							(KDPMaDiscreteScheduler,)
    lowerCamelCase		     =							10


    def 						snake_case__					(				self			:	Optional[Any],**lowercase_			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       {
            'num_train_timesteps': 1_1_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**lowercase_		)
        return config


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001],[0.0_002, 0.002, 0.02]		):
            self.check_over_configs(beta_start=lowercase_,beta_end=lowercase_		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Any:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Dict		)->   Any:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	List[str]		)->   str:





        '''simple docstring'''







        A__   				=       self.scheduler_classes[0]
        A__   				=       self.get_scheduler_config(prediction_type='v_prediction'		)
        A__   				=       scheduler_class(**lowercase_		)

        scheduler.set_timesteps(self.num_inference_steps		)

        A__   				=       self.dummy_model()
        A__   				=       self.dummy_sample_deter * scheduler.init_noise_sigma
        A__   				=       sample.to(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__   				=       scheduler.scale_model_input(lowercase_,lowercase_		)

            A__   				=       model(lowercase_,lowercase_		)

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_		)
            A__   				=       output.prev_sample

        A__   				=       torch.sum(torch.abs(lowercase_		)		)
        A__   				=       torch.mean(torch.abs(lowercase_		)		)

        if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.6_934E-07		) < 1E-2
            assert abs(result_mean.item() - 6.1_112E-10		) < 1E-3
        else:
            # CUDA
            assert abs(result_sum.item() - 4.693_428_650_170_972E-07		) < 1E-2
            assert abs(result_mean.item() - 0.0_002		) < 1E-3


    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        if torch_device == "mps":
            return
        A__   				=       self.scheduler_classes[0]
        A__   				=       self.get_scheduler_config()
        A__   				=       scheduler_class(**lowercase_		)

        scheduler.set_timesteps(self.num_inference_steps		)

        A__   				=       self.dummy_model()
        A__   				=       self.dummy_sample_deter * scheduler.init_noise_sigma
        A__   				=       sample.to(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__   				=       scheduler.scale_model_input(lowercase_,lowercase_		)

            A__   				=       model(lowercase_,lowercase_		)

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_		)
            A__   				=       output.prev_sample

        A__   				=       torch.sum(torch.abs(lowercase_		)		)
        A__   				=       torch.mean(torch.abs(lowercase_		)		)

        if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3
        else:
            # CUDA
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3


    def 						snake_case__					(				self			:	Union[str, Any]		)->   str:





        '''simple docstring'''







        if torch_device == "mps":
            return
        A__   				=       self.scheduler_classes[0]
        A__   				=       self.get_scheduler_config()
        A__   				=       scheduler_class(**lowercase_		)

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

        A__   				=       self.dummy_model()
        A__   				=       self.dummy_sample_deter.to(lowercase_		) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
            A__   				=       scheduler.scale_model_input(lowercase_,lowercase_		)

            A__   				=       model(lowercase_,lowercase_		)

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_		)
            A__   				=       output.prev_sample

        A__   				=       torch.sum(torch.abs(lowercase_		)		)
        A__   				=       torch.mean(torch.abs(lowercase_		)		)

        if str(lowercase_		).startswith('cpu'		):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3
        else:
            # CUDA
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int = 1000				)							->				int:





    '''simple docstring'''






    A__   				=       3
    A__   				=       0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


if __name__ == "__main__":
     print(f"""{solution() = }""")

