





'''simple docstring'''






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Any											=       (EulerDiscreteScheduler,)
      _lowercase:      Union[str, Any]											=       10




      def     lowercase__   (      self      :			Optional[int]      ,      **__snake_case      :			Union[str, Any]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							{
                """num_train_timesteps""": 11_00,
                """beta_start""": 0.00_01,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
            }

            config.update(**__snake_case  )
            return config




      def     lowercase__   (      self      :			Optional[int]  )    ->				Any:
            for timesteps in [10, 50, 1_00, 10_00]:
                  self.check_over_configs(num_train_timesteps=__snake_case  )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Optional[int]:
            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=__snake_case      ,      beta_end=__snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				Union[str, Any]:
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=__snake_case  )




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




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

            scheduler.set_timesteps(self.num_inference_steps  )

            _lowerCAmelCase												=							torch.manual_seed(0  )

            _lowerCAmelCase												=							self.dummy_model()
            _lowerCAmelCase												=							self.dummy_sample_deter * scheduler.init_noise_sigma
            _lowerCAmelCase												=							sample.to(__snake_case  )

            for i, t in enumerate(scheduler.timesteps  ):
                  _lowerCAmelCase												=							scheduler.scale_model_input(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							model(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							scheduler.step(__snake_case      ,      __snake_case      ,      __snake_case      ,      generator=__snake_case  )
                  _lowerCAmelCase												=							output.prev_sample

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

            assert abs(result_sum.item() - 10.08_07  ) < 1E-2
            assert abs(result_mean.item() - 0.01_31  ) < 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(**__snake_case  )

            scheduler.set_timesteps(self.num_inference_steps  )

            _lowerCAmelCase												=							torch.manual_seed(0  )

            _lowerCAmelCase												=							self.dummy_model()
            _lowerCAmelCase												=							self.dummy_sample_deter * scheduler.init_noise_sigma
            _lowerCAmelCase												=							sample.to(__snake_case  )

            for i, t in enumerate(scheduler.timesteps  ):
                  _lowerCAmelCase												=							scheduler.scale_model_input(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							model(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							scheduler.step(__snake_case      ,      __snake_case      ,      __snake_case      ,      generator=__snake_case  )
                  _lowerCAmelCase												=							output.prev_sample

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

            assert abs(result_sum.item() - 0.00_02  ) < 1E-2
            assert abs(result_mean.item() - 2.2_6_7_6E-0_6  ) < 1E-3




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

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

            _lowerCAmelCase												=							torch.manual_seed(0  )

            _lowerCAmelCase												=							self.dummy_model()
            _lowerCAmelCase												=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            _lowerCAmelCase												=							sample.to(__snake_case  )

            for t in scheduler.timesteps:
                  _lowerCAmelCase												=							scheduler.scale_model_input(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							model(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							scheduler.step(__snake_case      ,      __snake_case      ,      __snake_case      ,      generator=__snake_case  )
                  _lowerCAmelCase												=							output.prev_sample

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

            assert abs(result_sum.item() - 10.08_07  ) < 1E-2
            assert abs(result_mean.item() - 0.01_31  ) < 1E-3




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

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

            _lowerCAmelCase												=							torch.manual_seed(0  )

            _lowerCAmelCase												=							self.dummy_model()
            _lowerCAmelCase												=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            _lowerCAmelCase												=							sample.to(__snake_case  )

            for t in scheduler.timesteps:
                  _lowerCAmelCase												=							scheduler.scale_model_input(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							model(__snake_case      ,      __snake_case  )

                  _lowerCAmelCase												=							scheduler.step(__snake_case      ,      __snake_case      ,      __snake_case      ,      generator=__snake_case  )
                  _lowerCAmelCase												=							output.prev_sample

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

            assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19  ) < 1E-2
            assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63  ) < 1E-3













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase = 4_00_00_00  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      _lowerCAmelCase					,							_lowerCAmelCase												=							0, 1
      while b <= n:
            if b % 2 == 0:
                  even_fibs.append(lowerCAmelCase  )
            _lowerCAmelCase					,							_lowerCAmelCase												=							b, a + b
      return sum(lowerCAmelCase  )


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






