


'''simple docstring'''



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     (EulerDiscreteScheduler,)
     SCREAMING_SNAKE_CASE :				int							     =     1_0

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				**snake_case  :	List[str]    )		->							Tuple:
          __UpperCAmelCase			:				List[str]													=    {
              '''num_train_timesteps''': 1100,
              '''beta_start''': 0.0_001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**snake_case    )
          return config

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Optional[int]:
          for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=snake_case    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							List[Any]:
          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=snake_case							,				beta_end=snake_case    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							Dict:
          for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=snake_case    )

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

     def 				lowerCamelCase__				(       self  :	Any    )		->							List[Any]:
          __UpperCAmelCase			:				Dict													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				List[Any]													=    self.get_scheduler_config()
          __UpperCAmelCase			:				Optional[Any]													=    scheduler_class(**snake_case    )

          scheduler.set_timesteps(self.num_inference_steps    )

          __UpperCAmelCase			:				int													=    torch.manual_seed(0    )

          __UpperCAmelCase			:				str													=    self.dummy_model()
          __UpperCAmelCase			:				Optional[Any]													=    self.dummy_sample_deter * scheduler.init_noise_sigma
          __UpperCAmelCase			:				int													=    sample.to(snake_case    )

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

               __UpperCAmelCase			:				Dict													=    model(snake_case							,				snake_case    )

               __UpperCAmelCase			:				Dict													=    scheduler.step(snake_case							,				snake_case							,				snake_case							,				generator=snake_case    )
               __UpperCAmelCase			:				List[Any]													=    output.prev_sample

          __UpperCAmelCase			:				List[str]													=    torch.sum(torch.abs(snake_case    )    )
          __UpperCAmelCase			:				str													=    torch.mean(torch.abs(snake_case    )    )

          assert abs(result_sum.item() - 10.0_807    ) < 1E-2
          assert abs(result_mean.item() - 0.0_131    ) < 1E-3

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Any:
          __UpperCAmelCase			:				List[str]													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Optional[int]													=    self.get_scheduler_config(prediction_type='''v_prediction'''    )
          __UpperCAmelCase			:				List[str]													=    scheduler_class(**snake_case    )

          scheduler.set_timesteps(self.num_inference_steps    )

          __UpperCAmelCase			:				Any													=    torch.manual_seed(0    )

          __UpperCAmelCase			:				Optional[int]													=    self.dummy_model()
          __UpperCAmelCase			:				Any													=    self.dummy_sample_deter * scheduler.init_noise_sigma
          __UpperCAmelCase			:				Union[str, Any]													=    sample.to(snake_case    )

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

               __UpperCAmelCase			:				Dict													=    model(snake_case							,				snake_case    )

               __UpperCAmelCase			:				str													=    scheduler.step(snake_case							,				snake_case							,				snake_case							,				generator=snake_case    )
               __UpperCAmelCase			:				str													=    output.prev_sample

          __UpperCAmelCase			:				List[Any]													=    torch.sum(torch.abs(snake_case    )    )
          __UpperCAmelCase			:				Union[str, Any]													=    torch.mean(torch.abs(snake_case    )    )

          assert abs(result_sum.item() - 0.0_002    ) < 1E-2
          assert abs(result_mean.item() - 2.2676E-06    ) < 1E-3

     def 				lowerCamelCase__				(       self  :	str    )		->							List[Any]:
          __UpperCAmelCase			:				Any													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_scheduler_config()
          __UpperCAmelCase			:				str													=    scheduler_class(**snake_case    )

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

          __UpperCAmelCase			:				Optional[Any]													=    torch.manual_seed(0    )

          __UpperCAmelCase			:				Tuple													=    self.dummy_model()
          __UpperCAmelCase			:				Optional[int]													=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __UpperCAmelCase			:				List[Any]													=    sample.to(snake_case    )

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

               __UpperCAmelCase			:				Optional[int]													=    model(snake_case							,				snake_case    )

               __UpperCAmelCase			:				int													=    scheduler.step(snake_case							,				snake_case							,				snake_case							,				generator=snake_case    )
               __UpperCAmelCase			:				str													=    output.prev_sample

          __UpperCAmelCase			:				Any													=    torch.sum(torch.abs(snake_case    )    )
          __UpperCAmelCase			:				str													=    torch.mean(torch.abs(snake_case    )    )

          assert abs(result_sum.item() - 10.0_807    ) < 1E-2
          assert abs(result_mean.item() - 0.0_131    ) < 1E-3






     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							List[Any]:
          __UpperCAmelCase			:				str													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				List[str]													=    self.get_scheduler_config()
          __UpperCAmelCase			:				List[str]													=    scheduler_class(**snake_case							,				use_karras_sigmas=snake_case    )

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

          __UpperCAmelCase			:				List[Any]													=    torch.manual_seed(0    )

          __UpperCAmelCase			:				List[str]													=    self.dummy_model()
          __UpperCAmelCase			:				int													=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __UpperCAmelCase			:				Union[str, Any]													=    sample.to(snake_case    )

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

               __UpperCAmelCase			:				int													=    model(snake_case							,				snake_case    )

               __UpperCAmelCase			:				List[Any]													=    scheduler.step(snake_case							,				snake_case							,				snake_case							,				generator=snake_case    )
               __UpperCAmelCase			:				Optional[int]													=    output.prev_sample

          __UpperCAmelCase			:				Dict													=    torch.sum(torch.abs(snake_case    )    )
          __UpperCAmelCase			:				Union[str, Any]													=    torch.mean(torch.abs(snake_case    )    )

          assert abs(result_sum.item() - 124.52_299_499_511_719    ) < 1E-2
          assert abs(result_mean.item() - 0.16_213_932_633_399_963    ) < 1E-3



'''simple docstring'''



def 			_a			(		_lowercase      :			int = 4000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    []
     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(_lowercase						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    b, a + b
     return sum(_lowercase						)


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