





"""simple docstring"""




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      UpperCAmelCase_  : List[Any]				      =(EulerAncestralDiscreteScheduler,)
      UpperCAmelCase_  : Union[str, Any]				      =10







      def   UpperCAmelCase						(	self			,  **UpperCAmelCase				)    ->				List[str]:






        '''simple docstring'''






        __snake_case				:			str            =    {
            "num_train_timesteps": 1100,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**UpperCAmelCase				)
        return config







      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				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=UpperCAmelCase			,  beta_end=UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






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







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






        '''simple docstring'''






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







      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        __snake_case				:			List[str]            =    self.scheduler_classes[0]
        __snake_case				:			Dict            =    self.get_scheduler_config()
        __snake_case				:			Any            =    scheduler_class(**UpperCAmelCase				)

        scheduler.set_timesteps(self.num_inference_steps				)

        __snake_case				:			str            =    torch.manual_seed(0				)

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

        for i, t in enumerate(scheduler.timesteps				):
          __snake_case				:			Optional[Any]            =    scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase				)

          __snake_case				:			List[Any]            =    model(UpperCAmelCase			,  UpperCAmelCase				)

          __snake_case				:			Tuple            =    scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  generator=UpperCAmelCase				)
          __snake_case				:			Tuple            =    output.prev_sample

        __snake_case				:			Dict            =    torch.sum(torch.abs(UpperCAmelCase				)				)
        __snake_case				:			Optional[int]            =    torch.mean(torch.abs(UpperCAmelCase				)				)

        assert abs(result_sum.item() - 152.3_192				) < 1E-2
        assert abs(result_mean.item() - 0.1_983				) < 1E-3







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        __snake_case				:			List[str]            =    self.scheduler_classes[0]
        __snake_case				:			List[str]            =    self.get_scheduler_config(prediction_type="v_prediction"				)
        __snake_case				:			Union[str, Any]            =    scheduler_class(**UpperCAmelCase				)

        scheduler.set_timesteps(self.num_inference_steps				)

        __snake_case				:			Tuple            =    torch.manual_seed(0				)

        __snake_case				:			Union[str, Any]            =    self.dummy_model()
        __snake_case				:			Optional[Any]            =    self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case				:			Tuple            =    sample.to(UpperCAmelCase				)

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

          __snake_case				:			Tuple            =    model(UpperCAmelCase			,  UpperCAmelCase				)

          __snake_case				:			Union[str, Any]            =    scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  generator=UpperCAmelCase				)
          __snake_case				:			str            =    output.prev_sample

        __snake_case				:			Tuple            =    torch.sum(torch.abs(UpperCAmelCase				)				)
        __snake_case				:			Optional[Any]            =    torch.mean(torch.abs(UpperCAmelCase				)				)

        assert abs(result_sum.item() - 108.4_439				) < 1E-2
        assert abs(result_mean.item() - 0.1_412				) < 1E-3







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






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    self.scheduler_classes[0]
        __snake_case				:			Optional[int]            =    self.get_scheduler_config()
        __snake_case				:			Tuple            =    scheduler_class(**UpperCAmelCase				)

        scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCAmelCase				)
        __snake_case				:			Any            =    torch.manual_seed(0				)

        __snake_case				:			str            =    self.dummy_model()
        __snake_case				:			Any            =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case				:			Any            =    sample.to(UpperCAmelCase				)

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

          __snake_case				:			Dict            =    model(UpperCAmelCase			,  UpperCAmelCase				)

          __snake_case				:			List[Any]            =    scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  generator=UpperCAmelCase				)
          __snake_case				:			Tuple            =    output.prev_sample

        __snake_case				:			Union[str, Any]            =    torch.sum(torch.abs(UpperCAmelCase				)				)
        __snake_case				:			Optional[int]            =    torch.mean(torch.abs(UpperCAmelCase				)				)

        assert abs(result_sum.item() - 152.3_192				) < 1E-2
        assert abs(result_mean.item() - 0.1_983				) < 1E-3








def  lowerCAmelCase__(		lowercase    :      int = 10**6			)	->      int:

  if n <= 0:
    raise ValueError("Please enter an integer greater than 0"			)

  __snake_case				:			List[Any]            =    list(range(n + 1			)			)
  for number in range(2				,       n + 1			):
    if phi[number] == number:
      phi[number] -= 1
      for multiple in range(number * 2				,       n + 1				,       lowercase			):
        __snake_case				:			Optional[int]            =    (phi[multiple] // number) * (number - 1)

  __snake_case				:			List[Any]            =    1
  for number in range(1				,       n + 1			):
    if (answer / phi[answer]) < (number / phi[number]):
      __snake_case				:			List[str]            =    number

  return answer


if __name__ == "__main__":
     print(solution())

