


'''simple docstring'''





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							UpperCAmelCase ( _snake_case	):
 UpperCAmelCase				=				(EulerAncestralDiscreteScheduler,)
 UpperCAmelCase				=				10


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					**__lowerCamelCase       :  Tuple   ):
    UpperCAmelCase__  :Tuple            =			{
        '''num_train_timesteps''': 1_1_0_0,
        '''beta_start''': 0.00_01,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
    }

    config.update(**__lowerCamelCase   )
    return config


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    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 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :int            =			self.scheduler_classes[0]
    UpperCAmelCase__  :Optional[Any]            =			self.get_scheduler_config()
    UpperCAmelCase__  :int            =			scheduler_class(**__lowerCamelCase   )

    scheduler.set_timesteps(self.num_inference_steps   )

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

    UpperCAmelCase__  :Optional[Any]            =			self.dummy_model()
    UpperCAmelCase__  :List[Any]            =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    UpperCAmelCase__  :Any            =			sample.to(__lowerCamelCase   )

    for i, t in enumerate(scheduler.timesteps   ):
       UpperCAmelCase__  :Any            =			scheduler.scale_model_input(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :Any            =			model(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :List[Any]            =			scheduler.step(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					generator=__lowerCamelCase   )
       UpperCAmelCase__  :Any            =			output.prev_sample

    UpperCAmelCase__  :int            =			torch.sum(torch.abs(__lowerCamelCase   )   )
    UpperCAmelCase__  :Optional[Any]            =			torch.mean(torch.abs(__lowerCamelCase   )   )

    assert abs(result_sum.item() - 1_52.31_92   ) < 1e-2
    assert abs(result_mean.item() - 0.19_83   ) < 1e-3


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Union[str, Any]            =			self.scheduler_classes[0]
    UpperCAmelCase__  :Optional[Any]            =			self.get_scheduler_config(prediction_type='''v_prediction'''   )
    UpperCAmelCase__  :Dict            =			scheduler_class(**__lowerCamelCase   )

    scheduler.set_timesteps(self.num_inference_steps   )

    UpperCAmelCase__  :Optional[int]            =			torch.manual_seed(0   )

    UpperCAmelCase__  :Any            =			self.dummy_model()
    UpperCAmelCase__  :Optional[int]            =			self.dummy_sample_deter * scheduler.init_noise_sigma
    UpperCAmelCase__  :Optional[int]            =			sample.to(__lowerCamelCase   )

    for i, t in enumerate(scheduler.timesteps   ):
       UpperCAmelCase__  :List[Any]            =			scheduler.scale_model_input(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :List[str]            =			model(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :Any            =			scheduler.step(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					generator=__lowerCamelCase   )
       UpperCAmelCase__  :List[str]            =			output.prev_sample

    UpperCAmelCase__  :List[str]            =			torch.sum(torch.abs(__lowerCamelCase   )   )
    UpperCAmelCase__  :Any            =			torch.mean(torch.abs(__lowerCamelCase   )   )

    assert abs(result_sum.item() - 1_08.44_39   ) < 1e-2
    assert abs(result_mean.item() - 0.14_12   ) < 1e-3





 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :List[Any]            =			self.scheduler_classes[0]
    UpperCAmelCase__  :Dict            =			self.get_scheduler_config()
    UpperCAmelCase__  :Optional[Any]            =			scheduler_class(**__lowerCamelCase   )

    scheduler.set_timesteps(self.num_inference_steps ,					device=__lowerCamelCase   )
    UpperCAmelCase__  :str            =			torch.manual_seed(0   )

    UpperCAmelCase__  :str            =			self.dummy_model()
    UpperCAmelCase__  :Any            =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    UpperCAmelCase__  :Tuple            =			sample.to(__lowerCamelCase   )

    for t in scheduler.timesteps:
       UpperCAmelCase__  :List[Any]            =			scheduler.scale_model_input(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :List[Any]            =			model(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :int            =			scheduler.step(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					generator=__lowerCamelCase   )
       UpperCAmelCase__  :str            =			output.prev_sample

    UpperCAmelCase__  :List[Any]            =			torch.sum(torch.abs(__lowerCamelCase   )   )
    UpperCAmelCase__  :List[str]            =			torch.mean(torch.abs(__lowerCamelCase   )   )

    assert abs(result_sum.item() - 1_52.31_92   ) < 1e-2
    assert abs(result_mean.item() - 0.19_83   ) < 1e-3







'''simple docstring'''





def 					a__    (				UpperCamelCase_	:			int	):
   UpperCAmelCase__  :str            =			[True for i in range(n + 1	)]
   UpperCAmelCase__  :List[str]            =			[i - 1 for i in range(n + 1	)]
   UpperCAmelCase__  :str            =			[]
   for i in range(2,      n + 1	):
      if is_prime[i]:
         primes.append(UpperCamelCase_	)
      for j in range(0,      len(UpperCamelCase_	)	):
         if i * primes[j] >= n:
            break
         UpperCAmelCase__  :List[str]            =			False

         if i % primes[j] == 0:
            UpperCAmelCase__  :Any            =			totients[i] * primes[j]
            break

         UpperCAmelCase__  :str            =			totients[i] * (primes[j] - 1)

   return totients


if __name__ == "__main__":
    import doctest

    doctest.testmod()



