






"""simple docstring"""



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  lowerCAmelCase__ (						__magic_name__      ):
   SCREAMING_SNAKE_CASE_         =(KDPMaAncestralDiscreteScheduler,)
   SCREAMING_SNAKE_CASE_         =10
   def 			__a       (		self			:  Tuple       , **snake_case__			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						int       =       {
            "num_train_timesteps": 1_1_0_0,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**snake_case__       )
        return config
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=snake_case__       )
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       , [0.0002, 0.002, 0.02]       ):
             self.check_over_configs(beta_start=snake_case__       , beta_end=snake_case__       )
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=snake_case__       )
   def 			__a       (		self			:  Any       ):


        '''simple docstring'''
        if torch_device == "mps":
             return
        UpperCAmelCase__	:						Tuple       =       self.scheduler_classes[0]
        UpperCAmelCase__	:						List[Any]       =       self.get_scheduler_config()
        UpperCAmelCase__	:						List[str]       =       scheduler_class(**snake_case__       )

        scheduler.set_timesteps(self.num_inference_steps       )

        UpperCAmelCase__	:						Any       =       torch.manual_seed(0       )

        UpperCAmelCase__	:						str       =       self.dummy_model()
        UpperCAmelCase__	:						Dict       =       self.dummy_sample_deter * scheduler.init_noise_sigma
        UpperCAmelCase__	:						Dict       =       sample.to(snake_case__       )

        for i, t in enumerate(scheduler.timesteps       ):
             UpperCAmelCase__	:						str       =       scheduler.scale_model_input(snake_case__       , snake_case__       )

             UpperCAmelCase__	:						str       =       model(snake_case__       , snake_case__       )

             UpperCAmelCase__	:						int       =       scheduler.step(snake_case__       , snake_case__       , snake_case__       , generator=snake_case__       )
             UpperCAmelCase__	:						Tuple       =       output.prev_sample

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

        assert abs(result_sum.item() - 1_3849.3877       ) < 1e-2
        assert abs(result_mean.item() - 18.0331       ) < 5e-3
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=snake_case__       )
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        if torch_device == "mps":
             return
        UpperCAmelCase__	:						List[str]       =       self.scheduler_classes[0]
        UpperCAmelCase__	:						Any       =       self.get_scheduler_config(prediction_type="v_prediction"       )
        UpperCAmelCase__	:						Dict       =       scheduler_class(**snake_case__       )

        scheduler.set_timesteps(self.num_inference_steps       )

        UpperCAmelCase__	:						Any       =       self.dummy_model()
        UpperCAmelCase__	:						List[str]       =       self.dummy_sample_deter * scheduler.init_noise_sigma
        UpperCAmelCase__	:						Any       =       sample.to(snake_case__       )

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

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

             UpperCAmelCase__	:						Optional[int]       =       model(snake_case__       , snake_case__       )

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

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

        assert abs(result_sum.item() - 328.9970       ) < 1e-2
        assert abs(result_mean.item() - 0.4284       ) < 1e-3




   def 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        if torch_device == "mps":
             return
        UpperCAmelCase__	:						Optional[Any]       =       self.scheduler_classes[0]
        UpperCAmelCase__	:						Tuple       =       self.get_scheduler_config()
        UpperCAmelCase__	:						Optional[int]       =       scheduler_class(**snake_case__       )

        scheduler.set_timesteps(self.num_inference_steps       , device=snake_case__       )
        UpperCAmelCase__	:						List[Any]       =       torch.manual_seed(0       )

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

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

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

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

        UpperCAmelCase__	:						Tuple       =       torch.sum(torch.abs(snake_case__       )       )
        UpperCAmelCase__	:						Dict       =       torch.mean(torch.abs(snake_case__       )       )

        assert abs(result_sum.item() - 1_3849.3818       ) < 1e-1
        assert abs(result_mean.item() - 18.0331       ) < 1e-3










"""simple docstring"""



def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int							)->		list:






     '''simple docstring'''



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

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

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

     return totients


if __name__ == "__main__":
 import doctest

 doctest.testmod()


