





'''simple docstring'''







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



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

      def   _lowerCamelCase		(							self    : Optional[Any]   ,**UpperCamelCase    : Any  )		->  str:
           _lowercase							:      Optional[Any]  						=							{
               'num_train_timesteps': 1100,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
           }

           config.update(**UpperCamelCase  )
           return config

      def   _lowerCamelCase		(							self    : Union[str, Any]  )		->  Any:
           for timesteps in [10, 50, 100, 1000]:
                self.check_over_configs(num_train_timesteps=UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Union[str, Any]  )		->  Union[str, Any]:
           for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]   ,[0.0_0_0_2, 0.0_0_2, 0.0_2]  ):
                self.check_over_configs(beta_start=UpperCamelCase   ,beta_end=UpperCamelCase  )

      def   _lowerCamelCase		(							self    : str  )		->  Union[str, Any]:
           for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=UpperCamelCase  )

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

      def   _lowerCamelCase		(							self    : Union[str, Any]  )		->  List[Any]:
           _lowercase							:      List[str]  						=							self.scheduler_classes[0]
           _lowercase							:      Optional[Any]  						=							self.get_scheduler_config()
           _lowercase							:      Optional[Any]  						=							scheduler_class(**UpperCamelCase  )

           scheduler.set_timesteps(self.num_inference_steps  )

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

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

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

                _lowercase							:      Any  						=							model(UpperCamelCase   ,UpperCamelCase  )

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

           _lowercase							:      int  						=							torch.sum(torch.abs(UpperCamelCase  )  )
           _lowercase							:      Any  						=							torch.mean(torch.abs(UpperCamelCase  )  )

           assert abs(result_sum.item() - 1_5_2.3_1_9_2  ) < 1e-2
           assert abs(result_mean.item() - 0.1_9_8_3  ) < 1e-3

      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Tuple:
           _lowercase							:      Optional[Any]  						=							self.scheduler_classes[0]
           _lowercase							:      Optional[Any]  						=							self.get_scheduler_config(prediction_type='v_prediction'  )
           _lowercase							:      List[str]  						=							scheduler_class(**UpperCamelCase  )

           scheduler.set_timesteps(self.num_inference_steps  )

           _lowercase							:      int  						=							torch.manual_seed(0  )

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

           for i, t in enumerate(scheduler.timesteps  ):
                _lowercase							:      str  						=							scheduler.scale_model_input(UpperCamelCase   ,UpperCamelCase  )

                _lowercase							:      str  						=							model(UpperCamelCase   ,UpperCamelCase  )

                _lowercase							:      Any  						=							scheduler.step(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,generator=UpperCamelCase  )
                _lowercase							:      Dict  						=							output.prev_sample

           _lowercase							:      int  						=							torch.sum(torch.abs(UpperCamelCase  )  )
           _lowercase							:      Union[str, Any]  						=							torch.mean(torch.abs(UpperCamelCase  )  )

           assert abs(result_sum.item() - 1_0_8.4_4_3_9  ) < 1e-2
           assert abs(result_mean.item() - 0.1_4_1_2  ) < 1e-3



      def   _lowerCamelCase		(							self    : int  )		->  List[str]:
           _lowercase							:      Tuple  						=							self.scheduler_classes[0]
           _lowercase							:      Tuple  						=							self.get_scheduler_config()
           _lowercase							:      Any  						=							scheduler_class(**UpperCamelCase  )

           scheduler.set_timesteps(self.num_inference_steps   ,device=UpperCamelCase  )
           _lowercase							:      int  						=							torch.manual_seed(0  )

           _lowercase							:      List[str]  						=							self.dummy_model()
           _lowercase							:      str  						=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           _lowercase							:      Any  						=							sample.to(UpperCamelCase  )

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

                _lowercase							:      str  						=							model(UpperCamelCase   ,UpperCamelCase  )

                _lowercase							:      List[str]  						=							scheduler.step(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,generator=UpperCamelCase  )
                _lowercase							:      Tuple  						=							output.prev_sample

           _lowercase							:      Dict  						=							torch.sum(torch.abs(UpperCamelCase  )  )
           _lowercase							:      Any  						=							torch.mean(torch.abs(UpperCamelCase  )  )

           assert abs(result_sum.item() - 1_5_2.3_1_9_2  ) < 1e-2
           assert abs(result_mean.item() - 0.1_9_8_3  ) < 1e-3






'''simple docstring'''







from __future__ import annotations

import math
import random

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	int)			->						list[int]:

     '''simple docstring'''




     _lowercase							:      Optional[Any]  						=							d * e - 1
     _lowercase							:      Optional[Any]  						=							0
     _lowercase							:      Dict  						=							0
     while p == 0:
          _lowercase							:      Optional[Any]  						=							random.randint(2       ,  n - 1)
          _lowercase							:      int  						=							k
          while True:
               if t % 2 == 0:
                    _lowercase							:      int  						=							t // 2
                    _lowercase							:      Any  						=							(g**t) % n
                    _lowercase							:      Tuple  						=							math.gcd(x - 1       ,  lowerCAmelCase__)
                    if x > 1 and y > 1:
                         _lowercase							:      Union[str, Any]  						=							y
                         _lowercase							:      Dict  						=							n // y
                         break  # find the correct factors
               else:
                    break  # t is not divisible by 2, break and choose another g
     return sorted([p, q])


if __name__ == "__main__":
    import doctest

    doctest.testmod()