"""simple docstring"""


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class    a__    (   _a	):
   snake_case_							=      (DDIMParallelScheduler,)
   snake_case_							=      (("eta", 0.0), ("num_inference_steps", 50))


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							{
            "num_train_timesteps": 1000,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "clip_sample": True,
        }

        config.update(**_UpperCAmelCase  )
        return config


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							self.scheduler_classes[0]
        lowercase__				 =							self.get_scheduler_config(**_UpperCAmelCase  )
        lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

        lowercase__ ,						lowercase__				 =							10, 0.0

        lowercase__				 =							self.dummy_model()
        lowercase__				 =							self.dummy_sample_deter

        scheduler.set_timesteps(_UpperCAmelCase  )

        for t in scheduler.timesteps:
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        return sample


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=_UpperCAmelCase  )

        lowercase__				 =							self.scheduler_classes[0]
        lowercase__				 =							self.get_scheduler_config(steps_offset=1  )
        lowercase__				 =							scheduler_class(**_UpperCAmelCase  )
        scheduler.set_timesteps(5  )
        assert torch.equal(scheduler.timesteps,				torch.LongTensor([801, 601, 401, 201, 1]  )  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1],				[0.002, 0.02, 0.2, 2]  ):
             self.check_over_configs(beta_start=_UpperCAmelCase,				beta_end=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        self.check_over_configs(thresholding=_UpperCAmelCase  )
        for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=_UpperCAmelCase,				prediction_type=_UpperCAmelCase,				sample_max_value=_UpperCAmelCase,				)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for t in [1, 10, 49]:
             self.check_over_forward(time_step=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for t, num_inference_steps in zip([1, 10, 50],				[10, 50, 500]  ):
             self.check_over_forward(time_step=_UpperCAmelCase,				num_inference_steps=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for t, eta in zip([1, 10, 49],				[0.0, 0.5, 1.0]  ):
             self.check_over_forward(time_step=_UpperCAmelCase,				eta=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.scheduler_classes[0]
        lowercase__				 =							self.get_scheduler_config()
        lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

        assert torch.sum(torch.abs(scheduler._get_variance(0,				0  ) - 0.0  )  ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,				400  ) - 0.14_771  )  ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,				960  ) - 0.32_460  )  ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,				0  ) - 0.0  )  ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,				486  ) - 0.00_979  )  ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,				998  ) - 0.02  )  ) < 1E-5


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.scheduler_classes[0]
        lowercase__				 =							self.get_scheduler_config()
        lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

        lowercase__ ,						lowercase__				 =							10, 0.0
        scheduler.set_timesteps(_UpperCAmelCase  )

        lowercase__				 =							self.dummy_model()
        lowercase__				 =							self.dummy_sample_deter
        lowercase__				 =							self.dummy_sample_deter + 0.1
        lowercase__				 =							self.dummy_sample_deter - 0.1

        lowercase__				 =							samplea.shape[0]
        lowercase__				 =							torch.stack([samplea, samplea, samplea],				dim=0  )
        lowercase__				 =							torch.arange(_UpperCAmelCase  )[0:3, None].repeat(1,				_UpperCAmelCase  )

        lowercase__				 =							model(samples.flatten(0,				1  ),				timesteps.flatten(0,				1  )  )
        lowercase__				 =							scheduler.batch_step_no_noise(_UpperCAmelCase,				timesteps.flatten(0,				1  ),				samples.flatten(0,				1  ),				_UpperCAmelCase  )

        lowercase__				 =							torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_sum.item() - 1_147.7_904  ) < 1E-2
        assert abs(result_mean.item() - 0.4_982  ) < 1E-3


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop()

        lowercase__				 =							torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_sum.item() - 172.0_067  ) < 1E-2
        assert abs(result_mean.item() - 0.223_967  ) < 1E-3


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop(prediction_type="v_prediction"  )

        lowercase__				 =							torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_sum.item() - 52.5_302  ) < 1E-2
        assert abs(result_mean.item() - 0.0_684  ) < 1E-3


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop(set_alpha_to_one=_UpperCAmelCase,				beta_start=0.01  )
        lowercase__				 =							torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_sum.item() - 149.8_295  ) < 1E-2
        assert abs(result_mean.item() - 0.1_951  ) < 1E-3


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop(set_alpha_to_one=_UpperCAmelCase,				beta_start=0.01  )
        lowercase__				 =							torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_sum.item() - 149.0_784  ) < 1E-2
        assert abs(result_mean.item() - 0.1_941  ) < 1E-3





"""simple docstring"""


from __future__ import annotations

import math







def 					__a   (		A      ):






     '''simple docstring'''



     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5    ,				int(math.sqrt(A      ) + 1      )    ,				6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True


lowerCAmelCase_:					Optional[Any]     =					[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          raise ValueError("n must be an integer"      )
     if n <= 0:
          raise ValueError("n must be >= 0"      )

     lowercase__				 =							[]
     for num in range(len(A      )      ):
          lowercase__				 =							0
          while 2 * i * i <= odd_composites[num]:
               lowercase__				 =							odd_composites[num] - 2 * i * i
               if is_prime(A      ):
                    break
               i += 1
          else:
               list_nums.append(odd_composites[num]      )
               if len(A      ) == n:
                    return list_nums

     return []







def 					__a   (		):






     '''simple docstring'''



     return compute_nums(1      )[0]


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




