





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    (DDPMParallelScheduler,)




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	str						    =							{
                'num_train_timesteps': 1_0_0_0,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'variance_type': 'fixed_small',
                'clip_sample': True,
            }

            config.update(**__lowerCamelCase							)
            return config




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







            for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     int:







            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,						[0.002, 0.02, 0.2, 2]							):
                 self.check_over_configs(beta_start=__lowerCamelCase    ,						beta_end=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Any:







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




       def     UpperCamelCase_			(      self							)			->     str:







            for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     str:







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




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            self.check_over_configs(thresholding=__lowerCamelCase							)
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowerCamelCase    ,						prediction_type=__lowerCamelCase    ,						sample_max_value=__lowerCamelCase    ,						)




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







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




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







            for t in [0, 5_0_0, 9_9_9]:
                 self.check_over_forward(time_step=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							scheduler_class(**__lowerCamelCase							)

            assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0979							)							) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.02							)							) < 1E-5




       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	int						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							len(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.dummy_sample_deter
            _SCREAMING_SNAKE_CASE       :	int						    =							self.dummy_sample_deter + 0.1
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.dummy_sample_deter - 0.1

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							samplea.shape[0]
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							torch.stack([samplea, samplea, samplea]    ,						dim=0							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.arange(__lowerCamelCase							)[0:3, None].repeat(1    ,						__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(samples.flatten(0    ,						1							)    ,						timesteps.flatten(0    ,						1							)							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							scheduler.batch_step_no_noise(__lowerCamelCase    ,						timesteps.flatten(0    ,						1							)    ,						samples.flatten(0    ,						1							)							)

            _SCREAMING_SNAKE_CASE       :	str						    =							torch.sum(torch.abs(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							torch.mean(torch.abs(__lowerCamelCase							)							)

            assert abs(result_sum.item() - 1_1_5_3.1_8_3_3							) < 1E-2
            assert abs(result_mean.item() - 0.5005							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							len(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Any						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	int						    =							self.dummy_sample_deter
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.manual_seed(0							)

            for t in reversed(range(__lowerCamelCase							)							):
                 # 1. predict noise residual
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model(__lowerCamelCase    ,						__lowerCamelCase							)

                 # 2. predict previous mean of sample x_t-1
                 _SCREAMING_SNAKE_CASE       :	int						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						generator=__lowerCamelCase							).prev_sample

                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							pred_prev_sample

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

            assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
            assert abs(result_mean.item() - 0.3372							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_scheduler_config(prediction_type="v_prediction"							)
            _SCREAMING_SNAKE_CASE       :	int						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							len(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.dummy_sample_deter
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.manual_seed(0							)

            for t in reversed(range(__lowerCamelCase							)							):
                 # 1. predict noise residual
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model(__lowerCamelCase    ,						__lowerCamelCase							)

                 # 2. predict previous mean of sample x_t-1
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						generator=__lowerCamelCase							).prev_sample

                 _SCREAMING_SNAKE_CASE       :	str						    =							pred_prev_sample

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

            assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
            assert abs(result_mean.item() - 0.2631							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[1_0_0, 8_7, 5_0, 1, 0]

            scheduler.set_timesteps(timesteps=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							scheduler.timesteps

            for i, timestep in enumerate(__lowerCamelCase							):
                 if i == len(__lowerCamelCase							) - 1:
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							-1
                 else:
                      _SCREAMING_SNAKE_CASE       :	Dict						    =							timesteps[i + 1]

                 _SCREAMING_SNAKE_CASE       :	str						    =							scheduler.previous_timestep(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	int						    =							prev_t.item()

                 self.assertEqual(__lowerCamelCase    ,						__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							[1_0_0, 8_7, 5_0, 5_1, 0]

            with self.assertRaises(__lowerCamelCase    ,						msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	str						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							[1_0_0, 8_7, 5_0, 1, 0]
            _SCREAMING_SNAKE_CASE       :	int						    =							len(__lowerCamelCase							)

            with self.assertRaises(__lowerCamelCase    ,						msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."							):
                 scheduler.set_timesteps(num_inference_steps=__lowerCamelCase    ,						timesteps=__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							[scheduler.config.num_train_timesteps]

            with self.assertRaises(
                __lowerCamelCase    ,						msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"    ,						):
                 scheduler.set_timesteps(timesteps=__lowerCamelCase							)






from __future__ import annotations

UpperCamelCase__      =[True] * 100_0001
UpperCamelCase__      =2
while i * i <= 100_0000:
       if seive[i]:
              for j in range(i * i, 100_0001, i):
                     UpperCamelCase__      =False
       i += 1






def     lowerCamelCase__			(__lowerCamelCase	):
     return seive[n]






def     lowerCamelCase__			(__lowerCamelCase	):
     return any(digit in "02468" for digit in str(__lowerCamelCase	)	)






def     lowerCamelCase__			(__lowerCamelCase = 1000000	):
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[2]  # result already includes the number 2.
     for num in range(3,       limit + 1,       2	):
          if is_prime(__lowerCamelCase	) and not contains_an_even_digit(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							str(__lowerCamelCase	)
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[int(str_num[j:] + str_num[:j]	) for j in range(len(__lowerCamelCase	)	)]
               if all(is_prime(__lowerCamelCase	) for i in list_nums	):
                    result.append(__lowerCamelCase	)
     return result






def     lowerCamelCase__			():
     return len(find_circular_primes()	)


if __name__ == "__main__":
       print(f"{len(find_circular_primes()) = }")