


'''simple docstring'''



import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[int]							     =			(DPMSolverSDEScheduler,)
    lowerCamelCase  :					Dict							     =			1_0







    def       lowercase_				(							self			,     **SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	List[Any]								=		{
             'num_train_timesteps': 11_00,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'noise_sampler_seed': 0,
         }

         config.update(**SCREAMING_SNAKE_CASE_	)
         return config







    def       lowercase_				(							self	)				->     List[Any]:
         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     int:
         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=SCREAMING_SNAKE_CASE_			,     beta_end=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     List[str]:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     str:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	str								=		self.scheduler_classes[0]
         __lowerCamelCase   :	List[Any]								=		self.get_scheduler_config()
         __lowerCamelCase   :	int								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)

         scheduler.set_timesteps(self.num_inference_steps	)

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

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

              __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	Optional[int]								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Optional[int]								=		output.prev_sample

         __lowerCamelCase   :	Tuple								=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	List[Any]								=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5	) < 1E-2
              assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7	) < 1E-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6	) < 1E-2
              assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2	) < 1E-3
         else:
              assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2	) < 1E-2
              assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6	) < 1E-3







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Any								=		self.scheduler_classes[0]
         __lowerCamelCase   :	Optional[int]								=		self.get_scheduler_config(prediction_type='v_prediction'	)
         __lowerCamelCase   :	Dict								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)

         scheduler.set_timesteps(self.num_inference_steps	)

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

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

              __lowerCamelCase   :	Optional[Any]								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

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

         __lowerCamelCase   :	Union[str, Any]								=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	List[str]								=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3	) < 1E-2
              assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4	) < 1E-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3	) < 1E-2
              assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7	) < 1E-3
         else:
              assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5	) < 1E-2
              assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1	) < 1E-3







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	Tuple								=		self.scheduler_classes[0]
         __lowerCamelCase   :	Any								=		self.get_scheduler_config()
         __lowerCamelCase   :	Union[str, Any]								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)

         scheduler.set_timesteps(self.num_inference_steps			,     device=SCREAMING_SNAKE_CASE_	)

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

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

              __lowerCamelCase   :	Any								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	Optional[Any]								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Dict								=		output.prev_sample

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

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8	) < 1E-2
              assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5	) < 1E-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2	) < 1E-2
              assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1	) < 1E-3
         else:
              assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2	) < 1E-2
              assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6	) < 1E-3







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	int								=		self.scheduler_classes[0]
         __lowerCamelCase   :	Union[str, Any]								=		self.get_scheduler_config()
         __lowerCamelCase   :	Tuple								=		scheduler_class(**SCREAMING_SNAKE_CASE_			,     use_karras_sigmas=SCREAMING_SNAKE_CASE_	)

         scheduler.set_timesteps(self.num_inference_steps			,     device=SCREAMING_SNAKE_CASE_	)

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

         for t in scheduler.timesteps:
              __lowerCamelCase   :	Dict								=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	Dict								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

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

         __lowerCamelCase   :	Union[str, Any]								=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	Optional[int]								=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8	) < 1E-2
              assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1	) < 1E-2
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5	) < 1E-2
              assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1	) < 1E-2
         else:
              assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2	) < 1E-2
              assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1	) < 1E-2










'''simple docstring'''



from math import isqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int							)     ->		bool:
     return all(number % divisor != 0 for divisor in range(2      ,    isqrt(UpperCAmelCase_							) + 1							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int = 10**6							)     ->		int:
     __lowerCamelCase   :	Optional[Any]								=		0
     __lowerCamelCase   :	Optional[int]								=		1
     __lowerCamelCase   :	List[str]								=		7
     while prime_candidate < max_prime:
          primes_count += is_prime(UpperCAmelCase_							)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


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






