
"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							(UnCLIPScheduler,)



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					int:
          UpperCAmelCase__							: Any 							=						{
              "num_train_timesteps": 10_00,
              "variance_type": "fixed_small_log",
              "clip_sample": True,
              "clip_sample_range": 1.0,
              "prediction_type": "epsilon",
          }

          config.update(**__UpperCamelCase  )
          return config



     def  lowerCAmelCase__       (      self  )->					int:
          for timesteps in [1, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          for variance in ["fixed_small_log", "learned_range"]:
               self.check_over_configs(variance_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					str:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          for clip_sample_range in [1, 5, 10, 20]:
               self.check_over_configs(clip_sample_range=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(prediction_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          for time_step in [0, 5_00, 9_99]:
               for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
                    if prev_timestep is not None and prev_timestep >= time_step:
                         continue

                    self.check_over_forward(time_step=__UpperCamelCase ,			prev_timestep=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: int 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Optional[Any] 							=						self.get_scheduler_config(variance_type="fixed_small_log"  )
          UpperCAmelCase__							: Tuple 							=						scheduler_class(**__UpperCamelCase  )

          assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.00_00E-10  )  ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87  ) - 0.054_9625  )  ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99  ) - 0.999_4987  )  ) < 1E-5



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: List[str] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Optional[int] 							=						self.get_scheduler_config(variance_type="learned_range"  )
          UpperCAmelCase__							: str 							=						scheduler_class(**__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						0.5

          assert scheduler._get_variance(1 ,			predicted_variance=__UpperCamelCase  ) - -10.171_2790 < 1E-5
          assert scheduler._get_variance(4_87 ,			predicted_variance=__UpperCamelCase  ) - -5.799_8052 < 1E-5
          assert scheduler._get_variance(9_99 ,			predicted_variance=__UpperCamelCase  ) - -0.001_0011 < 1E-5



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Dict 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Any 							=						self.get_scheduler_config()
          UpperCAmelCase__							: Any 							=						scheduler_class(**__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						scheduler.timesteps

          UpperCAmelCase__							: Tuple 							=						self.dummy_model()
          UpperCAmelCase__							: int 							=						self.dummy_sample_deter
          UpperCAmelCase__							: Optional[int] 							=						torch.manual_seed(0  )

          for i, t in enumerate(__UpperCamelCase  ):
               # 1. predict noise residual
               UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase ,			__UpperCamelCase  )

               # 2. predict previous mean of sample x_t-1
               UpperCAmelCase__							: Dict 							=						scheduler.step(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			generator=__UpperCamelCase  ).prev_sample

               UpperCAmelCase__							: List[Any] 							=						pred_prev_sample

          UpperCAmelCase__							: Tuple 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: List[str] 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

          assert abs(result_sum.item() - 252.268_2495  ) < 1E-2
          assert abs(result_mean.item() - 0.328_4743  ) < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: List[Any] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Dict 							=						self.get_scheduler_config()
          UpperCAmelCase__							: str 							=						scheduler_class(**__UpperCamelCase  )

          scheduler.set_timesteps(25  )

          UpperCAmelCase__							: List[str] 							=						scheduler.timesteps

          UpperCAmelCase__							: List[str] 							=						self.dummy_model()
          UpperCAmelCase__							: List[str] 							=						self.dummy_sample_deter
          UpperCAmelCase__							: Union[str, Any] 							=						torch.manual_seed(0  )

          for i, t in enumerate(__UpperCamelCase  ):
               # 1. predict noise residual
               UpperCAmelCase__							: List[Any] 							=						model(__UpperCamelCase ,			__UpperCamelCase  )

               if i + 1 == timesteps.shape[0]:
                    UpperCAmelCase__							: Any 							=						None
               else:
                    UpperCAmelCase__							: Optional[Any] 							=						timesteps[i + 1]

               # 2. predict previous mean of sample x_t-1
               UpperCAmelCase__							: Any 							=						scheduler.step(
                   __UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			prev_timestep=__UpperCamelCase ,			generator=__UpperCamelCase  ).prev_sample

               UpperCAmelCase__							: Tuple 							=						pred_prev_sample

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

          assert abs(result_sum.item() - 258.204_4983  ) < 1E-2
          assert abs(result_mean.item() - 0.336_2038  ) < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          pass



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          pass





"""simple docstring"""




from math import isqrt



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[True] * max_number
     for i in range(2       ,    isqrt(max_number - 1		) + 1		):
          if is_prime[i]:
               for j in range(i**2       ,    lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: List[Any] 							=						False

     return [i for i in range(2       ,    lowerCAmelCase		) if is_prime[i]]



def 							a__ (				lowerCAmelCase	:       int = 10**8		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						calculate_prime_numbers(max_number // 2		)

     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Tuple 							=						len(lowerCAmelCase		) - 1
     while left <= right:
          while prime_numbers[left] * prime_numbers[right] >= max_number:
               right -= 1
          semiprimes_count += right - left + 1
          left += 1

     return semiprimes_count


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



