






'''simple docstring'''
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							(EulerDiscreteScheduler,)
 snake_case_  							=							10




 def      __magic_name__	(      self	:							Union[str, Any]					,      **__lowercase	:							List[Any]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''num_train_timesteps''': 11_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__lowercase      )
       return config




 def      __magic_name__	(      self	:							int      )	->						int:
       for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=__lowercase      )




 def      __magic_name__	(      self	:							Dict      )	->						str:
       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,      [0.0002, 0.002, 0.02]      ):
             self.check_over_configs(beta_start=__lowercase					,      beta_end=__lowercase      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						List[str]:
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=__lowercase      )




 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=__lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=scheduler_class(**__lowercase      )

       scheduler.set_timesteps(self.num_inference_steps      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.dummy_model()
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__     :				str									=sample.to(__lowercase      )

       for i, t in enumerate(scheduler.timesteps      ):
             SCREAMING_SNAKE_CASE__     :				int									=scheduler.scale_model_input(__lowercase					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(__lowercase					,      __lowercase      )

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

       SCREAMING_SNAKE_CASE__     :				List[str]									=torch.sum(torch.abs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				int									=torch.mean(torch.abs(__lowercase      )      )

       assert abs(result_sum.item() - 10.0807      ) < 1e-2
       assert abs(result_mean.item() - 0.0131      ) < 1e-3




 def      __magic_name__	(      self	:							Dict      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.get_scheduler_config(prediction_type='''v_prediction'''      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=scheduler_class(**__lowercase      )

       scheduler.set_timesteps(self.num_inference_steps      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Dict									=self.dummy_model()
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__     :				List[Any]									=sample.to(__lowercase      )

       for i, t in enumerate(scheduler.timesteps      ):
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=scheduler.scale_model_input(__lowercase					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				List[Any]									=model(__lowercase					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				Optional[int]									=scheduler.step(__lowercase					,      __lowercase					,      __lowercase					,      generator=__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=output.prev_sample

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.sum(torch.abs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Dict									=torch.mean(torch.abs(__lowercase      )      )

       assert abs(result_sum.item() - 0.0002      ) < 1e-2
       assert abs(result_mean.item() - 2.2676e-06      ) < 1e-3




 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__     :				List[str]									=scheduler_class(**__lowercase      )

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

       SCREAMING_SNAKE_CASE__     :				Dict									=torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.dummy_model()
       SCREAMING_SNAKE_CASE__     :				str									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=sample.to(__lowercase      )

       for t in scheduler.timesteps:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=scheduler.scale_model_input(__lowercase					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				List[str]									=model(__lowercase					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				str									=scheduler.step(__lowercase					,      __lowercase					,      __lowercase					,      generator=__lowercase      )
             SCREAMING_SNAKE_CASE__     :				int									=output.prev_sample

       SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.sum(torch.abs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.mean(torch.abs(__lowercase      )      )

       assert abs(result_sum.item() - 10.0807      ) < 1e-2
       assert abs(result_mean.item() - 0.0131      ) < 1e-3




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__     :				int									=scheduler_class(**__lowercase					,      use_karras_sigmas=__lowercase      )

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

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				int									=self.dummy_model()
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       SCREAMING_SNAKE_CASE__     :				Tuple									=sample.to(__lowercase      )

       for t in scheduler.timesteps:
             SCREAMING_SNAKE_CASE__     :				int									=scheduler.scale_model_input(__lowercase					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(__lowercase					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				int									=scheduler.step(__lowercase					,      __lowercase					,      __lowercase					,      generator=__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Any									=output.prev_sample

       SCREAMING_SNAKE_CASE__     :				int									=torch.sum(torch.abs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Any									=torch.mean(torch.abs(__lowercase      )      )

       assert abs(result_sum.item() - 124.52299499511719      ) < 1e-2
       assert abs(result_mean.item() - 0.16213932633399963      ) < 1e-3







'''simple docstring'''
from math import isqrt





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=[True] * max_number
      for i in range(2,	isqrt(max_number - 1							) + 1							):
            if is_prime[i]:
                  for j in range(i**2,	UpperCamelCase__,	UpperCamelCase__							):
                        SCREAMING_SNAKE_CASE__     :				Any									=False

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





def  _a(  UpperCamelCase__				:						int = 1_0**8							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=calculate_prime_numbers(max_number // 2							)

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							) - 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() = }''')