



'''simple docstring'''







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		Any		=      (LMSDiscreteScheduler,)
			__snake_case  :		Dict		=      1_0






			def 				_lowercase	(   self  :							Dict      ,       **SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      {
					    'num_train_timesteps': 1_100,
					    'beta_start': 0.00_01,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					}

					config.update(**SCREAMING_SNAKE_CASE	)
					return config






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]      ,       [0.00_02, 0.0_02, 0.02]	):
							self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE      ,       beta_end=SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					for t in [0, 500, 800]:
							self.check_over_forward(time_step=SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.get_scheduler_config()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      scheduler_class(**SCREAMING_SNAKE_CASE	)

					scheduler.set_timesteps(self.num_inference_steps	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      self.dummy_model()
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.dummy_sample_deter * scheduler.init_noise_sigma

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

							SCREAMING_SNAKE_CASE_						:Any										=      model(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							SCREAMING_SNAKE_CASE_						:Tuple										=      scheduler.step(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:List[str]										=      output.prev_sample

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE	)	)

					assert abs(result_sum.item() - 10_06.3_88	) < 1E-2
					assert abs(result_mean.item() - 1.31	) < 1E-3






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE_						:str										=      self.get_scheduler_config(prediction_type='v_prediction'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      scheduler_class(**SCREAMING_SNAKE_CASE	)

					scheduler.set_timesteps(self.num_inference_steps	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.dummy_model()
					SCREAMING_SNAKE_CASE_						:Dict										=      self.dummy_sample_deter * scheduler.init_noise_sigma

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

							SCREAMING_SNAKE_CASE_						:List[Any]										=      model(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							SCREAMING_SNAKE_CASE_						:Dict										=      scheduler.step(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      output.prev_sample

					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE	)	)

					assert abs(result_sum.item() - 0.00_17	) < 1E-2
					assert abs(result_mean.item() - 2.26_76E-06	) < 1E-3






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_scheduler_config()
					SCREAMING_SNAKE_CASE_						:List[Any]										=      scheduler_class(**SCREAMING_SNAKE_CASE	)

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

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.dummy_model()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					SCREAMING_SNAKE_CASE_						:int										=      sample.to(SCREAMING_SNAKE_CASE	)

					for i, t in enumerate(scheduler.timesteps	):
							SCREAMING_SNAKE_CASE_						:str										=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							SCREAMING_SNAKE_CASE_						:Dict										=      model(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      scheduler.step(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Any										=      output.prev_sample

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:int										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE	)	)

					assert abs(result_sum.item() - 10_06.3_88	) < 1E-2
					assert abs(result_mean.item() - 1.31	) < 1E-3






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.get_scheduler_config()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      scheduler_class(**SCREAMING_SNAKE_CASE      ,       use_karras_sigmas=SCREAMING_SNAKE_CASE	)

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

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

					for t in scheduler.timesteps:
							SCREAMING_SNAKE_CASE_						:Any										=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							SCREAMING_SNAKE_CASE_						:int										=      model(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							SCREAMING_SNAKE_CASE_						:str										=      scheduler.step(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Tuple										=      output.prev_sample

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:int										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE	)	)

					assert abs(result_sum.item() - 38_12.99_27	) < 2E-2
					assert abs(result_mean.item() - 4.96_48	) < 1E-3



import math
from collections.abc import Generator




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)		))
							for i in (n for n in numbers if n > 1):
														for j in range(2      ,  _A		):
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)		))
							for i in (n for n in numbers if n > 1):
														# only need to check for factors up to sqrt(i)
														lowerCamelCase_								=int(math.sqrt(_A		)		) + 1
														for j in range(2      ,  _A		):
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)      ,  2		))
							# It's useless to test even numbers as they will not be prime
							if max_n > 2:
														yield 2  # Because 2 will not be tested, it's necessary to yield it now
							for i in (n for n in numbers if n > 1):
														lowerCamelCase_								=int(math.sqrt(_A		)		) + 1
														for j in range(3      ,  _A      ,  2		):
																					# As we removed the even numbers, we don't need them now
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      )				->Dict:



							"""simple docstring"""
							from timeit import timeit

							lowerCamelCase_								="""from __main__ import slow_primes, primes, fast_primes"""
							print(timeit("""slow_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""fast_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)


if __name__ == "__main__":
	__A	:   Union[str, Any]	=					int(input('Calculate primes up to:\n>> ').strip())
	for ret in primes(number):
		print(ret)
	benchmark()
