


import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCamelCase						=		False





class  _a							(    unittest.TestCase):




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		int=32		)->	Optional[int]:
			set_seed(0		)
			lowerCAmelCase__       :      List[Any]   					= UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE		, in_channels=3		, out_channels=3		)
			lowerCAmelCase__       :      Union[str, Any]   					= torch.optim.SGD(model.parameters()		, lr=0.0001		)
			return model, optimizer




	@slow
	def 							UpperCAmelCase__( self						:		int		)->	Any:
			lowerCAmelCase__       :      Union[str, Any]   					= '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			lowerCAmelCase__       :      int   					= DDPMScheduler(
			    num_train_timesteps=1000		, beta_start=0.0001		, beta_end=0.02		, beta_schedule='''linear'''		, clip_sample=_SCREAMING_SNAKE_CASE		, )
			lowerCAmelCase__       :      Dict   					= DDIMScheduler(
			    num_train_timesteps=1000		, beta_start=0.0001		, beta_end=0.02		, beta_schedule='''linear'''		, clip_sample=_SCREAMING_SNAKE_CASE		, )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0		)
			lowerCAmelCase__       :      str   					= [torch.randn((4, 3, 32, 32)		).clip(-1		, 1		).to(_SCREAMING_SNAKE_CASE		) for _ in range(4		)]
			lowerCAmelCase__       :      Any   					= [torch.randn((4, 3, 32, 32)		).to(_SCREAMING_SNAKE_CASE		) for _ in range(4		)]
			lowerCAmelCase__       :      Optional[int]   					= [torch.randint(0		, 1000		, (4,)		).long().to(_SCREAMING_SNAKE_CASE		) for _ in range(4		)]

			# train with a DDPM scheduler
			lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= self.get_model_optimizer(resolution=32		)
			model.train().to(_SCREAMING_SNAKE_CASE		)
			for i in range(4		):
					optimizer.zero_grad()
					lowerCAmelCase__       :      Optional[Any]   					= ddpm_scheduler.add_noise(clean_images[i]		, noise[i]		, timesteps[i]		)
					lowerCAmelCase__       :      str   					= model(_SCREAMING_SNAKE_CASE		, timesteps[i]		).sample
					lowerCAmelCase__       :      Any   					= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE		, noise[i]		)
					loss.backward()
					optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= self.get_model_optimizer(resolution=32		)
			model.train().to(_SCREAMING_SNAKE_CASE		)
			for i in range(4		):
					optimizer.zero_grad()
					lowerCAmelCase__       :      Tuple   					= ddim_scheduler.add_noise(clean_images[i]		, noise[i]		, timesteps[i]		)
					lowerCAmelCase__       :      str   					= model(_SCREAMING_SNAKE_CASE		, timesteps[i]		).sample
					lowerCAmelCase__       :      Union[str, Any]   					= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE		, noise[i]		)
					loss.backward()
					optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, atol=1E-5		)		)
			self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, atol=1E-5		)		)








from math import isqrt






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






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

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






def 					lowerCamelCase_   (	_a = 10**8	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= calculate_prime_numbers(max_number // 2	)

		lowerCAmelCase__       :      Tuple   					= 0
		lowerCAmelCase__       :      List[Any]   					= 0
		lowerCAmelCase__       :      Optional[int]   					= len(_a	) - 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() = }''')




