


import torch

from diffusers import DiffusionPipeline





class       A    (   A_ ):


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									super().__init__()

									self.register_modules(unet=lowerCAmelCase      ,				scheduler=lowerCAmelCase					)







							def __call__(self					):
									__lowercase=			torch.randn(
									    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,				)
									__lowercase=			1

									__lowercase=			self.unet(lowerCAmelCase      ,				lowerCAmelCase					).sample
									__lowercase=			self.scheduler.step(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					).prev_sample

									__lowercase=			scheduler_output - scheduler_output + torch.ones_like(lowerCAmelCase					)

									return result










from math import isqrt







def        _lowerCamelCase(	lowercase__	)						->	bool:

		'''simple docstring'''







		return all(number % divisor != 0 for divisor in range(2   , isqrt(lowercase__	) + 1	)	)







def        _lowerCamelCase(	lowercase__ = 1_0**6	)						->	int:

		'''simple docstring'''







		__lowercase=			0
		__lowercase=			1
		__lowercase=			7
		while prime_candidate < max_prime:
				primes_count += is_prime(lowercase__	)

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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






