



from __future__ import annotations

import math


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5					,			int(math.sqrt(lowerCamelCase__						) + 1						)					,			6						):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True


lowerCAmelCase__					  =  [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				if not isinstance(lowerCamelCase__					,			lowerCamelCase__						):
								raise ValueError("n must be an integer"						)
				if n <= 0:
								raise ValueError("n must be >= 0"						)

				lowercase__					:   Dict													=					[]
				for num in range(len(lowerCamelCase__						)						):
								lowercase__					:   str													=					0
								while 2 * i * i <= odd_composites[num]:
												lowercase__					:   int													=					odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__						):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]						)
												if len(lowerCamelCase__						) == n:
																return list_nums

				return []


def  __lowerCamelCase (       ):

				"""simple docstring"""


				return compute_nums(1						)[0]


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









from typing import Union

import fire
import torch
from tqdm import tqdm


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__ = "cpu"					,			lowerCamelCase__ = None						):

				"""simple docstring"""


				lowercase__					:   Any													=					torch.load(lowerCamelCase__					,			map_location=lowerCamelCase__						)
				for k, v in tqdm(state_dict.items()						):
								if not isinstance(lowerCamelCase__					,			torch.Tensor						):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"						)
								lowercase__					:   int													=					v.half()
				if save_path is None:  # overwrite src_path
								lowercase__					:   Optional[Any]													=					src_path
				torch.save(lowerCamelCase__					,			lowerCamelCase__						)


if __name__ == "__main__":
				fire.Fire(convert)




