






'''simple docstring'''







def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							lowerCamelCase__ :     Optional[Any]	  =	1
							lowerCamelCase__ :     Optional[int]	  =	2
							while i * i <= n:
														lowerCamelCase__ :     Any	  =	0
														while n % i == 0:
																					n //= i
																					multiplicity += 1
														n_divisors *= multiplicity + 1
														i += 1
							if n > 1:
														n_divisors *= 2
							return n_divisors





def 			SCREAMING_SNAKE_CASE_	()				->	List[Any]:
							lowerCamelCase__ :     Dict	  =	1
							lowerCamelCase__ :     Union[str, Any]	  =	1

							while True:
														i += 1
														t_num += i

														if count_divisors(UpperCamelCase				) > 500:
																					break

							return t_num


if __name__ == "__main__":
			print(solution())









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

