





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

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





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   VectorOut:







	'''simple docstring'''







	return np.sqrt(np.sum((np.asarray(__snake_case							) - np.asarray(__snake_case							)) ** 2							)							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   VectorOut:







	'''simple docstring'''







	return sum((va - va) ** 2 for va, va in zip(__snake_case ,__snake_case							)							) ** (1 / 2)


if __name__ == "__main__":





			def lowerCAmelCase__()       ->   None:







				'''simple docstring'''







				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()










_a						=   "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_a						=   [{"type": "code", "content": INSTALL_CONTENT}]
_a						=   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



