

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     np.ndarray   ,      SCREAMING_SNAKE_CASE__      :     np.ndarray				)							->				float:





    '''simple docstring'''






    return math.sqrt(sum(pow(a - b   ,      2				) for a, b in zip(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     np.ndarray   ,      SCREAMING_SNAKE_CASE__      :     np.ndarray				)							->				list[list[list[float] | float]]:





    '''simple docstring'''






    if dataset.ndim != value_array.ndim:
        A__   				=       (
            'Wrong input data\'s dimensions... '
            f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__				)

    try:
        if dataset.shape[1] != value_array.shape[1]:
            A__   				=       (
                'Wrong input data\'s shape... '
                f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
            )
            raise ValueError(SCREAMING_SNAKE_CASE__				)
    except IndexError:
        if dataset.ndim != value_array.ndim:
            raise TypeError('Wrong shape'				)

    if dataset.dtype != value_array.dtype:
        A__   				=       (
            'Input data have different datatype... '
            f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
        )
        raise TypeError(SCREAMING_SNAKE_CASE__				)

    A__   				=       []

    for value in value_array:
        A__   				=       euclidean(SCREAMING_SNAKE_CASE__   ,      dataset[0]				)
        A__   				=       dataset[0].tolist()

        for dataset_value in dataset[1:]:
            A__   				=       euclidean(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

            if dist > temp_dist:
                A__   				=       temp_dist
                A__   				=       dataset_value.tolist()

        answer.append([vector, dist]				)

    return answer
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     np.ndarray   ,      SCREAMING_SNAKE_CASE__      :     np.ndarray				)							->				float:





    '''simple docstring'''






    return np.dot(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				) / (norm(SCREAMING_SNAKE_CASE__				) * norm(SCREAMING_SNAKE_CASE__				))


if __name__ == "__main__":
     import doctest

     doctest.testmod()




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int = 1000				)							->				int:





    '''simple docstring'''






    A__   				=       3
    A__   				=       0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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

