




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       float:


      '''simple docstring'''


      return math.sqrt(sum(pow(a - b			,						2     ) for a, b in zip(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )     )     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       list[list[list[float] | float]]:


      '''simple docstring'''


      if dataset.ndim != value_array.ndim:
            SCREAMING_SNAKE_CASE		=						(
                """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]:
                  SCREAMING_SNAKE_CASE		=						(
                      """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:
            SCREAMING_SNAKE_CASE		=						(
                """Input data have different datatype... """
                F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
            )
            raise TypeError(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						[]

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

            for dataset_value in dataset[1:]:
                  SCREAMING_SNAKE_CASE		=						euclidean(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

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

            answer.append([vector, dist]     )

      return answer



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       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()









from __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						2
      SCREAMING_SNAKE_CASE		=						[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(_SCREAMING_SNAKE_CASE     )
      if n > 1:
            factors.append(_SCREAMING_SNAKE_CASE     )
      return factors


if __name__ == "__main__":
      import doctest

      doctest.testmod()


