from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def     _a				(      lowercase__  :			np.ndarray     ,  lowercase__  :			np.ndarray ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			np.ndarray     ,  lowercase__  :			np.ndarray ):







     '''simple docstring'''


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

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

     if dataset.dtype != value_array.dtype:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		(
              'Input data have different datatype... '
              f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
          )
          raise TypeError(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]

     for value in value_array:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		euclidean(lowercase__     ,  dataset[0] )
          SCREAMING_SNAKE_CASE__				:				str			=		dataset[0].tolist()

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

               if dist > temp_dist:
                    SCREAMING_SNAKE_CASE__				:				List[str]			=		temp_dist
                    SCREAMING_SNAKE_CASE__				:				List[Any]			=		dataset_value.tolist()

          answer.append([vector, dist] )

     return answer







def     _a				(      lowercase__  :			np.ndarray     ,  lowercase__  :			np.ndarray ):







     '''simple docstring'''


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


if __name__ == "__main__":
      import doctest

      doctest.testmod()




from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



