






"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=UpperCAmelCase	)
class 						snake_case  (  UpperCAmelCase	):
    # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
    __magic_name__   					=					field(default='''question-answering-extractive'''       ,			metadata={'''include_in_asdict_even_if_is_default''': True}	)
    __magic_name__   					=					Features({'''question''': Value('''string'''	), '''context''': Value('''string'''	)}	)
    __magic_name__   					=					Features(
        {
            '''answers''': Sequence(
                {
                    '''text''': Value('''string'''	),
                    '''answer_start''': Value('''int32'''	),
                }	)
        }	)
    __magic_name__   					=					"question"
    __magic_name__   					=					"context"
    __magic_name__   					=					"answers"





    @property
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}












"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

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




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  if dataset.ndim != value_array.ndim:
    a					:      Optional[Any]    =       (
        'Wrong input data\'s dimensions... '
        f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
    )
    raise ValueError(A_				)

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

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

  a					:      Tuple    =       []

  for value in value_array:
    a					:      List[Any]    =       euclidean(A_     ,      dataset[0]				)
    a					:      int    =       dataset[0].tolist()

    for dataset_value in dataset[1:]:
      a					:      Optional[int]    =       euclidean(A_     ,      A_				)

      if dist > temp_dist:
        a					:      List[str]    =       temp_dist
        a					:      int    =       dataset_value.tolist()

    answer.append([vector, dist]				)

  return answer




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()




