



"""simple docstring"""






import datasets

from .evaluate import evaluate


lowerCAmelCase__      =    '''\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'''

lowerCAmelCase__      =    '''\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'''

lowerCAmelCase__      =    '''\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,					_KWARGS_DESCRIPTION)
class 	__snake_case	(     datasets.Metric):




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         return datasets.MetricInfo(
             description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
                 {
                     '''predictions''': {'''id''': datasets.Value('''string'''		), '''prediction_text''': datasets.Value('''string'''		)},
                     '''references''': {
                         '''id''': datasets.Value('''string'''		),
                         '''answers''': datasets.features.Sequence(
                             {
                                 '''text''': datasets.Value('''string'''		),
                                 '''answer_start''': datasets.Value('''int32'''		),
                             }		),
                     },
                 }		)						, codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']						, reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']						, )




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
         _lowerCamelCase						:    List[Any]         =			[
             {
                 '''paragraphs''': [
                     {
                         '''qas''': [
                             {
                                 '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                 '''id''': ref['''id'''],
                             }
                             for ref in references
                         ]
                     }
                 ]
             }
         ]
         _lowerCamelCase						:    int         =			evaluate(dataset=a__						, predictions=a__		)
         return score







"""simple docstring"""






from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[Any]         =			[randint(-10_00,    10_00						) for i in range(10						)]
       _lowerCamelCase						:    Tuple         =			randint(-50_00,    50_00						)
       return (arr, r)


lowerCAmelCase__      =    make_dataset()






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       for triplet in permutations(A_,    3						):
              if sum(A_						) == target:
                     return tuple(sorted(A_						)						)
       return (0, 0, 0)






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       arr.sort()
       _lowerCamelCase						:    Optional[Any]         =			len(A_						)
       for i in range(n - 1						):
              _lowerCamelCase      ,   _lowerCamelCase						:    int         =			i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       _lowerCamelCase						:    List[Any]         =			'''
triplet_sum1(*dataset)
'''
       _lowerCamelCase						:    str         =			'''
triplet_sum2(*dataset)
'''
       _lowerCamelCase						:    Optional[int]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       _lowerCamelCase						:    Union[str, Any]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       return (min(A_						), min(A_						))


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   lowerCAmelCase__      =    solution_times()
   print(F"""The time for naive implementation is {times[0]}.""")
   print(F"""The time for optimized implementation is {times[1]}.""")


