

import datasets

from .evaluate import evaluate


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

__lowercase				      =						"""
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

__lowercase				      =						"""
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""

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

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Optional[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     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
         A_							       =		[
             {
                 '''paragraphs''': [
                     {
                         '''qas''': [
                             {
                                 '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                 '''id''': ref['''id'''],
                             }
                             for ref in references
                         ]
                     }
                 ]
             }
         ]
         A_							       =		evaluate(dataset=lowerCamelCase__			,						predictions=lowerCamelCase__       )
         return score








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


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





