






'''simple docstring'''






import datasets

from .evaluate import evaluate


__A    	=   '''\
@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}
}
'''

__A    	=   '''
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.
'''

__A    	=   '''
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 a_    (    datasets.Metric							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case				:      int     							= {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
      __snake_case				:      Tuple     							= [
          {
              """paragraphs""": [
                  {
                      """qas""": [
                          {
                              """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                              """id""": ref["""id"""],
                          }
                          for ref in references
                      ]
                  }
              ]
          }
      ]
      __snake_case				:      int     							= evaluate(dataset=a_					,   predictions=a_)
      return score







'''simple docstring'''






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> int:







   """simple docstring"""
   if not is_accelerate_available():
      return method
   __snake_case				:      Optional[Any]     							= version.parse(accelerate.__version__  ).base_version
   if version.parse(A  ) < version.parse('0.17.0'  ):
      return method

   def wrapper(self  :       Optional[Any]  ,		*A  :       Optional[Any]  ,		**A  :       Optional[int]  ):
      if hasattr(self  ,		'_hf_hook'  ) and hasattr(self._hf_hook  ,		'pre_forward'  ):
         self._hf_hook.pre_forward(self  )
      return method(self  ,		*A  ,		**A  )

   return wrapper