




import datasets

from .evaluate import evaluate


lowerCamelCase__			:       Dict         =							'\\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__			:       List[str]         =							'\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__			:       List[str]         =							'\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 	lowerCamelCase_							(				datasets.Metric ):


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							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 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      {prediction['id']: prediction['prediction_text'] for prediction in predictions}
							SCREAMING_SNAKE_CASE_     						=      [
							    {
							        'paragraphs': [
							            {
							                'qas': [
							                    {
							                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
							                        'id': ref['id'],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							SCREAMING_SNAKE_CASE_     						=      evaluate(dataset=_lowerCAmelCase    ,    predictions=_lowerCAmelCase						)
							return score





def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Union[str, Any]							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      0
      while b > 0:
            if b & 1:
                  res += a

            a += a
            b >>= 1

      return res

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Tuple							) ->			int:
      SCREAMING_SNAKE_CASE_     						=      0
      while b > 0:
            if b & 1:
                  SCREAMING_SNAKE_CASE_     						=      ((res % c) + (a % c)) % c

            a += a
            b >>= 1

      return res