

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 						__magic_name__    (       datasets.Metric    ):


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Optional[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 _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: Union[str, Any]    )    ->      Dict:
   UpperCAmelCase					      =  {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
   UpperCAmelCase					      =  [
       {
           "paragraphs": [
               {
                   "qas": [
                       {
                           "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                           "id": ref["id"],
                       }
                       for ref in references
                   ]
               }
           ]
       }
   ]
   UpperCAmelCase					      =  evaluate(dataset=lowerCAmelCase__			,						predictions=lowerCAmelCase__    )
   return score






def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  while num > 0:
    digit_sum += num % 10
    num //= 10
  return digit_sum
def      _lowerCAmelCase(	__A = 100			):
  UpperCAmelCase					      =  1
  UpperCAmelCase					      =  2

  for i in range(2   ,       max_n + 1			):
    UpperCAmelCase					      =  pre_numerator
    UpperCAmelCase					      =  2 * i // 3 if i % 3 == 0 else 1
    UpperCAmelCase					      =  cur_numerator
    UpperCAmelCase					      =  e_cont * pre_numerator + temp

  return sum_digits(__A			)


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



