





import datasets

from .evaluate import evaluate


__A     =    "\\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"

__A     =    "\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"

__A     =    "\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        _SCREAMING_SNAKE_CASE						(     datasets.Metric   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->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    :       Any	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        ={prediction["id"]: prediction["prediction_text"] for prediction in predictions}
   lowerCamelCase__:				str        =[
       {
           "paragraphs": [
               {
                   "qas": [
                       {
                           "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                           "id": ref["id"],
                       }
                       for ref in references
                   ]
               }
           ]
       }
   ]
   lowerCamelCase__:				Any        =evaluate(dataset=lowercase_	,			predictions=lowercase_)
   return score







def 					lowerCAmelCase_			(	__a		)					->		str:







  """simple docstring"""





  if isinstance(__a     ,   __a		):
    raise TypeError("'float' object cannot be interpreted as an integer"		)
  if isinstance(__a     ,   __a		):
    raise TypeError("'str' object cannot be interpreted as an integer"		)

  if num == 0:
    return "0b0"

  lowerCamelCase__:				Optional[int]        =False

  if num < 0:
    lowerCamelCase__:				Optional[Any]        =True
    lowerCamelCase__:				List[Any]        =-num

  lowerCamelCase__:				list[int]        =[]
  while num > 0:
    binary.insert(0     ,   num % 2		)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(__a		) for e in binary		)

  return "0b" + "".join(str(__a		) for e in binary		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

