


import datasets

from .evaluate import evaluate


UpperCamelCase					:       Any			    =     """\
@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}
}
"""

UpperCamelCase					:       str			    =     """
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.
"""

UpperCamelCase					:       List[str]			    =     """
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  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  List[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			:  Dict				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  int       ):
        a__	:  Any     =   {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
        a__	:  Optional[int]     =   [
            {
                "paragraphs": [
                    {
                        "qas": [
                            {
                                "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                "id": ref["id"],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        a__	:  Any     =   evaluate(dataset=lowerCamelCase__				,				predictions=lowerCamelCase__       )
        return score




__lowercase				      =						[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						{
    0: """Sunday""",
    1: """Monday""",
    2: """Tuesday""",
    3: """Wednesday""",
    4: """Thursday""",
    5: """Friday""",
    6: """Saturday""",
}







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      assert len(str(SCREAMING_SNAKE_CASE   )   ) > 2, "year should be in YYYY format"
      assert 1 <= month <= 12, "month should be between 1 to 12"
      assert 1 <= day <= 31, "day should be between 1 to 31"

      # Doomsday algorithm:
      A_							       =		year // 100
      A_							       =		(5 * (century % 4) + 2) % 7
      A_							       =		year % 100
      A_							       =		centurian % 12
      A_							       =		(
          (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
      ) % 7
      A_							       =		(
          DOOMSDAY_NOT_LEAP[month - 1]
          if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
          else DOOMSDAY_LEAP[month - 1]
      )
      A_							       =		(dooms_day + day - day_anchor) % 7
      return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
      import doctest

      doctest.testmod()





