




import datasets

from .evaluate import evaluate


_lowerCamelCase	:		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}
}
"""

_lowerCamelCase	:		int							       =   """
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.
"""

_lowerCamelCase	:		Dict							       =   """
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 				UpperCamelCase_    ( datasets.Metric     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->int:





           '''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      :	List[str]					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Union[str, Any])						->Tuple:





           '''simple docstring'''

           A__       =    {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
           A__       =    [
               {
                   '''paragraphs''': [
                       {
                           '''qas''': [
                               {
                                   '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                   '''id''': ref['''id'''],
                               }
                               for ref in references
                           ]
                       }
                   ]
               }
           ]
           A__       =    evaluate(dataset=UpperCAmelCase__					, predictions=UpperCAmelCase__)
           return score








_lowerCamelCase	:		Optional[int]							       =   65521







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    1
       A__       =    0
       for plain_chr in plain_text:
              A__       =    (a + ord(lowercase_						)) % MOD_ADLER
              A__       =    (b + a) % MOD_ADLER
       return (b << 16) | a


