



import datasets

from .evaluate import evaluate


_lowercase						  =		"\\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"

_lowercase						  =		"\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"

_lowercase						  =		"\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     _UpperCAmelCase			(       datasets.Metric     ):




     def 		snake_case_		(				self):
         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 		snake_case_		(				self ,						a__ ,						a__):
         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=a__ ,						predictions=a__)
         return score





def 		lowerCAmelCase__    (					UpperCamelCase_				:       float ,       UpperCamelCase_				:       int	)->  float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase_	) ,       UpperCamelCase_	)
    return number - int(UpperCamelCase_	)


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))
