



"""simple docstring"""


import datasets

from .evaluate import evaluate


__A      :							Tuple   =      "\\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      :							Dict   =      "\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      :							int   =      "\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 	_a       (    datasets.Metric):
			"""simple docstring"""



			def        lowercase__  (						self   :			List[Any]      )->List[str]:
							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        lowercase__  (						self   :			str ,      __UpperCamelCase   :			int ,      __UpperCamelCase   :			List[Any]      )->Optional[int]:
							_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=__UpperCAmelCase ,      predictions=__UpperCAmelCase      )
							return score











def 						_a    (      a :int )							->			bool:
 a		  =      n ** (1 / 3)
 return (val * val * val) == n


if __name__ == "__main__":
     print(perfect_cube(27))
     print(perfect_cube(4))


