
"""simple docstring"""







import datasets

from .evaluate import evaluate


lowerCamelCase__ :    List[Any]        =		"\\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"

lowerCamelCase__ :    Tuple        =		"\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"

lowerCamelCase__ :    Union[str, Any]        =		"\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):

		def 							__lowerCamelCase							(				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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[int]						      =				{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      :						Tuple						      =				evaluate(dataset=a__	,		predictions=a__			)
			return score

"""simple docstring"""







from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels