





'''simple docstring'''





import datasets

from .evaluate import evaluate


A_												=     "\\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_												=     "\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_												=     "\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 SCREAMING_SNAKE_CASE_ (					datasets.Metric   ):





					"""simple docstring"""


					def 					__magic_name__ ( 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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
										lowerCamelCase__       =       [
										    {
										        "paragraphs": [
										            {
										                "qas": [
										                    {
										                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
										                        "id": ref["id"],
										                    }
										                    for ref in references
										                ]
										            }
										        ]
										    }
										]
										lowerCamelCase__       =       evaluate(dataset=lowercase_	,  predictions=lowercase_	)
										return score








def    __UpperCamelCase      (    a) ->str:

					if isinstance(a,     a):
										raise TypeError("'float' object cannot be interpreted as an integer")
					if isinstance(a,     a):
										raise TypeError("'str' object cannot be interpreted as an integer")

					if num == 0:
										return "0b0"

					lowerCamelCase__       =       False

					if num < 0:
										lowerCamelCase__       =       True
										lowerCamelCase__       =       -num

					lowerCamelCase__       =       []
					while num > 0:
										binary.insert(0,     num % 2)
										num >>= 1

					if negative:
										return "-0b" + "".join(str(a) for e in binary)

					return "0b" + "".join(str(a) for e in binary)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

