

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 _UpperCamelCase				(				datasets.Metric ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Optional[Any]:

				"""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 				__UpperCamelCase			(      self				:			Any   ,   a				:			Dict   ,   a				:			Dict						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
				SCREAMING_SNAKE_CASE :   Dict					  =							[
				    {
				        "paragraphs": [
				            {
				                "qas": [
				                    {
				                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
				                        "id": ref["id"],
				                    }
				                    for ref in references
				                ]
				            }
				        ]
				    }
				]
				SCREAMING_SNAKE_CASE :   Any					  =							evaluate(dataset=snake_case_   ,   predictions=snake_case_						)
				return score






def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return price * (1 + tax_rate)


if __name__ == "__main__":
 print(f"{price_plus_tax(100, 0.2_5) = }")
 print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) = }")





