


"""simple docstring"""


import datasets

from .evaluate import evaluate


A  :     Dict    					=    "\\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  :     str    					=    "\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  :     List[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 						_UpperCamelCase						(     datasets.Metric	):
							'''simple docstring'''







							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			):
										__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








"""simple docstring"""


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    len(_UpperCamelCase   )
			print("The following activities are selected:"   )

			# The first activity is always selected
			__lowerCAmelCase       					=    0
			print(_UpperCamelCase					,				end=","   )

			# Consider rest of the activities
			for j in range(_UpperCamelCase   ):
						# If this activity has start time greater than
						# or equal to the finish time of previously
						# selected activity, then select it
						if start[j] >= finish[i]:
									print(_UpperCamelCase					,				end=","   )
									__lowerCAmelCase       					=    j


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A  :     Dict    					=    [1, 3, 0, 5, 8, 5]
	A  :     int    					=    [2, 4, 6, 7, 9, 9]
	print_max_activities(start, finish)




