

'''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   :			Optional[int]         = "\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   UpperCamelCase__    (   datasets.Metric   ):


		"""simple docstring"""

		def       A_       ( self			):


							'''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       A_       ( self    ,			snake_case    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							str   					=   {prediction['id']: prediction['prediction_text'] for prediction in predictions}
							UpperCAmelCase		:							Any   					=   [
							    {
							        'paragraphs': [
							            {
							                'qas': [
							                    {
							                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
							                        'id': ref['id'],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							UpperCAmelCase		:							Optional[Any]   					=   evaluate(dataset=snake_case    ,			predictions=snake_case			)
							return score


class      A__							:






				def __init__(						self					:      Dict    ,     a					:      list[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		len(a   )
							lowerCAmelCase__      :      Optional[Any]         =		[0] * len_array

							if len_array > 0:
										lowerCAmelCase__      :      List[Any]         =		array[0]

							for i in range(1    ,     a   ):
										lowerCAmelCase__      :      Optional[int]         =		self.prefix_sum[i - 1] + array[i]






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							if start == 0:
										return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{0}
							for sum_item in self.prefix_sum:
										if sum_item - target_sum in sums:
													return True

										sums.add(a   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()