





'''simple docstring'''


import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE	 =					"\\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"

_SCREAMING_SNAKE_CASE	 =					"\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"

_SCREAMING_SNAKE_CASE	 =					"\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    lowerCAmelCase_		(		datasets.Metric		):




 def 							_snake_case   (	self		)    ->							int:
     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			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _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=_lowerCAmelCase			,     predictions=_lowerCAmelCase		)
     return score









'''simple docstring'''


from math import isqrt, loga


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [True] * max_number
    for i in range(2		,       isqrt(max_number - 1    ) + 1    ):
        if is_prime[i]:
            for j in range(i**2		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
                _lowerCAmelCase				=      False

    return [i for i in range(2		,       SCREAMING_SNAKE_CASE_    ) if is_prime[i]]


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 800800		,       SCREAMING_SNAKE_CASE_  :					int = 800800    ):



    '''simple docstring'''





    _lowerCAmelCase				=      degree * loga(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      int(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      calculate_prime_numbers(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      len(SCREAMING_SNAKE_CASE_    ) - 1
    while left < right:
        while (
            prime_numbers[right] * loga(prime_numbers[left]    )
            + prime_numbers[left] * loga(prime_numbers[right]    )
            > upper_bound
        ):
            right -= 1
        hybrid_integers_count += right - left
        left += 1

    return hybrid_integers_count


if __name__ == "__main__":
       print(f'''{solution() = }''')


