"""simple docstring"""







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


lowerCamelCase      : Any       ='''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

lowerCamelCase      : List[str]       ='''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

lowerCamelCase      : Any       ='''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,	_KWARGS_DESCRIPTION	)
class 							__snake_case(					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'	),
                         'no_answer_probability': datasets.Value('float32'	),
                     },
                     '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					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase=1.0	):




         '''simple docstring'''



         __A					:					Tuple								=		{p['id']: p['no_answer_probability'] for p in predictions}
         __A					:					Tuple								=		[{'paragraphs': [{'qas': references}]}]
         __A					:					Optional[Any]								=		{p['id']: p['prediction_text'] for p in predictions}

         __A					:					List[str]								=		make_qid_to_has_ans(__lowerCamelCase	)  # maps qid to True/False
         __A					:					str								=		[k for k, v in qid_to_has_ans.items() if v]
         __A					:					int								=		[k for k, v in qid_to_has_ans.items() if not v]

         __A						,		__A					:					Union[str, Any]								=		get_raw_scores(__lowerCamelCase					,       __lowerCamelCase	)
         __A					:					str								=		apply_no_ans_threshold(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	)
         __A					:					List[str]								=		apply_no_ans_threshold(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	)
         __A					:					Any								=		make_eval_dict(__lowerCamelCase					,       __lowerCamelCase	)

         if has_ans_qids:
              __A					:					Any								=		make_eval_dict(__lowerCamelCase					,       __lowerCamelCase					,       qid_list=__lowerCamelCase	)
              merge_eval(__lowerCamelCase					,       __lowerCamelCase					,       'HasAns'	)
         if no_ans_qids:
              __A					:					Union[str, Any]								=		make_eval_dict(__lowerCamelCase					,       __lowerCamelCase					,       qid_list=__lowerCamelCase	)
              merge_eval(__lowerCamelCase					,       __lowerCamelCase					,       'NoAns'	)
         find_all_best_thresh(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	)
         return dict(__lowerCamelCase	)




"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



