






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,
)


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						'''\\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 :    Optional[Any]    =						'''\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering 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\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'''

_SCREAMING_SNAKE_CASE :    int    =						'''\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no 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 list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'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}\n'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,				_KWARGS_DESCRIPTION		)
class 					a						( datasets.Metric		):






    def        UpperCamelCase	(	self					:							Dict )					->		Dict:
      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        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							List[Any]=1.0 )					->		int:
      lowerCamelCase_   					=						{p['id']: p['no_answer_probability'] for p in predictions}
      lowerCamelCase_   					=						[{'paragraphs': [{'qas': references}]}]
      lowerCamelCase_   					=						{p['id']: p['prediction_text'] for p in predictions}

      lowerCamelCase_   					=						make_qid_to_has_ans(__SCREAMING_SNAKE_CASE )  # maps qid to True/False
      lowerCamelCase_   					=						[k for k, v in qid_to_has_ans.items() if v]
      lowerCamelCase_   					=						[k for k, v in qid_to_has_ans.items() if not v]

      lowerCamelCase_					,     lowerCamelCase_   					=						get_raw_scores(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						apply_no_ans_threshold(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						apply_no_ans_threshold(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						make_eval_dict(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

      if has_ans_qids:
        lowerCamelCase_   					=						make_eval_dict(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						qid_list=__SCREAMING_SNAKE_CASE )
        merge_eval(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						'HasAns' )
      if no_ans_qids:
        lowerCamelCase_   					=						make_eval_dict(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						qid_list=__SCREAMING_SNAKE_CASE )
        merge_eval(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						'NoAns' )
      find_all_best_thresh(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
      return dict(__SCREAMING_SNAKE_CASE )












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		int = 1000			)				->    int:
  lowerCamelCase_   					=						0
  lowerCamelCase_   					=						(n - 1) // 3
  total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
  lowerCamelCase_   					=						(n - 1) // 5
  total += ((terms) * (10 + (terms - 1) * 5)) // 2
  lowerCamelCase_   					=						(n - 1) // 15
  total -= ((terms) * (30 + (terms - 1) * 15)) // 2
  return total


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




