





'''simple docstring'''




from __future__ import annotations

_A:					str							=				[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_A:					List[Any]							=				[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[float]:
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   len(UpperCAmelCase__    )

 for i in range(UpperCAmelCase__    ):
  __UpperCAmelCase					=   -1
  for j in range(i + 1       ,    UpperCAmelCase__    ):
   if arr[i] < arr[j]:
    __UpperCAmelCase					=   arr[j]
    break
  result.append(UpperCAmelCase__    )
 return result







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[float]:
 __UpperCAmelCase					=   []
 for i, outer in enumerate(UpperCAmelCase__    ):
  __UpperCAmelCase					=   -1
  for inner in arr[i + 1 :]:
   if outer < inner:
    __UpperCAmelCase					=   inner
    break
  result.append(UpperCAmelCase__    )
 return result







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[float]:
 __UpperCAmelCase					=   len(UpperCAmelCase__    )
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [-1] * arr_size

 for index in reversed(range(UpperCAmelCase__    )    ):
  if stack:
   while stack[-1] <= arr[index]:
    stack.pop()
    if not stack:
     break
  if stack:
   __UpperCAmelCase					=   stack[-1]
  stack.append(arr[index]    )
 return result


if __name__ == "__main__":
       from doctest import testmod
       from timeit import timeit

       testmod()
       print(next_greatest_element_slow(arr))
       print(next_greatest_element_fast(arr))
       print(next_greatest_element(arr))

       _A:					List[Any]							=				(
           "from __main__ import arr, next_greatest_element_slow, "
           "next_greatest_element_fast, next_greatest_element"
       )
       print(
           """next_greatest_element_slow():""",
           timeit("""next_greatest_element_slow(arr)""", setup=setup),
       )
       print(
           """next_greatest_element_fast():""",
           timeit("""next_greatest_element_fast(arr)""", setup=setup),
       )
       print(
           """     next_greatest_element():""",
           timeit("""next_greatest_element(arr)""", setup=setup),
       )












'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_A:					Tuple							=				"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

_A:					List[Any]							=				"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

_A:					List[Any]							=				"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 return float((preds == labels).mean()    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase="binary"    )->     Tuple:
 __UpperCAmelCase					=   simple_accuracy(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   float(fa_score(y_true=_lowerCAmelCase       ,    y_pred=_lowerCAmelCase       ,    average=_lowerCAmelCase    )    )
 return {
     "accuracy": acc,
     "f1": fa,
 }







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   {}
 for id_pred, label in zip(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  __UpperCAmelCase					=   F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
  __UpperCAmelCase					=   id_pred['prediction']
  if question_id in question_map:
   question_map[question_id].append((pred, label)    )
  else:
   __UpperCAmelCase					=   [(pred, label)]
 __UpperCAmelCase					,    __UpperCAmelCase					=   [], []
 for question, preds_labels in question_map.items():
  __UpperCAmelCase					,    __UpperCAmelCase					=   zip(*_lowerCAmelCase    )
  __UpperCAmelCase					=   fa_score(y_true=_lowerCAmelCase       ,    y_pred=_lowerCAmelCase       ,    average='macro'    )
  fas.append(_lowerCAmelCase    )
  __UpperCAmelCase					=   int(sum(pred == label for pred, label in preds_labels    ) == len(_lowerCAmelCase    )    )
  ems.append(_lowerCAmelCase    )
 __UpperCAmelCase					=   float(sum(_lowerCAmelCase    ) / len(_lowerCAmelCase    )    )
 __UpperCAmelCase					=   sum(_lowerCAmelCase    ) / len(_lowerCAmelCase    )
 __UpperCAmelCase					=   float(fa_score(y_true=_lowerCAmelCase       ,    y_pred=[id_pred['prediction'] for id_pred in ids_preds]    )    )
 return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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



    def 					__lowerCamelCase						(  self					):
     if self.config_name not in [
         "boolq",
         "cb",
         "copa",
         "multirc",
         "record",
         "rte",
         "wic",
         "wsc",
         "wsc.fixed",
         "axb",
         "axg",
     ]:
      raise KeyError(
          'You should supply a configuration name selected in '
          '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'					)
     return datasets.MetricInfo(
         description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(self._get_feature_types()					)     ,			codebase_urls=[]     ,			reference_urls=[]     ,			format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None     ,			)



    def 					__lowerCamelCase						(  self					):
     if self.config_name == "record":
      return {
          "predictions": {
              "idx": {
                  "passage": datasets.Value('int64'					),
                  "query": datasets.Value('int64'					),
              },
              "prediction_text": datasets.Value('string'					),
          },
          "references": {
              "idx": {
                  "passage": datasets.Value('int64'					),
                  "query": datasets.Value('int64'					),
              },
              "answers": datasets.Sequence(datasets.Value('string'					)					),
          },
      }
     elif self.config_name == "multirc":
      return {
          "predictions": {
              "idx": {
                  "answer": datasets.Value('int64'					),
                  "paragraph": datasets.Value('int64'					),
                  "question": datasets.Value('int64'					),
              },
              "prediction": datasets.Value('int64'					),
          },
          "references": datasets.Value('int64'					),
      }
     else:
      return {
          "predictions": datasets.Value('int64'					),
          "references": datasets.Value('int64'					),
      }




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     if self.config_name == "axb":
      return {"matthews_correlation": matthews_corrcoef(__A     ,			__A					)}
     elif self.config_name == "cb":
      return acc_and_fa(__A     ,			__A     ,			fa_avg='macro'					)
     elif self.config_name == "record":
      __UpperCAmelCase					=   [
          {
              'qas': [
                  {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                  for ref in references
              ]
          }
      ]
      __UpperCAmelCase					=   {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
      return evaluate_record(__A     ,			__A					)[0]
     elif self.config_name == "multirc":
      return evaluate_multirc(__A     ,			__A					)
     elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
      return {"accuracy": simple_accuracy(__A     ,			__A					)}
     else:
      raise KeyError(
          'You should supply a configuration name selected in '
          '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'					)





