


'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase__          = '''\
@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}
}
'''

UpperCamelCase__          = '''\
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.
'''

UpperCamelCase__          = '''
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    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      int:
    return float((preds == labels).mean()		)





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__="binary"		)       ->      int:
    UpperCAmelCase__     :						Optional[int]						=							simple_accuracy(lowerCAmelCase__    ,   lowerCAmelCase__		)
    UpperCAmelCase__     :						List[str]						=							float(fa_score(y_true=lowerCAmelCase__    ,   y_pred=lowerCAmelCase__    ,   average=lowerCAmelCase__		)		)
    return {
        "accuracy": acc,
        "f1": fa,
    }





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Optional[Any]:
    UpperCAmelCase__     :						Tuple						=							{}
    for id_pred, label in zip(lowerCAmelCase__    ,   lowerCAmelCase__		):
        UpperCAmelCase__     :						int						=							F"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
        UpperCAmelCase__     :						Dict						=							id_pred['''prediction''']
        if question_id in question_map:
            question_map[question_id].append((pred, label)		)
        else:
            UpperCAmelCase__     :						List[Any]						=							[(pred, label)]
    UpperCAmelCase__		,		UpperCAmelCase__     :						Any						=							[], []
    for question, preds_labels in question_map.items():
        UpperCAmelCase__		,		UpperCAmelCase__     :						Optional[int]						=							zip(*lowerCAmelCase__		)
        UpperCAmelCase__     :						Optional[int]						=							fa_score(y_true=lowerCAmelCase__    ,   y_pred=lowerCAmelCase__    ,   average='''macro'''		)
        fas.append(lowerCAmelCase__		)
        UpperCAmelCase__     :						List[Any]						=							int(sum(pred == label for pred, label in preds_labels		) == len(lowerCAmelCase__		)		)
        ems.append(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[str]						=							float(sum(lowerCAmelCase__		) / len(lowerCAmelCase__		)		)
    UpperCAmelCase__     :						Any						=							sum(lowerCAmelCase__		) / len(lowerCAmelCase__		)
    UpperCAmelCase__     :						Dict						=							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      lowerCamelCase_  (      datasets.Metric ):






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          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 lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          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 lowercase_				(   self							:       Optional[Any]		,		_A							:       List[Any]		,		_A							:       Optional[Any]					):


          '''simple docstring'''


          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__     :						List[Any]						=							[
                  {
                      '''qas''': [
                          {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
                          for ref in references
                      ]
                  }
              ]
              UpperCAmelCase__     :						str						=							{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",]'''					)


from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int | float]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int | float:
       if len(UpperCAmelCase_				) == 0:
              raise ValueError('''find_max() arg is an empty sequence'''				)
       if (
           left >= len(UpperCAmelCase_				)
           or left < -len(UpperCAmelCase_				)
           or right >= len(UpperCAmelCase_				)
           or right < -len(UpperCAmelCase_				)
       ):
              raise IndexError('''list index out of range'''				)
       if left == right:
              return nums[left]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=(left + right) >> 1  # the middle
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)  # find max in range[left, mid]
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							mid + 1			,							UpperCAmelCase_				)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
