



'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowerCAmelCase      						=	'''\
@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}
}
'''

_lowerCAmelCase      						=	'''\
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.
'''

_lowerCAmelCase      						=	'''
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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    return float((preds == labels).mean()			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase="binary"			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					simple_accuracy(UpperCamelCase							,    UpperCamelCase			)
    lowerCAmelCase__ :  Tuple							      =					float(fa_score(y_true=UpperCamelCase							,    y_pred=UpperCamelCase							,    average=UpperCamelCase			)			)
    return {
        "accuracy": acc,
        "f1": fa,
    }
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					{}
    for id_pred, label in zip(UpperCamelCase							,    UpperCamelCase			):
        lowerCAmelCase__ :  str							      =					f"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
        lowerCAmelCase__ :  Dict							      =					id_pred["""prediction"""]
        if question_id in question_map:
            question_map[question_id].append((pred, label)			)
        else:
            lowerCAmelCase__ :  Optional[int]							      =					[(pred, label)]
    lowerCAmelCase__      ,   lowerCAmelCase__ :  int							      =					[], []
    for question, preds_labels in question_map.items():
        lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					zip(*UpperCamelCase			)
        lowerCAmelCase__ :  List[Any]							      =					fa_score(y_true=UpperCamelCase							,    y_pred=UpperCamelCase							,    average="""macro"""			)
        fas.append(UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					int(sum(pred == label for pred, label in preds_labels			) == len(UpperCamelCase			)			)
        ems.append(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					float(sum(UpperCamelCase			) / len(UpperCamelCase			)			)
    lowerCAmelCase__ :  List[Any]							      =					sum(UpperCamelCase			) / len(UpperCamelCase			)
    lowerCAmelCase__ :  Dict							      =					float(fa_score(y_true=UpperCamelCase							,    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     ):
  '''simple docstring'''




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









def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




