'''simple docstring'''


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__snake_case						  =		"""\
@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}
}
"""

__snake_case						  =		"""\
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.
"""

__snake_case						  =		"""
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_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  return float((preds == labels).mean()						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_="binary"						)			->Optional[Any]:
  lowercase_     =       simple_accuracy(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  lowercase_     =       float(fa_score(y_true=SCREAMING_SNAKE_CASE_ ,	y_pred=SCREAMING_SNAKE_CASE_ ,	average=SCREAMING_SNAKE_CASE_						)						)
  return {
      "accuracy": acc,
      "f1": fa,
  }

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->str:
  lowercase_     =       {}
  for id_pred, label in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
    lowercase_     =       id_pred["""prediction"""]
    if question_id in question_map:
      question_map[question_id].append((pred, label)						)
    else:
      lowercase_     =       [(pred, label)]
  lowercase_     ,	lowercase_     =       [], []
  for question, preds_labels in question_map.items():
    lowercase_     ,	lowercase_     =       zip(*SCREAMING_SNAKE_CASE_						)
    lowercase_     =       fa_score(y_true=SCREAMING_SNAKE_CASE_ ,	y_pred=SCREAMING_SNAKE_CASE_ ,	average="""macro"""						)
    fas.append(SCREAMING_SNAKE_CASE_						)
    lowercase_     =       int(sum(pred == label for pred, label in preds_labels						) == len(SCREAMING_SNAKE_CASE_						)						)
    ems.append(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       float(sum(SCREAMING_SNAKE_CASE_						) / len(SCREAMING_SNAKE_CASE_						)						)
  lowercase_     =       sum(SCREAMING_SNAKE_CASE_						) / len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       float(fa_score(y_true=SCREAMING_SNAKE_CASE_ ,	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       _a    (		datasets.Metric      ):


  """simple docstring"""

  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''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 		lowerCamelCase__  (		self      :       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 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Tuple   ,				lowercase_      :       Dict      ):







    '''simple docstring'''


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


'''simple docstring'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if num < 0:
    return False

  lowercase_     =       num
  lowercase_     =       0
  while num > 0:
    lowercase_     =       rev_num * 10 + (num % 10)
    num //= 10

  return num_copy == rev_num


if __name__ == "__main__":
       import doctest

       doctest.testmod()

