




"""simple docstring"""


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


A_      			=       '''\
@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_      			=       '''\
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_      			=       '''
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       UpperCAmelCase__ (snake_case__							:      Any	,     snake_case__							:      int					):
  """simple docstring"""

  return float((preds == labels).mean()					)


def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      Tuple	,     snake_case__							:      List[Any]="binary"					):
  """simple docstring"""

  _snake_case							:	Any			=						simple_accuracy(snake_case__	,     snake_case__					)
  _snake_case							:	int			=						float(fa_score(y_true=snake_case__	,     y_pred=snake_case__	,     average=snake_case__					)					)
  return {
      "accuracy": acc,
      "f1": fa,
  }


def       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      str					):
  """simple docstring"""

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







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Union[str, 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 				UpperCamelCase_     (       self:	Tuple	):




   '''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 				UpperCamelCase_     (       self:	int,   a_:	Tuple,   a_:	int	):




   '''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":
     _snake_case							:	Optional[Any]			=						[
         {
             """qas""": [
                 {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                 for ref in references
             ]
         }
     ]
     _snake_case							:	int			=						{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\",]"""	)








"""simple docstring"""


from typing import Any


def       UpperCAmelCase__ (snake_case__							:      list					):
  """simple docstring"""

  if not input_list:
    return []
  _snake_case							:	List[Any]			=						[input_list.count(snake_case__					) for value in input_list]
  _snake_case							:	Optional[int]			=						max(snake_case__					)  # Gets the maximum count in the input list.
  # Gets values of modes
  return sorted({input_list[i] for i, value in enumerate(snake_case__					) if value == y}					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()


