"""simple docstring"""






from ..utils import DummyObject, requires_backends


class     UpperCAmelCase						(metaclass=__lowercase  ):



    """simple docstring"""

    _UpperCAmelCase	:Any						    = ['''keras_nlp''']






    def __init__(   self    ,			*_UpperCAmelCase    ,			**_UpperCAmelCase							):
       requires_backends(self    ,			['''keras_nlp''']							)




'''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_					( lowerCamelCase	:     str	, lowerCamelCase	:     Union[str, Any]				):
   return float((preds == labels).mean()				)





def 	a_					( lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str="binary"				):
   lowerCAmelCase     =			simple_accuracy(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			float(fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average=lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     List[Any]				):
   lowerCAmelCase     =			{}
   for id_pred, label in zip(lowerCamelCase	, lowerCamelCase				):
      lowerCAmelCase     =			f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
      lowerCAmelCase     =			id_pred['prediction']
      if question_id in question_map:
         question_map[question_id].append((pred, label)				)
      else:
         lowerCAmelCase     =			[(pred, label)]
   lowerCAmelCase     ,  lowerCAmelCase     =			[], []
   for question, preds_labels in question_map.items():
      lowerCAmelCase     ,  lowerCAmelCase     =			zip(*lowerCamelCase				)
      lowerCAmelCase     =			fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average='macro'				)
      fas.append(lowerCamelCase				)
      lowerCAmelCase     =			int(sum(pred == label for pred, label in preds_labels				) == len(lowerCamelCase				)				)
      ems.append(lowerCamelCase				)
   lowerCAmelCase     =			float(sum(lowerCamelCase				) / len(lowerCamelCase				)				)
   lowerCAmelCase     =			sum(lowerCamelCase				) / len(lowerCamelCase				)
   lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[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       :      Union[str, Any]    )				->  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       :      List[str]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Optional[Any]    )				->  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     =			[
               {
                   'qas': [
                       {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                       for ref in references
                   ]
               }
           ]
           lowerCAmelCase     =			{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",]'    )





