




"""simple docstring"""




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase							:    Union[str, Any]  							=							"""\
@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							:    Optional[int]  							=							"""\
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							:    int  							=							"""
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__				(       snake_case__ ,				snake_case__       )		->      List[Any]:
			return float((preds == labels).mean()       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__="binary"       )		->      int:
			lowerCamelCase       =      simple_accuracy(lowercase__ ,				lowercase__       )
			lowerCamelCase       =      float(fa_score(y_true=lowercase__ ,				y_pred=lowercase__ ,				average=lowercase__       )       )
			return {
			    "accuracy": acc,
			    "f1": fa,
			}



def 					a__				(       snake_case__ ,				snake_case__       )		->      Dict:
			lowerCamelCase       =      {}
			for id_pred, label in zip(lowercase__ ,				lowercase__       ):
						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(*lowercase__       )
						lowerCamelCase       =      fa_score(y_true=lowercase__ ,				y_pred=lowercase__ ,				average="""macro"""       )
						fas.append(lowercase__       )
						lowerCamelCase       =      int(sum(pred == label for pred, label in preds_labels       ) == len(lowercase__       )       )
						ems.append(lowercase__       )
			lowerCamelCase       =      float(sum(lowercase__       ) / len(lowercase__       )       )
			lowerCamelCase       =      sum(lowercase__       ) / len(lowercase__       )
			lowerCamelCase       =      float(fa_score(y_true=lowercase__ ,				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 					__magic_name__							( datasets.Metric       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""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				):





							"""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	,				_a	,				_a				):





							"""simple docstring"""







							if self.config_name == "axb":
										return {"matthews_correlation": matthews_corrcoef(lowerCAmelCase_	,				lowerCAmelCase_				)}
							elif self.config_name == "cb":
										return acc_and_fa(lowerCAmelCase_	,				lowerCAmelCase_	,				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(lowerCAmelCase_	,				lowerCAmelCase_				)[0]
							elif self.config_name == "multirc":
										return evaluate_multirc(lowerCAmelCase_	,				lowerCAmelCase_				)
							elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
										return {"accuracy": simple_accuracy(lowerCAmelCase_	,				lowerCAmelCase_				)}
							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 ..utils import DummyObject, requires_backends






class 					__magic_name__							( metaclass=UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		["note_seq"]





				def __init__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							requires_backends(self	,				["""note_seq"""]				)





				@classmethod
				def      _lowerCAmelCase       ( cls	,				*_a	,				**_a				):





							"""simple docstring"""
							requires_backends(cls	,				["""note_seq"""]				)





				@classmethod
				def      _lowerCAmelCase       ( cls	,				*_a	,				**_a				):





							"""simple docstring"""
							requires_backends(cls	,				["""note_seq"""]				)



