

'''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 						_A	(       A__	,							A__ ):






 """simple docstring"""



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


def 						_A	(       A__	,							A__	,							A__="binary" ):






 """simple docstring"""



 __lowercase				    =					simple_accuracy(A__	,							A__ )
 __lowercase				    =					float(fa_score(y_true=A__	,							y_pred=A__	,							average=A__ ) )
 return {
     "accuracy": acc,
     "f1": fa,
 }


def 						_A	(       A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					{}
 for id_pred, label in zip(A__	,							A__ ):
  __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(*A__ )
  __lowercase				    =					fa_score(y_true=A__	,							y_pred=A__	,							average='''macro''' )
  fas.append(A__ )
  __lowercase				    =					int(sum(pred == label for pred, label in preds_labels ) == len(A__ ) )
  ems.append(A__ )
 __lowercase				    =					float(sum(A__ ) / len(A__ ) )
 __lowercase				    =					sum(A__ ) / len(A__ )
 __lowercase				    =					float(fa_score(y_true=A__	,							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    SCREAMING_SNAKE_CASE ( self  :	str     ):
   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    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   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    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	Tuple   ,lowercase__  :	Tuple     ):
   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'''





# flake8: noqa
# Lint as: python3
__a  			=						[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
