


'''simple docstring'''






from sklearn.metrics import mean_squared_error

import datasets


__UpperCAmelCase       =    '''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

__UpperCAmelCase       =    '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


__UpperCAmelCase       =    '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,    _KWARGS_DESCRIPTION  )
class 		a__    (							datasets.Metric  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														return datasets.MetricInfo(
														    description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(self._get_feature_types()       ) ,   reference_urls=[
														        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
														    ] ,   )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														if self.config_name == "multilist":
																					return {
																					    "predictions": datasets.Sequence(datasets.Value('''float'''       )       ),
																					    "references": datasets.Sequence(datasets.Value('''float'''       )       ),
																					}
														else:
																					return {
																					    "predictions": datasets.Value('''float'''       ),
																					    "references": datasets.Value('''float'''       ),
																					}







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_=None ,   lowerCamelCase_="uniform_average" ,   lowerCamelCase_=True       )	->							Any:
														lowerCAmelCase__	       =     mean_squared_error(
														    lowerCAmelCase__ ,   lowerCAmelCase__ ,   sample_weight=lowerCAmelCase__ ,   multioutput=lowerCAmelCase__ ,   squared=lowerCAmelCase__       )

														return {"mse": mse}



'''simple docstring'''






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__UpperCAmelCase       =							'''\
@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}
}
'''

__UpperCAmelCase       =							'''\
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.
'''

__UpperCAmelCase       =							'''
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  _snake_case       (					A			,   A	)						->   List[Any]:
							return float((preds == labels).mean()	)


def  _snake_case       (					A			,   A			,   A="binary"	)						->   int:
							lowerCAmelCase__	       =     simple_accuracy(A			,   A	)
							lowerCAmelCase__	       =     float(fa_score(y_true=A			,   y_pred=A			,   average=A	)	)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


def  _snake_case       (					A			,   A	)						->   List[Any]:
							lowerCAmelCase__	       =     {}
							for id_pred, label in zip(A			,   A	):
														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(*A	)
														lowerCAmelCase__	       =     fa_score(y_true=A			,   y_pred=A			,   average='''macro'''	)
														fas.append(A	)
														lowerCAmelCase__	       =     int(sum(pred == label for pred, label in preds_labels	) == len(A	)	)
														ems.append(A	)
							lowerCAmelCase__	       =     float(sum(A	) / len(A	)	)
							lowerCAmelCase__	       =     sum(A	) / len(A	)
							lowerCAmelCase__	       =     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 		a__    (							datasets.Metric  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														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       )	->							Optional[int]:
														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 ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Dict:
														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",]'''       )