





"""simple docstring"""




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


a						: Dict     	=			'''\
@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						: 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.
'''

a						: Any     	=			'''
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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[str]    ,       _lowercase						:    List[Any]					) ->Optional[int]:






	'''simple docstring'''






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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any    ,       _lowercase						:    Optional[Any]    ,       _lowercase						:    Union[str, Any]="binary"					) ->List[Any]:






	'''simple docstring'''






	a      :		List[Any]	=				simple_accuracy(_lowercase    ,       _lowercase					)
	a      :		Dict	=				float(fa_score(y_true=_lowercase    ,       y_pred=_lowercase    ,       average=_lowercase					)					)
	return {
	    "accuracy": acc,
	    "f1": fa,
	}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[Any]    ,       _lowercase						:    List[str]					) ->Tuple:






	'''simple docstring'''






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






							def     __a   (   self      )	-> 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     __a   (   self      )	-> Any:
								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     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Any:
								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":
									a      :		int	=				[
									    {
									        "qas": [
									            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
									            for ref in references
									        ]
									    }
									]
									a      :		List[Any]	=				{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 __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


