
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase__:    Tuple      = '\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  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},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n'

lowerCAmelCase__:    Dict      = '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'

lowerCAmelCase__:    Union[str, Any]      = '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for \'record\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'prediction_text\': the predicted answer text\n        - for \'multirc\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question-answer pair as specified by the dataset\n            - \'prediction\': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for \'record\': list of question-answers dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'answers\': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for \'record\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1\': F1 score\n    - for \'multirc\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1_m\': Per-question macro-F1 score\n        - \'f1_a\': Average F1 score over all answers\n    - for \'axb\':\n        \'matthews_correlation\': Matthew Correlation\n    - for \'cb\':\n        - \'accuracy\': Accuracy\n        - \'f1\': F1 score\n    - for all others:\n        - \'accuracy\': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Any:
		return float((preds == labels).mean() )



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE="binary" )		-> List[Any]:
		SCREAMING_SNAKE_CASE_					:	Tuple		  =			simple_accuracy(SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			float(fa_score(y_true=SCREAMING_SNAKE_CASE ,    y_pred=SCREAMING_SNAKE_CASE ,    average=SCREAMING_SNAKE_CASE ) )
		return {
		    "accuracy": acc,
		    "f1": fa,
		}



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> List[str]:
		SCREAMING_SNAKE_CASE_					:	Any		  =			{}
		for id_pred, label in zip(SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ):
				SCREAMING_SNAKE_CASE_					:	Tuple		  =			f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
				SCREAMING_SNAKE_CASE_					:	str		  =			id_pred['prediction']
				if question_id in question_map:
						question_map[question_id].append((pred, label) )
				else:
						SCREAMING_SNAKE_CASE_					:	int		  =			[(pred, label)]
		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	int		  =			[], []
		for question, preds_labels in question_map.items():
				SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			zip(*SCREAMING_SNAKE_CASE )
				SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			fa_score(y_true=SCREAMING_SNAKE_CASE ,    y_pred=SCREAMING_SNAKE_CASE ,    average='macro' )
				fas.append(SCREAMING_SNAKE_CASE )
				SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			int(sum(pred == label for pred, label in preds_labels ) == len(SCREAMING_SNAKE_CASE ) )
				ems.append(SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE_					:	List[str]		  =			float(sum(SCREAMING_SNAKE_CASE ) / len(SCREAMING_SNAKE_CASE ) )
		SCREAMING_SNAKE_CASE_					:	str		  =			sum(SCREAMING_SNAKE_CASE ) / len(SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE_					:	Any		  =			float(fa_score(y_true=SCREAMING_SNAKE_CASE ,    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 							snake_case_ (				datasets.Metric    ):





			def 						__A						(		self      ):







					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      ):







					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      ):







					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":
							SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			[
							    {
							        'qas': [
							            {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
							            for ref in references
							        ]
							    }
							]
							SCREAMING_SNAKE_CASE_					:	List[str]		  =			{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'''

def _SCREAMING_SNAKE_CASE (A = 1_000			)			->			int:






       """simple docstring"""







       return sum(2 * a * ((a - 1) // 2) for a in range(3    ,					n + 1			)			)


if __name__ == "__main__":
 print(solution())




