



'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


a     :    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}
}
"""

a     :    List[str]      						=					"""\
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     :    Tuple      						=					"""
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    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[int]:
							return float((preds == labels).mean()	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase="binary"	)   ->    List[str]:
							UpperCAmelCase  :	Optional[int]							  =	simple_accuracy(_lowercase			,       _lowercase	)
							UpperCAmelCase  :	List[Any]							  =	float(fa_score(y_true=_lowercase			,       y_pred=_lowercase			,       average=_lowercase	)	)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	Any							  =	{}
							for id_pred, label in zip(_lowercase			,       _lowercase	):
														UpperCAmelCase  :	List[str]							  =	F'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
														UpperCAmelCase  :	int							  =	id_pred["""prediction"""]
														if question_id in question_map:
																					question_map[question_id].append((pred, label)	)
														else:
																					UpperCAmelCase  :	Any							  =	[(pred, label)]
							UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	[], []
							for question, preds_labels in question_map.items():
														UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	zip(*_lowercase	)
														UpperCAmelCase  :	int							  =	fa_score(y_true=_lowercase			,       y_pred=_lowercase			,       average="""macro"""	)
														fas.append(_lowercase	)
														UpperCAmelCase  :	Any							  =	int(sum(pred == label for pred, label in preds_labels	) == len(_lowercase	)	)
														ems.append(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	float(sum(_lowercase	) / len(_lowercase	)	)
							UpperCAmelCase  :	List[Any]							  =	sum(_lowercase	) / len(_lowercase	)
							UpperCAmelCase  :	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 				_lowercase(						self					)    ->    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 				_lowercase(						self					)    ->    Dict:
								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 				_lowercase(						self	,	A	,	A					)    ->    Tuple:
								if self.config_name == "axb":
															return {"matthews_correlation": matthews_corrcoef(A	,	A					)}
								elif self.config_name == "cb":
															return acc_and_fa(A	,	A	,	fa_avg="""macro"""					)
								elif self.config_name == "record":
															UpperCAmelCase  :	List[str]							  =	[
															    {
															        """qas""": [
															            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
															            for ref in references
															        ]
															    }
															]
															UpperCAmelCase  :	List[Any]							  =	{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
															return evaluate_record(A	,	A					)[0]
								elif self.config_name == "multirc":
															return evaluate_multirc(A	,	A					)
								elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
															return {"accuracy": simple_accuracy(A	,	A					)}
								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 is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

