


def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if a < 0 or b < 0:
														raise ValueError("the value of both inputs must be positive"			)
							__snake_case	:    Tuple											=     str(bin(lowerCamelCase_			)			)[2:]  # remove the leading "0b"
							__snake_case	:    str											=     str(bin(lowerCamelCase_			)			)[2:]
							__snake_case	:    List[str]											=     max(len(lowerCamelCase_			)							,			len(lowerCamelCase_			)			)
							return "0b" + "".join(
							    str(int("1" in (char_a, char_b)			)			)
							    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase_			)							,			b_binary.zfill(lowerCamelCase_			)			)			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_snake_case  :	List[Any]			      =       "\\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"

_snake_case  :	Any			      =       "\\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"

_snake_case  :	str			      =       "\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        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							return float((preds == labels).mean()			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase="binary"			):
							__snake_case	:    Union[str, Any]											=     simple_accuracy(__lowerCamelCase							,			__lowerCamelCase			)
							__snake_case	:    List[str]											=     float(fa_score(y_true=__lowerCamelCase							,			y_pred=__lowerCamelCase							,			average=__lowerCamelCase			)			)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    List[Any]											=     {}
							for id_pred, label in zip(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    int											=     F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
														__snake_case	:    str											=     id_pred["prediction"]
														if question_id in question_map:
																					question_map[question_id].append((pred, label)			)
														else:
																					__snake_case	:    Optional[Any]											=     [(pred, label)]
							__snake_case				,    __snake_case	:    Dict											=     [], []
							for question, preds_labels in question_map.items():
														__snake_case				,    __snake_case	:    int											=     zip(*__lowerCamelCase			)
														__snake_case	:    Optional[Any]											=     fa_score(y_true=__lowerCamelCase							,			y_pred=__lowerCamelCase							,			average="macro"			)
														fas.append(__lowerCamelCase			)
														__snake_case	:    Union[str, Any]											=     int(sum(pred == label for pred, label in preds_labels			) == len(__lowerCamelCase			)			)
														ems.append(__lowerCamelCase			)
							__snake_case	:    Tuple											=     float(sum(__lowerCamelCase			) / len(__lowerCamelCase			)			)
							__snake_case	:    Any											=     sum(__lowerCamelCase			) / len(__lowerCamelCase			)
							__snake_case	:    int											=     float(fa_score(y_true=__lowerCamelCase							,			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 					__snake_case      (							self :						List[Any]       )			->					Union[str, 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 					__snake_case      (							self :						Any       )			->					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 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						Optional[Any]       )			->					str:
														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":
																					__snake_case	:    Tuple											=     [
																					    {
																					        "qas": [
																					            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
																					            for ref in references
																					        ]
																					    }
																					]
																					__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\",]"       )

