





'''simple docstring'''







from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


snake_case_     :       Union[str, 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_     :       str						=  "\\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_     :       List[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_      (  SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	Optional[int]:
						return float((preds == labels).mean()   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Union[str, Any]="binary"   ) ->	Dict:
						UpperCAmelCase_			:      Union[str, Any]     						=  simple_accuracy(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Tuple     						=  float(fa_score(y_true=SCREAMING_SNAKE_CASE__,			y_pred=SCREAMING_SNAKE_CASE__,			average=SCREAMING_SNAKE_CASE__   )   )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}





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

							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													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     UpperCAmelCase__	(							self							:     Any   )					->       Any:







													"""simple docstring"""
													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     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     int       ,     __magic_name__							:     Any   )					->       Tuple:







													"""simple docstring"""
													if self.config_name == "axb":
																			return {"matthews_correlation": matthews_corrcoef(__magic_name__       ,     __magic_name__   )}
													elif self.config_name == "cb":
																			return acc_and_fa(__magic_name__       ,     __magic_name__       ,     fa_avg='''macro'''   )
													elif self.config_name == "record":
																			UpperCAmelCase_			:      List[Any]     						=  [
																			    {
																			        '''qas''': [
																			            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
																			            for ref in references
																			        ]
																			    }
																			]
																			UpperCAmelCase_			:      Dict     						=  {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
																			return evaluate_record(__magic_name__       ,     __magic_name__   )[0]
													elif self.config_name == "multirc":
																			return evaluate_multirc(__magic_name__       ,     __magic_name__   )
													elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																			return {"accuracy": simple_accuracy(__magic_name__       ,     __magic_name__   )}
													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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int = 10,			SCREAMING_SNAKE_CASE__      :			int = 22   ) ->	int:
						UpperCAmelCase_			:      Optional[int]     						=  range(1,			SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[Any]     						=  range(1,			SCREAMING_SNAKE_CASE__   )
						return sum(
						    1 for power in powers for base in bases if len(str(base**power   )   ) == power   )


if __name__ == "__main__":
					print(f'''{solution(10, 22) = }''')





