


'''simple docstring'''

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 (A    ,					A			)			->			Any:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A="binary"			)			->			List[Any]:






       """simple docstring"""







       lowercase__      =					simple_accuracy(A    ,					A			)
       lowercase__      =					float(fa_score(y_true=A    ,					y_pred=A    ,					average=A			)			)
       return {
           "accuracy": acc,
           "f1": fa,
       }

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			List[str]:






       """simple docstring"""







       lowercase__      =					{}
       for id_pred, label in zip(A    ,					A			):
              lowercase__      =					f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
              lowercase__      =					id_pred['''prediction''']
              if question_id in question_map:
                     question_map[question_id].append((pred, label)			)
              else:
                     lowercase__      =					[(pred, label)]
       lowercase__     ,lowercase__      =					[], []
       for question, preds_labels in question_map.items():
              lowercase__     ,lowercase__      =					zip(*A			)
              lowercase__      =					fa_score(y_true=A    ,					y_pred=A    ,					average='''macro'''			)
              fas.append(A			)
              lowercase__      =					int(sum(pred == label for pred, label in preds_labels			) == len(A			)			)
              ems.append(A			)
       lowercase__      =					float(sum(A			) / len(A			)			)
       lowercase__      =					sum(A			) / len(A			)
       lowercase__      =					float(fa_score(y_true=A    ,					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     __lowerCAmelCase		(datasets.Metric	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Tuple					):

             '''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       :       Union[str, 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       :       List[str]  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             if self.config_name == "axb":
                    return {"matthews_correlation": matthews_corrcoef(UpperCamelCase  ,		UpperCamelCase					)}
             elif self.config_name == "cb":
                    return acc_and_fa(UpperCamelCase  ,		UpperCamelCase  ,		fa_avg='''macro'''					)
             elif self.config_name == "record":
                    lowercase__      =					[
                        {
                            '''qas''': [
                                {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
                                for ref in references
                            ]
                        }
                    ]
                    lowercase__      =					{pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
                    return evaluate_record(UpperCamelCase  ,		UpperCamelCase					)[0]
             elif self.config_name == "multirc":
                    return evaluate_multirc(UpperCamelCase  ,		UpperCamelCase					)
             elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
                    return {"accuracy": simple_accuracy(UpperCamelCase  ,		UpperCamelCase					)}
             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 math import sqrt

def _SCREAMING_SNAKE_CASE (A = 1_000_000			)			->			int:






       """simple docstring"""







       lowercase__      =					0
       lowercase__      =					0
       lowercase__      =					42

       while num_cuboids <= limit:
              max_cuboid_size += 1
              for sum_shortest_sides in range(2    ,					2 * max_cuboid_size + 1			):
                     if sqrt(sum_shortest_sides**2 + max_cuboid_size**2			).is_integer():
                            num_cuboids += (
                                min(A    ,					sum_shortest_sides // 2			)
                                - max(1    ,					sum_shortest_sides - max_cuboid_size			)
                                + 1
                            )

       return max_cuboid_size


if __name__ == "__main__":
 print(f"""{solution() = }""")




