




'''simple docstring'''






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


A_			      =		"\\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"

A_			      =		"\\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"

A_			      =		"\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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   List[str]:
      return float((preds == labels).mean()		)






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






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





     def 			_lowerCAmelCase      (     self     :					List[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 			_lowerCAmelCase      (     self     :					str			):
           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 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Dict			):
           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__       :   Optional[Any]	      =    [
                     {
                         'qas': [
                             {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                             for ref in references
                         ]
                     }
                 ]
                 snake_case__       :   int	      =    {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'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    div.find('div'				,      attrs={'class': 'gs_fl'}		).find_all('a'		)
      return anchors[2].get_text()


if __name__ == "__main__":
      A_			      =		{
          "title": (
              "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
              "capacitance, volumetric capacitance, and energy density"
          ),
          "journal": "Chem. Mater.",
          "volume": 30,
          "pages": "3979-3990",
          "year": 2018,
          "hl": "en",
      }
      print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
