"""simple docstring"""




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_a				:		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'

_a				:		Tuple	   =       '\\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				:		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					(      _lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[Any]     )	->				Tuple:
  return float((preds == labels).mean()     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Tuple="binary"     )	->				List[str]:
  _lowerCAmelCase						:	Dict        =					simple_accuracy(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_     )
  _lowerCAmelCase						:	Optional[int]        =					float(fa_score(y_true=SCREAMING_SNAKE_CASE_      ,y_pred=SCREAMING_SNAKE_CASE_      ,average=SCREAMING_SNAKE_CASE_     )     )
  return {
      "accuracy": acc,
      "f1": fa,
  }





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




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
     from doctest import testmod

     testmod()




