


"""simple docstring"""






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowerCAmelCase							:    Dict					   =	'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  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},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

_lowerCAmelCase							:    Optional[Any]					   =	'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

_lowerCAmelCase							:    int					   =	'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





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







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase="binary"       )    ->     Tuple:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     simple_accuracy(_lowerCamelCase			,		_lowerCamelCase       )
   _lowerCamelCase   :				Union[str, Any]							=     float(fa_score(y_true=_lowerCamelCase			,		y_pred=_lowerCamelCase			,		average=_lowerCamelCase       )       )
   return {
       "accuracy": acc,
       "f1": fa,
   }







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





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


  def 							_lowercase					(			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 							_lowercase					(			self:						Optional[int]    ):







     '''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 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						int							,__lowerCAmelCase:						List[Any]    ):







     '''simple docstring'''







     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   :				Optional[Any]							=     [
            {
                "qas": [
                    {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                    for ref in references
                ]
            }
        ]
        _lowerCamelCase   :				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\",]"    )


__lowercase				      =						"""Alexander Joslin"""

import operator as op

from .stack import Stack







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		{'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

      A_							       =		Stack()
      A_							       =		Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(SCREAMING_SNAKE_CASE   )   )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(SCREAMING_SNAKE_CASE   )
            elif i == ")":
                  # RULE 4
                  A_							       =		operator_stack.peek()
                  operator_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()

                  A_							       =		operators[opr](SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                  operand_stack.push(SCREAMING_SNAKE_CASE   )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
      __lowercase				      =						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





