



"""simple docstring"""

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowercase__					:						Tuple												=			'''\
@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}
}
'''

lowercase__					:						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.
'''

lowercase__					:						str												=			'''
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 __lowercase    (							_a	,    _a					):
    return float((preds == labels).mean()					)




def __lowercase    (							_a	,    _a	,    _a="binary"					):
    snake_case_				:  List[Any]       =			simple_accuracy(_a	,    _a					)
    snake_case_				:  List[str]       =			float(fa_score(y_true=_a	,    y_pred=_a	,    average=_a					)					)
    return {
        "accuracy": acc,
        "f1": fa,
    }




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Any       =			{}
    for id_pred, label in zip(_a	,    _a					):
        snake_case_				:  Union[str, Any]       =			f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
        snake_case_				:  int       =			id_pred['''prediction''']
        if question_id in question_map:
            question_map[question_id].append((pred, label)					)
        else:
            snake_case_				:  List[str]       =			[(pred, label)]
    snake_case_				:  str       =			[], []
    for question, preds_labels in question_map.items():
        snake_case_				:  List[Any]       =			zip(*_a					)
        snake_case_				:  List[Any]       =			fa_score(y_true=_a	,    y_pred=_a	,    average='''macro'''					)
        fas.append(_a					)
        snake_case_				:  int       =			int(sum(pred == label for pred, label in preds_labels					) == len(_a					)					)
        ems.append(_a					)
    snake_case_				:  Union[str, Any]       =			float(sum(_a					) / len(_a					)					)
    snake_case_				:  str       =			sum(_a					) / len(_a					)
    snake_case_				:  Union[str, Any]       =			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  _UpperCAmelCase (    datasets.Metric):

 def 		_snake_case      (  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 		_snake_case      (  self	: Optional[Any]       ):
     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 		_snake_case      (  self	: Optional[int]						,  lowercase_	: Dict						,  lowercase_	: Any       ):
     if self.config_name == "axb":
         return {"matthews_correlation": matthews_corrcoef(lowercase_						,  lowercase_       )}
     elif self.config_name == "cb":
         return acc_and_fa(lowercase_						,  lowercase_						,  fa_avg='''macro'''       )
     elif self.config_name == "record":
         snake_case_				:  Tuple       =			[
             {
                 '''qas''': [
                     {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
                     for ref in references
                 ]
             }
         ]
         snake_case_				:  Union[str, Any]       =			{pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
         return evaluate_record(lowercase_						,  lowercase_       )[0]
     elif self.config_name == "multirc":
         return evaluate_multirc(lowercase_						,  lowercase_       )
     elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
         return {"accuracy": simple_accuracy(lowercase_						,  lowercase_       )}
     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 numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def __lowercase    (							_a					):
    return np.dot(_a	,    _a					)




class  _UpperCAmelCase :

 def __init__(  self	: int						,  *,
     lowercase_	: float = np.inf						,  lowercase_	: str = "linear"						,  lowercase_	: float = 0.0						,  ):
     snake_case_				:  Optional[Any]       =			regularization
     snake_case_				:  Tuple       =			gamma
     if kernel == "linear":
         snake_case_				:  int       =			self.__linear
     elif kernel == "rbf":
         if self.gamma == 0:
             raise ValueError('''rbf kernel requires gamma'''       )
         if not isinstance(self.gamma						,  (float, int)       ):
             raise ValueError('''gamma must be float or int'''       )
         if not self.gamma > 0:
             raise ValueError('''gamma must be > 0'''       )
         snake_case_				:  int       =			self.__rbf
         # in the future, there could be a default value like in sklearn
         # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
         # previously it was 1/(n_features)
     else:
         snake_case_				:  List[Any]       =			f"Unknown kernel: {kernel}"
         raise ValueError(lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.dot(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: list[ndarray]						,  lowercase_	: ndarray       ):
     snake_case_				:  Union[str, Any]       =			observations
     snake_case_				:  int       =			classes

     # using Wolfe's Dual to calculate w.
     # Primal problem: minimize 1/2*norm_squared(w)
     #   constraint: yn(w . xn + b) >= 1
     #
     # With l a vector
     # Dual problem: maximize sum_n(ln) -
     #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
     #   constraint: self.C >= ln >= 0
     #           and sum_n(ln*yn) = 0
     # Then we get w using w = sum_n(ln*yn*xn)
     # At the end we can get b ~= mean(yn - w . xn)
     #
     # Since we use kernels, we only need l_star to calculate b
     # and to classify observations

     ((snake_case_),			)				:  List[str]       =			np.shape(lowercase_       )

     def to_minimize(lowercase_	: ndarray       ) -> float:
         snake_case_				:  Tuple       =			0
         ((snake_case_),			)				:  Optional[Any]       =			np.shape(lowercase_       )
         for i in range(lowercase_       ):
             for j in range(lowercase_       ):
                 s += (
                     candidate[i]
                     * candidate[j]
                     * classes[i]
                     * classes[j]
                     * self.kernel(observations[i]						,  observations[j]       )
                 )
         return 1 / 2 * s - sum(lowercase_       )

     snake_case_				:  Optional[Any]       =			LinearConstraint(lowercase_						,  0						,  0       )
     snake_case_				:  str       =			Bounds(0						,  self.regularization       )

     snake_case_				:  int       =			minimize(
         lowercase_						,  np.ones(lowercase_       )						,  bounds=lowercase_						,  constraints=[ly_contraint]       ).x
     snake_case_				:  Optional[Any]       =			l_star

     # calculating mean offset of separation plane to points
     snake_case_				:  List[Any]       =			0
     for i in range(lowercase_       ):
         for j in range(lowercase_       ):
             s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                 observations[i]						,  observations[j]       )
     snake_case_				:  Union[str, Any]       =			s / n






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: ndarray       ):
     snake_case_				:  int       =			sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]						,  lowercase_       )
         for n in range(len(self.classes       )       )       )
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





