






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


UpperCAmelCase__ :     List[Any]				    =    """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

UpperCAmelCase__ :     str				    =    """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

UpperCAmelCase__ :     List[Any]				    =    """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""






def 					__lowercase      (     _A						,    _A			)     ->						int:
      return float((preds == labels).mean()			)






def 					__lowercase      (     _A						,    _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      List[str]       =   simple_accuracy(_A						,    _A			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   float(fa_score(y_true=_A						,    y_pred=_A			)			)
      return {
          "accuracy": acc,
          "f1": fa,
      }






def 					__lowercase      (     _A						,    _A			)     ->						List[Any]:
      SCREAMING_SNAKE_CASE :      Any       =   float(pearsonr(_A						,    _A			)[0]			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   float(spearmanr(_A						,    _A			)[0]			)
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,			_KWARGS_DESCRIPTION       )
class        a__       (   datasets.Metric       ):







  """simple docstring"""




  def  _lowercase      (			self    : Union[str, Any]		)					->Dict:
        """simple docstring"""







        if self.config_name not in [
            "sst2",
            "mnli",
            "mnli_mismatched",
            "mnli_matched",
            "cola",
            "stsb",
            "mrpc",
            "qqp",
            "qnli",
            "rte",
            "wnli",
            "hans",
        ]:
              raise KeyError(
                  """You should supply a configuration name selected in """
                  """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                  """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""		)
        return datasets.MetricInfo(
            description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(
                {
                    """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""		),
                    """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""		),
                }		)				,				codebase_urls=[]				,				reference_urls=[]				,				format="""numpy"""				,				)




  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : List[Any]		)					->int:
        """simple docstring"""







        if self.config_name == "cola":
              return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase__				,				UpperCAmelCase__		)}
        elif self.config_name == "stsb":
              return pearson_and_spearman(UpperCAmelCase__				,				UpperCAmelCase__		)
        elif self.config_name in ["mrpc", "qqp"]:
              return acc_and_fa(UpperCAmelCase__				,				UpperCAmelCase__		)
        elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
              return {"accuracy": simple_accuracy(UpperCAmelCase__				,				UpperCAmelCase__		)}
        else:
              raise KeyError(
                  """You should supply a configuration name selected in """
                  """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                  """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""		)







from typing import Any

import numpy as np






def 					__lowercase      (     _A			)     ->						bool:
      return np.array_equal(_A						,    matrix.conjugate().T			)






def 					__lowercase      (     _A						,    _A			)     ->						Any:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   v.conjugate().T
      SCREAMING_SNAKE_CASE :      List[Any]       =   v_star.dot(_A			)
      assert isinstance(_A						,    np.ndarray			)
      return (v_star_dot.dot(_A			)) / (v_star.dot(_A			))






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      Any       =   np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   np.array([[1], [2], [3]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      print(rayleigh_quotient(_A						,    _A			)			)

      SCREAMING_SNAKE_CASE :      Any       =   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      assert rayleigh_quotient(_A						,    _A			) == float(3			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()




