






__lowerCamelCase	:		Dict										=   """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""







def 			A__							(      _a  :					bytes	):





 '''simple docstring'''







 if not isinstance(_a		,  _a	):
  snake_case__  :    List[Any]							       =f"a bytes-like object is required, not '{data.__class__.__name__}'"
  raise TypeError(_a	)

 snake_case__  :    int							       ="""""".join(bin(_a	)[2:].zfill(8	) for byte in data	)

 snake_case__  :    Optional[int]							       =len(_a	) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  snake_case__  :    List[Any]							       =B"""=""" * ((6 - len(_a	) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_a	) % 6)
 else:
  snake_case__  :    Optional[int]							       =B""""""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]		,  2	)]
         for index in range(0		,  len(_a	)		,  6	)	).encode()
     + padding
 )







def 			A__							(      _a  :					str	):





 '''simple docstring'''







 if not isinstance(_a		,  _a	) and not isinstance(_a		,  _a	):
  snake_case__  :    Dict							       =(
      """argument should be a bytes-like object or ASCII string, """
      f"not '{encoded_data.__class__.__name__}'"
  )
  raise TypeError(_a	)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_a		,  _a	):
  try:
   snake_case__  :    Optional[Any]							       =encoded_data.decode("""utf-8"""	)
  except UnicodeDecodeError:
   raise ValueError("""base64 encoded data should only contain ASCII characters"""	)

 snake_case__  :    List[Any]							       =encoded_data.count("""="""	)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]	), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data	), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_a	) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  snake_case__  :    int							       =encoded_data[:-padding]

  snake_case__  :    int							       ="""""".join(
      bin(B64_CHARSET.index(_a	)	)[2:].zfill(6	) for char in encoded_data	)[: -padding * 2]
 else:
  snake_case__  :    Dict							       ="""""".join(
      bin(B64_CHARSET.index(_a	)	)[2:].zfill(6	) for char in encoded_data	)

 snake_case__  :    List[Any]							       =[
     int(binary_stream[index : index + 8]		,  2	)
     for index in range(0		,  len(_a	)		,  8	)
 ]

 return bytes(_a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()













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

import datasets


__lowerCamelCase	:		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}
}
"""

__lowerCamelCase	:		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.
"""

__lowerCamelCase	:		str										=   """
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 			A__							(      _a  :					int		,  _a  :					str	):





 '''simple docstring'''







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







def 			A__							(      _a  :					Optional[Any]		,  _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    str							       =simple_accuracy(_a		,  _a	)
 snake_case__  :    str							       =float(fa_score(y_true=_a		,  y_pred=_a	)	)
 return {
     "accuracy": acc,
     "f1": fa,
 }







def 			A__							(      _a  :					int		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    str							       =float(pearsonr(_a		,  _a	)[0]	)
 snake_case__  :    Optional[int]							       =float(spearmanr(_a		,  _a	)[0]	)
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       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				,			a				,			a   ):
       if self.config_name == "cola":
        return {"matthews_correlation": matthews_corrcoef(a				,			a   )}
       elif self.config_name == "stsb":
        return pearson_and_spearman(a				,			a   )
       elif self.config_name in ["mrpc", "qqp"]:
        return acc_and_fa(a				,			a   )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
        return {"accuracy": simple_accuracy(a				,			a   )}
       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\"]"""   )





