




'''simple docstring'''



import logging
import os

from .state import PartialState






class 							_a				(			logging.LoggerAdapter ):





       '''simple docstring'''






       @staticmethod
       def        UpperCamelCase_							(      A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						PartialState()
             return not main_process_only or (main_process_only and state.is_main_process)







       def        UpperCamelCase_							(      self,							A,							A,							*A,							**A  ):







             '''simple docstring'''







             if PartialState._shared_state == {}:
                   raise RuntimeError(
                       'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'  )
             SCREAMING_SNAKE_CASE   :      int    					=						kwargs.pop('main_process_only',							A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						kwargs.pop('in_order',							A  )

             if self.isEnabledFor(A  ):
                   if self._should_log(A  ):
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						self.process(A,							A  )
                         self.logger.log(A,							A,							*A,							**A  )

                   elif in_order:
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						PartialState()
                         for i in range(state.num_processes  ):
                               if i == state.process_index:
                                     SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						self.process(A,							A  )
                                     self.logger.log(A,							A,							*A,							**A  )
                               state.wait_for_everyone()




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					str = None    ):

      """simple docstring"""






      if log_level is None:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						os.environ.get('ACCELERATE_LOG_LEVEL'      ,__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						logging.getLogger(__UpperCamelCase    )
      if log_level is not None:
            logger.setLevel(log_level.upper()    )
            logger.root.setLevel(log_level.upper()    )
      return MultiProcessAdapter(__UpperCamelCase      ,{}    )










from sklearn.metrics import matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		"""
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

SCREAMING_SNAKE_CASE__   :      int				=		"""\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION			)
class     lowerCamelCase_					(   datasets.Metric			):







				def       A						(    self  ):
								"""simple docstring"""
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,       citation=_CITATION							,       inputs_description=_KWARGS_DESCRIPTION							,       features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''int32'''  ),
								            '''references''': datasets.Value('''int32'''  ),
								        }  )							,       reference_urls=[
								        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
								    ]							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								return {
								    "matthews_correlation": float(matthews_corrcoef(__lowerCAmelCase							,       __lowerCAmelCase							,       sample_weight=__lowerCAmelCase  )  ),
								}






