






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  )  ),
								}














from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	''''''
				a__       							=	'''hf-legacy'''  # "hf://"" is reserved for hffs







				def __init__(    self							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(self							,       **__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   repo_info
								__magic_name__		:Dict   =   token
								__magic_name__		:Optional[Any]   =   None







				def       A						(    self  ):
								"""simple docstring"""
								if self.dir_cache is None:
												__magic_name__		:Any   =   {}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																__magic_name__		:Optional[int]   =   {
																    '''name''': hf_file.rfilename,
																    '''size''': None,
																    '''type''': '''file''',
																}
																self.dir_cache.update(
																    {
																        str(__lowerCAmelCase  ): {'''name''': str(__lowerCAmelCase  ), '''size''': None, '''type''': '''directory'''}
																        for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
																    }  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = "rb"							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								if not isinstance(self.repo_info							,       __lowerCAmelCase  ):
												raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''  )
								__magic_name__		:Union[str, Any]   =   hf_hub_url(self.repo_info.id							,       __lowerCAmelCase							,       revision=self.repo_info.sha  )
								return fsspec.open(
								    __lowerCAmelCase							,       mode=__lowerCAmelCase							,       headers=get_authentication_headers_for_url(__lowerCAmelCase							,       use_auth_token=self.token  )							,       client_kwargs={'''trust_env''': True}							,       ).open()







				def       A						(    self							,       __lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:str   =   self._strip_protocol(__lowerCAmelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:Union[str, Any]   =   PurePosixPath(path.strip('''/'''  )  )
								__magic_name__		:Dict   =   {}
								for p, f in self.dir_cache.items():
												__magic_name__		:int   =   PurePosixPath(p.strip('''/'''  )  )
												__magic_name__		:Tuple   =   p.parent
												if root == path:
																__magic_name__		:Optional[Any]   =   f
								__magic_name__		:List[Any]   =   list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )






