





'''simple docstring'''






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					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ""
					_SCREAMING_SNAKE_CASE							   =    "hf-legacy"  # "hf://"" is reserved for hffs


					def __init__(					self	:  List[Any]       ,     __snake_case	:  Optional[DatasetInfo] = None       ,     __snake_case	:  Optional[str] = None       ,     **__snake_case	:  Optional[Any]       ,     ):





											'''simple docstring'''





											super().__init__(self       ,     **__snake_case       )
											_snake_case:   Optional[Any]  					=		repo_info
											_snake_case:   str  					=		token
											_snake_case:   int  					=		None


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											if self.dir_cache is None:
																	_snake_case:   Optional[Any]  					=		{}
																	for hf_file in self.repo_info.siblings:
																							# TODO(QL): add sizes
																							_snake_case:   Dict  					=		{
																							    'name': hf_file.rfilename,
																							    'size': None,
																							    'type': 'file',
																							}
																							self.dir_cache.update(
																							    {
																							        str(__snake_case       ): {'name': str(__snake_case       ), 'size': None, 'type': 'directory'}
																							        for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
																							    }       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  str       ,     __snake_case	:  str = "rb"       ,     **__snake_case	:  Union[str, Any]       ,     ):





											'''simple docstring'''





											if not isinstance(self.repo_info       ,     __snake_case       ):
																	raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''       )
											_snake_case:   Any  					=		hf_hub_url(self.repo_info.id       ,     __snake_case       ,     revision=self.repo_info.sha       )
											return fsspec.open(
											    __snake_case       ,     mode=__snake_case       ,     headers=get_authentication_headers_for_url(__snake_case       ,     use_auth_token=self.token       )       ,     client_kwargs={'trust_env': True}       ,     ).open()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  int       ,     **__snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											self._get_dirs()
											_snake_case:   Union[str, Any]  					=		self._strip_protocol(__snake_case       )
											if path in self.dir_cache:
																	return self.dir_cache[path]
											else:
																	raise FileNotFoundError(__snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  List[Any]=False       ,     **__snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											self._get_dirs()
											_snake_case:   Dict  					=		PurePosixPath(path.strip('/'       )       )
											_snake_case:   Dict  					=		{}
											for p, f in self.dir_cache.items():
																	_snake_case:   str  					=		PurePosixPath(p.strip('/'       )       )
																	_snake_case:   Dict  					=		p.parent
																	if root == path:
																							_snake_case:   List[str]  					=		f
											_snake_case:   int  					=		list(paths.values()       )
											if detail:
																	return out
											else:
																	return sorted(f['name'] for f in out       )












'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


A   : Dict             =			'\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'

A   : int             =			'\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n'

A   : Dict             =			'\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'







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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''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    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  int       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]=None       ):





											'''simple docstring'''





											return {
											    "matthews_correlation": float(matthews_corrcoef(__snake_case       ,     __snake_case       ,     sample_weight=__snake_case       )       ),
											}





