
from sklearn.metrics import matthews_corrcoef

import datasets


_A   =			"\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   =			"\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   =			"\\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 		UpperCAmelCase(self				:   List[str]       )						->						Dict:
         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 		UpperCAmelCase(self				:   Tuple    ,   _A				:   Union[str, Any]    ,   _A				:   List[Any]    ,   _A				:   List[Any]=None       )						->						Optional[int]:
         return {
             "matthews_correlation": float(matthews_corrcoef(_A    ,   _A    ,   sample_weight=_A       )       ),
         }



import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   Tuple       )						->						List[str]:
         snake_case								= ["a", "b", "c"]

         # Defaults to last layer if both are None
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   _A    ,   _A       )
         self.assertEqual(_A    ,   ["c"]       )
         self.assertEqual(_A    ,   [2]       )

         # Out indices set to match out features
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(["a", "c"]    ,   _A    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [0, 2]       )

         # Out features set to match out indices
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   [0, 2]    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [0, 2]       )

         # Out features selected from negative indices
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   [-3, -1]    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [-3, -1]       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         # Stage names must be set
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 1)    ,   _A       )

         # Out features must be a list
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(("a", "b")    ,   (0, 1)    ,   ["a", "b"]       )

         # Out features must be a subset of stage names
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 1)    ,   ["a"]       )

         # Out indices must be a list or tuple
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(_A    ,   0    ,   ["a", "b"]       )

         # Out indices must be a subset of stage names
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(_A    ,   (0, 1)    ,   ["a"]       )

         # Out features and out indices must be the same length
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0,)    ,   ["a", "b", "c"]       )

         # Out features should match out indices
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 2)    ,   ["a", "b", "c"]       )

         # Out features and out indices should be in order
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["b", "a"]    ,   (0, 1)    ,   ["a", "b"]       )

         # Check passes with valid inputs
         verify_out_features_out_indices(["a", "b", "d"]    ,   (0, 1, -1)    ,   ["a", "b", "c", "d"]       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						str:
         snake_case								= BackboneMixin()

         snake_case								= ["a", "b", "c"]
         snake_case								= ["a", "c"]
         snake_case								= [0, 2]

         # Check that the output features and indices are set correctly
         self.assertEqual(backbone.out_features    ,   ["a", "c"]       )
         self.assertEqual(backbone.out_indices    ,   [0, 2]       )

         # Check out features and indices are updated correctly
         snake_case								= ["a", "b"]
         self.assertEqual(backbone.out_features    ,   ["a", "b"]       )
         self.assertEqual(backbone.out_indices    ,   [0, 1]       )

         snake_case								= [-3, -1]
         self.assertEqual(backbone.out_features    ,   ["a", "c"]       )
         self.assertEqual(backbone.out_indices    ,   [-3, -1]       )

