


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCAmelCase__    :Union[str, Any]       =				logging.get_logger(__name__)

class 						__a (							UpperCAmelCase     ):
		_a      :    List[str]           =      "upernet"
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=[1, 2, 3, 6]     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=0.4     ,       _SCREAMING_SNAKE_CASE=384     ,       _SCREAMING_SNAKE_CASE=256     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=255     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									if backbone_config is None:
																logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
																_UpperCAmelCase											=     CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     backbone_config.get('model_type'		)
																_UpperCAmelCase											=     CONFIG_MAPPING[backbone_model_type]
																_UpperCAmelCase											=     config_class.from_dict(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     backbone_config
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     pool_scales
									_UpperCAmelCase											=     use_auxiliary_head
									_UpperCAmelCase											=     auxiliary_loss_weight
									_UpperCAmelCase											=     auxiliary_in_channels
									_UpperCAmelCase											=     auxiliary_channels
									_UpperCAmelCase											=     auxiliary_num_convs
									_UpperCAmelCase											=     auxiliary_concat_input
									_UpperCAmelCase											=     loss_ignore_index






		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     copy.deepcopy(self.__dict__		)
									_UpperCAmelCase											=     self.backbone_config.to_dict()
									_UpperCAmelCase											=     self.__class__.model_type
									return output





from sklearn.metrics import matthews_corrcoef

import datasets


a__   :					Any 						=      "\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__   :					Any 						=      "\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__   :					str 						=      "\\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    UpperCAmelCase__(				datasets.Metric						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[Any])						->					int:




         """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       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]=None)						->					Any:




         """simple docstring"""

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