




from sklearn.metrics import matthews_corrcoef

import datasets


lowerCamelCase							:						Optional[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"

lowerCamelCase							:						Union[str, 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"

lowerCamelCase							:						Optional[int]					       =  "\\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     A(		datasets.Metric  ):



 '''simple docstring'''


 def 							a__					(							self   :    Dict    )  ->      Optional[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 							a__					(							self   :    Any		,				A_   :    Optional[Any]		,				A_   :    List[Any]		,				A_   :    List[Any]=None    )  ->      Tuple:

       """simple docstring"""






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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''vit_mae'''


			def __init__( self     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=12     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=224     ,		lowerCamelCase__=16     ,		lowerCamelCase__=3     ,		lowerCamelCase__=True     ,		lowerCamelCase__=16     ,		lowerCamelCase__=512     ,		lowerCamelCase__=8     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=0.75     ,		lowerCamelCase__=False     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)

							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       qkv_bias
							__lowerCamelCase									=       decoder_num_attention_heads
							__lowerCamelCase									=       decoder_hidden_size
							__lowerCamelCase									=       decoder_num_hidden_layers
							__lowerCamelCase									=       decoder_intermediate_size
							__lowerCamelCase									=       mask_ratio
							__lowerCamelCase									=       norm_pix_loss


