'''simple docstring'''




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   :	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   :	List[Any] 							=   "\\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   ):






       def 			_snake_case						(      self      ):
              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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=None      ):
              return {
                  "matthews_correlation": float(matthews_corrcoef(UpperCamelCase_				,     UpperCamelCase_				,     sample_weight=UpperCamelCase_      )      ),
              }







'''simple docstring'''




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


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	List[Any] 							=   {
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}






class 		__A       (__magic_name__   ):
       snake_case       :Any				    =					"cvt"






       def __init__(      self				,     UpperCamelCase_=3				,     UpperCamelCase_=[7, 3, 3]				,     UpperCamelCase_=[4, 2, 2]				,     UpperCamelCase_=[2, 1, 1]				,     UpperCamelCase_=[64, 1_92, 3_84]				,     UpperCamelCase_=[1, 3, 6]				,     UpperCamelCase_=[1, 2, 10]				,     UpperCamelCase_=[4.0, 4.0, 4.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.1]				,     UpperCamelCase_=[True, True, True]				,     UpperCamelCase_=[False, False, True]				,     UpperCamelCase_=["dw_bn", "dw_bn", "dw_bn"]				,     UpperCamelCase_=[3, 3, 3]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[2, 2, 2]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     **UpperCamelCase_				,     ):
              super().__init__(**UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		num_channels
              __UpperCAmelCase  :				Optional[Any]       				=		patch_sizes
              __UpperCAmelCase  :				List[str]       				=		patch_stride
              __UpperCAmelCase  :				Tuple       				=		patch_padding
              __UpperCAmelCase  :				int       				=		embed_dim
              __UpperCAmelCase  :				str       				=		num_heads
              __UpperCAmelCase  :				Any       				=		depth
              __UpperCAmelCase  :				List[str]       				=		mlp_ratio
              __UpperCAmelCase  :				List[str]       				=		attention_drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_path_rate
              __UpperCAmelCase  :				str       				=		qkv_bias
              __UpperCAmelCase  :				Optional[int]       				=		cls_token
              __UpperCAmelCase  :				Optional[Any]       				=		qkv_projection_method
              __UpperCAmelCase  :				Tuple       				=		kernel_qkv
              __UpperCAmelCase  :				Optional[Any]       				=		padding_kv
              __UpperCAmelCase  :				Optional[int]       				=		stride_kv
              __UpperCAmelCase  :				Any       				=		padding_q
              __UpperCAmelCase  :				List[Any]       				=		stride_q
              __UpperCAmelCase  :				Union[str, Any]       				=		initializer_range
              __UpperCAmelCase  :				Any       				=		layer_norm_eps






