'''simple docstring'''





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class 			__A :
    a__			:		int
    a__			:		TreeNode | None			  =    None
    a__			:		TreeNode | None			  =    None




SCREAMING_SNAKE_CASE_:	Union[str, Any]    =namedtuple('CoinsDistribResult', 'moves excess')





def       lowerCAmelCase_							(   snake_case_			:					TreeNode | None     )       -> int:


     '''simple docstring'''




     if root is None:
          return 0

     # Validation
     def count_nodes(snake_case_			:					TreeNode | None     ) -> int:
          if node is None:
               return 0

          return count_nodes(node.left     ) + count_nodes(node.right     ) + 1

     def count_coins(snake_case_			:					TreeNode | None     ) -> int:
          if node is None:
               return 0

          return count_coins(node.left     ) + count_coins(node.right     ) + node.data

     if count_nodes(snake_case_     ) != count_coins(snake_case_     ):
          raise ValueError("The nodes number should be same as the number of coins"     )

     # Main calculation
     def get_distrib(snake_case_			:					TreeNode | None     ) -> CoinsDistribResult:

          if node is None:
               return CoinsDistribResult(0   ,       1     )

          UpperCAmelCase_       ,					UpperCAmelCase_			       =  get_distrib(node.left     )
          UpperCAmelCase_       ,					UpperCAmelCase_			       =  get_distrib(node.right     )

          UpperCAmelCase_			       =  1 - left_distrib_excess
          UpperCAmelCase_			       =  1 - right_distrib_excess

          UpperCAmelCase_			       =  (
              left_distrib_moves
              + right_distrib_moves
              + abs(snake_case_     )
              + abs(snake_case_     )
          )
          UpperCAmelCase_			       =  node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(snake_case_   ,       snake_case_     )

     return get_distrib(snake_case_     )[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()











'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase					    =	"\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"

__UpperCamelCase					    =	"\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"

__UpperCamelCase					    =	"\\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 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""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 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]=None			)					->    Optional[int]:


					"""simple docstring"""

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


