


'''simple docstring'''



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass



@dataclass
class 			a       :


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				int
     SCREAMING_SNAKE_CASE :				TreeNode | None							     =     None
     SCREAMING_SNAKE_CASE :				TreeNode | None							     =     None



__UpperCAmelCase    :Optional[int]				       =     namedtuple("CoinsDistribResult", "moves excess")




def 			_a			(		_lowercase      :			TreeNode | None						):





     '''simple docstring'''






     if root is None:
          return 0

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

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

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

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

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

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

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

          __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    get_distrib(node.left						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    get_distrib(node.right						)

          __UpperCAmelCase			:				Optional[int]													=    1 - left_distrib_excess
          __UpperCAmelCase			:				List[Any]													=    1 - right_distrib_excess

          __UpperCAmelCase			:				Tuple													=    (
              left_distrib_moves
              + right_distrib_moves
              + abs(_lowercase						)
              + abs(_lowercase						)
          )
          __UpperCAmelCase			:				Optional[Any]													=    node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(_lowercase    ,      _lowercase						)

     return get_distrib(_lowercase						)[0]


if __name__ == "__main__":
      import doctest

      doctest.testmod()



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


__UpperCAmelCase    :List[str]				       =     "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


__UpperCAmelCase    :List[Any]				       =     "\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION )
class 			a       (	datasets.Metric ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''float'''    ),
                      '''references''': datasets.Value('''float'''    ),
                  }    )							,				reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']							,				)






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}