



"""simple docstring"""






from __future__ import annotations






class        __snake_case       :




  def __init__( self     :						Optional[int]						,					__lowerCAmelCase     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= data
         _lowerCamelCase						:				Node | None    			= None
         _lowerCamelCase						:				Node | None    			= None






def        snake_case_  (      A_					:		Node | None						):  # In Order traversal of the tree


       '''simple docstring'''





       if tree:
              display(tree.left						)
              print(tree.data						)
              display(tree.right						)






def        snake_case_  (      A_					:		Node | None						):


       '''simple docstring'''





       return 1 + max(depth_of_tree(tree.left						),				depth_of_tree(tree.right						)						) if tree else 0






def        snake_case_  (      A_					:		Node						):


       '''simple docstring'''





       if not tree:
              return True
       if tree.left and tree.right:
              return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
       else:
              return not tree.left and not tree.right






def        snake_case_  (      ):  # Main function for testing.


       '''simple docstring'''





       _lowerCamelCase						:				Tuple    			= Node(1						)
       _lowerCamelCase						:				int    			= Node(2						)
       _lowerCamelCase						:				Optional[Any]    			= Node(3						)
       _lowerCamelCase						:				List[str]    			= Node(4						)
       _lowerCamelCase						:				List[Any]    			= Node(5						)
       _lowerCamelCase						:				int    			= Node(6						)
       _lowerCamelCase						:				str    			= Node(7						)
       _lowerCamelCase						:				Tuple    			= Node(8						)
       _lowerCamelCase						:				Union[str, Any]    			= Node(9						)

       print(is_full_binary_tree(SCREAMING_SNAKE_CASE__						)						)
       print(depth_of_tree(SCREAMING_SNAKE_CASE__						)						)
       print('''Tree is: '''						)
       display(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
   main()






from sklearn.metrics import mean_squared_error

import datasets


lowerCamelCase											=       '\\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'

lowerCamelCase											=       '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCamelCase											=       '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'




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






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             return datasets.MetricInfo(
                 description=_DESCRIPTION			,   citation=_CITATION			,   inputs_description=_KWARGS_DESCRIPTION			,   features=datasets.Features(self._get_feature_types() )			,   reference_urls=[
                     'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
                 ]			,   )






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             if self.config_name == "multilist":
                    return {
                        "predictions": datasets.Sequence(datasets.Value('float' ) ),
                        "references": datasets.Sequence(datasets.Value('float' ) ),
                    }
             else:
                    return {
                        "predictions": datasets.Value('float' ),
                        "references": datasets.Value('float' ),
                    }






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=None			,   lowercase_						:     Union[str, Any]="uniform_average"			,   lowercase_						:     Tuple=True )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=mean_squared_error(
                 lowercase_			,   lowercase_			,   sample_weight=lowercase_			,   multioutput=lowercase_			,   squared=lowercase_ )

             return {"mse": mse}


