

from sklearn.metrics import mean_absolute_error

import datasets


_SCREAMING_SNAKE_CASE							 =			'''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

_SCREAMING_SNAKE_CASE							 =			'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


_SCREAMING_SNAKE_CASE							 =			'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''


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




 def     a       (			self							):
   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_absolute_error.html'
       ]  ,  )




 def     a       (			self							):
   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     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase="uniform_average"							):
   __UpperCAmelCase	:  int		=     mean_absolute_error(_UpperCAmelCase  ,  _UpperCAmelCase  ,  sample_weight=_UpperCAmelCase  ,  multioutput=_UpperCAmelCase							)

   return {"mae": mae_score}








class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Any		=     data
   __UpperCAmelCase	:  BinaryTreeNode | None		=     None
   __UpperCAmelCase	:  BinaryTreeNode | None		=     None





def  _snake_case		(snake_case_					:     BinaryTreeNode | None    ,       snake_case_					:     int  )							->		BinaryTreeNode | None:
  if node is None:
    __UpperCAmelCase	:  List[Any]		=     BinaryTreeNode(snake_case_  )
    return node

  # binary search tree is not empty,
  # so we will insert it into the tree
  # if new_value is less than value of data in node,
  #  add it to left subtree and proceed recursively
  if new_value < node.data:
    __UpperCAmelCase	:  Union[str, Any]		=     insert(node.left_child    ,       snake_case_  )
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase	:  Tuple		=     insert(node.right_child    ,       snake_case_  )
  return node





def  _snake_case		(snake_case_					:     None | BinaryTreeNode  )							->		list[int]:  # if node is None,return
  if node:
    __UpperCAmelCase	:  str		=     inorder(node.left_child  )
    __UpperCAmelCase	:  Optional[int]		=     [*inorder_array, node.data]
    __UpperCAmelCase	:  str		=     inorder_array + inorder(node.right_child  )
  else:
    __UpperCAmelCase	:  Union[str, Any]		=     []
  return inorder_array





def  _snake_case		()							->		BinaryTreeNode | None:
  __UpperCAmelCase	:  Tuple		=     insert(snake_case_    ,       15  )
  insert(snake_case_    ,       10  )
  insert(snake_case_    ,       25  )
  insert(snake_case_    ,       6  )
  insert(snake_case_    ,       14  )
  insert(snake_case_    ,       20  )
  insert(snake_case_    ,       60  )
  return root





def  _snake_case		()							->		None:
  # main function
  __UpperCAmelCase	:  Any		=     make_tree()
  print('Printing values of binary search tree in Inorder Traversal.'  )
  inorder(snake_case_  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





