






'''simple docstring'''





from sklearn.metrics import mean_absolute_error

import datasets


a_      = '\\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'

a_      = '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


a_      = '\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\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'





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



		def _lowerCAmelCase     (    self:    Optional[int])  ->int:






								'''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_absolute_error.html"
								    ]				,   )



		def _lowerCAmelCase     (    self:    int)  ->str:






								'''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[str]				,   a:    Any				,   a:    Optional[Any]				,   a:    Dict=None				,   a:    List[str]="uniform_average")  ->Dict:






								'''simple docstring'''


								a_				     =   mean_absolute_error(a				,   a				,   sample_weight=a				,   multioutput=a)

								return {"mae": mae_score}











'''simple docstring'''





from __future__ import annotations

a_      = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Optional[int]				,   a:    int)  ->None:






								'''simple docstring'''


								a_				     =   -1
								a_				     =   -1
								a_				     =   [-1] * length
								a_				     =   None
								a_				     =   None




		def __repr__(    self:    Union[str, Any])  ->str:






								'''simple docstring'''


								return f"""Node(min_value={self.minn} max_value={self.maxx})"""





def 			__UpperCAmelCase				(lowercase__				)		->				Node | None:

						'''simple docstring'''


						a_				     =   Node(len(lowercase__				)				)
						a_     ,       a_				     =   min(lowercase__				), max(lowercase__				)
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						a_				     =   (root.minn + root.maxx) // 2

						a_				     =   []
						a_				     =   []

						for index, num in enumerate(lowercase__				):
												if num <= pivot:
																		left_arr.append(lowercase__				)
												else:
																		right_arr.append(lowercase__				)
												a_				     =   len(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						return root



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						a_				     =   (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left	,lowercase__	,node.map_left[index] - 1				)
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right	,lowercase__	,index - node.map_left[index]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if start > end:
												return 0
						a_				     =   rank_till_index(lowercase__	,lowercase__	,lowercase__				)
						a_				     =   rank_till_index(lowercase__	,lowercase__	,start - 1				)
						return rank_till_end - rank_before_start



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						a_				     =   node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left	,lowercase__	,(node.map_left[start - 1] if start else 0)	,node.map_left[end] - 1	,)
						else:
												return quantile(
												    node.right	,index - num_elements_in_left_tree	,start - (node.map_left[start - 1] if start else 0)	,end - node.map_left[end]	,)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						a_				     =   range_counting(
						    node.left	,(node.map_left[start - 1] if start else 0)	,node.map_left[end] - 1	,lowercase__	,lowercase__	,)
						a_				     =   range_counting(
						    node.right	,start - (node.map_left[start - 1] if start else 0)	,end - node.map_left[end]	,lowercase__	,lowercase__	,)
						return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()



