



'''simple docstring'''





from sklearn.metrics import mean_absolute_error

import datasets


_lowercase	:   Optional[Any]    	="\\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"

_lowercase	:   Any    	="\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


_lowercase	:   Union[str, Any]    	="\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      snake_case__       (datasets.Metric	):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   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_absolute_error.html"""
									    ]      ,     )

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
									"""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 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase=None      ,     __lowercase="uniform_average"						)      ->   str:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						mean_absolute_error(snake_case_      ,     snake_case_      ,     sample_weight=snake_case_      ,     multioutput=snake_case_						)

									return {"mae": mae_score}









from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class      snake_case__       (A__	):







						"""simple docstring"""




						@abstractmethod
						def __lt__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass

						@abstractmethod
						def __gt__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass

						@abstractmethod
						def __eq__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass




_lowercase	:   List[str]    	=TypeVar("T", bound=Comparable)

class      snake_case__       (Generic[T]	):







						"""simple docstring"""




						def __init__(     self						)      ->   None:
									"""simple docstring"""




									a__     :		list[T]		 =						[]
									a__     :		int		 =						0

						def __repr__(     self						)      ->   str:
									"""simple docstring"""




									return str(self.h						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									if child_idx > 0:
												return (child_idx - 1) // 2
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									a__     :		int		 =						2 * parent_idx + 1
									if left_child_index < self.heap_size:
												return left_child_index
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									a__     :		Optional[Any]		 =						2 * parent_idx + 2
									if right_child_index < self.heap_size:
												return right_child_index
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									if index < self.heap_size:
												a__     :		int		 =						index
												a__     :		Union[str, Any]		 =						self.left_child_idx(__lowercase						)
												a__     :		Any		 =						self.right_child_idx(__lowercase						)
												# check which child is larger than its parent
												if left_child is not None and self.h[left_child] > self.h[violation]:
															a__     :		Union[str, Any]		 =						left_child
												if right_child is not None and self.h[right_child] > self.h[violation]:
															a__     :		List[Any]		 =						right_child
												# if violation indeed exists
												if violation != index:
															# swap to fix the violation
															a__ ,		a__     :		Tuple		 =						self.h[index], self.h[violation]
															# fix the subsequent violation recursively if any
															self.max_heapify(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									a__     :		Tuple		 =						list(__lowercase						)
									a__     :		Union[str, Any]		 =						len(self.h						)
									if self.heap_size > 1:
												# max_heapify from right to left but exclude leaves (last level)
												for i in range(self.heap_size // 2 - 1      ,     -1      ,     -1						):
															self.max_heapify(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   T:
									"""simple docstring"""




									if self.heap_size >= 2:
												a__     :		Tuple		 =						self.h[0]
												a__     :		Union[str, Any]		 =						self.h.pop(-1						)
												self.heap_size -= 1
												self.max_heapify(0						)
												return me
									elif self.heap_size == 1:
												self.heap_size -= 1
												return self.h.pop(-1						)
									else:
												raise Exception("""Empty heap"""						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									self.h.append(__lowercase						)
									a__     :		Optional[int]		 =						(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
												self.max_heapify(__lowercase						)
												a__     :		Union[str, Any]		 =						(idx - 1) // 2

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   None:
									"""simple docstring"""




									a__     :		Any		 =						self.heap_size
									for j in range(size - 1      ,     0      ,     -1						):
												a__ ,		a__     :		Dict		 =						self.h[j], self.h[0]
												self.heap_size -= 1
												self.max_heapify(0						)
									a__     :		List[Any]		 =						size


if __name__ == "__main__":
		import doctest

		# run doc test
		doctest.testmod()

		# demo
		for unsorted in [
		    [0],
		    [2],
		    [3, 5],
		    [5, 3],
		    [5, 5],
		    [0, 0, 0, 0],
		    [1, 1, 1, 1],
		    [2, 2, 3, 5],
		    [0, 2, 2, 3, 5],
		    [2, 5, 3, 0, 2, 3, 0, 3],
		    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
		    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
		    [-45, -2, -5],
		]:
				print(f'unsorted array: {unsorted}')

				_lowercase	:   Heap[int]    	=Heap()
				heap.build_max_heap(unsorted)
				print(f'after build heap: {heap}')

				print(f'max value: {heap.extract_max()}')
				print(f'after max value removed: {heap}')

				heap.insert(100)
				print(f'after new value 100 inserted: {heap}')

				heap.heap_sort()
				print(f'heap-sorted array: {heap}\n')




