





from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_SCREAMING_SNAKE_CASE   						=		TypeVar("""T""", bound=bool)
class SCREAMING_SNAKE_CASE_    (       Generic[T]       ):






						def __init__(   self   :   str		, _A   :   T						)				->     None:







							"""simple docstring"""


							snake_case_		:      T           =						value
							snake_case_		:      RandomizedHeapNode[T] | None           =						None
							snake_case_		:      RandomizedHeapNode[T] | None           =						None






						@property
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     T:







							"""simple docstring"""


							return self._value






						@staticmethod
						def UpperCAmelCase_					(   _A   :   RandomizedHeapNode[T] | None		, _A   :   RandomizedHeapNode[T] | None						)				->     RandomizedHeapNode[T] | None:







							"""simple docstring"""


							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								snake_case_    ,snake_case_		:      Optional[int]           =						roota, roota

							if random.choice([True, False]						):
								snake_case_    ,snake_case_		:      Any           =						roota.right, roota.left

							snake_case_		:      List[str]           =						RandomizedHeapNode.merge(roota.left		, _A						)

							return roota
class SCREAMING_SNAKE_CASE_    (       Generic[T]       ):






						def __init__(   self   :   Optional[int]		, _A   :   Iterable[T] | None = ()						)				->     None:







							"""simple docstring"""


							snake_case_		:      RandomizedHeapNode[T] | None           =						None

							if data:
								for item in data:
									self.insert(_A						)






						def UpperCAmelCase_					(   self   :   Union[str, Any]		, _A   :   T						)				->     None:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						RandomizedHeapNode.merge(self._root		, RandomizedHeapNode(_A						)						)






						def UpperCAmelCase_					(   self   :   Tuple						)				->     T | None:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						self.top()

							if self._root is None:
								return None

							snake_case_		:      Tuple           =						RandomizedHeapNode.merge(self._root.left		, self._root.right						)

							return result






						def UpperCAmelCase_					(   self   :   str						)				->     T:







							"""simple docstring"""


							if not self._root:
								raise IndexError('Can\'t get top element for the empty heap.'						)
							return self._root.value






						def UpperCAmelCase_					(   self   :   List[str]						)				->     None:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						None






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







							"""simple docstring"""


							snake_case_		:      Any           =						[]
							while self:
								result.append(self.pop()						)

							return result






						def __bool__(   self   :   str						)				->     bool:







							"""simple docstring"""


							return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()









from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

