


from __future__ import annotations

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

snake_case__     							= TypeVar('T', bound=bool)



class     snake_case__  (      Generic[T]  ):
				def __init__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							T				=      value
										__magic_name__				:							SkewNode[T] | None				=      None
										__magic_name__				:							SkewNode[T] | None				=      None
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self._value





				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if not roota:
																return roota

										if not roota:
																return roota

										if roota.value > roota.value:
																__magic_name__		, __magic_name__				:							Union[str, Any]				=      roota, roota

										__magic_name__				:							int				=      roota
										__magic_name__				:							Optional[int]				=      roota.right
										__magic_name__				:							int				=      roota.left
										__magic_name__				:							Any				=      SkewNode.merge(_UpperCAmelCase     ,      _UpperCAmelCase      )

										return result






class     snake_case__  (      Generic[T]  ):
				def __init__(  self     ,      _UpperCAmelCase = ()      ):




										"""simple docstring"""




										__magic_name__				:							SkewNode[T] | None				=      None
										if data:
																for item in data:
																						self.insert(_UpperCAmelCase      )
				def __bool__(  self      ):




										"""simple docstring"""




										return self._root is not None
				def __iter__(  self      ):




										"""simple docstring"""




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

										# Pushing items back to the heap not to clear it.
										for item in result:
																self.insert(_UpperCAmelCase      )

										return iter(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      SkewNode.merge(self._root     ,      SkewNode(_UpperCAmelCase      )      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      self.top()
										__magic_name__				:							Tuple				=      (
										    SkewNode.merge(self._root.left     ,      self._root.right      ) if self._root else None
										)

										return result
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      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
snake_case__     							= 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
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

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


