
import numpy as np

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				Any       )     ->   Dict:
						lowercase       :    Optional[int]											=  n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						lowercase       :    int											=  np.floor(UpperCAmelCase_       )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						lowercase       :    Union[str, Any]											=  int(n_draws - floored.sum()       )
						if need_to_add > 0:
												lowercase       :    Dict											=  continuous - floored
												lowercase       :    Tuple											=  np.sort(np.unique(UpperCAmelCase_       )       )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		(lowercase				)       :    Optional[int]											=  np.where(remainder == value       )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		lowercase       :    Optional[int]											=  min(len(UpperCAmelCase_       )							,			UpperCAmelCase_       )
																		lowercase       :    List[str]											=  rng.choice(UpperCAmelCase_							,			size=UpperCAmelCase_							,			replace=UpperCAmelCase_       )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa       )




def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				Tuple=10       )     ->   List[str]:
						lowercase       :    List[Any]											=  np.unique(UpperCAmelCase_							,			return_inverse=UpperCAmelCase_       )
						lowercase       :    Optional[Any]											=  classes.shape[0]
						lowercase       :    str											=  np.bincount(UpperCAmelCase_       )
						if np.min(UpperCAmelCase_       ) < 2:
												raise ValueError('''Minimum class count error'''       )
						if n_train < n_classes:
												raise ValueError(
												    '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)       )
						if n_test < n_classes:
												raise ValueError(
												    '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)       )
						lowercase       :    Any											=  np.split(np.argsort(UpperCAmelCase_							,			kind='''mergesort'''       )							,			np.cumsum(UpperCAmelCase_       )[:-1]       )
						for _ in range(UpperCAmelCase_       ):
												lowercase       :    List[str]											=  approximate_mode(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    Tuple											=  class_counts - n_i
												lowercase       :    Any											=  approximate_mode(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_       )

												lowercase       :    Any											=  []
												lowercase       :    int											=  []

												for i in range(UpperCAmelCase_       ):
																		lowercase       :    Dict											=  rng.permutation(class_counts[i]       )
																		lowercase       :    List[Any]											=  class_indices[i].take(UpperCAmelCase_							,			mode='''clip'''       )
																		train.extend(perm_indices_class_i[: n_i[i]]       )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]       )
												lowercase       :    Optional[Any]											=  rng.permutation(UpperCAmelCase_       )
												lowercase       :    List[Any]											=  rng.permutation(UpperCAmelCase_       )

												yield train, test




class 							UpperCAmelCase	:

					def __init__(			self      :					Any ,      lowerCAmelCase      :					int					):
											lowercase       :    Tuple											=  data
											lowercase       :    BinaryTreeNode | None											=  None
											lowercase       :    BinaryTreeNode | None											=  None





def        lowerCamelCase_      (			UpperCAmelCase_							:				BinaryTreeNode | None							,			UpperCAmelCase_							:				int       ):
						if node is None:
												lowercase       :    Union[str, Any]											=  BinaryTreeNode(UpperCAmelCase_       )
												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:
												lowercase       :    List[Any]											=  insert(node.left_child							,			UpperCAmelCase_       )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowercase       :    Dict											=  insert(node.right_child							,			UpperCAmelCase_       )
						return node

def        lowerCamelCase_      (			UpperCAmelCase_							:				None | BinaryTreeNode       ):  # if node is None,return
						if node:
												lowercase       :    Optional[Any]											=  inorder(node.left_child       )
												lowercase       :    Tuple											=  [*inorder_array, node.data]
												lowercase       :    Optional[Any]											=  inorder_array + inorder(node.right_child       )
						else:
												lowercase       :    Optional[int]											=  []
						return inorder_array

def        lowerCamelCase_      (			):
						lowercase       :    Any											=  insert(UpperCAmelCase_							,			15       )
						insert(UpperCAmelCase_							,			10       )
						insert(UpperCAmelCase_							,			25       )
						insert(UpperCAmelCase_							,			6       )
						insert(UpperCAmelCase_							,			14       )
						insert(UpperCAmelCase_							,			20       )
						insert(UpperCAmelCase_							,			60       )
						return root

def        lowerCamelCase_      (			):
						# main function
						lowercase       :    Any											=  make_tree()
						print('''Printing values of binary search tree in Inorder Traversal.'''       )
						inorder(UpperCAmelCase_       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()


