def all_elements_in_two_binary_search_trees(self, root1: List[int],
root2: List[int]) -> List[int]:
root1 = TreeNode.deserialize(root1)
root2 = TreeNode.deserialize(root2)
def inorder(n):
if n is None: return
yield from inorder(n.left)
yield n.val
yield from inorder(n.right)
def consume(g):
try:
return next(g)
except StopIteration as e:
return None
def merge_values(root1, root2):
g1, g2 = inorder(root1), inorder(root2)
n1, n2 = consume(g1), consume(g2)
while n1 is not None and n2 is not None:
if n1 < n2:
yield n1
n1 = consume(g1)
else:
yield n2
n2 = consume(g2)
while n1 is not None:
yield n1
n1 = consume(g1)
while n2 is not None:
yield n2
n2 = consume(g2)
return list(merge_values(root1, root2))
def inorder_successor_in_bst(self, root: List[int],
p: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
p = TreeNode.deserialize(p)
suc, t = None, root
while t:
if t.val <= p.val:
t = t.right
else:
suc = t
t = t.left
return suc
def lowest_common_ancestor_of_a_binary_search_tree(self, root: List[int],
p: int,
q: int) -> TreeNode:
root = TreeNode.deserialize(root)
def find_node(node, num):
if not node: return None
if node.val == num: return node
l = find_node(node.left, num)
r = find_node(node.right, num)
return l or r
p = find_node(root, p)
q = find_node(root, q)
# Actual code starts from here
def find_lca(root):
if root in (None, p, q): return root
l = find_lca(root.left)
r = find_lca(root.right)
if l and r: return root
return l or r
n = find_lca(root)
return n.val
def recover_binary_search_tree(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
"""
Record first, second everytime first >= second
"""
first = second = prev = None
def inorder(n):
nonlocal first, second, prev
if n is None: return
inorder(n.left)
# 'prev is None' means root node can be included to first
if not first and (prev is None or prev.val >= n.val):
first = prev
if first and prev and prev.val >= n.val:
second = n
prev = n
inorder(n.right)
inorder(root)
first.val, second.val = second.val, first.val
return root
def binary_tree_level_order_traversal(self, root: typing.List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
if root is None: return None
sent = prev = TreeNode(0)
def inorder(node):
nonlocal prev
if node is None: return
inorder(node.left)
# process node
prev.right = node
node.left = prev
prev = node
inorder(node.right)
inorder(root)
head = sent.right
head.left = prev
prev.right = head
return head
# 1. Perform inorder traversal and create the doubly linked list on the fly
# 2. To access prev node after dfs, set prev as nonlocal variable
"""
def inorder_successor_in_bst(self, root: List[int],
p: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
p = TreeNode.deserialize(p)
prev, successor = None, None
def inorder(n):
nonlocal prev, successor
if n is None: return
inorder(n.left)
if prev is not None and prev.val == p.val:
successor = n
prev = n
inorder(n.right)
inorder(root)
return successor
def inorder_successor_in_bst(self, root: List[int],
p: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
p = TreeNode.deserialize(p)
prev, s, t = None, [], root
while s or t:
while t:
s.append(t)
t = t.left
t = s.pop()
if prev and prev.val == p.val:
return t
prev = t
t = t.right
return None
def binary_tree_postorder_traversal(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
def postorder(n):
if n is None: return
yield from postorder(n.left)
yield from postorder(n.right)
yield n.val
return list(postorder(root))
def binary_tree_inorder_traversal(
self, root: typing.List[int]) -> typing.List[int]:
root = TreeNode.deserialize(root)
def inorder(n):
if n is None: return
yield from inorder(n.left)
yield n.val
yield from inorder(n.right)
return list(inorder(root))
def merge_two_bsts(self, root1: List[int], root2: List[int]) -> TreeNode:
root1 = TreeNode.deserialize(root1)
root2 = TreeNode.deserialize(root2)
a1, a2 = [], []
def inorder(n, a):
if n is None: return
inorder(n.left, a)
a.append(n)
inorder(n.right, a)
def merge(a1, a2):
a = []
l1, l2 = 0, 0
while l1 < len(a1) and l2 < len(a2):
if a1[l1].val < a2[l2].val:
a.append(a1[l1])
l1 += 1
else:
a.append(a2[l2])
l2 += 1
while l1 < len(a1):
a.append(a1[l1])
l1 += 1
while l2 < len(a2):
a.append(a2[l2])
l2 += 1
return a
def bst(l, r, a):
if l > r: return None
mid = (r - l) // 2 + l
n = a[mid]
n.left = bst(l, mid - 1, a)
n.right = bst(mid + 1, r, a)
return n
inorder(root1, a1)
inorder(root2, a2)
return bst(0, len(a1) + len(a2) - 1, merge(a1, a2))
def binary_tree_inorder_traversal(
self, root: typing.List[int]) -> typing.List[int]:
root = TreeNode.deserialize(root)
s, t, res = [], root, []
while s or t:
while t:
s.append(t)
t = t.left
t = s.pop()
res.append(t.val)
t = t.right
return res
def check_completeness_of_a_binary_tree(
self, root: typing.List[int]) -> typing.List[int]:
root = TreeNode.deserialize(root)
if root is None: return True
q = collections.deque([root])
while q:
n = q.popleft()
if n is None: break
q.append(n.left)
q.append(n.right)
return not any(q)
def insert_into_a_binary_search_tree(self, root: List[int],
val: int) -> TreeNode:
root = TreeNode.deserialize(root)
def inorder(n):
if n is None: return TreeNode(val)
if val < n.val:
n.left = inorder(n.left)
else:
n.right = inorder(n.right)
return n
return inorder(root)
def binary_tree_inorder_traversal(
self, root: typing.List[int]) -> typing.List[int]:
root = TreeNode.deserialize(root)
res = []
def inorder(n):
if n is None: return
inorder(n.left)
res.append(n.val)
inorder(n.right)
inorder(root)
return res
def binary_tree_postorder_traversal(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
res = []
def postorder(n):
if n is None: return
postorder(n.left)
postorder(n.right)
res.append(n.val)
postorder(root)
return res
def binary_tree_level_order_traversal(self,
root: typing.List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
if root is None: return []
q = collections.deque([(0, root)])
result = []
while q:
l, node = q.popleft()
while len(result) <= l:
result.append([])
result[l].append(node.val)
if node.left: q.append((l + 1, node.left))
if node.right: q.append((l + 1, node.right))
return result
def binary_tree_postorder_traversal(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
if not root: return []
s, s1 = [root], []
while s:
n = s.pop()
s1.append(n)
if n.left is not None:
s.append(n.left)
if n.right is not None:
s.append(n.right)
return [n.val for n in reversed(s1)]
def single_value_tree(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
c = 0
def postord(t):
nonlocal c
if t is None: return set()
total = postord(t.left) | postord(t.right) | set([t.val])
if len(total) == 1:
c += 1
return total
postord(root)
return c
def upside_down(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
if not root: return None
def upside_down(n):
if n.left == n.right == None:
return n
new_root = upside_down(n.left)
n.left.right = n
n.left.left = n.right
n.right = n.left = None
return new_root
return upside_down(root)
def split_bst(self, root: List[int], V: int) -> List[List[int]]:
root = TreeNode.deserialize(root)
def preorder(node):
if node is None: return (None, None)
if node.val <= V:
left, right = preorder(node.right)
node.right = left
return (node, right)
else:
left, right = preorder(node.left)
node.left = right
return (left, node)
left, right = preorder(root)
return [TreeNode.serialize(left), TreeNode.serialize(right)]
def binary_tree_level_order_traversal(self,
root: typing.List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
if not root: return []
q = collections.deque([root])
result = []
while q:
l = len(q)
temp = []
for i in range(l):
node = q.popleft()
temp.append(node.val)
if node.left is not None: q.append(node.left)
if node.right is not None: q.append(node.right)
result.append(temp)
return result
def binary_tree_paths(self, root: List[int]) -> List[str]:
root = TreeNode.deserialize(root)
def preorder(n, slate):
if n is None:
return
slate.append(n.val)
if n.left == n.right == None:
res.append('->'.join(map(str, slate)))
else:
preorder(n.left, slate)
preorder(n.right, slate)
slate.pop()
res = []
preorder(root, [])
return res
def binary_tree_preorder_traversal(
self, root: typing.List[int]) -> typing.List[int]:
root = TreeNode.deserialize(root)
if not root: return []
def preorder(root):
s = [root]
while s:
n = s.pop()
yield n.val
if n.right is not None:
s.append(n.right)
if n.left is not None:
s.append(n.left)
return list(preorder(root))
def insert_into_a_binary_search_tree(self, root: List[int],
val: int) -> TreeNode:
root = TreeNode.deserialize(root)
new_node = TreeNode(val)
if root is None: return new_node
prev = None
cur = root
while cur is not None:
prev = cur
if val < cur.val:
cur = cur.left
else:
cur = cur.right
if prev.val > val:
prev.left = new_node
else:
prev.right = new_node
return root
def upside_down(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
if not root: return None
cur, s = root, [root]
while cur.left is not None:
cur = cur.left
s.append(cur)
new_root = cur = s.pop()
while s:
n = s.pop()
cur.left = n.right
cur.right = n
n.left = n.right = None
cur = n
return new_root
def path_sum_ii(self, root: typing.List[int],
sum: int) -> typing.List[typing.List[int]]:
root = TreeNode.deserialize(root)
res = []
def dfs(root, slate, cur):
if root is None: return
if root.left is None and root.right is None:
if cur + root.val == sum:
res.append(slate[:] + [root.val])
return
slate.append(root.val)
dfs(root.left, slate, cur + root.val)
dfs(root.right, slate, cur + root.val)
slate.pop()
dfs(root, [], 0)
return res
def boundary_of_binary_tree(self, root: List[int]) -> TreeNode:
root = TreeNode.deserialize(root)
res = []
def preord(node, isleft, isright):
if node is None: return
if isleft: res.append(node.val)
if (isleft == isright == False) and (node.left == node.right ==
None):
res.append(node.val)
preord(node.left, isleft, isright and node.right is None)
preord(node.right, isleft and node.left is None, isright)
if isright: res.append(node.val)
if not root: return []
res.append(root.val)
preord(root.left, True, False)
preord(root.right, False, True)
return res
def second_minimum_node_in_a_binary_tree(self, root: List[int]) -> int:
root = TreeNode.deserialize(root)
# Root has to be min1, since node value has to be the smaller node.
# So root value will be the leftest node value
def pre_order(t):
nonlocal min2
# base
if t is None: return
# rec
# current
if min1 < t.val < min2:
min2 = t.val
# left/right if current is same as min1
# keep looking for min2
if min1 == t.val:
pre_order(t.left)
pre_order(t.right)
min1, min2 = root.val, float("inf")
pre_order(root)
return min2 if min2 < float("inf") else -1
def all_nodes_distance_k_in_binary_tree(self, root: List[List[int]],
target: int, K: int) -> List[int]:
root = TreeNode.deserialize(root)
if not root: return []
g = collections.defaultdict(list)
def preorder(n, parent):
if n is None:
return
g[n.val]
if parent:
g[parent.val].append(n.val)
g[n.val].append(parent.val)
preorder(n.left, n)
preorder(n.right, n)
preorder(root, None)
# bfs
q = collections.deque([target])
visited = [True if i == target else False for i in range(len(g))]
count = 0
while q:
print(q)
if count == K: return list(q)
l = len(q)
for _ in range(l):
v = q.popleft()
for nv in g[v]:
if not visited[nv]:
visited[nv] = True
q.append(nv)
count += 1
return []