树、图和递归：表示层级、依赖与关系网络

from dataclasses import dataclass


@dataclass
class TreeNode:
    value: int
    left: "TreeNode | None" = None
    right: "TreeNode | None" = None


def inorder(node: TreeNode | None) -> list[int]:
    if node is None:
        return []
    return [*inorder(node.left), node.value, *inorder(node.right)]


root = TreeNode(4, TreeNode(2, TreeNode(1), TreeNode(3)), TreeNode(6))
print(inorder(root))

from collections import deque

graph = {
    "collect": ["clean", "validate"],
    "clean": ["analyze"],
    "validate": ["analyze"],
    "analyze": ["chart", "report"],
    "chart": ["report"],
    "report": [],
}


def dfs(node: str, visited: set[str] | None = None) -> list[str]:
    if visited is None:
        visited = set()
    if node in visited:
        return []
    visited.add(node)
    order = [node]
    for neighbor in graph[node]:
        order.extend(dfs(neighbor, visited))
    return order


def shortest_path(start: str, target: str) -> list[str] | None:
    queue = deque([(start, [start])])
    visited = {start}
    while queue:
        node, path = queue.popleft()
        if node == target:
            return path
        for neighbor in graph[node]:
            if neighbor not in visited:
                visited.add(neighbor)
                queue.append((neighbor, [*path, neighbor]))
    return None


print("DFS:", dfs("collect"))
print("path:", shortest_path("collect", "report"))