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Leetcode #1803: Count Pairs With XOR in a Range

In this guide, we solve Leetcode #1803 Count Pairs With XOR in a Range in Python and focus on the core idea that makes the solution efficient.

You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.

Leetcode

Problem Statement

Given a (0-indexed) integer array nums and two integers low and high, return the number of nice pairs. A nice pair is a pair (i, j) where 0 <= i < j < nums.length and low <= (nums[i] XOR nums[j]) <= high.

Quick Facts

  • Difficulty: Hard
  • Premium: No
  • Tags: Bit Manipulation, Trie, Array

Intuition

Prefix queries are most efficient with a trie.

Each character transitions to the next node in the tree.

Approach

Insert words into the trie and traverse by characters for queries.

Track terminal markers to distinguish full words from prefixes.

Steps:

  • Build the trie.
  • Traverse for each query.
  • Return matches or validations.

Example

Input: nums = [1,4,2,7], low = 2, high = 6 Output: 6 Explanation: All nice pairs (i, j) are as follows: - (0, 1): nums[0] XOR nums[1] = 5 - (0, 2): nums[0] XOR nums[2] = 3 - (0, 3): nums[0] XOR nums[3] = 6 - (1, 2): nums[1] XOR nums[2] = 6 - (1, 3): nums[1] XOR nums[3] = 3 - (2, 3): nums[2] XOR nums[3] = 5

Python Solution

class Trie: def __init__(self): self.children = [None] * 2 self.cnt = 0 def insert(self, x): node = self for i in range(15, -1, -1): v = x >> i & 1 if node.children[v] is None: node.children[v] = Trie() node = node.children[v] node.cnt += 1 def search(self, x, limit): node = self ans = 0 for i in range(15, -1, -1): if node is None: return ans v = x >> i & 1 if limit >> i & 1: if node.children[v]: ans += node.children[v].cnt node = node.children[v ^ 1] else: node = node.children[v] return ans class Solution: def countPairs(self, nums: List[int], low: int, high: int) -> int: ans = 0 tree = Trie() for x in nums: ans += tree.search(x, high + 1) - tree.search(x, low) tree.insert(x) return ans

Complexity

The time complexity is O(n×log⁡M)O(n \times \log M)O(n×logM), and the space complexity is O(n×log⁡M)O(n \times \log M)O(n×logM). The space complexity is O(n×log⁡M)O(n \times \log M)O(n×logM).

Edge Cases and Pitfalls

Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.

Summary

This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.


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