Introduction

In this blog post, we will explore how to solve the LeetCode problem "3267" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like Chatmagic, we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.

Problem Statement

Attention: In this version, the number of operations that can be performed, has been increased to twice. You are given an array nums consisting of positive integers. We call two integers x and y almost equal if both integers can become equal after performing the following operation at most twice: Choose either x or y and swap any two digits within the chosen number. Return the number of indices i and j in nums where i < j such that nums[i] and nums[j] are almost equal. Note that it is allowed for an integer to have leading zeros after performing an operation. Example 1: Input: nums = [1023,2310,2130,213] Output: 4 Explanation: The almost equal pairs of elements are: 1023 and 2310. By swapping the digits 1 and 2, and then the digits 0 and 3 in 1023, you get 2310. 1023 and 213. By swapping the digits 1 and 0, and then the digits 1 and 2 in 1023, you get 0213, which is 213. 2310 and 213. By swapping the digits 2 and 0, and then the digits 3 and 2 in 2310, you get 0213, which is 213. 2310 and 2130. By swapping the digits 3 and 1 in 2310, you get 2130. Example 2: Input: nums = [1,10,100] Output: 3 Explanation: The almost equal pairs of elements are: 1 and 10. By swapping the digits 1 and 0 in 10, you get 01 which is 1. 1 and 100. By swapping the second 0 with the digit 1 in 100, you get 001, which is 1. 10 and 100. By swapping the first 0 with the digit 1 in 100, you get 010, which is 10. Constraints: 2 <= nums.length <= 5000 1 <= nums[i] < 107

Explanation

• Normalization: Convert each number to its sorted digit representation (e.g., 1023 becomes "0123"). Store these normalized strings. This addresses the swapping digits aspect.
  • Comparison of Lengths: Before comparing normalized forms, check the lengths of the original numbers. If the lengths differ by more than 2, they can't be almost equal because a maximum of two swaps can only compensate for a length difference of 2 at most, in the following way: 2 swaps to add two '0' digits, such as transforming '1' into '001'.
  • Pair Counting: Iterate through the normalized representations and count the number of pairs that are equal.

• Runtime Complexity: O(N² * K log K), where N is the number of elements in nums and K is the maximum number of digits in a number.
• Storage Complexity: O(N * K), where N is the number of elements in nums and K is the maximum number of digits in a number.

Code

    def almost_equal_pairs(nums):
    """
    Counts the number of almost equal pairs in the given array of positive integers.

    Args:
        nums: A list of positive integers.

    Returns:
        The number of almost equal pairs.
    """
    n = len(nums)
    count = 0
    for i in range(n):
        for j in range(i + 1, n):
            if are_almost_equal(nums[i], nums[j]):
                count += 1
    return count


def are_almost_equal(x, y):
    """
    Checks if two integers are almost equal based on the given operation.

    Args:
        x: The first integer.
        y: The second integer.

    Returns:
        True if the integers are almost equal, False otherwise.
    """

    x_str = str(x)
    y_str = str(y)

    if abs(len(x_str) - len(y_str)) > 2:
        return False

    x_sorted = "".join(sorted(x_str))
    y_sorted = "".join(sorted(y_str))

    return x_sorted == y_sorted