# Solving Leetcode Interviews in Seconds with AI: Count Pairs That Form a Complete Day II


	# Introduction
	In this blog post, we will explore how to solve the LeetCode problem "3185" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like [Chatmagic](https://www.chatmagic.app), 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
	> Given an integer array hours representing times in hours, return an integer denoting the number of pairs i, j where i < j and hours[i] + hours[j] forms a complete day. A complete day is defined as a time duration that is an exact multiple of 24 hours. For example, 1 day is 24 hours, 2 days is 48 hours, 3 days is 72 hours, and so on.   Example 1:  Input: hours = [12,12,30,24,24] Output: 2 Explanation: The pairs of indices that form a complete day are (0, 1) and (3, 4).  Example 2:  Input: hours = [72,48,24,3] Output: 3 Explanation: The pairs of indices that form a complete day are (0, 1), (0, 2), and (1, 2).    Constraints:  1 <= hours.length <= 5 * 105 1 <= hours[i] <= 109  

	# Explanation
	Here's a breakdown of the solution:

*   **Core Idea:** The problem boils down to finding pairs `(i, j)` such that `hours[i] + hours[j]` is divisible by 24. We can use the modulo operator (%) to efficiently check for divisibility.
*   **Optimization with Hash Map (Counter):**  We can improve efficiency by using a hash map (specifically, a `Counter` in Python) to store the counts of each `hours[i] % 24`. This allows us to quickly find the number of elements that, when added to `hours[i] % 24`, result in a multiple of 24.
*   **Avoiding Double Counting:** Since we need to consider pairs (i, j) where i < j, we process each element one at a time, updating the count *after* calculating the number of valid pairs it forms with the elements processed so far.

*   **Time & Space Complexity:** O(n), where n is the length of the `hours` array, because we iterate through the array once. Space complexity is O(1) because the counter stores at most 24 different keys (the possible remainders when dividing by 24).

	
	# Code
	```python
	from collections import Counter

def complete_days(hours):
    """
    Calculates the number of pairs (i, j) where i < j and hours[i] + hours[j]
    is a multiple of 24.

    Args:
        hours: A list of integers representing times in hours.

    Returns:
        The number of pairs forming complete days.
    """

    count = 0
    remainders = Counter()

    for hour in hours:
        remainder = hour % 24
        complement = (24 - remainder) % 24  # Find the remainder that, when added to current remainder, results in 24.
        count += remainders[complement] # Add number of existing remainders which complement current remainder

        remainders[remainder] += 1 # Update the count

    return count
	```