# Solving Leetcode Interviews in Seconds with AI: Find Missing Observations


	# Introduction
	In this blog post, we will explore how to solve the LeetCode problem "2028" 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
	> You have observations of n + m 6-sided dice rolls with each face numbered from 1 to 6. n of the observations went missing, and you only have the observations of m rolls. Fortunately, you have also calculated the average value of the n + m rolls. You are given an integer array rolls of length m where rolls[i] is the value of the ith observation. You are also given the two integers mean and n. Return an array of length n containing the missing observations such that the average value of the n + m rolls is exactly mean. If there are multiple valid answers, return any of them. If no such array exists, return an empty array. The average value of a set of k numbers is the sum of the numbers divided by k. Note that mean is an integer, so the sum of the n + m rolls should be divisible by n + m.   Example 1:  Input: rolls = [3,2,4,3], mean = 4, n = 2 Output: [6,6] Explanation: The mean of all n + m rolls is (3 + 2 + 4 + 3 + 6 + 6) / 6 = 4.  Example 2:  Input: rolls = [1,5,6], mean = 3, n = 4 Output: [2,3,2,2] Explanation: The mean of all n + m rolls is (1 + 5 + 6 + 2 + 3 + 2 + 2) / 7 = 3.  Example 3:  Input: rolls = [1,2,3,4], mean = 6, n = 4 Output: [] Explanation: It is impossible for the mean to be 6 no matter what the 4 missing rolls are.    Constraints:  m == rolls.length 1 <= n, m <= 105 1 <= rolls[i], mean <= 6  

	# Explanation
	Here's the breakdown of the solution:

*   **Calculate the required sum:** Determine the total sum needed for all `n + m` rolls to achieve the target `mean`.
*   **Calculate the sum of existing rolls:** Sum the values in the `rolls` array. Subtract this sum from the required sum to find the total value needed from the missing `n` rolls.
*   **Distribute the missing value:** Divide the required sum by `n`. If the result is not within the range \[1, 6], no solution exists. Otherwise, distribute the sum as evenly as possible among the `n` rolls, ensuring each roll's value is between 1 and 6.

*   **Runtime Complexity:** O(m + n), where m is the length of the `rolls` array and n is the number of missing rolls.
*   **Storage Complexity:** O(n) for storing the result array.

	
	# Code
	```python
	def missingRolls(rolls, mean, n):
    m = len(rolls)
    total_sum = mean * (n + m)
    rolls_sum = sum(rolls)
    missing_sum = total_sum - rolls_sum

    if missing_sum < n or missing_sum > 6 * n:
        return []

    base = missing_sum // n
    remainder = missing_sum % n
    result = [base] * n
    for i in range(remainder):
        result[i] += 1

    return result
	```