# Solving Leetcode Interviews in Seconds with AI: Queries on a Permutation With Key


	# Introduction
	In this blog post, we will explore how to solve the LeetCode problem "1409" 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 the array queries of positive integers between 1 and m, you have to process all queries[i] (from i=0 to i=queries.length-1) according to the following rules:  In the beginning, you have the permutation P=[1,2,3,...,m]. For the current i, find the position of queries[i] in the permutation P (indexing from 0) and then move this at the beginning of the permutation P. Notice that the position of queries[i] in P is the result for queries[i].  Return an array containing the result for the given queries.   Example 1:  Input: queries = [3,1,2,1], m = 5 Output: [2,1,2,1]  Explanation: The queries are processed as follow:  For i=0: queries[i]=3, P=[1,2,3,4,5], position of 3 in P is 2, then we move 3 to the beginning of P resulting in P=[3,1,2,4,5].  For i=1: queries[i]=1, P=[3,1,2,4,5], position of 1 in P is 1, then we move 1 to the beginning of P resulting in P=[1,3,2,4,5].  For i=2: queries[i]=2, P=[1,3,2,4,5], position of 2 in P is 2, then we move 2 to the beginning of P resulting in P=[2,1,3,4,5].  For i=3: queries[i]=1, P=[2,1,3,4,5], position of 1 in P is 1, then we move 1 to the beginning of P resulting in P=[1,2,3,4,5].  Therefore, the array containing the result is [2,1,2,1].    Example 2:  Input: queries = [4,1,2,2], m = 4 Output: [3,1,2,0]  Example 3:  Input: queries = [7,5,5,8,3], m = 8 Output: [6,5,0,7,5]    Constraints:  1 <= m <= 10^3 1 <= queries.length <= m 1 <= queries[i] <= m  

	# Explanation
	*   **Simulate the permutation:** Maintain the permutation `P` as a list and process each query one by one.
*   **Find the index and move to the front:** For each query, find its index in `P`, store the index in the result, and then move the element to the beginning of `P`.
*   **List operations:** Implement the search and insertion in `P` using list operations.

*   **Runtime Complexity:** O(m\*n), where n is the length of queries and m is the value of m. This is because, in the worst-case scenario, the `index()` function takes O(m) time, and we perform this operation n times. Moving the element to the front also takes O(m) time in the worst case.
*   **Storage Complexity:** O(m), for storing the permutation P.

	
	# Code
	```python
	def processQueries(queries, m):
    """
    Processes queries according to the given rules and returns the result array.

    Args:
        queries: A list of positive integers between 1 and m.
        m: An integer representing the upper bound of the integers in queries.

    Returns:
        A list of integers representing the result for the given queries.
    """

    P = list(range(1, m + 1))  # Initialize the permutation P
    result = []

    for query in queries:
        index = P.index(query)  # Find the index of the query in P
        result.append(index)  # Store the index in the result
        P.insert(0, P.pop(index))  # Move the query to the beginning of P

    return result
	```