

1. The grid has 0-based indexing.
2. A rotten orange can affect the adjacent oranges 4 directionally i.e. Up, Down, Left, Right.
The first line of input contains two single space-separated integers 'N' and 'M' representing the number of rows and columns of the grid respectively.
The next 'N' lines contain 'M' single space-separated integers each representing the rows of the grid.
The only line of output contains a single integer i.e. The minimum time after which no cell has a fresh orange.
If it's impossible to rot all oranges, print -1.
You are not required to print the expected output, it has already been taken care of. Just implement the function.
1 <= N <= 500
1 <= M <= 500
0 <= grid[i][j] <= 2
Time Limit: 1 sec
The idea is very simple and naive. We will process the rotten oranges second by second. Each second, we rot all the fresh oranges that are adjacent to the already rotten oranges. The time by which there are no rotten oranges left to process will be our minimum time.
In the first traversal of the grid, we will process all the cells with value 2 (rotten oranges). We will also mark their adjacent cells as rotten for the next traversal. Now, we can’t mark them by assigning the same value i.e. 2 because then we won’t able to differentiate between the current processing cells and the cells which are going to be processed in the next traversal. So, we will mark them as value 3. More formally, we will be marking the adjacent cells as ‘CURR_ROTTEN’ + 1 in each traversal of the grid.
Here is the complete algorithm.
We can use Breadth-First-Search which is similar to the level order traversal of a Binary Tree, to solve this problem.
We will be using a Queue data structure and inserting cells into this Queue level by level. Here, all the already rotten oranges will be at level 0, all the fresh oranges adjacent to them will be at level 1, all the fresh oranges adjacent to level 1 oranges will be at level 2, and so on. The time to reach the last level will be our minimum time.
Here, is the complete algorithm-