input: Write a function to find the optimal solution to solve a knapsack problem using dynamic programming. The function should take four parameters: W (capacity of the knapsack), wt (list of weights of the items), val (list of values of the items), and n (number of items). The function should return the maximum value that can be put in the knapsack without exceeding the capacity W. If the capacity is 0 or there are no items, the function should return 0.
  assert knapSack(50, [10, 20, 30], [60, 100, 120], 3) == 220
  assert knapSack(0, [10, 20, 30], [60, 100, 120], 3) == 0
  assert knapSack(50, [], [], 0) == 0
  assert knapSack(30, [10, 20], [60, 100], 2) == 160
  assert knapSack(100, [5, 10, 15], [10, 30, 20], 3) == 60
output: 
  if W == 0 or n == 0:
      return 0
  
  dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
  
  for i in range(1, n + 1):
      for w in range(1, W + 1):
          if wt[i-1] <= w:
              dp[i][w] = max(val[i-1] + dp[i-1][w-wt[i-1]], dp[i-1][w])
          else:
              dp[i][w] = dp[i-1][w]
  
  return dp[n][W]
  