input: Write a function to find the optimal solution to solve a knapsack problem using dynamic programming. The function should take in the capacity of the knapsack, a list of item weights, and a list of item values, and return the maximum value that can be obtained with the given constraints. If the inputs are invalid (e.g., lists are of different lengths), return -1.
  def knapSack(W, wt, val):
      if len(wt) != len(val):
          return -1
      n = len(wt)
      dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
      
      for i in range(n + 1):
          for w in range(W + 1):
              if i == 0 or w == 0:
                  dp[i][w] = 0
              elif 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]
  
  assert knapSack(50, [10, 20, 30], [60, 100, 120])==220
  assert knapSack(0, [10, 20, 30], [60, 100, 120])==0
  assert knapSack(50, [10, 20], [60, 100, 120])==-1
  assert knapSack(50, [], [])==0
  assert knapSack(50, [40], [60])==60
output: def knapSack(W, wt, val):
      if len(wt) != len(val):
          return -1
      n = len(wt)
      dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
      
      for i in range(n + 1):
          for w in range(W + 1):
              if i == 0 or w == 0:
                  dp[i][w] = 0
              elif 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]