def solve_army(UNITS, DATA, RESOURCES):
  # Create the linear solver using the CBC backend
  solver = pywraplp.Solver('Minimize resource consumption', pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)

  # 1. Create the variables we want to optimize
  units = [solver.IntVar(0, solver.infinity(), unit) for unit in UNITS]

  # 2. Add constraints for each resource
  for r, _ in enumerate(RESOURCES):
    solver.Add(sum((10 * DATA[u][-2] + DATA[u][-1]) * units[u] for u, _ in enumerate(units)) >= 1000001)

  # Old constraints for limited resources
  for r, _ in enumerate(RESOURCES):
    solver.Add(sum(DATA[u][r] * units[u] for u, _ in enumerate(units)) <= RESOURCES[r])

  # 3. Minimize the objective function
  solver.Minimize(sum((DATA[u][0] + DATA[u][1] + DATA[u][2]) * units[u] for u, _ in enumerate(units)))

  # Solve problem
  status = solver.Solve()

  # If an optimal solution has been found, print results
  if status == pywraplp.Solver.OPTIMAL:
    print('================= Solution =================')
    print(f'Solved in {solver.wall_time():.2f} milliseconds in {solver.iterations()} iterations')
    print()

    power = sum((10 * DATA[u][-2] + DATA[u][-1]) * units[u].solution_value() for u, _ in enumerate(units))
    print(f'Optimal value = {solver.Objective().Value()} 🌾  🪵🪙resources')
    print(f'Power = 💪 {power}')
    print('Army:')
    for u, _ in enumerate(units):
      print(f' - {units[u].name()} = {units[u].solution_value()}')
    print()

    food = sum((DATA[u][0]) * units[u].solution_value() for u, _ in enumerate(units))
    wood = sum((DATA[u][1]) * units[u].solution_value() for u, _ in enumerate(units))
    gold = sum((DATA[u][2]) * units[u].solution_value() for u, _ in enumerate(units))
    print('Resources:')
    print(f' - 🌾  Food = {food}')
    print(f' - 🪵Wood = {wood}')
    print(f' - 🪙Gold = {gold}')
  else:
      print('The solver could not find an optimal solution.')

solve_army(UNITS, DATA, RESOURCES)