using BilevelJuMP, MibS_jll

model = BilevelModel()

K = 1:1
V = 1:3
v0 = 1
a = 10

C = 
[0  6  11;
10  0  7;   
8  9  0]

     
@variable(Upper(model), x1[k=K], Bin) 
@variable(Lower(model), x2[i=V, j=V] >= 0, Bin)

@objective(Upper(model), Max, a * sum(x1[k] for k in K))
@objective(Lower(model), Min, sum(C[i, j] * x2[i, j] for i in V for j in V if i != j))



for j in V
    @constraint(Lower(model), x2[j, j] <= 0)
end


for j in V
    @constraint(Lower(model), sum(x2[i, j] for i in V if i != j) <= x1[1])
    @constraint(Lower(model), sum(x2[i, j] for i in V if i != j) >= x1[1])
end


solution = BilevelJuMP.solve_with_MibS(model, MibS_jll.mibs; verbose_results=true)
for (var_name, value) in solution.all_lower
    println("$var_name = $value")
end



============================================
		Output:

Optimal solution:
Cost = -10
x[0] = 1
y[0] = 1
y[1] = 1
y[6] = 1
Number of problems (VF) solved = 1
Number of problems (UB) solved = 1
Time for solving problem (VF) = 0
Time for solving problem (UB) = 0
============================================

x2[3,2] = 0
x2[3,3] = 0
x2[2,1] = 1.0
x2[1,3] = 1.0
x2[1,1] = 1.0
x2[2,3] = 0
x2[3,1] = 0
x2[1,2] = 0
x2[2,2] = 0
============================================


============================================

    expected Output (lower variables):

x2[3,2] = 0
x2[3,3] = 0
x2[2,1] = 0
x2[1,3] = 0
x2[1,1] = 0
x2[2,3] = 1.0
x2[3,1] = 0
x2[1,2] = 1.0
x2[2,2] = 0
============================================