use ark_ec::{msm::VariableBaseMSM, AffineCurve, ProjectiveCurve};
use ark_ff::{PrimeField, UniformRand, Zero};
use rand::SeedableRng;
use rand_xorshift::XorShiftRng;

fn naive_var_base_msm<G: AffineCurve>(
    bases: &[G],
    scalars: &[<G::ScalarField as PrimeField>::BigInt],
) -> G::Projective {
    let mut acc = G::Projective::zero();

    for (base, scalar) in bases.iter().zip(scalars.iter()) {
        acc += &base.mul(*scalar);
    }
    acc
}

pub fn test_var_base_msm<G: AffineCurve>() {
    const SAMPLES: usize = 1 << 10;

    let mut rng = XorShiftRng::seed_from_u64(234872845u64);

    let v = (0..SAMPLES - 1)
        .map(|_| G::ScalarField::rand(&mut rng).into_repr())
        .collect::<Vec<_>>();
    let g = (0..SAMPLES)
        .map(|_| G::Projective::rand(&mut rng))
        .collect::<Vec<_>>();
    let g = <G::Projective as ProjectiveCurve>::batch_normalization_into_affine(&g);

    let naive = naive_var_base_msm(g.as_slice(), v.as_slice());
    let fast = VariableBaseMSM::multi_scalar_mul(g.as_slice(), v.as_slice());

    assert_eq!(naive.into_affine(), fast.into_affine());
}