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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());
}
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