mirror of https://github.com/zcash/halo2.git
435 lines
17 KiB
Rust
435 lines
17 KiB
Rust
use group::{
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ff::{BatchInvert, Field, PrimeField},
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Curve,
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};
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use rand_core::RngCore;
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use std::iter::{self, ExactSizeIterator};
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use super::super::{circuit::Any, ChallengeBeta, ChallengeGamma, ChallengeX};
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use super::{Argument, ProvingKey};
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use crate::{
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arithmetic::{eval_polynomial, parallelize, CurveAffine},
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plonk::{self, Error},
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poly::{
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self,
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commitment::{Blind, Params},
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multiopen::ProverQuery,
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Coeff, ExtendedLagrangeCoeff, LagrangeCoeff, Polynomial, Rotation,
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},
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transcript::{EncodedChallenge, TranscriptWrite},
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};
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pub struct CommittedSet<C: CurveAffine, Ev> {
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permutation_product_poly: Polynomial<C::Scalar, Coeff>,
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permutation_product_coset: poly::AstLeaf<Ev, ExtendedLagrangeCoeff>,
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permutation_product_blind: Blind<C::Scalar>,
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}
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pub(crate) struct Committed<C: CurveAffine, Ev> {
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sets: Vec<CommittedSet<C, Ev>>,
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}
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pub struct ConstructedSet<C: CurveAffine> {
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permutation_product_poly: Polynomial<C::Scalar, Coeff>,
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permutation_product_blind: Blind<C::Scalar>,
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}
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pub(crate) struct Constructed<C: CurveAffine> {
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sets: Vec<ConstructedSet<C>>,
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}
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pub(crate) struct Evaluated<C: CurveAffine> {
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constructed: Constructed<C>,
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}
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impl Argument {
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#[allow(clippy::too_many_arguments)]
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pub(in crate::plonk) fn commit<
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C: CurveAffine,
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E: EncodedChallenge<C>,
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Ev: Copy + Send + Sync,
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R: RngCore,
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T: TranscriptWrite<C, E>,
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>(
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&self,
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params: &Params<C>,
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pk: &plonk::ProvingKey<C>,
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pkey: &ProvingKey<C>,
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advice: &[Polynomial<C::Scalar, LagrangeCoeff>],
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fixed: &[Polynomial<C::Scalar, LagrangeCoeff>],
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instance: &[Polynomial<C::Scalar, LagrangeCoeff>],
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beta: ChallengeBeta<C>,
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gamma: ChallengeGamma<C>,
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evaluator: &mut poly::Evaluator<Ev, C::Scalar, ExtendedLagrangeCoeff>,
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mut rng: R,
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transcript: &mut T,
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) -> Result<Committed<C, Ev>, Error> {
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let domain = &pk.vk.domain;
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// How many columns can be included in a single permutation polynomial?
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// We need to multiply by z(X) and (1 - (l_last(X) + l_blind(X))). This
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// will never underflow because of the requirement of at least a degree
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// 3 circuit for the permutation argument.
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assert!(pk.vk.cs_degree >= 3);
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let chunk_len = pk.vk.cs_degree - 2;
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let blinding_factors = pk.vk.cs.blinding_factors();
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// Each column gets its own delta power.
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let mut deltaomega = C::Scalar::ONE;
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// Track the "last" value from the previous column set
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let mut last_z = C::Scalar::ONE;
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let mut sets = vec![];
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for (columns, permutations) in self
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.columns
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.chunks(chunk_len)
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.zip(pkey.permutations.chunks(chunk_len))
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{
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// Goal is to compute the products of fractions
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//
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// (p_j(\omega^i) + \delta^j \omega^i \beta + \gamma) /
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// (p_j(\omega^i) + \beta s_j(\omega^i) + \gamma)
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//
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// where p_j(X) is the jth column in this permutation,
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// and i is the ith row of the column.
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let mut modified_values = vec![C::Scalar::ONE; params.n as usize];
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// Iterate over each column of the permutation
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for (&column, permuted_column_values) in columns.iter().zip(permutations.iter()) {
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let values = match column.column_type() {
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Any::Advice => advice,
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Any::Fixed => fixed,
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Any::Instance => instance,
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};
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parallelize(&mut modified_values, |modified_values, start| {
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for ((modified_values, value), permuted_value) in modified_values
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.iter_mut()
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.zip(values[column.index()][start..].iter())
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.zip(permuted_column_values[start..].iter())
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{
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*modified_values *= &(*beta * permuted_value + &*gamma + value);
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}
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});
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}
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// Invert to obtain the denominator for the permutation product polynomial
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modified_values.batch_invert();
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// Iterate over each column again, this time finishing the computation
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// of the entire fraction by computing the numerators
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for &column in columns.iter() {
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let omega = domain.get_omega();
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let values = match column.column_type() {
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Any::Advice => advice,
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Any::Fixed => fixed,
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Any::Instance => instance,
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};
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parallelize(&mut modified_values, |modified_values, start| {
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let mut deltaomega = deltaomega * &omega.pow_vartime([start as u64, 0, 0, 0]);
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for (modified_values, value) in modified_values
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.iter_mut()
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.zip(values[column.index()][start..].iter())
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{
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// Multiply by p_j(\omega^i) + \delta^j \omega^i \beta
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*modified_values *= &(deltaomega * &*beta + &*gamma + value);
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deltaomega *= ω
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}
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});
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deltaomega *= &C::Scalar::DELTA;
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}
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// The modified_values vector is a vector of products of fractions
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// of the form
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//
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// (p_j(\omega^i) + \delta^j \omega^i \beta + \gamma) /
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// (p_j(\omega^i) + \beta s_j(\omega^i) + \gamma)
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//
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// where i is the index into modified_values, for the jth column in
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// the permutation
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// Compute the evaluations of the permutation product polynomial
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// over our domain, starting with z[0] = 1
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let mut z = vec![last_z];
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for row in 1..(params.n as usize) {
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let mut tmp = z[row - 1];
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tmp *= &modified_values[row - 1];
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z.push(tmp);
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}
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let mut z = domain.lagrange_from_vec(z);
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// Set blinding factors
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for z in &mut z[params.n as usize - blinding_factors..] {
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*z = C::Scalar::random(&mut rng);
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}
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// Set new last_z
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last_z = z[params.n as usize - (blinding_factors + 1)];
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let blind = Blind(C::Scalar::random(&mut rng));
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let permutation_product_commitment_projective = params.commit_lagrange(&z, blind);
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let permutation_product_blind = blind;
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let z = domain.lagrange_to_coeff(z);
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let permutation_product_poly = z.clone();
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let permutation_product_coset =
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evaluator.register_poly(domain.coeff_to_extended(z.clone()));
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let permutation_product_commitment =
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permutation_product_commitment_projective.to_affine();
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// Hash the permutation product commitment
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transcript.write_point(permutation_product_commitment)?;
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sets.push(CommittedSet {
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permutation_product_poly,
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permutation_product_coset,
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permutation_product_blind,
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});
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}
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Ok(Committed { sets })
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}
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}
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impl<C: CurveAffine, Ev: Copy + Send + Sync> Committed<C, Ev> {
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#[allow(clippy::too_many_arguments)]
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pub(in crate::plonk) fn construct<'a>(
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self,
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pk: &'a plonk::ProvingKey<C>,
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p: &'a Argument,
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advice_cosets: &'a [poly::AstLeaf<Ev, ExtendedLagrangeCoeff>],
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fixed_cosets: &'a [poly::AstLeaf<Ev, ExtendedLagrangeCoeff>],
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instance_cosets: &'a [poly::AstLeaf<Ev, ExtendedLagrangeCoeff>],
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permutation_cosets: &'a [poly::AstLeaf<Ev, ExtendedLagrangeCoeff>],
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l0: poly::AstLeaf<Ev, ExtendedLagrangeCoeff>,
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l_blind: poly::AstLeaf<Ev, ExtendedLagrangeCoeff>,
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l_last: poly::AstLeaf<Ev, ExtendedLagrangeCoeff>,
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beta: ChallengeBeta<C>,
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gamma: ChallengeGamma<C>,
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) -> (
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Constructed<C>,
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impl Iterator<Item = poly::Ast<Ev, C::Scalar, ExtendedLagrangeCoeff>> + 'a,
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) {
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let chunk_len = pk.vk.cs_degree - 2;
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let blinding_factors = pk.vk.cs.blinding_factors();
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let last_rotation = Rotation(-((blinding_factors + 1) as i32));
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let constructed = Constructed {
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sets: self
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.sets
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.iter()
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.map(|set| ConstructedSet {
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permutation_product_poly: set.permutation_product_poly.clone(),
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permutation_product_blind: set.permutation_product_blind,
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})
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.collect(),
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};
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let expressions = iter::empty()
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// Enforce only for the first set.
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// l_0(X) * (1 - z_0(X)) = 0
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.chain(
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self.sets
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.first()
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.map(|first_set| (poly::Ast::one() - first_set.permutation_product_coset) * l0),
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)
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// Enforce only for the last set.
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// l_last(X) * (z_l(X)^2 - z_l(X)) = 0
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.chain(self.sets.last().map(|last_set| {
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((poly::Ast::from(last_set.permutation_product_coset)
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* last_set.permutation_product_coset)
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- last_set.permutation_product_coset)
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* l_last
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}))
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// Except for the first set, enforce.
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// l_0(X) * (z_i(X) - z_{i-1}(\omega^(last) X)) = 0
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.chain(
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self.sets
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.iter()
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.skip(1)
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.zip(self.sets.iter())
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.map(|(set, last_set)| {
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(poly::Ast::from(set.permutation_product_coset)
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- last_set
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.permutation_product_coset
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.with_rotation(last_rotation))
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* l0
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})
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.collect::<Vec<_>>(),
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)
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// And for all the sets we enforce:
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// (1 - (l_last(X) + l_blind(X))) * (
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// z_i(\omega X) \prod_j (p(X) + \beta s_j(X) + \gamma)
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// - z_i(X) \prod_j (p(X) + \delta^j \beta X + \gamma)
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// )
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.chain(
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self.sets
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.into_iter()
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.zip(p.columns.chunks(chunk_len))
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.zip(permutation_cosets.chunks(chunk_len))
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.enumerate()
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.map(move |(chunk_index, ((set, columns), cosets))| {
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let mut left = poly::Ast::<_, C::Scalar, _>::from(
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set.permutation_product_coset
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.with_rotation(Rotation::next()),
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);
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for (values, permutation) in columns
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.iter()
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.map(|&column| match column.column_type() {
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Any::Advice => &advice_cosets[column.index()],
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Any::Fixed => &fixed_cosets[column.index()],
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Any::Instance => &instance_cosets[column.index()],
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})
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.zip(cosets.iter())
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{
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left *= poly::Ast::<_, C::Scalar, _>::from(*values)
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+ (poly::Ast::ConstantTerm(*beta) * poly::Ast::from(*permutation))
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+ poly::Ast::ConstantTerm(*gamma);
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}
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let mut right = poly::Ast::from(set.permutation_product_coset);
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let mut current_delta = *beta
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* &(C::Scalar::DELTA.pow_vartime([(chunk_index * chunk_len) as u64]));
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for values in columns.iter().map(|&column| match column.column_type() {
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Any::Advice => &advice_cosets[column.index()],
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Any::Fixed => &fixed_cosets[column.index()],
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Any::Instance => &instance_cosets[column.index()],
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}) {
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right *= poly::Ast::from(*values)
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+ poly::Ast::LinearTerm(current_delta)
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+ poly::Ast::ConstantTerm(*gamma);
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current_delta *= &C::Scalar::DELTA;
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}
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(left - right) * (poly::Ast::one() - (poly::Ast::from(l_last) + l_blind))
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}),
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);
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(constructed, expressions)
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}
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}
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impl<C: CurveAffine> super::ProvingKey<C> {
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pub(in crate::plonk) fn open(
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&self,
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x: ChallengeX<C>,
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) -> impl Iterator<Item = ProverQuery<'_, C>> + Clone {
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self.polys.iter().map(move |poly| ProverQuery {
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point: *x,
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poly,
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blind: Blind::default(),
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})
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}
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pub(in crate::plonk) fn evaluate<E: EncodedChallenge<C>, T: TranscriptWrite<C, E>>(
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&self,
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x: ChallengeX<C>,
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transcript: &mut T,
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) -> Result<(), Error> {
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// Hash permutation evals
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for eval in self.polys.iter().map(|poly| eval_polynomial(poly, *x)) {
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transcript.write_scalar(eval)?;
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}
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Ok(())
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}
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}
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impl<C: CurveAffine> Constructed<C> {
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pub(in crate::plonk) fn evaluate<E: EncodedChallenge<C>, T: TranscriptWrite<C, E>>(
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self,
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pk: &plonk::ProvingKey<C>,
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x: ChallengeX<C>,
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transcript: &mut T,
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) -> Result<Evaluated<C>, Error> {
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let domain = &pk.vk.domain;
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let blinding_factors = pk.vk.cs.blinding_factors();
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{
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let mut sets = self.sets.iter();
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while let Some(set) = sets.next() {
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let permutation_product_eval = eval_polynomial(&set.permutation_product_poly, *x);
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let permutation_product_next_eval = eval_polynomial(
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&set.permutation_product_poly,
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domain.rotate_omega(*x, Rotation::next()),
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);
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// Hash permutation product evals
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for eval in iter::empty()
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.chain(Some(&permutation_product_eval))
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.chain(Some(&permutation_product_next_eval))
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{
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transcript.write_scalar(*eval)?;
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}
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// If we have any remaining sets to process, evaluate this set at omega^u
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// so we can constrain the last value of its running product to equal the
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// first value of the next set's running product, chaining them together.
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if sets.len() > 0 {
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let permutation_product_last_eval = eval_polynomial(
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&set.permutation_product_poly,
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domain.rotate_omega(*x, Rotation(-((blinding_factors + 1) as i32))),
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);
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transcript.write_scalar(permutation_product_last_eval)?;
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}
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}
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}
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Ok(Evaluated { constructed: self })
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}
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}
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impl<C: CurveAffine> Evaluated<C> {
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pub(in crate::plonk) fn open<'a>(
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&'a self,
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pk: &'a plonk::ProvingKey<C>,
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x: ChallengeX<C>,
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) -> impl Iterator<Item = ProverQuery<'a, C>> + Clone {
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let blinding_factors = pk.vk.cs.blinding_factors();
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let x_next = pk.vk.domain.rotate_omega(*x, Rotation::next());
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let x_last = pk
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.vk
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.domain
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.rotate_omega(*x, Rotation(-((blinding_factors + 1) as i32)));
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iter::empty()
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.chain(self.constructed.sets.iter().flat_map(move |set| {
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iter::empty()
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// Open permutation product commitments at x and \omega x
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.chain(Some(ProverQuery {
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point: *x,
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poly: &set.permutation_product_poly,
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blind: set.permutation_product_blind,
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}))
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.chain(Some(ProverQuery {
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point: x_next,
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poly: &set.permutation_product_poly,
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blind: set.permutation_product_blind,
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}))
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}))
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// Open it at \omega^{last} x for all but the last set. This rotation is only
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// sensical for the first row, but we only use this rotation in a constraint
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// that is gated on l_0.
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.chain(
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self.constructed
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.sets
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.iter()
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.rev()
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.skip(1)
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.flat_map(move |set| {
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Some(ProverQuery {
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point: x_last,
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poly: &set.permutation_product_poly,
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blind: set.permutation_product_blind,
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})
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}),
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)
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}
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}
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