/// @file dpf/eval_inner_product.hpp
/// @brief Full / interval / sequence DPF evaluation that reduces against a
///        public vector instead of materializing the output.
/// @details Three local forms share the same interval / sequence domains:
///          - **Batched leaf walk** (no tag): one output, weights in
///            `eval_interval` layout, exterior AES batched like that walk,
///            O(1) accumulator. Cost shape matches `eval_interval` on the
///            same range (Θ(L) nodes) plus a multiply-add per packed slot.
///          - **`dpf::paired`** (row-wise): one row of the weight vector per
///            input. A row is a scalar (one output) or a `tuple` / `array`
///            zipped with several outputs — leaf slots or an ancestor prefix
///            plus the leaf — read off one path. Products use `operator*`;
///            they are summed with `operator+`. Same walk cost as the point
///            list, plus O(1) arithmetic per selected output per input.
///          - **`dpf::columns`** (transposed / column-wise): one output,
///            several weight streams, one accumulator per stream, one walk.
///            Products are *not* summed across streams. A stream is anything
///            with `w[i]` or `w(i)`. `dpf::project` maps the share before the
///            multiply; `dpf::also` sees the unmapped share. Cost is one path
///            walk plus O(stream count) arithmetic per input.
///
///          A single-stream `columns` result matches `paired` on that stream;
///          a single-output batched leaf walk matches `paired` when the
///          interval is leaf-aligned (covering-leaf weights equal the clipped
///          domain points). Unaligned intervals still weight every lane of the
///          covering leaves — same layout as one-key `eval_interval` buffers /
///          cohort interval inner products, not the clipped iterable. A sized
///          weight container shorter than that covering span throws
///          `std::invalid_argument`. `paired` and `columns` throw the same way
///          when a sized stream is shorter than the point list or the clipped
///          interval.

#ifndef LIBDPF_INCLUDE_DPF_EVAL_INNER_PRODUCT_HPP__
#define LIBDPF_INCLUDE_DPF_EVAL_INNER_PRODUCT_HPP__

#include <algorithm>
#include <array>
#include <iterator>
#include <vector>
#include <cstddef>
#include <cstring>
#include <limits>
#include <stdexcept>
#include <tuple>
#include <type_traits>
#include <utility>

#include "hedley/hedley.h"
#include <portable-snippets/exact-int/exact-int.h>
#include <simde/simde/x86/avx2.h>

#include "dpf/dpf_key.hpp"
#include "dpf/eval_interval.hpp"
#include "dpf/eval_sequence.hpp"
#include "dpf/eval_target.hpp"
#include "dpf/incremental.hpp"
#include "dpf/leaf_node.hpp"
#include "dpf/path_memoizer.hpp"
#include "dpf/twiddle.hpp"
#include "dpf/utils.hpp"
#include "dpf/xor_wrapper.hpp"

namespace dpf
{

namespace detail_ip_check
{

template <typename T, typename = void>
struct has_container_size : std::false_type
{
};

template <typename T>
struct has_container_size<T,
    std::void_t<decltype(std::size(std::declval<const T &>()))>>
    : std::true_type
{
};

/// @brief Throw when a sized weight container is shorter than the walk.
///        Unsizable weights (`w(i)` callables, raw pointers) are left to
///        the caller.
template <typename W>
void require_weight_count(const W & w, std::size_t need, const char * what)
{
    if constexpr (has_container_size<std::decay_t<W>>::value)
    {
        if (static_cast<std::size_t>(std::size(w)) < need)
            throw std::invalid_argument(what);
    }
}

}  // namespace detail_ip_check

namespace internal
{

template <typename T>
struct is_xor_wrapper : std::false_type {};

template <typename T>
struct is_xor_wrapper<dpf::xor_wrapper<T>> : std::true_type {};

template <typename T>
inline constexpr bool is_xor_wrapper_v = is_xor_wrapper<T>::value;

template <typename W>
HEDLEY_ALWAYS_INLINE
auto weight_as_u64(W && w, std::size_t i)
{
    return static_cast<psnip_uint64_t>(w[i]);
}

HEDLEY_ALWAYS_INLINE
simde__m128i load_weight_pair_u64(psnip_uint64_t lo, psnip_uint64_t hi)
{
    return simde_mm_set_epi64x(static_cast<int64_t>(hi),
                               static_cast<int64_t>(lo));
}

HEDLEY_ALWAYS_INLINE
simde__m128i mullo_epi64x2(simde__m128i a, simde__m128i b)
{
#if defined(__AVX512DQ__) && defined(__AVX512VL__)
    return _mm_mullo_epi64(a, b);
#else
    psnip_uint64_t av[2], bv[2];
    std::memcpy(av, &a, sizeof(av));
    std::memcpy(bv, &b, sizeof(bv));
    av[0] *= bv[0];
    av[1] *= bv[1];
    simde__m128i r;
    std::memcpy(&r, av, sizeof(r));
    return r;
#endif
}

template <typename NodeT,
          typename OutputsTuple,
          std::size_t ...Is>
struct ip_prg_range
{
    static constexpr std::size_t pos_min
        = const_min_size<block_offset_of_leaf_v<Is, NodeT, OutputsTuple>...>::value;
    static constexpr std::size_t pos_end
        = const_max_size<(block_offset_of_leaf_v<Is, NodeT, OutputsTuple>
            + block_length_of_leaf_v<std::tuple_element_t<Is, OutputsTuple>, NodeT>)...>::value;
    static constexpr std::size_t count = pos_end - pos_min;
};

template <typename OutputT>
struct ip_accum
{
    using output_type = OutputT;
    static constexpr bool xor_mode = is_xor_wrapper_v<OutputT>;
    static constexpr bool simd64 = (sizeof(OutputT) == 8);

    simde__m128i vacc = simde_mm_setzero_si128();
    output_type scalar{};

    template <typename LeafT, typename W>
    HEDLEY_ALWAYS_INLINE
    void mac(const LeafT & leaf, std::size_t base, std::size_t opl, W && w)
    {
HEDLEY_PRAGMA(GCC diagnostic push)
HEDLEY_PRAGMA(GCC diagnostic ignored "-Wignored-attributes")
        if constexpr (simd64 && std::is_same_v<LeafT, simde__m128i>)
        {
            if (HEDLEY_LIKELY(opl == 2))
            {
                simde__m128i ww = load_weight_pair_u64(
                    weight_as_u64(w, base),
                    weight_as_u64(w, base + 1));
                if constexpr (xor_mode)
                {
                    vacc = simde_mm_xor_si128(vacc,
                        simde_mm_and_si128(leaf, ww));
                }
                else
                {
                    vacc = simde_mm_add_epi64(vacc, mullo_epi64x2(leaf, ww));
                }
                return;
            }
        }
HEDLEY_PRAGMA(GCC diagnostic pop)

        for (std::size_t p = 0; p < opl; ++p)
        {
            output_type val;
            if constexpr (utils::is_packed_subbyte_v<output_type>)
            {
                val = extract_leaf<std::remove_cv_t<LeafT>, output_type>(leaf, p);
            }
            else
            {
                std::memcpy(&val,
                    reinterpret_cast<const unsigned char *>(std::addressof(leaf))
                        + p * sizeof(output_type),
                    sizeof(val));
            }
            const auto wt = weight_as_u64(w, base + p);
            if constexpr (xor_mode)
            {
                scalar = output_type{static_cast<typename output_type::value_type>(
                    static_cast<psnip_uint64_t>(scalar)
                    ^ (static_cast<psnip_uint64_t>(val) & wt))};
            }
            else if constexpr (utils::is_packed_subbyte_v<output_type>
                && !std::is_same_v<output_type, dpf::bit>)
            {
                constexpr unsigned mask
                    = (1u << utils::packed_lane_bits_v<output_type>) - 1u;
                const auto wlane = static_cast<output_type>(
                    static_cast<unsigned>(wt) & mask);
                scalar = scalar + val * wlane;
            }
            else
            {
                scalar = static_cast<output_type>(
                    static_cast<psnip_uint64_t>(scalar)
                    + static_cast<psnip_uint64_t>(val) * wt);
            }
        }
    }

    HEDLEY_ALWAYS_INLINE
    output_type finish() const
    {
        if constexpr (simd64)
        {
            psnip_uint64_t lanes[2];
            std::memcpy(lanes, &vacc, sizeof(lanes));
            if constexpr (xor_mode)
            {
                return output_type{static_cast<typename output_type::value_type>(
                    (lanes[0] ^ lanes[1])
                    ^ static_cast<psnip_uint64_t>(scalar))};
            }
            else
            {
                return output_type{
                    lanes[0] + lanes[1]
                    + static_cast<psnip_uint64_t>(scalar)};
            }
        }
        return scalar;
    }
};

template <std::size_t ...Is,
          typename DpfKey,
          typename Weights,
          typename IntervalMemoizer,
          typename IntegralT,
          std::size_t ...IIs>
void eval_inner_product_exterior(const DpfKey & dpf, IntegralT from_node,
    IntegralT to_node, Weights && weights, IntervalMemoizer && memoizer,
    std::index_sequence<IIs...>,
    std::tuple<ip_accum<typename DpfKey::concrete_output_type<Is>>...> & accs,
    std::size_t start = 0)
{
    assert_not_wildcard_output<Is...>(dpf);
    if (HEDLEY_UNLIKELY(to_node < from_node && to_node != IntegralT{0}))
        throw std::runtime_error("to_node<from_node");

    using node_type = typename DpfKey::exterior_node;
    using outputs_tuple = typename DpfKey::concrete_outputs_tuple;
    HEDLEY_PRAGMA(GCC diagnostic push)
    HEDLEY_PRAGMA(GCC diagnostic ignored "-Wignored-attributes")
    using range = ip_prg_range<node_type, outputs_tuple, Is...>;
    HEDLEY_PRAGMA(GCC diagnostic pop)
    constexpr std::size_t opl = DpfKey::outputs_per_leaf;

    std::size_t nodes_in_interval = static_cast<std::size_t>(to_node - from_node);
    // `to_node == 0` is the saturated exclusive end; the subtraction is the
    // leaf count. A real inverted range is rejected above.
    auto *nodes = memoizer[DpfKey::depth];
    auto cws = std::make_tuple(std::get<Is>(dpf.leaf_nodes).get()...);

    if constexpr (DpfKey::is_extractable)
    {
        std::size_t j = 0, k = start;
        for (; j < nodes_in_interval; ++j, ++k)
        {
            auto apply_output = [&](auto out_index, auto buf_index)
            {
                constexpr std::size_t out_i = decltype(out_index)::value;
                constexpr std::size_t buf_i = decltype(buf_index)::value;
                auto leaf = dpf.template traverse_exterior<out_i>(nodes[j]);
                std::get<buf_i>(accs).mac(leaf, k * opl, opl,
                    utils::get<buf_i>(weights));
            };
            (apply_output(std::integral_constant<std::size_t, Is>{},
                          std::integral_constant<std::size_t, IIs>{}), ...);
        }
        return;
    }

HEDLEY_PRAGMA(GCC diagnostic push)
HEDLEY_PRAGMA(GCC diagnostic ignored "-Wignored-attributes")
    auto apply_masks = [&](std::size_t k, const node_type & node,
        const node_type * HEDLEY_RESTRICT masks)
    {
        auto apply_output = [&](auto out_index, auto buf_index)
        {
            constexpr std::size_t out_i = decltype(out_index)::value;
            constexpr std::size_t buf_i = decltype(buf_index)::value;
            using output_type = typename DpfKey::concrete_output_type<out_i>;
            using leaf_type = dpf::leaf_node_t<node_type, output_type>;
            constexpr auto pos = block_offset_of_leaf_v<out_i, node_type, outputs_tuple>;
            leaf_type mask;
            std::memcpy(&mask, masks + (pos - range::pos_min), sizeof(leaf_type));
            // Subtractive share: CW_if_t − mask so reconstruct(y0, y1) = y0 − y1 = β.
            auto leaf = dpf::subtract_leaf<output_type>(
                get_if_lo_bit(std::get<buf_i>(cws), node), mask);
            std::get<buf_i>(accs).mac(leaf, k * opl, opl,
                utils::get<buf_i>(weights));
        };
        (apply_output(std::integral_constant<std::size_t, Is>{},
                      std::integral_constant<std::size_t, IIs>{}), ...);
    };

    std::size_t j = 0, k = start;
    if constexpr (range::count == 2 && range::pos_min == 0)
    {
        for (; j + 4 <= nodes_in_interval; j += 4, k += 4)
        {
            alignas(node_type) node_type seeds[4];
            alignas(node_type) node_type left[4];
            alignas(node_type) node_type right[4];
            DPF_UNROLL_LOOP
            for (std::size_t t = 0; t < 4; ++t)
            {
                seeds[t] = utils::to_exterior_node<node_type>(
                    unset_lo_2bits(nodes[j + t]));
            }
            DpfKey::exterior_prg::eval01_x4(seeds, left, right);
            DPF_UNROLL_LOOP
            for (std::size_t t = 0; t < 4; ++t)
            {
                node_type masks[2] = {left[t], right[t]};
                apply_masks(k + t, nodes[j + t], masks);
            }
        }
    }
    else if constexpr (range::count == 1)
    {
        const auto pos = static_cast<psnip_uint32_t>(range::pos_min);
        for (; j + 8 <= nodes_in_interval; j += 8, k += 8)
        {
            alignas(node_type) node_type seeds[8];
            alignas(node_type) node_type masks[8];
            HEDLEY_PRAGMA(GCC diagnostic push)
            HEDLEY_PRAGMA(GCC diagnostic ignored "-Warray-bounds")
            DPF_UNROLL_LOOP
            for (std::size_t t = 0; t < 8; ++t)
            {
                seeds[t] = utils::to_exterior_node<node_type>(
                    unset_lo_2bits(nodes[j + t]));
            }
            HEDLEY_PRAGMA(GCC diagnostic pop)
            DpfKey::exterior_prg::eval_x8(seeds, masks, pos);
            DPF_UNROLL_LOOP
            for (std::size_t t = 0; t < 8; ++t)
            {
                apply_masks(k + t, nodes[j + t], &masks[t]);
            }
        }
        for (; j + 4 <= nodes_in_interval; j += 4, k += 4)
        {
            alignas(node_type) node_type seeds[4];
            alignas(node_type) node_type masks[4];
            DPF_UNROLL_LOOP
            for (std::size_t t = 0; t < 4; ++t)
            {
                seeds[t] = utils::to_exterior_node<node_type>(
                    unset_lo_2bits(nodes[j + t]));
            }
            DpfKey::exterior_prg::eval_x4(seeds, masks, pos);
            DPF_UNROLL_LOOP
            for (std::size_t t = 0; t < 4; ++t)
            {
                apply_masks(k + t, nodes[j + t], &masks[t]);
            }
        }
    }

    DPF_UNROLL_LOOP
    for (; j < nodes_in_interval; ++j, ++k)
    {
        const auto & node = nodes[j];
        auto seed = utils::to_exterior_node<node_type>(unset_lo_2bits(node));
        std::array<node_type, range::count> masks;
        DpfKey::exterior_prg::eval(seed, masks.data(),
            static_cast<psnip_uint32_t>(range::count),
            static_cast<psnip_uint32_t>(range::pos_min));
        apply_masks(k, node, masks.data());
    }
HEDLEY_PRAGMA(GCC diagnostic pop)
}

template <typename DpfKey,
          typename InputT,
          typename IntervalMemoizer>
void eval_prepare_nodes(const DpfKey & dpf, InputT from, InputT to,
    IntervalMemoizer && memoizer)
{
    using dpf_type = DpfKey;
    using integral_type = typename DpfKey::integral_type;

    utils::flip_msb_if_signed_integral(from);
    utils::flip_msb_if_signed_integral(to);

    integral_type from_node = utils::get_from_node<dpf_type>(from);
    integral_type to_node = utils::get_to_node<dpf_type>(to);
    constexpr auto to_int = utils::to_integral_type<InputT>{};
    const bool wraps = utils::interval_wraps(
        static_cast<integral_type>(to_int(from)),
        static_cast<integral_type>(to_int(to)),
        utils::bitlength_of_v<InputT>);
    auto segs = utils::split_leaf_nodes(from_node, to_node, dpf.depth, wraps);
    // The memoizer keeps one interval. A wrap is two intervals, and walking
    // the first clobbers the second, so only a single segment can be cached.
    if (segs.n == 1)
    {
        eval_interval_interior(dpf, segs.seg[0].from_node, segs.seg[0].to_node,
            memoizer);
    }
}

template <std::size_t ...Is,
          typename DpfKey,
          typename InputT,
          typename Weights,
          typename IntervalMemoizer,
          std::size_t ...IIs>
auto eval_inner_product_impl(const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, IntervalMemoizer && memoizer,
    std::index_sequence<IIs...>)
{
    using dpf_type = DpfKey;
    using integral_type = typename DpfKey::integral_type;

    utils::flip_msb_if_signed_integral(from);
    utils::flip_msb_if_signed_integral(to);

    integral_type from_node = utils::get_from_node<dpf_type>(from);
    integral_type to_node = utils::get_to_node<dpf_type>(to);
    constexpr auto to_int = utils::to_integral_type<InputT>{};
    const bool wraps = utils::interval_wraps(
        static_cast<integral_type>(to_int(from)),
        static_cast<integral_type>(to_int(to)),
        utils::bitlength_of_v<InputT>);
    auto segs = utils::split_leaf_nodes(from_node, to_node, dpf.depth, wraps);

    constexpr std::size_t opl = DpfKey::outputs_per_leaf;
    const std::size_t lanes = segs.total * opl;
    auto check_one = [&](auto which)
    {
        constexpr std::size_t wi = decltype(which)::value;
        detail_ip_check::require_weight_count(
            utils::get<wi>(weights), lanes,
            "inner product weights are shorter than the covering leaves");
    };
    (check_one(std::integral_constant<std::size_t, IIs>{}), ...);

    auto accs = std::make_tuple(
        ip_accum<typename DpfKey::concrete_output_type<Is>>{}...);
    auto idxs = std::index_sequence<IIs...>{};

    std::size_t start = 0;
    for (std::size_t s = 0; s < segs.n; ++s)
    {
        const auto & seg = segs.seg[s];
        eval_interval_interior(dpf, seg.from_node, seg.to_node, memoizer);
        eval_inner_product_exterior<Is...>(dpf, seg.from_node, seg.to_node,
            weights, memoizer, idxs, accs, start);
        start += seg.count;
    }

    if constexpr (sizeof...(Is) == 1)
    {
        return std::get<0>(accs).finish();
    }
    else
    {
        return std::make_tuple(std::get<IIs>(accs).finish()...);
    }
}

}  // namespace internal

/// @brief Expand the interior tree for `[from, to]`. A wrapping interval is left
/// cold: the memoizer holds one half, and walking the first half of the later
/// inner product would clobber a cached second half. Safe to call before the
/// weight vector exists; a subsequent inner-product on the same memoizer
/// skips the interior AES when the interval did not wrap.
/// @tparam DpfKey DPF key type
/// @tparam InputT input domain type
/// @tparam IntervalMemoizer interval memoizer type
/// @param dpf the DPF key
/// @param from the inclusive start of the range
/// @param to the `to`
/// @param memoizer the memoizer built for this key
template <typename DpfKey,
          typename InputT,
          typename IntervalMemoizer>
HEDLEY_ALWAYS_INLINE
void eval_prepare_interval(const DpfKey & dpf, InputT from, InputT to,
    IntervalMemoizer && memoizer)
{
    internal::eval_prepare_nodes(dpf, dpf.offset_x(from), dpf.offset_x(to),
        memoizer);
}

template <typename DpfKey,
          typename IntervalMemoizer>
HEDLEY_ALWAYS_INLINE
void eval_prepare_full(const DpfKey & dpf, IntervalMemoizer && memoizer)
{
    using input_type = typename DpfKey::input_type;
    eval_prepare_interval(dpf,
        std::numeric_limits<input_type>::min(),
        std::numeric_limits<input_type>::max(),
        memoizer);
}

/// @brief `sum_x DPF_I(x) * w[x]` (additive) or `xor_x DPF_I(x) & w[x]` (XOR).
/// @details `w[j]` is the weight for the `j`-th lane of the *covering* leaves
/// of `[from, to]`, matching `eval_interval`'s destination buffer (not the
/// clipped iterable). Multiple `Is` take a tuple of weight ranges and return
/// a tuple of accumulators; a single `I` takes one range and returns one
/// accumulator.
/// @tparam I output index
/// @tparam Is is
/// @tparam DpfKey DPF key type
/// @tparam InputT input domain type
/// @tparam Weights weights
/// @tparam IntervalMemoizer interval memoizer type
/// @tparam DpfKey DPF key type
/// @param dpf the DPF key
/// @param from the inclusive start of the range
/// @param to the `to`
/// @param weights the weights
/// @param memoizer the memoizer built for this key
/// @return `sum_x DPF_I(x) * w[x]` (additive) or `xor_x DPF_I(x) & w[x]` (XOR)
/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0,
          std::size_t ...Is,
          typename DpfKey,
          typename InputT,
          typename Weights,
          typename IntervalMemoizer,
          std::enable_if_t<looks_like_dpf_key_v<DpfKey>
              && !is_multilevel_key_v<DpfKey>, bool> = true>
HEDLEY_ALWAYS_INLINE
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, IntervalMemoizer && memoizer)
{
    assert_not_wildcard_output<I, Is...>(dpf);
    return internal::eval_inner_product_impl<I, Is...>(
        dpf, dpf.offset_x(from), dpf.offset_x(to),
        weights, memoizer, std::make_index_sequence<1 + sizeof...(Is)>{});
}

/// @brief Inner product with a VDPF path proof over the same interval nodes.
/// @details Folds once per BFS node (same transcript as `prove_interval`), then
///          evaluates. Weights are not mixed into the token.
/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0,
          std::size_t ...Is,
          typename DpfKey,
          typename InputT,
          typename Weights,
          typename IntervalMemoizer,
          std::enable_if_t<looks_like_dpf_key_v<DpfKey>
              && !is_multilevel_key_v<DpfKey>, bool> = true>
HEDLEY_ALWAYS_INLINE
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, IntervalMemoizer && memoizer, prove_ref pr)
{
    static_assert(DpfKey::is_verifiable,
        "eval_inner_product(..., prove(π)): key must carry dpf::verifiable");
    detail::vdpf::init_proof(pr.token, dpf);
    prove_fold_interval(dpf, from, to, pr.token);
    detail::vdpf::fold_output_binding(pr.token, dpf);
    return eval_inner_product<I, Is...>(dpf, from, to,
        std::forward<Weights>(weights),
        std::forward<IntervalMemoizer>(memoizer));
}

/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0,
          std::size_t ...Is,
          typename DpfKey,
          typename InputT,
          typename Weights,
          std::enable_if_t<looks_like_dpf_key_v<DpfKey>
              && !is_multilevel_key_v<DpfKey>, bool> = true>
HEDLEY_ALWAYS_INLINE
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, prove_ref pr)
{
    return eval_inner_product<I, Is...>(dpf, from, to,
        std::forward<Weights>(weights),
        dpf::make_basic_interval_memoizer(dpf, from, to), pr);
}

template <std::size_t I = 0,
          std::size_t ...Is,
          typename DpfKey,
          typename Weights,
          typename IntervalMemoizer,
          std::enable_if_t<looks_like_dpf_key_v<DpfKey>
              && !is_multilevel_key_v<DpfKey>, bool> = true>
HEDLEY_ALWAYS_INLINE
HEDLEY_WARN_UNUSED_RESULT
auto eval_full_inner_product(const DpfKey & dpf, Weights && weights,
    IntervalMemoizer && memoizer)
{
    using input_type = typename DpfKey::input_type;
    return eval_inner_product<I, Is...>(dpf,
        std::numeric_limits<input_type>::min(),
        std::numeric_limits<input_type>::max(),
        weights, memoizer);
}

/// @brief Tag: row-wise zip of several DPF outputs with each weight element.
struct paired_t
{
};

inline constexpr paired_t paired{};

/// @brief Tag: transposed walk — one output, several weight streams kept apart.
/// @details Unlike `paired`, products are not summed across streams.
struct columns_t
{
};

inline constexpr columns_t columns{};

/// @brief Map a leaf share before it is multiplied by column weights.
template <typename F>
struct project_fn
{
    F fn;
};

/// @brief Wrap `fn` as the column projector. `fn` is called as `fn(share)`.
template <typename F>
HEDLEY_ALWAYS_INLINE
HEDLEY_WARN_UNUSED_RESULT
project_fn<std::decay_t<F>> project(F && fn)
{
    return project_fn<std::decay_t<F>>{std::forward<F>(fn)};
}

/// @brief Observe each unmapped share during a `columns` walk.
template <typename F>
struct also_fn
{
    F fn;
};

/// @brief Wrap `fn` as a `columns` side visit.
/// @details `fn` is called as `fn(i, x, share)`: list index, domain point,
///          then the share `project` has not seen.
template <typename F>
HEDLEY_ALWAYS_INLINE
HEDLEY_WARN_UNUSED_RESULT
also_fn<std::decay_t<F>> also(F && fn)
{
    return also_fn<std::decay_t<F>>{std::forward<F>(fn)};
}

/// @brief Column projector that returns the share unchanged.
struct identity_project
{
    template <typename T>
    HEDLEY_ALWAYS_INLINE
    constexpr T operator()(T value) const
    {
        return value;
    }
};

/// @brief Column side visit that ignores its arguments.
struct noop_also
{
    template <typename... A>
    HEDLEY_ALWAYS_INLINE
    void operator()(A && ...) const noexcept
    {
    }
};

namespace detail_ip
{

template <typename T, typename = void>
struct has_public_addends : std::false_type
{
};

template <typename T>
struct has_public_addends<T, std::void_t<decltype(std::declval<const T &>().public_addends)>>
    : std::true_type
{
};

template <typename Row>
struct is_std_array : std::false_type
{
};

template <typename T, std::size_t N>
struct is_std_array<std::array<T, N>> : std::true_type
{
};

/// @brief Component `K` of a row. A scalar row pairs with output 0 only.
template <std::size_t K, typename Row>
HEDLEY_ALWAYS_INLINE
decltype(auto) component(Row && row)
{
    using R = std::decay_t<Row>;
    if constexpr (utils::is_tuple_v<R> || is_std_array<R>::value)
        return std::get<K>(std::forward<Row>(row));
    else
    {
        static_assert(K == 0,
            "a scalar weight pairs with one DPF output; use a tuple or "
            "std::array row for several outputs");
        return std::forward<Row>(row);
    }
}

template <std::size_t I, typename Key>
HEDLEY_ALWAYS_INLINE
auto share_at(const Key & key, const typename Key::input_type & tx,
    const typename Key::interior_node & node)
{
    constexpr auto bits = utils::bitlength_of_v<typename Key::input_type>;
    constexpr auto prefix = Key::meta[I].prefix == 0
        ? bits : Key::meta[I].prefix;
    auto lane = detail::incr::lane_input(tx, prefix, bits);
    auto leaf = key.template traverse_exterior<I>(node);
    if constexpr (has_public_addends<Key>::value)
        detail::incr::absorb_public_addend_lane<I>(key, leaf, lane);
    using output_type = typename Key::template concrete_output_type<I>;
    return *make_eval_dpf_output<Key, output_type>(leaf, lane);
}

template <std::size_t... Outs, typename Key, typename Tx, typename Path,
          typename Row, typename Acc, std::size_t... Ks>
HEDLEY_ALWAYS_INLINE
void mac_row(const Key & key, const Tx & tx, Path & path, Row && row,
    Acc & acc, std::index_sequence<Ks...>)
{
    // One exterior expansion per output per leaf/ancestor bucket. Sequential
    // and sorted queries reuse the node already on the path.
    ((acc = acc + (share_at<Outs>(key, tx, path[Key::meta[Outs].tree_level])
        * component<Ks>(row))), ...);
}

template <std::size_t... Outs, typename Key, typename Point, typename Rows,
          typename Acc>
void accumulate_points(const Key & key, Point first, Point last, Rows && rows,
    Acc & acc)
{
    constexpr std::size_t nout = sizeof...(Outs);
    constexpr std::size_t deepest = std::max({std::size_t{0},
        Key::meta[Outs].tree_level...});
    auto path = make_basic_path_memoizer<Key>();
    std::size_t i = 0;
    for (auto it = first; it != last; ++it, ++i)
    {
        auto tx = key.offset_x(*it);
        utils::flip_msb_if_signed_integral(tx);
        detail::ensure_level(key, tx, path, deepest);
        mac_row<Outs...>(key, tx, path, rows[i], acc,
            std::make_index_sequence<nout>{});
    }
}

template <std::size_t I0, std::size_t... Rest>
struct pack_first
{
    static constexpr std::size_t value = I0;
};

template <std::size_t... Outs, typename Key, typename Rows>
auto accum_type_from(const Key &, Rows && rows)
{
    using row_type = std::decay_t<decltype(rows[std::size_t{0}])>;
    using y0 = decltype(share_at<pack_first<Outs...>::value>(
        std::declval<const Key &>(),
        std::declval<const typename Key::input_type &>(),
        std::declval<const typename Key::interior_node &>()));
    using w0 = std::decay_t<decltype(component<0>(std::declval<row_type &>()))>;
    using acc_type = decltype(std::declval<y0>() * std::declval<w0>());
    return acc_type{};
}

/// @brief `w[i]` when `w` is a range, otherwise `w(i)`.
template <typename W>
HEDLEY_ALWAYS_INLINE
decltype(auto) weight_at(W && w, std::size_t i)
{
    if constexpr (std::is_invocable_v<W &, std::size_t>)
        return w(i);
    else
        return w[i];
}

template <typename Weights>
struct is_column_pack : std::false_type
{
};

template <typename... Ts>
struct is_column_pack<std::tuple<Ts...>> : std::true_type
{
};

template <typename T, std::size_t N>
struct is_column_pack<std::array<T, N>> : std::true_type
{
};

template <std::size_t Out, std::size_t K, typename Key, typename Weights,
          typename Proj>
auto column_accum_type()
{
    using share_type = decltype(share_at<Out>(
        std::declval<const Key &>(),
        std::declval<const typename Key::input_type &>(),
        std::declval<const typename Key::interior_node &>()));
    using mapped = decltype(std::declval<Proj &>()(std::declval<share_type>()));
    using weight = decltype(weight_at(
        std::get<K>(std::declval<Weights &>()), std::size_t{0}));
    using acc_type = decltype(std::declval<mapped>() * std::declval<weight>());
    return acc_type{};
}

template <std::size_t Out, typename Key, typename Point, typename Weights,
          typename Proj, typename Sink, std::size_t... Ks>
auto accumulate_columns(const Key & key, Point first, Point last,
    Weights && weights, Proj && proj, Sink && sink,
    std::index_sequence<Ks...>)
{
    static_assert(sizeof...(Ks) > 0, "columns needs at least one weight stream");
    using acc_tuple = std::tuple<decltype(column_accum_type<Out, Ks, Key,
        std::decay_t<Weights>, std::decay_t<Proj>>())...>;
    acc_tuple acc{};
    if constexpr (std::is_base_of_v<std::random_access_iterator_tag,
        typename std::iterator_traits<Point>::iterator_category>)
    {
        const auto n = static_cast<std::size_t>(std::distance(first, last));
        auto guard = [&](auto which)
        {
            constexpr std::size_t k = decltype(which)::value;
            detail_ip_check::require_weight_count(std::get<k>(weights), n,
                "column weights are shorter than the point list");
        };
        (guard(std::integral_constant<std::size_t, Ks>{}), ...);
    }
    constexpr std::size_t deepest = Key::meta[Out].tree_level;
    auto path = make_basic_path_memoizer<Key>();
    std::size_t i = 0;
    for (auto it = first; it != last; ++it, ++i)
    {
        auto tx = key.offset_x(*it);
        utils::flip_msb_if_signed_integral(tx);
        detail::ensure_level(key, tx, path, deepest);
        auto share = share_at<Out>(key, tx, path[Key::meta[Out].tree_level]);
        sink(i, *it, share);
        auto y = proj(share);
        ((std::get<Ks>(acc) = std::get<Ks>(acc)
            + (y * weight_at(std::get<Ks>(weights), i))), ...);
    }
    return acc;
}

}  // namespace detail_ip

namespace internal_paired
{

template <typename Input, typename Fn>
void for_inclusive(Input from, Input to, Fn && fn)
{
    constexpr auto to_int = utils::to_integral_type<Input>{};
    using integral = decltype(to_int(from));
    const bool wraps = utils::interval_wraps(
        static_cast<integral>(to_int(from)),
        static_cast<integral>(to_int(to)),
        utils::bitlength_of_v<Input>);
    auto step = [&](Input x) { fn(x); };
    if (!wraps)
    {
        for (auto x = from;; ++x)
        {
            step(x);
            if (x == to)
                break;
        }
        return;
    }
    const auto hi = std::numeric_limits<Input>::max();
    const auto lo = std::numeric_limits<Input>::min();
    for (auto x = from;; ++x)
    {
        step(x);
        if (x == hi)
            break;
    }
    for (auto x = lo;; ++x)
    {
        step(x);
        if (x == to)
            break;
    }
}

template <std::size_t... Outs, typename Key, typename Rows>
auto run_points(const Key & key, const std::vector<typename Key::input_type> & xs,
    Rows && rows)
{
    assert_not_wildcard_output<Outs...>(key);
    if (xs.size() == 0)
    {
        using acc_type = decltype(detail_ip::accum_type_from<Outs...>(key, rows));
        return acc_type{};
    }
    detail_ip_check::require_weight_count(rows, xs.size(),
        "paired weights are shorter than the point list");
    using acc_type = decltype(detail_ip::accum_type_from<Outs...>(key, rows));
    acc_type acc{};
    detail_ip::accumulate_points<Outs...>(key, xs.begin(), xs.end(), rows, acc);
    return acc;
}

/// @brief Clipped interval dot via the interval tree, not one path per point.
///        Wrapping intervals stay on the path walk.
template <std::size_t I, typename DpfKey, typename InputT, typename Rows>
auto interval_scalar(const DpfKey & dpf, InputT from, InputT to, Rows && rows)
{
    constexpr auto to_int = utils::to_integral_type<InputT>{};
    using integral = decltype(to_int(from));
    const bool wraps = utils::interval_wraps(
        static_cast<integral>(to_int(from)),
        static_cast<integral>(to_int(to)),
        utils::bitlength_of_v<InputT>);
    if (wraps)
    {
        std::vector<InputT> xs;
        for_inclusive(from, to, [&](InputT x) { xs.push_back(x); });
        return run_points<I>(dpf, xs, std::forward<Rows>(rows));
    }
    const auto npoints = static_cast<std::size_t>(
        static_cast<integral>(to_int(to)) - static_cast<integral>(to_int(from)))
        + std::size_t{1};
    detail_ip_check::require_weight_count(rows, npoints,
        "paired weights are shorter than the interval");
    auto buf = make_output_buffer_for_interval<I>(dpf, from, to);
    auto iter = eval_interval<I>(dpf, from, to, buf);
    using share_t = std::decay_t<decltype(*iter.begin())>;
    using weight_t = std::decay_t<decltype(rows[std::size_t{0}])>;
    using acc_t = decltype(std::declval<share_t>() * std::declval<weight_t>());
    acc_t acc{};
    std::size_t i = 0;
    for (auto it = iter.begin(); it != iter.end(); ++it, ++i)
        acc = acc + (*it * rows[i]);
    return acc;
}

}  // namespace internal_paired

/// @brief `sum_i Σ_k DPF_{out_k}(x_i) * row_i[k]` over `[from, to]`.
/// @details One row of `rows` per input, in interval order (wrapping the same
///          way as `eval_interval`). A row is a scalar when one output is
///          selected, or a `std::tuple` / `std::array` with one component per
///          output. Ancestor slots and leaf slots are read off the same path.
///          Differs from the batched leaf walk: one path step per domain point
///          (reuse via the path memoizer), not batched exterior AES over leaf
///          nodes. For a single output the opened result matches the batched
///          form when `rows` is the interval weight vector.
/// \complexity One path ensure per input up to the deepest selected output,
///          plus one exterior expand and multiply-add per selected output per
///          input. Accumulator is O(1); no output buffer.
template <std::size_t I = 0, std::size_t... Is, typename DpfKey, typename InputT,
          typename Rows>
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(paired_t, const DpfKey & dpf, InputT from, InputT to,
    Rows && rows)
{
    using row_t = std::decay_t<decltype(std::declval<Rows &>()[std::size_t{0}])>;
    constexpr bool scalar = !utils::is_tuple_v<row_t>
        && !detail_ip::is_std_array<row_t>::value;
    if constexpr (scalar && sizeof...(Is) == 0 && !is_multilevel_key_v<DpfKey>)
    {
        return internal_paired::interval_scalar<I>(dpf, from, to,
            std::forward<Rows>(rows));
    }
    else
    {
        std::vector<InputT> xs;
        internal_paired::for_inclusive(from, to, [&](InputT x) { xs.push_back(x); });
        return internal_paired::run_points<I, Is...>(dpf, xs,
            std::forward<Rows>(rows));
    }
}

/// @brief Paired inner product over the whole input domain.
template <std::size_t I = 0, std::size_t... Is, typename DpfKey, typename Rows>
HEDLEY_WARN_UNUSED_RESULT
auto eval_full_inner_product(paired_t, const DpfKey & dpf, Rows && rows)
{
    using input_type = typename DpfKey::input_type;
    return eval_inner_product<I, Is...>(paired, dpf,
        std::numeric_limits<input_type>::min(),
        std::numeric_limits<input_type>::max(),
        std::forward<Rows>(rows));
}

/// @brief Paired inner product over a sorted point list.
/// @details Same order and sortedness rule as `eval_sequence`. Each point
///          pairs with `rows[i]`.
template <std::size_t I = 0, std::size_t... Is, typename DpfKey,
          typename ForwardIterator, typename Rows>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(const DpfKey & dpf, ForwardIterator begin,
    ForwardIterator end, Rows && rows)
{
    if (!std::is_sorted(begin, end))
        throw std::runtime_error("list must be sorted");
    std::vector<typename DpfKey::input_type> xs(begin, end);
    return internal_paired::run_points<I, Is...>(dpf, xs,
        std::forward<Rows>(rows));
}

/// @brief Paired inner product over a `sequence_recipe`'s points.
/// @details `points` is the same sorted list the recipe was built from. The
///          recipe drives nothing the path walk does not already share; it
///          checks that the list still matches the recipe's output count.
template <std::size_t I = 0, std::size_t... Is, typename DpfKey,
          typename ForwardIterator, typename Rows>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(const DpfKey & dpf,
    const sequence_recipe & recipe, ForwardIterator begin, ForwardIterator end,
    Rows && rows)
{
    const auto n = static_cast<std::size_t>(std::distance(begin, end));
    if (n != recipe.output_indices().size())
        throw std::invalid_argument(
            "eval_sequence_inner_product: recipe and point list differ");
    return eval_sequence_inner_product<I, Is...>(dpf, begin, end,
        std::forward<Rows>(rows));
}

namespace detail_columns
{

template <std::size_t I, typename DpfKey, typename ForwardIterator,
          typename Weights, typename Proj, typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto run(const DpfKey & dpf, ForwardIterator begin, ForwardIterator end,
    Weights && weights, Proj && proj, Sink && sink, bool require_sorted)
{
    using pack = std::decay_t<Weights>;
    static_assert(detail_ip::is_column_pack<pack>::value,
        "columns weights are a std::tuple or std::array of streams; "
        "each stream is w[i] or w(i)");
    if (require_sorted && !std::is_sorted(begin, end))
        throw std::runtime_error("list must be sorted");
    std::vector<typename DpfKey::input_type> xs(begin, end);
    constexpr auto n = std::tuple_size<pack>::value;
    return detail_ip::accumulate_columns<I>(dpf, xs.begin(), xs.end(),
        std::forward<Weights>(weights), std::forward<Proj>(proj),
        std::forward<Sink>(sink), std::make_index_sequence<n>{});
}

template <std::size_t I, typename DpfKey, typename InputT, typename Weights,
          std::size_t... Ks>
auto interval_dots(const DpfKey & dpf, InputT from, InputT to, Weights && weights,
    std::index_sequence<Ks...>)
{
    constexpr auto to_int = utils::to_integral_type<InputT>{};
    using integral = decltype(to_int(from));
    const bool wraps = utils::interval_wraps(
        static_cast<integral>(to_int(from)),
        static_cast<integral>(to_int(to)),
        utils::bitlength_of_v<InputT>);
    if (wraps)
    {
        std::vector<InputT> xs;
        internal_paired::for_inclusive(from, to, [&](InputT x) { xs.push_back(x); });
        return detail_ip::accumulate_columns<I>(dpf, xs.begin(), xs.end(),
            std::forward<Weights>(weights), identity_project{},
            noop_also{}, std::index_sequence<Ks...>{});
    }
    const auto npoints = static_cast<std::size_t>(
        static_cast<integral>(to_int(to)) - static_cast<integral>(to_int(from)))
        + std::size_t{1};
    auto guard = [&](auto which)
    {
        constexpr std::size_t k = decltype(which)::value;
        detail_ip_check::require_weight_count(std::get<k>(weights), npoints,
            "column weights are shorter than the interval");
    };
    (guard(std::integral_constant<std::size_t, Ks>{}), ...);
    auto buf = make_output_buffer_for_interval<I>(dpf, from, to);
    auto iter = eval_interval<I>(dpf, from, to, buf);
    using acc_tuple = std::tuple<decltype(detail_ip::column_accum_type<I, Ks,
        DpfKey, std::decay_t<Weights>, identity_project>())...>;
    acc_tuple acc{};
    std::size_t i = 0;
    for (auto it = iter.begin(); it != iter.end(); ++it, ++i)
    {
        auto y = *it;
        ((std::get<Ks>(acc) = std::get<Ks>(acc)
            + (y * detail_ip::weight_at(std::get<Ks>(weights), i))), ...);
    }
    return acc;
}

}  // namespace detail_columns

/// @brief Several independent dots of one output, one walk (transposed form).
/// @details `weights` is a `std::tuple` or `std::array` of streams. Stream
///          `k` is either `w[i]` or `w(i)`, `i` the position in the point
///          list (or in the interval, wrapping the same way as
///          `eval_interval`). The result is a tuple of accumulators,
///          `acc_k = sum_i project(DPF(x_i)) * stream_k(i)`.
///          `dpf::project(fn)` maps the share first. `dpf::also(fn)` is
///          called as `fn(i, x, share)` on the unmapped share. Pass either
///          tag, both, or neither. `also` then `project` is accepted too.
///          Sequence points are sorted nondecreasing, same as `eval_sequence`.
///          Relative to `paired`: same path walk for one output, but streams
///          stay separate (no cross-stream sum). One stream equals `paired`
///          on that stream. Relative to the batched leaf walk: path-per-point
///          instead of batched exterior AES; same opened scalar when the
///          single stream matches the interval weight layout.
/// \complexity One path ensure and one exterior expand per input, plus
///          O(stream count) multiply-adds per input. Accumulators are O(stream count).
/// @{

template <std::size_t I = 0, typename DpfKey, typename ForwardIterator,
          typename Weights>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(columns_t, const DpfKey & dpf,
    ForwardIterator begin, ForwardIterator end, Weights && weights)
{
    assert_not_wildcard_output<I>(dpf);
    return detail_columns::run<I>(dpf, begin, end,
        std::forward<Weights>(weights), identity_project{}, noop_also{}, true);
}

template <std::size_t I = 0, typename DpfKey, typename ForwardIterator,
          typename Weights, typename Proj>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(columns_t, const DpfKey & dpf,
    ForwardIterator begin, ForwardIterator end, Weights && weights,
    project_fn<Proj> proj)
{
    assert_not_wildcard_output<I>(dpf);
    return detail_columns::run<I>(dpf, begin, end,
        std::forward<Weights>(weights), proj.fn, noop_also{}, true);
}

template <std::size_t I = 0, typename DpfKey, typename ForwardIterator,
          typename Weights, typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(columns_t, const DpfKey & dpf,
    ForwardIterator begin, ForwardIterator end, Weights && weights,
    also_fn<Sink> sink)
{
    assert_not_wildcard_output<I>(dpf);
    return detail_columns::run<I>(dpf, begin, end,
        std::forward<Weights>(weights), identity_project{}, sink.fn, true);
}

template <std::size_t I = 0, typename DpfKey, typename ForwardIterator,
          typename Weights, typename Proj, typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(columns_t, const DpfKey & dpf,
    ForwardIterator begin, ForwardIterator end, Weights && weights,
    project_fn<Proj> proj, also_fn<Sink> sink)
{
    assert_not_wildcard_output<I>(dpf);
    return detail_columns::run<I>(dpf, begin, end,
        std::forward<Weights>(weights), proj.fn, sink.fn, true);
}

template <std::size_t I = 0, typename DpfKey, typename ForwardIterator,
          typename Weights, typename Proj, typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(columns_t, const DpfKey & dpf,
    ForwardIterator begin, ForwardIterator end, Weights && weights,
    also_fn<Sink> sink, project_fn<Proj> proj)
{
    return eval_sequence_inner_product<I>(columns, dpf, begin, end,
        std::forward<Weights>(weights), std::move(proj), std::move(sink));
}

template <std::size_t I = 0, typename DpfKey, typename ForwardIterator,
          typename Weights, typename... Extra>
HEDLEY_WARN_UNUSED_RESULT
auto eval_sequence_inner_product(columns_t, const DpfKey & dpf,
    const sequence_recipe & recipe, ForwardIterator begin,
    ForwardIterator end, Weights && weights, Extra && ... extra)
{
    const auto n = static_cast<std::size_t>(std::distance(begin, end));
    if (n != recipe.output_indices().size())
        throw std::invalid_argument(
            "eval_sequence_inner_product: recipe and point list differ");
    return eval_sequence_inner_product<I>(columns, dpf, begin, end,
        std::forward<Weights>(weights), std::forward<Extra>(extra)...);
}

/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0, typename DpfKey, typename InputT, typename Weights,
          typename Proj, typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(columns_t, const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, Proj && proj, Sink && sink)
{
    assert_not_wildcard_output<I>(dpf);
    if constexpr (std::is_same_v<std::decay_t<Proj>, identity_project>
        && std::is_same_v<std::decay_t<Sink>, noop_also>
        && !is_multilevel_key_v<DpfKey>)
    {
        using pack = std::decay_t<Weights>;
        static_assert(detail_ip::is_column_pack<pack>::value,
            "columns weights are a std::tuple or std::array of streams; "
            "each stream is w[i] or w(i)");
        constexpr auto nstreams = std::tuple_size<pack>::value;
        return detail_columns::interval_dots<I>(dpf, from, to,
            std::forward<Weights>(weights), std::make_index_sequence<nstreams>{});
    }
    else
    {
        std::vector<InputT> xs;
        internal_paired::for_inclusive(from, to, [&](InputT x) { xs.push_back(x); });
        return detail_columns::run<I>(dpf, xs.begin(), xs.end(),
            std::forward<Weights>(weights), std::forward<Proj>(proj),
            std::forward<Sink>(sink), false);
    }
}

/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0, typename DpfKey, typename InputT, typename Weights>
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(columns_t, const DpfKey & dpf, InputT from, InputT to,
    Weights && weights)
{
    return eval_inner_product<I>(columns, dpf, from, to,
        std::forward<Weights>(weights), identity_project{}, noop_also{});
}

/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0, typename DpfKey, typename InputT, typename Weights,
          typename Proj>
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(columns_t, const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, project_fn<Proj> proj)
{
    return eval_inner_product<I>(columns, dpf, from, to,
        std::forward<Weights>(weights), proj.fn, noop_also{});
}

/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0, typename DpfKey, typename InputT, typename Weights,
          typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(columns_t, const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, also_fn<Sink> sink)
{
    return eval_inner_product<I>(columns, dpf, from, to,
        std::forward<Weights>(weights), identity_project{}, sink.fn);
}

/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0, typename DpfKey, typename InputT, typename Weights,
          typename Proj, typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(columns_t, const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, project_fn<Proj> proj, also_fn<Sink> sink)
{
    return eval_inner_product<I>(columns, dpf, from, to,
        std::forward<Weights>(weights), proj.fn, sink.fn);
}

/// \complexity Same interior expansion as `eval_interval` on `[from, to]` (Θ(L) nodes, L = leaf nodes in the interval) plus a multiply-add per output slot into an O(1) accumulator. The basic memoizer still holds O(L) nodes.
template <std::size_t I = 0, typename DpfKey, typename InputT, typename Weights,
          typename Proj, typename Sink>
HEDLEY_WARN_UNUSED_RESULT
auto eval_inner_product(columns_t, const DpfKey & dpf, InputT from, InputT to,
    Weights && weights, also_fn<Sink> sink, project_fn<Proj> proj)
{
    return eval_inner_product<I>(columns, dpf, from, to,
        std::forward<Weights>(weights), proj.fn, sink.fn);
}

/// @brief `columns` over the whole input domain. Same streams as the interval form.
template <std::size_t I = 0, typename DpfKey, typename Weights, typename... Extra>
HEDLEY_WARN_UNUSED_RESULT
auto eval_full_inner_product(columns_t, const DpfKey & dpf, Weights && weights,
    Extra && ... extra)
{
    using input_type = typename DpfKey::input_type;
    return eval_inner_product<I>(columns, dpf,
        std::numeric_limits<input_type>::min(),
        std::numeric_limits<input_type>::max(),
        std::forward<Weights>(weights), std::forward<Extra>(extra)...);
}

/// @}

}  // namespace dpf

#endif  // LIBDPF_INCLUDE_DPF_EVAL_INNER_PRODUCT_HPP__