// Replay tests — the real tracker and registry driven from committed fixtures.
//
// TRACES: AR-004, AR-012, AR-013 | VR-001, VR-002 | IT-001
//
// Tier T2: composition, not units. The registry tests construct awkward states
// directly; these check that the pieces behave when wired together and fed real
// footage — 480x360 public-domain clips at 5 fps, with the cuts, gaps and
// crowded frames that actual film produces and synthetic input does not.
//
// No GPU and no model: the fixtures are HDF5 dumps taken after embedding, so
// everything here is CPU maths. That is what lets this run on the CI host at
// all (see docs/requirements.md, "CI never calls a model").
//
// Driving the node functors directly rather than through a KPN network is
// deliberate: functors are plain objects, so there are no threads, no channels
// and no scheduling — the same input gives the same output every time, which is
// exactly what a fixture-based test needs.
#include <catch2/catch_test_macros.hpp>

#include "config.hpp"
#include "evidence_discount.hpp"
#include "nodes/face_tracker_node.hpp"
#include "track_registry.hpp"
#include "types.hpp"

#include <H5Cpp.h>

#include <algorithm>
#include <cmath>
#include <memory>
#include <string>
#include <vector>

namespace {

// ── Fixture reader ───────────────────────────────────────────────────────────
// The flat/ragged layout of scripts/optimizer/SCHEMA.md: per-face arrays
// concatenated, with a per-frame index table pointing into them.
struct Dump {
    std::vector<double>    ts;
    std::vector<uint8_t>   is_cut;
    std::vector<int64_t>   face_offset;
    std::vector<int32_t>   face_count;
    std::vector<Embedding> emb;
    std::vector<float>     bbox;      // 4 per face
    std::string            embedder;

    std::size_t frames() const { return ts.size(); }
    std::size_t faces()  const { return emb.size(); }
};

template <typename T>
std::vector<T> read1d(H5::Group& g, const char* name, const H5::DataType& dt) {
    H5::DataSet ds = g.openDataSet(name);
    hsize_t n = 0;
    ds.getSpace().getSimpleExtentDims(&n, nullptr);
    std::vector<T> out(n);
    if (n) ds.read(out.data(), dt);
    return out;
}

Dump load(const std::string& path) {
    H5::H5File f(path, H5F_ACC_RDONLY);
    H5::Group frames = f.openGroup("frames");
    H5::Group faces  = f.openGroup("faces");

    Dump d;
    d.ts          = read1d<double>(frames, "timestamp_sec", H5::PredType::NATIVE_DOUBLE);
    d.is_cut      = read1d<uint8_t>(frames, "is_cut",       H5::PredType::NATIVE_UINT8);
    d.face_offset = read1d<int64_t>(frames, "face_offset",  H5::PredType::NATIVE_INT64);
    d.face_count  = read1d<int32_t>(frames, "face_count",   H5::PredType::NATIVE_INT32);

    H5::DataSet e = faces.openDataSet("embedding");
    hsize_t dims[2]{0, 0};
    e.getSpace().getSimpleExtentDims(dims, nullptr);
    std::vector<float> flat(dims[0] * dims[1]);
    if (!flat.empty()) e.read(flat.data(), H5::PredType::NATIVE_FLOAT);
    d.emb.resize(dims[0]);
    for (hsize_t i = 0; i < dims[0]; ++i)
        std::copy_n(flat.begin() + i * dims[1], 512, d.emb[i].begin());

    // bbox is 2-D [N,4]; reading it with the 1-D helper would size the buffer
    // from the first extent only and then read four times that many floats.
    {
        H5::DataSet bs = faces.openDataSet("bbox");
        hsize_t bd[2]{0, 0};
        bs.getSpace().getSimpleExtentDims(bd, nullptr);
        d.bbox.resize(bd[0] * bd[1]);
        if (!d.bbox.empty()) bs.read(d.bbox.data(), H5::PredType::NATIVE_FLOAT);
    }

    // GR-004: the dump records which embedder produced it, so a replay cannot
    // be silently scored against a gallery from a different model.
    if (f.attrExists("embedder_model")) {
        // Written as a variable-length string (embedding_dump_node.hpp:99), so
        // the read must name the same type explicitly.
        H5::StrType vlen(H5::PredType::C_S1, H5T_VARIABLE);
        f.openAttribute("embedder_model").read(vlen, d.embedder);
    }
    return d;
}

std::string fixture(const char* name) {
    return std::string(SAE_TEST_FIXTURES_DIR) + "/dumps/" + name;
}

// ── Harness ──────────────────────────────────────────────────────────────────
struct Replay {
    std::vector<DeadTrack> claims;
    std::vector<int>       track_ids;   // per face, in fixture order
    std::size_t            faces_seen{0};
};

Replay run(const Dump& d, double extinction = 10.0) {
    Replay r;
    TrackRegistry::Config rc;
    rc.extinction_sec = extinction;

    auto cal = [](float cos) { return std::max(0.f, cos); };
    auto reg = std::make_shared<TrackRegistry>(rc, EvidenceDiscounter(cal));
    reg->on_track_dead([&r](const DeadTrack& t) { r.claims.push_back(t); });

    Config cfg;
    cfg.track_assoc_min_prob = 0.5f;
    FaceTrackerFunc ft(cfg, reg, cal);

    for (std::size_t i = 0; i < d.frames(); ++i) {
        EmbeddedSceneFrame ef;
        ef.source.timestamp_sec = d.ts[i];
        ef.source.is_cut        = d.is_cut[i] != 0;

        const int64_t off = d.face_offset[i];
        const int32_t n   = d.face_count[i];
        for (int32_t k = 0; k < n; ++k) {
            DetectedFace face;
            const float* b = &d.bbox[(off + k) * 4];
            face.bbox = cv::Rect2f(b[0], b[1], b[2], b[3]);
            face.confidence = 1.0f;
            ef.faces.push_back(face);
            ef.crops.push_back(cv::Mat());
            ef.embeddings.push_back(d.emb[off + k]);
        }
        r.faces_seen += static_cast<std::size_t>(n);

        auto out = ft(std::move(ef));
        for (int id : out.track_ids) r.track_ids.push_back(id);
    }

    reg->flush(d.ts.empty() ? 0.0 : d.ts.back());
    return r;
}

}  // namespace

// ── AR-004 / VR-001 — the fixtures are intact and self-describing ────────────
TEST_CASE("fixtures are complete and carry their embedder identity",
          "[replay][AR-004][VR-001]") {
    // Frame counts are exact rather than approximate. Before node outputs
    // blocked on a full channel, generation lost most of a clip and what it
    // lost depended on timing — these numbers could not have been asserted.
    struct Expect { const char* file; std::size_t frames, faces; };
    const Expect all[] = {
        {"bali_13.h5", 385, 693},
        {"bali_27.h5", 335, 335},
        {"bali_28.h5", 345, 368},
        {"bali_31.h5", 145, 203},
        {"bali_46.h5", 385, 140},
    };

    for (const auto& x : all) {
        INFO(x.file);
        Dump d = load(fixture(x.file));
        CHECK(d.frames() == x.frames);
        CHECK(d.faces()  == x.faces);
        CHECK(d.embedder == "LVFace-B_Glint360K.onnx");

        // face_offset must be contiguous: a gap means faces went missing
        // between frames, which no consumer could detect.
        int64_t running = 0;
        for (std::size_t i = 0; i < d.frames(); ++i) {
            REQUIRE(d.face_offset[i] == running);
            running += d.face_count[i];
        }
        CHECK(static_cast<std::size_t>(running) == d.faces());
    }
}

// ── VR-002 — replay is deterministic ─────────────────────────────────────────
TEST_CASE("replaying a fixture twice gives identical tracks", "[replay][VR-002]") {
    // The property the whole fixture strategy rests on. If this fails, every
    // golden output derived from a fixture is unreliable and the CI replay
    // tier is worthless.
    Dump d = load(fixture("bali_28.h5"));
    Replay a = run(d);
    Replay b = run(d);

    REQUIRE(a.track_ids.size() == b.track_ids.size());
    CHECK(a.track_ids == b.track_ids);
    REQUIRE(a.claims.size() == b.claims.size());
    for (std::size_t i = 0; i < a.claims.size(); ++i) {
        CHECK(a.claims[i].first_seen == b.claims[i].first_seen);
        CHECK(a.claims[i].last_seen  == b.claims[i].last_seen);
    }
}

// ── AR-012 / AR-013 — window invariants on real footage ──────────────────────
TEST_CASE("every face is assigned a track and every track closes",
          "[replay][AR-012]") {
    Dump d = load(fixture("bali_13.h5"));
    Replay r = run(d);

    CHECK(r.track_ids.size() == r.faces_seen);
    for (int id : r.track_ids) CHECK(id >= 0);       // nothing silently unassigned

    // flush() must leave nothing behind: a track still open at EOF would be a
    // window that never reaches the output.
    CHECK(r.claims.size() > 0);
}

TEST_CASE("windows are well-formed and inside the clip", "[replay][AR-013]") {
    for (const char* f : {"bali_13.h5", "bali_27.h5", "bali_28.h5",
                          "bali_31.h5", "bali_46.h5"}) {
        INFO(f);
        Dump d = load(fixture(f));
        Replay r = run(d);
        const double t0 = d.ts.front(), t1 = d.ts.back();

        for (const auto& c : r.claims) {
            // A window ends at the last sighting, never after it — so it can
            // never extend past the footage that produced it.
            CHECK(c.first_seen <= c.last_seen);
            CHECK(c.first_seen >= t0);
            CHECK(c.last_seen  <= t1);
        }
    }
}

TEST_CASE("a longer extinction window yields fewer, longer tracks",
          "[replay][AR-013]") {
    // The timeout decides whether a gap is absorbed into one window or splits
    // it in two, so lengthening it must merge tracks rather than multiply them.
    // On sparse footage this is the difference the constant actually makes.
    Dump d = load(fixture("bali_46.h5"));   // 140 faces over 385 frames
    Replay tight = run(d, /*extinction=*/1.0);
    Replay loose = run(d, /*extinction=*/30.0);

    CHECK(loose.claims.size() <= tight.claims.size());
}

// ── AR-007 — cuts are exercised by the corpus, not just by construction ──────
TEST_CASE("the cut-heavy fixture actually contains cuts", "[replay][AR-007]") {
    // Guards the corpus rather than the code: if a regeneration produced a
    // fixture with no cuts, the association tests above would still pass while
    // silently testing nothing about viewpoint changes.
    Dump d = load(fixture("bali_28.h5"));
    const int cuts = std::count(d.is_cut.begin(), d.is_cut.end(), uint8_t{1});
    CHECK(cuts >= 5);
}