// wai-conformance: int-kernels
//! `wai.splat.int_codec` — **splat-equivalence**: a LEARNED Gaussian-splat attribute
//! codec whose integer synthesis decodes a factorized-rANS latent to the `[14,S,S]`
//! attribute tensor byte-identically on every machine, so the reconstructed cloud's
//! hash is portable — the int_hyper of 3D. Committed tiny vector under
//! `tests/vectors/int_splat/`; never skips.
#![cfg(feature = "splat_int")]

use std::path::PathBuf;

use wai::int_hyper_synth::IntModel;
use wai::int_splat::decode;

fn dir() -> PathBuf {
    PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("tests/vectors/int_splat")
}

#[test]
fn decodes_the_learned_splat_cloud_attribute_for_attribute() {
    // The committed integer model (WHM1) + factorized bitstream decode — no float, no
    // ML runtime — to the SAME [14,S,S] attribute tensor the Python integer reference
    // produced. A single disagreeing attribute would diverge this FNV-1a.
    let gs = IntModel::from_bytes(&std::fs::read(dir().join("splat_gs.bin")).unwrap()).expect("WHM1 g_s");
    let text = std::fs::read(dir().join("splat.json")).unwrap();
    let bs: serde_json::Value = serde_json::from_slice(&text).unwrap();

    let clip = decode(&gs, &text).expect("decode");
    assert_eq!(clip.n_attr, bs["attr"].as_u64().unwrap() as usize);
    assert_eq!(clip.attr.len(), clip.n_attr * clip.s * clip.s, "attribute tensor size");
    assert_eq!(clip.s * clip.s, bs["n_gaussians"].as_u64().unwrap() as usize, "N gaussians = S²");

    let got = format!("{:016x}", clip.attr_hash());
    assert_eq!(got, bs["attr_hash"].as_str().unwrap(), "attributes must reproduce their committed hash");
    println!("int_splat: {} gaussians × {} attrs -> attribute-exact, hash {got}", clip.s * clip.s, clip.n_attr);
}