//! Calibration for [`wai::jpegai_pipeline::efn_filter`] — the EFE luma-aided adaptive
//! nonlinear chroma post-filter. Runs on the reference fixture (luma `y`, input chroma
//! `c_in`, the 8 Q15 tile weights, luma min/max) and compares the filtered plane to the
//! reference output `c_want`. Float arithmetic ⇒ machine-precision match expected.
//!
//! env: WAI_JPEGAI_EFN_FIXTURE = efn_fixture.json
//!   { y:[f64], c_in:[f64], c_want:[f64], weights_q15:[i64], luma_min_q, luma_max_q }
#![cfg(feature = "neural_int")]

use wai::jpegai_pipeline::efn_filter;

#[test]
fn efn_filter_matches_reference() {
    let path = match std::env::var("WAI_JPEGAI_EFN_FIXTURE") {
        Ok(p) if std::path::Path::new(&p).exists() => p,
        _ => { eprintln!("skip: set WAI_JPEGAI_EFN_FIXTURE to efn_fixture.json"); return; }
    };
    let v: serde_json::Value = serde_json::from_slice(&std::fs::read(&path).unwrap()).unwrap();
    let f32v = |k: &str| v[k].as_array().unwrap().iter().map(|x| x.as_f64().unwrap() as f32).collect::<Vec<f32>>();
    let y = f32v("y");
    let c_in = f32v("c_in");
    let c_want = f32v("c_want");
    let weights: Vec<i64> = v["weights_q15"].as_array().unwrap().iter().map(|x| x.as_i64().unwrap()).collect();
    let (lmin, lmax) = (v["luma_min_q"].as_i64().unwrap(), v["luma_max_q"].as_i64().unwrap());

    let got = efn_filter(&y, &c_in, &weights, lmin, lmax);
    assert_eq!(got.len(), c_want.len(), "output size mismatch");

    let mut maxd = 0f32;
    let mut mse = 0f64;
    for i in 0..got.len() {
        let d = (got[i] - c_want[i]).abs();
        maxd = maxd.max(d);
        mse += (d as f64) * (d as f64);
    }
    mse /= got.len() as f64;
    let sig: f64 = c_want.iter().map(|&x| (x as f64) * (x as f64)).sum::<f64>() / got.len() as f64;
    let snr = if mse > 0.0 { 10.0 * (sig / mse).log10() } else { f64::INFINITY };
    eprintln!("efn_filter vs reference: max|Δ| = {maxd:.6}   SNR = {snr:.2} dB");
    assert!(maxd < 1e-2, "EFN filter diverged from the reference (max|Δ| = {maxd})");
}