//! Calibration for [`wai::jpegai_pipeline::efl_chroma`] — the EFE linear per-plane
//! cross-component filter. Runs on the reference fixture (luma + each chroma plane with
//! its `w_uv`/`w_y`/`mean`) and compares against the reference filtered plane. Float
//! arithmetic ⇒ machine-precision match expected.
//!
//! env: WAI_JPEGAI_EFL_FIXTURE = efl_fixture.json
//!   { y:[f64], planes:[ { x:[f64], want:[f64], w_uv, w_y, mean } ] }
#![cfg(feature = "neural_int")]

use wai::jpegai_pipeline::efl_chroma;

#[test]
fn efl_chroma_matches_reference() {
    let path = match std::env::var("WAI_JPEGAI_EFL_FIXTURE") {
        Ok(p) if std::path::Path::new(&p).exists() => p,
        _ => { eprintln!("skip: set WAI_JPEGAI_EFL_FIXTURE to efl_fixture.json"); return; }
    };
    let v: serde_json::Value = serde_json::from_slice(&std::fs::read(&path).unwrap()).unwrap();
    let f32arr = |a: &serde_json::Value| a.as_array().unwrap().iter().map(|x| x.as_f64().unwrap() as f32).collect::<Vec<f32>>();
    let y = f32arr(&v["y"]);

    for (pi, p) in v["planes"].as_array().unwrap().iter().enumerate() {
        let x = f32arr(&p["x"]);
        let want = f32arr(&p["want"]);
        let (w_uv, w_y, mean) = (p["w_uv"].as_f64().unwrap() as f32, p["w_y"].as_f64().unwrap() as f32, p["mean"].as_f64().unwrap() as f32);
        let got = efl_chroma(&x, &y, w_uv, w_y, mean);
        assert_eq!(got.len(), want.len(), "plane {pi}: size mismatch");
        let mut maxd = 0f32;
        for i in 0..got.len() { maxd = maxd.max((got[i] - want[i]).abs()); }
        eprintln!("efl_chroma plane {pi} vs reference: max|Δ| = {maxd:.6}");
        assert!(maxd < 1e-2, "plane {pi}: EFL diverged (max|Δ| = {maxd})");
    }
}