//! Phase 13.8 — slice 2d smoke test.
//!
//! Loads the real `emotion2vec/emotion2vec_plus_base` pickle from HF Hub,
//! runs a synthetic-audio forward pass, and dumps the 9 raw class logits
//! plus the argmax label. This is the first real-weight integration of
//! the candle port: every key in the upstream state dict must map to a
//! candle param of matching shape, or `load_from_pickle` will fail.
//!
//! Usage:
//! ```bash
//! cargo run -p rtx-csm --release --features metal --example emotion2vec_smoke
//! ```

use anyhow::{Context, Result};
use candle_core::{Device, Tensor};
use hf_hub::api::sync::Api;
use rtx_csm::emotion2vec::{Classifier, Emotion2Vec};

const REPO: &str = "emotion2vec/emotion2vec_plus_base";
const PT_FILE: &str = "model.pt";

fn main() -> Result<()> {
    tracing_subscriber::fmt().init();
    let device = if candle_core::utils::metal_is_available() {
        Device::new_metal(0)?
    } else {
        Device::Cpu
    };
    eprintln!("device: {device:?}");

    // Resolve the cached .pt (or download on first run).
    let api = Api::new().context("hf_hub init")?;
    let path = api
        .model(REPO.to_string())
        .get(PT_FILE)
        .with_context(|| format!("download {PT_FILE} from {REPO}"))?;
    eprintln!("pickle: {}", path.display());

    // Load the candle port from the real pickle.
    let load_t = std::time::Instant::now();
    let model = Emotion2Vec::load_from_pickle(&path, &device)?;
    eprintln!("loaded model in {:.2}s", load_t.elapsed().as_secs_f32());

    // Synthesize 2 seconds of low-amplitude noise as a smoke input.
    // (Real evaluation in slice 3 will use a known-emotion clip.)
    let audio = Tensor::randn(0f32, 0.05, (1, 1, 32_000), &device)?;

    let fwd_t = std::time::Instant::now();
    let logits = model.forward(&audio)?;
    eprintln!("forward in {:.0} ms", fwd_t.elapsed().as_millis());

    let logits_vec = logits.flatten_all()?.to_vec1::<f32>()?;
    let labels = [
        "angry",
        "disgusted",
        "fearful",
        "happy",
        "neutral",
        "other",
        "sad",
        "surprised",
        "<unk>",
    ];
    println!();
    println!("=== logits (raw, before softmax) ===");
    for (i, l) in logits_vec.iter().enumerate() {
        println!("  {}: {:>9.4}  ({})", i, l, labels[i]);
    }

    // Argmax → emotion label.
    let argmax = logits_vec
        .iter()
        .enumerate()
        .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
        .map(|(i, _)| i)
        .unwrap_or(0);
    let bucket = Classifier::tag_for_class(argmax as u32);
    println!();
    println!(
        "argmax: class {argmax} ({}) → 5-bucket label {}",
        labels[argmax],
        bucket.as_tag()
    );

    Ok(())
}