//! End-to-end LoRA fine-tuning step demo on real CSM-1B.
//!
//! Pipeline:
//!   1. Load CSM-1B (FP)
//!   2. Inject LoRA adapters on backbone q_proj/v_proj (StyleSpeech recipe)
//!   3. Mimi-encode reference audio → target codebook tokens
//!   4. Tokenize transcript
//!   5. Repeated forward_loss → backward → AdamW step → refresh_lora
//!   6. Verify loss decreases
//!
//! This proves the LoRA fine-tuning path works end-to-end. The next step is
//! to scale up: paired-data loader (multiple `(transcript, wav)` pairs),
//! multi-epoch driver, checkpoint export, and a generation script that loads
//! the trained adapter.
//!
//! Usage:
//!   cargo run -p rtx-csm --release --example lora_finetune_step -- \
//!       --wav /tmp/csm/hello.wav --text "Hello from Rust." --steps 30

use anyhow::Result;
use candle_nn::{AdamW, Optimizer, ParamsAdamW, VarMap};
use clap::Parser;
use rtx_csm::lora::LoraConfig;
use rtx_csm::{audio_io, Generator, Segment};
use std::path::PathBuf;

#[derive(Debug, Parser)]
struct Cli {
    #[arg(long)]
    wav: PathBuf,
    #[arg(long)]
    text: String,
    #[arg(long, default_value_t = 0)]
    speaker: u32,
    #[arg(long, default_value_t = 30)]
    steps: usize,
    #[arg(long, default_value_t = 5e-4)]
    lr: f64,
    #[arg(long, default_value_t = 8)]
    rank: usize,
    #[arg(long, default_value_t = 16.0)]
    alpha: f32,
    #[arg(long)]
    cpu: bool,
    /// Use Phase 12.1 extended coverage (q+k+v+output_proj + MLP) instead of
    /// q+v only. Useful for sanity-checking the wider adapter set on a single
    /// utterance before launching a real training run.
    #[arg(long, default_value_t = false)]
    extended_lora: bool,
}

fn main() -> Result<()> {
    tracing_subscriber::fmt().init();
    let cli = Cli::parse();

    let device = if cli.cpu {
        candle_core::Device::Cpu
    } else {
        Generator::default_device()?
    };
    println!("device: {device:?}");

    let mut generator = Generator::load_csm_1b(&device)?;

    // Audio side: encode reference WAV through Mimi.
    let audio = audio_io::load_mono_24k(&cli.wav)?;
    let codes = generator.mimi.encode(&audio)?;
    let (_b, _cb, t_frames) = codes.dims3()?;
    if t_frames < 1 {
        anyhow::bail!("audio too short; need at least one Mimi frame");
    }
    let target_codes = codes
        .narrow(2, 0, 1)?
        .squeeze(2)?
        .flatten_all()?
        .to_vec1::<u32>()?;
    println!("target codes (frame 0): {} tokens", target_codes.len());

    // Build prompt.
    let current = Segment::new_text(cli.speaker, &cli.text);
    let prompt = rtx_csm::prompt::build_prompt(
        &[],
        &current,
        &generator.model,
        &mut generator.mimi,
        &generator.tokenizer,
    )?;

    // Inject LoRA adapters into the backbone. `--extended-lora` opts into
    // Phase 12.1 coverage (q+k+v+output_proj + MLP).
    let base = if cli.extended_lora {
        LoraConfig::extended()
    } else {
        LoraConfig::default()
    };
    let lora_cfg = LoraConfig {
        rank: cli.rank,
        alpha: cli.alpha,
        ..base
    };
    let vm = VarMap::new();
    generator.model.inner.add_lora_to_backbone(&lora_cfg, &vm)?;
    let n_params: usize = vm
        .all_vars()
        .iter()
        .map(|v| v.shape().elem_count())
        .sum();
    println!(
        "LoRA: {} trainable params across {} adapter Vars (rank={} alpha={})",
        n_params,
        vm.all_vars().len(),
        cli.rank,
        cli.alpha,
    );

    // Direct sanity: take an actual LoRA Var that was injected into the model,
    // compute a trivial loss involving it, see if backward populates its gradient.
    {
        let some_var = &vm.all_vars()[0];
        let direct_loss = some_var.as_tensor().sum_all()?;
        let g = direct_loss.backward()?;
        println!(
            "DIRECT-on-injected-Var: {} grad entries; var has grad: {}",
            g.get_ids().count(),
            g.get(some_var).is_some(),
        );
    }

    // Sanity: isolated forward through a single LoRA delta, verify backward gives gradients.
    {
        use candle_core::{DType, Tensor};
        use candle_nn::VarMap;
        let test_vm = VarMap::new();
        // Note: LoraDelta::new init B=0, so initially the chain produces 0.
        // We need to force non-zero B for A to have nonzero gradient OR rely on
        // B getting the only grad. Either way, at least one Var should appear
        // in the GradStore after backward.
        let test_lora =
            rtx_csm::lora::LoraDelta::new(8, 16.0, 64, 64, "test", &test_vm, &device, DType::F32)?;
        let xs = Tensor::randn(0.0f32, 1.0, (4, 64), &device)?;
        let target = Tensor::randn(0.0f32, 1.0, (4, 64), &device)?;
        let pred = test_lora.forward(&xs)?;
        let diff = (pred - &target)?;
        let loss = diff.sqr()?.mean_all()?;
        let g = loss.backward()?;
        let n_with_grads = test_vm
            .all_vars()
            .iter()
            .filter(|v| g.get(v).is_some())
            .count();
        println!(
            "ISOLATED LoraDelta: {}/{} Vars have grads (loss={:.4})",
            n_with_grads,
            test_vm.all_vars().len(),
            loss.to_scalar::<f32>()?,
        );
    }

    // Initial loss (sanity check — should match the un-adapted forward_loss
    // since B is zero-initialized → adapter is a no-op at step 0).
    generator.model.clear_kv_cache();
    let init_loss_tensor = generator
        .model
        .inner
        .forward_loss(&prompt.tokens, &prompt.mask, 0, &target_codes)?;
    let init_loss = init_loss_tensor.to_scalar::<f32>()?;
    println!("\ninitial loss (LoRA-init zero): {init_loss:.4}");
    println!(
        "loss tensor: track_op={} is_variable={}",
        init_loss_tensor.track_op(),
        init_loss_tensor.is_variable()
    );

    // Optimizer.
    let mut optim = AdamW::new(
        vm.all_vars(),
        ParamsAdamW {
            lr: cli.lr,
            ..ParamsAdamW::default()
        },
    )?;

    let mut last_loss = init_loss;
    for step in 0..cli.steps {
        generator.model.clear_kv_cache();
        let loss = generator.model.inner.forward_loss(
            &prompt.tokens,
            &prompt.mask,
            0,
            &target_codes,
        )?;
        let grads = loss.backward()?;
        if step == 0 {
            let n_grads_present = vm
                .all_vars()
                .iter()
                .filter(|v| grads.get(v).is_some())
                .count();
            let n_total = vm.all_vars().len();
            // How many gradient entries does the GradStore have TOTAL?
            let total_grad_entries = grads.get_ids().count();
            println!(
                "DEBUG step 0: {n_grads_present}/{n_total} LoRA Vars have gradients (total grad entries: {total_grad_entries})"
            );
            // is_variable check on LoRA Vars
            let n_var_flagged = vm
                .all_vars()
                .iter()
                .filter(|v| v.as_tensor().is_variable())
                .count();
            println!("  LoRA Vars with is_variable=true: {n_var_flagged}/{n_total}");
            // Print first few var tensor ids vs first few grad ids
            print!("  LoRA Var ids (first 4): ");
            for v in vm.all_vars().iter().take(4) {
                print!("{:?} ", v.as_tensor().id());
            }
            println!();
            print!("  GradStore ids (first 4):  ");
            for id in grads.get_ids().take(4) {
                print!("{:?} ", id);
            }
            println!();
        }
        optim.step(&grads)?;
        // Pull updated A/B values into the LoraDelta tensors held by Attention.
        generator.model.inner.refresh_lora(&vm)?;
        let l = loss.to_scalar::<f32>()?;
        last_loss = l;
        if step % 5 == 0 || step == cli.steps - 1 {
            println!("step {step:>3}: loss = {l:.4}");
        }
    }

    println!(
        "\nfinal loss: {last_loss:.4} (start: {init_loss:.4}, change: {:+.2}%)",
        100.0 * (last_loss - init_loss) / init_loss,
    );
    if last_loss < init_loss * 0.95 {
        println!("✓ LoRA fine-tune is learning — loss dropped >5%");
    } else if last_loss < init_loss {
        println!("▲ loss dropped slightly — try more steps or higher LR");
    } else {
        println!("⚠ loss did not decrease — check gradient flow / LR");
    }
    Ok(())
}