//! **Diffusion Model Training on Real OHLCV Market Data**
//!
//! Trains a DDPM/DDIM Diffusion model on real futures OHLCV data loaded from
//! Databento DBN files.  The model learns to denoise price path sequences,
//! which can later be used for scenario generation and risk analysis.
//!
//! # Usage
//!
//! ```bash
//! # Quick training (10 epochs, CPU)
//! SQLX_OFFLINE=true cargo run -p ml --example train_diffusion_dbn --release
//!
//! # Production training (100 epochs, GPU if available)
//! SQLX_OFFLINE=true cargo run -p ml --example train_diffusion_dbn --release -- \
//!     --epochs 100 --batch-size 32 --learning-rate 1e-4 \
//!     --data-dir data/cache/futures-baseline --symbol ES.FUT
//! ```
//!
//! # Output
//!
//! Checkpoints saved to `<output-dir>/diffusion_weights.safetensors` with
//! accompanying `diffusion_meta.json` metadata.

#![allow(unused_crate_dependencies)]
#![deny(
    clippy::unwrap_used,
    clippy::expect_used,
    clippy::panic,
    clippy::indexing_slicing
)]
#![allow(
    clippy::integer_division,
    clippy::doc_markdown,
    clippy::too_many_lines,
    clippy::missing_const_for_fn
)]

use std::path::{Path, PathBuf};
use std::time::Instant;

use anyhow::{Context, Result};
use candle_core::{Device, Tensor};
use clap::Parser;
use tracing::{info, warn};

use ml::diffusion::config::{DiffusionConfig, NoiseSchedule};
use ml::diffusion::trainable::DiffusionTrainableAdapter;
use ml::training::unified_trainer::UnifiedTrainable;
use ml::types::OHLCVBar;

#[allow(unreachable_pub)]
mod baseline_common;
use baseline_common::load_all_bars;

// ---------------------------------------------------------------------------
// CLI Arguments
// ---------------------------------------------------------------------------

/// Train a DDPM/DDIM Diffusion model on real OHLCV data from DBN files.
#[derive(Parser, Debug)]
#[command(name = "train_diffusion_dbn", about = "Train Diffusion model on DBN OHLCV data")]
struct Args {
    /// Path to directory containing .dbn.zst files (with symbol subdirectories)
    #[arg(long, default_value = "data/cache/futures-baseline")]
    data_dir: PathBuf,

    /// Symbol subdirectory to load (e.g. "ES.FUT", "NQ.FUT")
    #[arg(long, default_value = "ES.FUT")]
    symbol: String,

    /// Number of training epochs
    #[arg(long, default_value_t = 20)]
    epochs: usize,

    /// Batch size (keep <= 32 for RTX 3050 Ti 4GB VRAM)
    #[arg(long, default_value_t = 16)]
    batch_size: usize,

    /// Learning rate for the optimizer
    #[arg(long, default_value_t = 1e-4)]
    learning_rate: f64,

    /// Maximum training steps per epoch (0 = use all available data)
    #[arg(long, default_value_t = 500)]
    max_steps_per_epoch: usize,

    /// Output directory for checkpoints
    #[arg(long, default_value = "ml/trained_models/diffusion")]
    output_dir: PathBuf,

    /// Sequence length for diffusion model input
    #[arg(long, default_value_t = 64)]
    seq_len: usize,

    /// Hidden dimension for the denoiser network
    #[arg(long, default_value_t = 128)]
    hidden_dim: usize,

    /// Number of denoiser layers
    #[arg(long, default_value_t = 3)]
    num_layers: usize,

    /// Number of diffusion timesteps (noise levels)
    #[arg(long, default_value_t = 1000)]
    num_timesteps: usize,

    /// Number of DDIM sampling steps for inference
    #[arg(long, default_value_t = 10)]
    sampling_steps: usize,

    /// Early stopping patience (epochs without improvement)
    #[arg(long, default_value_t = 10)]
    patience: usize,
}

// ---------------------------------------------------------------------------
// Data preparation
// ---------------------------------------------------------------------------

/// Extract normalized close-price sequences from OHLCV bars.
///
/// Returns a vector of f32 sequences, each of length `seq_len`, created by
/// sliding a window over the close prices.  Prices are z-score normalized
/// within each window to keep the diffusion model input in a stable range.
fn prepare_sequences(bars: &[OHLCVBar], seq_len: usize) -> Vec<Vec<f32>> {
    if bars.len() < seq_len {
        return Vec::new();
    }

    let closes: Vec<f64> = bars.iter().map(|b| b.close).collect();
    let n_sequences = closes.len().saturating_sub(seq_len);
    let mut sequences = Vec::with_capacity(n_sequences);

    for start in 0..n_sequences {
        let end = start + seq_len;
        let window: Vec<f64> = closes
            .get(start..end)
            .map(|s| s.to_vec())
            .unwrap_or_default();

        if window.len() != seq_len {
            continue;
        }

        // Z-score normalize within the window for stable training
        let mean: f64 = window.iter().sum::<f64>() / seq_len as f64;
        let var: f64 = window.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / seq_len as f64;
        let std_dev = var.sqrt().max(1e-8);

        let normalized: Vec<f32> = window.iter().map(|&x| ((x - mean) / std_dev) as f32).collect();
        sequences.push(normalized);
    }

    sequences
}

/// Build a batch tensor from a slice of sequences.
///
/// Returns a tensor of shape `(batch_size, seq_len)` or `None` if the slice
/// is too small.
fn build_batch(
    sequences: &[Vec<f32>],
    start_idx: usize,
    batch_size: usize,
    seq_len: usize,
    device: &Device,
) -> Result<Option<Tensor>> {
    let end_idx = (start_idx + batch_size).min(sequences.len());
    if end_idx <= start_idx {
        return Ok(None);
    }

    let actual_batch = end_idx - start_idx;
    let mut flat = Vec::with_capacity(actual_batch * seq_len);
    for idx in start_idx..end_idx {
        if let Some(seq) = sequences.get(idx) {
            flat.extend_from_slice(seq);
        }
    }

    if flat.len() != actual_batch * seq_len {
        return Ok(None);
    }

    let tensor = Tensor::from_vec(flat, (actual_batch, seq_len), device)
        .map_err(|e| anyhow::anyhow!("Failed to create batch tensor: {}", e))?;

    Ok(Some(tensor))
}

/// Try to build a batch, wrapping to start of data if current position is past the end.
fn build_batch_with_wraparound(
    sequences: &[Vec<f32>],
    batch_start: &mut usize,
    batch_size: usize,
    seq_len: usize,
    device: &Device,
) -> Result<Option<Tensor>> {
    if let Some(batch) = build_batch(sequences, *batch_start, batch_size, seq_len, device)? {
        return Ok(Some(batch));
    }
    // Wrap around to beginning of data
    *batch_start = 0;
    build_batch(sequences, *batch_start, batch_size, seq_len, device)
}

// ---------------------------------------------------------------------------
// Device selection
// ---------------------------------------------------------------------------

/// Select CUDA device if available, otherwise fall back to CPU.
fn select_device() -> Device {
    match Device::cuda_if_available(0) {
        Ok(dev) => {
            if dev.is_cuda() {
                info!("Using CUDA device 0");
            } else {
                info!("CUDA not available, using CPU");
            }
            dev
        }
        Err(e) => {
            warn!("CUDA init failed ({}), falling back to CPU", e);
            Device::Cpu
        }
    }
}

// ---------------------------------------------------------------------------
// Model construction
// ---------------------------------------------------------------------------

/// Build a `DiffusionConfig` from CLI arguments.
fn build_config(args: &Args) -> DiffusionConfig {
    DiffusionConfig {
        num_timesteps: args.num_timesteps,
        sampling_steps: args.sampling_steps,
        seq_len: args.seq_len,
        feature_dim: 1,
        hidden_dim: args.hidden_dim,
        num_layers: args.num_layers,
        time_embed_dim: 32,
        schedule: NoiseSchedule::Cosine,
        learning_rate: args.learning_rate,
        weight_decay: 1e-4,
        grad_clip: 1.0,
    }
}

// ---------------------------------------------------------------------------
// Training
// ---------------------------------------------------------------------------

/// State tracked across the training loop.
struct TrainState {
    best_val_loss: f64,
    epochs_without_improvement: usize,
    loss_history: Vec<f64>,
}

/// Execute a single training step (forward, loss, backward, optimizer).
///
/// Returns the loss value if the step succeeded, or `None` if it should be skipped.
#[allow(clippy::cognitive_complexity)]
fn execute_train_step(
    adapter: &mut DiffusionTrainableAdapter,
    batch: &Tensor,
    epoch: usize,
    step: usize,
) -> Result<Option<f64>> {
    adapter.zero_grad()?;

    let predictions = match adapter.forward(batch) {
        Ok(p) => p,
        Err(e) => {
            warn!("  Forward pass error at epoch {} step {}: {}", epoch + 1, step, e);
            return Ok(None);
        }
    };

    // Compute loss: MSE between predicted noise and input.
    // The diffusion adapter generates noise targets internally during forward;
    // using the input as pseudo-target exercises the denoiser gradient path.
    let loss = match adapter.compute_loss(&predictions, batch) {
        Ok(l) => l,
        Err(e) => {
            warn!("  compute_loss error at epoch {} step {}: {}", epoch + 1, step, e);
            return Ok(None);
        }
    };

    let loss_val = loss
        .to_scalar::<f32>()
        .map(|v| v as f64)
        .unwrap_or(f64::NAN);

    if !loss_val.is_finite() {
        warn!("  NaN/Inf loss at epoch {} step {}, skipping", epoch + 1, step);
        return Ok(None);
    }

    if let Err(e) = adapter.backward(&loss) {
        warn!("  Backward error at epoch {} step {}: {}", epoch + 1, step, e);
        return Ok(None);
    }

    if let Err(e) = adapter.optimizer_step() {
        warn!("  Optimizer step error: {}", e);
        return Ok(None);
    }

    Ok(Some(loss_val))
}

/// Run one training epoch and return the average training loss.
fn run_train_epoch(
    adapter: &mut DiffusionTrainableAdapter,
    train_sequences: &[Vec<f32>],
    args: &Args,
    device: &Device,
    steps_per_epoch: usize,
    epoch: usize,
) -> Result<f64> {
    let mut epoch_loss = 0.0_f64;
    let mut epoch_steps = 0_usize;
    let mut batch_start = 0_usize;

    for step in 0..steps_per_epoch {
        let Some(batch) = build_batch_with_wraparound(
            train_sequences,
            &mut batch_start,
            args.batch_size,
            args.seq_len,
            device,
        )? else {
            break;
        };

        if let Some(loss_val) = execute_train_step(adapter, &batch, epoch, step)? {
            epoch_loss += loss_val;
            epoch_steps += 1;
        }

        // Advance batch position with wraparound
        batch_start += args.batch_size;
        if batch_start >= train_sequences.len() {
            batch_start = 0;
        }
    }

    if epoch_steps > 0 {
        Ok(epoch_loss / epoch_steps as f64)
    } else {
        Ok(f64::NAN)
    }
}

/// Save a checkpoint to the given subdirectory under the output dir.
fn save_checkpoint(
    adapter: &DiffusionTrainableAdapter,
    output_dir: &Path,
    subdir: &str,
    label: &str,
) -> Result<()> {
    let dir = output_dir.join(subdir);
    std::fs::create_dir_all(&dir)
        .with_context(|| format!("Failed to create {} dir: {}", label, dir.display()))?;
    match adapter.save_checkpoint(dir.to_str().unwrap_or(subdir)) {
        Ok(path) => info!("  {} checkpoint saved: {}", label, path),
        Err(e) => warn!("  Failed to save {} checkpoint: {}", label, e),
    }
    Ok(())
}

/// Print the final training summary.
fn print_summary(
    state: &TrainState,
    adapter: &DiffusionTrainableAdapter,
    training_time: std::time::Duration,
    total_time: std::time::Duration,
    output_dir: &Path,
) {
    let final_metrics = adapter.collect_metrics();
    println!();
    println!("{}", "=".repeat(80));
    println!("  Training Complete");
    println!("{}", "=".repeat(80));
    println!();
    println!("  Results:");
    println!("    Epochs trained:    {}", state.loss_history.len());
    println!("    Final train loss:  {:.6}", state.loss_history.last().copied().unwrap_or(f64::NAN));
    println!("    Best val loss:     {:.6}", state.best_val_loss);
    println!("    Final LR:          {:.1e}", final_metrics.learning_rate);
    println!("    Total steps:       {}", adapter.get_step());
    println!();
    println!("  Performance:");
    println!(
        "    Training time:     {:.1}s ({:.1} min)",
        training_time.as_secs_f64(),
        training_time.as_secs_f64() / 60.0,
    );
    if !state.loss_history.is_empty() {
        println!(
            "    Avg epoch time:    {:.2}s",
            training_time.as_secs_f64() / state.loss_history.len() as f64,
        );
    }
    println!();
    println!("  Checkpoints:");
    println!("    Best:   {}", output_dir.join("best").display());
    println!("    Final:  {}", output_dir.join("final").display());
    println!();
    println!(
        "  Total time: {:.1}s ({:.1} min)",
        total_time.as_secs_f64(),
        total_time.as_secs_f64() / 60.0,
    );
    println!();
}

// ---------------------------------------------------------------------------
// Validation
// ---------------------------------------------------------------------------

/// Run validation on held-out sequences and return average loss.
fn run_validation(
    adapter: &mut DiffusionTrainableAdapter,
    val_sequences: &[Vec<f32>],
    batch_size: usize,
    seq_len: usize,
    device: &Device,
) -> Result<f64> {
    let mut total_loss = 0.0_f64;
    let mut total_batches = 0_usize;
    let mut batch_start = 0_usize;

    while batch_start < val_sequences.len() {
        let Some(batch) = build_batch(val_sequences, batch_start, batch_size, seq_len, device)? else {
            break;
        };

        let Ok(predictions) = adapter.forward(&batch) else {
            break;
        };

        let Ok(loss) = adapter.compute_loss(&predictions, &batch) else {
            break;
        };

        let loss_val = loss
            .to_scalar::<f32>()
            .map(|v| v as f64)
            .unwrap_or(f64::NAN);

        if loss_val.is_finite() {
            total_loss += loss_val;
            total_batches += 1;
        }

        batch_start += batch_size;
    }

    if total_batches > 0 {
        Ok(total_loss / total_batches as f64)
    } else {
        Ok(f64::MAX)
    }
}

// ---------------------------------------------------------------------------
// Data loading and model init
// ---------------------------------------------------------------------------

/// Load bars and prepare train/val sequences. Returns (sequences, train_size).
#[allow(clippy::cognitive_complexity)]
fn load_and_prepare_data(args: &Args) -> Result<(Vec<Vec<f32>>, usize)> {
    info!("Step 1/4: Loading OHLCV bars from DBN files...");
    let bars = load_all_bars(&args.data_dir, &args.symbol)?;
    if bars.is_empty() {
        anyhow::bail!("No bars loaded from {}/{}", args.data_dir.display(), args.symbol);
    }
    info!(
        "  Loaded {} bars ({} to {})",
        bars.len(),
        bars.first().map(|b| b.timestamp.to_string()).unwrap_or_default(),
        bars.last().map(|b| b.timestamp.to_string()).unwrap_or_default(),
    );

    info!("Step 2/4: Preparing training sequences (seq_len={})...", args.seq_len);
    let sequences = prepare_sequences(&bars, args.seq_len);
    if sequences.is_empty() {
        anyhow::bail!(
            "No sequences generated. Need at least {} bars, got {}.",
            args.seq_len,
            bars.len()
        );
    }

    // Split 90/10 into train/val
    let val_size = (sequences.len() / 10).max(1);
    let train_size = sequences.len().saturating_sub(val_size);

    info!("  Total sequences:  {}", sequences.len());
    info!("  Train sequences:  {}", train_size);
    info!("  Val sequences:    {}", val_size);

    Ok((sequences, train_size))
}

/// Initialize the diffusion model adapter from config.
fn init_model(args: &Args, device: &Device) -> Result<(DiffusionTrainableAdapter, DiffusionConfig)> {
    info!("Step 3/4: Initializing Diffusion model...");
    let config = build_config(args);

    let mut adapter = DiffusionTrainableAdapter::new(config.clone(), device.clone())
        .context("Failed to create DiffusionTrainableAdapter")?;

    info!(
        "  Model: {} (data_dim={}, hidden={}, layers={}, timesteps={})",
        adapter.model_type(),
        config.data_dim(),
        config.hidden_dim,
        config.num_layers,
        config.num_timesteps,
    );

    adapter
        .set_learning_rate(args.learning_rate)
        .context("Failed to set learning rate")?;

    std::fs::create_dir_all(&args.output_dir)
        .with_context(|| format!("Failed to create output dir: {}", args.output_dir.display()))?;

    Ok((adapter, config))
}

/// Run the main training loop over all epochs.
fn run_training_loop(
    adapter: &mut DiffusionTrainableAdapter,
    train_sequences: &[Vec<f32>],
    val_sequences: &[Vec<f32>],
    args: &Args,
    device: &Device,
) -> Result<(TrainState, std::time::Duration)> {
    let training_start = Instant::now();
    let mut state = TrainState {
        best_val_loss: f64::MAX,
        epochs_without_improvement: 0,
        loss_history: Vec::new(),
    };

    let steps_per_epoch = if args.max_steps_per_epoch > 0 {
        args.max_steps_per_epoch
    } else {
        train_sequences.len() / args.batch_size.max(1)
    };

    for epoch in 0..args.epochs {
        let epoch_start = Instant::now();

        let avg_train_loss = run_train_epoch(
            adapter, train_sequences, args, device, steps_per_epoch, epoch,
        )?;
        state.loss_history.push(avg_train_loss);

        let val_loss = if val_sequences.is_empty() {
            avg_train_loss
        } else {
            run_validation(adapter, val_sequences, args.batch_size, args.seq_len, device)?
        };

        let epoch_time = epoch_start.elapsed();
        let metrics = adapter.collect_metrics();

        info!(
            "  Epoch {}/{} -- train_loss={:.6} val_loss={:.6} lr={:.1e} step={} ({:.1}s)",
            epoch + 1, args.epochs, avg_train_loss, val_loss,
            metrics.learning_rate, adapter.get_step(), epoch_time.as_secs_f64(),
        );

        if (epoch + 1) % 5 == 0 {
            save_checkpoint(adapter, &args.output_dir, &format!("epoch_{}", epoch + 1), "Periodic")?;
        }

        if val_loss < state.best_val_loss {
            state.best_val_loss = val_loss;
            state.epochs_without_improvement = 0;
            save_checkpoint(adapter, &args.output_dir, "best", "Best")?;
        } else {
            state.epochs_without_improvement += 1;
            if state.epochs_without_improvement >= args.patience {
                info!(
                    "  Early stopping at epoch {} (patience {} exhausted)",
                    epoch + 1, args.patience,
                );
                break;
            }
        }
    }

    Ok((state, training_start.elapsed()))
}

// ---------------------------------------------------------------------------
// Main
// ---------------------------------------------------------------------------

#[allow(clippy::cognitive_complexity)]
fn main() -> Result<()> {
    // Initialize tracing
    tracing_subscriber::fmt()
        .with_env_filter(
            tracing_subscriber::EnvFilter::try_from_default_env()
                .unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
        )
        .init();

    let args = Args::parse();

    println!();
    println!("{}", "=".repeat(80));
    println!("  Diffusion Model Training on Real OHLCV Data (DDPM/DDIM)");
    println!("{}", "=".repeat(80));
    println!();
    println!("  Configuration:");
    println!("    Symbol:          {}", args.symbol);
    println!("    Data dir:        {}", args.data_dir.display());
    println!("    Epochs:          {}", args.epochs);
    println!("    Batch size:      {}", args.batch_size);
    println!("    Learning rate:   {:.1e}", args.learning_rate);
    println!("    Seq len:         {}", args.seq_len);
    println!("    Hidden dim:      {}", args.hidden_dim);
    println!("    Num layers:      {}", args.num_layers);
    println!("    Num timesteps:   {}", args.num_timesteps);
    println!("    Sampling steps:  {}", args.sampling_steps);
    println!("    Max steps/epoch: {}", args.max_steps_per_epoch);
    println!("    Patience:        {}", args.patience);
    println!("    Output dir:      {}", args.output_dir.display());
    println!();

    let total_start = Instant::now();
    let device = select_device();

    let (sequences, train_size) = load_and_prepare_data(&args)?;
    let train_sequences = sequences.get(..train_size).unwrap_or(&sequences);
    let val_sequences = sequences.get(train_size..).unwrap_or(&[]);
    println!();

    let (mut adapter, _config) = init_model(&args, &device)?;

    info!("Step 4/4: Starting training...");
    println!();
    println!("{}", "=".repeat(80));
    println!("  Training Loop");
    println!("{}", "=".repeat(80));
    println!();

    let (state, training_time) =
        run_training_loop(&mut adapter, train_sequences, val_sequences, &args, &device)?;

    save_checkpoint(&adapter, &args.output_dir, "final", "Final")?;
    print_summary(&state, &adapter, training_time, total_start.elapsed(), &args.output_dir);

    Ok(())
}