"""
CAISc Paper — 9-Model Mechanistic Decomposition Training on Modal.

Trains 3 architectures × 3 ACh model variants:
  - v14a (synaptic suppression only) — existing data in prod_5k_v13
  - v14b (depolarization only) — new data in v14b_5k
  - v14c (both mechanisms) — new data in v14c_5k

Each variant trains Model B (with ACh level as input feature).
Plus one Model A (no ACh) on v14a as the control baseline.

Total: 9 Model B variants + 3 Model A controls = 12 models (but Model A
is architecture-independent across data variants, so really 9 + 3 = 12).

Actually: 3 arch × 3 data variants = 9 (Model B only, since all data has ACh sweeps)
          + 3 arch × 1 = 3 Model A (baseline, from v14a ACh=0 only)
          = 12 total training runs

Usage:
  modal run train_decomposition.py              # Train all 12 models
  modal run train_decomposition.py --experiment  # Train + money experiment
"""

import modal

app = modal.App("caisc-decomp-train")
vol = modal.Volume.from_name("caisc-data")

train_image = (
    modal.Image.debian_slim(python_version="3.11")
    .pip_install(
        "torch>=2.2",
        "numpy",
        "scipy",
        "matplotlib",
        "scikit-learn",
        "xgboost",
    )
    .add_local_dir("training", remote_path="/root/training")
)

# Data paths for each ACh model variant
DATA_DIRS = {
    "v14a": "/data/sims/prod_5k_v13/circuits",   # synaptic suppression only
    "v14b": "/data/sims/v14b_5k/circuits",        # depolarization only
    "v14c": "/data/sims/v14c_5k/circuits",        # both mechanisms
}


@app.function(
    image=train_image,
    gpu="L4",
    volumes={"/data": vol},
    timeout=3600,
    memory=16384,
)
def train_single(data_variant: str, model_variant: str, arch: str) -> dict:
    """Train one model on one data variant with one architecture.
    
    Args:
        data_variant: "v14a", "v14b", or "v14c" — which ACh mechanism data
        model_variant: "A" (no ACh feature) or "B" (with ACh feature)
        arch: "mlp", "transformer", or "xgboost"
    """
    import logging, sys, os, json
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s",
                        handlers=[logging.StreamHandler(sys.stdout)])
    logger = logging.getLogger(f"{data_variant}_{model_variant}_{arch}")
    
    sys.path.insert(0, "/root")
    from training.config import TrainConfig
    from training.train import train_one_model
    
    import torch
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logger.info(f"Training {data_variant}/{model_variant}/{arch} on {device}")
    
    # Override config to point to correct data directory
    cfg = TrainConfig()
    cfg.sim_dir = DATA_DIRS[data_variant]
    
    # Use variant-specific checkpoint/log directories to avoid collisions
    cfg.checkpoint_dir = f"/data/training/decomp/checkpoints/{data_variant}"
    cfg.log_dir = f"/data/training/decomp/logs/{data_variant}"
    
    # Disable extra_ach0_dir (not relevant for decomposition)
    cfg.extra_ach0_dir = ""
    
    results = train_one_model(cfg, model_variant, device, arch=arch)
    
    # Tag results with data variant
    results["data_variant"] = data_variant
    results["arch"] = arch
    
    # Save combined results
    os.makedirs(f"/data/training/decomp/results", exist_ok=True)
    result_file = f"/data/training/decomp/results/{data_variant}_{model_variant}_{arch}.json"
    with open(result_file, "w") as f:
        json.dump(results, f, indent=2, default=str)
    
    vol.commit()
    logger.info(f"Done: {data_variant}/{model_variant}/{arch} — R²={results.get('mean_val_r2', '?')}")
    return results


@app.function(
    image=train_image,
    gpu="L4",
    volumes={"/data": vol},
    timeout=7200,
    memory=16384,
)
def run_decomposition_experiment() -> dict:
    """Run the full mechanistic decomposition money experiment.
    
    For each of the 9 Model B variants (3 data × 3 arch), predict Allen V1
    state changes and compare against real data.
    """
    import logging, sys, os, json, glob
    import numpy as np
    
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s",
                        handlers=[logging.StreamHandler(sys.stdout)])
    logger = logging.getLogger("decomp_experiment")
    
    sys.path.insert(0, "/root")
    from training.config import TrainConfig, INPUT_FEATURES_B, OUTPUT_STATS
    from training.dataset import Normalizer
    from training.model import build_model_b
    
    import torch
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # Load Allen data
    allen_dir = "/data/allen/epochs"
    allen_files = sorted(glob.glob(f"{allen_dir}/allen_*.json"))
    logger.info(f"Allen files: {len(allen_files)}")
    
    # Group by session
    sessions = {}
    for fpath in allen_files:
        with open(fpath) as f:
            epoch = json.load(f)
        sid = epoch["session_id"]
        state = epoch["epoch_type"]  # "running" or "stationary"
        if sid not in sessions:
            sessions[sid] = {"running": [], "stationary": []}
        if state in sessions[sid]:
            sessions[sid][state].append(epoch)
    
    # Filter to sessions with both states
    dual_sessions = {sid: data for sid, data in sessions.items()
                     if data["running"] and data["stationary"]}
    logger.info(f"Sessions with both states: {len(dual_sessions)}")
    
    # Canonical circuit parameters for prediction
    canonical = {
        "n_exc": 160, "n_inh": 40, "conn_prob": 0.06,
        "n_synapses": 2400, "mean_in_degree": 12.0,
        "gS_exc_effective": 5e-6, "ou_mu_effective": -0.001,
        "ou_sigma_effective": 0.001, "ou_tau": 5.0,
        "sim_duration_ms": 3000.0,
    }
    
    all_results = {}
    
    # For each data variant × architecture, load Model B and run experiment
    for data_var in ["v14a", "v14b", "v14c"]:
        for arch in ["mlp", "transformer", "xgboost"]:
            key = f"{data_var}_B_{arch}"
            logger.info(f"\n{'='*60}")
            logger.info(f"EXPERIMENT: {key}")
            logger.info(f"{'='*60}")
            
            # Load checkpoint
            if arch == "xgboost":
                # XGBoost doesn't save standard checkpoints — skip for now
                # or load from results JSON
                logger.info(f"  Skipping XGBoost (no checkpoint-based inference)")
                continue
            
            ckpt_path = f"/data/training/decomp/checkpoints/{data_var}/model_b/best.pt"
            if not os.path.exists(ckpt_path):
                logger.warning(f"  Checkpoint not found: {ckpt_path}")
                continue
            
            ckpt = torch.load(ckpt_path, map_location=device, weights_only=False)
            
            # Rebuild model
            cfg = TrainConfig()
            model_config = ckpt.get("config", {})
            actual_arch = model_config.get("arch", arch)
            model = build_model_b(cfg, arch=actual_arch)
            model.load_state_dict(ckpt["model_state_dict"])
            model = model.to(device)
            model.eval()
            
            # Rebuild normalizers
            x_norm = Normalizer.from_state_dict(ckpt["x_norm"])
            y_norm = Normalizer.from_state_dict(ckpt["y_norm"])
            
            # For each session, predict low vs high ACh
            session_results = []
            for sid, data in dual_sessions.items():
                # Mean observed stats
                running_stats = {}
                stationary_stats = {}
                for stat in OUTPUT_STATS:
                    running_vals = [e["statistics"][stat] for e in data["running"] if stat in e.get("statistics", {})]
                    stationary_vals = [e["statistics"][stat] for e in data["stationary"] if stat in e.get("statistics", {})]
                    if running_vals and stationary_vals:
                        running_stats[stat] = float(np.mean(running_vals))
                        stationary_stats[stat] = float(np.mean(stationary_vals))
                
                if not running_stats:
                    continue
                
                # Predict at low ACh (0.1) and high ACh (0.9)
                def make_input(ach_level):
                    x = np.array([[canonical[f] for f in INPUT_FEATURES_B[:-1]] + [ach_level]])
                    return x_norm.transform(x)
                
                with torch.no_grad():
                    x_low = torch.tensor(make_input(0.1), dtype=torch.float32).to(device)
                    x_high = torch.tensor(make_input(0.9), dtype=torch.float32).to(device)
                    pred_low = model(x_low).cpu().numpy()
                    pred_high = model(x_high).cpu().numpy()
                
                # Inverse normalize predictions
                pred_low_raw = y_norm.inverse(pred_low)[0]
                pred_high_raw = y_norm.inverse(pred_high)[0]
                
                # Compare directions
                sr = {}
                for i, stat in enumerate(OUTPUT_STATS):
                    if stat in running_stats:
                        obs_delta = running_stats[stat] - stationary_stats[stat]
                        pred_delta = pred_high_raw[i] - pred_low_raw[i]
                        sign_match = (np.sign(obs_delta) == np.sign(pred_delta))
                        sr[stat] = {
                            "obs_delta": round(float(obs_delta), 6),
                            "pred_delta": round(float(pred_delta), 6),
                            "sign_match": bool(sign_match),
                        }
                
                session_results.append({
                    "session_id": int(sid),
                    "n_running": len(data["running"]),
                    "n_stationary": len(data["stationary"]),
                    "stats": sr,
                })
            
            # Aggregate sign accuracy per stat
            sign_accuracy = {}
            for stat in OUTPUT_STATS:
                matches = [s["stats"][stat]["sign_match"] for s in session_results
                          if stat in s["stats"]]
                if matches:
                    sign_accuracy[stat] = round(sum(matches) / len(matches) * 100, 1)
            
            logger.info(f"\n  Sign accuracy ({len(session_results)} sessions):")
            for stat, acc in sign_accuracy.items():
                marker = "✅" if acc > 60 else "❌" if acc < 40 else "  "
                logger.info(f"    {stat:25s}: {acc:5.1f}% {marker}")
            
            mean_acc = float(np.mean(list(sign_accuracy.values()))) if sign_accuracy else 0
            logger.info(f"    {'MEAN':25s}: {mean_acc:5.1f}%")
            
            all_results[key] = {
                "data_variant": data_var,
                "arch": arch,
                "n_sessions": len(session_results),
                "sign_accuracy": sign_accuracy,
                "mean_sign_accuracy": round(mean_acc, 1),
                "session_details": session_results,
            }
    
    # Save full results
    os.makedirs("/data/training/decomp/results", exist_ok=True)
    with open("/data/training/decomp/results/decomposition_experiment.json", "w") as f:
        json.dump(all_results, f, indent=2, default=str)
    vol.commit()
    
    # Print summary table
    logger.info(f"\n\n{'='*80}")
    logger.info("MECHANISTIC DECOMPOSITION — SUMMARY TABLE")
    logger.info(f"{'='*80}")
    logger.info(f"{'Statistic':25s}  {'v14a(syn)':>10s}  {'v14b(dep)':>10s}  {'v14c(both)':>10s}")
    logger.info(f"{'-'*25}  {'-'*10}  {'-'*10}  {'-'*10}")
    
    for stat in OUTPUT_STATS:
        vals = []
        for dv in ["v14a", "v14b", "v14c"]:
            # Use MLP results (or first available)
            for arch in ["mlp", "transformer"]:
                k = f"{dv}_B_{arch}"
                if k in all_results:
                    vals.append(all_results[k]["sign_accuracy"].get(stat, 0))
                    break
            else:
                vals.append(0)
        logger.info(f"{stat:25s}  {vals[0]:9.1f}%  {vals[1]:9.1f}%  {vals[2]:9.1f}%")
    
    logger.info(f"{'='*80}")
    
    return all_results


@app.function(
    image=train_image,
    gpu="L4",
    volumes={"/data": vol},
    timeout=10800,  # 3 hours
    memory=16384,
)
def orchestrate(run_experiment: bool = False) -> dict:
    """Launch all training jobs in parallel, then optionally run experiment."""
    import logging, sys, time, json, os, glob
    
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s",
                        handlers=[logging.StreamHandler(sys.stdout)])
    logger = logging.getLogger("orchestrator")
    
    # Check data availability
    for name, path in DATA_DIRS.items():
        import glob as g
        # Can't check from orchestrator (different container) — just log
        logger.info(f"  {name}: {path}")
    
    # Launch all training jobs in parallel
    logger.info("=" * 70)
    logger.info("LAUNCHING PARALLEL TRAINING: 3 data × 3 arch × Model B + 3 Model A")
    logger.info("=" * 70)
    
    t0 = time.time()
    handles = {}
    
    # Model B: 3 data variants × 2 neural architectures (MLP, Transformer)
    # XGBoost separately (no GPU needed but uses same function)
    for data_var in ["v14a", "v14b", "v14c"]:
        for arch in ["mlp", "transformer", "xgboost"]:
            key = f"{data_var}_B_{arch}"
            handles[key] = train_single.spawn(data_var, "B", arch)
            logger.info(f"  Spawned {key}")
    
    # Model A baseline: only from v14a (ACh=0 data), 3 architectures
    for arch in ["mlp", "transformer", "xgboost"]:
        key = f"v14a_A_{arch}"
        handles[key] = train_single.spawn("v14a", "A", arch)
        logger.info(f"  Spawned {key}")
    
    logger.info(f"\n{len(handles)} jobs launched. Waiting for completion...\n")
    
    # Collect results
    results = {}
    for name, handle in handles.items():
        logger.info(f"Waiting for {name}...")
        try:
            r = handle.get()
            results[name] = r
            logger.info(f"  {name}: R²={r.get('mean_val_r2', '?')}, Time={r.get('total_time_s', 0):.0f}s")
        except Exception as e:
            logger.error(f"  {name} FAILED: {e}")
            results[name] = {"error": str(e)}
    
    total_time = time.time() - t0
    logger.info(f"\nAll training complete in {total_time:.0f}s ({total_time/60:.1f} min)")
    
    # Print summary table
    logger.info(f"\n{'='*90}")
    logger.info("MODEL B — Validation R² by Data Variant × Architecture")
    logger.info(f"{'='*90}")
    logger.info(f"{'Data':8s} {'Arch':15s} {'Mean R²':>10s} {'Best Epoch':>12s} {'Time(s)':>10s}")
    logger.info(f"{'-'*8} {'-'*15} {'-'*10} {'-'*12} {'-'*10}")
    
    for data_var in ["v14a", "v14b", "v14c"]:
        for arch in ["mlp", "transformer", "xgboost"]:
            key = f"{data_var}_B_{arch}"
            r = results.get(key, {})
            if "error" in r:
                logger.info(f"{data_var:8s} {arch:15s} {'FAILED':>10s}")
            else:
                logger.info(f"{data_var:8s} {arch:15s} {r.get('mean_val_r2', 0):10.4f} {r.get('best_epoch', 0):12d} {r.get('total_time_s', 0):10.0f}")
    
    logger.info(f"\nModel A baselines:")
    for arch in ["mlp", "transformer", "xgboost"]:
        key = f"v14a_A_{arch}"
        r = results.get(key, {})
        if "error" not in r:
            logger.info(f"  A_{arch}: R²={r.get('mean_val_r2', 0):.4f}")
    
    logger.info(f"{'='*90}")
    
    vol.commit()
    
    # Save all results
    os.makedirs("/data/training/decomp/results", exist_ok=True)
    with open("/data/training/decomp/results/all_training_results.json", "w") as f:
        json.dump(results, f, indent=2, default=str)
    vol.commit()
    
    # Run experiment if requested
    if run_experiment:
        logger.info("\n\nRunning mechanistic decomposition experiment...")
        vol.reload()
        experiment_results = run_decomposition_experiment.remote()
        results["experiment"] = experiment_results
        
        with open("/data/training/decomp/results/all_results_with_experiment.json", "w") as f:
            json.dump(results, f, indent=2, default=str)
        vol.commit()
    
    return results


@app.local_entrypoint()
def main(experiment: bool = False, experiment_only: bool = False):
    """Launch the full mechanistic decomposition training + experiment.
    
    Args:
        experiment: Also run the money experiment after training.
        experiment_only: Skip training, just run the money experiment (requires prior training).
    """
    if experiment_only:
        print("=" * 60)
        print("CAISc — EXPERIMENT ONLY (skipping training)")
        print("=" * 60)
        results = run_decomposition_experiment.remote()
        print(f"\nEXPERIMENT RESULTS:")
        for key, r in results.items():
            if isinstance(r, dict) and "mean_sign_accuracy" in r:
                print(f"  {key:25s}: {r['mean_sign_accuracy']:.1f}% mean sign accuracy")
        print(f"\nResults saved to Modal volume: /data/training/decomp/results/")
        return
    
    print("=" * 60)
    print("CAISc MECHANISTIC DECOMPOSITION")
    print("  Training: 3 data variants × 3 architectures × Model B")
    print("  + 3 Model A baselines")
    print(f"  Experiment: {experiment}")
    print("=" * 60)
    
    results = orchestrate.remote(run_experiment=experiment)
    
    print(f"\n{'='*60}")
    print("TRAINING COMPLETE")
    print(f"{'='*60}")
    
    for key, r in results.items():
        if key == "experiment":
            continue
        if isinstance(r, dict) and "error" not in r:
            print(f"  {key:25s}: R²={r.get('mean_val_r2', '?'):>8s}, Epoch={r.get('best_epoch', '?')}")
        elif isinstance(r, dict):
            print(f"  {key:25s}: FAILED — {r.get('error', '?')[:50]}")
    
    if "experiment" in results:
        print(f"\nEXPERIMENT RESULTS:")
        exp = results["experiment"]
        for key, r in exp.items():
            if isinstance(r, dict) and "mean_sign_accuracy" in r:
                print(f"  {key:25s}: {r['mean_sign_accuracy']:.1f}% mean sign accuracy")
    
    print(f"\nResults saved to Modal volume: /data/training/decomp/results/")