#!/usr/bin/env python3
"""
Train 3D Gaussian Splats for all DX.GL multi-view datasets.

Downloads datasets (if needed), trains each with nerfstudio splatfacto,
exports the PLY, and converts to .splat for web viewers.

Usage:
  python train_all.py                              # train all objects
  python train_all.py --object apple               # train specific object
  python train_all.py --data-dir ./dxgl-datasets   # custom dataset location
  python train_all.py --output ./splats            # custom output directory
  python train_all.py --iterations 30000           # custom iteration count
  python train_all.py --dry-run                    # show what would be trained

Requires:
  pip install nerfstudio requests plyfile numpy
"""

import argparse
import glob
import json
import os
import struct
import subprocess
import sys
import time

try:
    import numpy as np
    from plyfile import PlyData
except ImportError:
    print("Please install dependencies: pip install plyfile numpy requests")
    sys.exit(1)

MANIFEST_LOCAL = os.path.join(os.path.dirname(os.path.abspath(__file__)), "manifest.json")

# Validated training params (from tuning on RTX 4000 Pro Ada)
DEFAULT_ITERATIONS = 20000
TRAIN_PARAMS = [
    "--pipeline.model.sh-degree", "3",
    "--pipeline.model.background-color", "white",
    "--pipeline.model.cull-alpha-thresh", "0.2",
    "--pipeline.model.densify-size-thresh", "0.005",
    "--pipeline.model.use-scale-regularization", "True",
    "--pipeline.model.max-gauss-ratio", "5.0",
]


# ── PLY → .splat conversion (from scripts/ply-to-splat.py) ──────────────

SH_C0 = 0.28209479177387814  # 1 / (2 * sqrt(pi))


def _ply_field(v, *names):
    """Find the first matching field name in PLY vertex data."""
    available = v.data.dtype.names if hasattr(v.data, "dtype") else v.dtype.names
    for name in names:
        if name in available:
            return v[name]
    raise KeyError(f"No field matching: {names}. Available: {available}")


def ply_to_splat(input_path: str, output_path: str):
    """Convert a nerfstudio Gaussian Splatting PLY to .splat format."""
    ply = PlyData.read(input_path)
    v = ply["vertex"]
    n = len(v)

    xyz = np.column_stack([v["x"], v["y"], v["z"]]).astype(np.float32)

    s0 = _ply_field(v, "f_scale_0", "scale_0", "sx")
    s1 = _ply_field(v, "f_scale_1", "scale_1", "sy")
    s2 = _ply_field(v, "f_scale_2", "scale_2", "sz")
    scales = np.exp(np.column_stack([s0, s1, s2])).astype(np.float32)

    raw_opacity = _ply_field(v, "opacity", "f_opacity")
    opacity = (1.0 / (1.0 + np.exp(-raw_opacity.astype(np.float64)))).astype(np.float64)

    dc0 = _ply_field(v, "f_dc_0", "f_rest_0", "red")
    dc1 = _ply_field(v, "f_dc_1", "f_rest_1", "green")
    dc2 = _ply_field(v, "f_dc_2", "f_rest_2", "blue")
    if dc0.max() > 10:
        r = np.clip(dc0, 0, 255).astype(np.uint8)
        g = np.clip(dc1, 0, 255).astype(np.uint8)
        b = np.clip(dc2, 0, 255).astype(np.uint8)
    else:
        r = np.clip((0.5 + SH_C0 * dc0) * 255, 0, 255).astype(np.uint8)
        g = np.clip((0.5 + SH_C0 * dc1) * 255, 0, 255).astype(np.uint8)
        b = np.clip((0.5 + SH_C0 * dc2) * 255, 0, 255).astype(np.uint8)
    a = np.clip(opacity * 255, 0, 255).astype(np.uint8)

    qw = _ply_field(v, "rot_0", "qw", "f_rot_0").astype(np.float64)
    qx = _ply_field(v, "rot_1", "qx", "f_rot_1").astype(np.float64)
    qy = _ply_field(v, "rot_2", "qy", "f_rot_2").astype(np.float64)
    qz = _ply_field(v, "rot_3", "qz", "f_rot_3").astype(np.float64)
    norm = np.sqrt(qw * qw + qx * qx + qy * qy + qz * qz)
    qw /= norm; qx /= norm; qy /= norm; qz /= norm
    rot_x = np.clip(qx * 128 + 128, 0, 255).astype(np.uint8)
    rot_y = np.clip(qy * 128 + 128, 0, 255).astype(np.uint8)
    rot_z = np.clip(qz * 128 + 128, 0, 255).astype(np.uint8)
    rot_w = np.clip(qw * 128 + 128, 0, 255).astype(np.uint8)

    order = np.argsort(-opacity)

    buf = bytearray(n * 32)
    for i in range(n):
        idx = order[i]
        off = i * 32
        struct.pack_into("3f", buf, off, xyz[idx, 0], xyz[idx, 1], xyz[idx, 2])
        struct.pack_into("3f", buf, off + 12, scales[idx, 0], scales[idx, 1], scales[idx, 2])
        buf[off + 24] = r[idx]
        buf[off + 25] = g[idx]
        buf[off + 26] = b[idx]
        buf[off + 27] = a[idx]
        buf[off + 28] = rot_w[idx]
        buf[off + 29] = rot_x[idx]
        buf[off + 30] = rot_y[idx]
        buf[off + 31] = rot_z[idx]

    with open(output_path, "wb") as f:
        f.write(buf)

    ply_mb = os.path.getsize(input_path) / 1e6
    splat_mb = len(buf) / 1e6
    return n, ply_mb, splat_mb


# ── Nerfstudio helpers ───────────────────────────────────────────────────

def find_latest_config(output_base: str, experiment_name: str):
    """Find the most recent config.yml from nerfstudio outputs."""
    pattern = os.path.join(output_base, experiment_name, "splatfacto", "*", "config.yml")
    configs = sorted(glob.glob(pattern))
    if not configs:
        return None
    return configs[-1]  # latest timestamp


def train_splatfacto(data_dir: str, experiment_name: str, output_base: str,
                     max_iterations: int):
    """Run ns-train splatfacto for a single dataset."""
    cmd = [
        "ns-train", "splatfacto",
        "--data", data_dir,
        "--output-dir", output_base,
        "--experiment-name", experiment_name,
        "--max-num-iterations", str(max_iterations),
        *TRAIN_PARAMS,
    ]
    print(f"  Command: {' '.join(cmd)}")
    result = subprocess.run(cmd)
    if result.returncode != 0:
        raise RuntimeError(f"ns-train failed with exit code {result.returncode}")


def export_ply(config_path: str, export_dir: str):
    """Run ns-export gaussian-splat to get the PLY file."""
    os.makedirs(export_dir, exist_ok=True)
    cmd = [
        "ns-export", "gaussian-splat",
        "--load-config", config_path,
        "--output-dir", export_dir,
    ]
    print(f"  Export command: {' '.join(cmd)}")
    result = subprocess.run(cmd)
    if result.returncode != 0:
        raise RuntimeError(f"ns-export failed with exit code {result.returncode}")

    # ns-export writes splat.ply in the output dir
    ply_path = os.path.join(export_dir, "splat.ply")
    if not os.path.exists(ply_path):
        # Some versions write point_cloud.ply
        alt = os.path.join(export_dir, "point_cloud.ply")
        if os.path.exists(alt):
            return alt
        raise FileNotFoundError(f"No PLY found in {export_dir}")
    return ply_path


# ── Main ─────────────────────────────────────────────────────────────────

def load_manifest():
    if os.path.exists(MANIFEST_LOCAL):
        with open(MANIFEST_LOCAL) as f:
            return json.load(f)
    try:
        import requests
        url = "https://huggingface.co/datasets/dxgl/multiview-datasets/resolve/main/manifest.json"
        print(f"Downloading manifest from {url} ...")
        resp = requests.get(url)
        resp.raise_for_status()
        return resp.json()
    except ImportError:
        print("manifest.json not found locally and requests not installed.")
        sys.exit(1)


def main():
    parser = argparse.ArgumentParser(
        description="Train 3DGS splats for all DX.GL multi-view datasets"
    )
    parser.add_argument("--object", default=None,
                        help="Train only a specific object (by name, case-insensitive)")
    parser.add_argument("--data-dir", default="./dxgl-datasets",
                        help="Directory containing extracted datasets (default: ./dxgl-datasets)")
    parser.add_argument("--output", default="./dxgl-splats",
                        help="Output directory for .ply and .splat files (default: ./dxgl-splats)")
    parser.add_argument("--ns-output", default="./ns-outputs",
                        help="Nerfstudio outputs/checkpoints directory (default: ./ns-outputs)")
    parser.add_argument("--iterations", type=int, default=DEFAULT_ITERATIONS,
                        help=f"Max training iterations (default: {DEFAULT_ITERATIONS})")
    parser.add_argument("--dry-run", action="store_true",
                        help="Show what would be trained without running")
    parser.add_argument("--export-only", action="store_true",
                        help="Skip training, only export/convert from existing ns-outputs")
    args = parser.parse_args()

    manifest = load_manifest()
    objects = manifest["objects"]

    if args.object:
        objects = [o for o in objects if args.object.lower() in o["name"].lower()]
        if not objects:
            print(f"No object matching '{args.object}' found in manifest.")
            sys.exit(1)

    os.makedirs(args.output, exist_ok=True)
    os.makedirs(args.ns_output, exist_ok=True)

    results = []
    total_time = 0

    for i, obj in enumerate(objects, 1):
        name = obj["name"]
        slug = name.lower().replace(" ", "_")
        splat_out = os.path.join(args.output, f"{slug}.splat")
        ply_out = os.path.join(args.output, f"{slug}.ply")

        print(f"\n{'='*60}")
        print(f"[{i}/{len(objects)}] {name}")
        print(f"{'='*60}")

        # Check if already done
        if os.path.exists(splat_out) and not args.export_only:
            size_mb = os.path.getsize(splat_out) / 1e6
            print(f"  ✓ Already done ({size_mb:.1f} MB .splat) — skipping")
            results.append({"name": name, "status": "skipped"})
            continue

        # Find dataset
        data_dir = os.path.join(args.data_dir, slug)
        transforms = os.path.join(data_dir, "transforms.json")
        if not os.path.exists(transforms):
            # Try nested: dataset might be inside a subdirectory
            nested = os.path.join(data_dir, "dataset", "transforms.json")
            if os.path.exists(nested):
                data_dir = os.path.join(data_dir, "dataset")
            else:
                print(f"  ✗ Dataset not found at {data_dir}")
                print(f"    Run download_all.py first, or specify --data-dir")
                results.append({"name": name, "status": "missing_data"})
                continue

        if args.dry_run:
            print(f"  Would train: {data_dir}")
            print(f"  Iterations:  {args.iterations}")
            print(f"  Output:      {splat_out}")
            results.append({"name": name, "status": "dry_run"})
            continue

        t0 = time.time()
        experiment = slug

        # Step 1: Train (unless export-only)
        if not args.export_only:
            print(f"  Training splatfacto ({args.iterations} iterations) ...")
            try:
                train_splatfacto(data_dir, experiment, args.ns_output, args.iterations)
            except RuntimeError as e:
                print(f"  ✗ Training failed: {e}")
                results.append({"name": name, "status": "train_error", "error": str(e)})
                continue

        # Step 2: Find config and export PLY
        config_path = find_latest_config(args.ns_output, experiment)
        if not config_path:
            print(f"  ✗ No config.yml found in {args.ns_output}/{experiment}/")
            results.append({"name": name, "status": "no_config"})
            continue

        print(f"  Exporting PLY from {config_path} ...")
        export_dir = os.path.join(args.ns_output, experiment, "export")
        try:
            exported_ply = export_ply(config_path, export_dir)
        except (RuntimeError, FileNotFoundError) as e:
            print(f"  ✗ Export failed: {e}")
            results.append({"name": name, "status": "export_error", "error": str(e)})
            continue

        # Step 3: Copy PLY to output
        import shutil
        shutil.copy2(exported_ply, ply_out)
        ply_mb = os.path.getsize(ply_out) / 1e6
        print(f"  PLY: {ply_mb:.1f} MB → {ply_out}")

        # Step 4: Convert to .splat
        print(f"  Converting to .splat ...")
        try:
            n_gaussians, _, splat_mb = ply_to_splat(ply_out, splat_out)
        except Exception as e:
            print(f"  ✗ Conversion failed: {e}")
            results.append({"name": name, "status": "convert_error", "error": str(e)})
            continue

        elapsed = time.time() - t0
        total_time += elapsed
        print(f"  ✓ Done: {n_gaussians:,} gaussians, {splat_mb:.1f} MB .splat ({elapsed:.0f}s)")
        results.append({
            "name": name, "status": "done",
            "gaussians": n_gaussians, "ply_mb": round(ply_mb, 1),
            "splat_mb": round(splat_mb, 1), "seconds": round(elapsed),
        })

    # Summary
    print(f"\n{'='*60}")
    print("SUMMARY")
    print(f"{'='*60}")
    done = [r for r in results if r["status"] == "done"]
    skipped = [r for r in results if r["status"] == "skipped"]
    errors = [r for r in results if r["status"] not in ("done", "skipped", "dry_run")]

    if done:
        print(f"\n  Trained: {len(done)}")
        for r in done:
            print(f"    {r['name']}: {r['gaussians']:,} gaussians, "
                  f"{r['splat_mb']} MB, {r['seconds']}s")
    if skipped:
        print(f"\n  Skipped (already done): {len(skipped)}")
    if errors:
        print(f"\n  Errors: {len(errors)}")
        for r in errors:
            print(f"    {r['name']}: {r['status']} — {r.get('error', '')}")

    if total_time > 0:
        print(f"\n  Total training time: {total_time/60:.1f} minutes")
    print(f"  Output: {os.path.abspath(args.output)}")


if __name__ == "__main__":
    main()