"""
Offline preprocessing: download LeRobot episodes and convert to .rrd files.
Downloads 3 episodes each from 6 OpenX datasets covering diverse robot types.
Reads parquet + mp4 files directly (no lerobot dependency needed).
"""
import json
import os
import numpy as np
from pathlib import Path
import rerun as rr
import pandas as pd
import av
from huggingface_hub import hf_hub_download

DATA_DIR = Path(__file__).parent / "data"

# ---- Dataset Configuration ----
# chunk_size is how many episodes per chunk directory (1000 for most v2.0 datasets)
DATASETS = [
    {
        "slug": "fractal20220817_data",
        "repo_id": "IPEC-COMMUNITY/fractal20220817_data_lerobot",
        "robot_type": "Google Robot",
        "episodes": [0, 1, 2],
        "camera_keys": ["observation.images.image"],
        "camera_labels": ["main"],
        "fps": 3,
        "chunk_size": 1000,
        "state_dim": 8,
        "action_dim": 7,
        "num_episodes_total": 87212,
    },
    {
        "slug": "droid",
        "repo_id": "IPEC-COMMUNITY/droid_lerobot",
        "robot_type": "Franka Panda",
        "episodes": [0, 1, 2],
        "camera_keys": [
            "observation.images.exterior_image_1_left",
            "observation.images.exterior_image_2_left",
            "observation.images.wrist_image_left",
        ],
        "camera_labels": ["exterior_left", "exterior_right", "wrist"],
        "fps": 15,
        "chunk_size": 1000,
        "state_dim": 8,
        "action_dim": 7,
        "num_episodes_total": 92233,
    },
    {
        "slug": "bridge_orig",
        "repo_id": "IPEC-COMMUNITY/bridge_orig_lerobot",
        "robot_type": "WidowX",
        "episodes": [0, 1, 2],
        "camera_keys": [
            "observation.images.image_0",
            "observation.images.image_1",
            "observation.images.image_2",
            "observation.images.image_3",
        ],
        "camera_labels": ["cam_0", "cam_1", "cam_2", "cam_3"],
        "fps": 5,
        "chunk_size": 1000,
        "state_dim": 8,
        "action_dim": 7,
        "num_episodes_total": 53192,
    },
    {
        "slug": "dobbe",
        "repo_id": "IPEC-COMMUNITY/dobbe_lerobot",
        "robot_type": "Hello Stretch",
        "episodes": [0, 1, 2],
        "camera_keys": ["observation.images.wrist_image"],
        "camera_labels": ["wrist"],
        "fps": 3.75,
        "chunk_size": 1000,
        "state_dim": 8,
        "action_dim": 7,
        "num_episodes_total": 5208,
    },
    {
        "slug": "jaco_play",
        "repo_id": "IPEC-COMMUNITY/jaco_play_lerobot",
        "robot_type": "Kinova Jaco",
        "episodes": [0, 1, 2],
        "camera_keys": [
            "observation.images.image",
            "observation.images.image_wrist",
        ],
        "camera_labels": ["main", "wrist"],
        "fps": 10,
        "chunk_size": 1000,
        "state_dim": 8,
        "action_dim": 7,
        "num_episodes_total": 976,
    },
    {
        "slug": "kuka",
        "repo_id": "IPEC-COMMUNITY/kuka_lerobot",
        "robot_type": "KUKA IIWA",
        "episodes": [0, 1, 2],
        "camera_keys": ["observation.images.image"],
        "camera_labels": ["main"],
        "fps": 10,
        "chunk_size": 1000,
        "state_dim": 8,
        "action_dim": 7,
        "num_episodes_total": 209880,
    },
]


def estimate_intrinsics(h, w):
    """Estimate pinhole intrinsics from image resolution."""
    fx = fy = max(h, w) * 1.2
    cx, cy = w / 2, h / 2
    return np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)


def download_file(repo_id, path, cache_dir=None):
    """Download a file from HF and return local path. Returns None if not found."""
    try:
        return hf_hub_download(repo_id, path, repo_type="dataset", cache_dir=cache_dir)
    except Exception:
        return None


def read_video_frames(video_path):
    """Decode all frames from an MP4 file. Returns list of (H, W, C) numpy arrays."""
    container = av.open(video_path)
    stream = container.streams.video[0]
    frames = []
    for frame in container.decode(stream):
        img = frame.to_ndarray(format="rgb24")  # (H, W, 3) uint8
        frames.append(img)
    container.close()
    return frames


def process_episode(ds_config, ep_idx):
    """Download and convert a single episode to .rrd format.
    Returns (rrd_path, num_frames) or (None, None) on failure."""
    slug = ds_config["slug"]
    repo_id = ds_config["repo_id"]
    chunk_size = ds_config["chunk_size"]
    camera_keys = ds_config["camera_keys"]
    camera_labels = ds_config["camera_labels"]
    fps = ds_config["fps"]

    out_dir = DATA_DIR / slug
    out_dir.mkdir(parents=True, exist_ok=True)

    rrd_path = out_dir / f"episode_{ep_idx:06d}.rrd"
    if rrd_path.exists():
        # Get frame count from cached parquet
        chunk = ep_idx // chunk_size
        ep_str = f"episode_{ep_idx:06d}"
        pq_path = f"data/chunk-{chunk:03d}/{ep_str}.parquet"
        local_pq = download_file(repo_id, pq_path)
        num_frames = len(pd.read_parquet(local_pq)) if local_pq else "?"
        print(f"  Episode {ep_idx}: already exists ({rrd_path.stat().st_size / 1024:.0f} KB)")
        return rrd_path, num_frames

    chunk = ep_idx // chunk_size
    ep_str = f"episode_{ep_idx:06d}"

    # Download parquet file
    pq_path = f"data/chunk-{chunk:03d}/{ep_str}.parquet"
    local_pq = download_file(repo_id, pq_path)
    if local_pq is None:
        print(f"  Episode {ep_idx}: parquet not found ({pq_path})")
        return None, None

    # Read parquet data
    df = pd.read_parquet(local_pq)
    num_frames = len(df)
    print(f"  Episode {ep_idx}: {num_frames} frames, parquet OK, downloading videos...")

    # Download and decode video for each camera
    camera_frames = {}
    for ck, cl in zip(camera_keys, camera_labels):
        video_path = f"videos/chunk-{chunk:03d}/{ck}/{ep_str}.mp4"
        local_video = download_file(repo_id, video_path)
        if local_video is None:
            print(f"    Camera '{cl}': video not found, skipping")
            continue
        frames = read_video_frames(local_video)
        camera_frames[cl] = frames
        print(f"    Camera '{cl}': {len(frames)} frames decoded")

    if not camera_frames:
        print(f"  Episode {ep_idx}: no video data found, skipping")
        return None, None

    # Create .rrd recording
    rec_id = f"{slug}_ep{ep_idx:06d}"
    rr.init("openx_viewer", recording_id=rec_id)

    # Determine camera positions (spread around the scene)
    positions = [
        (0.8, 0.2, 0.6),
        (-0.8, 0.2, 0.6),
        (0.2, 0.8, 0.4),
        (-0.2, -0.8, 0.4),
    ]

    # Get image resolution from first frame of first camera
    first_cam = list(camera_frames.keys())[0]
    first_img = camera_frames[first_cam][0]
    h, w = first_img.shape[:2]

    # Log camera setups (static)
    for i, (cl, frames) in enumerate(camera_frames.items()):
        pos = positions[i % len(positions)]
        cam_path = f"cameras/{cl}"
        rr.log(cam_path, rr.Transform3D(translation=pos), static=True)
        rr.log(
            f"{cam_path}/image",
            rr.Pinhole(
                image_from_camera=estimate_intrinsics(h, w),
                resolution=[w, h],
                camera_xyz=rr.ViewCoordinates.RDF,
                image_plane_distance=0.5,
            ),
            static=True,
        )
        # Note: subsequent cameras might have different resolutions but we keep same intrinsics
        # as they're estimates anyway

    # State/action labels
    state_cols = [c for c in df.columns if c == "observation.state"]
    action_cols = [c for c in df.columns if c == "action"]
    state_labels = ["x", "y", "z", "rx", "ry", "rz", "rw", "gripper"]
    action_labels = ["ax", "ay", "az", "aroll", "apitch", "ayaw", "agripper"]

    # Log frames
    n_video_frames = min(len(frames) for frames in camera_frames.values())
    for frame_i in range(n_video_frames):
        rr.set_time_sequence("frame", frame_i)
        rr.set_time_seconds("time", frame_i / fps)

        # Log camera images
        for cl, frames in camera_frames.items():
            if frame_i < len(frames):
                rr.log(f"cameras/{cl}/image", rr.Image(frames[frame_i], color_model="RGB"))

        # Log state if available and within bounds
        if state_cols and frame_i < num_frames:
            state = np.asarray(df.iloc[frame_i][state_cols[0]]).flatten()
            for j, label in enumerate(state_labels):
                if j < len(state):
                    rr.log(f"state/{label}", rr.Scalars([float(state[j])]))

        # Log action if available and within bounds
        if action_cols and frame_i < num_frames:
            action = np.asarray(df.iloc[frame_i][action_cols[0]]).flatten()
            for j, label in enumerate(action_labels):
                if j < len(action):
                    rr.log(f"action/{label}", rr.Scalars([float(action[j])]))

    rr.save(str(rrd_path))
    rr.disconnect()

    size_kb = rrd_path.stat().st_size / 1024
    print(f"  Episode {ep_idx}: saved {size_kb:.0f} KB")
    return rrd_path, num_frames


def process_dataset(ds_config):
    """Process all episodes for a dataset."""
    slug = ds_config["slug"]
    print(f"\n{'='*60}")
    print(f"{ds_config['robot_type']} — {ds_config['repo_id']}")
    print(f"  Cameras: {ds_config['camera_labels']}")
    print(f"  Episodes: {ds_config['episodes']}")

    manifest_entry = {
        "slug": slug,
        "repo_id": ds_config["repo_id"],
        "robot_type": ds_config["robot_type"],
        "fps": ds_config["fps"],
        "camera_keys": ds_config["camera_keys"],
        "camera_labels": ds_config["camera_labels"],
        "state_dim": ds_config.get("state_dim", 8),
        "action_dim": ds_config.get("action_dim", 7),
        "num_episodes_total": ds_config.get("num_episodes_total", 0),
        "episodes_available": [],
    }

    for ep_idx in ds_config["episodes"]:
        rrd_path, num_frames = process_episode(ds_config, ep_idx)
        if rrd_path:
            manifest_entry["episodes_available"].append({
                "index": ep_idx,
                "frames": num_frames,
                "file": f"data/{slug}/episode_{ep_idx:06d}.rrd",
            })

    return manifest_entry


def main():
    manifests = []
    for ds_config in DATASETS:
        meta = process_dataset(ds_config)
        manifests.append(meta)

    manifest_path = DATA_DIR / "manifest.json"
    manifest_path.write_text(json.dumps(manifests, indent=2))
    print(f"\n{'='*60}")
    print(f"Manifest written to {manifest_path}")
    print(f"Total datasets: {len(manifests)}")
    total_eps = sum(len(m["episodes_available"]) for m in manifests)
    print(f"Total episodes: {total_eps}")


if __name__ == "__main__":
    main()