"""
Batch Process DROID Episodes with Mesh + CoTracker

Processes multiple episodes and saves:
- NPZ files with tracks and metadata
- MP4 videos showing tracked trajectories (using mediapy)
"""

import os
import numpy as np
from pathlib import Path
import argparse
import cv2
import sys

# Import torch first (needs GPU)
import torch
import mediapy as media
from tqdm import tqdm
import datetime
import re

# Import TensorFlow and configure it for CPU only to avoid conflicts
import tensorflow as tf
# Disable GPU for TensorFlow to leave it available for PyTorch/CoTracker
tf.config.set_visible_devices([], 'GPU')
import tensorflow_datasets as tfds

# Add parent directory to path
sys.path.append(str(Path(__file__).parent.parent))

from utils.load_camera_calibration import CameraCalibrationLoader
from utils.franka_mesh_projection import FrankaMeshProjector


def load_cotracker():
    """Load CoTracker v3 model."""
    from cotracker.predictor import CoTrackerPredictor

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = CoTrackerPredictor(checkpoint='/mnt/kevin/vlm_models/cotracker/scaled_offline.pth')
    model = model.to(device)
    model.eval()

    return model, device


def find_closest_calibration(episode, uuid_list, calib_loader):
    """Find closest calibration by timestamp."""
    try:
        recording_path = episode['episode_metadata']['recording_folderpath'].numpy().decode('utf-8')

        match = re.search(r'/([A-Z]+)/success/(\d{4}-\d{2}-\d{2})/\w+_\w+_+\d+_(\d{2}):(\d{2}):(\d{2})_\d{4}/', recording_path)

        if not match:
            return None

        lab = match.group(1)
        date = match.group(2)
        hour = match.group(3)
        minute = match.group(4)
        second = match.group(5)

        episode_time = datetime.datetime.strptime(f"{date} {hour}:{minute}:{second}", "%Y-%m-%d %H:%M:%S")

        # Find matching calibrations
        matching_calibs = []
        for calib_uuid in uuid_list:
            if calib_uuid.startswith(f"{lab}+") and f"+{date}-" in calib_uuid:
                matching_calibs.append(calib_uuid)

        if len(matching_calibs) == 0:
            return None

        # Find closest by time
        best_uuid = None
        min_time_diff = float('inf')

        for calib_uuid in matching_calibs:
            parts = calib_uuid.split('+')
            if len(parts) >= 3:
                time_str = parts[2].replace('_cameras', '')
                match_time = re.search(r'(\d{2})h-(\d{2})m-(\d{2})s', time_str)
                if match_time:
                    calib_hour = int(match_time.group(1))
                    calib_min = int(match_time.group(2))
                    calib_sec = int(match_time.group(3))

                    calib_time = datetime.datetime.strptime(
                        f"{date} {calib_hour}:{calib_min}:{calib_sec}",
                        "%Y-%m-%d %H:%M:%S"
                    )

                    time_diff = abs((episode_time - calib_time).total_seconds())
                    if time_diff < min_time_diff:
                        min_time_diff = time_diff
                        best_uuid = calib_uuid

        return best_uuid

    except Exception as e:
        return None


def process_episode(episode, episode_idx, uuid_list, calib_loader, projector, cotracker, device,
                    camera_view='exterior', max_frames=16, num_sample_points=300,
                    num_chunks=3):
    """
    Process single episode by chunking into multiple segments.

    Args:
        num_sample_points: Total number of points to track (default 300)
        num_chunks: Number of 16-frame chunks to process and concatenate (default 3)
                   Total frames = max_frames * num_chunks

    Returns:
        dict with 'success', 'uuid', 'tracks', 'visibility', 'frames', 'num_points'
        or None if failed
    """
    # Find calibration
    uuid = find_closest_calibration(episode, uuid_list, calib_loader)
    if uuid is None:
        return None

    # Load calibration
    try:
        dual_params = calib_loader.get_dual_view_params(uuid, param_type='refined', require_refined=False)
        if dual_params is None:
            return None

        if not calib_loader.has_refined_extrinsics(uuid):
            return None

    except Exception as e:
        return None

    # Collect frames - get enough for all chunks
    total_frames_needed = max_frames * num_chunks
    frames = []
    action_positions = []  # Use action xyz instead of cartesian_position

    for step_idx, step in enumerate(episode['steps']):
        if step_idx >= total_frames_needed:
            break

        action = step['action'].numpy()
        action_positions.append(action)

        if camera_view == 'exterior':
            img = step['observation']['exterior_image_1_left'].numpy()
        else:
            img = step['observation']['wrist_image_left'].numpy()

        if img is None or len(img.shape) != 3:
            return None

        # Keep original resolution
        frames.append(img)

    if len(frames) < 10:
        return None

    # Adjust num_chunks if we don't have enough frames
    actual_chunks = min(num_chunks, len(frames) // max_frames)
    if actual_chunks == 0:
        actual_chunks = 1

    # Get actual image dimensions from first frame
    img_h, img_w = frames[0].shape[:2]
    print(f"  Image resolution: {img_w}x{img_h}")

    # Get camera params
    if camera_view == 'exterior':
        K, E = dual_params['exterior_1']
    else:
        K, E = dual_params['wrist']

    # Project end-effector position from action[:3]
    action_pos_0 = action_positions[0]
    eef_pos_3d = action_pos_0[:3].reshape(1, 3)  # Just xyz from action

    eef_2d, eef_vis = projector._project_3d_to_2d(
        eef_pos_3d, K, E, img_h=img_h, img_w=img_w
    )

    # Use EEF as the single mesh point for verification
    mesh_2d = eef_2d
    mesh_vis = eef_vis

    visible_mesh = np.sum(mesh_vis)
    if visible_mesh < 1:  # Need at least the EEF visible
        return None

    # Get visible mesh points (just EEF)
    visible_mesh_2d = mesh_2d[mesh_vis]
    num_mesh = len(visible_mesh_2d)

    print(f"  Action xyz 3D: {eef_pos_3d[0]}")
    print(f"  Action projected 2D: {visible_mesh_2d[0] if len(visible_mesh_2d) > 0 else 'NOT VISIBLE'}")

    # Only track the single EEF point for debugging
    query_points = visible_mesh_2d  # Just the EEF
    num_points = len(query_points)

    # Track indices for visualization (everything is the EEF)
    num_random_actual = 0
    num_cluster = 0

    # Keep these variables for chunk processing
    points_per_mesh = 0  # Not used
    mesh_radius = 0  # Not used
    random_points = np.empty((0, 2))  # Empty

    # Process in chunks to avoid GPU OOM
    all_tracks = []
    all_visibility = []

    print(f"  Processing {actual_chunks} chunks of {max_frames} frames each...")

    for chunk_idx in range(actual_chunks):
        chunk_start = chunk_idx * max_frames
        chunk_end = min(chunk_start + max_frames, len(frames))
        chunk_frames = frames[chunk_start:chunk_end]

        if len(chunk_frames) < 4:  # Skip very short chunks
            continue

        # Get EEF position from action at the start of this chunk
        action_pos_chunk = action_positions[chunk_start]
        eef_pos_3d_chunk = action_pos_chunk[:3].reshape(1, 3)

        # Project EEF for this chunk
        eef_2d_chunk, eef_vis_chunk = projector._project_3d_to_2d(
            eef_pos_3d_chunk, K, E, img_h=img_h, img_w=img_w
        )

        if not eef_vis_chunk[0]:
            print(f"    Chunk {chunk_idx+1}: Action xyz not visible, skipping")
            continue

        print(f"    Chunk {chunk_idx+1}: Action xyz 3D={eef_pos_3d_chunk[0]}, 2D={eef_2d_chunk[0]}")

        # Only use the single EEF point for this chunk
        visible_mesh_2d_chunk = eef_2d_chunk[eef_vis_chunk]
        query_points_chunk = visible_mesh_2d_chunk  # Just the EEF point

        # Prepare chunk video
        chunk_video_np = np.array(chunk_frames)
        chunk_video_np = chunk_video_np.transpose(0, 3, 1, 2)
        chunk_video_tensor = torch.from_numpy(chunk_video_np).float() / 255.0
        chunk_video_tensor = chunk_video_tensor.unsqueeze(0).to(device)

        # Queries for this chunk using the updated EEF position
        queries = np.zeros((len(query_points_chunk), 3))
        queries[:, 0] = 0  # Start tracking from first frame of chunk
        queries[:, 1] = query_points_chunk[:, 0]
        queries[:, 2] = query_points_chunk[:, 1]
        queries_tensor = torch.from_numpy(queries).float().unsqueeze(0).to(device)

        # Run CoTracker on chunk
        with torch.no_grad():
            pred_tracks, pred_visibility = cotracker(
                chunk_video_tensor,
                queries=queries_tensor,
                backward_tracking=False
            )

        chunk_tracks = pred_tracks[0].cpu().numpy()  # [T_chunk, N, 2]
        chunk_visibility = pred_visibility[0].cpu().numpy()  # [T_chunk, N]

        all_tracks.append(chunk_tracks)
        all_visibility.append(chunk_visibility)

        print(f"    Chunk {chunk_idx+1}/{actual_chunks}: {chunk_tracks.shape[0]} frames")

    # Concatenate all chunks along time dimension
    tracks = np.concatenate(all_tracks, axis=0)  # [T_total, N, 2]
    visibility = np.concatenate(all_visibility, axis=0)  # [T_total, N]

    # Trim frames to match actual processed length
    frames = frames[:tracks.shape[0]]

    print(f"  Total concatenated frames: {tracks.shape[0]}")

    return {
        'success': True,
        'uuid': uuid,
        'tracks': tracks,
        'visibility': visibility,
        'frames': frames,
        'num_points': num_points,
        'visible_mesh': visible_mesh,
        'num_random': num_random_actual,
        'num_cluster': num_cluster,
        'num_mesh': num_mesh
    }


def visualize_tracks_on_frame(frame, tracks, visibility, frame_idx, num_points,
                             num_random, num_cluster, num_mesh, title=""):
    """
    Draw tracks on single frame.

    Point ordering: [random_points | cluster_points | mesh_vertices]
    """
    viz = frame.copy()

    # Define color scheme
    # Random: light gray trajectories, small blue dots
    # Cluster: light yellow trajectories, small yellow dots
    # Mesh: bright green trajectories, large green dots with labels

    for pt_idx in range(num_points):
        # Determine point type
        if pt_idx < num_random:
            traj_color = (180, 180, 180)  # Light gray
            point_color = (255, 100, 0)   # Blue
            point_size = 1
            is_mesh = False
        elif pt_idx < num_random + num_cluster:
            traj_color = (100, 200, 255)  # Light yellow
            point_color = (0, 200, 255)   # Yellow
            point_size = 2
            is_mesh = False
        else:
            traj_color = (100, 255, 100)  # Light green
            point_color = (0, 255, 0)     # Bright green
            point_size = 5
            is_mesh = True

        # Draw trajectory (past 10 frames)
        if frame_idx > 0:
            for t in range(max(0, frame_idx-10), frame_idx):
                if visibility[t, pt_idx] and visibility[t+1, pt_idx]:
                    pt1 = tuple(tracks[t, pt_idx].astype(int))
                    pt2 = tuple(tracks[t+1, pt_idx].astype(int))
                    cv2.line(viz, pt1, pt2, traj_color, 1)

        # Draw current point
        if visibility[frame_idx, pt_idx]:
            pt = tuple(tracks[frame_idx, pt_idx].astype(int))
            cv2.circle(viz, pt, point_size, point_color, -1)

            # Label mesh vertices
            if is_mesh:
                mesh_idx = pt_idx - num_random - num_cluster
                cv2.putText(viz, str(mesh_idx), (pt[0]+7, pt[1]-7),
                           cv2.FONT_HERSHEY_SIMPLEX, 0.4, point_color, 1)

    # Add title
    if title:
        cv2.putText(viz, title, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)

    visible_count = np.sum(visibility[frame_idx])
    cv2.putText(viz, f"Visible: {visible_count}/{num_points}", (10, 60),
               cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 1)

    return viz


def batch_process_episodes(droid_path: str,
                           calib_dir: str,
                           output_dir: str,
                           num_episodes: int = 10,
                           start_index: int = 0,
                           camera_view: str = 'exterior',
                           max_frames: int = 16,
                           num_sample_points: int = 300,
                           num_chunks: int = 3):
    """Batch process multiple episodes."""

    print("=" * 80)
    print("DROID Batch Processing: Mesh + CoTracker")
    print("=" * 80)
    print(f"  Episodes: {num_episodes} (starting from {start_index})")
    print(f"  Camera: {camera_view}")
    print(f"  Frames per chunk: {max_frames}")
    print(f"  Number of chunks: {num_chunks}")
    print(f"  Total frames per episode: {max_frames * num_chunks}")
    print(f"  Output: {output_dir}")
    print()

    # Create output directories
    output_path = Path(output_dir)
    npz_path = output_path / "npz"
    video_path = output_path / "videos"
    npz_path.mkdir(parents=True, exist_ok=True)
    video_path.mkdir(parents=True, exist_ok=True)

    # Initialize tools
    calib_loader = CameraCalibrationLoader(calib_dir)
    projector = FrankaMeshProjector(use_gui=False)
    cotracker, device = load_cotracker()

    # Get calibration UUIDs
    calib_path = Path(calib_dir)
    calib_files = sorted(calib_path.glob("*_cameras.json"))
    uuid_list = [f.stem.replace('_cameras', '') for f in calib_files]
    print(f"Loaded {len(uuid_list)} camera calibrations")

    # Load DROID dataset
    print("Loading DROID dataset...")
    builder = tfds.builder_from_directory(droid_path)
    dataset = builder.as_dataset(split='train')

    # Process episodes
    processed = 0
    skipped = 0

    pbar = tqdm(total=num_episodes, desc="Processing")

    for episode_idx, episode in enumerate(dataset):
        if episode_idx < start_index:
            continue

        if processed >= num_episodes:
            break

        try:
            result = process_episode(
                episode, episode_idx, uuid_list, calib_loader, projector,
                cotracker, device, camera_view, max_frames,
                num_sample_points=num_sample_points, num_chunks=num_chunks
            )

            if result is None:
                skipped += 1
                continue

            # Save NPZ
            npz_file = npz_path / f"episode_{processed:04d}.npz"
            np.savez_compressed(
                npz_file,
                tracks=result['tracks'],
                visibility=result['visibility'],
                uuid=result['uuid'],
                num_points=result['num_points'],
                visible_mesh=result['visible_mesh'],
                episode_index=episode_idx
            )

            # Create video with mediapy
            video_frames = []
            for frame_idx, frame in enumerate(result['frames']):
                viz = visualize_tracks_on_frame(
                    frame,
                    result['tracks'],
                    result['visibility'],
                    frame_idx,
                    result['num_points'],
                    result['num_random'],
                    result['num_cluster'],
                    result['num_mesh'],
                    title=f"Episode {processed} | Frame {frame_idx}/{len(result['frames'])}"
                )
                video_frames.append(viz)

            video_file = video_path / f"episode_{processed:04d}.mp4"
            media.write_video(str(video_file), video_frames, fps=10)

            processed += 1
            pbar.update(1)

        except Exception as e:
            print(f"\nError processing episode {episode_idx}: {e}")
            skipped += 1
            continue

    pbar.close()

    print("\n" + "=" * 80)
    print("Batch Processing Complete")
    print("=" * 80)
    print(f"  Processed: {processed} episodes")
    print(f"  Skipped: {skipped} episodes")
    print(f"  NPZ files: {npz_path}")
    print(f"  Videos: {video_path}")
    print("=" * 80)


def main():
    parser = argparse.ArgumentParser(description="Batch process DROID episodes with CoTracker")

    parser.add_argument('--droid-path', type=str,
                       default='/mnt/kevin/data/droid/droid/1.0.0',
                       help='Path to DROID RLDS dataset')
    parser.add_argument('--calib-dir', type=str,
                       default='/root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main/vision/u/wenlongh/datasets/droid_v4/cameras',
                       help='Path to camera calibration directory')
    parser.add_argument('--output-dir', type=str,
                       default='/tmp/droid_batch_cotracker',
                       help='Output directory')
    parser.add_argument('--num-episodes', type=int, default=10,
                       help='Number of episodes to process')
    parser.add_argument('--start-index', type=int, default=0,
                       help='Starting episode index')
    parser.add_argument('--camera', type=str, default='exterior',
                       choices=['exterior', 'wrist'],
                       help='Camera view')
    parser.add_argument('--max-frames', type=int, default=16,
                       help='Frames per chunk (to avoid GPU OOM)')
    parser.add_argument('--num-chunks', type=int, default=3,
                       help='Number of chunks to process and concatenate')
    parser.add_argument('--num-points', type=int, default=300,
                       help='Total number of points to track')

    args = parser.parse_args()

    batch_process_episodes(
        args.droid_path,
        args.calib_dir,
        args.output_dir,
        args.num_episodes,
        args.start_index,
        args.camera,
        args.max_frames,
        args.num_points,
        args.num_chunks
    )


if __name__ == "__main__":
    main()