#!/usr/bin/env python3
"""
HuggingFace Jobs script: Collect teacher outputs with metadata tracking.

Saves for each image:
- Full SAM 3D Body outputs (.npz)
- Metadata: num_humans, image_width, image_height, processing_time
"""
import argparse
import os
from pathlib import Path
import warnings
warnings.filterwarnings('ignore')
import logging
import sys

# Configure logging to stdout (so HF Jobs can capture it)
logging.basicConfig(
    level=logging.INFO,
    format='[%(asctime)s] %(levelname)s: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    stream=sys.stdout,
    force=True
)
logger = logging.getLogger(__name__)

# Also flush stdout immediately
sys.stdout.reconfigure(line_buffering=True) if hasattr(sys.stdout, 'reconfigure') else None

import numpy as np
import torch
from datasets import load_dataset, Dataset as HFDataset, Features, Value
from PIL import Image
import cv2
from typing import List, Dict, Optional
import time
import functools
import json
from collections import defaultdict
import subprocess

# SAM 3D Body imports
import sys
sam_repo = Path(__file__).parent.parent / "sam-3d-body"
if str(sam_repo) not in sys.path:
    sys.path.insert(0, str(sam_repo))
from sam_3d_body import load_sam_3d_body, SAM3DBodyEstimator

# Set environment variable
os.environ['PYOPENGL_PLATFORM'] = 'osmesa'


class GazeEstimator:
    """Gaze estimation using L2CS-Net"""
    
    def __init__(self, device='cuda'):
        self.device = device
        logger.info("Installing L2CS-Net...")
        # Install L2CS-Net package
        try:
            subprocess.run(
                ['pip', 'install', '-q', 'git+https://github.com/edavalosanaya/L2CS-Net.git@main'],
                check=True,
                capture_output=True
            )
            logger.info("✓ L2CS-Net installed")
        except Exception as e:
            print(f"Warning: L2CS-Net installation failed: {e}")
        
        print("Loading L2CS-Net gaze estimator...")
        try:
            from l2cs import Pipeline
            # Use Gaze360 pretrained weights (better for unconstrained images)
            self.pipeline = Pipeline(
                weights='L2CSNet_gaze360.pkl',  # Will download automatically
                arch='ResNet50',
                device=device
            )
            self.enabled = True
            print("✓ L2CS-Net gaze estimator loaded")
        except Exception as e:
            print(f"Warning: Could not load L2CS-Net: {e}")
            print("Gaze estimation will be disabled")
            self.enabled = False
    
    def estimate_gaze(self, bbox, detections=None, image_bgr=None):
        """
        Estimate gaze direction for a bbox using optional precomputed detections.
        
        Args:
            bbox: [x1, y1, x2, y2]
            detections: cached L2CS pipeline outputs for the full image
            image_bgr: optional BGR image (used only when detections missing)
        """
        if not self.enabled:
            return None
        
        try:
            if detections is None and image_bgr is not None:
                detections = self.pipeline.step(image_bgr)
            if not detections:
                return None
            
            x1, y1, x2, y2 = bbox
            bbox_center = np.array([(x1 + x2) / 2, (y1 + y2) / 2])
            best_result = None
            min_dist = float('inf')
            
            for result in detections:
                face_bbox = result.get('bbox')
                if face_bbox is None:
                    continue
                fx1, fy1, fx2, fy2 = face_bbox
                face_center = np.array([(fx1 + fx2) / 2, (fy1 + fy2) / 2])
                dist = np.linalg.norm(bbox_center - face_center)
                if dist < min_dist:
                    min_dist = dist
                    best_result = result
            
            if best_result is not None:
                pitch = float(best_result.get('pitch', 0))
                yaw = float(best_result.get('yaw', 0))
                return {'pitch': pitch, 'yaw': yaw}
            return None
        except Exception as e:
            print(f"Gaze estimation error: {e}")
            return None
    
    def run_pipeline(self, image_bgr):
        """Run L2CS pipeline once per image and reuse detections."""
        if not self.enabled:
            return None
        try:
            return self.pipeline.step(image_bgr)
        except Exception as e:
            print(f"Warning: L2CS pipeline failed: {e}")
            return None


class FaceEmbedder:
    """Face embedding extraction using InsightFace ArcFace (ResNet100-IR)"""
    
    def __init__(self, device='cuda'):
        self.device = device
        print("Installing InsightFace...")
        # Install InsightFace package
        try:
            subprocess.run(
                ['pip', 'install', '-q', 'insightface', 'onnxruntime-gpu' if device.type == 'cuda' else 'onnxruntime'],
                check=True,
                capture_output=True
            )
            print("✓ InsightFace installed")
        except Exception as e:
            print(f"Warning: InsightFace installation failed: {e}")
        
        print("Loading InsightFace ArcFace (ResNet100-IR)...")
        try:
            import insightface
            from insightface.app import FaceAnalysis
            
            # Initialize with ArcFace ResNet100 model
            # det_size=(640, 640) for better detection
            self.app = FaceAnalysis(
                name='buffalo_l',  # Uses ResNet100 backbone
                providers=['CUDAExecutionProvider'] if device.type == 'cuda' else ['CPUExecutionProvider']
            )
            self.app.prepare(ctx_id=0 if device.type == 'cuda' else -1, det_size=(640, 640))
            self.enabled = True
            print("✓ InsightFace ArcFace loaded (ResNet100-IR)")
            print("  Model: buffalo_l (ResNet100 + ArcFace head)")
            print("  Robust to: occlusion, lighting, blur, pose variations, aging")
        except Exception as e:
            print(f"Warning: Could not load InsightFace: {e}")
            print("Face embeddings will be disabled")
            self.enabled = False
    
    def extract_embedding(self, image, bbox=None, keypoints_2d=None):
        """
        Extract 512-dimensional ArcFace embedding from face.
        
        Args:
            image: PIL Image or BGR numpy array
            bbox: [x1, y1, x2, y2] in pixel coordinates (optional, for cropping)
            keypoints_2d: Face keypoints for alignment (optional)
            
        Returns:
            dict with 'embedding' (512-dim vector), 'det_score' (confidence), or None if failed
        """
        if not self.enabled:
            return None
        
        try:
            # Convert to numpy BGR (InsightFace expects BGR)
            if isinstance(image, Image.Image):
                image_np = np.array(image)
                if image_np.shape[2] == 3:
                    image_bgr = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
                else:
                    image_bgr = image_np
            else:
                image_bgr = image
            
            # Optionally crop to bbox region for efficiency
            if bbox is not None:
                x1, y1, x2, y2 = map(int, bbox)
                # Add padding for better detection
                pad = 20
                h, w = image_bgr.shape[:2]
                x1 = max(0, x1 - pad)
                y1 = max(0, y1 - pad)
                x2 = min(w, x2 + pad)
                y2 = min(h, y2 + pad)
                image_bgr = image_bgr[y1:y2, x1:x2]
            
            # Detect and extract faces
            faces = self.app.get(image_bgr)
            
            if len(faces) == 0:
                return None
            
            # Use the largest/most confident face
            face = max(faces, key=lambda x: x.det_score)
            
            # Extract embedding (512-dim ArcFace feature)
            embedding = face.embedding  # numpy array, shape (512,)
            det_score = float(face.det_score)
            
            # Normalize embedding (L2 norm = 1)
            embedding_norm = embedding / np.linalg.norm(embedding)
            
            return {
                'embedding': embedding_norm.astype(np.float32).tolist(),
                'det_score': det_score,
                'embedding_dim': len(embedding)
            }
            
        except Exception as e:
            print(f"Face embedding extraction error: {e}")
            return None


class NSFWClassifier:
    """NSFW classification using EraX-NSFW-V1.0 YOLO model"""
    
    def __init__(self, device='cuda'):
        self.device = device
        print("Loading EraX-NSFW YOLO model...")
        try:
            # Download the model if not already downloaded
            from huggingface_hub import snapshot_download
            snapshot_download(repo_id="erax-ai/EraX-NSFW-V1.0", local_dir="./", force_download=False)
            
            from ultralytics import YOLO
            # Use the m model for better accuracy
            self.model = YOLO('erax_nsfw_yolo11m.pt')
            self.enabled = True
            print("✓ EraX-NSFW classifier loaded (YOLO11m)")
        except Exception as e:
            print(f"Warning: Could not load EraX-NSFW: {e}")
            print("NSFW classification will be disabled")
            self.enabled = False
    
    def classify_crop(self, image_pil, bbox):
        """
        Classify NSFW content in a crop defined by bbox.
        
        Args:
            image_pil: PIL Image
            bbox: [x1, y1, x2, y2] in pixel coordinates
            
        Returns:
            dict with class scores, or None if failed
        """
        if not self.enabled:
            return None
        
        try:
            # Convert bbox to ultralytics format [x1, y1, x2, y2]
            x1, y1, x2, y2 = bbox
            
            # Crop the image
            crop = image_pil.crop((x1, y1, x2, y2))
            
            # Convert PIL to numpy for ultralytics
            crop_np = np.array(crop)
            
            # Run inference with confidence and IoU thresholds
            results = self.model(crop_np, conf=0.2, iou=0.3, verbose=False)
            
            detections = []
            if len(results) > 0 and len(results[0].boxes) > 0:
                boxes = results[0].boxes
                for box in boxes:
                    class_id = int(box.cls.item())
                    confidence = box.conf.item()
                    
                    # Model classes: ['anus', 'make_love', 'nipple', 'penis', 'vagina']
                    class_names = ['anus', 'make_love', 'nipple', 'penis', 'vagina']
                    class_name = class_names[class_id] if class_id < len(class_names) else f'class_{class_id}'
                    
                    # Get bbox relative to crop and convert to absolute coordinates
                    dx1, dy1, dx2, dy2 = box.xyxy[0].tolist()
                    abs_bbox = [x1 + dx1, y1 + dy1, x1 + dx2, y1 + dy2]
                    
                    detections.append({
                        'class': class_name,
                        'confidence': confidence,
                        'bbox': abs_bbox
                    })
            
            if detections:
                return detections
            else:
                # No detections - consider safe
                return [{'class': 'safe', 'confidence': 1.0, 'bbox': [x1, y1, x2, y2]}]
                
        except Exception as e:
            print(f"! NSFW classification failed: {e}")
            return None


def compute_face_orientation(vertices, keypoints_3d):
    """
    Compute face orientation vector from 3D mesh vertices and keypoints.
    Uses nose→head_top vector as face direction.
    
    Args:
        vertices: (N, 3) array of 3D vertices
        keypoints_3d: (70, 3) array of 3D keypoints (MHR70 format)
        
    Returns:
        (3,) normalized face orientation vector [x, y, z] or None
    """
    if vertices is None or keypoints_3d is None:
        return None
    
    try:
        # MHR70 keypoint indices (from sam-3d-body/sam_3d_body/metadata/mhr70.py)
        # 0: nose, 1: left-eye, 2: right-eye
        # Check if face keypoints are valid (not all zeros)
        nose_3d = keypoints_3d[0]
        left_eye_3d = keypoints_3d[1]
        right_eye_3d = keypoints_3d[2]
        
        # Verify face keypoints are valid (not at origin)
        if (np.linalg.norm(nose_3d) < 1e-6 or 
            np.linalg.norm(left_eye_3d) < 1e-6 or 
            np.linalg.norm(right_eye_3d) < 1e-6):
            return None  # Face keypoints not detected
        
        # Find topmost vertex as head top (highest Y coordinate in body frame)
        head_top_idx = np.argmax(vertices[:, 1])  # Y is up in SMPL convention
        head_top_3d = vertices[head_top_idx]
        
        # Face orientation = nose → head_top (points upward/forward from face)
        face_orientation = head_top_3d - nose_3d
        
        # Normalize
        norm = np.linalg.norm(face_orientation)
        if norm > 1e-6:
            face_orientation = face_orientation / norm
            return face_orientation.astype(np.float32)
        
        return None
        
    except Exception as e:
        print(f"Face orientation computation failed: {e}")
        return None


def compute_bbox_from_keypoints(keypoints_2d, indices):
    """
    Compute bounding box from a set of 2D keypoints.
    
    Args:
        keypoints_2d: (70, 2) array of 2D keypoints
        indices: list of keypoint indices to include
        
    Returns:
        [x1, y1, x2, y2] or None if no valid keypoints
    """
    if keypoints_2d is None or len(keypoints_2d) < max(indices) + 1:
        return None
    
    valid_points = []
    for idx in indices:
        kp = keypoints_2d[idx]
        if kp[0] >= 0 and kp[1] >= 0:  # Check if keypoint is valid (not -1, -1)
            valid_points.append(kp)
    
    if len(valid_points) < 2:  # Need at least 2 points for a bbox
        return None
    
    points = np.array(valid_points)
    x1, y1 = points.min(axis=0)
    x2, y2 = points.max(axis=0)
    
    # Add some padding (10% of bbox size)
    width = x2 - x1
    height = y2 - y1
    padding_x = width * 0.1
    padding_y = height * 0.1
    
    x1 = max(0, x1 - padding_x)
    y1 = max(0, y1 - padding_y)
    x2 = x2 + padding_x
    y2 = y2 + padding_y
    
    return [float(x1), float(y1), float(x2), float(y2)]


def process_batch(batch, teacher, nsfw_classifier, gaze_estimator, face_embedder, faces, out_dir):
    """
    Process a batch of samples using dataset.map() with batched NSFW inference
    
    Args:
        batch: dict with 'image' list and optional 'image_path' list
        ... (other args)
        
    Returns:
        dict with 'metadata' list
    """
    images = batch['image']
    image_paths = batch.get('image_path', [f'img_{i:06d}' for i in range(len(images))])
    
    # First pass: process images and collect humans data (without NSFW)
    humans_data_list = []
    outputs_list = []
    image_rgbs = []  # cache RGB numpy arrays for later crops
    image_bgrs = []  # cache BGR arrays for gaze/face embedding
    gaze_detections = []
    
    for img_idx, image_pil in enumerate(images):
        img_width, img_height = image_pil.size
        image_rgb = np.array(image_pil.convert('RGB'))
        image_rgbs.append(image_rgb)
        image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
        image_bgrs.append(image_bgr)
        
        detections = gaze_estimator.run_pipeline(image_bgr) if gaze_estimator is not None else None
        gaze_detections.append(detections)
        
        with torch.inference_mode():
            outputs = teacher.process_one_image(image_bgr)
        outputs_list.append(outputs)
        
        if not outputs:
            humans_data_list.append([])
            continue
        
        humans_data = []
        for human_idx, pred in enumerate(outputs):
            vertices = pred.get('pred_vertices')
            cam_t = pred.get('pred_cam_t')
            focal_length = pred.get('focal_length')
            kpts2d = pred.get('pred_keypoints_2d')
            kpts3d = pred.get('pred_keypoints_3d')
            bbox = pred.get('bbox', None)
            
            # Check face
            has_face = False
            if kpts2d is not None and kpts3d is not None and len(kpts2d) >= 3 and len(kpts3d) >= 3:
                nose_2d = kpts2d[0]
                left_eye_2d = kpts2d[1]
                right_eye_2d = kpts2d[2]
                nose_3d = kpts3d[0]
                left_eye_3d = kpts3d[1]
                right_eye_3d = kpts3d[2]
                
                keypoints_valid_3d = (np.linalg.norm(nose_3d) > 1e-6 and 
                                     np.linalg.norm(left_eye_3d) > 1e-6 and 
                                     np.linalg.norm(right_eye_3d) > 1e-6)
                
                keypoints_in_image = True
                if keypoints_valid_3d:
                    for kp in [nose_2d, left_eye_2d, right_eye_2d]:
                        if (kp[0] < 0 or kp[0] >= img_width or 
                            kp[1] < 0 or kp[1] >= img_height):
                            keypoints_in_image = False
                            break
                
                has_face = keypoints_valid_3d and keypoints_in_image
            
            face_orientation = None
            if has_face:
                face_orientation = compute_face_orientation(vertices, kpts3d)
            
            gaze_direction = None
            if has_face and bbox is not None and gaze_estimator is not None:
                try:
                    gaze_direction = gaze_estimator.estimate_gaze(bbox, detections=gaze_detections[img_idx])
                except Exception as e:
                    gaze_direction = None
            
            face_embedding = None
            if has_face and bbox is not None and face_embedder is not None:
                try:
                    face_embedding = face_embedder.extract_embedding(image_bgrs[img_idx], bbox, kpts2d)
                except Exception as e:
                    face_embedding = None
            
            # Compute bboxes
            left_hand_bbox = None
            right_hand_bbox = None
            left_foot_bbox = None
            right_foot_bbox = None
            
            if kpts2d is not None:
                left_hand_indices = list(range(42, 62))
                left_hand_bbox = compute_bbox_from_keypoints(kpts2d, left_hand_indices)
                
                right_hand_indices = list(range(21, 41))
                right_hand_bbox = compute_bbox_from_keypoints(kpts2d, right_hand_indices)
                
                left_foot_indices = [15, 16, 17]
                left_foot_bbox = compute_bbox_from_keypoints(kpts2d, left_foot_indices)
                
                right_foot_indices = [18, 19, 20]
                right_foot_bbox = compute_bbox_from_keypoints(kpts2d, right_foot_indices)
            
            humans_data.append({
                'human_idx': human_idx,
                'bbox': bbox.tolist() if bbox is not None else None,
                'left_hand_bbox': left_hand_bbox,
                'right_hand_bbox': right_hand_bbox,
                'left_foot_bbox': left_foot_bbox,
                'right_foot_bbox': right_foot_bbox,
                'has_face': has_face,
                'face_orientation': face_orientation.tolist() if face_orientation is not None else None,
                'gaze_direction': gaze_direction,
                'face_embedding': face_embedding,
                'has_mesh': vertices is not None,
                'nsfw_scores': None  # Will fill later
            })
        
        humans_data_list.append(humans_data)
    
    # Batch NSFW classification
    crops = []
    crop_info = []  # (img_idx, human_idx)
    
    for img_idx, humans_data in enumerate(humans_data_list):
        image_pil = images[img_idx]
        for human_idx, human in enumerate(humans_data):
            bbox = human['bbox']
            if bbox is not None:
                x1, y1, x2, y2 = bbox
                ix1, iy1, ix2, iy2 = map(lambda v: max(0, int(round(v))), [x1, y1, x2, y2])
                ix1, iy1 = min(ix1, image_rgbs[img_idx].shape[1]-1), min(iy1, image_rgbs[img_idx].shape[0]-1)
                ix2, iy2 = max(ix1+1, min(ix2, image_rgbs[img_idx].shape[1])), max(iy1+1, min(iy2, image_rgbs[img_idx].shape[0]))
                crop_np = np.ascontiguousarray(image_rgbs[img_idx][iy1:iy2, ix1:ix2])
                crops.append(crop_np)
                crop_info.append((img_idx, human_idx))
    
    if crops and nsfw_classifier is not None and nsfw_classifier.enabled:
        try:
            results = nsfw_classifier.model(crops, conf=0.2, iou=0.3, verbose=False)
            
            for crop_idx, result in enumerate(results):
                img_idx, human_idx = crop_info[crop_idx]
                bbox = humans_data_list[img_idx][human_idx]['bbox']
                x1, y1, x2, y2 = bbox
                
                detections = []
                if result.boxes:
                    for box in result.boxes:
                        class_id = int(box.cls.item())
                        confidence = box.conf.item()
                        class_names = ['anus', 'make_love', 'nipple', 'penis', 'vagina']
                        class_name = class_names[class_id] if class_id < len(class_names) else f'class_{class_id}'
                        
                        dx1, dy1, dx2, dy2 = box.xyxy[0].tolist()
                        abs_bbox = [x1 + dx1, y1 + dy1, x1 + dx2, y1 + dy2]
                        
                        detections.append({
                            'class': class_name,
                            'confidence': confidence,
                            'bbox': abs_bbox
                        })
                
                if detections:
                    humans_data_list[img_idx][human_idx]['nsfw_scores'] = detections
                else:
                    humans_data_list[img_idx][human_idx]['nsfw_scores'] = [{'class': 'safe', 'confidence': 1.0, 'bbox': [x1, y1, x2, y2]}]
        except Exception as e:
            print(f"! Batched NSFW failed: {e}")
            # Fallback: set safe for all
            for img_idx, humans_data in enumerate(humans_data_list):
                for human in humans_data:
                    if human['bbox'] is not None:
                        x1, y1, x2, y2 = human['bbox']
                        human['nsfw_scores'] = [{'class': 'safe', 'confidence': 1.0, 'bbox': [x1, y1, x2, y2]}]
    
    # Save NPZ files and create metadata
    metadatas = []
    for img_idx, (humans_data, image_path) in enumerate(zip(humans_data_list, image_paths)):
        image_pil = images[img_idx]
        image_id = Path(image_path).stem if image_path else f'img_{img_idx:06d}'
        img_width, img_height = image_pil.size
        
        out_path = out_dir / f"{image_id}.npz"
        if out_path.exists():
            metadatas.append(None)
            continue
        
        if not humans_data:
            metadata = {
                'image_id': image_id,
                'num_humans': 0,
                'image_width': img_width,
                'image_height': img_height,
                'processing_time_ms': 0,  # Not tracked in batch
                'status': 'no_detection',
                'humans': []
            }
        else:
            num_humans = len(humans_data)
            
            # Save first human's mesh
            pred = outputs_list[img_idx][0]
            vertices = pred.get('pred_vertices')
            cam_t = pred.get('pred_cam_t')
            focal_length = pred.get('focal_length')
            kpts2d = pred.get('pred_keypoints_2d')
            kpts3d = pred.get('pred_keypoints_3d')
            bbox_0 = pred.get('bbox', None)
            
            np.savez_compressed(
                out_path,
                vertices=vertices.astype(np.float32) if vertices is not None else None,
                faces=faces.astype(np.int32),
                cam_t=cam_t.astype(np.float32) if cam_t is not None else None,
                focal_length=np.array([focal_length], dtype=np.float32) if focal_length is not None else None,
                keypoints_2d=kpts2d.astype(np.float32) if kpts2d is not None else None,
                keypoints_3d=kpts3d.astype(np.float32) if kpts3d is not None else None,
                bbox=np.array(bbox_0, dtype=np.float32) if bbox_0 is not None else None,
                image_id=image_id,
                num_humans=num_humans,
                image_width=img_width,
                image_height=img_height,
                humans_metadata=json.dumps(humans_data)
            )
            
            metadata = {
                'image_id': image_id,
                'num_humans': num_humans,
                'image_width': img_width,
                'image_height': img_height,
                'processing_time_ms': 0,  # Not tracked
                'status': 'success',
                'npz_size_bytes': out_path.stat().st_size,
                'humans': humans_data
            }
        
        metadatas.append(metadata)
    
    return {'metadata': metadatas}


def main():
    logger.info("="*60)
    logger.info("SAM 3D Body Metadata Collection with Face Features")
    logger.info("="*60)
    sys.stdout.flush()
    
    ap = argparse.ArgumentParser()
    ap.add_argument('--input-dataset', type=str, required=True)
    ap.add_argument('--output-dataset', type=str, required=True)
    ap.add_argument('--split', type=str, default='train')
    ap.add_argument('--checkpoint', type=str, default='checkpoints/sam-3d-body-dinov3/model.ckpt')
    ap.add_argument('--mhr-path', type=str, default='checkpoints/sam-3d-body-dinov3/assets/mhr_model.pt')
    ap.add_argument('--limit', type=int, default=0)
    ap.add_argument('--shard-index', type=int, default=0)
    ap.add_argument('--num-shards', type=int, default=1)
    args = ap.parse_args()

    logger.info(f"Arguments: {vars(args)}")
    sys.stdout.flush()

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    logger.info(f"Using device: {device}")
    if torch.cuda.is_available():
        logger.info(f"  GPU: {torch.cuda.get_device_name(0)}")
        logger.info(f"  CUDA version: {torch.version.cuda}")
    sys.stdout.flush()

    # Load gaze estimator
    logger.info("Loading gaze estimator...")
    sys.stdout.flush()
    gaze_estimator = GazeEstimator(device=device)
    
    # Load face embedder (InsightFace ArcFace)
    logger.info("Loading face embedder (InsightFace ArcFace)...")
    sys.stdout.flush()
    face_embedder = FaceEmbedder(device=device)
    
    # Load NSFW classifier
    logger.info("Loading NSFW classifier...")
    sys.stdout.flush()
    nsfw_classifier = NSFWClassifier(device=device)

    # Load teacher
    logger.info("Loading SAM 3D Body teacher...")
    sys.stdout.flush()
    start_load = time.time()
    model, model_cfg = load_sam_3d_body(args.checkpoint, device=device, mhr_path=args.mhr_path)
    model.eval()
    
    teacher = SAM3DBodyEstimator(
        sam_3d_body_model=model,
        model_cfg=model_cfg,
        human_detector=None,
        human_segmentor=None,
        fov_estimator=None,
    )
    logger.info(f"✓ Model loaded in {time.time() - start_load:.1f}s")
    sys.stdout.flush()

    # Load dataset
    logger.info(f"Loading dataset {args.input_dataset}...")
    sys.stdout.flush()
    start_ds = time.time()
    ds = load_dataset(args.input_dataset, split=args.split, streaming=True)
    
    if args.num_shards > 1:
        ds = ds.shard(num_shards=args.num_shards, index=args.shard_index)
        logger.info(f"Using shard {args.shard_index+1}/{args.num_shards} (~{100/args.num_shards:.1f}% of dataset)")
    
    if args.limit and args.limit > 0:
        ds = ds.take(args.limit)
    logger.info(f"✓ Dataset ready in {time.time() - start_ds:.1f}s")
    sys.stdout.flush()

    # Prepare output directory and shared mesh topology
    out_dir = Path('teacher_labels')
    out_dir.mkdir(exist_ok=True)
    faces = teacher.faces
    logger.info(f"Mesh topology: {faces.shape[0]} faces")
    sys.stdout.flush()

    # Process using dataset.map() for efficient batching
    batch_size = 4  # Adjust based on GPU memory (higher = more efficient)
    logger.info(f"Processing with batch_size={batch_size} using dataset.map()")
    sys.stdout.flush()
    
    process_batch_partial = functools.partial(
        process_batch,
        teacher=teacher,
        nsfw_classifier=nsfw_classifier,
        gaze_estimator=gaze_estimator,
        face_embedder=face_embedder,
        faces=faces,
        out_dir=out_dir
    )
    
    processed_ds = ds.map(
        process_batch_partial,
        batched=True,
        batch_size=batch_size,
        remove_columns=ds.column_names  # Remove original columns, keep only metadata
    )
    
    # Collect metadata from processed dataset
    metadata_records = []
    batch_count = 0
    start_process = time.time()
    
    for batch_result in processed_ds:
        metadata_records.extend(batch_result['metadata'])
        batch_count += 1
        
        if batch_count % 10 == 0:
            elapsed = time.time() - start_process
            processed = sum(1 for m in metadata_records if m and m['status'] == 'success')
            speed = processed / elapsed if elapsed > 0 else 0
            logger.info(f"[{batch_count} batches] success={processed}, speed={speed:.2f} img/s")
            sys.stdout.flush()
    
    total_time = time.time() - start_process
    processed = sum(1 for m in metadata_records if m and m['status'] == 'success')
    no_detection = sum(1 for m in metadata_records if m and m['status'] == 'no_detection')
    failed = sum(1 for m in metadata_records if m and m['status'] == 'error')
    
    logger.info("="*60)
    logger.info(f"✓ Processing complete!")
    logger.info(f"  Processed: {processed} images in {total_time:.1f}s ({processed/total_time:.2f} img/s)")
    logger.info(f"  No detection: {no_detection}, Failed: {failed}")
    logger.info("="*60)
    sys.stdout.flush()
    
    # Compute metadata statistics
    if metadata_records:
        successful = [m for m in metadata_records if m['status'] == 'success']
        if successful:
            total_humans = sum(m['num_humans'] for m in successful)
            avg_humans = total_humans / len(successful)
            avg_width = sum(m['image_width'] for m in successful) / len(successful)
            avg_height = sum(m['image_height'] for m in successful) / len(successful)
            avg_time = sum(m['processing_time_ms'] for m in successful) / len(successful)
            
            # NSFW statistics
            nsfw_stats = defaultdict(list)
            for m in successful:
                for human in m.get('humans', []):
                    nsfw_list = human.get('nsfw_scores', [])
                    for detection in nsfw_list:
                        label = detection['class']
                        score = detection['confidence']
                        nsfw_stats[label].append(score)
            
            print(f"\nMetadata Statistics:")
            print(f"  Total humans detected: {total_humans}")
            print(f"  Avg humans per image: {avg_humans:.2f}")
            print(f"  Avg image size: {avg_width:.0f}x{avg_height:.0f}")
            print(f"  Avg processing time: {avg_time:.0f}ms")
            
            if nsfw_stats:
                print(f"\nNSFW Classification Statistics:")
                for label, scores in nsfw_stats.items():
                    avg_score = sum(scores) / len(scores)
                    max_score = max(scores)
                    print(f"  {label}: avg={avg_score:.3f}, max={max_score:.3f}, n={len(scores)}")
            
            # Face orientation and gaze statistics
            face_orientation_count = sum(1 for m in successful for h in m.get('humans', []) if h.get('face_orientation'))
            gaze_count = sum(1 for m in successful for h in m.get('humans', []) if h.get('gaze_direction'))
            face_embedding_count = sum(1 for m in successful for h in m.get('humans', []) if h.get('face_embedding'))
            print(f"\nFace Orientation & Gaze Statistics:")
            print(f"  Face orientations computed: {face_orientation_count}/{total_humans}")
            print(f"  Gaze directions estimated: {gaze_count}/{total_humans}")
            print(f"  Face embeddings extracted: {face_embedding_count}/{total_humans}")
            
            # Face embedding quality statistics
            if face_embedding_count > 0:
                det_scores = [h['face_embedding']['det_score'] for m in successful 
                             for h in m.get('humans', []) if h.get('face_embedding')]
                avg_det_score = sum(det_scores) / len(det_scores)
                min_det_score = min(det_scores)
                print(f"\nFace Embedding Quality (InsightFace ArcFace):")
                print(f"  Model: ResNet100-IR + ArcFace head (512-dim)")
                print(f"  Avg detection confidence: {avg_det_score:.3f}")
                print(f"  Min detection confidence: {min_det_score:.3f}")

    
    # Save metadata JSON locally
    metadata_path = Path('metadata.json')
    with open(metadata_path, 'w') as f:
        json.dump(metadata_records, f, indent=2)
    print(f"Saved metadata to {metadata_path}")
    
    # Upload labels
    print(f"\nUploading labels to {args.output_dataset}...")
    
    label_files = sorted(out_dir.glob('*.npz'))
    data = {'image_id': [], 'label_data': []}
    
    for npz_path in label_files:
        data['image_id'].append(npz_path.stem)
        with open(npz_path, 'rb') as f:
            data['label_data'].append(f.read())
    
    features = Features({
        'image_id': Value('string'),
        'label_data': Value('binary'),
    })
    
    label_ds = HFDataset.from_dict(data, features=features)
    label_ds.push_to_hub(
        args.output_dataset,
        split=args.split,
        token=os.environ.get('HF_TOKEN'),
        private=True,
    )
    logger.info(f"✓ Uploaded {len(label_files)} labels to {args.output_dataset}")
    sys.stdout.flush()
    
    # Upload metadata JSON
    from huggingface_hub import HfApi
    api = HfApi(token=os.environ.get('HF_TOKEN'))
    api.upload_file(
        path_or_fileobj=str(metadata_path),
        path_in_repo=f'metadata_shard{args.shard_index}.json',
        repo_id=args.output_dataset,
        repo_type='dataset'
    )
    logger.info(f"✓ Uploaded metadata to {args.output_dataset}/metadata_shard{args.shard_index}.json")
    sys.stdout.flush()


if __name__ == '__main__':
    main()