"""Local HSS metric evaluation for S23DR 2026."""

import numpy as np
from typing import Dict, List, Tuple, Optional
import time


def compute_hss(pred_vertices, pred_edges, gt_vertices, gt_edges, vert_thresh=0.5, edge_thresh=0.5):
    try:
        from hoho2025.metric_helper import hss
        t0 = time.time()
        result = hss(pred_vertices, pred_edges, gt_vertices, gt_edges, vert_thresh=vert_thresh, edge_thresh=edge_thresh)
        elapsed = time.time() - t0
        return {'hss': float(result.hss), 'f1': float(result.f1), 'iou': float(result.iou), 'time': elapsed}
    except Exception as e:
        return {'hss': 0.0, 'f1': 0.0, 'iou': 0.0, 'time': 0.0, 'error': str(e)}


def compute_corner_f1(pred_vertices, gt_vertices, thresh=0.5):
    from scipy.optimize import linear_sum_assignment
    if len(pred_vertices) == 0 or len(gt_vertices) == 0:
        return {'f1': 0.0, 'precision': 0.0, 'recall': 0.0, 'tp': 0}
    diff = pred_vertices[:, None, :] - gt_vertices[None, :, :]
    dists = np.sqrt((diff ** 2).sum(axis=-1))
    row_ind, col_ind = linear_sum_assignment(dists)
    tp = (dists[row_ind, col_ind] <= thresh).sum()
    precision = tp / len(pred_vertices)
    recall = tp / len(gt_vertices)
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
    return {'f1': float(f1), 'precision': float(precision), 'recall': float(recall), 'tp': int(tp), 'num_pred': len(pred_vertices), 'num_gt': len(gt_vertices)}


def evaluate_batch(predictions, ground_truths, verbose=True):
    hss_scores, f1_scores, iou_scores, times, errors = [], [], [], [], []
    for i, ((pred_v, pred_e), (gt_v, gt_e)) in enumerate(zip(predictions, ground_truths)):
        result = compute_hss(pred_v, pred_e, gt_v, gt_e)
        hss_scores.append(result['hss'])
        f1_scores.append(result['f1'])
        iou_scores.append(result['iou'])
        times.append(result['time'])
        if 'error' in result:
            errors.append((i, result['error']))
        if verbose and (i + 1) % 10 == 0:
            print(f"  [{i+1}/{len(predictions)}] HSS={np.mean(hss_scores):.4f} F1={np.mean(f1_scores):.4f} IoU={np.mean(iou_scores):.4f}")
    return {
        'hss_mean': float(np.mean(hss_scores)), 'hss_std': float(np.std(hss_scores)),
        'hss_median': float(np.median(hss_scores)), 'f1_mean': float(np.mean(f1_scores)),
        'iou_mean': float(np.mean(iou_scores)), 'total_time': float(sum(times)),
        'avg_time': float(np.mean(times)), 'num_samples': len(predictions),
        'num_errors': len(errors), 'errors': errors[:10],
    }


def quick_vertex_eval(pred_vertices, gt_vertices):
    return compute_corner_f1(pred_vertices, gt_vertices)