"""
cell_g_class_probe_v3.py — three-way geometric probe

Tests the same geometric battery of metrics on three batteries:
  H2a:     Q-rank02 (D=4, V=32, 40K params, 1000 batches Adam)
  G-Cand:  Q-rank09 (D=3, V=32, 29K params, 1000 batches Adam)
  h2-64:   single-noise gaussian battery (D=8, V=64, 57K params, 10 epochs)

Key question: is the antipodal+rotational structure found in G-Cand a
property of D=3 specifically, or a property of LOW-band attractors at
ANY D? h2-64 has D=8 which sits in LOW band naturally (CV ~0.21).

Predicted outcomes:
  - h2-64 looks like H2 (uniform sphere, stable rows): G-class is D=3-specific
  - h2-64 looks like G (antipodal pairs, rotating frame): G-class is the
    universal LOW-band character; H2a is the OUTLIER for being so static
  - h2-64 looks like neither (some third pattern): D=8 has its own
    geometric character we haven't seen yet

Loading h2-64 from `loaded` if defined in session, else fetches from HF.
"""

import json
import math
import sys
from pathlib import Path

import numpy as np
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D  # noqa
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

CKPT_DIR = Path("/content/phaseQ_reports")
RANK09_CKPT = CKPT_DIR / "Q_rank09_h64_V32_D3_dp0_nx0_adam" / "epoch_1_checkpoint.pt"
RANK02_CKPT = CKPT_DIR / "Q_rank02_h64_V32_D4_dp0_nx0_adam" / "epoch_1_checkpoint.pt"
OUTPUT_PLOT = CKPT_DIR / "g_class_probe_v3.png"
OUTPUT_JSON = CKPT_DIR / "g_class_probe_v3.json"


# ════════════════════════════════════════════════════════════════════
# Loading
# ════════════════════════════════════════════════════════════════════

def load_qsweep_model(variant_str, ckpt_path):
    """Load Q-sweep model (rank02 or rank09)."""
    cfgs = get_phaseQ_configs()
    cfg_dict = next(c for c in cfgs if variant_str in c['variant'])
    cfg = build_run_config(cfg_dict)
    overrides = cfg_dict['overrides']

    model = PatchSVAE_F_Ablation(
        matrix_v=cfg.matrix_v, D=cfg.D, patch_size=cfg.patch_size,
        hidden=cfg.hidden, depth=cfg.depth,
        n_cross_layers=cfg.n_cross_layers, n_heads=cfg.n_heads,
        max_alpha=overrides.get('max_alpha', cfg.max_alpha),
        alpha_init=cfg.alpha_init,
        activation=overrides.get('activation', 'gelu'),
        row_norm=overrides.get('row_norm', 'sphere'),
        svd_mode=overrides.get('svd', 'fp64'),
        linear_readout=overrides.get('linear_readout', False),
        match_params=overrides.get('match_params', True),
        init_scheme=overrides.get('init', 'orthogonal'),
    )

    ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=False)
    state_dict = (
        ckpt.get('model_state')
        or ckpt.get('model_state_dict')
        or ckpt.get('state_dict')
        or ckpt
    )
    model.load_state_dict(state_dict)
    model.eval()
    return model, cfg


def load_h2_64_battery(battery_idx=0, phase='final'):
    """Get one battery from the h2-64 array.

    Tries `loaded` from globals first (already in Colab session),
    falls back to AutoModel.from_pretrained.

    Returns (bank_module, V, D, patch_size, img_size).
    """
    array_model = globals().get('loaded')

    if array_model is None:
        print(f"  `loaded` not found, fetching from HF...")
        # Importing geolip_svae.arrays auto-registers BatteryArrayConfig
        # with HF Auto* — without this, model_type='battery_array' is unknown.
        import geolip_svae.arrays  # noqa: F401
        from transformers import AutoModel
        array_model = AutoModel.from_pretrained(
            "AbstractPhil/geolip-svae-h2-64")
        print(f"  Loaded h2-64 from HF")
    else:
        print(f"  Using `loaded` from global session")

    # Get the specific battery bank
    bank = array_model.bank(battery_idx, phase)
    bank.eval()

    # Get architecture from config
    cfg_dict = array_model.config.batteries[battery_idx]
    print(f"  Battery {battery_idx} ({phase}): "
          f"subgroup={cfg_dict.get('subgroup')}, "
          f"variant={cfg_dict.get('variant')}, "
          f"noise_types={cfg_dict.get('noise_types')}")

    # Architecture is uniform across h2-64 batteries
    V = 64
    D = 8
    patch_size = 2
    img_size = 64

    return bank, V, D, patch_size, img_size


# ════════════════════════════════════════════════════════════════════
# Collect M rows
# ════════════════════════════════════════════════════════════════════

def collect_per_sample_M(model, V, D, patch_size, img_size,
                          n_batches=8, batch_size=64,
                          is_h2_64_bank=False):
    """Collect [n_samples, V, D] M tensors from gaussian inputs."""
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = model.to(device)

    ds = OmegaNoiseDataset(
        size=n_batches * batch_size,
        img_size=img_size,
        allowed_types=[0])  # gaussian
    loader = torch.utils.data.DataLoader(
        ds, batch_size=batch_size, shuffle=False)

    all_M = []
    with torch.no_grad():
        for imgs, _ in loader:
            imgs = imgs.to(device)
            out = model(imgs)
            # Both PatchSVAE and h2-64 banks return dict with 'svd' or
            # similar — the M tensor is at out['svd']['M'][:, 0]
            if 'svd' in out and 'M' in out['svd']:
                M_patch0 = out['svd']['M'][:, 0]  # [B, V, D]
            elif 'M' in out:
                M_patch0 = out['M'][:, 0]
            else:
                # Fall back: try to access via internal encode_patches
                from johanna_F_trainer import extract_patches
                patches = extract_patches(imgs, patch_size)
                enc = model.encode_patches(patches)
                M_patch0 = enc['M'][:, 0]
            all_M.append(M_patch0.cpu())

    return torch.cat(all_M, dim=0).numpy()  # [n_samples, V, D]


# ════════════════════════════════════════════════════════════════════
# Tests (carry over from v2)
# ════════════════════════════════════════════════════════════════════

def test_sphere_norm(all_M):
    row_norms = np.linalg.norm(all_M, axis=2)
    return {
        'min': float(row_norms.min()),
        'max': float(row_norms.max()),
        'mean': float(row_norms.mean()),
        'std': float(row_norms.std()),
        'sphere_normed': bool(
            abs(row_norms.mean() - 1.0) < 0.05 and row_norms.std() < 0.05),
    }


def test_row_stability(all_M):
    mean_dirs = all_M.mean(axis=0)
    mean_dir_norms = np.linalg.norm(mean_dirs, axis=1)
    return {
        'mean': float(mean_dir_norms.mean()),
        'min': float(mean_dir_norms.min()),
        'max': float(mean_dir_norms.max()),
        'std': float(mean_dir_norms.std()),
        'mean_dir_norms': mean_dir_norms.tolist(),
    }


def test_per_sample_clustering(all_M, k_test=5, n_samples=20):
    silhouettes = []
    for i in range(min(n_samples, all_M.shape[0])):
        M = all_M[i]
        try:
            km = KMeans(n_clusters=k_test, n_init=10, random_state=42)
            labels = km.fit_predict(M)
            if len(set(labels)) >= 2:
                sil = silhouette_score(M, labels)
                silhouettes.append(sil)
        except Exception:
            pass
    silhouettes = np.array(silhouettes)
    return {
        'k_tested': k_test,
        'mean': float(silhouettes.mean()) if len(silhouettes) else None,
        'std': float(silhouettes.std()) if len(silhouettes) else None,
        'silhouettes_per_sample': silhouettes.tolist(),
    }


def test_angular_distribution(all_M):
    all_rows = all_M.reshape(-1, all_M.shape[-1])
    norms = np.linalg.norm(all_rows, axis=1, keepdims=True)
    unit_rows = all_rows / np.clip(norms, 1e-12, None)

    n_subset = min(500, unit_rows.shape[0])
    idx = np.random.RandomState(42).choice(
        unit_rows.shape[0], n_subset, replace=False)
    subset = unit_rows[idx]

    cosines = subset @ subset.T
    triu_idx = np.triu_indices(n_subset, k=1)
    pairwise_cos = cosines[triu_idx]
    pairwise_angles = np.arccos(np.clip(pairwise_cos, -1, 1))

    return {
        'mean_angle': float(pairwise_angles.mean()),
        'median_angle': float(np.median(pairwise_angles)),
        'fraction_near_zero': float((pairwise_angles < 0.5).mean()),
        'fraction_near_pi': float((pairwise_angles > math.pi - 0.5).mean()),
        'fraction_near_perp': float(
            ((pairwise_angles > math.pi/2 - 0.3) &
             (pairwise_angles < math.pi/2 + 0.3)).mean()),
        'pairwise_angles_subset': pairwise_angles[:200].tolist(),
    }


def test_antipodal(all_M):
    mean_dirs = all_M.mean(axis=0)
    norms = np.linalg.norm(mean_dirs, axis=1, keepdims=True)
    unit_dirs = mean_dirs / np.clip(norms, 1e-12, None)

    cosines = unit_dirs @ unit_dirs.T
    np.fill_diagonal(cosines, 1.0)
    most_anti_cos = cosines.min(axis=1)
    n_pairs = (most_anti_cos < -0.9).sum() // 2

    return {
        'min_cos': float(most_anti_cos.min()),
        'mean_cos': float(most_anti_cos.mean()),
        'fraction_with_antipode': float((most_anti_cos < -0.9).mean()),
        'estimated_pairs': int(n_pairs),
        'max_possible_pairs': all_M.shape[1] // 2,
    }


def test_effective_rank(all_M):
    M_avg = all_M.mean(axis=0)
    sv = np.linalg.svd(M_avg, compute_uv=False)
    sv_norm = sv / sv.sum()
    erank = math.exp(-(sv_norm * np.log(sv_norm + 1e-12)).sum())
    return {
        'singular_values': sv.tolist(),
        'normalized_SV': sv_norm.tolist(),
        'effective_rank': float(erank),
        'D': int(all_M.shape[2]),
        'utilization': float(erank / all_M.shape[2]),
        'top1_share': float(sv_norm[0]),
    }


def run_all_tests(all_M, label):
    print(f"\n[{label}]")
    print(f"  Shape: {all_M.shape}")

    sphere = test_sphere_norm(all_M)
    print(f"  Sphere-norm: mean={sphere['mean']:.4f}, "
          f"std={sphere['std']:.4f} → {'YES' if sphere['sphere_normed'] else 'NO'}")

    stability = test_row_stability(all_M)
    print(f"  Row stability: mean={stability['mean']:.3f}, "
          f"range=[{stability['min']:.3f}, {stability['max']:.3f}]")

    cluster = test_per_sample_clustering(all_M)
    if cluster['mean'] is not None:
        print(f"  Cluster (k=5): silhouette mean={cluster['mean']:.3f}, "
              f"std={cluster['std']:.3f}")

    angular = test_angular_distribution(all_M)
    print(f"  Angular: mean={angular['mean_angle']:.3f} "
          f"(uniform=π/2={math.pi/2:.3f})")
    print(f"           near-perp: {angular['fraction_near_perp']:.3f}, "
          f"near-π: {angular['fraction_near_pi']:.3f}")

    antipodal = test_antipodal(all_M)
    print(f"  Antipodal: {antipodal['estimated_pairs']}/"
          f"{antipodal['max_possible_pairs']} pairs, "
          f"frac with antipode={antipodal['fraction_with_antipode']:.3f}")

    erank = test_effective_rank(all_M)
    print(f"  Effective rank: {erank['effective_rank']:.2f} of {erank['D']} "
          f"({erank['utilization']*100:.0f}% utilization)")

    return {
        'sphere_norm': sphere,
        'stability': stability,
        'clustering': cluster,
        'angular': angular,
        'antipodal': antipodal,
        'rank': erank,
    }


# ════════════════════════════════════════════════════════════════════
# Composite character classification
# ════════════════════════════════════════════════════════════════════

def classify_battery_character(results):
    """Determine if battery is H2-like (sphere-solver) or G-like
    (rotating-antipodal) or something else."""
    stab = results['stability']['mean']
    antipodal_frac = results['antipodal']['fraction_with_antipode']
    cluster_sil = results['clustering']['mean']
    rank_util = results['rank']['utilization']

    # H2-like: high stability, low antipodal fraction, full rank
    is_h2_like = (
        stab > 0.85 and
        antipodal_frac < 0.55 and
        rank_util > 0.95
    )
    # G-like: low stability, high antipodal fraction
    is_g_like = (
        stab < 0.65 and
        antipodal_frac > 0.80
    )
    # Hybrid: somewhere in between

    if is_h2_like:
        return f"H2-LIKE (static sphere-solver)"
    elif is_g_like:
        return f"G-LIKE (rotating antipodal frame)"
    elif stab < 0.65 and antipodal_frac < 0.55:
        return f"DIFFUSE (low stability, no antipodal structure)"
    else:
        return (f"HYBRID (stab={stab:.2f}, antipodal_frac="
                f"{antipodal_frac:.2f})")


# ════════════════════════════════════════════════════════════════════
# Main
# ════════════════════════════════════════════════════════════════════

def main():
    print("=" * 70)
    print("Loading three batteries for comparative analysis")
    print("=" * 70)

    print("\n[1/3] H2a (Q-rank02, D=4, 1000-batch Adam)")
    h2_model, h2_cfg = load_qsweep_model('rank02', RANK02_CKPT)
    print(f"  V={h2_cfg.matrix_v}, D={h2_cfg.D}, "
          f"params={sum(p.numel() for p in h2_model.parameters()):,}")

    print("\n[2/3] G-Class candidate (Q-rank09, D=3, 1000-batch Adam)")
    g_model, g_cfg = load_qsweep_model('rank09', RANK09_CKPT)
    print(f"  V={g_cfg.matrix_v}, D={g_cfg.D}, "
          f"params={sum(p.numel() for p in g_model.parameters()):,}")

    print("\n[3/3] h2-64 single-noise gaussian battery (D=8, 10 epochs converged)")
    h264_bank, h264_V, h264_D, h264_ps, h264_img = load_h2_64_battery(
        battery_idx=0, phase='final')
    print(f"  V={h264_V}, D={h264_D}, patch_size={h264_ps}, img_size={h264_img}")

    # ════════════════════════════════════════════════════════════════
    # Collect M rows
    # ════════════════════════════════════════════════════════════════

    print("\n" + "=" * 70)
    print("Collecting M rows (gaussian inputs, 512 samples each)")
    print("=" * 70)

    print("\n  H2a...")
    all_M_h2 = collect_per_sample_M(
        h2_model, h2_cfg.matrix_v, h2_cfg.D,
        h2_cfg.patch_size, h2_cfg.img_size)

    print("  G-Cand...")
    all_M_g = collect_per_sample_M(
        g_model, g_cfg.matrix_v, g_cfg.D,
        g_cfg.patch_size, g_cfg.img_size)

    print("  h2-64 gaussian...")
    all_M_h264 = collect_per_sample_M(
        h264_bank, h264_V, h264_D, h264_ps, h264_img,
        is_h2_64_bank=True)

    # ════════════════════════════════════════════════════════════════
    # Run tests on each
    # ════════════════════════════════════════════════════════════════

    print("\n" + "=" * 70)
    print("GEOMETRIC ANALYSIS")
    print("=" * 70)

    results_h2 = run_all_tests(all_M_h2, "H2a (D=4, 1000-batch Adam)")
    results_g = run_all_tests(all_M_g, "G-Cand (D=3, 1000-batch Adam)")
    results_h264 = run_all_tests(
        all_M_h264, "h2-64 gaussian (D=8, 10 epochs)")

    # ════════════════════════════════════════════════════════════════
    # Side-by-side comparison
    # ════════════════════════════════════════════════════════════════

    print("\n" + "=" * 70)
    print("THREE-WAY COMPARISON")
    print("=" * 70)

    headers = f"{'Metric':<32} {'H2a (D=4)':>12} {'G-Cand (D=3)':>14} {'h2-64 (D=8)':>14}"
    print(f"\n  {headers}")
    print("  " + "-" * len(headers))

    rows = [
        ('Effective rank',
         results_h2['rank']['effective_rank'],
         results_g['rank']['effective_rank'],
         results_h264['rank']['effective_rank'],
         '.2f'),
        ('Dim utilization (%)',
         results_h2['rank']['utilization'] * 100,
         results_g['rank']['utilization'] * 100,
         results_h264['rank']['utilization'] * 100,
         '.0f'),
        ('Row stability',
         results_h2['stability']['mean'],
         results_g['stability']['mean'],
         results_h264['stability']['mean'],
         '.3f'),
        ('Per-sample silhouette (k=5)',
         results_h2['clustering']['mean'] or 0,
         results_g['clustering']['mean'] or 0,
         results_h264['clustering']['mean'] or 0,
         '.3f'),
        ('Mean pairwise angle (rad)',
         results_h2['angular']['mean_angle'],
         results_g['angular']['mean_angle'],
         results_h264['angular']['mean_angle'],
         '.3f'),
        ('Antipodal pair fraction',
         results_h2['antipodal']['fraction_with_antipode'],
         results_g['antipodal']['fraction_with_antipode'],
         results_h264['antipodal']['fraction_with_antipode'],
         '.3f'),
        ('Estimated antipodal pairs',
         results_h2['antipodal']['estimated_pairs'],
         results_g['antipodal']['estimated_pairs'],
         results_h264['antipodal']['estimated_pairs'],
         'd'),
    ]

    for row in rows:
        name, h2v, gv, h264v, fmt = row
        if fmt == 'd':
            print(f"  {name:<32} {h2v:>12d} {gv:>14d} {h264v:>14d}")
        else:
            print(f"  {name:<32} {h2v:>12{fmt}} {gv:>14{fmt}} {h264v:>14{fmt}}")

    print()
    char_h2 = classify_battery_character(results_h2)
    char_g = classify_battery_character(results_g)
    char_h264 = classify_battery_character(results_h264)

    print(f"  Character verdict:")
    print(f"    H2a:    {char_h2}")
    print(f"    G-Cand: {char_g}")
    print(f"    h2-64:  {char_h264}")

    # Headline conclusion
    print("\n" + "=" * 70)
    print("CONCLUSION")
    print("=" * 70)

    if "G-LIKE" in char_h264:
        print("  h2-64 (D=8, fully converged) shows G-CLASS character.")
        print("  → The antipodal+rotational structure is NOT D=3-specific.")
        print("  → It's the LOW-band attractor's natural geometry.")
        print("  → H2a (D=4 at HIGH band) is the OUTLIER — its sphere-solver")
        print("    rigidity is HIGH-band-specific, not the universal pattern.")
    elif "H2-LIKE" in char_h264:
        print("  h2-64 (D=8, fully converged) shows H2 sphere-solver character.")
        print("  → G-Class at D=3 is genuinely different from sphere-solvers.")
        print("  → D=3 specifically can't form a stable static 32-row arrangement,")
        print("    so it falls into the rotating-antipodal regime.")
        print("  → Higher D recovers static sphere-solver behavior even in LOW band.")
    elif "HYBRID" in char_h264 or "DIFFUSE" in char_h264:
        print("  h2-64 (D=8) shows mixed character — partial G-like features.")
        print("  → Possible spectrum: HIGH-band → static sphere (H2),")
        print("    LOW-band → progressively more antipodal as D decreases.")
        print("  → D=8 sits in transition; D=3 is fully G-class; D=4 HIGH is fully H2.")

    all_results = {
        'h2a': results_h2,
        'g_class_candidate': results_g,
        'h2_64_gaussian': results_h264,
        'characters': {
            'h2a': char_h2,
            'g_class': char_g,
            'h2_64': char_h264,
        },
    }

    with open(OUTPUT_JSON, 'w') as f:
        json.dump(all_results, f, indent=2, default=str)
    print(f"\n  Saved: {OUTPUT_JSON}")

    # Plot
    plot_three_way(all_M_h2, all_M_g, all_M_h264,
                    results_h2, results_g, results_h264, OUTPUT_PLOT)
    print(f"  Saved: {OUTPUT_PLOT}")

    return all_results


def plot_three_way(M_h2, M_g, M_h264, r_h2, r_g, r_h264, output_path):
    """6-panel comparison figure: 3 batteries × 2 metrics each."""
    fig = plt.figure(figsize=(18, 14))

    # Row 1: Single-sample row scatters (project to first 3 dims)
    ax1 = fig.add_subplot(3, 3, 1, projection='3d')
    s = M_h2[0]
    ax1.scatter(s[:, 0], s[:, 1], s[:, 2], c=np.arange(len(s)),
                 cmap='viridis', s=80, edgecolors='black', linewidths=0.5)
    ax1.set_title(f'H2a (D=4) — single sample\nrows projected to first 3 dims')

    ax2 = fig.add_subplot(3, 3, 2, projection='3d')
    s = M_g[0]
    ax2.scatter(s[:, 0], s[:, 1], s[:, 2], c=np.arange(len(s)),
                 cmap='viridis', s=80, edgecolors='black', linewidths=0.5)
    ax2.set_title(f'G-Cand (D=3) — single sample\nfull native dims')

    ax3 = fig.add_subplot(3, 3, 3, projection='3d')
    s = M_h264[0]
    ax3.scatter(s[:, 0], s[:, 1], s[:, 2], c=np.arange(len(s)),
                 cmap='viridis', s=80, edgecolors='black', linewidths=0.5)
    ax3.set_title(f'h2-64 gaussian (D=8) — single sample\nrows projected to first 3 dims')

    # Row 2: Per-row stability sorted (descending)
    ax4 = fig.add_subplot(3, 3, 4)
    ax4.plot(sorted(r_h2['stability']['mean_dir_norms'], reverse=True),
              'o-', color='blue', markersize=4)
    ax4.set_title(f"H2a row stability\nmean={r_h2['stability']['mean']:.3f}")
    ax4.set_xlabel('Row index (sorted)')
    ax4.set_ylabel('Mean direction norm')
    ax4.set_ylim([0, 1.05])
    ax4.grid(alpha=0.3)

    ax5 = fig.add_subplot(3, 3, 5)
    ax5.plot(sorted(r_g['stability']['mean_dir_norms'], reverse=True),
              'o-', color='red', markersize=4)
    ax5.set_title(f"G-Cand row stability\nmean={r_g['stability']['mean']:.3f}")
    ax5.set_xlabel('Row index (sorted)')
    ax5.set_ylabel('Mean direction norm')
    ax5.set_ylim([0, 1.05])
    ax5.grid(alpha=0.3)

    ax6 = fig.add_subplot(3, 3, 6)
    ax6.plot(sorted(r_h264['stability']['mean_dir_norms'], reverse=True),
              'o-', color='green', markersize=4)
    ax6.set_title(f"h2-64 row stability\nmean={r_h264['stability']['mean']:.3f}")
    ax6.set_xlabel('Row index (sorted)')
    ax6.set_ylabel('Mean direction norm')
    ax6.set_ylim([0, 1.05])
    ax6.grid(alpha=0.3)

    # Row 3: Pairwise angle distributions
    ax7 = fig.add_subplot(3, 3, 7)
    ax7.hist(r_h2['angular']['pairwise_angles_subset'], bins=30,
              color='blue', alpha=0.7, density=True)
    ax7.axvline(math.pi/2, color='black', linestyle='--', alpha=0.5)
    ax7.set_title(f"H2a pairwise angles\nmean={r_h2['angular']['mean_angle']:.3f}")
    ax7.set_xlabel('Angle (radians)')

    ax8 = fig.add_subplot(3, 3, 8)
    ax8.hist(r_g['angular']['pairwise_angles_subset'], bins=30,
              color='red', alpha=0.7, density=True)
    ax8.axvline(math.pi/2, color='black', linestyle='--', alpha=0.5)
    ax8.set_title(f"G-Cand pairwise angles\nmean={r_g['angular']['mean_angle']:.3f}")
    ax8.set_xlabel('Angle (radians)')

    ax9 = fig.add_subplot(3, 3, 9)
    ax9.hist(r_h264['angular']['pairwise_angles_subset'], bins=30,
              color='green', alpha=0.7, density=True)
    ax9.axvline(math.pi/2, color='black', linestyle='--', alpha=0.5)
    ax9.set_title(f"h2-64 pairwise angles\nmean={r_h264['angular']['mean_angle']:.3f}")
    ax9.set_xlabel('Angle (radians)')

    plt.tight_layout()
    plt.savefig(output_path, dpi=120, bbox_inches='tight')
    plt.show()


if __name__ == '__main__':
    results = main()