Wrinkle/WrinkleBrane-Research/08_signed_priority/test_learnable_codes.py

#!/usr/bin/env python3
"""Test suite for Direction 6: Learnable Code Evolution.

Tests the learnable codebook system:
1. Backward compat — frozen learnable bank ≡ plain 1D bank
2. Gradient flow — C_raw receives gradients through write+read paths
3. Orthogonality regulariser — penalises non-orthogonal codes
4. Reconstruction training — codes improve fidelity over training steps
5. Hadamard vs learned — compare on structured data distributions
6. Coherence tracking — monitor coherence_stats during training
7. Overload regime — can learned codes extend capacity beyond K=L?
8. Continuous addressing — learnable codes + continuous read

Conventions (matching project test style):
- torch.manual_seed(42), np.random.seed(42)
- Setup → Store → Retrieve → Measure → Report
- PSNR thresholds: >100 dB EXCELLENT, >80 dB HIGH FIDELITY, >50 dB GOOD
"""

from __future__ import annotations

import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent / "src"))

import math
import numpy as np
import torch
from torch import nn

from wrinklebrane.codes import (
    hadamard_codes,
    dct_codes,
    gaussian_codes,
    coherence_stats,
    normalize_columns,
)
from wrinklebrane.membrane_1d import (
    MembraneBank1D,
    store_pairs_1d,
    Slicer1D,
    cosine_similarity_matrix,
    token_retrieval_accuracy,
    soft_code_weights_1d,
)
from wrinklebrane.learnable_codes import (
    LearnableCodebook,
    LearnableMemoryBank1D,
    orthogonality_loss,
    reconstruction_loss,
    train_codebook,
)


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------

def _make_embeddings(T: int, D: int, seed: int = 42, signed: bool = True) -> torch.Tensor:
    gen = torch.Generator().manual_seed(seed)
    if signed:
        return torch.randn(T, D, generator=gen) * 0.5
    return torch.rand(T, D, generator=gen)


def _psnr(pred: torch.Tensor, target: torch.Tensor) -> float:
    mse = float((pred.detach() - target.detach()).pow(2).mean())
    if mse < 1e-30:
        return 300.0
    dr = float(target.detach().abs().max())
    if dr < 1e-10:
        dr = 1.0
    return 10.0 * math.log10(dr ** 2 / mse)


# =====================================================================
# Test 1: Backward Compatibility
# =====================================================================
def test_1_backward_compat():
    """Frozen learnable bank should produce identical results to plain 1D."""
    print("\n" + "=" * 60)
    print("TEST 1: Backward Compatibility (frozen learnable ≡ plain 1D)")
    print("=" * 60)

    torch.manual_seed(42)
    np.random.seed(42)

    L, K, D = 64, 8, 128
    B = 1
    T = K

    C = hadamard_codes(L, K)
    embeddings = _make_embeddings(T, D)
    keys = torch.arange(T, dtype=torch.long)
    alphas = torch.ones(T)

    # --- Plain 1D pipeline ---
    M_plain = torch.zeros(B, L, D)
    M_plain = store_pairs_1d(M_plain, C, keys, embeddings, alphas)
    slicer = Slicer1D(C, bias=False, relu=False)
    Y_plain = slicer(M_plain)

    # --- Frozen learnable bank ---
    bank = LearnableMemoryBank1D(L, K, D, init="hadamard", freeze_codes=True)
    bank.allocate(B)
    bank.store(keys, embeddings, alphas)
    Y_learn = bank.retrieve()

    max_diff = float((Y_plain - Y_learn).abs().max())
    mean_diff = float((Y_plain - Y_learn).abs().mean())

    print(f"\n  Configuration: L={L}, K={K}, D={D}")
    print(f"  Max  |Y_plain - Y_learn|: {max_diff:.2e}")
    print(f"  Mean |Y_plain - Y_learn|: {mean_diff:.2e}")

    passed = max_diff < 1e-6
    print(f"\n  {'PASS' if passed else 'FAIL'}: Frozen learnable ≡ plain 1D "
          f"(max diff = {max_diff:.2e})")
    return passed


# =====================================================================
# Test 2: Gradient Flow
# =====================================================================
def test_2_gradient_flow():
    """C_raw receives gradients through the shared write+read path."""
    print("\n" + "=" * 60)
    print("TEST 2: Gradient Flow Through Shared Codebook")
    print("=" * 60)

    torch.manual_seed(42)

    L, K, D = 16, 4, 32
    B = 1
    T = K

    embeddings = _make_embeddings(T, D)
    keys = torch.arange(T, dtype=torch.long)
    alphas = torch.ones(T)

    bank = LearnableMemoryBank1D(L, K, D, init="hadamard")
    bank.allocate(B)

    # Forward
    bank.store(keys, embeddings, alphas)
    Y = bank.retrieve()

    # Loss: reconstruct stored embeddings
    target = embeddings.unsqueeze(0).expand(B, -1, -1)
    loss = reconstruction_loss(Y, target)
    loss.backward()

    print(f"\n  Configuration: L={L}, K={K}, D={D}")
    print(f"  Loss: {loss.item():.6f}")

    grad = bank.codebook.C_raw.grad
    if grad is not None:
        norm = float(grad.norm())
        nonzero = int((grad.abs() > 1e-10).sum())
        total = grad.numel()
        print(f"  C_raw gradient: norm = {norm:.6f} | nz = {nonzero}/{total}")
        has_grad = norm > 1e-10
    else:
        print(f"  C_raw gradient: NONE")
        has_grad = False

    passed = has_grad
    print(f"\n  {'PASS' if passed else 'FAIL'}: C_raw receives gradients")
    return passed


# =====================================================================
# Test 3: Orthogonality Regulariser
# =====================================================================
def test_3_orthogonality_regulariser():
    """Orthogonality loss is zero for Hadamard, nonzero for random."""
    print("\n" + "=" * 60)
    print("TEST 3: Orthogonality Regulariser")
    print("=" * 60)

    torch.manual_seed(42)

    L, K = 64, 8

    configs = [
        ("hadamard", "hadamard"),
        ("dct", "dct"),
        ("gaussian", "gaussian"),
        ("random", "random"),
        ("identity", "identity"),
    ]

    print(f"\n  Configuration: L={L}, K={K}")
    print(f"\n  {'Init':>12} {'OrthoLoss':>12} {'MaxCoherence':>14} "
          f"{'MeanCoherence':>14}")
    print(f"  {'-'*12} {'-'*12} {'-'*14} {'-'*14}")

    results = {}
    for label, init in configs:
        cb = LearnableCodebook(L, K, init=init)
        loss = float(cb.ortho_loss())
        coh = cb.coherence()
        results[label] = {
            "loss": loss,
            "max_coh": coh["max_abs_offdiag"],
            "mean_coh": coh["mean_abs_offdiag"],
        }
        print(f"  {label:>12} {loss:>11.6f} {coh['max_abs_offdiag']:>13.6f} "
              f"{coh['mean_abs_offdiag']:>13.6f}")

    # Hadamard should be near-zero; random should be significantly nonzero
    hadamard_low = results["hadamard"]["loss"] < 1e-6
    random_higher = results["random"]["loss"] > results["hadamard"]["loss"]

    # Verify loss is differentiable
    cb = LearnableCodebook(L, K, init="random")
    loss = cb.ortho_loss()
    loss.backward()
    grad_exists = cb.C_raw.grad is not None and float(cb.C_raw.grad.norm()) > 0

    print(f"\n  Hadamard ortho_loss ≈ 0: {'YES' if hadamard_low else 'NO'}")
    print(f"  Random > Hadamard: {'YES' if random_higher else 'NO'}")
    print(f"  Loss is differentiable: {'YES' if grad_exists else 'NO'}")

    passed = hadamard_low and random_higher and grad_exists
    print(f"\n  {'PASS' if passed else 'FAIL'}: Orthogonality regulariser")
    return passed


# =====================================================================
# Test 4: Reconstruction Training
# =====================================================================
def test_4_reconstruction_training():
    """Training with reconstruction loss + ortho reg improves fidelity."""
    print("\n" + "=" * 60)
    print("TEST 4: Reconstruction Training")
    print("=" * 60)

    torch.manual_seed(42)

    L, K, D = 32, 8, 64
    B = 1
    T = K

    # Fixed data distribution: same embeddings every step
    embeddings = _make_embeddings(T, D, seed=42)
    keys = torch.arange(T, dtype=torch.long)
    alphas = torch.ones(T)

    def data_fn():
        return keys, embeddings, alphas

    # --- Baseline: random init, no training ---
    bank_before = LearnableMemoryBank1D(L, K, D, init="random", freeze_codes=True)
    bank_before.allocate(B)
    bank_before.store(keys, embeddings, alphas)
    Y_before = bank_before.retrieve()
    psnr_before = sum(
        _psnr(Y_before[0, k], embeddings[k]) for k in range(T)
    ) / T

    # --- Train from random init ---
    torch.manual_seed(42)
    bank = LearnableMemoryBank1D(L, K, D, init="random")
    history = train_codebook(
        bank, data_fn,
        n_steps=200, lr=1e-2, ortho_lambda=0.01,
        B=B, log_every=20,
    )

    # Evaluate after training
    bank.allocate(B)
    bank.store(keys, embeddings, alphas)
    Y_after = bank.retrieve()
    psnr_after = sum(
        _psnr(Y_after[0, k], embeddings[k]) for k in range(T)
    ) / T

    print(f"\n  Configuration: L={L}, K={K}, D={D}")
    print(f"  Init: random codes | Steps: 200 | lr: 1e-2 | λ_ortho: 0.01")

    print(f"\n  Training trajectory:")
    print(f"  {'Step':>6} {'Total':>10} {'Recon':>10} {'Ortho':>10} "
          f"{'MaxCoh':>10} {'MeanCoh':>10}")
    print(f"  {'-'*6} {'-'*10} {'-'*10} {'-'*10} {'-'*10} {'-'*10}")
    for h in history:
        print(f"  {h['step']:>6} {h['total_loss']:>9.6f} "
              f"{h['recon_loss']:>9.6f} {h['ortho_loss']:>9.6f} "
              f"{h['max_coherence']:>9.6f} {h['mean_coherence']:>9.6f}")

    print(f"\n  PSNR before training: {psnr_before:.1f} dB")
    print(f"  PSNR after training:  {psnr_after:.1f} dB")
    print(f"  Improvement: {psnr_after - psnr_before:.1f} dB")

    # Training should improve fidelity
    improved = psnr_after > psnr_before + 5.0  # At least 5 dB improvement
    loss_decreased = history[-1]["total_loss"] < history[0]["total_loss"]

    passed = improved and loss_decreased
    print(f"\n  PSNR improved by >5 dB: {'YES' if improved else 'NO'}")
    print(f"  Loss decreased: {'YES' if loss_decreased else 'NO'}")
    print(f"\n  {'PASS' if passed else 'FAIL'}: Reconstruction training")
    return passed


# =====================================================================
# Test 5: Hadamard vs Learned
# =====================================================================
def test_5_hadamard_vs_learned():
    """Compare fixed Hadamard vs learned codes on structured data."""
    print("\n" + "=" * 60)
    print("TEST 5: Hadamard vs Learned Codes")
    print("=" * 60)

    torch.manual_seed(42)

    L, K, D = 32, 8, 64
    B = 1
    T = K

    # Structured data: correlated embeddings (e.g. smooth, low-frequency)
    gen = torch.Generator().manual_seed(42)
    base = torch.randn(1, D, generator=gen) * 0.5
    embeddings = base + torch.randn(T, D, generator=gen) * 0.1
    keys = torch.arange(T, dtype=torch.long)
    alphas = torch.ones(T)

    def data_fn():
        return keys, embeddings, alphas

    # --- Fixed Hadamard ---
    bank_had = LearnableMemoryBank1D(L, K, D, init="hadamard", freeze_codes=True)
    bank_had.allocate(B)
    bank_had.store(keys, embeddings, alphas)
    Y_had = bank_had.retrieve()
    psnr_had = sum(_psnr(Y_had[0, k], embeddings[k]) for k in range(T)) / T
    coh_had = bank_had.coherence()

    # --- Trained from Hadamard init ---
    torch.manual_seed(42)
    bank_learn_h = LearnableMemoryBank1D(L, K, D, init="hadamard")
    train_codebook(
        bank_learn_h, data_fn,
        n_steps=200, lr=1e-2, ortho_lambda=0.01, B=B, log_every=200,
    )
    bank_learn_h.allocate(B)
    bank_learn_h.store(keys, embeddings, alphas)
    Y_learn_h = bank_learn_h.retrieve()
    psnr_learn_h = sum(
        _psnr(Y_learn_h[0, k], embeddings[k]) for k in range(T)
    ) / T
    coh_learn_h = bank_learn_h.coherence()

    # --- Trained from random init ---
    torch.manual_seed(42)
    bank_learn_r = LearnableMemoryBank1D(L, K, D, init="random")
    train_codebook(
        bank_learn_r, data_fn,
        n_steps=200, lr=1e-2, ortho_lambda=0.01, B=B, log_every=200,
    )
    bank_learn_r.allocate(B)
    bank_learn_r.store(keys, embeddings, alphas)
    Y_learn_r = bank_learn_r.retrieve()
    psnr_learn_r = sum(
        _psnr(Y_learn_r[0, k], embeddings[k]) for k in range(T)
    ) / T
    coh_learn_r = bank_learn_r.coherence()

    print(f"\n  Configuration: L={L}, K={K}, D={D}")
    print(f"  Data: correlated embeddings (base + noise)")

    print(f"\n  {'Config':<28} {'PSNR':>8} {'MaxCoh':>10} {'MeanCoh':>10}")
    print(f"  {'-'*28} {'-'*8} {'-'*10} {'-'*10}")
    print(f"  {'Fixed Hadamard':<28} {psnr_had:>7.1f}dB "
          f"{coh_had['max_abs_offdiag']:>9.6f} "
          f"{coh_had['mean_abs_offdiag']:>9.6f}")
    print(f"  {'Learned (Hadamard init)':<28} {psnr_learn_h:>7.1f}dB "
          f"{coh_learn_h['max_abs_offdiag']:>9.6f} "
          f"{coh_learn_h['mean_abs_offdiag']:>9.6f}")
    print(f"  {'Learned (random init)':<28} {psnr_learn_r:>7.1f}dB "
          f"{coh_learn_r['max_abs_offdiag']:>9.6f} "
          f"{coh_learn_r['mean_abs_offdiag']:>9.6f}")

    # Fixed Hadamard should already be excellent (orthogonal codes)
    hadamard_excellent = psnr_had > 100.0
    # Learned from random should significantly improve over random baseline
    learned_competitive = psnr_learn_r > 50.0

    passed = hadamard_excellent and learned_competitive
    print(f"\n  Fixed Hadamard > 100 dB: {'YES' if hadamard_excellent else 'NO'}")
    print(f"  Learned (random init) > 50 dB: "
          f"{'YES' if learned_competitive else 'NO'}")
    print(f"\n  {'PASS' if passed else 'FAIL'}: Hadamard vs learned")
    return passed


# =====================================================================
# Test 6: Coherence Tracking
# =====================================================================
def test_6_coherence_tracking():
    """Monitor coherence_stats during training — should stay controlled."""
    print("\n" + "=" * 60)
    print("TEST 6: Coherence Tracking During Training")
    print("=" * 60)

    torch.manual_seed(42)

    L, K, D = 32, 8, 64
    B = 1
    T = K

    embeddings = _make_embeddings(T, D, seed=42)
    keys = torch.arange(T, dtype=torch.long)
    alphas = torch.ones(T)

    def data_fn():
        return keys, embeddings, alphas

    # Train with different ortho_lambda values
    lambdas = [0.0, 0.01, 0.1, 1.0]

    print(f"\n  Configuration: L={L}, K={K}, D={D}")
    print(f"  Training 200 steps from random init at various λ_ortho")

    print(f"\n  {'λ_ortho':>8} {'Final PSNR':>12} {'Final MaxCoh':>14} "
          f"{'Final MeanCoh':>14} {'Final OrthoL':>14}")
    print(f"  {'-'*8} {'-'*12} {'-'*14} {'-'*14} {'-'*14}")

    results = {}
    for lam in lambdas:
        torch.manual_seed(42)
        bank = LearnableMemoryBank1D(L, K, D, init="random")
        history = train_codebook(
            bank, data_fn,
            n_steps=200, lr=1e-2, ortho_lambda=lam, B=B, log_every=200,
        )

        bank.allocate(B)
        bank.store(keys, embeddings, alphas)
        Y = bank.retrieve()
        psnr = sum(_psnr(Y[0, k], embeddings[k]) for k in range(T)) / T
        coh = bank.coherence()

        results[lam] = {
            "psnr": psnr,
            "max_coh": coh["max_abs_offdiag"],
            "mean_coh": coh["mean_abs_offdiag"],
            "ortho_loss": history[-1]["ortho_loss"],
        }
        print(f"  {lam:>8.2f} {psnr:>11.1f}dB "
              f"{coh['max_abs_offdiag']:>13.6f} "
              f"{coh['mean_abs_offdiag']:>13.6f} "
              f"{history[-1]['ortho_loss']:>13.6f}")

    # Higher λ should produce lower coherence (more orthogonal)
    coherence_controlled = (
        results[1.0]["max_coh"] < results[0.0]["max_coh"] or
        results[1.0]["max_coh"] < 0.3  # absolute threshold
    )
    # λ=0 may overfit to data but lose orthogonality
    no_reg_higher_coh = results[0.0]["max_coh"] > results[1.0]["max_coh"]

    passed = coherence_controlled
    print(f"\n  λ=1.0 controls coherence: "
          f"{'YES' if coherence_controlled else 'NO'}")
    print(f"  λ=0 has higher coherence: "
          f"{'YES' if no_reg_higher_coh else 'NO'}")
    print(f"\n  {'PASS' if passed else 'FAIL'}: Coherence tracking")
    return passed


# =====================================================================
# Test 7: Overload Regime
# =====================================================================
def test_7_overload_regime():
    """Can learned codes extend effective capacity beyond K=L?"""
    print("\n" + "=" * 60)
    print("TEST 7: Overload Regime (K > L)")
    print("=" * 60)

    torch.manual_seed(42)

    L = 16
    K = 32  # 2× overload
    D = 64
    B = 1
    T = K

    embeddings = _make_embeddings(T, D, seed=42)
    keys = torch.arange(T, dtype=torch.long)
    alphas = torch.ones(T)

    def data_fn():
        return keys, embeddings, alphas

    # --- Fixed Hadamard at overload ---
    bank_had = LearnableMemoryBank1D(L, K, D, init="hadamard", freeze_codes=True)
    bank_had.allocate(B)
    bank_had.store(keys, embeddings, alphas)
    Y_had = bank_had.retrieve()
    psnr_had = sum(_psnr(Y_had[0, k], embeddings[k]) for k in range(T)) / T

    # --- Learned codes at overload ---
    torch.manual_seed(42)
    bank_learn = LearnableMemoryBank1D(L, K, D, init="hadamard")
    history = train_codebook(
        bank_learn, data_fn,
        n_steps=300, lr=1e-2, ortho_lambda=0.001, B=B, log_every=50,
    )

    bank_learn.allocate(B)
    bank_learn.store(keys, embeddings, alphas)
    Y_learn = bank_learn.retrieve()
    psnr_learn = sum(
        _psnr(Y_learn[0, k], embeddings[k]) for k in range(T)
    ) / T

    print(f"\n  Configuration: L={L}, K={K} (K/L = {K/L:.1f}×), D={D}")

    print(f"\n  Training trajectory:")
    print(f"  {'Step':>6} {'Total':>10} {'Recon':>10} {'Ortho':>10}")
    print(f"  {'-'*6} {'-'*10} {'-'*10} {'-'*10}")
    for h in history:
        print(f"  {h['step']:>6} {h['total_loss']:>9.6f} "
              f"{h['recon_loss']:>9.6f} {h['ortho_loss']:>9.6f}")

    print(f"\n  Fixed Hadamard at 2× overload: {psnr_had:.1f} dB")
    print(f"  Learned codes at 2× overload:  {psnr_learn:.1f} dB")

    # Learned codes should improve over fixed at overload
    learned_better = psnr_learn > psnr_had
    # Both should be degraded compared to within-capacity (< 140 dB)
    both_degraded = psnr_had < 50.0 and psnr_learn < 100.0

    passed = learned_better
    print(f"\n  Learned > Fixed at overload: "
          f"{'YES' if learned_better else 'NO'} "
          f"({psnr_learn:.1f} vs {psnr_had:.1f} dB)")
    print(f"  Both degraded vs capacity: {'YES' if both_degraded else 'NO'}")
    print(f"\n  {'PASS' if passed else 'FAIL'}: Overload regime")
    return passed


# =====================================================================
# Test 8: Continuous Addressing Integration
# =====================================================================
def test_8_continuous_addressing():
    """Learnable codes work with continuous read path."""
    print("\n" + "=" * 60)
    print("TEST 8: Continuous Addressing + Learnable Codes")
    print("=" * 60)

    torch.manual_seed(42)

    L, K, D = 32, 8, 64
    D_query = L  # match codebook dimension
    B = 1
    T = K

    embeddings = _make_embeddings(T, D, seed=42)
    keys = torch.arange(T, dtype=torch.long)
    alphas = torch.ones(T)

    bank = LearnableMemoryBank1D(L, K, D, init="hadamard")
    bank.allocate(B)

    # Store discrete (high fidelity)
    bank.store(keys, embeddings, alphas)

    # Read continuous: use codebook columns as queries
    C = bank.codebook()  # [L, K]
    queries = C.T  # [K, L=D_query] — each query is a code column
    projection = C.detach().clone()  # [L, K] — project into code space

    temperatures = [1e-6, 0.01, 0.1, 1.0]

    print(f"\n  Configuration: L={L}, K={K}, D={D}, D_query={D_query}")
    print(f"  Store: discrete  |  Read: continuous at varying T")

    print(f"\n  {'Temp':>8} {'Avg PSNR':>10} {'Avg CosSim':>12}")
    print(f"  {'-'*8} {'-'*10} {'-'*12}")

    results = {}
    for temp in temperatures:
        Y = bank.retrieve_continuous(queries, projection, temperature=temp)

        psnrs = [_psnr(Y[0, k], embeddings[k]) for k in range(T)]
        metrics = token_retrieval_accuracy(
            Y[0].detach(), embeddings, threshold=0.999
        )
        avg_p = sum(psnrs) / len(psnrs)

        results[temp] = {"psnr": avg_p, "cos": metrics["cosine_sim"]}
        print(f"  {temp:>8.1e} {avg_p:>9.1f}dB {metrics['cosine_sim']:>11.6f}")

    # Verify gradient flows through learnable codes to continuous read
    bank.allocate(B)
    bank.store(keys, embeddings, alphas)
    proj_param = nn.Parameter(C.detach().clone())
    Y = bank.retrieve_continuous(queries.detach(), proj_param, temperature=0.01)
    target = embeddings.unsqueeze(0)
    loss = reconstruction_loss(Y, target)
    loss.backward()

    code_grad = bank.codebook.C_raw.grad is not None
    proj_grad = proj_param.grad is not None

    print(f"\n  Gradient through C_raw: {'YES' if code_grad else 'NO'}")
    print(f"  Gradient through projection: {'YES' if proj_grad else 'NO'}")

    low_t_ok = results[1e-6]["psnr"] > 80.0
    degrades = results[1.0]["psnr"] < results[1e-6]["psnr"]

    passed = low_t_ok and degrades and code_grad
    print(f"\n  Low T PSNR > 80 dB: {'YES' if low_t_ok else 'NO'} "
          f"({results[1e-6]['psnr']:.1f} dB)")
    print(f"  Degrades at high T: {'YES' if degrades else 'NO'}")
    print(f"\n  {'PASS' if passed else 'FAIL'}: Continuous addressing + learnable codes")
    return passed


# =====================================================================
# Main
# =====================================================================

def main():
    print("=" * 60)
    print("  DIRECTION 6: LEARNABLE CODE EVOLUTION")
    print("  Learnable Codebook Test Suite")
    print("=" * 60)

    tests = [
        ("Backward Compat", test_1_backward_compat),
        ("Gradient Flow", test_2_gradient_flow),
        ("Orthogonality Regulariser", test_3_orthogonality_regulariser),
        ("Reconstruction Training", test_4_reconstruction_training),
        ("Hadamard vs Learned", test_5_hadamard_vs_learned),
        ("Coherence Tracking", test_6_coherence_tracking),
        ("Overload Regime", test_7_overload_regime),
        ("Continuous Addressing", test_8_continuous_addressing),
    ]

    results = {}
    for name, test_fn in tests:
        try:
            results[name] = test_fn()
        except Exception as e:
            print(f"\n  ERROR in {name}: {e}")
            import traceback
            traceback.print_exc()
            results[name] = False

    print("\n" + "=" * 60)
    print("  SUMMARY")
    print("=" * 60)
    for name, passed in results.items():
        status = "PASS" if passed else "FAIL"
        print(f"  [{status}] {name}")

    n_pass = sum(1 for p in results.values() if p)
    n_total = len(results)
    print(f"\n  {n_pass}/{n_total} tests passed")

    if n_pass == n_total:
        print("\n  ALL TESTS PASSED")
    else:
        print(f"\n  {n_total - n_pass} FAILURES")

    return n_pass == n_total


if __name__ == "__main__":
    success = main()
    sys.exit(0 if success else 1)