"""Seal all evidence without mistaking diagnostic coverage for learning."""
from context import MODEL, EXP, read, save, digest, check, integrity, budget, seal


def main():
    check()
    from summarize import run
    summary = run(verify_only=True)
    assert summary['arithmetic_and_coverage_pass'] and summary['primary_trials'] == 120 and summary['exact_repeats'] == 24
    refs = read(EXP / 'regression/frozen-five.json')
    assert refs['pass_all'] and len(refs['trials']) == 5 and all(all(r['exact'].values()) for r in refs['trials'])
    old = integrity()
    save(EXP / 'regression/old-integrity-after.json', old)
    status = 'PASS-CONDITIONAL-CREDIT-TRANSFER-AUDIT-NOT-LEARNING'
    result = dict(status=status, **summary, old_integrity_pass=all(r['pass'] for r in old.values()),
        frozen_five_reference_pass=refs['pass_all'], payload_bytes_before_manifest=budget(),
        frozen_eligibility_and_mask_unchanged=True, base_weights_unchanged=True,
        scientific_summary_sha256=digest(EXP / 'audit/scientific-summary.json'),
        summary_all_144_baseline_raw_states_and_repeat_pair_metrics_reverified=True,
        next_required_gate='Only if the diagnostic evidence supports it, separately preregister compartment-specific reinforcement and independent whole-brain evaluation; no automatic conditioning unlock')
    save(EXP / 'audit/final-status.json', result)
    with (EXP / 'REPORT.md').open('x', encoding='utf-8') as f:
        f.write('# Phase 4AL — conditional credit/MBON transfer audit\n\nStatus: `' + status + '`.\n\n')
        f.write('All 120 primary trials and 24 repeats are retained. Full events/counts/final V/g/ref baseline exactness is required.\n')
        f.write('Virtual eligibility is sampled after tick 900; no DAN is injected, no delta is learned.\n')
        f.write('Uniform 25% mask depression is a temporary conditional replay; every source train, including feedback, is clamped.\n')
        f.write('All 60 odor pair-mode records and all 97 MBON targets are reported; no best pair/parameter selection.\n\n')
        for name, block in summary['blocks'].items():
            f.write(f"- {name}: specific KC/edge sets {block['pairs_with_both_specific_KC_and_edge_sets']}/15; conditional event change {block['conditional25_event_changed_trials']}/30; count/first-latency change {block['conditional25_count_or_first_latency_changed_trials']}/30.\n")
        f.write('\nNo biological learning PASS or candidate. This does not complete the learning goal.\n')
        f.write('Read EVIDENCE.md in the model for provenance, unmatched biology and conditional/closed-loop distinctions.\n')
    check(); seal(MODEL); seal(EXP)
    print(status, flush=True)


if __name__ == '__main__':
    main()