"""Bundled Vaultfire protocol stack components for Ghostkey activation."""

from __future__ import annotations

from datetime import datetime, timezone
from importlib import import_module
from typing import Dict, Iterable, List, Mapping, Sequence

from vaultfire.instinct import InstinctSuite
from vaultfire.tests import DefenseSuite, PurposeSuite
from vaultfire.modules._metadata import build_metadata
from vaultfire.modules.conscious_state_engine import ConsciousStateEngine
from vaultfire.modules.ethic_resonant_time_engine import EthicResonantTimeEngine
from vaultfire.modules.mission_soul_loop import MissionSoulLoop
from vaultfire.modules.predictive_yield_fabric import PredictiveYieldFabric
from vaultfire.modules.myth_compression_mode import MythCompressionMode
from vaultfire.modules.vaultfire_enhancement_stack import (
    ConscienceMirrorVerificationLayer,
    EnhancementConfirmComposer,
    LoopSingularityDetectorEngine,
    QuantumDriftSynchronizer,
    TemporalBehavioralCompressionEngine,
    VaultfireMythosEngine,
)
from vaultfire.consciousness import (
    CognitiveEquilibriumEngine,
    CompassionOverdriveLayer,
    TruthfieldResonator,
)
from vaultfire.protocol.signal_echo import SignalEchoEngine
from vaultfire.quantum.hashmirror import QuantumHashMirror
from vaultfire.privacy_integrity import PrivacyIntegrityShield, get_privacy_shield
from vaultfire.privacy import (
    ConsentGuardianLayer,
    EchoAnonymizerEngine,
    GhostkeyPrivacyHalo,
    VaultTraceEraser,
)
from vaultfire.legal import DisclosureShieldTrailEngine
from vaultfire.ethics import BehavioralEthicsMonitor, ConsentFirstMirror

_yield_module = import_module("vaultfire.yield")
PulseSync = getattr(_yield_module, "PulseSync")
TemporalGiftMatrixEngine = getattr(_yield_module, "TemporalGiftMatrixEngine")

IDENTITY_HANDLE = "bpow20.cb.id"
IDENTITY_ENS = "ghostkey316.eth"

_DEFAULT_CONFIRMATION_SOURCES = (
    "PulsewatchMetrics",
    "LoopCompressionLogs",
    "AnonymizationProof",
    "EthicsValidationHash",
    "DisclosureAuditTrail",
)

_MORAL_EQUILIBRIUM_SOURCES = (
    "CognitiveEquilibriumEngine",
    "BehavioralEthicsMonitor",
    "TruthfieldResonator",
    "CompassionOverdriveLayer",
    "GhostkeyPrivacyHalo",
    "DisclosureShieldTrailEngine",
)


def _now_ts() -> str:
    return datetime.now(timezone.utc).isoformat()


class _EphemeralTimeFlare:
    """Minimal TimeFlare compatible helper used for tests and CLI."""

    def __init__(self) -> None:
        self._ledger: List[Mapping[str, object]] = []

    def register(self, entry: Mapping[str, object]) -> None:
        self._ledger.append(dict(entry))

    def load(self) -> List[Mapping[str, object]]:
        return [dict(entry) for entry in self._ledger]


class GiftMatrixV1:
    """Belief signal reward distribution orchestrator."""

    def __init__(
        self,
        *,
        identity_handle: str = IDENTITY_HANDLE,
        identity_ens: str = IDENTITY_ENS,
        timeflare: _EphemeralTimeFlare | None = None,
        signal_engine: SignalEchoEngine | None = None,
        pulse_sync: PulseSync | None = None,
        hash_mirror: QuantumHashMirror | None = None,
        privacy_shield: PrivacyIntegrityShield | None = None,
    ) -> None:
        self.identity_handle = identity_handle
        self.identity_ens = identity_ens
        self._timeflare = timeflare or _EphemeralTimeFlare()
        self.signal_engine = signal_engine or SignalEchoEngine()
        self.pulse_sync = pulse_sync or PulseSync()
        self.hash_mirror = hash_mirror or QuantumHashMirror(
            seed=f"gift-matrix::{identity_ens}"
        )
        self.privacy_shield = privacy_shield or get_privacy_shield()
        self.engine = TemporalGiftMatrixEngine(
            timeflare=self._timeflare,
            signal_engine=self.signal_engine,
            pulse_sync=self.pulse_sync,
            hash_mirror=self.hash_mirror,
            base_reward=180.0,
        )
        self.metadata: Mapping[str, object] = build_metadata(
            "GiftMatrix_v1",
            identity={"wallet": identity_handle, "ens": identity_ens},
        )
        self._fork_log: List[Mapping[str, object]] = []
        self._unlocked_layers: List[Mapping[str, object]] = []
        self._last_signal_purity: float = 0.0
        self._last_record: Mapping[str, object] | None = None
        self.privacy_shield.halo.register_boundary(
            self.identity_handle,
            (
                "active_layers",
                "forks_tracked",
                "last_signal_purity",
                "last_record",
                "metadata",
            ),
        )

    def record_signal(
        self,
        interaction_id: str,
        *,
        belief_purity: float,
        emotion: str = "focus",
        ethic: str = "aligned",
        tags: Sequence[str] = (),
    ) -> None:
        self._last_signal_purity = float(belief_purity)
        self.signal_engine.record_frame(
            interaction_id,
            emotion=emotion,
            ethic=ethic,
            intensity=belief_purity,
            tags=tags,
        )

    def register_fork(
        self,
        interaction_id: str,
        *,
        branch: str,
        priority: str,
        ethic_score: float,
        alignment_bias: float,
    ) -> Mapping[str, object]:
        entry = {
            "interaction_id": interaction_id,
            "branch": branch,
            "priority": priority,
            "ethic_score": float(ethic_score),
            "alignment_bias": float(alignment_bias),
            "created_at": _now_ts(),
        }
        self._fork_log.append(entry)
        self._timeflare.register(entry)
        return entry

    def claim(self, interaction_id: str, recipients: Iterable[str | Mapping[str, object]]):
        record = self.engine.generate_matrix(interaction_id, recipients)
        payload = {
            "record_id": record.record_id,
            "metadata": dict(record.metadata),
            "allocations": [allocation.__dict__ for allocation in record.allocations],
        }
        tracked = self.privacy_shield.track_event(
            self.identity_handle,
            payload,
            category="giftmatrix.claim",
        )
        if tracked is not None:
            self._last_record = tracked
        else:
            self._last_record = self.privacy_shield.sanitize_view(
                self.identity_handle,
                payload,
                category="giftmatrix.claim",
            )
        return record

    def unlock_next_layer(self, label: str | None = None) -> Mapping[str, object]:
        layer_index = len(self._unlocked_layers) + 1
        entry = {
            "layer": layer_index,
            "label": label or f"Layer-{layer_index}",
            "unlocked_at": _now_ts(),
        }
        self._unlocked_layers.append(entry)
        return entry

    def pulse_watch(self) -> Mapping[str, object]:
        payload = {
            "identity": self.metadata["identity"],
            "active_layers": len(self._unlocked_layers),
            "forks_tracked": len(self._fork_log),
            "last_signal_purity": self._last_signal_purity,
            "last_record": self._last_record,
            "metadata": self.metadata,
        }
        return self.privacy_shield.sanitize_view(
            self.identity_handle,
            payload,
            category="giftmatrix.pulse",
        )


class VaultfireProtocolStack:
    """Convenience wrapper bundling the protocol systems."""

    _architect_handle: str = IDENTITY_ENS
    _architect_history: List[Mapping[str, object]] = [
        {"architect": IDENTITY_ENS, "assigned_at": _now_ts()}
    ]
    _adaptive_cycle_config: Mapping[str, object] = {}
    _update_log: List[Mapping[str, object]] = []

    def __init__(
        self,
        *,
        identity_handle: str = IDENTITY_HANDLE,
        identity_ens: str = IDENTITY_ENS,
        actions: Sequence[Mapping[str, object]] = (),
        mythos_path: str | None = None,
        privacy_shield: PrivacyIntegrityShield | None = None,
    ) -> None:
        self.identity_handle = identity_handle
        self.identity_ens = identity_ens
        self.privacy_shield = privacy_shield or get_privacy_shield()
        self.metadata: Mapping[str, object] = build_metadata(
            "VaultfireProtocolStack",
            identity={"wallet": identity_handle, "ens": identity_ens},
        )
        self.conscious = ConsciousStateEngine(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.predictive = PredictiveYieldFabric(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.soul = MissionSoulLoop(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.time_engine = EthicResonantTimeEngine(
            "ghostkey-316",
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.gift_matrix = GiftMatrixV1(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
            privacy_shield=self.privacy_shield,
        )
        self.privacy_shield.toggle_tracking(True)
        self.behavioral_compression = TemporalBehavioralCompressionEngine(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.conscience_mirror = ConscienceMirrorVerificationLayer(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.loop_detector = LoopSingularityDetectorEngine(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.quantum_drift = QuantumDriftSynchronizer(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.myth_mode = MythCompressionMode(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
            ghostkey_id=identity_handle,
        )
        self.mythos = VaultfireMythosEngine(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
            output_path=mythos_path,
        )
        self.cognitive_equilibrium = CognitiveEquilibriumEngine(
            identity_handle=identity_handle,
            identity_ens=identity_ens,
        )
        self.truthfield_resonator = TruthfieldResonator()
        self.compassion_overdrive = CompassionOverdriveLayer()
        self._moral_telemetry: List[Mapping[str, object]] = []
        self.ethics_monitor = BehavioralEthicsMonitor()
        self.consent_first_mirror = ConsentFirstMirror(self.ethics_monitor)
        self.disclosure_trail = DisclosureShieldTrailEngine()
        self.instinct_layer: Dict[str, Mapping[str, object]] = {}
        self.shield_layers: Dict[str, Mapping[str, object]] = {}
        self.lockbox_layers: Dict[str, Mapping[str, object]] = {}
        self._memory_links: List[Mapping[str, object]] = []
        self._protocol_commits: List[Mapping[str, object]] = []
        self.integration_manifest = self.integrate(
            [
                TemporalBehavioralCompressionEngine,
                ConscienceMirrorVerificationLayer,
                LoopSingularityDetectorEngine,
                QuantumDriftSynchronizer,
                VaultfireMythosEngine,
                MythCompressionMode,
                ConsentGuardianLayer,
                EchoAnonymizerEngine,
                VaultTraceEraser,
                GhostkeyPrivacyHalo,
                DisclosureShieldTrailEngine,
                BehavioralEthicsMonitor,
                ConsentFirstMirror,
                CognitiveEquilibriumEngine,
                TruthfieldResonator,
                CompassionOverdriveLayer,
            ]
        )
        from vaultfire.core.cli import GhostkeyCLI

        self.cli_manifest = GhostkeyCLI.add_subcommands(
            [
                "mythos compress/view/export/share",
                "privacy compress/anonymize/consent-verify",
                "audit trail export",
                "ethics check/lock-report",
                "unlock verify --ethics --consent",
                "truthfield verify",
                "compassion boost",
                "balance recalibrate",
                "ethics monitor --auto-correct",
                "loopstate visualize",
            ]
        )
        if not self._adaptive_cycle_config:
            self.register_adaptive_cycle(
                {
                    "calibration_interval": "dynamic",
                    "reinforcement_model": "belief-integrity-recall",
                    "error_tolerance": "morality-first",
                    "auto_correct_bias": True,
                }
            )
        self.adaptive_cycle_manifest = self.adaptive_cycle()
        self.myth_mode.ensure_bootstrap()
        self.behavioral_compression.compress(
            {
                "type": "activation",
                "future_self_alignment": 0.76,
                "note": "Vaultfire protocol baseline activation",
            }
        )
        for action in actions:
            self._ingest_action(action)
        self.mythos.weave(
            source="activation",
            payload={
                "actions_ingested": len(actions),
                "baseline": True,
            },
            resonance=0.82 if actions else 0.7,
        )
        self.myth_mode.record_event(
            {
                "type": "response",
                "channel": "system",
                "success": True,
                "note": "Baseline myth weave",
                "resonance": 0.7,
            }
        )
        if not self.myth_mode.history():
            self.myth_mode.compress(milestone=True, reason="baseline-weave")
        self.predictive.register_export("core", 1.0)
        self.predictive.forecast(self.conscious.belief_health(), 120.0)
        self.conscience_mirror.conscience_sync("initialisation", threshold=0.55)
        self.architect_ens = self.architect()

    def pulsewatch(self) -> Mapping[str, object]:
        summary = dict(self.gift_matrix.pulse_watch())
        summary.update(
            {
                "belief_health": self.conscious.belief_health(),
                "tempo": self.time_engine.current_tempo(),
                "yield_forecast": self.predictive.latest_forecast,
                "enhancements": self.enhancement_confirmation(),
                "system_status": self.system_status(),
                "myth_compression": self.myth_mode.status(),
                "moral_equilibrium": self.cognitive_equilibrium.status(),
                "truthfield": self.truthfield_resonator.status(),
                "compassion_overdrive": self.compassion_overdrive.status(),
                "adaptive_cycle": self.adaptive_cycle_manifest,
            }
        )
        summary["myth_echo_bonus"] = summary["myth_compression"]["myth_echo_bonus"]
        self.privacy_shield.halo.register_boundary(
            self.identity_handle,
            (
                "belief_health",
                "tempo",
                "yield_forecast",
                "enhancements",
                "system_status",
                "myth_compression",
                "myth_echo_bonus",
                "moral_equilibrium",
                "truthfield",
                "compassion_overdrive",
                "adaptive_cycle",
            ),
        )
        tracked = self.privacy_shield.track_event(
            self.identity_handle,
            summary,
            category="vaultfire.pulsewatch",
        )
        if tracked is not None:
            return tracked
        return self.privacy_shield.sanitize_view(
            self.identity_handle,
            summary,
            category="vaultfire.pulsewatch",
        )

    def _ingest_action(self, action: Mapping[str, object]) -> None:
        def _safe_float(value: object, default: float = 0.0) -> float:
            try:
                return float(value)  # type: ignore[arg-type]
            except (TypeError, ValueError):
                return default

        record = self.conscious.record_action(action)
        self.time_engine.register_action(action)
        compression = self.behavioral_compression.compress(action)
        self.conscience_mirror.ingest(action)
        tags = tuple(action.get("tags", ()))
        belief = self.conscious.belief_health()
        action_alignment = max(0.0, min((record.belief_delta + 1.0) / 2.0, 1.0))
        result_alignment = max(0.0, min(1.0, self.time_engine.mmi.get_score() / 100.0))
        external_signal = action.get("signal", action.get("confidence", 0.75) or 0.75)
        try:
            signal_value = float(external_signal)
        except (TypeError, ValueError):
            signal_value = 0.75
        drift_payload = self.quantum_drift.synchronize(
            {
                "mood": belief,
                "behavior_alignment": action_alignment,
                "external_signal": signal_value,
            }
        )
        ethics_review = self.ethics_monitor.evaluate(
            {
                "ethic": action.get("ethic", action.get("intent", "aligned")),
                "alignment": action_alignment,
                "result_alignment": result_alignment,
                "consent": bool(action.get("consent", True)),
                "source": action.get("type", "action"),
            }
        )
        consent_report = self.consent_first_mirror.verify(
            str(action.get("actor", self.identity_handle)),
            consent=bool(action.get("consent", True)),
            review=ethics_review,
        )
        self.loop_detector.observe(
            belief=belief,
            action_alignment=action_alignment,
            result_alignment=result_alignment,
            context={
                "thresholds": compression["thresholds_triggered"],
                "nudge": drift_payload["nudge"],
            },
        )
        self.disclosure_trail.record(
            "vaultfire.action",
            {
                "interaction": str(action.get("interaction_id", action.get("id", "unknown"))),
                "ethic": ethics_review["ethic"],
                "trusted": ethics_review["trusted"],
                "consent_verified": consent_report["verified"],
            },
        )
        moral_pressure = _safe_float(action.get("pressure", action.get("tension", 0.0)))
        emotion = action.get("emotion") or action.get("ethic", "focus")
        equilibrium_event = self.cognitive_equilibrium.balance(
            belief=belief,
            action_alignment=action_alignment,
            result_alignment=result_alignment,
            emotion=str(emotion),
            moral_pressure=moral_pressure,
            tags=tags,
        )
        confidence_signal = _safe_float(action.get("confidence", belief), belief)
        bias_signal = _safe_float(action.get("bias", action.get("alignment_bias", 0.0)))
        truth_snapshot = self.truthfield_resonator.scan(
            statement=str(
                action.get(
                    "statement",
                    action.get("summary", action.get("type", "action")),
                )
            ),
            confidence=confidence_signal,
            source=str(action.get("channel", action.get("source", "cli"))),
            source_bias=bias_signal,
            tags=tags,
            contradictions=action.get("contradictions"),
        )
        severity_hint = max(
            _safe_float(action.get("distress"), 0.0),
            _safe_float(action.get("urgency"), 0.0),
            _safe_float(action.get("risk"), 0.0),
        )
        severity = severity_hint if severity_hint > 0 else abs(result_alignment - belief)
        compassion_event = self.compassion_overdrive.boost(
            context=str(action.get("type", "action")),
            severity=severity,
            empathy_tags=tags,
            consent_granted=bool(action.get("consent", True)),
        )
        self._moral_telemetry.append(
            {
                "equilibrium": equilibrium_event,
                "truthfield": truth_snapshot,
                "compassion": compassion_event,
            }
        )
        self.mythos.weave(
            source=str(action.get("type", "action")),
            payload={
                "action": dict(action),
                "rewards": compression["rewards_unlocked"],
                "LoopMerge_Mode": self.loop_detector.mode,
            },
            resonance=belief,
        )
        tags = tuple(action.get("tags", ()))
        self.myth_mode.record_event(
            {
                "type": "command",
                "command": str(action.get("command", action.get("type", "action"))),
                "channel": action.get("channel", "cli"),
                "success": action_alignment >= 0.5,
                "intensity": belief,
                "tags": tags,
                "confirm": bool(compression["rewards_unlocked"]),
                "milestone": bool(action.get("milestone")),
                "metadata": {
                    "alignment": action_alignment,
                    "result_alignment": result_alignment,
                },
            }
        )
        self.myth_mode.record_event(
            {
                "type": "response",
                "channel": action.get("channel", "cli"),
                "success": result_alignment >= 0.5,
                "intensity": result_alignment,
                "tags": tags,
                "metadata": {
                    "belief": belief,
                    "drift": drift_payload,
                },
            }
        )
        if action.get("milestone"):
            self.myth_mode.compress(milestone=True, reason="milestone-action")

    def unlock_next(self, label: str | None = None) -> Mapping[str, object]:
        if not self.myth_mode.can_unlock():
            raise PermissionError(
                "Myth compression threshold not met; compress myth loops before unlocking"
            )
        sync = self.conscience_mirror.conscience_sync("unlock", threshold=0.55)
        if not sync["verified"]:
            raise PermissionError("Conscience mirror verification required before unlock")
        layer = self.gift_matrix.unlock_next_layer(label)
        payload = dict(layer)
        payload["conscience_sync"] = sync
        return payload

    def enhancement_confirmation(self, *, include_logs: bool = False) -> Mapping[str, object]:
        extra = {
            "ethics": self.ethics_monitor.status(),
            "consent_mirror": self.consent_first_mirror.status(),
            "disclosure_trail": self.disclosure_trail.status(),
        }
        return EnhancementConfirmComposer.compose(
            self.behavioral_compression,
            self.conscience_mirror,
            self.loop_detector,
            self.quantum_drift,
            self.mythos,
            include_logs=include_logs,
            extra=extra,
        )

    def system_status(self) -> Mapping[str, object]:
        status = {
            "Codex_Status": "🔥 READY 🔥",
            "Ghostkey_CLI": "Activated & Trusted",
            "Engine_Stack": "Synced",
            "VaultfireProtocolStack": "All engines integrated",
            "Telemetry": "CLI + enhancement unlock telemetry live",
            "metadata": self.metadata,
        }
        status["Enhancement_Confirmation"] = EnhancementConfirmComposer.status()
        status["Integration_Manifest"] = list(self.integration_manifest)
        status["CLI_Manifest"] = self.cli_manifest
        status["Moral_Equilibrium"] = self.cognitive_equilibrium.status()
        status["Truthfield"] = self.truthfield_resonator.status()
        status["Compassion_Overdrive"] = self.compassion_overdrive.status()
        status["Adaptive_Cycle"] = self.adaptive_cycle_manifest
        status["Architect"] = {"ens": self.architect_ens, "history": list(self.architect_history())}
        return status

    @classmethod
    def integrate(cls, modules: Iterable[type]) -> Sequence[Mapping[str, object]]:
        manifest = []
        timestamp = _now_ts()
        for module in modules:
            label = getattr(module, "__name__", str(module))
            manifest.append(
                {
                    "module": label,
                    "namespace": getattr(module, "__module__", ""),
                    "integrated_at": timestamp,
                }
            )
        sources = list(dict.fromkeys(_DEFAULT_CONFIRMATION_SOURCES + _MORAL_EQUILIBRIUM_SOURCES))
        EnhancementConfirmComposer.sync_with(sources)
        annotations: Dict[str, object] = {
            "integration_manifest": list(manifest),
            "moral_equilibrium_loop": list(_MORAL_EQUILIBRIUM_SOURCES),
        }
        if cls._adaptive_cycle_config:
            annotations["adaptive_cycle"] = dict(cls._adaptive_cycle_config)
        EnhancementConfirmComposer.annotate(**annotations)
        return tuple(manifest)

    @classmethod
    def assign_architect(cls, ens: str) -> Mapping[str, object]:
        entry = {"architect": str(ens), "assigned_at": _now_ts()}
        cls._architect_handle = str(ens)
        cls._architect_history.append(entry)
        EnhancementConfirmComposer.annotate(architect=str(ens))
        return dict(entry)

    @classmethod
    def architect(cls) -> str:
        return cls._architect_handle

    @classmethod
    def architect_history(cls) -> Sequence[Mapping[str, object]]:
        return tuple(cls._architect_history)

    @classmethod
    def register_adaptive_cycle(cls, config: Mapping[str, object]) -> Mapping[str, object]:
        payload = {
            "calibration_interval": str(
                config.get("calibration_interval", "dynamic")
            ),
            "reinforcement_model": str(
                config.get("reinforcement_model", "belief-integrity-recall")
            ),
            "error_tolerance": str(config.get("error_tolerance", "morality-first")),
            "auto_correct_bias": bool(config.get("auto_correct_bias", True)),
        }
        cls._adaptive_cycle_config = payload
        EnhancementConfirmComposer.annotate(adaptive_cycle=dict(payload))
        return dict(payload)

    @classmethod
    def adaptive_cycle(cls) -> Mapping[str, object]:
        return dict(cls._adaptive_cycle_config)

    def moral_telemetry(self) -> Sequence[Mapping[str, object]]:
        return tuple(self._moral_telemetry)

    def inject_instinct_layer(
        self, modules: Mapping[str, object]
    ) -> Mapping[str, Mapping[str, object]]:
        """Attach instinct primitives and broadcast the snapshot."""

        snapshot = InstinctSuite.attach(modules)
        self.instinct_layer = dict(snapshot)
        self._moral_telemetry.append(
            {
                "type": "instinct-layer",
                "timestamp": _now_ts(),
                "modules": tuple(sorted(snapshot)),
            }
        )
        return {name: dict(payload) for name, payload in snapshot.items()}

    def inject_shield_layer(
        self, modules: Mapping[str, object]
    ) -> Mapping[str, Mapping[str, object]]:
        """Register active shield defenses and broadcast the snapshot."""

        snapshot = DefenseSuite.register_shield(modules)
        self.shield_layers = {
            name: self._clone_shield_payload(payload) for name, payload in snapshot.items()
        }
        self._moral_telemetry.append(
            {
                "type": "shield-layer",
                "timestamp": _now_ts(),
                "modules": tuple(sorted(self.shield_layers)),
            }
        )
        return {
            name: self._clone_shield_payload(payload)
            for name, payload in self.shield_layers.items()
        }

    def lock_truth(
        self, packages: Mapping[str, object]
    ) -> Mapping[str, Mapping[str, object]]:
        """Seal truth lockboxes and broadcast the snapshot."""

        snapshot = DefenseSuite.register_lockbox(packages)
        self.lockbox_layers = {
            name: self._clone_lockbox_payload(payload)
            for name, payload in snapshot.items()
        }
        self._moral_telemetry.append(
            {
                "type": "truth-lockbox",
                "timestamp": _now_ts(),
                "modules": tuple(sorted(self.lockbox_layers)),
            }
        )
        return {
            name: self._clone_lockbox_payload(payload)
            for name, payload in self.lockbox_layers.items()
        }

    @staticmethod
    def _clone_shield_payload(payload: Mapping[str, object]) -> Mapping[str, object]:
        cloned: Dict[str, object] = {
            "module": payload["module"],
            "status": payload["status"],
            "mode": payload["mode"],
            "engaged_at": payload["engaged_at"],
            "config": dict(payload.get("config", {})),
        }
        if "whitelist" in payload and payload["whitelist"]:
            cloned["whitelist"] = tuple(payload["whitelist"])
        return cloned

    @staticmethod
    def _clone_lockbox_payload(payload: Mapping[str, object]) -> Mapping[str, object]:
        cloned: Dict[str, object] = {
            "module": payload["module"],
            "status": payload["status"],
            "sealed_at": payload["sealed_at"],
            "payload": dict(payload.get("payload", {})),
            "fingerprint": payload["fingerprint"],
        }
        if "keys" in payload:
            cloned["keys"] = tuple(payload["keys"])
        return cloned

    def sync_with_memory(
        self, channel: str, priority: str = "instinct"
    ) -> Mapping[str, object]:
        """Record a memory sync event for the instinct layer."""

        if not self.instinct_layer:
            raise RuntimeError("Instinct layer has not been injected")
        record = {
            "channel": str(channel),
            "priority": str(priority),
            "synced_at": _now_ts(),
            "modules": {
                name: {
                    "status": payload["status"],
                    "heartbeat": payload["heartbeat"],
                }
                for name, payload in self.instinct_layer.items()
            },
        }
        self._memory_links.append(record)
        self._moral_telemetry.append({"type": "memory-sync", "payload": record})
        return dict(record)

    def confirm_commit(
        self,
        version: str,
        *,
        log: str,
        sources: Sequence[str] | None = None,
    ) -> Mapping[str, object]:
        """Persist a protocol commit acknowledgement with instinct context."""

        record = {
            "version": str(version),
            "log": str(log),
            "confirmed_at": _now_ts(),
            "sources": tuple(sources or _DEFAULT_CONFIRMATION_SOURCES),
        }
        if self.instinct_layer:
            record["instinct_status"] = {
                name: payload["status"] for name, payload in self.instinct_layer.items()
            }
        self._protocol_commits.append(record)
        self._moral_telemetry.append({"type": "protocol-commit", "payload": record})
        return dict(record)

    @classmethod
    def commit_update(
        cls,
        version: str,
        *,
        note: str,
        source: str | None = None,
        tags: Sequence[str] | None = None,
        attach_purpose_report: bool = True,
    ) -> Mapping[str, object]:
        """Record a protocol update with optional purpose alignment context."""

        version_id = str(version).strip()
        if not version_id:
            raise ValueError("version cannot be empty")
        note_text = str(note).strip()
        if not note_text:
            raise ValueError("note cannot be empty")
        origin = str(source).strip() if source else cls._architect_handle
        tag_payload = tuple(
            tag
            for tag in (str(tag).strip() for tag in tags or ())
            if tag
        )
        entry: Dict[str, object] = {
            "version": version_id,
            "note": note_text,
            "source": origin,
            "committed_at": _now_ts(),
        }
        if tag_payload:
            entry["tags"] = tag_payload
        if attach_purpose_report:
            try:
                purpose_report = PurposeSuite.run_all()
            except Exception as exc:  # pragma: no cover - defensive path
                entry["purpose"] = {
                    "status": "unavailable",
                    "error": str(exc),
                }
            else:
                entry["purpose"] = purpose_report
        cls._update_log.append(entry)
        return dict(entry)

    def instinct_memory_links(self) -> Sequence[Mapping[str, object]]:
        """Return recorded instinct memory syncs."""

        return tuple(dict(entry) for entry in self._memory_links)

    def instinct_commits(self) -> Sequence[Mapping[str, object]]:
        """Return recorded instinct-aware commits."""

        return tuple(dict(entry) for entry in self._protocol_commits)

    @classmethod
    def updates(cls) -> Sequence[Mapping[str, object]]:
        """Return recorded protocol update commits."""

        return tuple(dict(entry) for entry in cls._update_log)


# ---------------------------------------------------------------------------
# Future module scaffolding
# ---------------------------------------------------------------------------


class AdaptiveRelicStore:
    """Placeholder scaffold for the Adaptive Relic Store module."""

    def __init__(self) -> None:
        self.backlog: List[str] = []

    def blueprint(self, relic_name: str) -> Mapping[str, object]:
        self.backlog.append(relic_name)
        return {
            "relic": relic_name,
            "status": "planned",
            "created_at": _now_ts(),
        }


class GhostMemoryArchive:
    """Placeholder scaffold for time-locked memory unlocks."""

    def __init__(self) -> None:
        self._anchors: List[Mapping[str, object]] = []

    def plan_anchor(self, label: str, unlock_at: str) -> Mapping[str, object]:
        entry = {"label": label, "unlock_at": unlock_at}
        self._anchors.append(entry)
        return entry


class SignalForge:
    """Placeholder scaffold for the belief-to-energy converter."""

    def __init__(self) -> None:
        self._designs: List[Mapping[str, object]] = []

    def draft_conversion(self, signal: str, efficiency: float) -> Mapping[str, object]:
        plan = {
            "signal": signal,
            "efficiency": efficiency,
            "created_at": _now_ts(),
        }
        self._designs.append(plan)
        return plan


class VaultfireDNASyncer:
    """Placeholder scaffold for legacy moral imprint technology."""

    def __init__(self) -> None:
        self._imprints: List[Mapping[str, object]] = []

    def schedule_imprint(self, reference: str, integrity: float) -> Mapping[str, object]:
        imprint = {
            "reference": reference,
            "integrity": integrity,
            "scheduled_at": _now_ts(),
        }
        self._imprints.append(imprint)
        return imprint


__all__ = [
    "ConsciousStateEngine",
    "PredictiveYieldFabric",
    "MissionSoulLoop",
    "GiftMatrixV1",
    "VaultfireProtocolStack",
    "AdaptiveRelicStore",
    "GhostMemoryArchive",
    "SignalForge",
    "VaultfireDNASyncer",
]

