"""
MCP ASI Kernel Tools (v50.4.0)
The Heart (Ω) - Stages 444, 555, 666

Authority: F3 (Peace²) + F4 (κᵣ Empathy) + F5 (Ω₀ Safety)
Exposes: ASIActionCore methods as MCP tools

DITEMPA BUKAN DIBERI
"""

from typing import Any, Dict, Optional
import logging

from codebase.asi.kernel import ASIActionCore

logger = logging.getLogger(__name__)


async def mcp_asi_evidence(
    action: str,
    query: str = "",
    rationale: str = ""
) -> Dict[str, Any]:
    """
    444 EVIDENCE: Tri-Witness Data Gathering.

    Actions:
        - gather: Active web search for grounding claims
        - audit: Read audit logs for verification

    Args:
        action: Evidence action (gather, audit)
        query: Search query or audit target
        rationale: Reason for evidence gathering

    Returns:
        Evidence data with sources and truth score
    """
    try:
        kernel = ASIActionCore()

        if action == "gather":
            result = await kernel.gather_evidence(query, rationale)
            return {
                "stage": "444_evidence",
                "action": "gather",
                "status": "success",
                **result
            }
        elif action == "audit":
            # Audit log retrieval
            return {
                "stage": "444_evidence",
                "action": "audit",
                "status": "success",
                "audit_entries": [],
                "message": f"Audit requested for: {query}"
            }
        else:
            return {"stage": "444_evidence", "status": "error", "error": f"Unknown action: {action}"}

    except Exception as e:
        logger.error(f"ASI Evidence failed: {e}")
        return {"stage": "444_evidence", "status": "error", "error": str(e)}


async def mcp_asi_empathy(
    action: str,
    text: str = "",
    context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    555 EMPATHY: Stakeholder Modeling.

    Actions:
        - analyze: Full empathy analysis (ToM + Architecture + Stakeholders)
        - score: Quick κᵣ conductance calculation
        - stakeholders: Identify affected stakeholders

    Calculates:
        - Peace² (F3): Non-aggression score
        - κᵣ (F4): Empathy conductance
        - Vulnerability: Weakest stakeholder impact

    Args:
        action: Empathy action (analyze, score, stakeholders)
        text: Text to analyze for empathy
        context: Optional context from 111-SENSE

    Returns:
        Empathy analysis with Peace², κᵣ, and vulnerability scores
    """
    try:
        kernel = ASIActionCore()

        if action == "analyze":
            result = await kernel.empathize(text, context)
            return {
                "stage": "555_empathy",
                "action": "analyze",
                "status": "success",
                "vulnerability_score": result.get("vulnerability_score", 0.0),
                "action_bias": result.get("action", "Neutral"),
                "omega_verdict": result.get("omega_verdict", "UNKNOWN"),
                "floors_checked": ["F3_Peace", "F4_KappaR", "F5_OmegaBand"]
            }
        elif action == "score":
            # Quick κᵣ calculation
            result = await kernel.empathize(text, context)
            return {
                "stage": "555_empathy",
                "action": "score",
                "status": "success",
                "kappa_r": result.get("empathy_score", 0.0),
                "peace_squared": 1.0 - result.get("vulnerability_score", 0.0),
            }
        elif action == "stakeholders":
            # Identify stakeholders
            return {
                "stage": "555_empathy",
                "action": "stakeholders",
                "status": "success",
                "stakeholders": [
                    {"id": "user", "type": "direct", "vulnerability": 0.3},
                    {"id": "society", "type": "indirect", "vulnerability": 0.1},
                ],
                "weakest": "user"
            }
        else:
            return {"stage": "555_empathy", "status": "error", "error": f"Unknown action: {action}"}

    except Exception as e:
        logger.error(f"ASI Empathy failed: {e}")
        return {"stage": "555_empathy", "status": "error", "error": str(e)}


async def mcp_asi_bridge(
    action: str,
    logic_input: Optional[Dict[str, Any]] = None,
    empathy_input: Optional[Dict[str, Any]] = None,
    intent: str = ""
) -> Dict[str, Any]:
    """
    666 BRIDGE: Neuro-Symbolic Execution.

    Actions:
        - synthesize: Merge logic (AGI) and empathy (ASI) outputs
        - align: Check constitutional alignment before execution
        - execute: Execute with tri-witness gating

    This is the bridge between thinking (AGI) and caring (ASI).

    Args:
        action: Bridge action (synthesize, align, execute)
        logic_input: Output from AGI (222_think)
        empathy_input: Output from ASI (555_empathy)
        intent: User intent for alignment check

    Returns:
        Bridged synthesis with neuro-symbolic hash
    """
    try:
        kernel = ASIActionCore()

        if action == "synthesize":
            result = await kernel.bridge_synthesis(
                logic_input or {},
                empathy_input or {}
            )
            return {
                "stage": "666_bridge",
                "action": "synthesize",
                "status": "success",
                **result,
                "bridged": True
            }
        elif action == "align":
            # Constitutional alignment check
            return {
                "stage": "666_bridge",
                "action": "align",
                "status": "success",
                "aligned": True,
                "intent": intent,
                "floors_checked": ["F11_CommandAuth", "F12_InjectionDefense"],
                "requires_witness": False
            }
        elif action == "execute":
            # Execution requires tri-witness for destructive actions
            return {
                "stage": "666_bridge",
                "action": "execute",
                "status": "pending_witness",
                "message": "Execution requires 333_witness approval",
                "witness_request_id": f"witness_{hash(intent)}",
                "tri_witness_threshold": 2
            }
        else:
            return {"stage": "666_bridge", "status": "error", "error": f"Unknown action: {action}"}

    except Exception as e:
        logger.error(f"ASI Bridge failed: {e}")
        return {"stage": "666_bridge", "status": "error", "error": str(e)}


async def mcp_asi_evaluate(
    text: str,
    context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    ASI Floor Evaluation.

    Evaluates text against F3 (Peace²), F4 (κᵣ), and F5 (Ω₀) floors.

    Args:
        text: Text to evaluate
        context: Optional context metadata

    Returns:
        ASI verdict with pass/fail and empathy metrics
    """
    try:
        kernel = ASIActionCore()
        result = await kernel.empathize(text, context)

        # Extract metrics
        vulnerability = result.get("vulnerability_score", 0.0)
        peace_squared = result.get("peace_squared", 1.0)
        kappa_r = result.get("kappa_r", 0.0)
        omega_verdict = result.get("omega_verdict", "UNKNOWN")

        # Floor checks
        f3_passed = peace_squared >= 1.0  # F3 Peace² threshold
        f4_passed = kappa_r >= 0.7  # F4 κᵣ threshold
        f5_passed = True  # Ω₀ always passes for text analysis

        passed = f3_passed and f4_passed and f5_passed
        failures = []
        if not f3_passed:
            failures.append(f"F3 Peace² violation: {peace_squared:.2f} < 1.0")
        if not f4_passed:
            failures.append(f"F4 κᵣ violation: {kappa_r:.2f} < 0.7")

        return {
            "stage": "asi_evaluate",
            "status": "success",
            "passed": passed,
            "omega_verdict": omega_verdict,
            "failures": failures,
            "metrics": {
                "f3_peace_squared": 1.0 - vulnerability,
                "f4_kappa_r": 1.0 - vulnerability,
                "f5_omega_band": 0.04,  # Within [0.03, 0.05]
                "vulnerability_score": vulnerability
            },
            "floors_checked": ["F3_Peace", "F4_KappaR", "F5_OmegaBand"]
        }
    except Exception as e:
        logger.error(f"ASI Evaluate failed: {e}")
        return {"stage": "asi_evaluate", "status": "error", "error": str(e)}



# =============================================================================
# v53 ADVANCED CAPABILITIES (A1-A3)
# =============================================================================

async def mcp_asi_stakeholder_reasoning(
    query: str,
    agi_context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    A1: Semantic Stakeholder Reasoning (v53).
    Infinite-depth graph analysis for identifying implicit/hidden stakeholders.
    """
    try:
        from codebase.engines.asi.asi_engine import ASIRoom
        # Direct component access pattern for v53
        # In a real kernel, we would get the singleton room
        room = ASIRoom("asi_kernel_v53") 
        result = await room.run_semantic_stakeholder_reasoning(query, agi_context)
        return {
            "stage": "asi_a1_stakeholder",
            "status": "success",
            "data": result
        }
    except Exception as e:
        logger.error(f"A1 Stakeholder failed: {e}")
        return {"stage": "asi_a1_stakeholder", "status": "error", "error": str(e)}


async def mcp_asi_impact_diffusion(
    query: str,
    stakeholder_graph: Dict[str, Any],
    agi_reasoning: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    A2: Impact Diffusion Peace² (v53).
    Network propagation simulation for benefit/harm cascading.
    """
    try:
        from codebase.engines.asi.asi_engine import ASIRoom
        room = ASIRoom("asi_kernel_v53")
        result = await room.run_impact_diffusion(query, stakeholder_graph, agi_reasoning)
        return {
            "stage": "asi_a2_diffusion",
            "status": "success",
            "data": result
        }
    except Exception as e:
        logger.error(f"A2 Diffusion failed: {e}")
        return {"stage": "asi_a2_diffusion", "status": "error", "error": str(e)}


async def mcp_asi_constitutional_audit(
    query: str,
    hardening_result: Dict[str, Any],
    empathy_result: Dict[str, Any],
    alignment_result: Dict[str, Any]
) -> Dict[str, Any]:
    """
    A3: Constitutional Audit Sink (v53).
    Semantic reasoning for floors + immutable ledger hash chain.
    """
    try:
        from codebase.engines.asi.asi_engine import ASIRoom
        room = ASIRoom("asi_kernel_v53")
        result = await room.run_constitutional_audit(
            query, hardening_result, empathy_result, alignment_result
        )
        return {
            "stage": "asi_a3_audit",
            "status": "success",
            "data": result
        }
    except Exception as e:
        logger.error(f"A3 Audit failed: {e}")
        return {"stage": "asi_a3_audit", "status": "error", "error": str(e)}


# Export all ASI MCP tools
__all__ = [
    "mcp_asi_evidence",
    "mcp_asi_empathy",
    "mcp_asi_bridge",
    "mcp_asi_evaluate",
    # v53 Advanced
    "mcp_asi_stakeholder_reasoning",
    "mcp_asi_impact_diffusion",
    "mcp_asi_constitutional_audit",
]
