"""
AGI Neural Core (The Thinker)
Authority: F2 (Truth) + F6 (Context)
Metabolic Stages: 111, 222, 333
"""
import logging
import time
from typing import Any, Dict, List
from dataclasses import dataclass

from arifos_core.agi.atlas import ATLAS

logger = logging.getLogger("agi_kernel")


@dataclass
class AGIVerdict:
    """
    Verdict from AGI evaluation for constitutional compliance.

    Attributes:
        passed: Whether the evaluation passed all AGI floors
        reason: Human-readable reason for the verdict
        failures: List of floor failures if any
        f4_delta_s: Entropy change measurement (F4/F6 - ΔS)
        truth_score: Truth confidence score (F2)
    """
    passed: bool
    reason: str
    failures: List[str]
    f4_delta_s: float = 0.0
    truth_score: float = 0.0

class AGINeuralCore:
    """
    The Orthogonal Thinking Kernel.
    Pure Logic. No Side Effects.
    """

    @staticmethod
    async def sense(query: str, context_meta: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Stage 111: Active Context Sensing via ATLAS.
        Maps query to Governance Placement Vector (GPV).
        """
        timestamp = time.time()

        # Use ATLAS for Lane Classification and Context mapping
        gpv = ATLAS.map(query, context_meta)

        return {
            "meta": {
                "timestamp": timestamp,
                "lane": gpv.lane,
                "truth_demand": gpv.truth_demand,
                "care_demand": gpv.care_demand,
                "risk_level": gpv.risk_level,
                "origin_context": context_meta.get("origin", "User_Direct") if context_meta else "User_Direct"
            }
        }

    @staticmethod
    async def reflect(thought: str, thought_number: int, total_thoughts: int, next_needed: bool) -> Dict[str, Any]:
        """Stage 222: Sequential Reflection."""
        # In a real implementation, this would invoke the SequentialThinking model
        # For the kernel, we validate the step
        return {
            "status": "Reflected",
            "thought_index": f"{thought_number}/{total_thoughts}",
            "requires_more": next_needed,
            "integrity_hash": str(hash(thought))
        }

    @staticmethod
    async def atlas_tac_analysis(inputs: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Stage 333: TAC Engine (Theory of Anomalous Contrast)."""
        # Kept as stub for now, focusing on 111-SENSE wiring
        pass

    def evaluate(self, query: str, response: str) -> AGIVerdict:
        """
        Evaluate query-response pair for AGI constitutional compliance.

        Checks F2 (Truth) and F6 (ΔS - Clarity/Entropy).

        Args:
            query: The user query
            response: The draft response to evaluate

        Returns:
            AGIVerdict with pass/fail and floor metrics
        """
        failures = []

        # F2: Truth check (simplified - would use actual fact-checking in production)
        truth_score = 0.95  # Placeholder - would calculate from actual verification
        if truth_score < 0.99:
            failures.append(f"F2 Truth score {truth_score:.2f} < 0.99")

        # F6: ΔS check (entropy/clarity)
        # Simplified: measure response complexity vs query
        response_entropy = len(response.split()) / max(len(query.split()), 1)
        f4_delta_s = response_entropy - 1.0  # Delta from baseline

        if f4_delta_s < 0:  # Requires ΔS ≥ 0
            failures.append(f"F6 ΔS {f4_delta_s:.2f} < 0 (too concise, information loss)")

        passed = len(failures) == 0
        reason = "AGI evaluation passed" if passed else f"AGI evaluation failed: {'; '.join(failures)}"

        return AGIVerdict(
            passed=passed,
            reason=reason,
            failures=failures,
            f4_delta_s=f4_delta_s,
            truth_score=truth_score
        )


# Backward Compatibility
AGIKernel = AGINeuralCore
