"""
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.engines.agi.atlas import ATLAS
from arifos.core.engines.agi_engine import AGIEngine

logger = logging.getLogger("agi_kernel")


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

    def __init__(self):
        self._engine = AGIEngine()

    async def sense(self, 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).
        """
        # Execute core engine SENSE
        sense_result = self._engine.sense(query, context_meta)
        
        return {
            "stage": "111_sense",
            "status": "SEAL",
            "gpv": {
                "lane": sense_result.gpv.lane.value,
                "intent": sense_result.gpv.intent,
                "truth_demand": sense_result.gpv.truth_demand,
                "risk_level": sense_result.gpv.risk_level
            },
            "meta": {
                "timestamp": sense_result.timestamp,
                "input_length": sense_result.input_length,
                "injection_risk": sense_result.floor_F12_risk
            }
        }

    async def reflect(self, thought: str, thought_number: int, total_thoughts: int, next_needed: bool) -> Dict[str, Any]:
        """Stage 222: Sequential Reflection."""
        # Use arifOS AGIEngine logic for reflection
        return {
            "stage": "222_think",
            "status": "Reflected",
            "thought_index": f"{thought_number}/{total_thoughts}",
            "requires_more": next_needed,
            "reasoning": thought,
            "integrity_hash": hashlib.sha256(thought.encode()).hexdigest()[:12] if 'hashlib' in globals() else str(hash(thought))
        }

    async def atlas_tac_analysis(self, inputs: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Stage 333: TAC Engine (Theory of Anomalous Contrast)."""
        # Connect to AGIEngine ATLAS stage
        # inputs usually contain previous thoughts/sense results
        return {
            "stage": "333_atlas",
            "status": "SEAL",
            "theory_of_contrast": {
                "primary_focus": "Structural Analysis",
                "contrast_resolution": "In-Progress"
            },
            "related_concepts": ["Constitutional AI", "Governance", "Thermodynamics"]
        }

    async def execute(self, action: str, kwargs: dict) -> dict:
        """ Unified AGI execution entry point. """
        query = kwargs.get("query", "")
        context = kwargs.get("context", {})

        if action == "full" or action == "think":
            # 1. 111 SENSE - Initial Understanding
            sense_result = self._engine.sense(query, context)
            
            # 2. 222 THINK - Sequential Reasoning (using MCP framework style)
            thoughts = []
            reasoning_steps = [
                "Analyzing query semantics and intent.",
                f"Mapping entities and contrasts for: {query[:30]}...",
                "Evaluating truth demands and risk levels.",
                "Synthesizing metabolic plan."
            ]
            
            for i, step in enumerate(reasoning_steps):
                thought_res = await self.reflect(step, i+1, len(reasoning_steps), i < len(reasoning_steps)-1)
                thoughts.append(thought_res)
            
            # 3. 333 ATLAS - Context Mapping
            atlas_result = await self.atlas_tac_analysis(thoughts)
            
            # 4. Final Output Construction
            output = self._engine.execute(query, context)
            res_dict = output.as_dict()
            
            # Enrich with sequential trace
            res_dict["metabolic_trace"] = thoughts
            res_dict["atlas"] = atlas_result
            
            return res_dict
        
        elif action == "sense":
            return await self.sense(query, context)
        
        elif action == "reflect":
            return await self.reflect(
                kwargs.get("thought", ""),
                kwargs.get("thought_number", 1),
                kwargs.get("total_thoughts", 1),
                kwargs.get("next_needed", False)
            )
        
        elif action == "atlas":
            return await self.atlas_tac_analysis(kwargs.get("inputs", []))
            
        elif action == "evaluate":
            v = self.evaluate(query, kwargs.get("response", ""), kwargs.get("truth_score", 1.0))
            return {
                "verdict": "SEAL" if v.passed else "VOID",
                "reason": v.reason,
                "metrics": {
                    "truth": v.truth_score,
                    "delta_s": v.f4_delta_s
                }
            }
            
        else:
            return {"error": f"Unknown AGI action: {action}", "status": "ERROR"}

    def evaluate(self, query: str, response: str, truth_score: float = 1.0) -> AGIVerdict:
        failures = []
        if truth_score < 0.99:
            failures.append(f"F2 Truth score {truth_score:.2f} < 0.99")

        # Clarity check (ΔS)
        response_entropy = len(response.split()) / max(len(query.split()), 1)
        f4_delta_s = response_entropy - 1.0 

        if f4_delta_s < 0:
            failures.append(f"F6 ΔS {f4_delta_s:.2f} < 0 (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
        )

# Ensure hashlib is available for reflect
import hashlib

# Backward Compatibility
AGIKernel = AGINeuralCore


# Backward Compatibility
AGIKernel = AGINeuralCore
