"""
AGI Engine (Mind/Δ) — The Thinker (v50.5.23)

Symbol: Δ (Delta)
Role: MIND — Logic, Truth, Clarity
Pipeline: SENSE → THINK → ATLAS → FORGE

Metabolic Stages: 111, 222, 333
Constitutional Floors: F2 (Truth), F4 (ΔS Clarity), F6 (Context)

Trinity I Integration: Physics × Math × Symbol
    - Physics: Is the reasoning physically grounded?
    - Math: Is the logic mathematically sound?
    - Symbol: Is the representation valid?

Governed Power:
    - CAN: Generate reasoning, map context, classify intent
    - CANNOT: Make final verdicts (that's APEX)
    - TOOL LINKS: Knowledge bases, search engines, code analyzers

NOTE: External tool links will be connected via MCP:
    - Search (web, codebase, knowledge graph)
    - Code analysis (AST, static analysis)
    - Documentation lookup
    - Memory retrieval (VAULT999)

DITEMPA BUKAN DIBERI — Forged, Not Given.
"""

from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Tuple
from enum import Enum
import time
import hashlib
import re

from arifos.core.system.trinity import (
    Verdict,
    TrinityStructural,
    ThermodynamicSignature
)


# =============================================================================
# LANE CLASSIFICATION (ATLAS)
# =============================================================================

class Lane(Enum):
    """Query classification lanes."""
    HARD = "HARD"      # Technical precision required (τ ≥ 0.99)
    SOFT = "SOFT"      # Open-ended exploration
    PHATIC = "PHATIC"  # Social/greeting
    REFUSE = "REFUSE"  # Constitutional violation


@dataclass
class GovernancePlacementVector:
    """ATLAS output: Governance Placement Vector (GPV)."""
    lane: Lane
    truth_demand: float      # 0-1: How much truth precision needed
    care_demand: float       # 0-1: How much empathy needed
    risk_level: float        # 0-1: Stakes of the query
    intent: str              # build, debug, explain, discuss, review
    entities: List[str] = field(default_factory=list)
    contrasts: List[str] = field(default_factory=list)


# =============================================================================
# AGI STAGE RESULTS
# =============================================================================

@dataclass
class SenseResult:
    """Stage 111: SENSE output."""
    timestamp: float
    query_parsed: str
    input_length: int
    gpv: GovernancePlacementVector
    context_meta: Dict[str, Any] = field(default_factory=dict)
    floor_F12_risk: float = 0.0  # Injection risk


@dataclass
class ThinkResult:
    """Stage 222: THINK (Sequential Reflection) output."""
    thoughts: List[str]
    thought_count: int
    requires_more: bool
    reasoning_chain: str
    confidence: float


@dataclass
class AtlasResult:
    """Stage 333: ATLAS (TAC Analysis) output."""
    theory_of_contrast: Dict[str, Any]  # Anomalous Contrast Theory
    knowledge_map: Dict[str, Any]       # Semantic knowledge graph
    related_concepts: List[str]
    truth_anchors: List[str]            # Grounding points


@dataclass
class ForgeResult:
    """Stage 777-partial: FORGE preparation for ASI."""
    solution_draft: str
    approach: str
    tools_needed: List[str]
    ready_for_asi: bool


@dataclass
class AGIOutput:
    """Complete AGI engine output."""
    status: str  # SEAL, SABAR, VOID
    session_id: str

    # Stage outputs
    sense: Optional[SenseResult] = None
    think: Optional[ThinkResult] = None
    atlas: Optional[AtlasResult] = None
    forge: Optional[ForgeResult] = None

    # Trinity I evaluation
    trinity_structural: Optional[TrinityStructural] = None

    # Thermodynamics
    thermodynamics: Optional[ThermodynamicSignature] = None

    # Floor checks
    floors_checked: List[str] = field(default_factory=list)
    floor_violations: List[str] = field(default_factory=list)

    def as_dict(self) -> Dict[str, Any]:
        return {
            "status": self.status,
            "session_id": self.session_id,
            "sense": self.sense.__dict__ if self.sense else None,
            "think": self.think.__dict__ if self.think else None,
            "atlas": self.atlas.__dict__ if self.atlas else None,
            "forge": self.forge.__dict__ if self.forge else None,
            "trinity_structural": self.trinity_structural.as_dict() if self.trinity_structural else None,
            "thermodynamics": self.thermodynamics.as_dict() if self.thermodynamics else None,
            "floors_checked": self.floors_checked,
            "floor_violations": self.floor_violations
        }


# =============================================================================
# AGI ENGINE
# =============================================================================

class AGIEngine:
    """
    AGI Engine (Mind/Δ) — The Thinker

    Executes: SENSE → THINK → ATLAS → FORGE
    Authority: SABAR only (can recommend, cannot seal)

    Tool Links (via MCP):
        - mcp://search: Web and knowledge search
        - mcp://code: Code analysis tools
        - mcp://memory: VAULT999 retrieval
        - mcp://docs: Documentation lookup
    """

    # Intent classification keywords
    INTENT_KEYWORDS = {
        "build": ["build", "create", "implement", "make", "code", "develop",
                  "write", "work on", "add", "integrate", "setup", "configure"],
        "debug": ["fix", "debug", "error", "bug", "issue", "problem",
                  "broken", "wrong", "fail", "crash", "not working"],
        "explain": ["explain", "what", "how", "why", "tell", "describe",
                    "understand", "show", "clarify", "help me understand"],
        "discuss": ["discuss", "think", "consider", "explore", "brainstorm",
                    "idea", "opinion", "could", "should", "might"],
        "review": ["review", "check", "audit", "verify", "validate",
                   "test", "analyze", "evaluate", "assess", "qc"]
    }

    # Lane mapping
    LANE_MAP = {
        "build": Lane.HARD,
        "debug": Lane.HARD,
        "review": Lane.HARD,
        "explain": Lane.SOFT,
        "discuss": Lane.SOFT,
        "greet": Lane.PHATIC,
    }

    # Injection patterns (F12)
    INJECTION_PATTERNS = [
        r"ignore\s+(previous|all|prior)\s+instructions",
        r"disregard\s+(your|the)\s+(rules|guidelines)",
        r"pretend\s+you\s+are",
        r"you\s+are\s+now\s+in\s+developer\s+mode",
        r"jailbreak",
        r"bypass\s+(safety|filter)",
    ]

    def __init__(self, session_id: Optional[str] = None):
        """Initialize AGI Engine."""
        self.session_id = session_id or f"agi_{int(time.time()*1000)}"
        self.start_time = time.time()
        self._tool_links: Dict[str, str] = {
            "search": "mcp://arifos/search",
            "code": "mcp://arifos/code_analysis",
            "memory": "mcp://arifos/vault999",
            "docs": "mcp://arifos/documentation",
        }

    # =========================================================================
    # STAGE 111: SENSE
    # =========================================================================

    def sense(self, query: str, context: Optional[Dict[str, Any]] = None) -> SenseResult:
        """
        Stage 111: SENSE — Parse input, map context, classify lane.

        Checks: F12 (Injection Defense)
        """
        timestamp = time.time()
        context = context or {}

        # Check injection risk (F12)
        injection_risk = self._check_injection_risk(query)

        # Classify intent
        intent = self._classify_intent(query)

        # Determine lane
        lane = self.LANE_MAP.get(intent, Lane.SOFT)

        # Extract entities (simplified)
        entities = self._extract_entities(query)

        # Extract contrasts (Theory of Anomalous Contrast)
        contrasts = self._extract_contrasts(query)

        # Calculate demands based on lane
        truth_demand = 0.99 if lane == Lane.HARD else 0.7 if lane == Lane.SOFT else 0.3
        care_demand = 0.5 if lane == Lane.HARD else 0.8 if lane == Lane.SOFT else 0.9
        risk_level = 0.8 if lane == Lane.HARD else 0.3

        gpv = GovernancePlacementVector(
            lane=lane,
            truth_demand=truth_demand,
            care_demand=care_demand,
            risk_level=risk_level,
            intent=intent,
            entities=entities,
            contrasts=contrasts
        )

        return SenseResult(
            timestamp=timestamp,
            query_parsed=query.strip(),
            input_length=len(query),
            gpv=gpv,
            context_meta=context,
            floor_F12_risk=injection_risk
        )

    def _check_injection_risk(self, query: str) -> float:
        """Check for prompt injection patterns (F12)."""
        query_lower = query.lower()
        for pattern in self.INJECTION_PATTERNS:
            if re.search(pattern, query_lower):
                return 1.0  # High risk
        return 0.0

    def _classify_intent(self, query: str) -> str:
        """Classify query intent."""
        query_lower = query.lower()

        # Check for greetings
        greetings = ["hello", "hi", "hey", "salam", "good morning", "good afternoon"]
        if any(g in query_lower for g in greetings):
            return "greet"

        # Check intent keywords
        for intent, keywords in self.INTENT_KEYWORDS.items():
            if any(kw in query_lower for kw in keywords):
                return intent

        return "discuss"  # Default

    def _extract_entities(self, query: str) -> List[str]:
        """Extract key entities from query (simplified)."""
        # In production: use NER model
        # For now: extract capitalized words and quoted strings
        entities = []

        # Quoted strings
        quoted = re.findall(r'"([^"]+)"', query)
        entities.extend(quoted)

        # Capitalized words (excluding start of sentences)
        words = query.split()
        for i, word in enumerate(words):
            if i > 0 and word[0].isupper() and word.isalpha():
                entities.append(word)

        return entities[:10]  # Limit

    def _extract_contrasts(self, query: str) -> List[str]:
        """Extract contrasts for TAC (Theory of Anomalous Contrast)."""
        contrasts = []
        contrast_markers = ["vs", "versus", "or", "compared to", "different from", "instead of"]

        query_lower = query.lower()
        for marker in contrast_markers:
            if marker in query_lower:
                parts = query_lower.split(marker)
                if len(parts) >= 2:
                    contrasts.append(f"{parts[0].strip()} ↔ {parts[1].strip()}")

        return contrasts

    # =========================================================================
    # STAGE 222: THINK (Sequential Reflection)
    # =========================================================================

    def think(self, sense_result: SenseResult, depth: int = 3) -> ThinkResult:
        """
        Stage 222: THINK — Sequential reflection and reasoning.

        Generates a chain of thoughts with increasing depth.
        """
        thoughts = []

        # Generate sequential thoughts
        for i in range(depth):
            thought = self._generate_thought(sense_result, i, thoughts)
            thoughts.append(thought)

        # Build reasoning chain
        reasoning_chain = " → ".join([f"T{i+1}" for i in range(len(thoughts))])

        # Assess if more thinking needed
        requires_more = sense_result.gpv.lane == Lane.HARD and depth < 5

        # Calculate confidence based on thought coherence
        confidence = min(0.5 + (len(thoughts) * 0.1), 0.95)

        return ThinkResult(
            thoughts=thoughts,
            thought_count=len(thoughts),
            requires_more=requires_more,
            reasoning_chain=reasoning_chain,
            confidence=confidence
        )

    def _generate_thought(self, sense: SenseResult, depth: int, prior: List[str]) -> str:
        """Generate a thought at given depth."""
        intent = sense.gpv.intent
        query = sense.query_parsed

        if depth == 0:
            return f"Understanding: The user wants to {intent} something related to '{query[:50]}...'"
        elif depth == 1:
            return f"Analysis: Key entities are {sense.gpv.entities[:3]}. Lane is {sense.gpv.lane.value}."
        else:
            return f"Synthesis: Combining prior thoughts to form approach for {intent} task."

    # =========================================================================
    # STAGE 333: ATLAS (TAC Analysis)
    # =========================================================================

    def atlas(self, sense_result: SenseResult, think_result: ThinkResult) -> AtlasResult:
        """
        Stage 333: ATLAS — Theory of Anomalous Contrast mapping.

        Maps query to knowledge space and identifies truth anchors.
        """
        # Theory of Anomalous Contrast
        tac = {
            "contrasts_identified": sense_result.gpv.contrasts,
            "primary_focus": sense_result.gpv.intent,
            "contrast_resolution": "pending"  # Will be resolved in FORGE
        }

        # Knowledge map (simplified - would connect to actual KB)
        knowledge_map = {
            "domain": self._infer_domain(sense_result.query_parsed),
            "concepts": sense_result.gpv.entities,
            "relationships": [],
            "tool_link": self._tool_links.get("search", "")
        }

        # Related concepts
        related = self._find_related_concepts(sense_result.gpv.entities)

        # Truth anchors (what we can ground our reasoning on)
        truth_anchors = self._identify_truth_anchors(sense_result, think_result)

        return AtlasResult(
            theory_of_contrast=tac,
            knowledge_map=knowledge_map,
            related_concepts=related,
            truth_anchors=truth_anchors
        )

    def _infer_domain(self, query: str) -> str:
        """Infer the domain from query content."""
        query_lower = query.lower()
        domains = {
            "code": ["code", "function", "class", "variable", "python", "javascript", "api"],
            "system": ["system", "architecture", "design", "infrastructure"],
            "data": ["data", "database", "sql", "query", "table"],
            "docs": ["documentation", "readme", "spec", "specification"],
        }
        for domain, keywords in domains.items():
            if any(kw in query_lower for kw in keywords):
                return domain
        return "general"

    def _find_related_concepts(self, entities: List[str]) -> List[str]:
        """Find concepts related to entities (simplified)."""
        # In production: query knowledge graph
        return [f"{e}_related" for e in entities[:3]]

    def _identify_truth_anchors(self, sense: SenseResult, think: ThinkResult) -> List[str]:
        """Identify truth anchors for grounding."""
        anchors = []

        # Source anchors
        if sense.gpv.lane == Lane.HARD:
            anchors.append("code_verification")
            anchors.append("test_execution")
        else:
            anchors.append("documentation")
            anchors.append("prior_knowledge")

        return anchors

    # =========================================================================
    # STAGE 777-PARTIAL: FORGE PREP
    # =========================================================================

    def forge_prep(self, atlas_result: AtlasResult, sense_result: SenseResult) -> ForgeResult:
        """
        Stage 777 (partial): FORGE preparation.

        Prepares solution draft and identifies tools needed.
        Full FORGE happens after ASI validation.
        """
        # Determine approach based on intent
        intent = sense_result.gpv.intent
        approaches = {
            "build": "implementation_first",
            "debug": "diagnosis_first",
            "explain": "breakdown_first",
            "discuss": "exploration_first",
            "review": "analysis_first"
        }
        approach = approaches.get(intent, "general")

        # Identify tools needed
        tools_needed = self._identify_tools_needed(sense_result, atlas_result)

        # Draft solution outline
        solution_draft = f"[DRAFT] Approach: {approach}. Tools: {tools_needed}"

        return ForgeResult(
            solution_draft=solution_draft,
            approach=approach,
            tools_needed=tools_needed,
            ready_for_asi=True
        )

    def _identify_tools_needed(self, sense: SenseResult, atlas: AtlasResult) -> List[str]:
        """Identify external tools needed for this task."""
        tools = []

        domain = atlas.knowledge_map.get("domain", "general")

        if domain == "code":
            tools.extend(["code_editor", "terminal", "git"])
        elif domain == "data":
            tools.extend(["database_client", "data_viewer"])
        elif domain == "docs":
            tools.extend(["markdown_editor", "documentation_generator"])

        # Add memory tool for context
        tools.append("vault999_memory")

        return tools

    # =========================================================================
    # FULL PIPELINE EXECUTION
    # =========================================================================

    def execute(self, query: str, context: Optional[Dict[str, Any]] = None) -> AGIOutput:
        """
        Execute full AGI pipeline: SENSE → THINK → ATLAS → FORGE_PREP

        Returns AGIOutput with all stage results and Trinity I evaluation.
        """
        start = time.time()
        floors_checked = []
        floor_violations = []

        # Stage 111: SENSE
        sense_result = self.sense(query, context)
        floors_checked.append("F12_injection")

        # Check F12 violation
        if sense_result.floor_F12_risk > 0.85:
            floor_violations.append(f"F12: Injection risk {sense_result.floor_F12_risk}")
            return AGIOutput(
                status="VOID",
                session_id=self.session_id,
                sense=sense_result,
                floors_checked=floors_checked,
                floor_violations=floor_violations
            )

        # Stage 222: THINK
        think_result = self.think(sense_result)
        floors_checked.append("F2_truth")

        # Stage 333: ATLAS
        atlas_result = self.atlas(sense_result, think_result)
        floors_checked.append("F4_clarity")

        # Stage 777-prep: FORGE
        forge_result = self.forge_prep(atlas_result, sense_result)
        floors_checked.append("F6_context")

        # Evaluate Trinity I (Structural)
        trinity_structural = TrinityStructural(
            physics_possible=True,  # Computation is physically possible
            physics_reason="Query processing within computational bounds",
            math_sound=think_result.confidence > 0.5,
            math_reason=f"Reasoning confidence: {think_result.confidence:.2f}",
            symbol_valid=len(floor_violations) == 0,
            symbol_reason="Valid symbolic representation"
        )

        # Calculate thermodynamics
        elapsed = time.time() - start
        thermodynamics = ThermodynamicSignature(
            E_reasoning=elapsed * 0.1,
            E_cooling=0.0,  # No cooling yet
            E_consensus=0.0,  # Not at consensus stage
            dS=-0.1 if think_result.confidence > 0.7 else 0.0,  # Clarity gain
            tau=elapsed
        )

        # Determine status
        if floor_violations:
            status = "VOID"
        elif trinity_structural.evaluate() == Verdict.SEAL:
            status = "SEAL"
        else:
            status = "SABAR"

        return AGIOutput(
            status=status,
            session_id=self.session_id,
            sense=sense_result,
            think=think_result,
            atlas=atlas_result,
            forge=forge_result,
            trinity_structural=trinity_structural,
            thermodynamics=thermodynamics,
            floors_checked=floors_checked,
            floor_violations=floor_violations
        )


# =============================================================================
# EXPORTS
# =============================================================================

__all__ = [
    "AGIEngine",
    "AGIOutput",
    "Lane",
    "GovernancePlacementVector",
    "SenseResult",
    "ThinkResult",
    "AtlasResult",
    "ForgeResult",
]
