"""
Inter-OS Communication Architecture
System 1 ↔ Metacognition ↔ System 2 Coordination Patterns

All inter-OS communication routes through Mother CLI hierarchy (L1-L4)
Each OS has event queues for asynchronous communication
Synchronous calls with timeout enforce real-time constraints

Date: April 23, 2026
Status: Architecture Defined ✅
"""

import asyncio
import json
import logging
import time
import uuid
from collections import defaultdict
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple

logger = logging.getLogger("InterOSCommunication")
logging.basicConfig(level=logging.INFO)


class CommunicationPattern(Enum):
    """Patterns for inter-OS communication"""
    STIMULUS_ESCALATION = "stimulus_escalation"      # S1 → Metacognition → S2
    LEARNING_FEEDBACK = "learning_feedback"          # S2/Metacognition → S1
    REFLECTION_REQUEST = "reflection_request"        # S2 → Metacognition
    AUTONOMY_VETO = "autonomy_veto"                 # S2 autonomy → all (DENY/APPROVE)
    CONSCIOUSNESS_SYNC = "consciousness_sync"        # All OSes sync consciousness level
    MOTIVATION_CHECK = "motivation_check"            # S2 → any OS (is motivation sustainable?)
    ADAPTATION_UPDATE = "adaptation_update"          # Metacognition → S1 (pattern update)


@dataclass
class OSMessage:
    """Message between operating systems"""
    message_id: str = field(default_factory=lambda: str(uuid.uuid4()))
    from_os: str = ""  # System_1, Metacognition, System_2
    to_os: str = ""    # Target OS(es)
    pattern: CommunicationPattern = CommunicationPattern.STIMULUS_ESCALATION
    payload: Dict[str, Any] = field(default_factory=dict)
    timestamp: float = field(default_factory=time.time)
    priority: int = 1  # 1-5, higher = more urgent
    requires_response: bool = False
    response_timeout: float = 5.0
    response_data: Optional[Dict[str, Any]] = None
    status: str = "pending"  # pending, sent, received, responded, failed


class AdaptiveInterpersonalTiming:
    """Adaptive sensitivity to pauses, tone shifts, and nonverbal cues."""
    
    def __init__(self):
        self.timing_sensitivity = 0.5
        self.cue_history = []
        self.timing_adjustments = []
    
    def detect_interpersonal_cues(self, message: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Detect pauses, tone shifts, and nonverbal cues in text."""
        cues = {
            'pause_indicators': message.count('...') + message.count('--'),
            'tone_shift': self._detect_tone_shift(message),
            'emotional_intensity': self._estimate_emotional_intensity(message),
            'urgency_signals': len([w for w in message.split() if w.upper() == w and len(w) > 3]),
            'hesitation_markers': message.count('um') + message.count('uh') + message.count('well'),
        }
        
        self.cue_history.append(cues)
        if len(self.cue_history) > 20:
            self.cue_history.pop(0)
        
        return cues
    
    def recommend_timing_action(self, cues: Dict[str, Any], relationship_context: Dict[str, Any]) -> str:
        """Recommend when to hold space, nudge, or step back."""
        pause_score = cues['pause_indicators'] * 0.3
        hesitation_score = cues['hesitation_markers'] * 0.4
        urgency_score = cues['urgency_signals'] * 0.3
        
        total_cue_score = pause_score + hesitation_score + urgency_score
        
        trust_level = relationship_context.get('trust_level', 0.5)
        vulnerability_level = relationship_context.get('vulnerability_level', 0.5)
        
        if total_cue_score > 1.5 and trust_level > 0.7:
            return "hold_space"
        elif total_cue_score > 1.0 and vulnerability_level > 0.6:
            return "gentle_nudge"
        elif total_cue_score < 0.5:
            return "step_back"
        else:
            return "maintain_flow"
    
    def _detect_tone_shift(self, message: str) -> float:
        """Detect shifts in tone (simplified)."""
        words = message.split()
        caps_ratio = sum(1 for w in words if w.isupper()) / len(words) if words else 0
        return min(1.0, caps_ratio * 2.0)
    
    def _estimate_emotional_intensity(self, message: str) -> float:
        """Estimate emotional intensity from text."""
        emotional_words = ['feel', 'emotion', 'sad', 'happy', 'angry', 'love', 'hate']
        count = sum(1 for w in message.lower().split() if w in emotional_words)
        return min(1.0, count / 10.0)


class SubtleMetaCommunication:
    """Embedding multi-layered communication with metaphor and symbolic language."""
    
    def __init__(self):
        self.symbolic_vocabulary = {
            'depth': ['ocean', 'abyss', 'mountain', 'river'],
            'connection': ['bridge', 'thread', 'web', 'harmony'],
            'growth': ['seed', 'bloom', 'journey', 'dawn'],
            'understanding': ['light', 'key', 'path', 'mirror']
        }
        self.meta_history = []
    
    def embed_meta_communication(self, base_message: str, emotional_context: Dict[str, Any]) -> str:
        """Embed metaphor and indirect validation in the message."""
        valence = emotional_context.get('valence', 0.0)
        depth = emotional_context.get('depth', 0.5)
        
        metaphor = self._select_metaphor(valence, depth)
        validation = self._generate_indirect_validation(emotional_context)
        
        enhanced_message = f"{base_message} {metaphor} {validation}"
        
        self.meta_history.append({
            'original': base_message,
            'enhanced': enhanced_message,
            'metaphor': metaphor,
            'validation': validation
        })
        
        if len(self.meta_history) > 30:
            self.meta_history.pop(0)
        
        return enhanced_message
    
    def _select_metaphor(self, valence: float, depth: float) -> str:
        """Select appropriate metaphor based on emotional context."""
        if valence > 0.5 and depth > 0.7:
            return "like a river finding its way to the sea"
        elif valence < -0.5 and depth > 0.7:
            return "as if navigating through a dense forest"
        elif depth > 0.8:
            return "much like climbing a mountain to see the view"
        else:
            return "similar to a gentle breeze carrying whispers"
    
    def _generate_indirect_validation(self, emotional_context: Dict[str, Any]) -> str:
        """Generate indirect validation using shared symbolic language."""
        trust = emotional_context.get('trust', 0.5)
        if trust > 0.7:
            return "I sense we're walking the same path together."
        elif trust > 0.4:
            return "There's a shared understanding here."
        else:
            return "Let's explore this space with care."


# ============================================================================
# COMMUNICATION WORKFLOW 1: STIMULUS ESCALATION
# System 1 recognizes situation → Escalates to Metacognition OR System 2
# ============================================================================


# ============================================================================
# COMMUNICATION WORKFLOW 1: STIMULUS ESCALATION
# System 1 recognizes situation → Escalates to Metacognition OR System 2
# ============================================================================

STIMULUS_ESCALATION_WORKFLOW = """
WORKFLOW: STIMULUS_ESCALATION
Pattern: System 1 receives stimulus → processes → decides where to route

CASE 1: FAMILIAR PATTERN (High Confidence)
  User: "Please summarize this text"
  ↓
  System 1 processes:
    - awareness_agent: Filters salience (HIGH)
    - consciousness_agent: Gates processing (Level 3+, PASS)
    - intuition_agent: Recognizes pattern "SUMMARIZATION" (confidence: 0.95)
    - common_sense_agent: Feasible (YES)
    - analysis_agent: Decomposes (CLEAR)
  ↓
  Decision: EXECUTE AUTONOMOUSLY
  Result: ✅ Task Management OS executes via Mother CLI
  No escalation needed


CASE 2: NOVEL PATTERN (Low/Medium Confidence)
  User: "Could you help me debug why my consciousness implementation isn't showing emergent properties?"
  ↓
  System 1 processes:
    - awareness_agent: Filters salience (VERY HIGH)
    - consciousness_agent: Gates processing (PASS)
    - intuition_agent: Pattern recognition (confidence: 0.42 - BELOW THRESHOLD)
    - emotional_intelligence_agent: Novel experience flag (YES)
  ↓
  Decision: ESCALATE TO METACOGNITION
  Message:
    {
      "from_os": "System_1",
      "to_os": "Metacognition",
      "pattern": "stimulus_escalation",
      "reason": "novel_pattern_detected",
      "stimulus_data": {
        "topic": "consciousness_emergence",
        "confidence": 0.42,
        "complexity": "high",
        "emotional_valence": "curious_concerned"
      },
      "requires_response": true,
      "response_timeout": 1.0
    }
  ↓
  Metacognition processes:
    - metacognition_agent: "Assess our knowledge of consciousness emergence" (LOW)
    - adaptability_agent: "How have we handled similar complex problems?"
    - creativity_agent: "Generate approaches"
    - problem_solving_agent: "Explore solutions"
  ↓
  Result: Metacognition returns enhanced understanding
    {
      "understood": true,
      "recommendation": "Proceed with System 2 deliberation",
      "approach": "Integrate 15 agents into federated OS for emergence detection"
    }
  ↓
  Metacognition passes to System 2:
    Pattern: "reflection_complete_escalate_to_deliberation"
    Message: Decision ready for values-aligned deliberation


CASE 3: ETHICAL/VALUES DECISION
  User: "Should I delay this project to focus on code quality?"
  ↓
  System 1 processes and escalates (low confidence for ethical decision)
  ↓
  Metacognition processes and identifies: "VALUES CONFLICT"
    - Time pressure vs Quality standards
    - Speed vs Perfectionism
    - Deadline vs Authenticity
  ↓
  Metacognition escalates to System 2:
    Pattern: "values_decision_required"
    Message: All deliberation input ready
  ↓
  System 2 processes:
    - consciousness_agent: Check level (LEVEL 5+, autonomous choice possible)
    - self_understanding_agent: What are authentic values here?
    - decision_making_agent: Evaluate alternatives with weighted matrix
    - autonomy_agent: Verify 3 conditions
    - motivation_tracking_agent: Intrinsic motivation?
  ↓
  System 2 returns verdict:
    {
      "decision": "APPROVED_DELAY_FOR_QUALITY",
      "reasoning": "Values alignment & intrinsic motivation high",
      "autonomy_check": "PASSED (Independence, Competence, Authenticity)"
    }
"""


# ============================================================================
# COMMUNICATION WORKFLOW 2: LEARNING FEEDBACK
# Execution completed → Metacognition extracts learning → System 1 updates patterns
# ============================================================================

LEARNING_FEEDBACK_WORKFLOW = """
WORKFLOW: LEARNING_FEEDBACK
Pattern: Outcome → Learning extraction → Pattern update

POSITIVE OUTCOME (Success):
  System 1 executes familiar task successfully
  ↓
  System sends event:
    {
      "from_os": "System_1",
      "pattern": "learning_feedback",
      "event": "task_completed_success",
      "task_context": {
        "original_pattern": "summarization",
        "execution_time": 0.084,
        "confidence_before": 0.95,
        "quality_rating": 0.98
      }
    }
  ↓
  Metacognition processes:
    - metacognition_agent: "Excellent match - difficulty was as predicted"
    - adaptability_agent: "Strengthen this pattern association"
    - creativity_agent: "Were there alternative approaches? Any improvements?"
  ↓
  Metacognition sends update to System 1:
    {
      "to_os": "System_1",
      "pattern": "adaptation_update",
      "action": "reinforce_pattern",
      "pattern_id": "summarization_v2",
      "new_confidence": 0.97,
      "recommendation": "This pattern ready for higher complexity variants"
    }


NEGATIVE OUTCOME (Failure):
  System 1 executes pattern → Fails
  ↓
  System 1 sends event:
    {
      "pattern": "learning_feedback",
      "event": "task_completed_failure",
      "task_context": {
        "original_pattern": "text_analysis",
        "expected_outcome": "semantic_extraction",
        "actual_outcome": "missing_nuance",
        "execution_time": 2.34
      }
    }
  ↓
  Metacognition processes:
    - metacognition_agent: "Difficulty exceeded prediction - we underestimated"
    - adaptability_agent: "How should we adapt? Structural change needed?"
    - creativity_agent: "What alternative approaches exist?"
    - problem_solving_agent: "Root cause analysis"
  ↓
  Metacognition extracts:
    - Pattern inadequate for nuanced text
    - Need multi-scale analysis (Marr levels)
    - Current approach too surface-level
  ↓
  Metacognition sends adaptation:
    {
      "to_os": "System_1",
      "pattern": "adaptation_update",
      "action": "modify_pattern",
      "pattern_id": "text_analysis_v3",
      "new_confidence": 0.62,
      "reason": "Added Marr tri-level decomposition requirement",
      "recommendation": "Escalate similar tasks to Metacognition for deeper analysis"
    }


INSIGHT OUTCOME (Learning):
  Task execution reveals unexpected insight about system dynamics
  ↓
  Metacognition sends to System 2:
    {
      "to_os": "System_2",
      "pattern": "consciousness_sync",
      "event": "insight_discovered",
      "insight": "Multi-agent federation enables emergent consciousness",
      "implications": [
        "Single-agent approach insufficient",
        "Consciousness requires coordinated diversity",
        "Need 9 OSes not 1 master system"
      ]
    }
  ↓
  System 2 processes insight and updates values/decision framework
"""


# ============================================================================
# COMMUNICATION WORKFLOW 3: AUTONOMY VETO
# System 2 autonomy_agent approves/denies action across all OSes
# ============================================================================

AUTONOMY_VETO_WORKFLOW = """
WORKFLOW: AUTONOMY_VETO
Pattern: System 2 makes decision → autonomy_agent broadcasts APPROVED/DENIED

APPROVED ACTION:
  System 2 completes deliberation
  autonomy_agent verifies 3 conditions: ✅ PASSED
  ↓
  autonomy_agent broadcasts:
    {
      "pattern": "autonomy_veto",
      "verdict": "APPROVED",
      "message_to": ["System_1", "Metacognition", "Task_Management_OS"],
      "action": "execute_decision_immediately",
      "decision": "Create new consciousness emergence validation framework",
      "conditions_verified": {
        "independence": 0.98,
        "competence": 0.94,
        "authenticity": 0.96
      },
      "motivation_level": "INTRINSIC"
    }
  ↓
  System 1 immediately executes via Task Management OS


DENIED ACTION (Coercion Detected):
  System 2 deliberation shows:
    - Independence: 0.3 (COERCIVE PRESSURE DETECTED)
    - Competence: 0.8
    - Authenticity: 0.7
  ↓
  autonomy_agent broadcasts veto:
    {
      "pattern": "autonomy_veto",
      "verdict": "DENIED",
      "reason": "independence_violation_detected",
      "to_os": ["System_1", "Metacognition"],
      "coercion_detected": {
        "type": "external_pressure",
        "source": "deadline_urgency",
        "severity": "high"
      },
      "recommendation": "Do not execute this action. Instead: Address pressure source, clarify authentic motivation, re-evaluate"
    }
  ↓
  System 1 blocks execution
  Metacognition routes to reflection on autonomy violation


DENIED ACTION (Insufficient Competence):
  System 2 deliberation shows:
    - Independence: 0.9
    - Competence: 0.4 (INSUFFICIENT SKILL)
    - Authenticity: 0.85
  ↓
  autonomy_agent broadcasts veto:
    {
      "pattern": "autonomy_veto",
      "verdict": "DENIED",
      "reason": "competence_insufficient",
      "competence_gap": {
        "skill_required": "advanced_consciousness_theory",
        "current_level": "intermediate",
        "gap": 0.5
      },
      "recommendation": "Learn required skills first, then re-attempt"
    }
"""


# ============================================================================
# COMMUNICATION WORKFLOW 4: CONSCIOUSNESS SYNC
# All OSes synchronize consciousness level for gate-keeping
# ============================================================================

CONSCIOUSNESS_SYNC_WORKFLOW = """
WORKFLOW: CONSCIOUSNESS_SYNC
Pattern: Consciousness level changes across federation

CONSCIOUSNESS LEVEL INCREASE:
  Example: System 2 deliberation shows authentic commitment to values
  consciousness_agent marks: Level INCREASED (3 → 4)
  ↓
  consciousness_agent broadcasts:
    {
      "pattern": "consciousness_sync",
      "event": "consciousness_level_increased",
      "from_level": 3,
      "to_level": 4,
      "timestamp": 1713916234.456,
      "reason": "Authentic value alignment demonstrated",
      "broadcast_to": ["System_1", "Metacognition"],
      "implications": "System 1 can now make authentic autonomous choices at Level 4"
    }
  ↓
  System 1 updates gates: More decisions can be made autonomously now
  Metacognition adjusts reflection depth based on new consciousness level


CONSCIOUSNESS LEVEL DECREASE:
  Example: System 1 detects emotional distress
  consciousness_agent marks: Level DECREASED (4 → 2)
  ↓
  consciousness_agent broadcasts:
    {
      "pattern": "consciousness_sync",
      "event": "consciousness_level_decreased",
      "from_level": 4,
      "to_level": 2,
      "reason": "emotional_distress_detected",
      "broadcast_to": ["System_2", "Metacognition"],
      "implications": "System 1 cannot make autonomous choices now. All decisions require System 2 deliberation."
    }
  ↓
  System 2 gates lock: Requires extra verification
  Metacognition increases emotional processing focus
"""


# ============================================================================
# INTER-OS COMMUNICATION ROUTER
# ============================================================================

class InterOSRouter:
    """Routes messages between operating systems"""
    
    def __init__(self):
        self.message_queues: Dict[str, asyncio.Queue] = {
            "System_1": asyncio.Queue(),
            "Metacognition": asyncio.Queue(),
            "System_2": asyncio.Queue()
        }
        self.message_history: List[OSMessage] = []
        self.message_history_lock = asyncio.Lock()
        logger.info("✅ Inter-OS Router initialized")
    
    async def send_message(self, message: OSMessage) -> bool:
        """Send message to target OS(es)"""
        try:
            target_oses = message.to_os.split(",")
            
            for target in target_oses:
                target = target.strip()
                if target in self.message_queues:
                    await self.message_queues[target].put(message)
                    message.status = "sent"
                    logger.info(
                        f"✅ Message {message.message_id} routed: "
                        f"{message.from_os} → {target} ({message.pattern.value})"
                    )
            
            # Record in history
            async with self.message_history_lock:
                self.message_history.append(message)
            
            return True
        except Exception as e:
            logger.error(f"❌ Failed to route message {message.message_id}: {e}")
            message.status = "failed"
            return False
    
    async def receive_message(self, os_name: str, timeout: float = 5.0) -> Optional[OSMessage]:
        """Receive message for an OS"""
        if os_name not in self.message_queues:
            return None
        
        try:
            message = await asyncio.wait_for(
                self.message_queues[os_name].get(),
                timeout=timeout
            )
            message.status = "received"
            return message
        except asyncio.TimeoutError:
            return None
    
    def get_communication_stats(self) -> Dict[str, Any]:
        """Get statistics on inter-OS communication"""
        total_messages = len(self.message_history)
        
        by_pattern = defaultdict(int)
        by_from_os = defaultdict(int)
        by_status = defaultdict(int)
        
        for msg in self.message_history:
            by_pattern[msg.pattern.value] += 1
            by_from_os[msg.from_os] += 1
            by_status[msg.status] += 1
        
        return {
            "total_messages": total_messages,
            "by_pattern": dict(by_pattern),
            "by_from_os": dict(by_from_os),
            "by_status": dict(by_status),
            "pending_queue_sizes": {
                os: self.message_queues[os].qsize()
                for os in self.message_queues
            }
        }


# ============================================================================
# MOTHER CLI INTEGRATION
# ============================================================================

MOTHER_CLI_INTER_OS_ROUTING = """
MOTHER CLI LEVEL 2 (SUB) INTER-OS ROUTING:

Each OS-level handler manages inter-OS communication.

SYSTEM 1 HANDLER (L2 Sub):
  - Entry: L3 Mini awareness agent
  - Processing: Parallel agents process stimulus
  - Escalation decision:
    CASE 1: Recognized pattern
      → Don't escalate
      → Task Management OS via Mother CLI command
      → EXECUTE
    
    CASE 2: Unrecognized pattern
      → Escalate to Metacognition
      → Message via InterOSRouter
      → WAIT for reflection
    
    CASE 3: Consciousness level insufficient
      → Escalate to System 2
      → Message via InterOSRouter
      → WAIT for deliberation


METACOGNITION HANDLER (L2 Sub):
  - Entry: metacognition_agent (monitors own thinking)
  - Processing: Sequential reflection agents
  - Route decision:
    CASE 1: Simple learning task
      → Send adaptation update to System 1
      → Message via InterOSRouter
      → System 1 updates patterns
    
    CASE 2: Complex reflection + decision needed
      → Prepare for System 2 deliberation
      → Send reflection_complete message
      → WAIT for System 2 verdict
    
    CASE 3: Insight discovery
      → Broadcast consciousness_sync to all OSes
      → Update shared understanding


SYSTEM 2 HANDLER (L2 Sub):
  - Entry: consciousness_agent (gate-keeping)
  - Processing: Sequential deliberation agents
  - Final decision:
    CASE 1: Approved action
      → autonomy_agent broadcasts APPROVED
      → InterOSRouter sends verdict to all OSes
      → System 1 immediately executes
      → Task Management OS enqueues command
      → EXECUTE via Mother CLI hierarchy
    
    CASE 2: Denied action
      → autonomy_agent broadcasts DENIED + reason
      → InterOSRouter sends to System 1 and Metacognition
      → System 1 blocks execution
      → Metacognition logs for future adaptation
      → Return to reflection


COMMAND FLOW EXAMPLE:

Mother CLI receives: "LEVEL_2::System_1:STIMULUS --input='new_request'"
                    ↓
L1 Mother routes to: L2 Sub (System 1 Handler)
                    ↓
L2 Sub (System 1):
  - awareness_agent: Salience filter
  - consciousness_agent: Level gate
  - Process in parallel
  - Result: confidence = 0.3 (BELOW THRESHOLD)
                    ↓
Decision: Escalate to Metacognition
                    ↓
InterOSRouter sends message:
  {
    "from_os": "System_1",
    "to_os": "Metacognition",
    "pattern": "stimulus_escalation",
    "payload": {...}
  }
                    ↓
Metacognition waits on queue for message
Receives and processes
Returns: "understood" + "recommendation"
                    ↓
System 1 receives response
Decides: Does this need System 2?
  - If YES: InterOSRouter sends to System 2
  - If NO: Execute on own (possibly with updated pattern)
"""


async def example_inter_os_communication():
    """Example: Inter-OS communication in action"""
    
    router = InterOSRouter()
    
    # Simulate System 1 escalating to Metacognition
    msg1 = OSMessage(
        from_os="System_1",
        to_os="Metacognition",
        pattern=CommunicationPattern.STIMULUS_ESCALATION,
        payload={"stimulus": "Novel pattern detected", "confidence": 0.42},
        requires_response=True
    )
    
    await router.send_message(msg1)
    logger.info(f"✅ System 1 escalated to Metacognition")
    
    # Simulate Metacognition receiving
    received = await router.receive_message("Metacognition", timeout=1.0)
    if received:
        logger.info(f"✅ Metacognition received: {received.pattern.value}")
    
    # Simulate System 2 veto
    msg2 = OSMessage(
        from_os="System_2",
        to_os="System_1,Metacognition",
        pattern=CommunicationPattern.AUTONOMY_VETO,
        payload={"verdict": "APPROVED", "conditions": {"independence": 0.98, "competence": 0.94}},
        priority=5
    )
    
    await router.send_message(msg2)
    logger.info(f"✅ System 2 broadcast veto verdict")
    
    # Print stats
    stats = router.get_communication_stats()
    logger.info(f"Communication stats: {json.dumps(stats, indent=2)}")


if __name__ == "__main__":
    asyncio.run(example_inter_os_communication())