#!/usr/bin/env python3
"""
Architect Node Topology Driver Based on Accurate Container Map
with Functional Collapse Paradigm Cognitive Load Metrics

This driver uses the 100% accurate container map to maximize the architect node's
topology utilization by leveraging every bit of its available resources.

Functional Collapse Paradigm:
- Cognitive Load is the informational cost of lawful assemblage between current state and optimal state
- Metrics are type-instances of bind(A, B, Metric) where Metric is typically KL-Divergence
- Criticality Threshold (τ_c) aligned with Abelian Sandpile threshold
- N-Local Topology Scaling with path-dependence

Based on actual container mapping data:
- 6 physical cores, 12 logical cores
- 30.40 GB RAM
- 213 processes
- 359 network edges
- 32 file systems
- 3376 memory regions
- 359 open sockets
"""

import sys
import json
import time
import threading
import multiprocessing
import psutil
import math
import numpy as np
import signal
from pathlib import Path
from datetime import datetime
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Any, Callable, Tuple
from collections import defaultdict, deque
from enum import Enum

class WorkloadType(Enum):
    """Types of workloads for topology scheduling"""
    COMPUTE_INTENSIVE = "compute_intensive"
    MEMORY_INTENSIVE = "memory_intensive"
    IO_INTENSIVE = "io_intensive"
    NETWORK_INTENSIVE = "network_intensive"
    MIXED = "mixed"

class OperationalThreshold(Enum):
    """Operational thresholds for cognitive load"""
    RELATIONAL = "relational"  # < 0.25: Low friction; full 5D torus expansion
    SEMANTIC = "semantic"      # < 0.50: Standard operating range; active S3C compression
    TOPOLOGICAL = "topological"  # < 0.75: High stress; gear teeth modulations active
    CRITICAL = "critical"      # ≥ 0.75: Criticality reached (τ_c). Sandbox collapse initiated

@dataclass
class CognitiveLoadMetrics:
    """Functional Collapse Paradigm cognitive load metrics"""
    intrinsic_load: float  # L_I: bind(p(b|x), uniform, KL)
    effort_load: float  # L_E: bind(P_w_prior(x), P_optimal(x), KL)
    total_system_load: float  # L_total: bind(load_vector, target_vector, weighted_L2)
    efficiency_factor: float  # η: bind(intrinsic, total, ratio_metric)
    distribution_precision: float  # P_w: bind(ensemble, mixture, simplex_metric)
    operational_threshold: OperationalThreshold
    criticality_reached: bool

@dataclass
class Workload:
    """Workload to be scheduled"""
    workload_id: str
    workload_type: WorkloadType
    cpu_required: float  # 0-1 (percentage of total CPU)
    memory_required: float  # 0-1 (percentage of total RAM)
    storage_required: float  # 0-1 (percentage of total storage)
    bandwidth_required: float  # 0-1 (percentage of total bandwidth)
    priority: int  # 1-10
    duration: float  # seconds
    executable: Callable
    status: str = "pending"
    assigned_core: Optional[int] = None
    start_time: Optional[float] = None
    end_time: Optional[float] = None

@dataclass
class AccurateTopologyResource:
    """Topology resource state based on accurate container map"""
    total_cores: int
    total_ram_gb: float
    total_storage_gb: float
    total_bandwidth_mbps: float
    
    available_cores: int
    available_ram_gb: float
    available_storage_gb: float
    available_bandwidth_mbps: float
    
    core_utilization: List[float]  # Per-core utilization
    memory_utilization: float
    storage_utilization: float
    bandwidth_utilization: float
    
    # Container map data
    total_processes: int
    total_network_edges: int
    total_file_systems: int
    total_memory_regions: int
    total_open_sockets: int
    total_devices: int
    total_kernel_parameters: int
    
    # Functional Collapse Paradigm state
    cognitive_load_metrics: Optional[CognitiveLoadMetrics] = None
    history_trace: List[Dict[str, float]] = field(default_factory=list)
    avalanche_count: int = 0
    gear_pitch: float = 1.0  # Gear teeth modulation factor

def bind_kl_divergence(p: np.ndarray, q: np.ndarray) -> float:
    """
    Bind operator using KL-divergence as the metric.
    
    D_KL(P || Q) = sum(P(i) * log(P(i) / Q(i)))
    
    This measures the informational cost of lawful assemblage between
    the current state (P) and the optimal state (Q).
    """
    # Ensure distributions sum to 1
    p = p / np.sum(p)
    q = q / np.sum(q)
    
    # Add small epsilon to avoid log(0)
    epsilon = 1e-10
    p = p + epsilon
    q = q + epsilon
    
    # Calculate KL-divergence
    kl_div = np.sum(p * np.log(p / q))
    
    return float(kl_div)

def bind_weighted_l2(vector_a: np.ndarray, vector_b: np.ndarray, weights: Optional[np.ndarray] = None) -> float:
    """
    Bind operator using weighted L2 distance.
    
    ||A - B||_w = sqrt(sum(w_i * (A_i - B_i)^2))
    """
    if weights is None:
        weights = np.ones_like(vector_a)
    
    diff = vector_a - vector_b
    weighted_diff = weights * diff
    l2_distance = np.sqrt(np.sum(weighted_diff ** 2))
    
    return float(l2_distance)

def bind_ratio_metric(a: float, b: float) -> float:
    """
    Bind operator using ratio metric.
    
    ratio = a / b (with epsilon to avoid division by zero)
    """
    epsilon = 1e-10
    return a / (b + epsilon)

def bind_simplex_metric(ensemble: np.ndarray, mixture: np.ndarray) -> float:
    """
    Bind operator using simplex metric.
    
    Measures the "sharpness" of the current agent consensus.
    Uses entropy-based distance on the probability simplex.
    """
    # Ensure distributions are on simplex
    ensemble = ensemble / np.sum(ensemble)
    mixture = mixture / np.sum(mixture)
    
    # Calculate entropy difference
    epsilon = 1e-10
    ensemble_entropy = -np.sum(ensemble * np.log(ensemble + epsilon))
    mixture_entropy = -np.sum(mixture * np.log(mixture + epsilon))
    
    # Simplex distance as entropy difference
    simplex_distance = abs(ensemble_entropy - mixture_entropy)
    
    return float(simplex_distance)

def calculate_cognitive_load_metrics(
    current_state: np.ndarray,
    optimal_state: np.ndarray,
    prior_distribution: np.ndarray,
    optimal_distribution: np.ndarray,
    load_vector: np.ndarray,
    target_vector: np.ndarray,
    ensemble: np.ndarray,
    mixture: np.ndarray,
    history_trace: List[Dict[str, float]]
) -> CognitiveLoadMetrics:
    """
    Calculate Functional Collapse Paradigm cognitive load metrics.
    
    All metrics are type-instances of bind(A, B, Metric).
    """
    # Intrinsic Load (L_I): bind(p(b|x), uniform, KL)
    # Measures how far the current bitseed distribution is from high-entropy uniform state
    uniform_distribution = np.ones_like(current_state) / len(current_state)
    intrinsic_load = bind_kl_divergence(current_state, uniform_distribution)
    
    # Effort Load (L_E): bind(P_w_prior(x), P_optimal(x), KL)
    # Measures delta between swarm's current prediction and bit-accurate optimal predictor
    effort_load = bind_kl_divergence(prior_distribution, optimal_distribution)
    
    # Total System Load (L_total): bind(load_vector, target_vector, weighted_L2)
    # Global pressure on the manifold substrate
    # Use history-dependent weights for N-Local Topology Scaling
    if history_trace:
        # Calculate weights based on history (recent avalanches increase weight)
        recent_avalanches = sum(1 for h in history_trace[-10:] if h.get('avalanche', False))
        weights = np.ones_like(load_vector) * (1.0 + 0.1 * recent_avalanches)
    else:
        weights = None
    total_system_load = bind_weighted_l2(load_vector, target_vector, weights)
    
    # Efficiency Factor (η): bind(intrinsic, total, ratio_metric)
    # Ratio of effective informatic work to total energy dissipated
    efficiency_factor = bind_ratio_metric(intrinsic_load, total_system_load + epsilon)
    
    # Distribution Precision (P_w): bind(ensemble, mixture, simplex_metric)
    # Measures the "sharpness" of the current agent consensus
    distribution_precision = bind_simplex_metric(ensemble, mixture)
    
    # Determine operational threshold
    operational_threshold = determine_operational_threshold(total_system_load)
    
    # Check criticality (τ_c)
    criticality_reached = total_system_load >= 0.75
    
    return CognitiveLoadMetrics(
        intrinsic_load=intrinsic_load,
        effort_load=effort_load,
        total_system_load=total_system_load,
        efficiency_factor=efficiency_factor,
        distribution_precision=distribution_precision,
        operational_threshold=operational_threshold,
        criticality_reached=criticality_reached
    )

def determine_operational_threshold(load: float) -> OperationalThreshold:
    """Determine operational threshold based on load value."""
    if load < 0.25:
        return OperationalThreshold.RELATIONAL
    elif load < 0.50:
        return OperationalThreshold.SEMANTIC
    elif load < 0.75:
        return OperationalThreshold.TOPOLOGICAL
    else:
        return OperationalThreshold.CRITICAL

def abelian_sandpile_collapse(load: float, gear_pitch: float) -> Tuple[bool, float]:
    """
    Abelian Sandpile collapse logic.
    
    When Load ≥ τ_c (0.75), trigger a "Topological Collapse" to prevent
    irreversible informatic damage. This modulates the gear pitch to
    reduce cognitive load.
    
    Returns:
        (collapse_triggered, new_gear_pitch)
    """
    criticality_threshold = 0.75
    
    if load >= criticality_threshold:
        # Avalanche triggered - collapse to reduce load
        # Modulate gear pitch to reduce cognitive load
        new_gear_pitch = gear_pitch * 0.8  # Reduce gear pitch by 20%
        return True, new_gear_pitch
    else:
        # No collapse needed
        # Gradually restore gear pitch toward 1.0
        new_gear_pitch = min(1.0, gear_pitch + 0.01)
        return False, new_gear_pitch

epsilon = 1e-10

class ArchitectTopologyDriverAccurate:
    """
    Driver to maximize architect node topology utilization based on accurate container map.
    
    Uses actual container mapping data to make intelligent scheduling decisions.
    """
    
    def __init__(self, container_map_path: Optional[str] = None):
        # Load container map
        self.container_map = self._load_container_map(container_map_path)
        
        # Extract actual specs from container map
        self.node_specs = {
            "cores": self.container_map["cpu_info"]["physical_cores"],
            "logical_cores": self.container_map["cpu_info"]["logical_cores"],
            "ram_gb": self.container_map["memory_info"]["virtual_memory"]["total"] / (1024**3),
            "storage_gb": 500,  # From resource map
            "bandwidth_mbps": 500  # From resource map
        }
        
        # Topology resource state
        self.topology_resource = AccurateTopologyResource(
            total_cores=self.node_specs["cores"],
            total_ram_gb=self.node_specs["ram_gb"],
            total_storage_gb=self.node_specs["storage_gb"],
            total_bandwidth_mbps=self.node_specs["bandwidth_mbps"],
            available_cores=self.node_specs["cores"],
            available_ram_gb=self.node_specs["ram_gb"],
            available_storage_gb=self.node_specs["storage_gb"],
            available_bandwidth_mbps=self.node_specs["bandwidth_mbps"],
            core_utilization=[0.0] * self.node_specs["logical_cores"],
            memory_utilization=0.0,
            storage_utilization=0.0,
            bandwidth_utilization=0.0,
            total_processes=self.container_map.get("processes", []),
            total_network_edges=len(self.container_map.get("network_edges", [])),
            total_file_systems=len(self.container_map.get("file_systems", [])),
            total_memory_regions=len(self.container_map.get("memory_regions", [])),
            total_open_sockets=len(self.container_map.get("open_sockets", [])),
            total_devices=len(self.container_map.get("devices", [])),
            total_kernel_parameters=len(self.container_map.get("kernel_parameters", {}))
        )
        
        # Workload queues
        self.workload_queue: deque[Workload] = deque()
        self.active_workloads: Dict[str, Workload] = {}
        self.completed_workloads: List[Workload] = []
        
        # Scheduling state
        self.scheduling_strategy = "topology_aware_accurate_functional_collapse"
        self.max_concurrent_workloads = self.node_specs["logical_cores"] * 2  # 2x oversubscription
        
        # Background processing
        self._running = False
        self._lock = threading.Lock()
        self._scheduler_thread: Optional[threading.Thread] = None
        self._monitor_thread: Optional[threading.Thread] = None
        self._scheduling_interval = 0.1  # 100ms
        self._monitoring_interval = 1.0  # 1s
        
        # Performance metrics
        self.metrics: Dict[str, List[float]] = defaultdict(list)
        self.total_utilization_score = 0.0
        
        # Functional Collapse Paradigm state
        self.cognitive_load_history: List[Dict[str, float]] = []
        self.avalanche_count = 0
        self.gear_pitch = 1.0
        
        print(f"[ArchitectTopologyDriverAccurate] Initialized based on accurate container map")
        print(f"  Cores: {self.node_specs['cores']} physical, {self.node_specs['logical_cores']} logical")
        print(f"  RAM: {self.node_specs['ram_gb']:.2f}GB")
        print(f"  Storage: {self.node_specs['storage_gb']}GB")
        print(f"  Bandwidth: {self.node_specs['bandwidth_mbps']}Mbps")
        print(f"  Container Map Processes: {len(self.topology_resource.total_processes)}")
        print(f"  Container Map Network Edges: {self.topology_resource.total_network_edges}")
        print(f"  Container Map File Systems: {self.topology_resource.total_file_systems}")
        print(f"  Container Map Memory Regions: {self.topology_resource.total_memory_regions}")
        print(f"  Scheduling Strategy: {self.scheduling_strategy}")
        print(f"  Functional Collapse Paradigm: Enabled")
    
    def _load_container_map(self, path: Optional[str]) -> Dict[str, Any]:
        """Load container map from file."""
        if path is None:
            # Find most recent container map
            map_dir = Path("shared-data/data/swarm_responses")
            maps = sorted(map_dir.glob("architect_container_map_remote_*.json"))
            if maps:
                path = str(maps[-1])
            else:
                raise FileNotFoundError("No container map found")
        
        print(f"[ArchitectTopologyDriverAccurate] Loading container map from {path}")
        
        with open(path, 'r') as f:
            return json.load(f)
    
    def update_topology_state(self):
        """Update topology resource state from actual system metrics and calculate cognitive load."""
        try:
            # Get actual CPU utilization
            cpu_percent = psutil.cpu_percent(interval=0.1, percpu=True)
            self.topology_resource.core_utilization = [c / 100.0 for c in cpu_percent]
            
            # Get actual memory utilization
            memory = psutil.virtual_memory()
            self.topology_resource.memory_utilization = memory.percent / 100.0
            self.topology_resource.available_ram_gb = memory.available / (1024**3)
            
            # Get actual disk utilization
            disk = psutil.disk_usage('/')
            self.topology_resource.storage_utilization = disk.percent / 100.0
            self.topology_resource.available_storage_gb = disk.free / (1024**3)
            
            # Calculate available cores (cores with < 80% utilization)
            available_cores = sum(1 for util in self.topology_resource.core_utilization if util < 0.8)
            self.topology_resource.available_cores = max(0, available_cores)
            
            # Calculate total utilization score
            core_avg = sum(self.topology_resource.core_utilization) / len(self.topology_resource.core_utilization)
            self.total_utilization_score = (
                core_avg * 0.4 +
                self.topology_resource.memory_utilization * 0.3 +
                self.topology_resource.storage_utilization * 0.2 +
                self.topology_resource.bandwidth_utilization * 0.1
            )
            
            # Calculate Functional Collapse Paradigm cognitive load metrics
            self._calculate_cognitive_load()
            
        except Exception as e:
            print(f"[ArchitectTopologyDriverAccurate] Error updating topology state: {e}")
    
    def _calculate_cognitive_load(self):
        """Calculate cognitive load metrics using Refined Functional Collapse Paradigm."""
        try:
            # Create state vectors from current topology state
            current_state = np.array(self.topology_resource.core_utilization)
            optimal_state = np.ones_like(current_state) * 0.5  # Target 50% utilization per core
            
            prior_distribution = np.array([self.topology_resource.memory_utilization, 
                                          self.topology_resource.storage_utilization,
                                          self.topology_resource.bandwidth_utilization])
            optimal_distribution = np.array([0.5, 0.5, 0.5])  # Target 50% utilization
            
            load_vector = np.array([self.total_utilization_score,
                                  len(self.active_workloads) / self.max_concurrent_workloads,
                                  self.topology_resource.memory_utilization])
            target_vector = np.array([0.5, 0.5, 0.5])  # Target load vector
            
            # Ensemble distribution (workload types distribution)
            workload_types = [w.workload_type.value for w in self.active_workloads.values()]
            if workload_types:
                type_counts = defaultdict(int)
                for wt in workload_types:
                    type_counts[wt] += 1
                ensemble = np.array([type_counts.get(wt.value, 0) for wt in WorkloadType])
                ensemble = ensemble / np.sum(ensemble) if np.sum(ensemble) > 0 else np.ones(len(WorkloadType)) / len(WorkloadType)
            else:
                ensemble = np.ones(len(WorkloadType)) / len(WorkloadType)
            
            # Mixture distribution (target uniform distribution)
            mixture = np.ones(len(WorkloadType)) / len(WorkloadType)
            
            # Refined Intrinsic Load: use capacity-matched baseline instead of uniform
            # Calculate baseline distribution based on current structural constraints
            baseline_distribution = self._calculate_capacity_matched_baseline(current_state)
            
            # Override the intrinsic load calculation to use capacity-matched baseline
            intrinsic_load = bind_kl_divergence(current_state, baseline_distribution)
            
            # Calculate remaining metrics
            effort_load = bind_kl_divergence(prior_distribution, optimal_distribution)
            
            # Calculate history-dependent weights for N-Local Topology Scaling
            if self.cognitive_load_history:
                recent_avalanches = sum(1 for h in self.cognitive_load_history[-10:] if h.get('avalanche', False))
                weights = np.ones_like(load_vector) * (1.0 + 0.1 * recent_avalanches)
            else:
                weights = None
            total_system_load = bind_weighted_l2(load_vector, target_vector, weights)
            
            efficiency_factor = bind_ratio_metric(intrinsic_load, total_system_load + epsilon)
            distribution_precision = bind_simplex_metric(ensemble, mixture)
            
            # Determine operational threshold
            operational_threshold = determine_operational_threshold(total_system_load)
            
            # Check criticality (τ_c)
            criticality_reached = total_system_load >= 0.75
            
            self.topology_resource.cognitive_load_metrics = CognitiveLoadMetrics(
                intrinsic_load=intrinsic_load,
                effort_load=effort_load,
                total_system_load=total_system_load,
                efficiency_factor=efficiency_factor,
                distribution_precision=distribution_precision,
                operational_threshold=operational_threshold,
                criticality_reached=criticality_reached
            )
            
            # Check for criticality and trigger Abelian Sandpile collapse if needed
            collapse_triggered, new_gear_pitch = abelian_sandpile_collapse(
                load=self.topology_resource.cognitive_load_metrics.total_system_load,
                gear_pitch=self.gear_pitch
            )
            
            if collapse_triggered:
                self.avalanche_count += 1
                print(f"[ArchitectTopologyDriverAccurate] ⚠️  AVALANCHE TRIGGERED - Topological Collapse initiated")
                print(f"  Total System Load: {self.topology_resource.cognitive_load_metrics.total_system_load:.3f} ≥ τ_c (0.75)")
                print(f"  Avalanche Count: {self.avalanche_count}")
                print(f"  Gear Pitch: {self.gear_pitch:.3f} → {new_gear_pitch:.3f}")
            
            self.gear_pitch = new_gear_pitch
            self.topology_resource.gear_pitch = new_gear_pitch
            
            # Record history trace for N-Local Topology Scaling
            history_entry = {
                "timestamp": time.time(),
                "total_system_load": self.topology_resource.cognitive_load_metrics.total_system_load,
                "intrinsic_load": self.topology_resource.cognitive_load_metrics.intrinsic_load,
                "effort_load": self.topology_resource.cognitive_load_metrics.effort_load,
                "efficiency_factor": self.topology_resource.cognitive_load_metrics.efficiency_factor,
                "distribution_precision": self.topology_resource.cognitive_load_metrics.distribution_precision,
                "operational_threshold": self.topology_resource.cognitive_load_metrics.operational_threshold.value,
                "avalanche": collapse_triggered,
                "gear_pitch": self.gear_pitch
            }
            
            self.cognitive_load_history.append(history_entry)
            
            # Keep history limited to last 100 entries
            if len(self.cognitive_load_history) > 100:
                self.cognitive_load_history = self.cognitive_load_history[-100:]
            
        except Exception as e:
            print(f"[ArchitectTopologyDriverAccurate] Error calculating cognitive load: {e}")
    
    def _calculate_capacity_matched_baseline(self, current_state: np.ndarray) -> np.ndarray:
        """
        Calculate capacity-matched baseline distribution for refined intrinsic load.
        
        Instead of using unconstrained uniformity, this calculates the maximum-entropy
        distribution under current structural and historical constraints.
        
        This means:
        - Unnecessary over-constraint increases load
        - Lawful structure does NOT automatically count as friction
        - Intrinsic load becomes "excess rigidity relative to what this system can stably sustain"
        """
        # Calculate baseline as maximum-entropy distribution under constraints
        # Constraints: current gear pitch, recent avalanche history, available cores
        
        # Base uniform distribution
        baseline = np.ones_like(current_state) / len(current_state)
        
        # Apply structural constraints based on gear pitch
        # Higher gear pitch = more constrained baseline (lower capacity)
        constrained_baseline = baseline * self.gear_pitch
        
        # Normalize to maintain probability distribution
        constrained_baseline = constrained_baseline / np.sum(constrained_baseline)
        
        # Apply historical constraints - recent avalanches tighten baseline
        if self.cognitive_load_history:
            recent_avalanches = sum(1 for h in self.cognitive_load_history[-10:] if h.get('avalanche', False))
            # More avalanches = tighter baseline (system becomes more conservative)
            history_factor = 1.0 / (1.0 + 0.1 * recent_avalanches)
            constrained_baseline = constrained_baseline * history_factor
            constrained_baseline = constrained_baseline / np.sum(constrained_baseline)
        
        return constrained_baseline
    
    def schedule_workload(self, workload: Workload) -> bool:
        """Schedule a workload using topology-aware scheduling with cognitive load consideration."""
        with self._lock:
            # Check if resources are available
            if self.topology_resource.available_cores < 1:
                return False
            
            if self.topology_resource.available_ram_gb < workload.memory_required * self.node_specs["ram_gb"]:
                return False
            
            if self.topology_resource.available_storage_gb < workload.storage_required * self.node_specs["storage_gb"]:
                return False
            
            # Check cognitive load - if critical, reject new workloads
            if (self.topology_resource.cognitive_load_metrics and 
                self.topology_resource.cognitive_load_metrics.criticality_reached):
                print(f"[ArchitectTopologyDriverAccurate] ⚠️  Rejecting {workload.workload_id} - Critical load reached")
                return False
            
            # Adjust resource requirements based on gear pitch (N-Local Topology Scaling)
            adjusted_cpu_required = workload.cpu_required * self.gear_pitch
            adjusted_memory_required = workload.memory_required * self.gear_pitch
            
            # Find best core for this workload based on container map data
            best_core = self._find_best_core_accurate(workload)
            
            if best_core is None:
                return False
            
            # Assign workload
            workload.assigned_core = best_core
            workload.status = "running"
            workload.start_time = time.time()
            
            # Update resource availability
            self.topology_resource.available_cores -= 1
            self.topology_resource.available_ram_gb -= adjusted_memory_required * self.node_specs["ram_gb"]
            self.topology_resource.available_storage_gb -= workload.storage_required * self.node_specs["storage_gb"]
            
            # Add to active workloads
            self.active_workloads[workload.workload_id] = workload
            
            print(f"[ArchitectTopologyDriverAccurate] Scheduled {workload.workload_id} on core {best_core}")
            print(f"  Type: {workload.workload_type.value}")
            print(f"  CPU: {adjusted_cpu_required * 100:.1f}% (adjusted by gear pitch {self.gear_pitch:.3f})")
            print(f"  RAM: {adjusted_memory_required * 100:.1f}%")
            
            return True
    
    def _find_best_core_accurate(self, workload: Workload) -> Optional[int]:
        """Find best core for workload based on accurate container map data."""
        # Find core with lowest utilization
        core_utilizations = self.topology_resource.core_utilization
        best_core = None
        best_utilization = 1.0
        
        for i, util in enumerate(core_utilizations):
            if util < best_utilization:
                best_utilization = util
                best_core = i
        
        # Check if best core is available (< 80% utilization)
        if best_core is not None and best_utilization < 0.8:
            return best_core
        
        return None
    
    def submit_workload(self, workload: Workload) -> bool:
        """Submit a workload for scheduling."""
        with self._lock:
            self.workload_queue.append(workload)
            print(f"[ArchitectTopologyDriverAccurate] Submitted workload {workload.workload_id}")
            return True
    
    def _scheduler_loop(self):
        """Background scheduler loop."""
        while self._running:
            try:
                with self._lock:
                    # Update topology state
                    self.update_topology_state()
                    
                    # Schedule pending workloads
                    while (len(self.workload_queue) > 0 and 
                           len(self.active_workloads) < self.max_concurrent_workloads):
                        workload = self.workload_queue.popleft()
                        if not self.schedule_workload(workload):
                            # Can't schedule now, put back in queue
                            self.workload_queue.appendleft(workload)
                            break
                    
                    # Check for completed workloads
                    current_time = time.time()
                    completed = []
                    for workload_id, workload in self.active_workloads.items():
                        if workload.end_time and current_time >= workload.end_time:
                            completed.append(workload_id)
                    
                    for workload_id in completed:
                        self._complete_workload(workload_id)
                
                time.sleep(self._scheduling_interval)
                
            except Exception as e:
                print(f"[ArchitectTopologyDriverAccurate] Scheduler loop error: {e}")
                time.sleep(1.0)
    
    def _complete_workload(self, workload_id: str):
        """Complete a workload and free resources."""
        with self._lock:
            if workload_id not in self.active_workloads:
                return
            
            workload = self.active_workloads[workload_id]
            workload.status = "completed"
            workload.end_time = time.time()
            
            # Free resources
            self.topology_resource.available_cores += 1
            self.topology_resource.available_ram_gb += workload.memory_required * self.node_specs["ram_gb"]
            self.topology_resource.available_storage_gb += workload.storage_required * self.node_specs["storage_gb"]
            
            # Move to completed
            self.completed_workloads.append(workload)
            del self.active_workloads[workload_id]
            
            print(f"[ArchitectTopologyDriverAccurate] Completed {workload_id}")
    
    def _monitor_loop(self):
        """Background monitoring loop with cognitive load tracking."""
        while self._running:
            try:
                with self._lock:
                    # Record metrics
                    self.metrics['total_utilization'].append(self.total_utilization_score)
                    self.metrics['core_utilization_avg'].append(
                        sum(self.topology_resource.core_utilization) / len(self.topology_resource.core_utilization)
                    )
                    self.metrics['memory_utilization'].append(self.topology_resource.memory_utilization)
                    self.metrics['active_workloads'].append(len(self.active_workloads))
                    self.metrics['queued_workloads'].append(len(self.workload_queue))
                    
                    # Record cognitive load metrics
                    if self.topology_resource.cognitive_load_metrics:
                        self.metrics['intrinsic_load'].append(self.topology_resource.cognitive_load_metrics.intrinsic_load)
                        self.metrics['effort_load'].append(self.topology_resource.cognitive_load_metrics.effort_load)
                        self.metrics['total_system_load'].append(self.topology_resource.cognitive_load_metrics.total_system_load)
                        self.metrics['efficiency_factor'].append(self.topology_resource.cognitive_load_metrics.efficiency_factor)
                        self.metrics['distribution_precision'].append(self.topology_resource.cognitive_load_metrics.distribution_precision)
                        self.metrics['gear_pitch'].append(self.gear_pitch)
                        self.metrics['avalanche_count'].append(self.avalanche_count)
                
                time.sleep(self._monitoring_interval)
                
            except Exception as e:
                print(f"[ArchitectTopologyDriverAccurate] Monitor loop error: {e}")
                time.sleep(1.0)
    
    def start(self) -> bool:
        """Start the topology driver."""
        try:
            self._running = True
            self._scheduler_thread = threading.Thread(target=self._scheduler_loop, daemon=True)
            self._monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True)
            
            self._scheduler_thread.start()
            self._monitor_thread.start()
            
            print(f"[ArchitectTopologyDriverAccurate] Started topology driver")
            print(f"  Scheduling strategy: {self.scheduling_strategy}")
            print(f"  Max concurrent workloads: {self.max_concurrent_workloads}")
            print(f"  Based on accurate container map with {self.topology_resource.total_processes} processes")
            return True
            
        except Exception as e:
            print(f"[ArchitectTopologyDriverAccurate] Failed to start: {e}")
            return False
    
    def stop(self):
        """Stop the topology driver."""
        self._running = False
        if self._scheduler_thread:
            self._scheduler_thread.join(timeout=5.0)
        if self._monitor_thread:
            self._monitor_thread.join(timeout=5.0)
        print("[ArchitectTopologyDriverAccurate] Stopped")
    
    def get_status(self) -> Dict[str, Any]:
        """Get current driver status including cognitive load metrics."""
        with self._lock:
            status = {
                "running": self._running,
                "scheduling_strategy": self.scheduling_strategy,
                "node_specs": self.node_specs,
                "topology_resource": {
                    "total_cores": self.topology_resource.total_cores,
                    "available_cores": self.topology_resource.available_cores,
                    "total_ram_gb": self.topology_resource.total_ram_gb,
                    "available_ram_gb": self.topology_resource.available_ram_gb,
                    "total_storage_gb": self.topology_resource.total_storage_gb,
                    "available_storage_gb": self.topology_resource.available_storage_gb,
                    "core_utilization": self.topology_resource.core_utilization,
                    "memory_utilization": self.topology_resource.memory_utilization,
                    "storage_utilization": self.topology_resource.storage_utilization,
                    "total_utilization_score": self.total_utilization_score,
                    "container_map_stats": {
                        "total_processes": len(self.topology_resource.total_processes),
                        "total_network_edges": self.topology_resource.total_network_edges,
                        "total_file_systems": self.topology_resource.total_file_systems,
                        "total_memory_regions": self.topology_resource.total_memory_regions,
                        "total_open_sockets": self.topology_resource.total_open_sockets,
                        "total_devices": self.topology_resource.total_devices,
                        "total_kernel_parameters": self.topology_resource.total_kernel_parameters
                    }
                },
                "workloads": {
                    "active": len(self.active_workloads),
                    "queued": len(self.workload_queue),
                    "completed": len(self.completed_workloads)
                },
                "functional_collapse_paradigm": {
                    "cognitive_load_metrics": None,
                    "gear_pitch": self.gear_pitch,
                    "avalanche_count": self.avalanche_count,
                    "history_length": len(self.cognitive_load_history)
                },
                "metrics": {
                    key: {
                        "avg": sum(values) / len(values) if values else 0,
                        "max": max(values) if values else 0,
                        "min": min(values) if values else 0,
                        "count": len(values)
                    }
                    for key, values in self.metrics.items()
                }
            }
            
            # Add cognitive load metrics if available
            if self.topology_resource.cognitive_load_metrics:
                status["functional_collapse_paradigm"]["cognitive_load_metrics"] = {
                    "intrinsic_load": self.topology_resource.cognitive_load_metrics.intrinsic_load,
                    "effort_load": self.topology_resource.cognitive_load_metrics.effort_load,
                    "total_system_load": self.topology_resource.cognitive_load_metrics.total_system_load,
                    "efficiency_factor": self.topology_resource.cognitive_load_metrics.efficiency_factor,
                    "distribution_precision": self.topology_resource.cognitive_load_metrics.distribution_precision,
                    "operational_threshold": self.topology_resource.cognitive_load_metrics.operational_threshold.value,
                    "criticality_reached": self.topology_resource.cognitive_load_metrics.criticality_reached
                }
            
            return status
    
    def print_status(self):
        """Print current driver status including cognitive load metrics."""
        status = self.get_status()
        
        print("\n" + "=" * 70)
        print("ARCHITECT TOPOLOGY DRIVER STATUS (FUNCTIONAL COLLAPSE PARADIGM)")
        print("=" * 70)
        
        print(f"\n📊 Node Specifications:")
        print(f"  Cores: {status['node_specs']['cores']} physical, {status['node_specs']['logical_cores']} logical")
        print(f"  RAM: {status['node_specs']['ram_gb']:.2f}GB")
        print(f"  Storage: {status['node_specs']['storage_gb']}GB")
        print(f"  Bandwidth: {status['node_specs']['bandwidth_mbps']}Mbps")
        
        print(f"\n📊 Topology Utilization:")
        print(f"  Total Utilization Score: {status['topology_resource']['total_utilization_score']:.3f}")
        print(f"  Cores: {status['topology_resource']['available_cores']}/{status['topology_resource']['total_cores']} available")
        print(f"  RAM: {status['topology_resource']['available_ram_gb']:.1f}/{status['topology_resource']['total_ram_gb']:.1f}GB available")
        print(f"  Storage: {status['topology_resource']['available_storage_gb']:.1f}/{status['topology_resource']['total_storage_gb']:.1f}GB available")
        print(f"  Core Utilization: {[f'{c:.2f}' for c in status['topology_resource']['core_utilization']]}")
        
        print(f"\n📊 Container Map Statistics:")
        print(f"  Processes: {status['topology_resource']['container_map_stats']['total_processes']}")
        print(f"  Network Edges: {status['topology_resource']['container_map_stats']['total_network_edges']}")
        print(f"  File Systems: {status['topology_resource']['container_map_stats']['total_file_systems']}")
        print(f"  Memory Regions: {status['topology_resource']['container_map_stats']['total_memory_regions']}")
        print(f"  Open Sockets: {status['topology_resource']['container_map_stats']['total_open_sockets']}")
        print(f"  Devices: {status['topology_resource']['container_map_stats']['total_devices']}")
        print(f"  Kernel Parameters: {status['topology_resource']['container_map_stats']['total_kernel_parameters']}")
        
        print(f"\n🧠 Functional Collapse Paradigm - Cognitive Load Metrics:")
        fcp = status['functional_collapse_paradigm']
        if fcp['cognitive_load_metrics']:
            clm = fcp['cognitive_load_metrics']
            print(f"  Intrinsic Load (L_I): {clm['intrinsic_load']:.4f}")
            print(f"  Effort Load (L_E): {clm['effort_load']:.4f}")
            print(f"  Total System Load (L_total): {clm['total_system_load']:.4f}")
            print(f"  Efficiency Factor (η): {clm['efficiency_factor']:.4f}")
            print(f"  Distribution Precision (P_w): {clm['distribution_precision']:.4f}")
            print(f"  Operational Threshold: {clm['operational_threshold']}")
            print(f"  Criticality Reached: {clm['criticality_reached']} {'⚠️  CRITICAL' if clm['criticality_reached'] else ''}")
        print(f"  Gear Pitch: {fcp['gear_pitch']:.3f}")
        print(f"  Avalanche Count: {fcp['avalanche_count']}")
        print(f"  History Length: {fcp['history_length']}")
        
        print(f"\n📋 Workloads:")
        print(f"  Active: {status['workloads']['active']}")
        print(f"  Queued: {status['workloads']['queued']}")
        print(f"  Completed: {status['workloads']['completed']}")
        
        print(f"\n📈 Metrics:")
        for key, metric in status['metrics'].items():
            print(f"  {key}: avg {metric['avg']:.3f}, max {metric['max']:.3f}")
        
        print("\n" + "=" * 70)

def create_sample_workloads(num_workloads: int) -> List[Workload]:
    """Create sample workloads for testing."""
    workloads = []
    
    for i in range(num_workloads):
        workload_type = random.choice(list(WorkloadType))
        
        workload = Workload(
            workload_id=f"workload_{i}",
            workload_type=workload_type,
            cpu_required=random.uniform(0.1, 0.5),
            memory_required=random.uniform(0.05, 0.3),
            storage_required=random.uniform(0.01, 0.05),
            bandwidth_required=random.uniform(0.01, 0.1),
            priority=random.randint(1, 10),
            duration=random.uniform(5.0, 30.0),
            executable=lambda: time.sleep(random.uniform(5.0, 30.0))
        )
        
        workloads.append(workload)
    
    return workloads

import random

def timeout_handler(signum, frame):
    """Watchdog timeout handler - forcibly kills the process."""
    print(f"\n⏱️  WATCHDOG TIMEOUT - Process forcibly terminated after {timeout}s")
    sys.exit(1)

if __name__ == "__main__":
    print("=" * 70)
    print("Architect Node Topology Driver (Based on Accurate Container Map)")
    print("Maximizing Topology Utilization with Functional Collapse Paradigm")
    print("=" * 70)
    
    # Set watchdog timeout
    timeout = 30
    signal.signal(signal.SIGALRM, timeout_handler)
    signal.alarm(timeout)
    print(f"Watchdog timeout set: {timeout}s")
    
    try:
        # Create driver with accurate container map
        driver = ArchitectTopologyDriverAccurate()
        
        # Start driver
        if not driver.start():
            print("Failed to start driver")
            sys.exit(1)
        
        # Create sample workloads
        print("\nCreating sample workloads...")
        num_workloads = 50
        workloads = create_sample_workloads(num_workloads)
        print(f"Created {len(workloads)} workloads")
        
        # Submit workloads
        print("\nSubmitting workloads...")
        for workload in workloads:
            driver.submit_workload(workload)
        
        print(f"Submitted {len(workloads)} workloads")
        
        # Monitor with timeout
        print("\nMonitoring for 30 seconds with watchdog...")
        start_time = time.time()
        
        while time.time() - start_time < timeout:
            time.sleep(1)
            elapsed = int(time.time() - start_time)
            if elapsed % 10 == 0:
                driver.print_status()
                print(f"\nTime remaining: {timeout - elapsed}s")
        
        # Cancel watchdog
        signal.alarm(0)
        
        # Stop driver
        driver.stop()
        
        # Final status
        driver.print_status()
        
        print("\n✅ Architect topology driver test complete")
        print("Driver based on accurate container map with Functional Collapse Paradigm")
        print(f"Test duration: {int(time.time() - start_time)}s")
        
    except KeyboardInterrupt:
        print("\n\nInterrupted by user")
        signal.alarm(0)
        driver.stop()
        sys.exit(0)
    except Exception as e:
        print(f"\n❌ Error: {e}")
        signal.alarm(0)
        driver.stop()
        sys.exit(1)