"""
TNFR Computational Centralization Hub

This module implements the natural centralization that emerges from the nodal equation:
∂EPI/∂t = νf · ΔNFR(t)

The hub recognizes that all TNFR computations share the same mathematical foundation
and can be unified under a single computational infrastructure:

Centralization Principles:
1. **Single Mathematical Source**: All operations derive from nodal equation
2. **Unified Resource Management**: Shared memory, compute, and cache coordination  
3. **Cross-Engine Communication**: Direct data sharing between optimization engines
4. **Intelligent Load Balancing**: Route computations to optimal engines
5. **Emergent Optimization**: System learns and adapts automatically
6. **Hierarchical Coordination**: Multi-scale operation from local to global

Key Features:
- Unified computation dispatch across all engines
- Shared memory pools for cross-engine data transfer
- Intelligent caching with mathematical dependency tracking
- Automatic backend selection (NumPy/JAX/PyTorch/GPU)
- Performance learning and adaptation
- Resource pooling and load balancing
- Mathematical consistency guarantees

Status: CANONICAL COMPUTATIONAL CENTRALIZATION HUB
"""

import numpy as np
from typing import Dict, Any, Optional, Union, Callable, Set
from dataclasses import dataclass, field
from enum import Enum
import time
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from queue import Queue, PriorityQueue
import uuid

try:
    import networkx as nx
    HAS_NETWORKX = True
except ImportError:
    HAS_NETWORKX = False
    nx = None

# Import all engines
try:
    from .unified_backend import TNFRUnifiedBackend, ComputationType, UnifiedComputationRequest
    from .optimization_orchestrator import TNFROptimizationOrchestrator, OptimizationStrategy
    from .advanced_fft_arithmetic import TNFRAdvancedFFTEngine, SpectralOperation
    from .multi_modal_cache import TNFRUnifiedMultiModalCache, CacheEntryType
    from .nodal_optimizer import NodalEquationOptimizer
    from .fft_engine import FFTDynamicsEngine  
    from .structural_cache import StructuralCoherenceCache
    from .adelic import AdelicDynamics
    HAS_ALL_ENGINES = True
except ImportError:
    HAS_ALL_ENGINES = False

# Import mathematical backends
try:
    from ..mathematics.backend import get_backend, available_backends
    HAS_MATH_BACKENDS = True
except ImportError:
    HAS_MATH_BACKENDS = False
    get_backend = None
    available_backends = None


class ComputationPriority(Enum):
    """Priority levels for computation requests."""
    CRITICAL = 1      # Real-time operator applications
    HIGH = 2          # Interactive computations
    NORMAL = 3        # Standard analysis
    LOW = 4           # Background optimization
    BATCH = 5         # Large batch processing


class EngineType(Enum):
    """Available computational engines."""
    UNIFIED_BACKEND = "unified_backend"
    OPTIMIZATION_ORCHESTRATOR = "orchestrator" 
    ADVANCED_FFT = "advanced_fft"
    NODAL_OPTIMIZER = "nodal_optimizer"
    FFT_ENGINE = "fft_engine"
    STRUCTURAL_CACHE = "structural_cache"
    ADELIC_DYNAMICS = "adelic_dynamics"
    MULTI_MODAL_CACHE = "multi_modal_cache"


@dataclass
class ComputationRequest:
    """Unified computation request."""
    request_id: str = field(default_factory=lambda: str(uuid.uuid4()))
    engine_type: EngineType = EngineType.UNIFIED_BACKEND
    operation: str = "general_computation"
    graph: Optional[Any] = None
    parameters: Dict[str, Any] = field(default_factory=dict)
    priority: ComputationPriority = ComputationPriority.NORMAL
    callback: Optional[Callable] = None
    dependencies: Set[str] = field(default_factory=set)
    timeout_seconds: float = 300.0
    enable_cache: bool = True
    require_accuracy: bool = True


@dataclass
class ComputationResult:
    """Unified computation result."""
    request_id: str
    engine_used: EngineType
    operation: str
    success: bool
    result_data: Any = None
    execution_time: float = 0.0
    cache_hits: int = 0
    cache_misses: int = 0
    memory_used_mb: float = 0.0
    backend_used: str = "numpy"
    accuracy_metrics: Dict[str, float] = field(default_factory=dict)
    error_message: Optional[str] = None
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class SystemResources:
    """System resource status."""
    total_memory_mb: float = 0.0
    available_memory_mb: float = 0.0
    cpu_count: int = 1
    gpu_available: bool = False
    active_computations: int = 0
    cache_utilization: float = 0.0
    load_average: float = 0.0


class TNFRComputationalHub:
    """
    Centralized computational hub for all TNFR operations.
    
    This hub emerges naturally from recognizing that all TNFR computations
    are variations of the same mathematical structure and can benefit from
    unified resource management and cross-engine optimization.
    """
    
    def __init__(
        self,
        max_workers: int = 4,
        memory_budget_mb: float = 1024.0,
        enable_gpu: bool = True,
        cache_size_mb: float = 512.0
    ):
        self.max_workers = max_workers
        self.memory_budget_mb = memory_budget_mb
        self.enable_gpu = enable_gpu
        self.cache_size_mb = cache_size_mb
        
        # Initialize all engines
        self._engines = {}
        if HAS_ALL_ENGINES:
            self._engines[EngineType.UNIFIED_BACKEND] = TNFRUnifiedBackend()
            self._engines[EngineType.OPTIMIZATION_ORCHESTRATOR] = TNFROptimizationOrchestrator()
            self._engines[EngineType.ADVANCED_FFT] = TNFRAdvancedFFTEngine()
            self._engines[EngineType.MULTI_MODAL_CACHE] = TNFRUnifiedMultiModalCache(cache_size_mb)
            self._engines[EngineType.NODAL_OPTIMIZER] = NodalEquationOptimizer()
            self._engines[EngineType.FFT_ENGINE] = FFTDynamicsEngine()
            self._engines[EngineType.STRUCTURAL_CACHE] = StructuralCoherenceCache()
            self._engines[EngineType.ADELIC_DYNAMICS] = AdelicDynamics()
            
        # Computation coordination
        self._request_queue = PriorityQueue()
        self._result_cache = {}
        self._active_requests = {}
        
        # Thread pool for parallel execution
        self._executor = ThreadPoolExecutor(max_workers=max_workers)
        self._queue_thread = None
        self._shutdown = False
        
        # Resource management
        self._resource_monitor = SystemResources()
        self._load_balancer = {}
        
        # Performance tracking
        self._performance_history = []
        self._engine_performance = {engine: [] for engine in EngineType}
        
        # Cross-engine shared memory
        self._shared_memory_pool = {}
        self._memory_locks = {}
        
        # Start background processing
        self._start_queue_processor()
        
    def _start_queue_processor(self) -> None:
        """Start background queue processing thread."""
        def process_queue():
            while not self._shutdown:
                try:
                    if not self._request_queue.empty():
                        priority, timestamp, request = self._request_queue.get(timeout=1.0)
                        self._process_request_async(request)
                except Exception:
                    continue  # Keep processing
                    
        self._queue_thread = threading.Thread(target=process_queue, daemon=True)
        self._queue_thread.start()
        
    def submit_computation(
        self, 
        request: ComputationRequest
    ) -> str:
        """
        Submit computation request to hub.
        
        Returns request ID for tracking.
        """
        # Validate request
        if not self._validate_request(request):
            raise ValueError(f"Invalid computation request: {request}")
            
        # Add to queue with priority
        timestamp = time.time()
        self._request_queue.put((request.priority.value, timestamp, request))
        self._active_requests[request.request_id] = request
        
        return request.request_id
        
    def get_result(
        self, 
        request_id: str, 
        timeout: Optional[float] = None
    ) -> Optional[ComputationResult]:
        """Get computation result by request ID."""
        start_time = time.time()
        
        while True:
            # Check if result is ready
            if request_id in self._result_cache:
                result = self._result_cache.pop(request_id)
                self._active_requests.pop(request_id, None)
                return result
                
            # Check timeout
            if timeout and (time.time() - start_time) > timeout:
                return None
                
            # Brief sleep to avoid busy waiting
            time.sleep(0.01)
            
    def execute_computation_sync(
        self,
        request: ComputationRequest
    ) -> ComputationResult:
        """Execute computation synchronously."""
        request_id = self.submit_computation(request)
        result = self.get_result(request_id, timeout=request.timeout_seconds)
        
        if result is None:
            return ComputationResult(
                request_id=request_id,
                engine_used=request.engine_type,
                operation=request.operation,
                success=False,
                error_message="Computation timed out"
            )
            
        return result
        
    def _process_request_async(self, request: ComputationRequest) -> None:
        """Process computation request asynchronously."""
        def process():
            try:
                result = self._execute_computation(request)
                self._result_cache[request.request_id] = result
                
                # Call callback if provided
                if request.callback:
                    request.callback(result)
                    
            except Exception as e:
                error_result = ComputationResult(
                    request_id=request.request_id,
                    engine_used=request.engine_type,
                    operation=request.operation,
                    success=False,
                    error_message=str(e)
                )
                self._result_cache[request.request_id] = error_result
                
        # Submit to thread pool
        self._executor.submit(process)
        
    def _execute_computation(self, request: ComputationRequest) -> ComputationResult:
        """Execute single computation request."""
        start_time = time.time()
        
        # Select optimal engine for this computation
        selected_engine = self._select_optimal_engine(request)
        
        # Route to appropriate engine
        try:
            if selected_engine == EngineType.UNIFIED_BACKEND:
                result_data = self._execute_unified_backend(request)
                
            elif selected_engine == EngineType.OPTIMIZATION_ORCHESTRATOR:
                result_data = self._execute_optimization_orchestrator(request)
                
            elif selected_engine == EngineType.ADVANCED_FFT:
                result_data = self._execute_advanced_fft(request)
                
            elif selected_engine == EngineType.NODAL_OPTIMIZER:
                result_data = self._execute_nodal_optimizer(request)
                
            elif selected_engine == EngineType.FFT_ENGINE:
                result_data = self._execute_fft_engine(request)
                
            elif selected_engine == EngineType.ADELIC_DYNAMICS:
                result_data = self._execute_adelic_dynamics(request)
                
            else:
                raise ValueError(f"Unknown engine type: {selected_engine}")
                
        except Exception as e:
            return ComputationResult(
                request_id=request.request_id,
                engine_used=selected_engine,
                operation=request.operation,
                success=False,
                error_message=str(e)
            )
            
        execution_time = time.perf_counter() - start_time
        
        # Update performance tracking
        self._engine_performance[selected_engine].append(execution_time)
        
        return ComputationResult(
            request_id=request.request_id,
            engine_used=selected_engine,
            operation=request.operation,
            success=True,
            result_data=result_data,
            execution_time=execution_time
        )
        
    def _select_optimal_engine(self, request: ComputationRequest) -> EngineType:
        """
        Select optimal engine based on request characteristics and system state.
        
        This selection emerges from mathematical analysis of the computation type.
        """
        # Use specified engine if available and suitable
        if request.engine_type in self._engines:
            return request.engine_type
            
        # Intelligent selection based on operation and graph properties
        if request.graph and HAS_NETWORKX:
            num_nodes = len(request.graph.nodes())
            
            # Large graphs benefit from specialized FFT engines
            if num_nodes > 100:
                if request.operation in ["spectral_analysis", "harmonic_analysis"]:
                    return EngineType.ADVANCED_FFT
                elif request.operation in ["temporal_evolution", "multi_step"]:
                    return EngineType.FFT_ENGINE
                    
            # Medium graphs good for nodal optimization
            elif 20 <= num_nodes <= 100:
                if request.operation in ["nodal_evolution", "operator_sequence"]:
                    return EngineType.NODAL_OPTIMIZER
                    
        # Default to optimization orchestrator for intelligent routing
        if EngineType.OPTIMIZATION_ORCHESTRATOR in self._engines:
            return EngineType.OPTIMIZATION_ORCHESTRATOR
            
        # Fallback to unified backend
        return EngineType.UNIFIED_BACKEND
        
    def _execute_unified_backend(self, request: ComputationRequest) -> Any:
        """Execute using unified backend."""
        engine = self._engines[EngineType.UNIFIED_BACKEND]
        
        # Map to unified computation request
        unified_request = UnifiedComputationRequest(
            computation_type=ComputationType.NODAL_EVOLUTION,  # Default
            graph=request.graph,
            parameters=request.parameters,
            enable_cache=request.enable_cache
        )
        
        result = engine.execute_computation(unified_request)
        return result.results
        
    def _execute_optimization_orchestrator(self, request: ComputationRequest) -> Any:
        """Execute using optimization orchestrator."""
        engine = self._engines[EngineType.OPTIMIZATION_ORCHESTRATOR]
        
        # Analyze and execute with optimal strategy
        profile = engine.analyze_optimization_profile(request.graph, request.operation)
        strategy = engine.select_optimal_strategy(profile)
        
        result = engine.execute_optimization(
            request.graph,
            request.operation,
            strategy,
            **request.parameters
        )
        
        return {
            "strategy_used": result.strategy_used.value,
            "execution_time": result.execution_time,
            "speedup_factor": result.speedup_factor,
            "cache_performance": {
                "hits": result.cache_hits,
                "misses": result.cache_misses
            },
            "details": result.details
        }
        
    def _execute_advanced_fft(self, request: ComputationRequest) -> Any:
        """Execute using advanced FFT engine."""
        engine = self._engines[EngineType.ADVANCED_FFT]
        
        operation = request.parameters.get('spectral_operation', 'harmonic_analysis')
        
        if operation == "harmonic_analysis":
            result = engine.harmonic_analysis(request.graph)
        elif operation == "spectral_filtering":
            result = engine.spectral_filtering(request.graph, **request.parameters)
        elif operation == "coherence_analysis":
            # Requires second graph
            graph2 = request.parameters.get('graph2')
            if graph2:
                result = engine.cross_spectral_coherence(request.graph, graph2)
            else:
                raise ValueError("Coherence analysis requires second graph")
        else:
            result = engine.spectral_convolution(request.graph, **request.parameters)
            
        return result.output_data
        
    def _execute_nodal_optimizer(self, request: ComputationRequest) -> Any:
        """Execute using nodal optimizer."""
        engine = self._engines[EngineType.NODAL_OPTIMIZER]
        
        dt = request.parameters.get('dt', 0.01)
        result = engine.compute_vectorized_nodal_evolution(request.graph, dt)
        
        return {
            "nodal_evolution": result,
            "optimization_stats": engine.get_optimization_stats()
        }
        
    def _execute_fft_engine(self, request: ComputationRequest) -> Any:
        """Execute using FFT engine."""
        engine = self._engines[EngineType.FFT_ENGINE]
        
        num_steps = request.parameters.get('num_steps', 10)
        dt = request.parameters.get('dt', 0.01)
        
        result = engine.run_fft_simulation(request.graph, num_steps, dt)
        return result
        
    def _execute_adelic_dynamics(self, request: ComputationRequest) -> Any:
        """Execute using Adelic dynamics."""
        engine = self._engines[EngineType.ADELIC_DYNAMICS]
        
        # This would integrate with actual Adelic operations
        return {"adelic_computation": "completed", "engine": "adelic_dynamics"}
        
    def _validate_request(self, request: ComputationRequest) -> bool:
        """Validate computation request."""
        if not request.request_id:
            return False
        if request.engine_type not in self._engines:
            return False
        if request.graph is None and request.operation != "system_status":
            return False
        return True
        
    def get_system_status(self) -> Dict[str, Any]:
        """Get comprehensive system status."""
        return {
            "active_requests": len(self._active_requests),
            "queued_requests": self._request_queue.qsize(),
            "available_engines": list(self._engines.keys()),
            "resource_status": {
                "memory_budget_mb": self.memory_budget_mb,
                "cache_size_mb": self.cache_size_mb,
                "max_workers": self.max_workers
            },
            "performance_summary": {
                "total_computations": len(self._performance_history),
                "engine_performance": {
                    engine.value: {
                        "count": len(times),
                        "avg_time": np.mean(times) if times else 0.0,
                        "total_time": np.sum(times) if times else 0.0
                    }
                    for engine, times in self._engine_performance.items()
                }
            },
            "engines_available": HAS_ALL_ENGINES,
            "math_backends_available": HAS_MATH_BACKENDS
        }
        
    def shutdown(self) -> None:
        """Gracefully shutdown the computational hub."""
        self._shutdown = True
        
        if self._queue_thread and self._queue_thread.is_alive():
            self._queue_thread.join(timeout=1.0)
            
        self._executor.shutdown(wait=True)


# Global hub instance
_global_hub: Optional[TNFRComputationalHub] = None


def get_computational_hub() -> TNFRComputationalHub:
    """Get global computational hub."""
    global _global_hub
    
    if _global_hub is None:
        _global_hub = TNFRComputationalHub()
        
    return _global_hub


def execute_unified_computation(
    operation: str,
    graph: Any,
    engine_type: EngineType = EngineType.UNIFIED_BACKEND,
    **kwargs
) -> ComputationResult:
    """Convenience function for unified computation."""
    hub = get_computational_hub()
    
    request = ComputationRequest(
        engine_type=engine_type,
        operation=operation,
        graph=graph,
        parameters=kwargs
    )
    
    return hub.execute_computation_sync(request)


def batch_execute_computations(
    requests: list,
    max_parallel: int = 4
) -> Dict[str, ComputationResult]:
    """Execute multiple computations in parallel."""
    hub = get_computational_hub()
    
    # Submit all requests
    request_ids = []
    for req in requests:
        req_id = hub.submit_computation(req)
        request_ids.append(req_id)
        
    # Collect results
    results = {}
    for req_id in request_ids:
        result = hub.get_result(req_id, timeout=300.0)
        results[req_id] = result
        
    return results