from typing import List, Dict, Any, Optional, Tuple
import numpy as np
from .scheduler import QUBOScheduler, QUBOTerm
from .quantum_reasoning import QuantumReasoningState, DecisionPath
from concurrent.futures import ThreadPoolExecutor, as_completed

class EnhancedQUBOScheduler(QUBOScheduler):
    """Scheduler with quantum orchestration capabilities for large-scale operations."""
    
    def __init__(self):
        super().__init__()
        self.hierarchical_levels: List[np.ndarray] = []
        self.cluster_assignments: Dict[str, str] = {}
        self.max_parallel_jobs = 4  # Maximum number of parallel quantum jobs
        
    def build_hierarchical_qubo(self, tasks: List[Dict], 
                              clusters: Dict[str, Dict],
                              max_cluster_size: int = 100) -> List[np.ndarray]:
        """
        Build multi-level QUBO for large-scale scheduling.
        
        Args:
            tasks: List of tasks to schedule
            clusters: Dictionary of cluster information
            max_cluster_size: Maximum size for each cluster
            
        Returns:
            List[np.ndarray]: List of QUBO matrices for each hierarchical level
        """
        # Reset hierarchical levels
        self.hierarchical_levels = []
        
        try:
            # Group tasks into clusters
            task_clusters = self._assign_tasks_to_clusters(tasks, clusters, max_cluster_size)
            
            # Build and solve QUBO for each cluster in parallel
            with ThreadPoolExecutor(max_workers=self.max_parallel_jobs) as executor:
                futures = []
                
                for cluster_id, cluster_tasks in task_clusters.items():
                    if not cluster_tasks:
                        continue
                        
                    future = executor.submit(
                        self._build_and_solve_cluster_qubo,
                        cluster_tasks,
                        len(cluster_tasks)
                    )
                    futures.append((cluster_id, future))
                
                # Collect results
                for cluster_id, future in futures:
                    try:
                        result = future.result()
                        if result is not None:
                            self.hierarchical_levels.append(result)
                    except Exception as e:
                        print(f"Error processing cluster {cluster_id}: {e}")
            
            return self.hierarchical_levels
            
        except Exception as e:
            print(f"Error in hierarchical QUBO build: {e}")
            return []

    def _build_and_solve_cluster_qubo(self, tasks: List[Dict], horizon: int) -> Optional[np.ndarray]:
        """Build and solve QUBO for a cluster using Azure Quantum."""
        try:
            # Create reasoning state for this cluster
            state = QuantumReasoningState()
            
            # Add decision paths based on task dependencies
            self._add_cluster_decision_paths(state, tasks)
            
            # Build QUBO terms
            terms = self.build_qubo_with_reasoning(horizon, state)
            
            # Solve using quantum computer
            solution = self._solve_quantum(terms, horizon)
            
            # Convert solution to QUBO matrix
            Q = np.zeros((horizon, horizon))
            for i in range(horizon):
                for j in range(horizon):
                    if solution[i] and solution[j]:
                        Q[i, j] = 1.0
            
            return Q
            
        except Exception as e:
            print(f"Error in cluster QUBO processing: {e}")
            return None

    def _add_cluster_decision_paths(self, state: QuantumReasoningState, tasks: List[Dict]) -> None:
        """Add decision paths to reasoning state based on task dependencies."""
        for i, task in enumerate(tasks):
            # Create paths for each possible task position
            for pos in range(len(tasks)):
                # Check if position respects dependencies
                valid = True
                if 'dependencies' in task:
                    for dep_id in task['dependencies']:
                        dep_idx = next((j for j, t in enumerate(tasks) if t['id'] == dep_id), None)
                        if dep_idx is not None and dep_idx >= pos:
                            valid = False
                            break
                
                if valid:
                    path = DecisionPath(
                        id=f"task_{task['id']}_pos_{pos}",
                        probability=1.0 / len(tasks),
                        actions=[f"schedule_{pos}"]
                    )
                    state.add_decision_path(path, np.sqrt(1.0 / len(tasks)))
        
    def optimize_schedule_with_reasoning(self, tasks: List[Dict], 
                                      horizon: int,
                                      reasoning_state: QuantumReasoningState) -> Dict:
        """Override to use hierarchical quantum optimization."""
        # Build hierarchical QUBO
        self.build_hierarchical_qubo(tasks, {}, horizon)
        
        # Use parent class optimization with quantum solving
        return super().optimize_schedule_with_reasoning(tasks, horizon, reasoning_state)
        
    def optimize_cluster_assignments(self, tasks: List[Dict], 
                                  clusters: Dict[str, Dict]) -> Dict[str, str]:
        """
        Optimize agent cluster assignments.
        
        Args:
            tasks: List of tasks to assign
            clusters: Dictionary of cluster information
            
        Returns:
            Dict[str, str]: Mapping of task IDs to cluster IDs
        """
        if not tasks or not clusters:
            return {}
            
        # Calculate task-cluster affinities
        affinities = np.zeros((len(tasks), len(clusters)))
        
        for i, task in enumerate(tasks):
            for j, (cluster_id, cluster) in enumerate(clusters.items()):
                # Calculate affinity based on resource requirements
                task_resources = set(task.get('required_resources', []))
                cluster_resources = set(cluster.get('available_resources', []))
                resource_match = len(task_resources & cluster_resources)
                
                # Consider cluster load
                cluster_load = cluster.get('current_load', 0) / cluster.get('capacity', 1)
                load_factor = 1 - cluster_load
                
                affinities[i, j] = resource_match * load_factor
                
        # Assign tasks to clusters greedily based on affinities
        assignments = {}
        for i, task in enumerate(tasks):
            best_cluster_idx = np.argmax(affinities[i])
            cluster_id = list(clusters.keys())[best_cluster_idx]
            assignments[task['id']] = cluster_id
            
        self.cluster_assignments = assignments
        return assignments
        
    def _assign_tasks_to_clusters(self, tasks: List[Dict], 
                               clusters: Dict[str, Dict],
                               max_cluster_size: int) -> Dict[str, List[Dict]]:
        """
        Group tasks into clusters based on optimization criteria.
        
        Args:
            tasks: List of tasks to assign
            clusters: Dictionary of cluster information
            max_cluster_size: Maximum size for each cluster
            
        Returns:
            Dict[str, List[Dict]]: Mapping of cluster IDs to lists of tasks
        """
        # Get cluster assignments
        if not self.cluster_assignments:
            self.optimize_cluster_assignments(tasks, clusters)
            
        # Group tasks by assigned cluster
        task_clusters: Dict[str, List[Dict]] = {
            cluster_id: [] for cluster_id in clusters.keys()
        }
        
        for task in tasks:
            cluster_id = self.cluster_assignments.get(task['id'])
            if cluster_id:
                cluster_tasks = task_clusters[cluster_id]
                if len(cluster_tasks) < max_cluster_size:
                    cluster_tasks.append(task)
                    
        return task_clusters