from typing import Dict, List, Optional
import numpy as np

class QuantumOrchestrator:
    """Manages large-scale agent coordination using quantum algorithms."""
    
    def __init__(self):
        self.agent_clusters: Dict = {}  # Hierarchical agent organization
        self.resource_map: Dict = {}    # Resource allocation tracking
        self.optimization_state: Optional[np.ndarray] = None  # Current optimization state

    def optimize_resource_allocation(self) -> Dict:
        """
        Use QAOA for resource optimization.
        
        Returns:
            Dict: Optimized resource allocation mapping
        """
        # Initialize optimization parameters
        num_resources = len(self.resource_map)
        num_clusters = len(self.agent_clusters)
        
        if num_resources == 0 or num_clusters == 0:
            return {}
            
        # Create resource allocation matrix
        allocation_matrix = np.zeros((num_clusters, num_resources))
        
        # TODO: Implement QAOA optimization
        # For now, using a simple greedy allocation
        for cluster_idx, (cluster_id, cluster) in enumerate(self.agent_clusters.items()):
            for resource_idx, (resource_id, resource) in enumerate(self.resource_map.items()):
                if resource.get('available', 0) > 0:
                    allocation_matrix[cluster_idx, resource_idx] = 1
                    
        self.optimization_state = allocation_matrix
        return self._convert_matrix_to_allocation(allocation_matrix)

    def manage_agent_clusters(self) -> Dict:
        """
        Handle agent group organization.
        
        Returns:
            Dict: Updated cluster organization
        """
        if not self.agent_clusters:
            return {}
            
        # Implement basic cluster management
        organized_clusters = {}
        
        for cluster_id, cluster in self.agent_clusters.items():
            # Basic load balancing
            if cluster.get('size', 0) > 100:  # Split large clusters
                new_cluster_id = f"{cluster_id}_split"
                organized_clusters[cluster_id] = {
                    'size': cluster.get('size', 0) // 2,
                    'resources': cluster.get('resources', [])
                }
                organized_clusters[new_cluster_id] = {
                    'size': cluster.get('size', 0) // 2,
                    'resources': cluster.get('resources', []).copy()
                }
            else:
                organized_clusters[cluster_id] = cluster
                
        self.agent_clusters = organized_clusters
        return organized_clusters

    def add_cluster(self, cluster_id: str, cluster_data: Dict) -> bool:
        """
        Add a new agent cluster to the orchestrator.
        
        Args:
            cluster_id: Unique identifier for the cluster
            cluster_data: Dictionary containing cluster information
            
        Returns:
            bool: Success status of the operation
        """
        if cluster_id in self.agent_clusters:
            return False
            
        self.agent_clusters[cluster_id] = cluster_data
        return True

    def add_resource(self, resource_id: str, resource_data: Dict) -> bool:
        """
        Add a new resource to the resource map.
        
        Args:
            resource_id: Unique identifier for the resource
            resource_data: Dictionary containing resource information
            
        Returns:
            bool: Success status of the operation
        """
        if resource_id in self.resource_map:
            return False
            
        self.resource_map[resource_id] = resource_data
        return True

    def _convert_matrix_to_allocation(self, matrix: np.ndarray) -> Dict:
        """
        Convert optimization matrix to resource allocation mapping.
        
        Args:
            matrix: NumPy array representing resource allocation
            
        Returns:
            Dict: Resource allocation mapping
        """
        allocation = {}
        cluster_ids = list(self.agent_clusters.keys())
        resource_ids = list(self.resource_map.keys())
        
        for i, cluster_id in enumerate(cluster_ids):
            allocation[cluster_id] = {
                'resources': [
                    resource_ids[j] for j in range(len(resource_ids))
                    if matrix[i, j] > 0
                ]
            }
            
        return allocation