use daa_swarm::{SwarmCoordinator, AgentRegistry, TaskQueue};
use tokio::sync::{RwLock, broadcast};
use std::collections::HashMap;

pub struct HierarchicalCoordinator {
    agent_registry: Arc<RwLock<AgentRegistry>>,
    task_queue: Arc<TaskQueue>,
    leadership_layer: LeadershipLayer,
    coordination_channel: broadcast::Sender<CoordinationMessage>,
    performance_metrics: SwarmMetrics,
}

impl HierarchicalCoordinator {
    pub async fn new(config: SwarmConfig) -> Result<Self, SwarmError> {
        let (tx, _) = broadcast::channel(1000);
        
        Ok(Self {
            agent_registry: Arc::new(RwLock::new(AgentRegistry::new())),
            task_queue: Arc::new(TaskQueue::with_capacity(10000)),
            leadership_layer: LeadershipLayer::initialize(config.leadership).await?,
            coordination_channel: tx,
            performance_metrics: SwarmMetrics::new(),
        })
    }
    
    pub async fn coordinate_swarm(&self) -> Result<(), SwarmError> {
        loop {
            // Task distribution phase
            self.distribute_tasks().await?;
            
            // Agent health monitoring
            self.monitor_agent_health().await?;
            
            // Performance optimization
            self.optimize_performance().await?;
            
            // Consensus operations
            self.run_consensus_round().await?;
            
            tokio::time::sleep(Duration::from_millis(100)).await;
        }
    }
    
    async fn distribute_tasks(&self) -> Result<(), SwarmError> {
        let available_tasks = self.task_queue.get_pending_tasks(100).await?;
        let agents = self.agent_registry.read().await;
        
        for task in available_tasks {
            let best_agent = agents.find_best_match(&task)?;
            
            if let Some(agent) = best_agent {
                self.assign_task_to_agent(task, agent.id()).await?;
                self.performance_metrics.record_task_assignment();
            } else {
                self.task_queue.requeue_with_delay(task, Duration::from_secs(5)).await?;
            }
        }
        
        Ok(())
    }
}

pub struct MeshCoordinator {
    peer_network: P2PNetwork,
    consensus_engine: ConsensusEngine,
    message_router: MessageRouter,
    state_machine: SwarmStateMachine,
}

impl MeshCoordinator {
    pub async fn initialize_mesh(&self, bootstrap_peers: Vec<PeerId>) -> Result<(), MeshError> {
        // Connect to bootstrap peers
        for peer in bootstrap_peers {
            self.peer_network.connect(peer).await?;
        }
        
        // Initialize consensus protocol
        self.consensus_engine.start_consensus().await?;
        
        // Begin message routing
        self.message_router.start_routing().await?;
        
        Ok(())
    }
    
    pub async fn handle_peer_message(&self, message: PeerMessage) -> Result<(), MeshError> {
        match message.message_type {
            MessageType::TaskProposal => {
                let proposal: TaskProposal = message.deserialize()?;
                self.evaluate_task_proposal(proposal).await?;
            }
            MessageType::ConsensusVote => {
                let vote: ConsensusVote = message.deserialize()?;
                self.consensus_engine.process_vote(vote).await?;
            }
            MessageType::StateUpdate => {
                let update: StateUpdate = message.deserialize()?;
                self.state_machine.apply_update(update).await?;
            }
            MessageType::ResourceShare => {
                let share: ResourceShare = message.deserialize()?;
                self.handle_resource_sharing(share).await?;
            }
        }
        
        Ok(())
    }
}

use ml_models::{FederatedLearning, CollectiveMemory, PatternRecognition};

pub struct CollectiveIntelligence {
    federated_learning: FederatedLearning,
    collective_memory: Arc<RwLock<CollectiveMemory>>,
    pattern_recognition: PatternRecognition,
    knowledge_graph: KnowledgeGraph,
}

impl CollectiveIntelligence {
    pub async fn learn_from_swarm_experience(&mut self, experiences: Vec<AgentExperience>) -> Result<(), LearningError> {
        // Aggregate experiences from all agents
        let aggregated_data = self.aggregate_experiences(experiences).await?;
        
        // Update federated learning model
        let model_update = self.federated_learning.train_round(aggregated_data).await?;
        
        // Distribute model updates to all agents
        self.distribute_model_update(model_update).await?;
        
        // Update collective memory
        self.update_collective_memory(&aggregated_data).await?;
        
        // Identify new patterns
        let patterns = self.pattern_recognition.identify_patterns(&aggregated_data)?;
        self.knowledge_graph.add_patterns(patterns).await?;
        
        Ok(())
    }
    
    pub async fn make_collective_decision(&self, decision_context: DecisionContext) -> Result<CollectiveDecision, DecisionError> {
        // Query collective memory for relevant experiences
        let relevant_experiences = self.collective_memory
            .read()
            .await
            .query_similar_contexts(&decision_context)?;
        
        // Get recommendations from knowledge graph
        let graph_recommendations = self.knowledge_graph
            .get_recommendations(&decision_context)
            .await?;
        
        // Use federated model for prediction
        let model_prediction = self.federated_learning
            .predict(&decision_context)
            .await?;
        
        // Combine all inputs for final decision
        let decision = CollectiveDecision::synthesize(
            relevant_experiences,
            graph_recommendations,
            model_prediction,
        )?;
        
        Ok(decision)
    }
}

pub struct EmergentBehaviorMonitor {
    behavior_patterns: HashMap<BehaviorId, BehaviorPattern>,
    emergence_detector: EmergenceDetector,
    complexity_analyzer: ComplexityAnalyzer,
    adaptation_engine: AdaptationEngine,
}

impl EmergentBehaviorMonitor {
    pub async fn monitor_emergence(&mut self) -> Result<(), MonitoringError> {
        let swarm_state = self.collect_swarm_state().await?;
        
        // Detect emerging patterns
        let emerging_patterns = self.emergence_detector
            .detect_patterns(&swarm_state)?;
        
        for pattern in emerging_patterns {
            // Analyze pattern complexity
            let complexity = self.complexity_analyzer
                .analyze_complexity(&pattern)?;
            
            // Check if pattern is beneficial
            if self.is_beneficial_pattern(&pattern, complexity).await? {
                // Reinforce beneficial pattern
                self.adaptation_engine
                    .reinforce_pattern(&pattern)
                    .await?;
            } else {
                // Suppress harmful pattern
                self.adaptation_engine
                    .suppress_pattern(&pattern)
                    .await?;
            }
        }
        
        Ok(())
    }
}

use consensus::{ByzantineConsensus, Vote, Proposal, ConsensusRound};

pub struct BFTConsensus {
    node_id: NodeId,
    validators: Vec<ValidatorInfo>,
    current_round: ConsensusRound,
    vote_collector: VoteCollector,
    proposal_buffer: ProposalBuffer,
}

impl BFTConsensus {
    pub async fn propose_value(&mut self, value: ConsensusValue) -> Result<(), ConsensusError> {
        let proposal = Proposal {
            round: self.current_round,
            value,
            proposer: self.node_id,
            timestamp: SystemTime::now(),
        };
        
        // Sign proposal with quantum-resistant signature
        let signed_proposal = self.sign_proposal(proposal).await?;
        
        // Broadcast to all validators
        self.broadcast_proposal(signed_proposal).await?;
        
        Ok(())
    }
    
    pub async fn process_proposal(&mut self, proposal: SignedProposal) -> Result<(), ConsensusError> {
        // Validate proposal signature and content
        self.validate_proposal(&proposal).await?;
        
        // Add to proposal buffer
        self.proposal_buffer.add(proposal.clone()).await?;
        
        // Cast vote
        let vote = Vote {
            round: proposal.round,
            value_hash: proposal.value_hash(),
            voter: self.node_id,
            signature: self.sign_vote(&proposal).await?,
        };
        
        // Broadcast vote
        self.broadcast_vote(vote).await?;
        
        Ok(())
    }
    
    pub async fn check_consensus(&mut self) -> Result<Option<ConsensusValue>, ConsensusError> {
        let votes = self.vote_collector.get_votes_for_round(self.current_round).await?;
        
        // Check if we have enough votes (2f + 1 out of 3f + 1)
        let required_votes = (self.validators.len() * 2 / 3) + 1;
        
        for proposal in self.proposal_buffer.get_proposals(self.current_round).await? {
            let votes_for_proposal = votes.iter()
                .filter(|v| v.value_hash == proposal.value_hash())
                .count();
            
            if votes_for_proposal >= required_votes {
                // We have consensus!
                self.finalize_consensus(proposal.value).await?;
                return Ok(Some(proposal.value));
            }
        }
        
        Ok(None)
    }
}

use prometheus::{Counter, Histogram, IntGauge, IntCounterVec};

pub struct SwarmMetrics {
    // Coordination metrics
    pub agents_active: IntGauge,
    pub tasks_queued: IntGauge,
    pub tasks_completed: Counter,
    pub coordination_latency: Histogram,
    
    // Consensus metrics
    pub consensus_rounds: Counter,
    pub consensus_time: Histogram,
    pub byzantine_failures: Counter,
    
    // Intelligence metrics
    pub learning_iterations: Counter,
    pub pattern_discoveries: Counter,
    pub collective_decisions: Counter,
    
    // Performance metrics
    pub message_throughput: Counter,
    pub bandwidth_usage: IntGauge,
    pub cpu_utilization: IntGauge,
    pub memory_usage: IntGauge,
}

impl SwarmMetrics {
    pub fn record_task_completion(&self, task_type: &str, duration: Duration) {
        self.tasks_completed.inc();
        self.coordination_latency.observe(duration.as_secs_f64());
    }
    
    pub fn record_consensus_round(&self, participants: u32, duration: Duration) {
        self.consensus_rounds.inc();
        self.consensus_time.observe(duration.as_secs_f64());
    }
    
    pub fn record_pattern_discovery(&self, pattern_complexity: f64) {
        self.pattern_discoveries.inc();
        // Additional complexity metrics could be recorded here
    }
}