use ruv_swarm_core::{Swarm, TopologyType, CognitiveDiversity};
use ruv_swarm_agents::{Agent, AgentType, CognitivePattern};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize cognitive diversity swarm
    let mut swarm = Swarm::builder()
        .topology(TopologyType::Hierarchical)
        .max_agents(5)
        .cognitive_diversity(CognitiveDiversity::Balanced)
        .ml_optimization(true)
        .build()
        .await?;
    
    // Spawn specialized agents with neural capabilities
    let researcher = Agent::new(AgentType::Researcher)
        .with_model("lstm-optimizer")
        .with_pattern(CognitivePattern::Divergent)
        .spawn(&mut swarm).await?;
        
    let coder = Agent::new(AgentType::Coder)
        .with_model("tcn-pattern-detector")
        .with_pattern(CognitivePattern::Convergent)
        .spawn(&mut swarm).await?;
    
    // Orchestrate complex task
    let task = swarm.orchestrate_task()
        .description("Analyze codebase and suggest optimizations")
        .strategy(OrchestrationStrategy::CognitiveDiversity)
        .agents(vec![researcher.id, coder.id])
        .execute()
        .await?;
    
    // Get optimized solution
    let solution = task.await_completion().await?;
    println!("Solution achieved in {}ms with {:.1}% token reduction", 
             solution.duration_ms, solution.token_efficiency);
    
    Ok(())
}

pub enum CognitivePattern {
    Convergent,     // Focus on single optimal solution
    Divergent,      // Explore multiple creative approaches
    Lateral,        // Think outside conventional boundaries
    Systems,        // Consider holistic interactions
    Critical,       // Analyze and validate thoroughly
    Abstract,       // Work with high-level concepts
    Hybrid,         // Combine multiple patterns dynamically
}

pub enum AgentType {
    Researcher,     // Information gathering and analysis
    Coder,         // Implementation and development
    Analyst,       // Data analysis and insights
    Optimizer,     // Performance and efficiency
    Coordinator,   // Task orchestration
    Tester,        // Quality assurance
    Reviewer,      // Code and content review
    Architect,     // System design and planning
    Monitor,       // Performance and health monitoring
}

use ruv_swarm_core::*;

// Create specialized swarm for software development
let dev_swarm = Swarm::builder()
    .topology(TopologyType::Hierarchical)
    .max_agents(8)
    .cognitive_diversity(CognitiveDiversity::Balanced)
    .with_ml_models(vec![
        "lstm-coding-optimizer",
        "tcn-pattern-detector", 
        "nbeats-task-decomposer",
        "transformer-code-analyzer"
    ])
    .build()
    .await?;

// Spawn development team
let architect = dev_swarm.spawn_agent(AgentType::Architect)
    .with_pattern(CognitivePattern::Systems)
    .with_model("transformer-code-analyzer")
    .await?;

let lead_coder = dev_swarm.spawn_agent(AgentType::Coder)
    .with_pattern(CognitivePattern::Convergent)
    .with_model("lstm-coding-optimizer")
    .await?;

let researcher = dev_swarm.spawn_agent(AgentType::Researcher)
    .with_pattern(CognitivePattern::Divergent)
    .with_model("tcn-pattern-detector")
    .await?;

// Orchestrate development task
let development_task = dev_swarm.orchestrate()
    .task("Build microservice with authentication and rate limiting")
    .strategy(OrchestrationStrategy::CognitiveDiversity)
    .agents(vec![architect.id, lead_coder.id, researcher.id])
    .with_optimization(OptimizationConfig {
        minimize_tokens: true,
        maximize_speed: true,
        quality_threshold: 0.95,
    })
    .execute()
    .await?;

use ruv_swarm_core::memory::*;

// Persistent memory across swarm sessions
let memory_config = MemoryConfig::new()
    .with_sqlite_persistence("swarm_memory.db")
    .with_cross_session_memory(true)
    .with_ttl_hours(24)
    .with_compression(true);

let swarm = Swarm::builder()
    .memory_config(memory_config)
    .build()
    .await?;

// Agents share memory automatically
let shared_memory = swarm.memory();
shared_memory.store("project_context", project_analysis).await?;
shared_memory.store("coding_patterns", discovered_patterns).await?;

// Retrieve shared knowledge
let context = shared_memory.retrieve("project_context").await?;
let patterns = shared_memory.retrieve("coding_patterns").await?;

use ruv_swarm_core::*;
use ruv_swarm_agents::*;

#[tokio::main]
async fn software_development() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize development-focused swarm
    let mut swarm = Swarm::builder()
        .topology(TopologyType::Hierarchical)
        .max_agents(6)
        .cognitive_diversity(CognitiveDiversity::Development)
        .with_specialization(SwarmSpecialization::SoftwareDevelopment)
        .build()
        .await?;

    // Spawn specialized development team
    let system_architect = swarm.spawn_agent(AgentType::Architect)
        .with_pattern(CognitivePattern::Systems)
        .with_model("transformer-architecture-analyzer")
        .with_expertise(vec!["system-design", "microservices", "scalability"])
        .await?;

    let senior_developer = swarm.spawn_agent(AgentType::Coder)
        .with_pattern(CognitivePattern::Convergent)  
        .with_model("lstm-coding-optimizer")
        .with_expertise(vec!["rust", "async", "performance"])
        .await?;

    let qa_engineer = swarm.spawn_agent(AgentType::Tester)
        .with_pattern(CognitivePattern::Critical)
        .with_model("tcn-test-generator")
        .with_expertise(vec!["testing", "edge-cases", "integration"])
        .await?;

    let performance_optimizer = swarm.spawn_agent(AgentType::Optimizer)
        .with_pattern(CognitivePattern::Convergent)
        .with_model("nbeats-performance-predictor")
        .with_expertise(vec!["profiling", "optimization", "bottlenecks"])
        .await?;

    // Orchestrate complex development project
    let project = swarm.orchestrate_task()
        .description("Build high-performance distributed cache system")
        .requirements(vec![
            "Handle 1M+ requests/second",
            "Sub-millisecond latency",
            "Horizontal scaling",
            "Redis compatibility",
            "Comprehensive test suite"
        ])
        .strategy(OrchestrationStrategy::PipelineWithFeedback)
        .phases(vec![
            TaskPhase::new("Architecture Design")
                .agents(vec![system_architect.id])
                .deliverables(vec!["system_design.md", "api_spec.yaml"]),
            
            TaskPhase::new("Core Implementation")
                .agents(vec![senior_developer.id])
                .depends_on(vec!["Architecture Design"])
                .deliverables(vec!["core_engine.rs", "networking.rs"]),
            
            TaskPhase::new("Testing & Validation")
                .agents(vec![qa_engineer.id])
                .depends_on(vec!["Core Implementation"])
                .deliverables(vec!["test_suite.rs", "benchmarks.rs"]),
            
            TaskPhase::new("Performance Optimization")
                .agents(vec![performance_optimizer.id])
                .depends_on(vec!["Testing & Validation"])
                .deliverables(vec!["optimized_core.rs", "perf_report.md"]),
        ])
        .execute()
        .await?;

    // Monitor progress with real-time feedback
    let mut progress_monitor = project.monitor().await?;
    while let Some(update) = progress_monitor.next().await {
        match update {
            ProgressUpdate::PhaseCompleted { phase, deliverables, metrics } => {
                println!("✅ Phase '{}' completed", phase);
                println!("   Deliverables: {:?}", deliverables);
                println!("   Metrics: {} lines, {:.2}s duration", 
                         metrics.lines_of_code, metrics.duration_seconds);
            }
            ProgressUpdate::AgentInsight { agent_id, insight } => {
                println!("💡 Agent {} insight: {}", agent_id, insight);
            }
            ProgressUpdate::OptimizationFound { description, impact } => {
                println!("⚡ Optimization found: {} (impact: {:.1}%)", 
                         description, impact * 100.0);
            }
        }
    }

    let result = project.await_completion().await?;
    println!("🎉 Project completed!");
    println!("   Duration: {:.2} hours", result.total_duration_hours);
    println!("   Code quality: {:.1}%", result.quality_score * 100.0);
    println!("   Performance: {}x faster than baseline", result.performance_multiplier);
    println!("   Token efficiency: {:.1}% cost reduction", result.token_savings * 100.0);

    Ok(())
}

async fn data_analysis_swarm() -> Result<(), Box<dyn std::error::Error>> {
    let mut swarm = Swarm::builder()
        .topology(TopologyType::Mesh)  // High collaboration needed
        .max_agents(4)
        .cognitive_diversity(CognitiveDiversity::Analytical)
        .build()
        .await?;

    // Spawn data analysis team
    let data_scientist = swarm.spawn_agent(AgentType::Analyst)
        .with_pattern(CognitivePattern::Systems)
        .with_model("lstm-time-series-analyzer")
        .with_expertise(vec!["statistics", "machine-learning", "visualization"])
        .await?;

    let domain_expert = swarm.spawn_agent(AgentType::Researcher)
        .with_pattern(CognitivePattern::Divergent)
        .with_model("tcn-pattern-detector")
        .with_expertise(vec!["business-intelligence", "domain-knowledge"])
        .await?;

    let ml_engineer = swarm.spawn_agent(AgentType::Optimizer)
        .with_pattern(CognitivePattern::Convergent)
        .with_model("nbeats-optimization-predictor")
        .with_expertise(vec!["model-optimization", "feature-engineering"])
        .await?;

    // Complex data analysis task
    let analysis_task = swarm.orchestrate_task()
        .description("Analyze sales data and predict Q4 revenue")
        .data_sources(vec![
            "sales_history.csv",
            "customer_data.parquet", 
            "market_trends.json",
            "economic_indicators.xlsx"
        ])
        .strategy(OrchestrationStrategy::DataPipeline)
        .execute()
        .await?;

    let results = analysis_task.await_completion().await?;
    println!("Analysis complete: {:.1}% confidence in predictions", 
             results.confidence * 100.0);

    Ok(())
}

async fn research_swarm() -> Result<(), Box<dyn std::error::Error>> {
    let mut swarm = Swarm::builder()
        .topology(TopologyType::Ring)  // Sequential deep thinking
        .max_agents(5)
        .cognitive_diversity(CognitiveDiversity::Research)
        .build()
        .await?;

    // Multi-disciplinary research team
    let literature_researcher = swarm.spawn_agent(AgentType::Researcher)
        .with_pattern(CognitivePattern::Divergent)
        .with_model("transformer-knowledge-extractor")
        .with_expertise(vec!["literature-review", "citation-analysis"])
        .await?;

    let hypothesis_generator = swarm.spawn_agent(AgentType::Researcher)
        .with_pattern(CognitivePattern::Abstract)
        .with_model("lstm-hypothesis-generator")
        .with_expertise(vec!["hypothesis-formation", "experimental-design"])
        .await?;

    let critical_analyzer = swarm.spawn_agent(AgentType::Reviewer)
        .with_pattern(CognitivePattern::Critical)
        .with_model("tcn-critique-analyzer")
        .with_expertise(vec!["peer-review", "methodology-validation"])
        .await?;

    let synthesizer = swarm.spawn_agent(AgentType::Analyst)
        .with_pattern(CognitivePattern::Systems)
        .with_model("nbeats-synthesis-engine")
        .with_expertise(vec!["knowledge-synthesis", "conclusion-drawing"])
        .await?;

    // Research orchestration
    let research_project = swarm.orchestrate_task()
        .description("Investigate novel approaches to distributed neural networks")
        .methodology(ResearchMethodology::SystematicReview)
        .strategy(OrchestrationStrategy::SequentialDeepThinking)
        .execute()
        .await?;

    Ok(())
}

use ruv_fann::*;
use ruv_swarm_core::*;

// Integrate custom neural networks into swarm agents
async fn custom_neural_swarm() -> Result<(), Box<dyn std::error::Error>> {
    // Create specialized neural network for agent
    let agent_network = NetworkBuilder::<f32>::new()
        .input_layer(128)   // Agent observations
        .hidden_layer_with_activation(256, ActivationFunction::ReLU, 1.0)
        .hidden_layer_with_activation(128, ActivationFunction::Swish, 1.0)
        .output_layer_with_activation(64, ActivationFunction::Linear, 1.0)  // Agent actions
        .build();

    // Train network for specific agent behavior
    let training_data = load_agent_training_data("coder_behavior.data")?;
    let mut trainer = AdamTrainer::new()
        .learning_rate(0.001)
        .target_error(0.01);
    
    trainer.train(&mut agent_network, &training_data)?;

    // Create agent with custom neural network
    let mut swarm = Swarm::new().await?;
    let custom_agent = swarm.spawn_agent(AgentType::Coder)
        .with_neural_network(agent_network)
        .with_pattern(CognitivePattern::Convergent)
        .await?;

    Ok(())
}

use neuro_divergent::*;
use ruv_swarm_core::*;

// Use forecasting models within swarm agents
async fn forecasting_swarm() -> Result<(), Box<dyn std::error::Error>> {
    let mut swarm = Swarm::new().await?;

    // Agent with LSTM for time series prediction
    let predictor_agent = swarm.spawn_agent(AgentType::Analyst)
        .with_forecasting_model(
            LSTM::builder()
                .horizon(12)
                .hidden_size(128)
                .build()?
        )
        .with_pattern(CognitivePattern::Systems)
        .await?;

    // Agent with N-BEATS for decomposition analysis
    let decomposer_agent = swarm.spawn_agent(AgentType::Researcher)
        .with_forecasting_model(
            NBEATS::builder()
                .horizon(12)
                .stacks(4) 
                .build()?
        )
        .with_pattern(CognitivePattern::Divergent)
        .await?;

    // Orchestrate forecasting task
    let forecast_task = swarm.orchestrate_task()
        .description("Predict market trends and identify opportunities")
        .agents(vec![predictor_agent.id, decomposer_agent.id])
        .strategy(OrchestrationStrategy::EnsembleForecast)
        .execute()
        .await?;

    Ok(())
}

use ruv_swarm_core::cognitive::*;

// Define custom cognitive pattern
#[derive(Clone, Debug)]
struct InnovativeProblemSolver {
    creativity_weight: f64,
    analytical_weight: f64,
    risk_tolerance: f64,
}

impl CognitivePattern for InnovativeProblemSolver {
    fn process_input(&self, input: &AgentInput) -> CognitiveResponse {
        // Custom thinking process
        let creative_analysis = self.generate_creative_solutions(input);
        let analytical_validation = self.validate_analytically(input);
        let risk_assessment = self.assess_risks(input);
        
        CognitiveResponse::new()
            .with_solutions(creative_analysis)
            .with_validation(analytical_validation)
            .with_risk_profile(risk_assessment)
    }
    
    fn learning_rate(&self) -> f64 { 0.05 }
    fn exploration_factor(&self) -> f64 { self.creativity_weight }
}

// Use custom pattern
let agent = swarm.spawn_agent(AgentType::Researcher)
    .with_custom_pattern(InnovativeProblemSolver {
        creativity_weight: 0.8,
        analytical_weight: 0.6,
        risk_tolerance: 0.4,
    })
    .await?;

use ruv_swarm_core::monitoring::*;

// Configure comprehensive monitoring
let monitoring_config = MonitoringConfig::new()
    .with_metrics_collection(true)
    .with_performance_tracking(true)
    .with_token_usage_optimization(true)
    .with_real_time_dashboard(true)
    .with_export_interval(Duration::from_secs(60));

let swarm = Swarm::builder()
    .monitoring_config(monitoring_config)
    .build()
    .await?;

// Real-time performance monitoring
let monitor = swarm.performance_monitor().await?;
while let Some(metrics) = monitor.next().await {
    println!("Swarm Performance:");
    println!("  Active agents: {}", metrics.active_agents);
    println!("  Tasks completed: {}", metrics.completed_tasks);
    println!("  Average response time: {:.2}ms", metrics.avg_response_time_ms);
    println!("  Token efficiency: {:.1}% savings", metrics.token_savings * 100.0);
    println!("  Memory usage: {:.1}MB", metrics.memory_usage_mb);
    
    // Adaptive optimization
    if metrics.avg_response_time_ms > 100.0 {
        swarm.optimize_performance().await?;
    }
}

use ruv_swarm_ml::*;

// Define custom ML model for agents
#[derive(Clone)]
struct CustomAgentModel {
    base_network: Network<f32>,
    attention_mechanism: AttentionLayer,
    memory_buffer: CircularBuffer<AgentMemory>,
}

impl MLModel for CustomAgentModel {
    fn predict(&mut self, input: &AgentObservation) -> AgentAction {
        // Custom prediction logic
        let features = self.extract_features(input);
        let attention_weights = self.attention_mechanism.compute(&features);
        let context = self.memory_buffer.get_relevant_context(&attention_weights);
        
        let network_input = self.combine_features_and_context(&features, &context);
        let raw_output = self.base_network.run(&network_input);
        
        self.postprocess_output(raw_output)
    }
    
    fn update(&mut self, experience: &AgentExperience) {
        // Online learning
        self.memory_buffer.add(experience.clone());
        if self.memory_buffer.len() >= 32 {
            let batch = self.memory_buffer.sample_batch(32);
            self.train_on_batch(&batch);
        }
    }
}

// Use custom model in agent
let agent = swarm.spawn_agent(AgentType::Coder)
    .with_custom_model(Box::new(custom_model))
    .await?;

// Performance optimization configuration
let optimization_config = OptimizationConfig::new()
    .with_simd_acceleration(true)
    .with_neural_quantization(true)
    .with_memory_pooling(true)
    .with_task_parallelization(8)  // 8 parallel tasks
    .with_caching_strategy(CachingStrategy::Intelligent)
    .with_token_optimization(TokenOptimization::Aggressive);

let swarm = Swarm::builder()
    .optimization_config(optimization_config)
    .build()
    .await?;

// Check network connectivity
let health = swarm.health_check().await?;
if !health.all_agents_connected() {
    swarm.repair_connections().await?;
}

// Verify topology configuration
let topology_status = swarm.topology_status().await?;
if topology_status.has_isolated_agents() {
    swarm.rebuild_topology().await?;
}

// Monitor resource usage
let resources = swarm.resource_usage().await?;
if resources.memory_usage_mb > 2048 {
    swarm.optimize_memory().await?;
}

if resources.cpu_usage_percent > 80.0 {
    swarm.scale_down().await?;
}

// Enable performance profiling
swarm.enable_profiling(true).await?;

// Implement fault tolerance
let fault_tolerance = FaultToleranceConfig::new()
    .with_agent_recovery(true)
    .with_task_redistribution(true)
    .with_graceful_degradation(true);

swarm.configure_fault_tolerance(fault_tolerance).await?;

// Monitor agent health
let unhealthy_agents = swarm.get_unhealthy_agents().await?;
for agent_id in unhealthy_agents {
    swarm.restart_agent(agent_id).await?;
}