use ruv_fann::metrics::{MetricsCollector, MetricType};
use std::time::Instant;

// Initialize metrics collector
let mut collector = MetricsCollector::new()
    .with_namespace("ruv_fann")
    .with_flush_interval(Duration::from_secs(10))
    .build()?;

// System metrics
collector.gauge("system.cpu_usage", cpu_usage_percent());
collector.gauge("system.memory_usage_mb", memory_usage_mb());
collector.gauge("system.disk_io_mbps", disk_io_mbps());
collector.gauge("system.network_bandwidth_mbps", network_bandwidth());

// Neural network metrics
collector.counter("nn.forward_passes", 1);
collector.histogram("nn.inference_time_ms", inference_time.as_millis() as f64);
collector.gauge("nn.model_parameters", model.parameter_count());
collector.gauge("nn.gradient_norm", gradient_norm);

// GPU metrics
#[cfg(feature = "cuda")]
{
    collector.gauge("gpu.utilization_percent", gpu_utilization());
    collector.gauge("gpu.memory_used_mb", gpu_memory_used());
    collector.gauge("gpu.temperature_celsius", gpu_temperature());
    collector.gauge("gpu.power_watts", gpu_power_consumption());
}

// SIMD metrics
collector.gauge("simd.utilization_percent", simd_utilization());
collector.counter("simd.operations", simd_op_count);
collector.histogram("simd.speedup_factor", speedup_factor);

// Process-level metrics
collector.gauge("process.threads", thread_count());
collector.gauge("process.file_descriptors", fd_count());
collector.gauge("process.heap_size_mb", heap_size_mb());
collector.histogram("process.gc_pause_ms", gc_pause_ms);

use ruv_fann::metrics::performance::{PerformanceMonitor, PerfConfig};

// Configure performance monitoring
let perf_monitor = PerformanceMonitor::new(PerfConfig {
    sample_rate: 0.1,  // Sample 10% of operations
    trace_level: TraceLevel::Detailed,
    enable_profiling: true,
});

// Monitor training performance
let mut epoch_monitor = perf_monitor.start_epoch();

for batch in dataset.batches() {
    let batch_timer = epoch_monitor.start_batch();
    
    // Forward pass monitoring
    let forward_timer = batch_timer.start_phase("forward");
    let output = model.forward(&batch.inputs);
    forward_timer.end();
    
    // Loss calculation monitoring
    let loss_timer = batch_timer.start_phase("loss");
    let loss = criterion.compute_loss(&output, &batch.targets);
    loss_timer.end();
    
    // Backward pass monitoring
    let backward_timer = batch_timer.start_phase("backward");
    model.backward(&loss);
    backward_timer.end();
    
    // Optimizer step monitoring
    let optimizer_timer = batch_timer.start_phase("optimizer");
    optimizer.step();
    optimizer_timer.end();
    
    batch_timer.end();
}

// Get performance report
let report = epoch_monitor.generate_report();
println!("Epoch Performance: {:#?}", report);

use ruv_swarm::metrics::{SwarmMonitor, AgentMetrics};

// Monitor swarm coordination
let swarm_monitor = SwarmMonitor::new()
    .track_communication_latency()
    .track_consensus_time()
    .track_task_distribution()
    .build();

// Agent-level metrics
impl Agent for CustomAgent {
    async fn execute_task(&mut self, task: Task) -> Result<TaskResult> {
        let metrics = AgentMetrics::start();
        
        // Task execution
        let result = self.process_task(&task).await?;
        
        // Record metrics
        metrics.record_execution_time();
        metrics.record_memory_usage();
        metrics.record_cpu_cycles();
        
        Ok(result)
    }
}

// SIMD operation monitoring
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;

struct SimdMonitor {
    vector_ops: AtomicU64,
    scalar_fallbacks: AtomicU64,
    cache_misses: AtomicU64,
}

impl SimdMonitor {
    fn monitor_matrix_multiply(&self, a: &Matrix, b: &Matrix) -> Matrix {
        let start = Instant::now();
        let start_cycles = unsafe { _rdtsc() };
        
        let result = if is_simd_available() {
            self.vector_ops.fetch_add(1, Ordering::Relaxed);
            simd_matrix_multiply(a, b)
        } else {
            self.scalar_fallbacks.fetch_add(1, Ordering::Relaxed);
            scalar_matrix_multiply(a, b)
        };
        
        let cycles = unsafe { _rdtsc() } - start_cycles;
        let duration = start.elapsed();
        
        // Record performance metrics
        METRICS.histogram("simd.matrix_multiply_cycles", cycles as f64);
        METRICS.histogram("simd.matrix_multiply_us", duration.as_micros() as f64);
        
        result
    }
}

use opentelemetry::{global, trace::{Tracer, Span}};
use opentelemetry_jaeger::JaegerExporter;

// Initialize distributed tracing
fn init_tracing() -> Result<()> {
    let exporter = JaegerExporter::builder()
        .with_agent_endpoint("localhost:6831")
        .with_service_name("ruv-fann")
        .build()?;
    
    let tracer = opentelemetry::sdk::trace::TracerProvider::builder()
        .with_simple_exporter(exporter)
        .build()
        .get_tracer("ruv-fann", Some(env!("CARGO_PKG_VERSION")));
    
    global::set_tracer_provider(tracer);
    Ok(())
}

// Trace neural network operations
pub fn trace_forward_pass<T>(
    model: &NeuralNetwork,
    input: &Tensor<T>,
) -> Result<Tensor<T>> {
    let tracer = global::tracer("ruv-fann");
    let mut span = tracer.start("neural_network.forward");
    
    span.set_attribute("model.name", model.name());
    span.set_attribute("input.shape", format!("{:?}", input.shape()));
    span.set_attribute("model.parameters", model.parameter_count() as i64);
    
    let result = model.forward(input)?;
    
    span.set_attribute("output.shape", format!("{:?}", result.shape()));
    span.end();
    
    Ok(result)
}

// Distributed swarm tracing
impl SwarmOrchestrator {
    async fn orchestrate_with_tracing(&mut self, task: Task) -> Result<TaskResult> {
        let tracer = global::tracer("ruv-swarm");
        let mut span = tracer.start("swarm.orchestrate");
        
        span.set_attribute("task.id", task.id.clone());
        span.set_attribute("task.priority", task.priority as i64);
        span.set_attribute("swarm.agents", self.agent_count() as i64);
        
        // Trace agent assignments
        let assignments = self.assign_agents(&task);
        for (agent_id, subtask) in &assignments {
            let agent_span = tracer.start(format!("agent.{}.execute", agent_id));
            agent_span.set_attribute("subtask.type", subtask.task_type());
            
            // Execute with tracing context
            let result = self.execute_subtask(agent_id, subtask).await?;
            agent_span.end();
        }
        
        span.end();
        Ok(result)
    }
}

use slog::{Logger, o, info, warn, error};
use slog_async::Async;
use slog_json::Json;

// Initialize structured logging
fn init_logging() -> Logger {
    let drain = Json::new(std::io::stdout())
        .add_default_keys()
        .build()
        .fuse();
    
    let drain = Async::new(drain)
        .overflow_strategy(slog_async::OverflowStrategy::DropNewest)
        .build()
        .fuse();
    
    Logger::root(drain, o!(
        "service" => "ruv-fann",
        "version" => env!("CARGO_PKG_VERSION"),
        "environment" => std::env::var("ENV").unwrap_or_else(|_| "development".to_string()),
    ))
}

// Log neural network events
impl NeuralNetwork {
    fn log_training_event(&self, logger: &Logger, event: TrainingEvent) {
        match event {
            TrainingEvent::EpochStart { epoch } => {
                info!(logger, "Training epoch started";
                    "epoch" => epoch,
                    "learning_rate" => self.learning_rate(),
                    "batch_size" => self.batch_size(),
                );
            }
            TrainingEvent::LossUpdate { loss, accuracy } => {
                info!(logger, "Training metrics updated";
                    "loss" => loss,
                    "accuracy" => accuracy,
                    "gradient_norm" => self.gradient_norm(),
                );
            }
            TrainingEvent::ValidationComplete { metrics } => {
                info!(logger, "Validation completed";
                    "val_loss" => metrics.loss,
                    "val_accuracy" => metrics.accuracy,
                    "val_f1_score" => metrics.f1_score,
                );
            }
        }
    }
}

// Centralized log aggregation
struct LogAggregator {
    backends: Vec<Box<dyn LogBackend>>,
    buffer: RingBuffer<LogEntry>,
    flush_interval: Duration,
}

impl LogAggregator {
    async fn run(&mut self) {
        let mut interval = interval(self.flush_interval);
        
        loop {
            interval.tick().await;
            
            // Batch process logs
            let logs: Vec<_> = self.buffer.drain().collect();
            if logs.is_empty() {
                continue;
            }
            
            // Send to all backends in parallel
            let futures: Vec<_> = self.backends.iter()
                .map(|backend| backend.send_batch(&logs))
                .collect();
            
            futures::future::join_all(futures).await;
        }
    }
}

// Elasticsearch backend
struct ElasticsearchBackend {
    client: elasticsearch::Elasticsearch,
    index_pattern: String,
}

impl LogBackend for ElasticsearchBackend {
    async fn send_batch(&self, logs: &[LogEntry]) -> Result<()> {
        let body: Vec<_> = logs.iter()
            .flat_map(|log| {
                vec![
                    json!({ "index": { "_index": self.get_index_name() } }),
                    serde_json::to_value(log).unwrap(),
                ]
            })
            .collect();
        
        self.client
            .bulk(elasticsearch::BulkParts::Index(&self.index_pattern))
            .body(body)
            .send()
            .await?;
        
        Ok(())
    }
}

use ruv_fann::alerts::{AlertManager, AlertRule, AlertAction};

// Configure alert manager
let alert_manager = AlertManager::builder()
    .add_rule(AlertRule {
        name: "high_loss",
        condition: |metrics| metrics.training_loss > 1.0,
        severity: Severity::Warning,
        cooldown: Duration::from_secs(300),
    })
    .add_rule(AlertRule {
        name: "training_stalled",
        condition: |metrics| metrics.loss_improvement < 0.001,
        severity: Severity::Critical,
        cooldown: Duration::from_secs(600),
    })
    .add_action(AlertAction::Email {
        to: "[email protected]",
        template: "alert_email.html",
    })
    .add_action(AlertAction::Webhook {
        url: "https://hooks.slack.com/services/...",
        method: "POST",
    })
    .build();

// Monitor training
model.set_alert_manager(alert_manager);

use ruv_fann::dashboard::{Dashboard, Widget, Layout};

// Create custom dashboard
let dashboard = Dashboard::builder()
    .title("Neural Network Training Dashboard")
    .add_widget(Widget::LineChart {
        title: "Loss Over Time",
        metric: "training.loss",
        aggregation: Aggregation::Average,
        time_window: Duration::from_hours(1),
    })
    .add_widget(Widget::Heatmap {
        title: "Layer Activation Patterns",
        metric: "nn.layer_activations",
        color_scheme: ColorScheme::Viridis,
    })
    .add_widget(Widget::Gauge {
        title: "GPU Memory Usage",
        metric: "gpu.memory_percent",
        thresholds: vec![
            (0.0, Color::Green),
            (70.0, Color::Yellow),
            (90.0, Color::Red),
        ],
    })
    .add_widget(Widget::Table {
        title: "Top Resource Consumers",
        query: "sort_desc(topk(10, process_memory_mb))",
        columns: vec!["Process", "Memory (MB)", "CPU %"],
    })
    .layout(Layout::Grid {
        columns: 3,
        row_height: 300,
    })
    .build();

// Serve dashboard
dashboard.serve("0.0.0.0:3000").await?;

use ruv_fann::metrics::{Metric, MetricRegistry};

// Define custom metrics
#[derive(Metric)]
struct CartanMatrixMetrics {
    #[metric(type = "gauge", help = "Orthogonality measure of Cartan matrix")]
    orthogonality: f64,
    
    #[metric(type = "histogram", help = "Eigenvalue distribution")]
    eigenvalues: Vec<f64>,
    
    #[metric(type = "counter", help = "Number of matrix updates")]
    update_count: u64,
}

// Implement metric collection
impl CartanMatrix {
    fn collect_metrics(&self) -> CartanMatrixMetrics {
        CartanMatrixMetrics {
            orthogonality: self.compute_orthogonality(),
            eigenvalues: self.compute_eigenvalues(),
            update_count: self.updates,
        }
    }
}

// Register with global registry
METRIC_REGISTRY.register::<CartanMatrixMetrics>();

use prometheus::{Encoder, TextEncoder, Counter, Gauge, Histogram};
use warp::Filter;

// Metrics endpoint
pub async fn serve_metrics(port: u16) {
    let metrics_route = warp::path!("metrics")
        .and(warp::get())
        .map(|| {
            let encoder = TextEncoder::new();
            let metric_families = prometheus::gather();
            let mut buffer = vec![];
            encoder.encode(&metric_families, &mut buffer).unwrap();
            String::from_utf8(buffer).unwrap()
        });
    
    warp::serve(metrics_route)
        .run(([0, 0, 0, 0], port))
        .await;
}

// Export custom metrics
lazy_static! {
    static ref NN_FORWARD_COUNTER: Counter = Counter::new(
        "nn_forward_passes_total", "Total number of forward passes"
    ).unwrap();
    
    static ref NN_LOSS_GAUGE: Gauge = Gauge::new(
        "nn_training_loss", "Current training loss"
    ).unwrap();
    
    static ref NN_INFERENCE_HISTOGRAM: Histogram = Histogram::with_opts(
        HistogramOpts::new("nn_inference_duration_seconds", "Inference time distribution")
            .buckets(vec![0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0])
    ).unwrap();
}

// Good metric names
"nn_training_loss"                  // Clear namespace and purpose
"swarm_agent_task_duration_seconds" // Units included
"gpu_memory_used_bytes"            // Specific measurement

// Bad metric names
"loss"                             // Too generic
"agent_time"                       // Missing units
"memory"                           // Ambiguous

// Use sampling for high-frequency metrics
impl MetricCollector {
    fn should_sample(&self, metric_name: &str) -> bool {
        match self.sampling_rates.get(metric_name) {
            Some(rate) => rand::random::<f64>() < *rate,
            None => true,
        }
    }
    
    fn record(&mut self, name: &str, value: f64) {
        if self.should_sample(name) {
            self.buffer.push(Metric { name, value, timestamp: Instant::now() });
        }
    }
}

// Smart alert suppression
struct AlertSuppressor {
    recent_alerts: HashMap<String, Instant>,
    suppression_window: Duration,
}

impl AlertSuppressor {
    fn should_alert(&mut self, alert_name: &str) -> bool {
        match self.recent_alerts.get(alert_name) {
            Some(last_alert) => {
                if last_alert.elapsed() > self.suppression_window {
                    self.recent_alerts.insert(alert_name.to_string(), Instant::now());
                    true
                } else {
                    false
                }
            }
            None => {
                self.recent_alerts.insert(alert_name.to_string(), Instant::now());
                true
            }
        }
    }
}

// Asynchronous metric collection
impl MetricCollector {
    async fn collect_expensive_metrics(&self) {
        // Offload to background task
        tokio::spawn(async move {
            let metrics = expensive_computation().await;
            METRIC_BUFFER.lock().unwrap().extend(metrics);
        });
    }
}

// Batch metric updates
impl MetricBuffer {
    fn flush(&mut self) {
        if self.buffer.len() >= self.batch_size || self.last_flush.elapsed() > self.flush_interval {
            self.backend.send_batch(&self.buffer);
            self.buffer.clear();
            self.last_flush = Instant::now();
        }
    }
}