//! Chaos Engineering Module for Foxhunt HFT Trading System
//!
//! This module provides comprehensive chaos engineering capabilities specifically
//! designed for high-frequency trading systems with sub-100ms recovery requirements.
//!
//! # Status: Disabled
//!
//! All submodules and tests are commented out because they require infrastructure
//! that is not available in the standard test environment:
//!
//! - **ML gRPC service** (`ML_SERVICE_ENDPOINT`) must be running to inject training faults
//! - **GPU infrastructure** for ML model chaos tests (checkpoint corruption, OOM simulation)
//! - **Isolated environment** to safely inject network partitions, broker disconnects, and
//!   process crashes without affecting real trading or CI stability
//!
//! To run chaos tests, use the nightly chaos runner with `CHAOS_WEBHOOK_URL` configured
//! and all dependent services deployed (see `tests/chaos/README.md` for setup instructions).

// COMMENTED OUT - Need proper integration test harness with running services
// pub mod chaos_cli;
// pub mod chaos_framework;
// pub mod examples;
// pub mod ml_training_chaos;
// pub mod nightly_chaos_runner;

// COMMENTED OUT - All imports depend on disabled modules
// use anyhow::Result;
// use chrono;
// use std::path::PathBuf;
// use tracing::info;
// use chaos_framework::ChaosOrchestrator;
// use ml_training_chaos::{MLChaosConfig, MLChaosResult, MLTrainingChaosTests, ModelType};
// use nightly_chaos_runner::{NightlyChaosConfig, NightlyChaosRunner};

/*
/// Initialize chaos engineering for the Foxhunt system (DISABLED)
pub async fn initialize_foxhunt_chaos() -> Result<NightlyChaosRunner> {
    info!("Initializing Foxhunt chaos engineering framework");

    // Configure chaos testing for HFT requirements
    let chaos_config = NightlyChaosConfig {
        enabled: true,
        schedule_time: chrono::NaiveTime::from_hms_opt(2, 0, 0).unwrap(), // 2 AM UTC
        timezone: "UTC".to_string(),
        max_duration_hours: 3, // Complete chaos testing within 3 hours
        notification_webhook: std::env::var("CHAOS_WEBHOOK_URL").ok(),
        report_storage_path: PathBuf::from("./chaos_reports"),
        ml_chaos_config: MLChaosConfig {
            ml_service_endpoint: std::env::var("ML_SERVICE_ENDPOINT")
                .unwrap_or_else(|_| "http://localhost:8080".to_string()),
            checkpoint_base_path: PathBuf::from("./ml_checkpoints"),
            model_types: vec![
                ModelType::TLOB,   // Ultra-low latency transformer
                ModelType::MAMBA2, // State space model
                ModelType::DQN,    // Deep Q-learning
                ModelType::PPO,    // Policy optimization
                ModelType::Liquid, // Liquid neural networks
                ModelType::TFT,    // Temporal fusion transformer
            ],
            training_timeout_secs: 300,    // 5 minutes max training time
            max_recovery_time_ms: 100,     // HFT requirement: sub-100ms recovery
            gpu_memory_threshold_mb: 8192, // 8GB GPU memory threshold
        },
        exclude_weekends: true, // Skip weekends for production safety
        retry_on_failure: true,
        max_retries: 2,
    };

    let runner = NightlyChaosRunner::new(chaos_config);

    info!("Foxhunt chaos engineering framework initialized");
    Ok(runner)
}

/// Quick chaos test for development/CI
pub async fn run_quick_chaos_test() -> Result<Vec<MLChaosResult>> {
    info!("Running quick chaos test for CI/development");

    let ml_config = MLChaosConfig {
        ml_service_endpoint: "http://localhost:8080".to_string(),
        checkpoint_base_path: PathBuf::from("/tmp/test_checkpoints"),
        model_types: vec![ModelType::TLOB], // Just test TLOB for speed
        training_timeout_secs: 60,          // 1 minute for quick test
        max_recovery_time_ms: 100,
        gpu_memory_threshold_mb: 2048, // Lower threshold for CI
    };

    let ml_chaos = MLTrainingChaosTests::new(ml_config);

    // Run a subset of chaos tests
    let experiment_ids = ml_chaos.initialize_experiments().await?;
    let first_experiment = experiment_ids
        .into_iter()
        .next()
        .ok_or_else(|| anyhow::anyhow!("No experiments available"))?;

    // Execute just one experiment for quick testing
    let orchestrator = ChaosOrchestrator::new(1);
    let _result = orchestrator.execute_experiment(first_experiment).await?;

    // Return empty results for now (would implement actual quick test)
    Ok(vec![])
}

*/

// Tests disabled - require full service infrastructure
/*
#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_chaos_initialization() {
        let runner = initialize_foxhunt_chaos().await;
        assert!(runner.is_ok());
    }

    #[tokio::test]
    async fn test_quick_chaos_test() {
        // This would require actual ML service running
        // For now just test that the function exists
        let result = run_quick_chaos_test().await;
        // In CI without services running, this might fail, so we don't assert success
        println!("Quick chaos test result: {:?}", result);
    }
}
*/