torsh-distributed 0.1.2

//! Ray integration for ToRSh distributed training
//!
//! This module provides compatibility with Ray's distributed computing framework,
//! allowing users to leverage Ray Train, Ray Tune, and other Ray components
//! with ToRSh distributed training.
//!
//! Ray is a unified framework for scaling AI and Python applications that provides:
//! - Ray Train: Distributed training with fault tolerance
//! - Ray Tune: Scalable hyperparameter tuning
//! - Ray Serve: Scalable model serving
//! - Ray Data: Distributed data processing
//! - Ray Core: General distributed computing primitives

use crate::{TorshDistributedError, TorshResult};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::Path;

/// Ray configuration compatible with ToRSh
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayConfig {
    /// Ray cluster configuration
    pub cluster: Option<RayClusterConfig>,
    /// Ray Train configuration
    pub train: Option<RayTrainConfig>,
    /// Ray Tune configuration
    pub tune: Option<RayTuneConfig>,
    /// Ray Serve configuration
    pub serve: Option<RayServeConfig>,
    /// Ray Data configuration
    pub data: Option<RayDataConfig>,
    /// Resource configuration
    pub resources: Option<RayResourceConfig>,
    /// Fault tolerance configuration
    pub fault_tolerance: Option<RayFaultToleranceConfig>,
}

/// Ray cluster configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayClusterConfig {
    /// Cluster address
    pub address: Option<String>,
    /// Redis address
    pub redis_address: Option<String>,
    /// Number of CPUs per node
    pub num_cpus: Option<u32>,
    /// Number of GPUs per node
    pub num_gpus: Option<u32>,
    /// Memory per node (GB)
    pub memory_gb: Option<f32>,
    /// Object store memory (GB)
    pub object_store_memory_gb: Option<f32>,
    /// Ray namespace
    pub namespace: Option<String>,
    /// Dashboard host
    pub dashboard_host: Option<String>,
    /// Dashboard port
    pub dashboard_port: Option<u16>,
    /// Include dashboard
    pub include_dashboard: Option<bool>,
}

/// Ray Train configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayTrainConfig {
    /// Backend type for training
    pub backend: RayTrainBackend,
    /// Number of workers
    pub num_workers: u32,
    /// Use GPU
    pub use_gpu: Option<bool>,
    /// Resources per worker
    pub resources_per_worker: Option<HashMap<String, f32>>,
    /// Placement group strategy
    pub placement_group_strategy: Option<RayPlacementGroupStrategy>,
    /// Scaling configuration
    pub scaling_config: Option<RayScalingConfig>,
    /// Run configuration
    pub run_config: Option<RayRunConfig>,
    /// Checkpoint configuration
    pub checkpoint_config: Option<RayCheckpointConfig>,
    /// Failure handling configuration
    pub failure_config: Option<RayFailureConfig>,
}

/// Ray Train backend types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RayTrainBackend {
    /// PyTorch backend
    Torch,
    /// TensorFlow backend
    TensorFlow,
    /// Horovod backend
    Horovod,
    /// MPI backend
    MPI,
    /// Custom backend
    Custom,
}

/// Ray placement group strategy
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RayPlacementGroupStrategy {
    /// Strict pack strategy
    StrictPack,
    /// Pack strategy
    Pack,
    /// Strict spread strategy
    StrictSpread,
    /// Spread strategy
    Spread,
}

/// Ray scaling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayScalingConfig {
    /// Number of workers
    pub num_workers: Option<u32>,
    /// Use GPU
    pub use_gpu: Option<bool>,
    /// Resources per worker
    pub resources_per_worker: Option<HashMap<String, f32>>,
    /// Placement group strategy
    pub placement_group_strategy: Option<RayPlacementGroupStrategy>,
    /// Trainer resources
    pub trainer_resources: Option<HashMap<String, f32>>,
}

/// Ray run configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayRunConfig {
    /// Experiment name
    pub name: Option<String>,
    /// Storage path
    pub storage_path: Option<String>,
    /// Stop conditions
    pub stop: Option<HashMap<String, f32>>,
    /// Checkpoint frequency
    pub checkpoint_freq: Option<u32>,
    /// Keep checkpoints number
    pub keep_checkpoints_num: Option<u32>,
    /// Checkpoint score attribute
    pub checkpoint_score_attr: Option<String>,
    /// Checkpoint mode
    pub checkpoint_mode: Option<RayCheckpointMode>,
    /// Verbose logging
    pub verbose: Option<u32>,
    /// Progress reporter
    pub progress_reporter: Option<RayProgressReporter>,
}

/// Ray checkpoint mode
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RayCheckpointMode {
    /// Maximize checkpoint score
    Max,
    /// Minimize checkpoint score
    Min,
}

/// Ray progress reporter
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RayProgressReporter {
    /// Default reporter
    Default,
    /// JSON reporter
    Json,
    /// TensorBoard reporter
    TensorBoard,
    /// Weights & Biases reporter
    WandB,
    /// MLflow reporter
    MLflow,
}

/// Ray checkpoint configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayCheckpointConfig {
    /// Number of checkpoints to keep
    pub num_to_keep: Option<u32>,
    /// Checkpoint frequency
    pub checkpoint_frequency: Option<u32>,
    /// Checkpoint at end
    pub checkpoint_at_end: Option<bool>,
    /// Checkpoint score attribute
    pub checkpoint_score_attribute: Option<String>,
    /// Checkpoint mode
    pub checkpoint_mode: Option<RayCheckpointMode>,
}

/// Ray failure configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayFailureConfig {
    /// Maximum failures
    pub max_failures: Option<u32>,
    /// Failure handling strategy
    pub failure_handling: Option<RayFailureHandling>,
}

/// Ray failure handling strategy
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RayFailureHandling {
    /// Restart failed workers
    Restart,
    /// Ignore failures
    Ignore,
    /// Fail entire job
    Fail,
}

/// Ray Tune configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayTuneConfig {
    /// Search algorithm
    pub search_alg: Option<RaySearchAlgorithm>,
    /// Scheduler
    pub scheduler: Option<RayScheduler>,
    /// Number of samples
    pub num_samples: Option<u32>,
    /// Concurrent trials
    pub max_concurrent_trials: Option<u32>,
    /// Resources per trial
    pub resources_per_trial: Option<HashMap<String, f32>>,
    /// Parameter space
    pub param_space: Option<HashMap<String, serde_json::Value>>,
    /// Metric to optimize
    pub metric: Option<String>,
    /// Mode (min or max)
    pub mode: Option<String>,
    /// Time budget
    pub time_budget_s: Option<f32>,
}

/// Ray search algorithms
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RaySearchAlgorithm {
    /// Basic variant generation
    BasicVariant,
    /// Random search
    Random,
    /// Grid search
    Grid,
    /// Bayesian optimization
    BayesOpt,
    /// Hyperband
    Hyperband,
    /// BOHB
    BOHB,
    /// Population based training
    PopulationBasedTraining,
}

/// Ray schedulers
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RayScheduler {
    /// FIFO scheduler
    FIFO,
    /// Hyperband scheduler
    Hyperband,
    /// ASHA scheduler
    ASHA,
    /// Median stopping rule
    MedianStopping,
    /// Population based training
    PopulationBasedTraining,
}

/// Ray Serve configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayServeConfig {
    /// HTTP options
    pub http_options: Option<RayServeHttpOptions>,
    /// gRPC options
    pub grpc_options: Option<RayServeGrpcOptions>,
    /// Deployment configuration
    pub deployments: Option<Vec<RayServeDeploymentConfig>>,
}

/// Ray Serve HTTP options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayServeHttpOptions {
    /// Host
    pub host: Option<String>,
    /// Port
    pub port: Option<u16>,
    /// Root path
    pub root_path: Option<String>,
}

/// Ray Serve gRPC options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayServeGrpcOptions {
    /// Port
    pub port: Option<u16>,
    /// gRPC servicer functions
    pub grpc_servicer_functions: Option<Vec<String>>,
}

/// Ray Serve deployment configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayServeDeploymentConfig {
    /// Deployment name
    pub name: String,
    /// Number of replicas
    pub num_replicas: Option<u32>,
    /// Resources per replica
    pub ray_actor_options: Option<HashMap<String, serde_json::Value>>,
    /// User configuration
    pub user_config: Option<HashMap<String, serde_json::Value>>,
    /// Max concurrent queries
    pub max_concurrent_queries: Option<u32>,
    /// Autoscaling configuration
    pub autoscaling_config: Option<RayServeAutoscalingConfig>,
}

/// Ray Serve autoscaling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayServeAutoscalingConfig {
    /// Minimum replicas
    pub min_replicas: Option<u32>,
    /// Maximum replicas
    pub max_replicas: Option<u32>,
    /// Target number of ongoing requests per replica
    pub target_num_ongoing_requests_per_replica: Option<f32>,
    /// Metrics interval
    pub metrics_interval_s: Option<f32>,
    /// Look back period
    pub look_back_period_s: Option<f32>,
    /// Smoothing factor
    pub smoothing_factor: Option<f32>,
}

/// Ray Data configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayDataConfig {
    /// Data format
    pub format: Option<RayDataFormat>,
    /// Parallelism
    pub parallelism: Option<u32>,
    /// Batch size
    pub batch_size: Option<u32>,
    /// Prefetch
    pub prefetch: Option<u32>,
    /// Shuffle
    pub shuffle: Option<bool>,
    /// Shuffle buffer size
    pub shuffle_buffer_size: Option<u32>,
}

/// Ray Data formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RayDataFormat {
    /// Parquet format
    Parquet,
    /// CSV format
    CSV,
    /// JSON format
    JSON,
    /// Image format
    Image,
    /// Text format
    Text,
    /// Arrow format
    Arrow,
}

/// Ray resource configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayResourceConfig {
    /// CPU resources
    pub num_cpus: Option<f32>,
    /// GPU resources
    pub num_gpus: Option<f32>,
    /// Memory (bytes)
    pub memory: Option<u64>,
    /// Object store memory (bytes)
    pub object_store_memory: Option<u64>,
    /// Custom resources
    pub custom_resources: Option<HashMap<String, f32>>,
}

/// Ray fault tolerance configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RayFaultToleranceConfig {
    /// Maximum restarts
    pub max_restarts: Option<u32>,
    /// Restart delay (seconds)
    pub restart_delay_s: Option<f32>,
    /// Health check interval (seconds)
    pub health_check_interval_s: Option<f32>,
    /// Enable fault tolerance
    pub enabled: Option<bool>,
}

/// Ray integration statistics
#[derive(Debug, Clone, Default)]
pub struct RayStats {
    /// Number of training runs
    pub training_runs: u64,
    /// Total training time (seconds)
    pub training_time_sec: f64,
    /// Number of tuning trials
    pub tuning_trials: u64,
    /// Total tuning time (seconds)
    pub tuning_time_sec: f64,
    /// Number of served requests
    pub served_requests: u64,
    /// Number of data processing tasks
    pub data_processing_tasks: u64,
    /// Number of worker failures
    pub worker_failures: u64,
    /// Number of restarts
    pub restarts: u64,
    /// Resource utilization
    pub resource_utilization: f64,
    /// Checkpoint frequency
    pub checkpoint_frequency: f64,
}

/// Ray compatibility integration
pub struct RayIntegration {
    /// Configuration
    config: RayConfig,
    /// Statistics
    stats: RayStats,
    /// Initialization status
    initialized: bool,
    /// Process rank
    rank: u32,
    /// World size
    world_size: u32,
    /// Local rank
    local_rank: u32,
    /// Local size
    local_size: u32,
    /// Ray session active
    ray_session_active: bool,
}

impl RayIntegration {
    /// Create a new Ray integration
    pub fn new(config: RayConfig) -> Self {
        Self {
            config,
            stats: RayStats::default(),
            initialized: false,
            rank: 0,
            world_size: 1,
            local_rank: 0,
            local_size: 1,
            ray_session_active: false,
        }
    }

    /// Load configuration from JSON file
    pub fn from_file<P: AsRef<Path>>(path: P) -> TorshResult<Self> {
        let content = std::fs::read_to_string(path).map_err(|e| {
            TorshDistributedError::configuration_error(format!(
                "Failed to read Ray config file: {}",
                e
            ))
        })?;

        let config: RayConfig = serde_json::from_str(&content).map_err(|e| {
            TorshDistributedError::configuration_error(format!("Failed to parse Ray config: {}", e))
        })?;

        Ok(Self::new(config))
    }

    /// Initialize Ray integration
    pub fn initialize(
        &mut self,
        rank: u32,
        world_size: u32,
        local_rank: u32,
        local_size: u32,
    ) -> TorshResult<()> {
        if self.initialized {
            return Err(TorshDistributedError::configuration_error(
                "Ray integration already initialized",
            ));
        }

        self.rank = rank;
        self.world_size = world_size;
        self.local_rank = local_rank;
        self.local_size = local_size;

        self.validate_config()?;
        self.setup_ray_cluster()?;
        self.setup_ray_train()?;
        self.setup_ray_tune()?;
        self.setup_ray_serve()?;
        self.setup_ray_data()?;
        self.setup_fault_tolerance()?;

        self.initialized = true;
        self.ray_session_active = true;

        tracing::info!(
            "Ray integration initialized - rank: {}, world_size: {}, local_rank: {}",
            self.rank,
            self.world_size,
            self.local_rank
        );

        Ok(())
    }

    /// Validate Ray configuration
    fn validate_config(&self) -> TorshResult<()> {
        // Validate cluster configuration
        if let Some(ref cluster) = self.config.cluster {
            if let Some(num_cpus) = cluster.num_cpus {
                if num_cpus == 0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Ray cluster num_cpus must be greater than 0",
                    ));
                }
            }

            if let Some(memory_gb) = cluster.memory_gb {
                if memory_gb <= 0.0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Ray cluster memory_gb must be greater than 0",
                    ));
                }
            }
        }

        // Validate training configuration
        if let Some(ref train) = self.config.train {
            if train.num_workers == 0 {
                return Err(TorshDistributedError::configuration_error(
                    "Ray Train num_workers must be greater than 0",
                ));
            }

            if let Some(ref scaling) = train.scaling_config {
                if let Some(num_workers) = scaling.num_workers {
                    if num_workers == 0 {
                        return Err(TorshDistributedError::configuration_error(
                            "Ray Train scaling num_workers must be greater than 0",
                        ));
                    }
                }
            }
        }

        // Validate tuning configuration
        if let Some(ref tune) = self.config.tune {
            if let Some(num_samples) = tune.num_samples {
                if num_samples == 0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Ray Tune num_samples must be greater than 0",
                    ));
                }
            }

            if let Some(max_concurrent) = tune.max_concurrent_trials {
                if max_concurrent == 0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Ray Tune max_concurrent_trials must be greater than 0",
                    ));
                }
            }
        }

        Ok(())
    }

    /// Setup Ray cluster
    fn setup_ray_cluster(&self) -> TorshResult<()> {
        if let Some(ref cluster) = self.config.cluster {
            tracing::info!("Setting up Ray cluster");

            if let Some(ref address) = cluster.address {
                tracing::debug!("Ray cluster address: {}", address);
            }

            let num_cpus = cluster.num_cpus.unwrap_or(1);
            tracing::debug!("Ray cluster CPUs: {}", num_cpus);

            let num_gpus = cluster.num_gpus.unwrap_or(0);
            tracing::debug!("Ray cluster GPUs: {}", num_gpus);

            let memory_gb = cluster.memory_gb.unwrap_or(4.0);
            tracing::debug!("Ray cluster memory: {} GB", memory_gb);

            let object_store_memory_gb = cluster.object_store_memory_gb.unwrap_or(2.0);
            tracing::debug!("Ray object store memory: {} GB", object_store_memory_gb);

            if let Some(ref namespace) = cluster.namespace {
                tracing::debug!("Ray namespace: {}", namespace);
            }

            let include_dashboard = cluster.include_dashboard.unwrap_or(true);
            if include_dashboard {
                let default_host = "127.0.0.1".to_string();
                let dashboard_host = cluster.dashboard_host.as_ref().unwrap_or(&default_host);
                let dashboard_port = cluster.dashboard_port.unwrap_or(8265);
                tracing::debug!("Ray dashboard: {}:{}", dashboard_host, dashboard_port);
            }
        }
        Ok(())
    }

    /// Setup Ray Train
    fn setup_ray_train(&self) -> TorshResult<()> {
        if let Some(ref train) = self.config.train {
            tracing::info!("Setting up Ray Train");

            tracing::debug!("Ray Train backend: {:?}", train.backend);
            tracing::debug!("Ray Train workers: {}", train.num_workers);

            let use_gpu = train.use_gpu.unwrap_or(false);
            tracing::debug!("Ray Train use GPU: {}", use_gpu);

            if let Some(ref resources) = train.resources_per_worker {
                tracing::debug!("Ray Train resources per worker: {:?}", resources);
            }

            let placement_strategy = train
                .placement_group_strategy
                .unwrap_or(RayPlacementGroupStrategy::Pack);
            tracing::debug!(
                "Ray Train placement group strategy: {:?}",
                placement_strategy
            );

            if let Some(ref scaling) = train.scaling_config {
                tracing::debug!("Ray Train scaling configuration: {:?}", scaling);
            }

            if let Some(ref run_config) = train.run_config {
                if let Some(ref name) = run_config.name {
                    tracing::debug!("Ray Train experiment name: {}", name);
                }

                if let Some(ref storage_path) = run_config.storage_path {
                    tracing::debug!("Ray Train storage path: {}", storage_path);
                }
            }

            if let Some(ref checkpoint) = train.checkpoint_config {
                let num_to_keep = checkpoint.num_to_keep.unwrap_or(3);
                tracing::debug!("Ray Train checkpoints to keep: {}", num_to_keep);
            }

            if let Some(ref failure) = train.failure_config {
                let max_failures = failure.max_failures.unwrap_or(3);
                tracing::debug!("Ray Train max failures: {}", max_failures);
            }
        }
        Ok(())
    }

    /// Setup Ray Tune
    fn setup_ray_tune(&self) -> TorshResult<()> {
        if let Some(ref tune) = self.config.tune {
            tracing::info!("Setting up Ray Tune");

            if let Some(search_alg) = tune.search_alg {
                tracing::debug!("Ray Tune search algorithm: {:?}", search_alg);
            }

            if let Some(scheduler) = tune.scheduler {
                tracing::debug!("Ray Tune scheduler: {:?}", scheduler);
            }

            let num_samples = tune.num_samples.unwrap_or(10);
            tracing::debug!("Ray Tune samples: {}", num_samples);

            let max_concurrent = tune.max_concurrent_trials.unwrap_or(4);
            tracing::debug!("Ray Tune max concurrent trials: {}", max_concurrent);

            if let Some(ref resources) = tune.resources_per_trial {
                tracing::debug!("Ray Tune resources per trial: {:?}", resources);
            }

            if let Some(ref metric) = tune.metric {
                tracing::debug!("Ray Tune optimization metric: {}", metric);
            }

            if let Some(ref mode) = tune.mode {
                tracing::debug!("Ray Tune optimization mode: {}", mode);
            }

            if let Some(time_budget) = tune.time_budget_s {
                tracing::debug!("Ray Tune time budget: {} seconds", time_budget);
            }
        }
        Ok(())
    }

    /// Setup Ray Serve
    fn setup_ray_serve(&self) -> TorshResult<()> {
        if let Some(ref serve) = self.config.serve {
            tracing::info!("Setting up Ray Serve");

            if let Some(ref http) = serve.http_options {
                let default_host = "127.0.0.1".to_string();
                let host = http.host.as_ref().unwrap_or(&default_host);
                let port = http.port.unwrap_or(8000);
                tracing::debug!("Ray Serve HTTP: {}:{}", host, port);

                if let Some(ref root_path) = http.root_path {
                    tracing::debug!("Ray Serve HTTP root path: {}", root_path);
                }
            }

            if let Some(ref grpc) = serve.grpc_options {
                let port = grpc.port.unwrap_or(9000);
                tracing::debug!("Ray Serve gRPC port: {}", port);

                if let Some(ref functions) = grpc.grpc_servicer_functions {
                    tracing::debug!("Ray Serve gRPC servicer functions: {:?}", functions);
                }
            }

            if let Some(ref deployments) = serve.deployments {
                for deployment in deployments {
                    tracing::debug!("Ray Serve deployment: {}", deployment.name);

                    let num_replicas = deployment.num_replicas.unwrap_or(1);
                    tracing::debug!("  Replicas: {}", num_replicas);

                    if let Some(ref autoscaling) = deployment.autoscaling_config {
                        let min_replicas = autoscaling.min_replicas.unwrap_or(1);
                        let max_replicas = autoscaling.max_replicas.unwrap_or(10);
                        tracing::debug!("  Autoscaling: {} - {}", min_replicas, max_replicas);
                    }
                }
            }
        }
        Ok(())
    }

    /// Setup Ray Data
    fn setup_ray_data(&self) -> TorshResult<()> {
        if let Some(ref data) = self.config.data {
            tracing::info!("Setting up Ray Data");

            if let Some(format) = data.format {
                tracing::debug!("Ray Data format: {:?}", format);
            }

            let parallelism = data.parallelism.unwrap_or(4);
            tracing::debug!("Ray Data parallelism: {}", parallelism);

            let batch_size = data.batch_size.unwrap_or(32);
            tracing::debug!("Ray Data batch size: {}", batch_size);

            let prefetch = data.prefetch.unwrap_or(2);
            tracing::debug!("Ray Data prefetch: {}", prefetch);

            let shuffle = data.shuffle.unwrap_or(false);
            tracing::debug!("Ray Data shuffle: {}", shuffle);

            if shuffle {
                let shuffle_buffer_size = data.shuffle_buffer_size.unwrap_or(1000);
                tracing::debug!("Ray Data shuffle buffer size: {}", shuffle_buffer_size);
            }
        }
        Ok(())
    }

    /// Setup fault tolerance
    fn setup_fault_tolerance(&self) -> TorshResult<()> {
        if let Some(ref fault_tolerance) = self.config.fault_tolerance {
            tracing::info!("Setting up Ray fault tolerance");

            let enabled = fault_tolerance.enabled.unwrap_or(true);
            tracing::debug!("Ray fault tolerance enabled: {}", enabled);

            if enabled {
                let max_restarts = fault_tolerance.max_restarts.unwrap_or(3);
                tracing::debug!("Ray max restarts: {}", max_restarts);

                let restart_delay = fault_tolerance.restart_delay_s.unwrap_or(5.0);
                tracing::debug!("Ray restart delay: {} seconds", restart_delay);

                let health_check_interval = fault_tolerance.health_check_interval_s.unwrap_or(10.0);
                tracing::debug!(
                    "Ray health check interval: {} seconds",
                    health_check_interval
                );
            }
        }
        Ok(())
    }

    /// Convert Ray config to ToRSh elastic config
    pub fn to_elastic_config(&self) -> TorshResult<Option<crate::fault_tolerance::ElasticConfig>> {
        if let Some(ref train) = self.config.train {
            use crate::fault_tolerance::ElasticConfig;

            let min_workers = if let Some(ref scaling) = train.scaling_config {
                scaling.num_workers.unwrap_or(train.num_workers)
            } else {
                train.num_workers
            };

            let max_workers = min_workers * 2; // Default scaling

            let config = ElasticConfig {
                min_workers: min_workers as usize,
                max_workers: max_workers as usize,
                scaling_timeout: std::time::Duration::from_secs(300),
                scaling_check_interval: std::time::Duration::from_secs(30),
                enable_elastic_scheduling: true,
                rendezvous_backend: "etcd".to_string(),
                rendezvous_endpoint: "localhost:2379".to_string(),
            };

            Ok(Some(config))
        } else {
            Ok(None)
        }
    }

    /// Get the current configuration
    pub fn config(&self) -> &RayConfig {
        &self.config
    }

    /// Get current statistics
    pub fn stats(&self) -> &RayStats {
        &self.stats
    }

    /// Check if Ray integration is initialized
    pub fn is_initialized(&self) -> bool {
        self.initialized
    }

    /// Get current rank
    pub fn rank(&self) -> u32 {
        self.rank
    }

    /// Get world size
    pub fn world_size(&self) -> u32 {
        self.world_size
    }

    /// Get local rank
    pub fn local_rank(&self) -> u32 {
        self.local_rank
    }

    /// Get local size
    pub fn local_size(&self) -> u32 {
        self.local_size
    }

    /// Check if Ray session is active
    pub fn is_ray_session_active(&self) -> bool {
        self.ray_session_active
    }

    /// Simulate Ray Train run
    pub fn run_training(&mut self, train_func_name: &str, num_epochs: u32) -> TorshResult<()> {
        if !self.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        let start_time = std::time::Instant::now();

        tracing::info!(
            "Running Ray Train: {} for {} epochs",
            train_func_name,
            num_epochs
        );

        // Simulate training
        for epoch in 1..=num_epochs {
            tracing::debug!("Ray Train epoch {}/{}", epoch, num_epochs);

            // Simulate potential worker failure and restart
            if epoch % 10 == 0 && self.config.fault_tolerance.is_some() {
                self.handle_worker_failure()?;
            }
        }

        // Update statistics
        self.stats.training_runs += 1;
        self.stats.training_time_sec += start_time.elapsed().as_secs_f64();

        tracing::info!("Ray Train completed: {}", train_func_name);
        Ok(())
    }

    /// Simulate Ray Tune run
    pub fn run_tuning(&mut self, tune_config_name: &str) -> TorshResult<()> {
        if !self.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        let start_time = std::time::Instant::now();

        let num_trials = self
            .config
            .tune
            .as_ref()
            .and_then(|t| t.num_samples)
            .unwrap_or(10);

        tracing::info!(
            "Running Ray Tune: {} with {} trials",
            tune_config_name,
            num_trials
        );

        // Simulate tuning trials
        for trial in 1..=num_trials {
            tracing::debug!("Ray Tune trial {}/{}", trial, num_trials);
            self.stats.tuning_trials += 1;
        }

        // Update statistics
        self.stats.tuning_time_sec += start_time.elapsed().as_secs_f64();

        tracing::info!("Ray Tune completed: {}", tune_config_name);
        Ok(())
    }

    /// Handle worker failure
    fn handle_worker_failure(&mut self) -> TorshResult<()> {
        tracing::warn!("Simulating Ray worker failure");
        self.stats.worker_failures += 1;

        if let Some(ref fault_tolerance) = self.config.fault_tolerance {
            if fault_tolerance.enabled.unwrap_or(true) {
                let max_restarts = fault_tolerance.max_restarts.unwrap_or(3);

                if self.stats.restarts < max_restarts as u64 {
                    tracing::info!("Restarting failed Ray worker");
                    self.stats.restarts += 1;

                    let restart_delay = fault_tolerance.restart_delay_s.unwrap_or(5.0);
                    tracing::debug!("Ray restart delay: {} seconds", restart_delay);
                } else {
                    return Err(TorshDistributedError::process_failure(
                        self.rank,
                        "ray_worker",
                        "Maximum restart attempts exceeded",
                    ));
                }
            }
        }

        Ok(())
    }

    /// Shutdown Ray integration
    pub fn shutdown(&mut self) -> TorshResult<()> {
        if self.ray_session_active {
            tracing::info!("Shutting down Ray integration");
            self.ray_session_active = false;
            self.initialized = false;
        }
        Ok(())
    }

    /// Create a default Ray configuration
    pub fn default_config() -> RayConfig {
        RayConfig {
            cluster: Some(RayClusterConfig {
                address: None,
                redis_address: None,
                num_cpus: Some(4),
                num_gpus: Some(0),
                memory_gb: Some(8.0),
                object_store_memory_gb: Some(2.0),
                namespace: None,
                dashboard_host: Some("127.0.0.1".to_string()),
                dashboard_port: Some(8265),
                include_dashboard: Some(true),
            }),
            train: Some(RayTrainConfig {
                backend: RayTrainBackend::Torch,
                num_workers: 4,
                use_gpu: Some(false),
                resources_per_worker: None,
                placement_group_strategy: Some(RayPlacementGroupStrategy::Pack),
                scaling_config: None,
                run_config: None,
                checkpoint_config: None,
                failure_config: Some(RayFailureConfig {
                    max_failures: Some(3),
                    failure_handling: Some(RayFailureHandling::Restart),
                }),
            }),
            tune: None,
            serve: None,
            data: Some(RayDataConfig {
                format: Some(RayDataFormat::Parquet),
                parallelism: Some(4),
                batch_size: Some(32),
                prefetch: Some(2),
                shuffle: Some(false),
                shuffle_buffer_size: Some(1000),
            }),
            resources: Some(RayResourceConfig {
                num_cpus: Some(4.0),
                num_gpus: Some(0.0),
                memory: Some(8 * 1024 * 1024 * 1024), // 8GB
                object_store_memory: Some(2 * 1024 * 1024 * 1024), // 2GB
                custom_resources: None,
            }),
            fault_tolerance: Some(RayFaultToleranceConfig {
                max_restarts: Some(3),
                restart_delay_s: Some(5.0),
                health_check_interval_s: Some(10.0),
                enabled: Some(true),
            }),
        }
    }

    /// Create a configuration for hyperparameter tuning
    pub fn config_with_tune(num_samples: u32, search_alg: RaySearchAlgorithm) -> RayConfig {
        let mut config = Self::default_config();

        config.tune = Some(RayTuneConfig {
            search_alg: Some(search_alg),
            scheduler: Some(RayScheduler::ASHA),
            num_samples: Some(num_samples),
            max_concurrent_trials: Some(4),
            resources_per_trial: Some([("cpu".to_string(), 1.0)].into_iter().collect()),
            param_space: None,
            metric: Some("accuracy".to_string()),
            mode: Some("max".to_string()),
            time_budget_s: Some(3600.0), // 1 hour
        });

        config
    }

    /// Create a configuration for model serving
    pub fn config_with_serve(num_replicas: u32) -> RayConfig {
        let mut config = Self::default_config();

        config.serve = Some(RayServeConfig {
            http_options: Some(RayServeHttpOptions {
                host: Some("0.0.0.0".to_string()),
                port: Some(8000),
                root_path: None,
            }),
            grpc_options: None,
            deployments: Some(vec![RayServeDeploymentConfig {
                name: "model".to_string(),
                num_replicas: Some(num_replicas),
                ray_actor_options: Some(
                    [(
                        "num_cpus".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(1)),
                    )]
                    .into_iter()
                    .collect(),
                ),
                user_config: None,
                max_concurrent_queries: Some(100),
                autoscaling_config: Some(RayServeAutoscalingConfig {
                    min_replicas: Some(1),
                    max_replicas: Some(num_replicas * 2),
                    target_num_ongoing_requests_per_replica: Some(10.0),
                    metrics_interval_s: Some(10.0),
                    look_back_period_s: Some(30.0),
                    smoothing_factor: Some(1.0),
                }),
            }]),
        });

        config
    }
}

impl Default for RayConfig {
    fn default() -> Self {
        RayIntegration::default_config()
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_ray_config_validation() {
        let config = RayIntegration::default_config();
        let mut integration = RayIntegration::new(config);

        // Should succeed with valid parameters
        assert!(integration.initialize(0, 4, 0, 2).is_ok());
        assert!(integration.is_initialized());
        assert!(integration.is_ray_session_active());
        assert_eq!(integration.rank(), 0);
        assert_eq!(integration.world_size(), 4);
        assert_eq!(integration.local_rank(), 0);
    }

    #[test]
    fn test_ray_training_simulation() {
        let config = RayIntegration::default_config();
        let mut integration = RayIntegration::new(config);

        assert!(integration.initialize(0, 4, 0, 2).is_ok());

        // Simulate training runs
        assert!(integration.run_training("my_train_func", 5).is_ok());
        assert!(integration.run_training("another_train_func", 3).is_ok());

        let stats = integration.stats();
        assert_eq!(stats.training_runs, 2);
        assert!(stats.training_time_sec >= 0.0); // Allow for very fast execution in tests
    }

    #[test]
    fn test_ray_tuning_simulation() {
        let config = RayIntegration::config_with_tune(20, RaySearchAlgorithm::BayesOpt);
        let mut integration = RayIntegration::new(config);

        assert!(integration.initialize(0, 4, 0, 2).is_ok());

        // Simulate tuning run
        assert!(integration.run_tuning("hyperparameter_search").is_ok());

        let stats = integration.stats();
        assert_eq!(stats.tuning_trials, 20);
        assert!(stats.tuning_time_sec > 0.0);
    }

    #[test]
    fn test_ray_elastic_config_conversion() {
        let config = RayIntegration::default_config();
        let mut integration = RayIntegration::new(config);

        assert!(integration.initialize(0, 4, 0, 2).is_ok());

        // Test elastic config conversion
        let elastic_config = integration.to_elastic_config().unwrap();
        assert!(elastic_config.is_some());

        if let Some(config) = elastic_config {
            assert_eq!(config.min_workers, 4);
            assert_eq!(config.max_workers, 8);
            assert!(config.enable_elastic_scheduling);
            assert_eq!(config.rendezvous_backend, "etcd");
        }
    }

    #[test]
    fn test_ray_worker_failure_handling() {
        let config = RayIntegration::default_config();
        let mut integration = RayIntegration::new(config);

        assert!(integration.initialize(0, 4, 0, 2).is_ok());

        // Simulate worker failures
        assert!(integration.handle_worker_failure().is_ok());
        assert!(integration.handle_worker_failure().is_ok());
        assert!(integration.handle_worker_failure().is_ok());

        let stats = integration.stats();
        assert_eq!(stats.worker_failures, 3);
        assert_eq!(stats.restarts, 3);

        // Should fail after max restarts
        assert!(integration.handle_worker_failure().is_err());
    }

    #[test]
    fn test_ray_shutdown() {
        let config = RayIntegration::default_config();
        let mut integration = RayIntegration::new(config);

        assert!(integration.initialize(0, 4, 0, 2).is_ok());
        assert!(integration.is_ray_session_active());

        assert!(integration.shutdown().is_ok());
        assert!(!integration.is_ray_session_active());
        assert!(!integration.is_initialized());
    }

    #[test]
    fn test_ray_serve_config() {
        let config = RayIntegration::config_with_serve(4);
        let mut integration = RayIntegration::new(config);

        assert!(integration.initialize(0, 4, 0, 2).is_ok());

        // Check serve configuration
        assert!(integration.config().serve.is_some());

        if let Some(ref serve) = integration.config().serve {
            assert!(serve.http_options.is_some());
            assert!(serve.deployments.is_some());

            if let Some(ref deployments) = serve.deployments {
                assert_eq!(deployments.len(), 1);
                assert_eq!(deployments[0].name, "model");
                assert_eq!(deployments[0].num_replicas, Some(4));
            }
        }
    }

    #[test]
    fn test_ray_config_serialization() {
        let config = RayIntegration::config_with_tune(10, RaySearchAlgorithm::Random);

        // Test JSON serialization
        let json = serde_json::to_string(&config).unwrap();
        assert!(json.contains("Random"));
        assert!(json.contains("ASHA"));
        assert!(json.contains("accuracy"));

        // Test deserialization
        let deserialized: RayConfig = serde_json::from_str(&json).unwrap();
        assert!(deserialized.tune.is_some());

        if let Some(tune) = deserialized.tune {
            assert_eq!(tune.search_alg, Some(RaySearchAlgorithm::Random));
            assert_eq!(tune.scheduler, Some(RayScheduler::ASHA));
            assert_eq!(tune.num_samples, Some(10));
        }
    }

    #[test]
    fn test_ray_invalid_config() {
        let mut config = RayIntegration::default_config();

        // Make configuration invalid
        if let Some(ref mut train) = config.train {
            train.num_workers = 0; // Invalid: 0 workers
        }

        let mut integration = RayIntegration::new(config);

        // Should fail validation
        assert!(integration.initialize(0, 4, 0, 2).is_err());
    }
}