torsh-distributed 0.1.2

//! Dask integration for ToRSh distributed training
//!
//! This module provides compatibility with Dask's parallel computing framework,
//! allowing users to leverage Dask's distributed computing capabilities
//! with ToRSh distributed training.
//!
//! Dask is a flexible library for parallel computing in Python that provides:
//! - Dask Array: Parallel NumPy-like arrays
//! - Dask DataFrame: Parallel Pandas-like dataframes  
//! - Dask Bag: Parallel collections for unstructured data
//! - Dask Distributed: Distributed computing with task scheduling
//! - Dask ML: Machine learning algorithms
//! - Dask Gateway: Secure cluster management

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

/// Dask configuration compatible with ToRSh
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskConfig {
    /// Dask cluster configuration
    pub cluster: Option<DaskClusterConfig>,
    /// Dask scheduler configuration
    pub scheduler: Option<DaskSchedulerConfig>,
    /// Dask worker configuration
    pub worker: Option<DaskWorkerConfig>,
    /// Dask array configuration
    pub array: Option<DaskArrayConfig>,
    /// Dask dataframe configuration
    pub dataframe: Option<DaskDataFrameConfig>,
    /// Dask bag configuration
    pub bag: Option<DaskBagConfig>,
    /// Dask ML configuration
    pub ml: Option<DaskMLConfig>,
    /// Dask distributed configuration
    pub distributed: Option<DaskDistributedConfig>,
}

/// Dask cluster configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskClusterConfig {
    /// Cluster type
    pub cluster_type: DaskClusterType,
    /// Number of workers
    pub n_workers: Option<u32>,
    /// Threads per worker
    pub threads_per_worker: Option<u32>,
    /// Memory per worker
    pub memory_limit: Option<String>,
    /// Processes instead of threads
    pub processes: Option<bool>,
    /// Dashboard address
    pub dashboard_address: Option<String>,
    /// Silence logs
    pub silence_logs: Option<bool>,
    /// Security configuration
    pub security: Option<DaskSecurityConfig>,
    /// Cluster scaling configuration
    pub scaling: Option<DaskScalingConfig>,
}

/// Dask cluster types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DaskClusterType {
    /// Local cluster
    Local,
    /// LocalCluster with processes
    LocalProcess,
    /// Kubernetes cluster
    Kubernetes,
    /// SLURM cluster
    Slurm,
    /// PBS cluster
    PBS,
    /// SGE cluster
    SGE,
    /// Custom cluster
    Custom,
}

/// Dask security configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskSecurityConfig {
    /// TLS certificate file
    pub tls_cert: Option<String>,
    /// TLS key file
    pub tls_key: Option<String>,
    /// TLS CA file
    pub tls_ca_file: Option<String>,
    /// Require encryption
    pub require_encryption: Option<bool>,
}

/// Dask scaling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskScalingConfig {
    /// Minimum workers
    pub minimum: Option<u32>,
    /// Maximum workers
    pub maximum: Option<u32>,
    /// Target CPU utilization
    pub target_cpu: Option<f32>,
    /// Target memory utilization
    pub target_memory: Option<f32>,
    /// Scale up threshold
    pub scale_up_threshold: Option<f32>,
    /// Scale down threshold
    pub scale_down_threshold: Option<f32>,
    /// Interval for scaling decisions
    pub interval: Option<f32>,
}

/// Dask scheduler configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskSchedulerConfig {
    /// Scheduler address
    pub address: Option<String>,
    /// Scheduler port
    pub port: Option<u16>,
    /// Dashboard port
    pub dashboard_port: Option<u16>,
    /// Bokeh port
    pub bokeh_port: Option<u16>,
    /// Worker timeout
    pub worker_timeout: Option<f32>,
    /// Idle timeout
    pub idle_timeout: Option<f32>,
    /// Transition log length
    pub transition_log_length: Option<u32>,
    /// Task duration overhead
    pub task_duration_overhead: Option<f32>,
    /// Allowed failures
    pub allowed_failures: Option<u32>,
}

/// Dask worker configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskWorkerConfig {
    /// Number of workers
    pub nworkers: Option<u32>,
    /// Threads per worker
    pub nthreads: Option<u32>,
    /// Memory limit per worker
    pub memory_limit: Option<String>,
    /// Worker port range
    pub worker_port: Option<String>,
    /// Nanny port range
    pub nanny_port: Option<String>,
    /// Dashboard port
    pub dashboard_port: Option<u16>,
    /// Death timeout
    pub death_timeout: Option<f32>,
    /// Preload modules
    pub preload: Option<Vec<String>>,
    /// Environment variables
    pub env: Option<HashMap<String, String>>,
    /// Resources
    pub resources: Option<HashMap<String, f32>>,
}

/// Dask array configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskArrayConfig {
    /// Default chunk size
    pub chunk_size: Option<String>,
    /// Array backend
    pub backend: Option<String>,
    /// Overlap for sliding window operations
    pub overlap: Option<u32>,
    /// Boundary conditions
    pub boundary: Option<String>,
    /// Trim excess data
    pub trim: Option<bool>,
    /// Rechunk threshold
    pub rechunk_threshold: Option<f32>,
}

/// Dask dataframe configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskDataFrameConfig {
    /// Default partition size
    pub partition_size: Option<String>,
    /// Shuffle method
    pub shuffle_method: Option<DaskShuffleMethod>,
    /// Query planning
    pub query_planning: Option<bool>,
    /// Dataframe backend
    pub backend: Option<String>,
    /// Index optimization
    pub optimize_index: Option<bool>,
}

/// Dask shuffle methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DaskShuffleMethod {
    /// Disk-based shuffle
    Disk,
    /// Tasks-based shuffle
    Tasks,
    /// P2P shuffle
    P2P,
}

/// Dask bag configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskBagConfig {
    /// Default partition size
    pub partition_size: Option<u64>,
    /// Compression for storage
    pub compression: Option<String>,
    /// Text encoding
    pub encoding: Option<String>,
    /// Split by lines
    pub linedelimiter: Option<String>,
}

/// Dask ML configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskMLConfig {
    /// Model selection configuration
    pub model_selection: Option<DaskMLModelSelectionConfig>,
    /// Preprocessing configuration
    pub preprocessing: Option<DaskMLPreprocessingConfig>,
    /// Linear models configuration
    pub linear_model: Option<DaskMLLinearModelConfig>,
    /// Ensemble configuration
    pub ensemble: Option<DaskMLEnsembleConfig>,
    /// Clustering configuration
    pub cluster: Option<DaskMLClusterConfig>,
}

/// Dask ML model selection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskMLModelSelectionConfig {
    /// Cross-validation folds
    pub cv_folds: Option<u32>,
    /// Scoring metric
    pub scoring: Option<String>,
    /// N jobs for parallel execution
    pub n_jobs: Option<i32>,
    /// Hyperparameter search method
    pub search_method: Option<DaskMLSearchMethod>,
}

/// Dask ML search methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DaskMLSearchMethod {
    /// Grid search
    GridSearch,
    /// Random search
    RandomSearch,
    /// Successive halving
    SuccessiveHalving,
    /// Hyperband
    Hyperband,
}

/// Dask ML preprocessing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskMLPreprocessingConfig {
    /// Standardization method
    pub standardization: Option<String>,
    /// Categorical encoding
    pub categorical_encoding: Option<String>,
    /// Feature selection
    pub feature_selection: Option<String>,
    /// Dimensionality reduction
    pub dimensionality_reduction: Option<String>,
}

/// Dask ML linear model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskMLLinearModelConfig {
    /// Solver for linear models
    pub solver: Option<String>,
    /// Regularization parameter
    pub alpha: Option<f32>,
    /// Maximum iterations
    pub max_iter: Option<u32>,
    /// Tolerance for convergence
    pub tol: Option<f32>,
}

/// Dask ML ensemble configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskMLEnsembleConfig {
    /// Number of estimators
    pub n_estimators: Option<u32>,
    /// Bootstrap sampling
    pub bootstrap: Option<bool>,
    /// Random state
    pub random_state: Option<u32>,
    /// Out of bag score
    pub oob_score: Option<bool>,
}

/// Dask ML clustering configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskMLClusterConfig {
    /// Number of clusters
    pub n_clusters: Option<u32>,
    /// Initialization method
    pub init: Option<String>,
    /// Maximum iterations
    pub max_iter: Option<u32>,
    /// Tolerance
    pub tol: Option<f32>,
    /// Number of init runs
    pub n_init: Option<u32>,
}

/// Dask distributed configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskDistributedConfig {
    /// Communication configuration
    pub comm: Option<DaskCommConfig>,
    /// Serialization configuration
    pub serialization: Option<DaskSerializationConfig>,
    /// Client configuration
    pub client: Option<DaskClientConfig>,
    /// Task scheduling configuration
    pub scheduling: Option<DaskSchedulingConfig>,
    /// Diagnostics configuration
    pub diagnostics: Option<DaskDiagnosticsConfig>,
}

/// Dask communication configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskCommConfig {
    /// Compression algorithm
    pub compression: Option<String>,
    /// Default serializers
    pub serializers: Option<Vec<String>>,
    /// Timeouts
    pub timeouts: Option<DaskTimeoutsConfig>,
    /// TCP configuration
    pub tcp: Option<DaskTcpConfig>,
}

/// Dask timeouts configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskTimeoutsConfig {
    /// Connect timeout
    pub connect: Option<f32>,
    /// TCP timeout
    pub tcp: Option<f32>,
}

/// Dask TCP configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskTcpConfig {
    /// Reuse port
    pub reuse_port: Option<bool>,
    /// No delay
    pub no_delay: Option<bool>,
    /// Keep alive
    pub keep_alive: Option<bool>,
}

/// Dask serialization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskSerializationConfig {
    /// Compression algorithms
    pub compression: Option<Vec<String>>,
    /// Default serializers
    pub default_serializers: Option<Vec<String>>,
    /// Pickle protocol
    pub pickle_protocol: Option<u32>,
}

/// Dask client configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskClientConfig {
    /// Heartbeat interval
    pub heartbeat_interval: Option<f32>,
    /// Scheduler info interval
    pub scheduler_info_interval: Option<f32>,
    /// Task metadata
    pub task_metadata: Option<Vec<String>>,
    /// Set as default
    pub set_as_default: Option<bool>,
}

/// Dask scheduling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskSchedulingConfig {
    /// Work stealing
    pub work_stealing: Option<bool>,
    /// Work stealing interval
    pub work_stealing_interval: Option<f32>,
    /// Unknown task duration
    pub unknown_task_duration: Option<f32>,
    /// Default task durations
    pub default_task_durations: Option<HashMap<String, f32>>,
}

/// Dask diagnostics configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaskDiagnosticsConfig {
    /// Progress bar
    pub progress_bar: Option<bool>,
    /// Profile
    pub profile: Option<bool>,
    /// Memory profiling
    pub memory_profiling: Option<bool>,
    /// Task stream
    pub task_stream: Option<bool>,
    /// Resource monitor
    pub resource_monitor: Option<bool>,
}

/// Dask integration statistics
#[derive(Debug, Clone, Default)]
pub struct DaskStats {
    /// Number of tasks executed
    pub tasks_executed: u64,
    /// Total task execution time (seconds)
    pub task_execution_time_sec: f64,
    /// Number of workers connected
    pub workers_connected: u32,
    /// Total data transferred (bytes)
    pub data_transferred_bytes: u64,
    /// Number of task retries
    pub task_retries: u64,
    /// Number of worker failures
    pub worker_failures: u64,
    /// Memory usage (bytes)
    pub memory_usage_bytes: u64,
    /// CPU utilization
    pub cpu_utilization: f64,
    /// Network bandwidth (bytes/sec)
    pub network_bandwidth_bytes_per_sec: f64,
    /// Average task duration (seconds)
    pub average_task_duration_sec: f64,
}

/// Dask compatibility integration
pub struct DaskIntegration {
    /// Configuration
    config: DaskConfig,
    /// Statistics
    stats: DaskStats,
    /// Initialization status
    initialized: bool,
    /// Process rank
    rank: u32,
    /// World size
    world_size: u32,
    /// Local rank
    local_rank: u32,
    /// Local size
    local_size: u32,
    /// Dask client active
    client_active: bool,
}

impl DaskIntegration {
    /// Create a new Dask integration
    pub fn new(config: DaskConfig) -> Self {
        Self {
            config,
            stats: DaskStats::default(),
            initialized: false,
            rank: 0,
            world_size: 1,
            local_rank: 0,
            local_size: 1,
            client_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 Dask config file: {}",
                e
            ))
        })?;

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

        Ok(Self::new(config))
    }

    /// Initialize Dask 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(
                "Dask 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_cluster()?;
        self.setup_scheduler()?;
        self.setup_workers()?;
        self.setup_client()?;
        self.setup_ml()?;
        self.setup_distributed()?;

        self.initialized = true;
        self.client_active = true;

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

        Ok(())
    }

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

            if let Some(threads_per_worker) = cluster.threads_per_worker {
                if threads_per_worker == 0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Dask cluster threads_per_worker must be greater than 0",
                    ));
                }
            }

            if let Some(ref scaling) = cluster.scaling {
                if let Some(minimum) = scaling.minimum {
                    if let Some(maximum) = scaling.maximum {
                        if minimum > maximum {
                            return Err(TorshDistributedError::configuration_error(
                                "Dask scaling minimum workers cannot exceed maximum workers",
                            ));
                        }
                    }
                }
            }
        }

        // Validate scheduler configuration
        if let Some(ref scheduler) = self.config.scheduler {
            if let Some(port) = scheduler.port {
                if port == 0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Dask scheduler port must be greater than 0",
                    ));
                }
            }

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

        // Validate worker configuration
        if let Some(ref worker) = self.config.worker {
            if let Some(nworkers) = worker.nworkers {
                if nworkers == 0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Dask worker nworkers must be greater than 0",
                    ));
                }
            }

            if let Some(nthreads) = worker.nthreads {
                if nthreads == 0 {
                    return Err(TorshDistributedError::configuration_error(
                        "Dask worker nthreads must be greater than 0",
                    ));
                }
            }
        }

        Ok(())
    }

    /// Setup Dask cluster
    fn setup_cluster(&self) -> TorshResult<()> {
        if let Some(ref cluster) = self.config.cluster {
            tracing::info!("Setting up Dask cluster: {:?}", cluster.cluster_type);

            let n_workers = cluster.n_workers.unwrap_or(4);
            tracing::debug!("Dask cluster workers: {}", n_workers);

            let threads_per_worker = cluster.threads_per_worker.unwrap_or(2);
            tracing::debug!("Dask threads per worker: {}", threads_per_worker);

            if let Some(ref memory_limit) = cluster.memory_limit {
                tracing::debug!("Dask memory limit per worker: {}", memory_limit);
            }

            let processes = cluster.processes.unwrap_or(false);
            tracing::debug!("Dask use processes: {}", processes);

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

            let silence_logs = cluster.silence_logs.unwrap_or(false);
            tracing::debug!("Dask silence logs: {}", silence_logs);

            if let Some(ref security) = cluster.security {
                tracing::debug!("Dask security enabled");
                if let Some(ref tls_cert) = security.tls_cert {
                    tracing::debug!("Dask TLS cert: {}", tls_cert);
                }
                if let Some(ref tls_key) = security.tls_key {
                    tracing::debug!("Dask TLS key: {}", tls_key);
                }
                let require_encryption = security.require_encryption.unwrap_or(false);
                tracing::debug!("Dask require encryption: {}", require_encryption);
            }

            if let Some(ref scaling) = cluster.scaling {
                let minimum = scaling.minimum.unwrap_or(1);
                let maximum = scaling.maximum.unwrap_or(n_workers * 2);
                tracing::debug!("Dask scaling: {} - {} workers", minimum, maximum);

                let target_cpu = scaling.target_cpu.unwrap_or(0.8);
                tracing::debug!("Dask target CPU utilization: {}", target_cpu);

                let interval = scaling.interval.unwrap_or(30.0);
                tracing::debug!("Dask scaling interval: {} seconds", interval);
            }
        }
        Ok(())
    }

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

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

            let port = scheduler.port.unwrap_or(8786);
            tracing::debug!("Dask scheduler port: {}", port);

            let dashboard_port = scheduler.dashboard_port.unwrap_or(8787);
            tracing::debug!("Dask scheduler dashboard port: {}", dashboard_port);

            let worker_timeout = scheduler.worker_timeout.unwrap_or(60.0);
            tracing::debug!("Dask scheduler worker timeout: {} seconds", worker_timeout);

            let idle_timeout = scheduler.idle_timeout.unwrap_or(1800.0);
            tracing::debug!("Dask scheduler idle timeout: {} seconds", idle_timeout);

            let allowed_failures = scheduler.allowed_failures.unwrap_or(3);
            tracing::debug!("Dask scheduler allowed failures: {}", allowed_failures);
        }
        Ok(())
    }

    /// Setup Dask workers
    fn setup_workers(&mut self) -> TorshResult<()> {
        if let Some(ref worker) = self.config.worker {
            tracing::info!("Setting up Dask workers");

            let nworkers = worker.nworkers.unwrap_or(4);
            tracing::debug!("Dask number of workers: {}", nworkers);

            let nthreads = worker.nthreads.unwrap_or(2);
            tracing::debug!("Dask threads per worker: {}", nthreads);

            if let Some(ref memory_limit) = worker.memory_limit {
                tracing::debug!("Dask worker memory limit: {}", memory_limit);
            }

            if let Some(ref worker_port) = worker.worker_port {
                tracing::debug!("Dask worker port range: {}", worker_port);
            }

            if let Some(ref nanny_port) = worker.nanny_port {
                tracing::debug!("Dask nanny port range: {}", nanny_port);
            }

            let death_timeout = worker.death_timeout.unwrap_or(60.0);
            tracing::debug!("Dask worker death timeout: {} seconds", death_timeout);

            if let Some(ref preload) = worker.preload {
                tracing::debug!("Dask worker preload modules: {:?}", preload);
            }

            if let Some(ref env) = worker.env {
                tracing::debug!("Dask worker environment variables: {:?}", env);
            }

            if let Some(ref resources) = worker.resources {
                tracing::debug!("Dask worker resources: {:?}", resources);
            }
        }

        // Update stats
        self.stats.workers_connected = self
            .config
            .worker
            .as_ref()
            .and_then(|w| w.nworkers)
            .unwrap_or(4);

        Ok(())
    }

    /// Setup Dask client
    fn setup_client(&self) -> TorshResult<()> {
        if let Some(ref distributed) = self.config.distributed {
            if let Some(ref client) = distributed.client {
                tracing::info!("Setting up Dask client");

                let heartbeat_interval = client.heartbeat_interval.unwrap_or(5.0);
                tracing::debug!(
                    "Dask client heartbeat interval: {} seconds",
                    heartbeat_interval
                );

                let scheduler_info_interval = client.scheduler_info_interval.unwrap_or(2.0);
                tracing::debug!(
                    "Dask client scheduler info interval: {} seconds",
                    scheduler_info_interval
                );

                if let Some(ref task_metadata) = client.task_metadata {
                    tracing::debug!("Dask client task metadata: {:?}", task_metadata);
                }

                let set_as_default = client.set_as_default.unwrap_or(true);
                tracing::debug!("Dask client set as default: {}", set_as_default);
            }
        }
        Ok(())
    }

    /// Setup Dask ML
    fn setup_ml(&self) -> TorshResult<()> {
        if let Some(ref ml) = self.config.ml {
            tracing::info!("Setting up Dask ML");

            if let Some(ref model_selection) = ml.model_selection {
                let cv_folds = model_selection.cv_folds.unwrap_or(5);
                tracing::debug!("Dask ML cross-validation folds: {}", cv_folds);

                if let Some(ref scoring) = model_selection.scoring {
                    tracing::debug!("Dask ML scoring metric: {}", scoring);
                }

                if let Some(search_method) = model_selection.search_method {
                    tracing::debug!("Dask ML search method: {:?}", search_method);
                }
            }

            if let Some(ref preprocessing) = ml.preprocessing {
                if let Some(ref standardization) = preprocessing.standardization {
                    tracing::debug!("Dask ML standardization: {}", standardization);
                }

                if let Some(ref encoding) = preprocessing.categorical_encoding {
                    tracing::debug!("Dask ML categorical encoding: {}", encoding);
                }
            }

            if let Some(ref linear_model) = ml.linear_model {
                if let Some(ref solver) = linear_model.solver {
                    tracing::debug!("Dask ML linear model solver: {}", solver);
                }

                let max_iter = linear_model.max_iter.unwrap_or(1000);
                tracing::debug!("Dask ML linear model max iterations: {}", max_iter);
            }

            if let Some(ref ensemble) = ml.ensemble {
                let n_estimators = ensemble.n_estimators.unwrap_or(100);
                tracing::debug!("Dask ML ensemble estimators: {}", n_estimators);

                let bootstrap = ensemble.bootstrap.unwrap_or(true);
                tracing::debug!("Dask ML ensemble bootstrap: {}", bootstrap);
            }

            if let Some(ref cluster) = ml.cluster {
                let n_clusters = cluster.n_clusters.unwrap_or(8);
                tracing::debug!("Dask ML clustering clusters: {}", n_clusters);

                let max_iter = cluster.max_iter.unwrap_or(300);
                tracing::debug!("Dask ML clustering max iterations: {}", max_iter);
            }
        }
        Ok(())
    }

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

            if let Some(ref comm) = distributed.comm {
                if let Some(ref compression) = comm.compression {
                    tracing::debug!("Dask communication compression: {}", compression);
                }

                if let Some(ref serializers) = comm.serializers {
                    tracing::debug!("Dask communication serializers: {:?}", serializers);
                }

                if let Some(ref timeouts) = comm.timeouts {
                    let connect_timeout = timeouts.connect.unwrap_or(10.0);
                    tracing::debug!(
                        "Dask communication connect timeout: {} seconds",
                        connect_timeout
                    );

                    let tcp_timeout = timeouts.tcp.unwrap_or(30.0);
                    tracing::debug!("Dask communication TCP timeout: {} seconds", tcp_timeout);
                }

                if let Some(ref tcp) = comm.tcp {
                    let reuse_port = tcp.reuse_port.unwrap_or(false);
                    tracing::debug!("Dask TCP reuse port: {}", reuse_port);

                    let no_delay = tcp.no_delay.unwrap_or(true);
                    tracing::debug!("Dask TCP no delay: {}", no_delay);

                    let keep_alive = tcp.keep_alive.unwrap_or(false);
                    tracing::debug!("Dask TCP keep alive: {}", keep_alive);
                }
            }

            if let Some(ref serialization) = distributed.serialization {
                if let Some(ref compression) = serialization.compression {
                    tracing::debug!("Dask serialization compression: {:?}", compression);
                }

                let pickle_protocol = serialization.pickle_protocol.unwrap_or(4);
                tracing::debug!("Dask serialization pickle protocol: {}", pickle_protocol);
            }

            if let Some(ref scheduling) = distributed.scheduling {
                let work_stealing = scheduling.work_stealing.unwrap_or(true);
                tracing::debug!("Dask scheduling work stealing: {}", work_stealing);

                if work_stealing {
                    let interval = scheduling.work_stealing_interval.unwrap_or(0.1);
                    tracing::debug!("Dask work stealing interval: {} seconds", interval);
                }

                let unknown_task_duration = scheduling.unknown_task_duration.unwrap_or(0.5);
                tracing::debug!(
                    "Dask unknown task duration: {} seconds",
                    unknown_task_duration
                );
            }

            if let Some(ref diagnostics) = distributed.diagnostics {
                let progress_bar = diagnostics.progress_bar.unwrap_or(true);
                tracing::debug!("Dask diagnostics progress bar: {}", progress_bar);

                let profile = diagnostics.profile.unwrap_or(false);
                tracing::debug!("Dask diagnostics profile: {}", profile);

                let memory_profiling = diagnostics.memory_profiling.unwrap_or(false);
                tracing::debug!("Dask diagnostics memory profiling: {}", memory_profiling);
            }
        }
        Ok(())
    }

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

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

    /// Check if Dask 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 Dask client is active
    pub fn is_client_active(&self) -> bool {
        self.client_active
    }

    /// Submit task to Dask cluster
    pub fn submit_task(&mut self, task_name: &str, task_size: usize) -> TorshResult<String> {
        if !self.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

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

        tracing::debug!("Submitting Dask task: {} ({} bytes)", task_name, task_size);

        // Simulate task execution
        let task_id = format!("task_{}_{}", task_name, self.stats.tasks_executed);

        // Update statistics
        self.stats.tasks_executed += 1;
        let execution_time = start_time.elapsed().as_secs_f64();
        self.stats.task_execution_time_sec += execution_time;
        self.stats.average_task_duration_sec =
            self.stats.task_execution_time_sec / self.stats.tasks_executed as f64;
        self.stats.data_transferred_bytes += task_size as u64;

        tracing::debug!("Dask task submitted: {} (ID: {})", task_name, task_id);
        Ok(task_id)
    }

    /// Compute Dask collection
    pub fn compute(&mut self, collection_name: &str) -> TorshResult<()> {
        if !self.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

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

        tracing::info!("Computing Dask collection: {}", collection_name);

        // Simulate computation
        let num_tasks = 10; // Simulate breaking down into tasks
        for i in 0..num_tasks {
            self.submit_task(&format!("{}_task_{}", collection_name, i), 1024)?;
        }

        let execution_time = start_time.elapsed().as_secs_f64();
        tracing::info!(
            "Dask collection computed: {} in {:.2}s",
            collection_name,
            execution_time
        );
        Ok(())
    }

    /// Scale Dask cluster
    pub fn scale_cluster(&mut self, target_workers: u32) -> TorshResult<()> {
        if !self.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        tracing::info!("Scaling Dask cluster to {} workers", target_workers);

        if let Some(ref cluster) = self.config.cluster {
            if let Some(ref scaling) = cluster.scaling {
                let minimum = scaling.minimum.unwrap_or(1);
                let maximum = scaling.maximum.unwrap_or(100);

                if target_workers < minimum {
                    return Err(TorshDistributedError::invalid_argument(
                        "target_workers",
                        format!("Cannot scale below minimum: {}", minimum),
                        format!("At least {} workers", minimum),
                    ));
                }

                if target_workers > maximum {
                    return Err(TorshDistributedError::invalid_argument(
                        "target_workers",
                        format!("Cannot scale above maximum: {}", maximum),
                        format!("At most {} workers", maximum),
                    ));
                }
            }
        }

        self.stats.workers_connected = target_workers;
        tracing::info!("Dask cluster scaled to {} workers", target_workers);
        Ok(())
    }

    /// Handle worker failure
    pub fn handle_worker_failure(&mut self, worker_id: u32) -> TorshResult<()> {
        tracing::warn!("Dask worker {} failed", worker_id);
        self.stats.worker_failures += 1;

        // Decrease worker count
        if self.stats.workers_connected > 0 {
            self.stats.workers_connected -= 1;
        }

        // Auto-scale if configured
        if let Some(ref cluster) = self.config.cluster {
            if let Some(ref scaling) = cluster.scaling {
                let minimum = scaling.minimum.unwrap_or(1);
                if self.stats.workers_connected < minimum {
                    tracing::info!("Auto-scaling Dask cluster due to worker failure");
                    self.scale_cluster(minimum)?;
                }
            }
        }

        Ok(())
    }

    /// Shutdown Dask integration
    pub fn shutdown(&mut self) -> TorshResult<()> {
        if self.client_active {
            tracing::info!("Shutting down Dask integration");
            self.client_active = false;
            self.initialized = false;
            self.stats.workers_connected = 0;
        }
        Ok(())
    }

    /// Create a default Dask configuration
    pub fn default_config() -> DaskConfig {
        DaskConfig {
            cluster: Some(DaskClusterConfig {
                cluster_type: DaskClusterType::Local,
                n_workers: Some(4),
                threads_per_worker: Some(2),
                memory_limit: Some("4GB".to_string()),
                processes: Some(false),
                dashboard_address: Some("127.0.0.1:8787".to_string()),
                silence_logs: Some(false),
                security: None,
                scaling: Some(DaskScalingConfig {
                    minimum: Some(1),
                    maximum: Some(10),
                    target_cpu: Some(0.8),
                    target_memory: Some(0.8),
                    scale_up_threshold: Some(0.8),
                    scale_down_threshold: Some(0.2),
                    interval: Some(30.0),
                }),
            }),
            scheduler: Some(DaskSchedulerConfig {
                address: None,
                port: Some(8786),
                dashboard_port: Some(8787),
                bokeh_port: Some(8788),
                worker_timeout: Some(60.0),
                idle_timeout: Some(1800.0),
                transition_log_length: Some(100000),
                task_duration_overhead: Some(0.1),
                allowed_failures: Some(3),
            }),
            worker: Some(DaskWorkerConfig {
                nworkers: Some(4),
                nthreads: Some(2),
                memory_limit: Some("4GB".to_string()),
                worker_port: Some("40000:40100".to_string()),
                nanny_port: Some("40100:40200".to_string()),
                dashboard_port: Some(8789),
                death_timeout: Some(60.0),
                preload: None,
                env: None,
                resources: None,
            }),
            array: Some(DaskArrayConfig {
                chunk_size: Some("128MB".to_string()),
                backend: Some("numpy".to_string()),
                overlap: Some(0),
                boundary: Some("reflect".to_string()),
                trim: Some(true),
                rechunk_threshold: Some(4.0),
            }),
            dataframe: Some(DaskDataFrameConfig {
                partition_size: Some("128MB".to_string()),
                shuffle_method: Some(DaskShuffleMethod::Tasks),
                query_planning: Some(false),
                backend: Some("pandas".to_string()),
                optimize_index: Some(true),
            }),
            bag: Some(DaskBagConfig {
                partition_size: Some(134217728), // 128MB
                compression: Some("gzip".to_string()),
                encoding: Some("utf-8".to_string()),
                linedelimiter: Some("\n".to_string()),
            }),
            ml: None,
            distributed: Some(DaskDistributedConfig {
                comm: Some(DaskCommConfig {
                    compression: Some("lz4".to_string()),
                    serializers: Some(vec!["pickle".to_string(), "msgpack".to_string()]),
                    timeouts: Some(DaskTimeoutsConfig {
                        connect: Some(10.0),
                        tcp: Some(30.0),
                    }),
                    tcp: Some(DaskTcpConfig {
                        reuse_port: Some(false),
                        no_delay: Some(true),
                        keep_alive: Some(false),
                    }),
                }),
                serialization: Some(DaskSerializationConfig {
                    compression: Some(vec!["lz4".to_string(), "zlib".to_string()]),
                    default_serializers: Some(vec!["pickle".to_string()]),
                    pickle_protocol: Some(4),
                }),
                client: Some(DaskClientConfig {
                    heartbeat_interval: Some(5.0),
                    scheduler_info_interval: Some(2.0),
                    task_metadata: Some(vec!["task_name".to_string(), "worker".to_string()]),
                    set_as_default: Some(true),
                }),
                scheduling: Some(DaskSchedulingConfig {
                    work_stealing: Some(true),
                    work_stealing_interval: Some(0.1),
                    unknown_task_duration: Some(0.5),
                    default_task_durations: None,
                }),
                diagnostics: Some(DaskDiagnosticsConfig {
                    progress_bar: Some(true),
                    profile: Some(false),
                    memory_profiling: Some(false),
                    task_stream: Some(false),
                    resource_monitor: Some(false),
                }),
            }),
        }
    }

    /// Create a configuration for machine learning workloads
    pub fn config_with_ml() -> DaskConfig {
        let mut config = Self::default_config();

        config.ml = Some(DaskMLConfig {
            model_selection: Some(DaskMLModelSelectionConfig {
                cv_folds: Some(5),
                scoring: Some("accuracy".to_string()),
                n_jobs: Some(-1),
                search_method: Some(DaskMLSearchMethod::RandomSearch),
            }),
            preprocessing: Some(DaskMLPreprocessingConfig {
                standardization: Some("StandardScaler".to_string()),
                categorical_encoding: Some("OneHotEncoder".to_string()),
                feature_selection: Some("SelectKBest".to_string()),
                dimensionality_reduction: Some("PCA".to_string()),
            }),
            linear_model: Some(DaskMLLinearModelConfig {
                solver: Some("lbfgs".to_string()),
                alpha: Some(1.0),
                max_iter: Some(1000),
                tol: Some(1e-4),
            }),
            ensemble: Some(DaskMLEnsembleConfig {
                n_estimators: Some(100),
                bootstrap: Some(true),
                random_state: Some(42),
                oob_score: Some(true),
            }),
            cluster: Some(DaskMLClusterConfig {
                n_clusters: Some(8),
                init: Some("k-means++".to_string()),
                max_iter: Some(300),
                tol: Some(1e-4),
                n_init: Some(10),
            }),
        });

        config
    }

    /// Create a configuration for large-scale distributed computing
    pub fn config_with_large_scale(n_workers: u32, memory_per_worker: &str) -> DaskConfig {
        let mut config = Self::default_config();

        if let Some(ref mut cluster) = config.cluster {
            cluster.n_workers = Some(n_workers);
            cluster.memory_limit = Some(memory_per_worker.to_string());
            cluster.processes = Some(true); // Use processes for large scale

            if let Some(ref mut scaling) = cluster.scaling {
                scaling.minimum = Some(n_workers / 2);
                scaling.maximum = Some(n_workers * 2);
            }
        }

        if let Some(ref mut worker) = config.worker {
            worker.nworkers = Some(n_workers);
            worker.memory_limit = Some(memory_per_worker.to_string());
        }

        // Optimize for large scale
        if let Some(ref mut distributed) = config.distributed {
            if let Some(ref mut comm) = distributed.comm {
                comm.compression = Some("zstd".to_string()); // Better compression for large data
            }

            if let Some(ref mut scheduling) = distributed.scheduling {
                scheduling.work_stealing_interval = Some(0.5); // Less aggressive for stability
            }
        }

        config
    }
}

impl Default for DaskConfig {
    fn default() -> Self {
        DaskIntegration::default_config()
    }
}

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

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

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

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

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

        // Submit tasks
        let task_id1 = integration.submit_task("compute_gradient", 1024).unwrap();
        let task_id2 = integration.submit_task("update_parameters", 2048).unwrap();

        assert!(task_id1.contains("compute_gradient"));
        assert!(task_id2.contains("update_parameters"));

        let stats = integration.stats();
        assert_eq!(stats.tasks_executed, 2);
        assert!(stats.task_execution_time_sec > 0.0);
        assert_eq!(stats.data_transferred_bytes, 3072);
        assert!(stats.average_task_duration_sec > 0.0);
    }

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

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

        // Compute collection (should submit multiple tasks internally)
        assert!(integration.compute("training_dataset").is_ok());

        let stats = integration.stats();
        assert_eq!(stats.tasks_executed, 10); // Should create 10 tasks
        assert!(stats.task_execution_time_sec > 0.0);
    }

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

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

        // Scale up
        assert!(integration.scale_cluster(8).is_ok());
        assert_eq!(integration.stats().workers_connected, 8);

        // Scale down
        assert!(integration.scale_cluster(2).is_ok());
        assert_eq!(integration.stats().workers_connected, 2);

        // Should fail scaling below minimum
        assert!(integration.scale_cluster(0).is_err());

        // Should fail scaling above maximum
        assert!(integration.scale_cluster(20).is_err());
    }

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

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

        // Simulate worker failure
        assert!(integration.handle_worker_failure(1).is_ok());
        assert_eq!(integration.stats().worker_failures, 1);
        assert_eq!(integration.stats().workers_connected, 3);

        // Should auto-scale back to minimum if configured
        assert!(integration.handle_worker_failure(2).is_ok());
        assert!(integration.handle_worker_failure(3).is_ok());
        assert_eq!(integration.stats().worker_failures, 3);
        assert_eq!(integration.stats().workers_connected, 1); // Auto-scaled to minimum
    }

    #[test]
    fn test_dask_ml_config() {
        let config = DaskIntegration::config_with_ml();
        let mut integration = DaskIntegration::new(config);

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

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

        if let Some(ref ml) = integration.config().ml {
            assert!(ml.model_selection.is_some());
            assert!(ml.preprocessing.is_some());
            assert!(ml.linear_model.is_some());
            assert!(ml.ensemble.is_some());
            assert!(ml.cluster.is_some());

            if let Some(ref model_selection) = ml.model_selection {
                assert_eq!(model_selection.cv_folds, Some(5));
                assert_eq!(
                    model_selection.search_method,
                    Some(DaskMLSearchMethod::RandomSearch)
                );
            }
        }
    }

    #[test]
    fn test_dask_large_scale_config() {
        let config = DaskIntegration::config_with_large_scale(16, "8GB");
        let mut integration = DaskIntegration::new(config);

        assert!(integration.initialize(0, 16, 0, 4).is_ok());

        // Check large scale configuration
        if let Some(ref cluster) = integration.config().cluster {
            assert_eq!(cluster.n_workers, Some(16));
            assert_eq!(cluster.memory_limit, Some("8GB".to_string()));
            assert_eq!(cluster.processes, Some(true));

            if let Some(ref scaling) = cluster.scaling {
                assert_eq!(scaling.minimum, Some(8));
                assert_eq!(scaling.maximum, Some(32));
            }
        }
    }

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

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

        assert!(integration.shutdown().is_ok());
        assert!(!integration.is_client_active());
        assert!(!integration.is_initialized());
        assert_eq!(integration.stats().workers_connected, 0);
    }

    #[test]
    fn test_dask_config_serialization() {
        let config = DaskIntegration::config_with_ml();

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

        // Test deserialization
        let deserialized: DaskConfig = serde_json::from_str(&json).unwrap();
        assert!(deserialized.cluster.is_some());
        assert!(deserialized.ml.is_some());

        if let Some(cluster) = deserialized.cluster {
            assert_eq!(cluster.cluster_type, DaskClusterType::Local);
        }
    }

    #[test]
    fn test_dask_invalid_config() {
        let mut config = DaskIntegration::default_config();

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

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

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

    #[test]
    fn test_dask_scaling_validation() {
        let mut config = DaskIntegration::default_config();

        // Set invalid scaling config
        if let Some(ref mut cluster) = config.cluster {
            if let Some(ref mut scaling) = cluster.scaling {
                scaling.minimum = Some(10);
                scaling.maximum = Some(5); // Invalid: min > max
            }
        }

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

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