mlmf 0.2.0

//! Core distributed functionality implementation.
//!
//! This module provides the essential distributed model loading capabilities
//! with a focus on practical deployment scenarios.

use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
use std::path::Path;
use std::time::Instant;

use crate::distributed::*;
use crate::distributed_loader::*;
use crate::cached_loader::CachedModelLoader;
use anyhow::{Result, anyhow};
// Simplified without LoadedModel dependency for now

/// Simple distributed model manager for practical deployment
pub struct SimpleDistributedManager {
    /// Configuration
    config: Arc<DistributedConfig>,
    
    /// Node registry
    nodes: Arc<RwLock<HashMap<String, SimpleNodeInfo>>>,
    
    /// Model registry
    models: Arc<RwLock<HashMap<String, SimpleDistributedModel>>>,
    
    /// Cached loader for individual nodes
    cached_loader: Arc<CachedModelLoader>,
    
    /// Simple load balancer
    load_balancer: Arc<SimpleLoadBalancer>,
}

/// Simplified node information
#[derive(Debug, Clone)]
pub struct SimpleNodeInfo {
    /// Node configuration
    pub config: NodeConfig,
    
    /// Current load (0.0 to 1.0)
    pub current_load: f32,
    
    /// Available memory in bytes
    pub available_memory: u64,
    
    /// Node health status
    pub healthy: bool,
    
    /// Last heartbeat
    pub last_heartbeat: Instant,
    
    /// Active model shards
    pub active_shards: Vec<String>,
}

/// Simplified distributed model
#[derive(Debug, Clone)]
pub struct SimpleDistributedModel {
    /// Model ID
    pub model_id: String,
    
    /// Sharding configuration
    pub shards: Vec<SimpleModelShard>,
    
    /// Load balancing strategy
    pub load_strategy: LoadBalancingStrategy,
    
    /// Deployment status
    pub status: DeploymentStatus,
}

/// Simplified model shard
#[derive(Debug, Clone)]
pub struct SimpleModelShard {
    /// Shard ID
    pub shard_id: String,
    
    /// Node hosting this shard
    pub node_id: String,
    
    /// Model path on the node
    pub model_path: String,
    
    /// Shard size estimate
    pub size_bytes: u64,
    
    /// Load status
    pub loaded: bool,
}

/// Simple load balancer implementation
pub struct SimpleLoadBalancer {
    /// Current routing strategy
    strategy: LoadBalancingStrategy,
    
    /// Round-robin counter
    round_robin_counter: Arc<RwLock<usize>>,
    
    /// Node weights for weighted strategies
    node_weights: Arc<RwLock<HashMap<String, f32>>>,
}

/// Inference request for distributed models
#[derive(Debug, Clone)]
pub struct InferenceRequest {
    /// Model ID to use for inference
    pub model_id: String,
    
    /// Input data for inference
    pub input_data: Vec<f32>, // Simplified input format
    
    /// Optional session ID for sticky sessions
    pub session_id: Option<String>,
    
    /// Request priority
    pub priority: RequestPriority,
}

/// Priority levels for inference requests
#[derive(Debug, Clone, PartialEq, PartialOrd)]
pub enum RequestPriority {
    /// Low priority request
    Low,
    
    /// Normal priority request
    Normal,
    
    /// High priority request
    High,
    
    /// Critical priority request
    Critical,
}

/// Response from distributed inference
#[derive(Debug, Clone)]
pub struct InferenceResponse {
    /// Request ID
    pub request_id: String,
    
    /// Output data from inference
    pub output_data: Vec<f32>, // Simplified output format
    
    /// Node that processed the request
    pub processed_by: String,
    
    /// Processing time in milliseconds
    pub processing_time_ms: u64,
    
    /// Model version used
    pub model_version: String,
}

impl SimpleDistributedManager {
    /// Create a new simple distributed manager
    pub fn new(config: DistributedConfig) -> Result<Self> {
        let config = Arc::new(config);
        let cached_loader = Arc::new(CachedModelLoader::new());
        let load_balancer = Arc::new(SimpleLoadBalancer::new(LoadBalancingStrategy::RoundRobin));
        
        Ok(Self {
            config,
            nodes: Arc::new(RwLock::new(HashMap::new())),
            models: Arc::new(RwLock::new(HashMap::new())),
            cached_loader,
            load_balancer,
        })
    }

    /// Register a node with the distributed manager
    pub async fn register_node(&self, node_config: NodeConfig) -> Result<()> {
        let node_info = SimpleNodeInfo {
            config: node_config.clone(),
            current_load: 0.0,
            available_memory: node_config.capabilities.total_memory,
            healthy: true,
            last_heartbeat: Instant::now(),
            active_shards: Vec::new(),
        };

        let mut nodes = self.nodes.write().await;
        nodes.insert(node_config.node_id.clone(), node_info);
        
        println!("✅ Registered node: {}", node_config.node_id);
        Ok(())
    }

    /// Deploy a model across available nodes
    pub async fn deploy_model<P: AsRef<Path>>(
        &self,
        model_path: P,
        model_id: String,
        sharding_strategy: ShardingStrategy,
    ) -> Result<()> {
        let model_path = model_path.as_ref();
        let nodes = self.nodes.read().await;
        
        if nodes.is_empty() {
            return Err(anyhow!("No nodes available for deployment"));
        }

        // Create deployment plan based on sharding strategy
        let shards = self.create_sharding_plan(&model_id, &sharding_strategy, &nodes).await?;
        
        // Deploy shards to nodes
        for shard in &shards {
            self.deploy_shard_to_node(model_path, &shard).await?;
        }

        // Register the distributed model
        let distributed_model = SimpleDistributedModel {
            model_id: model_id.clone(),
            shards,
            load_strategy: self.config.load_balancing.strategy.clone(),
            status: DeploymentStatus::Ready,
        };

        let mut models = self.models.write().await;
        models.insert(model_id.clone(), distributed_model);
        
        println!("✅ Deployed distributed model: {}", model_id);
        Ok(())
    }

    /// Create a sharding plan for model deployment
    async fn create_sharding_plan(
        &self,
        model_id: &str,
        strategy: &ShardingStrategy,
        nodes: &HashMap<String, SimpleNodeInfo>,
    ) -> Result<Vec<SimpleModelShard>> {
        let mut shards = Vec::new();
        
        match strategy {
            ShardingStrategy::NoSharding => {
                // Deploy full model to all nodes
                for (node_id, _) in nodes {
                    shards.push(SimpleModelShard {
                        shard_id: format!("{}_full", model_id),
                        node_id: node_id.clone(),
                        model_path: format!("/models/{}", model_id),
                        size_bytes: 1_000_000_000, // Estimate 1GB
                        loaded: true,
                    });
                }
            }
            
            ShardingStrategy::LayerSharding { layers_per_shard } => {
                // Create layer-based shards
                let available_nodes: Vec<_> = nodes.keys().collect();
                let num_shards = (20 / layers_per_shard).max(1); // Assume 20 layers
                
                for i in 0..num_shards {
                    let node_id = available_nodes[i % available_nodes.len()].clone();
                    shards.push(SimpleModelShard {
                        shard_id: format!("{}_layers_{}_{}", model_id, i * layers_per_shard, (i + 1) * layers_per_shard),
                        node_id,
                        model_path: format!("/models/{}/shard_{}", model_id, i),
                        size_bytes: 500_000_000, // Estimate 500MB per shard
                        loaded: true,
                    });
                }
            }
            
            ShardingStrategy::PipelineSharding { num_stages } => {
                // Create pipeline stages
                let available_nodes: Vec<_> = nodes.keys().collect();
                
                for i in 0..*num_stages {
                    let node_id = available_nodes[i % available_nodes.len()].clone();
                    shards.push(SimpleModelShard {
                        shard_id: format!("{}_stage_{}", model_id, i),
                        node_id,
                        model_path: format!("/models/{}/stage_{}", model_id, i),
                        size_bytes: 800_000_000 / *num_stages as u64, // Distribute evenly
                        loaded: true,
                    });
                }
            }
            
            _ => {
                // Default to no sharding for unsupported strategies
                for (node_id, _) in nodes {
                    shards.push(SimpleModelShard {
                        shard_id: format!("{}_default", model_id),
                        node_id: node_id.clone(),
                        model_path: format!("/models/{}", model_id),
                        size_bytes: 1_000_000_000,
                        loaded: true,
                    });
                }
            }
        }
        
        Ok(shards)
    }

    /// Deploy a single shard to a node
    async fn deploy_shard_to_node<P: AsRef<Path>>(
        &self,
        model_path: P,
        shard: &SimpleModelShard,
    ) -> Result<()> {
        // In a real implementation, this would:
        // 1. Transfer model data to the target node
        // 2. Load the shard into memory on the node
        // 3. Register the shard as available
        
        println!("📦 Deploying shard {} to node {}", shard.shard_id, shard.node_id);
        
        // Simulate deployment delay
        tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
        
        Ok(())
    }

    /// Process an inference request
    pub async fn inference(&self, request: InferenceRequest) -> Result<InferenceResponse> {
        let models = self.models.read().await;
        let model = models.get(&request.model_id)
            .ok_or_else(|| anyhow!("Model not found: {}", request.model_id))?;

        // Select a node using load balancing
        let selected_node = self.load_balancer
            .select_node(&model.shards, &request)
            .await?;

        // Process inference on selected node
        let start_time = Instant::now();
        
        // Simulate inference processing
        let output_data = self.process_inference_on_node(
            &selected_node,
            &request,
        ).await?;
        
        let processing_time = start_time.elapsed();
        
        Ok(InferenceResponse {
            request_id: uuid::Uuid::new_v4().to_string(),
            output_data,
            processed_by: selected_node,
            processing_time_ms: processing_time.as_millis() as u64,
            model_version: "1.0.0".to_string(),
        })
    }

    /// Process inference on a specific node
    async fn process_inference_on_node(
        &self,
        node_id: &str,
        request: &InferenceRequest,
    ) -> Result<Vec<f32>> {
        // In a real implementation, this would:
        // 1. Send the request to the target node
        // 2. Execute inference using the loaded model shard
        // 3. Return the results
        
        println!("🧠 Processing inference for model {} on node {}", 
                request.model_id, node_id);
        
        // Simulate processing
        tokio::time::sleep(tokio::time::Duration::from_millis(50)).await;
        
        // Return dummy output
        Ok(vec![0.5, 0.3, 0.2, 0.1])
    }

    /// Get cluster status
    pub async fn get_cluster_status(&self) -> ClusterStatus {
        let nodes = self.nodes.read().await;
        let models = self.models.read().await;
        
        let healthy_nodes = nodes.values()
            .filter(|node| node.healthy)
            .count();
        
        let total_shards = models.values()
            .map(|model| model.shards.len())
            .sum();
        
        let loaded_shards = models.values()
            .flat_map(|model| &model.shards)
            .filter(|shard| shard.loaded)
            .count();

        ClusterStatus {
            total_nodes: nodes.len(),
            healthy_nodes,
            total_models: models.len(),
            total_shards,
            loaded_shards,
            cluster_health: if healthy_nodes == nodes.len() {
                ClusterHealthStatus::Healthy
            } else if healthy_nodes > nodes.len() / 2 {
                ClusterHealthStatus::Degraded
            } else {
                ClusterHealthStatus::Critical
            },
        }
    }

    /// List all distributed models
    pub async fn list_models(&self) -> Vec<String> {
        let models = self.models.read().await;
        models.keys().cloned().collect()
    }

    /// Get model information
    pub async fn get_model_info(&self, model_id: &str) -> Option<SimpleDistributedModel> {
        let models = self.models.read().await;
        models.get(model_id).cloned()
    }
}

/// Cluster status information
#[derive(Debug, Clone)]
pub struct ClusterStatus {
    /// Total number of nodes
    pub total_nodes: usize,
    
    /// Number of healthy nodes
    pub healthy_nodes: usize,
    
    /// Total number of models
    pub total_models: usize,
    
    /// Total number of shards
    pub total_shards: usize,
    
    /// Number of loaded shards
    pub loaded_shards: usize,
    
    /// Overall cluster health
    pub cluster_health: ClusterHealthStatus,
}

impl SimpleLoadBalancer {
    /// Create a new simple load balancer
    pub fn new(strategy: LoadBalancingStrategy) -> Self {
        Self {
            strategy,
            round_robin_counter: Arc::new(RwLock::new(0)),
            node_weights: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Select a node for processing a request
    pub async fn select_node(
        &self,
        shards: &[SimpleModelShard],
        request: &InferenceRequest,
    ) -> Result<String> {
        if shards.is_empty() {
            return Err(anyhow!("No shards available for request"));
        }

        match self.strategy {
            LoadBalancingStrategy::RoundRobin => {
                let mut counter = self.round_robin_counter.write().await;
                let index = *counter % shards.len();
                *counter += 1;
                Ok(shards[index].node_id.clone())
            }
            
            LoadBalancingStrategy::WeightedRoundRobin => {
                // For simplicity, fall back to round-robin
                let mut counter = self.round_robin_counter.write().await;
                let index = *counter % shards.len();
                *counter += 1;
                Ok(shards[index].node_id.clone())
            }
            
            _ => {
                // Default to first available shard
                Ok(shards[0].node_id.clone())
            }
        }
    }

    /// Update node weights for weighted load balancing
    pub async fn update_node_weight(&self, node_id: String, weight: f32) {
        let mut weights = self.node_weights.write().await;
        weights.insert(node_id, weight);
    }
}

/// Convenience functions for distributed operations
impl SimpleDistributedManager {
    /// Create a simple single-node deployment
    pub async fn create_single_node_deployment<P: AsRef<Path>>(
        model_path: P,
        model_id: String,
        node_address: std::net::SocketAddr,
    ) -> Result<Self> {
        let node_config = NodeConfig {
            node_id: "node-0".to_string(),
            address: node_address,
            devices: vec![DeviceInfo {
                device_id: "cpu".to_string(),
                device_type: DeviceType::Cpu,
                memory_bytes: 8 * 1024 * 1024 * 1024, // 8GB
                compute_score: 1.0,
                utilization: 0.0,
            }],
            capabilities: NodeCapabilities {
                total_memory: 8 * 1024 * 1024 * 1024,
                network_bandwidth: 1_000_000_000, // 1Gbps
                storage_capacity: 1_000_000_000_000, // 1TB
                supported_dtypes: vec!["f32".to_string(), "f16".to_string()],
                special_capabilities: vec!["inference".to_string()],
            },
            role: NodeRole::Hybrid(vec![NodeRole::Coordinator, NodeRole::Worker]),
        };

        let config = DistributedConfig {
            nodes: vec![node_config.clone()],
            sharding_strategy: ShardingStrategy::NoSharding,
            device_placement: DevicePlacementConfig::default(),
            communication: CommunicationConfig::default(),
            load_balancing: LoadBalancingConfig::default(),
            fault_tolerance: FaultToleranceConfig::default(),
        };

        let manager = Self::new(config)?;
        manager.register_node(node_config).await?;
        manager.deploy_model(
            model_path, 
            model_id, 
            ShardingStrategy::NoSharding
        ).await?;

        Ok(manager)
    }

    /// Create a multi-node cluster deployment
    pub async fn create_cluster_deployment<P: AsRef<Path>>(
        model_path: P,
        model_id: String,
        node_addresses: Vec<std::net::SocketAddr>,
        sharding_strategy: ShardingStrategy,
    ) -> Result<Self> {
        let mut nodes = Vec::new();
        
        for (i, address) in node_addresses.iter().enumerate() {
            let node_config = NodeConfig {
                node_id: format!("node-{}", i),
                address: *address,
                devices: vec![DeviceInfo {
                    device_id: "cpu".to_string(),
                    device_type: DeviceType::Cpu,
                    memory_bytes: 16 * 1024 * 1024 * 1024, // 16GB
                    compute_score: 1.0,
                    utilization: 0.0,
                }],
                capabilities: NodeCapabilities {
                    total_memory: 16 * 1024 * 1024 * 1024,
                    network_bandwidth: 10_000_000_000, // 10Gbps
                    storage_capacity: 2_000_000_000_000, // 2TB
                    supported_dtypes: vec!["f32".to_string(), "f16".to_string()],
                    special_capabilities: vec!["inference".to_string(), "distributed".to_string()],
                },
                role: if i == 0 { NodeRole::Coordinator } else { NodeRole::Worker },
            };
            nodes.push(node_config);
        }

        let config = DistributedConfig {
            nodes: nodes.clone(),
            sharding_strategy: sharding_strategy.clone(),
            device_placement: DevicePlacementConfig::default(),
            communication: CommunicationConfig::default(),
            load_balancing: LoadBalancingConfig {
                strategy: LoadBalancingStrategy::RoundRobin,
                health_check: HealthCheckConfig::default(),
                routing: RoutingConfig::default(),
            },
            fault_tolerance: FaultToleranceConfig::default(),
        };

        let manager = Self::new(config)?;
        
        // Register all nodes
        for node in nodes {
            manager.register_node(node).await?;
        }
        
        // Deploy the model with specified sharding
        manager.deploy_model(model_path, model_id, sharding_strategy).await?;

        Ok(manager)
    }
}