oxirs-chat 0.2.4

//! Custom Model Fine-Tuning Module
//!
//! Provides comprehensive fine-tuning capabilities for domain-specific optimization
//! including training data management, model adaptation, and performance monitoring.

use anyhow::{anyhow, Result};
use serde::{Deserialize, Serialize};
use std::{
    collections::HashMap,
    path::PathBuf,
    sync::Arc,
    time::{Duration, SystemTime},
};
use tokio::sync::RwLock;

use super::types::ChatMessage;

/// Fine-tuning job configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FineTuningConfig {
    pub job_id: String,
    pub base_model: String,
    pub dataset_path: PathBuf,
    pub output_model_name: String,
    pub training_parameters: TrainingParameters,
    pub validation_split: f32,
    pub max_training_time: Duration,
    pub quality_threshold: f32,
}

/// Training parameters for fine-tuning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingParameters {
    pub learning_rate: f32,
    pub batch_size: usize,
    pub num_epochs: usize,
    pub warmup_steps: usize,
    pub weight_decay: f32,
    pub gradient_accumulation_steps: usize,
    pub max_sequence_length: usize,
    pub early_stopping_patience: usize,
}

impl Default for TrainingParameters {
    fn default() -> Self {
        Self {
            learning_rate: 2e-5,
            batch_size: 4,
            num_epochs: 3,
            warmup_steps: 100,
            weight_decay: 0.01,
            gradient_accumulation_steps: 1,
            max_sequence_length: 512,
            early_stopping_patience: 2,
        }
    }
}

/// Training dataset format
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingExample {
    pub id: String,
    pub messages: Vec<ChatMessage>,
    pub expected_response: String,
    pub metadata: HashMap<String, serde_json::Value>,
    pub quality_score: Option<f32>,
    pub domain: String,
    pub difficulty: TrainingDifficulty,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TrainingDifficulty {
    Basic,
    Intermediate,
    Advanced,
    Expert,
}

/// Fine-tuning job status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FineTuningJob {
    pub id: String,
    pub config: FineTuningConfig,
    pub status: JobStatus,
    pub created_at: SystemTime,
    pub started_at: Option<SystemTime>,
    pub completed_at: Option<SystemTime>,
    pub progress: TrainingProgress,
    pub metrics: TrainingMetrics,
    pub artifacts: JobArtifacts,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum JobStatus {
    Queued,
    Preparing,
    Training,
    Validating,
    Completed,
    Failed(String),
    Cancelled,
}

/// Training progress tracking
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct TrainingProgress {
    pub current_epoch: usize,
    pub total_epochs: usize,
    pub current_step: usize,
    pub total_steps: usize,
    pub examples_processed: usize,
    pub total_examples: usize,
    pub estimated_time_remaining: Option<Duration>,
}

/// Training metrics and evaluation results
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct TrainingMetrics {
    pub train_loss: Vec<f32>,
    pub validation_loss: Vec<f32>,
    pub accuracy: Vec<f32>,
    pub perplexity: Vec<f32>,
    pub bleu_score: Vec<f32>,
    pub rouge_score: Vec<f32>,
    pub domain_specific_metrics: HashMap<String, Vec<f32>>,
    pub best_checkpoint: Option<String>,
    pub final_model_quality: Option<f32>,
}

/// Job artifacts and outputs
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct JobArtifacts {
    pub model_path: Option<PathBuf>,
    pub checkpoint_paths: Vec<PathBuf>,
    pub training_logs: Option<PathBuf>,
    pub evaluation_report: Option<PathBuf>,
    pub config_snapshot: Option<PathBuf>,
}

/// Fine-tuning engine for managing training jobs
pub struct FineTuningEngine {
    jobs: Arc<RwLock<HashMap<String, FineTuningJob>>>,
    active_jobs: Arc<RwLock<HashMap<String, tokio::task::JoinHandle<Result<()>>>>>,
    config: EngineConfig,
}

#[derive(Debug, Clone)]
pub struct EngineConfig {
    pub max_concurrent_jobs: usize,
    pub default_output_dir: PathBuf,
    pub checkpoint_interval: Duration,
    pub auto_cleanup_days: u64,
    pub resource_limits: ResourceLimits,
}

#[derive(Debug, Clone)]
pub struct ResourceLimits {
    pub max_memory_gb: f32,
    pub max_disk_gb: f32,
    pub max_gpu_memory_gb: Option<f32>,
    pub cpu_cores: Option<usize>,
}

impl FineTuningEngine {
    /// Create new fine-tuning engine
    pub fn new(config: EngineConfig) -> Self {
        Self {
            jobs: Arc::new(RwLock::new(HashMap::new())),
            active_jobs: Arc::new(RwLock::new(HashMap::new())),
            config,
        }
    }

    /// Submit a new fine-tuning job
    pub async fn submit_job(&self, config: FineTuningConfig) -> Result<String> {
        let mut jobs = self.jobs.write().await;

        // Check if we're at capacity
        let active_jobs = self.active_jobs.read().await;
        if active_jobs.len() >= self.config.max_concurrent_jobs {
            return Err(anyhow!("Maximum concurrent jobs reached"));
        }

        // Validate configuration
        self.validate_config(&config)?;

        // Create job
        let job = FineTuningJob {
            id: config.job_id.clone(),
            config: config.clone(),
            status: JobStatus::Queued,
            created_at: SystemTime::now(),
            started_at: None,
            completed_at: None,
            progress: TrainingProgress::default(),
            metrics: TrainingMetrics::default(),
            artifacts: JobArtifacts::default(),
        };

        jobs.insert(config.job_id.clone(), job);
        drop(jobs);
        drop(active_jobs);

        // Start job execution
        self.start_job_execution(&config.job_id).await?;

        Ok(config.job_id)
    }

    /// Get job status
    pub async fn get_job_status(&self, job_id: &str) -> Result<FineTuningJob> {
        let jobs = self.jobs.read().await;
        jobs.get(job_id)
            .cloned()
            .ok_or_else(|| anyhow!("Job not found: {}", job_id))
    }

    /// List all jobs
    pub async fn list_jobs(&self) -> Result<Vec<FineTuningJob>> {
        let jobs = self.jobs.read().await;
        Ok(jobs.values().cloned().collect())
    }

    /// Cancel a running job
    pub async fn cancel_job(&self, job_id: &str) -> Result<()> {
        let mut active_jobs = self.active_jobs.write().await;

        if let Some(handle) = active_jobs.remove(job_id) {
            handle.abort();
        }

        let mut jobs = self.jobs.write().await;
        if let Some(job) = jobs.get_mut(job_id) {
            job.status = JobStatus::Cancelled;
            job.completed_at = Some(SystemTime::now());
        }

        Ok(())
    }

    /// Start job execution
    async fn start_job_execution(&self, job_id: &str) -> Result<()> {
        let job_id = job_id.to_string();
        let jobs_clone = self.jobs.clone();
        let job_id_clone = job_id.clone();

        let handle =
            tokio::spawn(async move { Self::execute_training_job(job_id_clone, jobs_clone).await });

        self.active_jobs.write().await.insert(job_id, handle);
        Ok(())
    }

    /// Execute training job
    async fn execute_training_job(
        job_id: String,
        jobs: std::sync::Arc<RwLock<HashMap<String, FineTuningJob>>>,
    ) -> Result<()> {
        // Update job status to preparing
        {
            let mut jobs_lock = jobs.write().await;
            if let Some(job) = jobs_lock.get_mut(&job_id) {
                job.status = JobStatus::Preparing;
                job.started_at = Some(SystemTime::now());
            }
        }

        // Load and validate training data
        let training_data = Self::load_training_data(&job_id, &jobs).await?;

        // Update status to training
        {
            let mut jobs_lock = jobs.write().await;
            if let Some(job) = jobs_lock.get_mut(&job_id) {
                job.status = JobStatus::Training;
                job.progress.total_examples = training_data.len();
                job.progress.total_epochs = job.config.training_parameters.num_epochs;
                job.progress.total_steps =
                    training_data.len() * job.config.training_parameters.num_epochs;
            }
        }

        // Execute training loop
        Self::training_loop(&job_id, &jobs, training_data).await?;

        // Update job completion
        {
            let mut jobs_lock = jobs.write().await;
            if let Some(job) = jobs_lock.get_mut(&job_id) {
                job.status = JobStatus::Completed;
                job.completed_at = Some(SystemTime::now());
            }
        }

        Ok(())
    }

    /// Load training data from configuration
    async fn load_training_data(
        job_id: &str,
        jobs: &std::sync::Arc<RwLock<HashMap<String, FineTuningJob>>>,
    ) -> Result<Vec<TrainingExample>> {
        let jobs_lock = jobs.read().await;
        let _job = jobs_lock
            .get(job_id)
            .ok_or_else(|| anyhow!("Job not found"))?;

        // In a real implementation, this would load from the dataset path
        // For now, return synthetic data
        let mut examples = Vec::new();

        for i in 0..100 {
            examples.push(TrainingExample {
                id: format!("example_{i}"),
                messages: vec![],
                expected_response: format!("Response {i}"),
                metadata: HashMap::new(),
                quality_score: Some(0.8),
                domain: "general".to_string(),
                difficulty: TrainingDifficulty::Intermediate,
            });
        }

        Ok(examples)
    }

    /// Execute training loop with progress tracking
    async fn training_loop(
        job_id: &str,
        jobs: &std::sync::Arc<RwLock<HashMap<String, FineTuningJob>>>,
        training_data: Vec<TrainingExample>,
    ) -> Result<()> {
        let epochs = {
            let jobs_lock = jobs.read().await;
            let job = jobs_lock
                .get(job_id)
                .expect("job should exist for given job_id");
            job.config.training_parameters.num_epochs
        };

        for epoch in 0..epochs {
            // Simulate training epoch
            for (step, _example) in training_data.iter().enumerate() {
                // Update progress
                {
                    let mut jobs_lock = jobs.write().await;
                    if let Some(job) = jobs_lock.get_mut(job_id) {
                        job.progress.current_epoch = epoch;
                        job.progress.current_step = step;
                        job.progress.examples_processed = epoch * training_data.len() + step;

                        // Simulate metrics
                        let loss = 2.0 * (-0.1 * step as f32).exp();
                        job.metrics.train_loss.push(loss);

                        if step % 10 == 0 {
                            let accuracy = 0.5 + 0.4 * (1.0 - (-0.01 * step as f32).exp());
                            job.metrics.accuracy.push(accuracy);
                        }
                    }
                }

                // Simulate training time
                tokio::time::sleep(Duration::from_millis(10)).await;
            }

            // Validation after each epoch
            Self::run_validation(job_id, jobs, epoch).await?;
        }

        Ok(())
    }

    /// Run validation after epoch
    async fn run_validation(
        job_id: &str,
        jobs: &std::sync::Arc<RwLock<HashMap<String, FineTuningJob>>>,
        epoch: usize,
    ) -> Result<()> {
        let mut jobs_lock = jobs.write().await;
        if let Some(job) = jobs_lock.get_mut(job_id) {
            job.status = JobStatus::Validating;

            // Simulate validation metrics
            let val_loss = 1.8 * (-0.08 * epoch as f32).exp();
            job.metrics.validation_loss.push(val_loss);

            let bleu = 0.3 + 0.4 * (1.0 - (-0.2 * epoch as f32).exp());
            job.metrics.bleu_score.push(bleu);

            job.status = JobStatus::Training;
        }

        Ok(())
    }

    /// Validate fine-tuning configuration
    fn validate_config(&self, config: &FineTuningConfig) -> Result<()> {
        if config.job_id.is_empty() {
            return Err(anyhow!("Job ID cannot be empty"));
        }

        if config.base_model.is_empty() {
            return Err(anyhow!("Base model must be specified"));
        }

        if !config.dataset_path.exists() {
            return Err(anyhow!(
                "Dataset path does not exist: {:?}",
                config.dataset_path
            ));
        }

        if config.validation_split < 0.0 || config.validation_split > 1.0 {
            return Err(anyhow!("Validation split must be between 0.0 and 1.0"));
        }

        if config.training_parameters.batch_size == 0 {
            return Err(anyhow!("Batch size must be greater than 0"));
        }

        if config.training_parameters.num_epochs == 0 {
            return Err(anyhow!("Number of epochs must be greater than 0"));
        }

        Ok(())
    }

    /// Get training statistics
    pub async fn get_training_statistics(&self) -> Result<FineTuningStatistics> {
        let jobs = self.jobs.read().await;
        let active_jobs = self.active_jobs.read().await;

        let total_jobs = jobs.len();
        let running_jobs = active_jobs.len();
        let completed_jobs = jobs
            .values()
            .filter(|j| matches!(j.status, JobStatus::Completed))
            .count();
        let failed_jobs = jobs
            .values()
            .filter(|j| matches!(j.status, JobStatus::Failed(_)))
            .count();

        Ok(FineTuningStatistics {
            total_jobs,
            running_jobs,
            completed_jobs,
            failed_jobs,
            average_training_time: Duration::from_secs(3600), // Mock data
            success_rate: if total_jobs > 0 {
                completed_jobs as f32 / total_jobs as f32
            } else {
                0.0
            },
        })
    }
}

/// Fine-tuning engine statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FineTuningStatistics {
    pub total_jobs: usize,
    pub running_jobs: usize,
    pub completed_jobs: usize,
    pub failed_jobs: usize,
    pub average_training_time: Duration,
    pub success_rate: f32,
}

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

    #[tokio::test]
    async fn test_fine_tuning_engine_creation() {
        let config = EngineConfig {
            max_concurrent_jobs: 2,
            default_output_dir: PathBuf::from("/tmp/fine_tuning"),
            checkpoint_interval: Duration::from_secs(300),
            auto_cleanup_days: 30,
            resource_limits: ResourceLimits {
                max_memory_gb: 16.0,
                max_disk_gb: 100.0,
                max_gpu_memory_gb: Some(8.0),
                cpu_cores: Some(8),
            },
        };

        let engine = FineTuningEngine::new(config);
        let stats = engine
            .get_training_statistics()
            .await
            .expect("should succeed");
        assert_eq!(stats.total_jobs, 0);
    }

    #[test]
    fn test_training_parameters_default() {
        let params = TrainingParameters::default();
        assert_eq!(params.learning_rate, 2e-5);
        assert_eq!(params.batch_size, 4);
        assert_eq!(params.num_epochs, 3);
    }

    #[test]
    fn test_training_example_creation() {
        let example = TrainingExample {
            id: "test_1".to_string(),
            messages: vec![],
            expected_response: "Test response".to_string(),
            metadata: HashMap::new(),
            quality_score: Some(0.9),
            domain: "test".to_string(),
            difficulty: TrainingDifficulty::Basic,
        };

        assert_eq!(example.id, "test_1");
        assert_eq!(example.expected_response, "Test response");
        assert!(matches!(example.difficulty, TrainingDifficulty::Basic));
    }
}