//! RLHF trainer implementation providing unified training interface.

use crate::rlhf::{ConstitutionalPrinciple, HumanFeedback, PreferencePair, RLHFMetrics, RLHFPhase};
use std::collections::HashMap;
use trustformers_core::errors::{invalid_config, Result};

/// RLHF trainer configuration
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct RLHFTrainerConfig {
    /// Training phase
    pub phase: RLHFPhase,
    /// Learning rate
    pub learning_rate: f32,
    /// Batch size
    pub batch_size: usize,
    /// Number of training epochs
    pub epochs: usize,
    /// KL penalty coefficient
    pub kl_penalty: f32,
    /// Reward scaling factor
    pub reward_scale: f32,
    /// Maximum sequence length
    pub max_seq_length: usize,
    /// Constitutional AI principles (if using Constitutional phase)
    pub constitutional_principles: Vec<ConstitutionalPrinciple>,
}

impl Default for RLHFTrainerConfig {
    fn default() -> Self {
        Self {
            phase: RLHFPhase::SFT,
            learning_rate: 1e-5,
            batch_size: 8,
            epochs: 3,
            kl_penalty: 0.1,
            reward_scale: 1.0,
            max_seq_length: 512,
            constitutional_principles: Vec::new(),
        }
    }
}

/// Unified RLHF trainer supporting multiple phases
pub struct RLHFTrainer {
    /// Training configuration
    config: RLHFTrainerConfig,
    /// Training metrics
    metrics: RLHFMetrics,
    /// Feedback data
    feedback_data: Vec<HumanFeedback>,
    /// Preference pairs
    preference_pairs: Vec<PreferencePair>,
}

impl RLHFTrainer {
    /// Create a new RLHF trainer
    pub fn new(config: RLHFTrainerConfig) -> Self {
        Self {
            config,
            metrics: RLHFMetrics::default(),
            feedback_data: Vec::new(),
            preference_pairs: Vec::new(),
        }
    }

    /// Add human feedback data
    pub fn add_feedback(&mut self, feedback: HumanFeedback) {
        self.feedback_data.push(feedback);
    }

    /// Add preference pair
    pub fn add_preference_pair(&mut self, pair: PreferencePair) {
        self.preference_pairs.push(pair);
    }

    /// Get current training metrics
    pub fn metrics(&self) -> &RLHFMetrics {
        &self.metrics
    }

    /// Update training phase
    pub fn set_phase(&mut self, phase: RLHFPhase) {
        self.config.phase = phase;
        self.metrics.phase = phase;
    }

    /// Start training for the current phase
    pub async fn train(&mut self) -> Result<RLHFMetrics> {
        match self.config.phase {
            RLHFPhase::SFT => self.train_supervised().await,
            RLHFPhase::RewardModel => self.train_reward_model().await,
            RLHFPhase::PPO => self.train_ppo().await,
            RLHFPhase::DPO => self.train_dpo().await,
            RLHFPhase::Constitutional => self.train_constitutional().await,
        }
    }

    /// Train using supervised fine-tuning
    async fn train_supervised(&mut self) -> Result<RLHFMetrics> {
        if self.feedback_data.is_empty() {
            return Err(invalid_config(
                "No feedback data available for supervised fine-tuning",
                "train_supervised",
            ));
        }

        let mut total_loss = 0.0;
        let num_batches =
            (self.feedback_data.len() + self.config.batch_size - 1) / self.config.batch_size;

        // Simulate training epochs
        for epoch in 0..self.config.epochs {
            let mut epoch_loss = 0.0;

            // Process batches
            for batch_idx in 0..num_batches {
                let start_idx = batch_idx * self.config.batch_size;
                let end_idx =
                    std::cmp::min(start_idx + self.config.batch_size, self.feedback_data.len());
                let batch = &self.feedback_data[start_idx..end_idx];

                // Simulate forward pass and loss calculation
                let batch_loss = self.compute_supervised_loss(batch)?;
                epoch_loss += batch_loss;

                // Simulate learning rate decay
                let adjusted_lr = self.config.learning_rate * (0.95_f32).powi(epoch as i32);
                epoch_loss *= 1.0 - adjusted_lr;
            }

            total_loss += epoch_loss / num_batches as f32;
        }

        // Update metrics with realistic values
        self.metrics.policy_loss = Some(total_loss / self.config.epochs as f32);
        self.metrics.phase = RLHFPhase::SFT;

        Ok(self.metrics.clone())
    }

    /// Compute supervised fine-tuning loss
    fn compute_supervised_loss(&self, batch: &[HumanFeedback]) -> Result<f32> {
        let mut loss = 0.0;

        for feedback in batch {
            // Simulate cross-entropy loss based on feedback quality
            let target_quality = feedback.rating / 5.0; // Normalize to 0-1
            let predicted_quality = 0.5 + (target_quality - 0.5) * 0.8; // Simulate prediction

            // Cross-entropy loss approximation
            let ce_loss = -(target_quality * predicted_quality.ln()
                + (1.0 - target_quality) * (1.0 - predicted_quality).ln());

            loss += ce_loss;
        }

        Ok(loss / batch.len() as f32)
    }

    /// Train reward model
    async fn train_reward_model(&mut self) -> Result<RLHFMetrics> {
        if self.preference_pairs.is_empty() {
            return Err(invalid_config(
                "No preference pairs available for reward model training",
                "train_reward_model",
            ));
        }

        let mut total_accuracy = 0.0;
        #[allow(unused_variables)]
        let mut total_loss = 0.0;
        let num_batches =
            (self.preference_pairs.len() + self.config.batch_size - 1) / self.config.batch_size;

        // Simulate training epochs
        for _epoch in 0..self.config.epochs {
            let mut epoch_accuracy = 0.0;
            let mut epoch_loss = 0.0;

            // Process batches
            for batch_idx in 0..num_batches {
                let start_idx = batch_idx * self.config.batch_size;
                let end_idx = std::cmp::min(
                    start_idx + self.config.batch_size,
                    self.preference_pairs.len(),
                );
                let batch = &self.preference_pairs[start_idx..end_idx];

                // Simulate reward model training
                let (batch_accuracy, batch_loss) = self.train_reward_batch(batch)?;
                epoch_accuracy += batch_accuracy;
                epoch_loss += batch_loss;
            }

            total_accuracy += epoch_accuracy / num_batches as f32;
            total_loss += epoch_loss / num_batches as f32;
        }

        // Update metrics with realistic values
        self.metrics.reward_accuracy = Some(total_accuracy / self.config.epochs as f32);
        self.metrics.phase = RLHFPhase::RewardModel;

        Ok(self.metrics.clone())
    }

    /// Train reward model on a batch of preference pairs
    fn train_reward_batch(&self, batch: &[PreferencePair]) -> Result<(f32, f32)> {
        let mut correct_predictions = 0;
        let mut total_loss = 0.0;

        for pair in batch {
            // Simulate reward prediction for chosen and rejected responses
            let chosen_reward = self.simulate_reward_prediction(&pair.chosen);
            let rejected_reward = self.simulate_reward_prediction(&pair.rejected);

            // Check if model correctly predicts preference
            if chosen_reward > rejected_reward {
                correct_predictions += 1;
            }

            // Compute ranking loss (Bradley-Terry model)
            let logit_diff = chosen_reward - rejected_reward;
            let loss = -f32::ln(1.0 / (1.0 + f32::exp(-logit_diff)));
            total_loss += loss;
        }

        let accuracy = correct_predictions as f32 / batch.len() as f32;
        let avg_loss = total_loss / batch.len() as f32;

        Ok((accuracy, avg_loss))
    }

    /// Simulate reward prediction for a response
    fn simulate_reward_prediction(&self, response: &str) -> f32 {
        // Simple heuristic based on response length and character diversity
        let length_score = (response.len() as f32 / self.config.max_seq_length as f32).min(1.0);
        let char_diversity =
            response.chars().collect::<std::collections::HashSet<_>>().len() as f32 / 26.0;

        // Combine factors with some randomness
        let base_score = 0.5 + 0.2 * length_score + 0.2 * char_diversity;
        let noise = (response.len() % 7) as f32 / 7.0 * 0.2 - 0.1; // Deterministic "randomness"

        (base_score + noise).clamp(0.0, 1.0)
    }

    /// Train using PPO
    async fn train_ppo(&mut self) -> Result<RLHFMetrics> {
        if self.feedback_data.is_empty() {
            return Err(invalid_config(
                "No feedback data available for PPO training",
                "train_ppo",
            ));
        }

        let mut total_ppo_objective = 0.0;
        let mut total_kl_divergence = 0.0;
        #[allow(unused_variables)]
        let mut total_value_loss = 0.0;
        let clip_epsilon = 0.2; // PPO clip parameter

        // Simulate PPO training epochs
        for _epoch in 0..self.config.epochs {
            let mut epoch_ppo_obj = 0.0;
            let mut epoch_kl_div = 0.0;
            let mut epoch_value_loss = 0.0;

            let num_batches =
                (self.feedback_data.len() + self.config.batch_size - 1) / self.config.batch_size;

            for batch_idx in 0..num_batches {
                let start_idx = batch_idx * self.config.batch_size;
                let end_idx =
                    std::cmp::min(start_idx + self.config.batch_size, self.feedback_data.len());
                let batch = &self.feedback_data[start_idx..end_idx];

                // Simulate PPO update
                let (ppo_obj, kl_div, value_loss) = self.compute_ppo_update(batch, clip_epsilon)?;
                epoch_ppo_obj += ppo_obj;
                epoch_kl_div += kl_div;
                epoch_value_loss += value_loss;
            }

            total_ppo_objective += epoch_ppo_obj / num_batches as f32;
            total_kl_divergence += epoch_kl_div / num_batches as f32;
            total_value_loss += epoch_value_loss / num_batches as f32;
        }

        // Update metrics with realistic values
        self.metrics.ppo_objective = Some(total_ppo_objective / self.config.epochs as f32);
        self.metrics.kl_divergence = total_kl_divergence / self.config.epochs as f32;
        self.metrics.phase = RLHFPhase::PPO;

        Ok(self.metrics.clone())
    }

    /// Compute PPO update for a batch
    fn compute_ppo_update(
        &self,
        batch: &[HumanFeedback],
        clip_epsilon: f32,
    ) -> Result<(f32, f32, f32)> {
        let mut total_ppo_objective = 0.0;
        let mut total_kl_divergence = 0.0;
        let mut total_value_loss = 0.0;

        for feedback in batch {
            // Simulate policy ratio and advantage
            let reward = feedback.rating / 5.0; // Normalize to 0-1
            let advantage = reward - 0.5; // Simulate baseline subtraction

            // Simulate old and new policy probabilities
            let old_log_prob = 0.5 + advantage * 0.1; // Simulate old policy
            let new_log_prob = old_log_prob + self.config.learning_rate * advantage; // Simulate policy update

            let ratio = f32::exp(new_log_prob - old_log_prob);

            // PPO clipped objective
            let clipped_ratio = ratio.clamp(1.0 - clip_epsilon, 1.0 + clip_epsilon);
            let ppo_objective = f32::min(ratio * advantage, clipped_ratio * advantage);

            // KL divergence approximation
            let kl_divergence = (old_log_prob - new_log_prob).powi(2) / 2.0;

            // Value function loss
            let predicted_value = reward + advantage * 0.5; // Simulate value prediction
            let value_loss = (predicted_value - reward).powi(2);

            total_ppo_objective += ppo_objective;
            total_kl_divergence += kl_divergence;
            total_value_loss += value_loss;
        }

        let avg_ppo_obj = total_ppo_objective / batch.len() as f32;
        let avg_kl_div = total_kl_divergence / batch.len() as f32;
        let avg_value_loss = total_value_loss / batch.len() as f32;

        Ok((avg_ppo_obj, avg_kl_div, avg_value_loss))
    }

    /// Train using DPO
    async fn train_dpo(&mut self) -> Result<RLHFMetrics> {
        if self.preference_pairs.is_empty() {
            return Err(invalid_config(
                "No preference pairs available for DPO training",
                "train_dpo",
            ));
        }

        #[allow(unused_variables)]
        let mut total_dpo_loss = 0.0;
        let mut total_reward = 0.0;
        let beta = 0.1; // Temperature parameter for DPO

        // Simulate DPO training epochs
        for _epoch in 0..self.config.epochs {
            let mut epoch_dpo_loss = 0.0;
            let mut epoch_reward = 0.0;

            let num_batches =
                (self.preference_pairs.len() + self.config.batch_size - 1) / self.config.batch_size;

            for batch_idx in 0..num_batches {
                let start_idx = batch_idx * self.config.batch_size;
                let end_idx = std::cmp::min(
                    start_idx + self.config.batch_size,
                    self.preference_pairs.len(),
                );
                let batch = &self.preference_pairs[start_idx..end_idx];

                // Simulate DPO update
                let (dpo_loss, avg_reward) = self.compute_dpo_update(batch, beta)?;
                epoch_dpo_loss += dpo_loss;
                epoch_reward += avg_reward;
            }

            total_dpo_loss += epoch_dpo_loss / num_batches as f32;
            total_reward += epoch_reward / num_batches as f32;
        }

        // Update metrics with realistic values
        self.metrics.avg_reward = total_reward / self.config.epochs as f32;
        self.metrics.phase = RLHFPhase::DPO;

        Ok(self.metrics.clone())
    }

    /// Compute DPO update for a batch
    fn compute_dpo_update(&self, batch: &[PreferencePair], beta: f32) -> Result<(f32, f32)> {
        let mut total_dpo_loss = 0.0;
        let mut total_reward = 0.0;

        for pair in batch {
            // Simulate log probabilities for chosen and rejected responses
            let chosen_logp = self.simulate_log_probability(&pair.chosen);
            let rejected_logp = self.simulate_log_probability(&pair.rejected);

            // Simulate reference model probabilities (used in DPO)
            let ref_chosen_logp = chosen_logp * 0.9; // Reference model is slightly worse
            let ref_rejected_logp = rejected_logp * 0.9;

            // DPO loss computation
            let chosen_ratio = chosen_logp - ref_chosen_logp;
            let rejected_ratio = rejected_logp - ref_rejected_logp;

            let dpo_loss =
                -f32::ln(1.0 / (1.0 + f32::exp(-beta * (chosen_ratio - rejected_ratio))));

            // Estimate implicit reward
            let implicit_reward = beta * chosen_ratio;

            total_dpo_loss += dpo_loss;
            total_reward += implicit_reward;
        }

        let avg_dpo_loss = total_dpo_loss / batch.len() as f32;
        let avg_reward = total_reward / batch.len() as f32;

        Ok((avg_dpo_loss, avg_reward))
    }

    /// Simulate log probability for a response
    fn simulate_log_probability(&self, response: &str) -> f32 {
        // Simple heuristic based on response characteristics
        let length_factor = -(response.len() as f32 / self.config.max_seq_length as f32).ln();
        let repetition_penalty = self.calculate_repetition_penalty(response);

        length_factor + repetition_penalty
    }

    /// Calculate repetition penalty for response quality
    fn calculate_repetition_penalty(&self, response: &str) -> f32 {
        let words: Vec<&str> = response.split_whitespace().collect();
        if words.is_empty() {
            return 0.0;
        }

        let unique_words = words.iter().collect::<std::collections::HashSet<_>>().len();
        let repetition_ratio = unique_words as f32 / words.len() as f32;

        // Higher repetition ratio (less repetition) gets lower penalty
        -((1.0 - repetition_ratio) * 2.0)
    }

    /// Train using Constitutional AI
    async fn train_constitutional(&mut self) -> Result<RLHFMetrics> {
        if self.config.constitutional_principles.is_empty() {
            return Err(invalid_config(
                "No constitutional principles defined for Constitutional AI training",
                "train_constitutional",
            ));
        }

        if self.feedback_data.is_empty() {
            return Err(invalid_config(
                "No feedback data available for Constitutional AI training",
                "train_constitutional",
            ));
        }

        let mut total_violations = 0;
        #[allow(unused_variables)]
        let mut total_constitutional_loss = 0.0;

        // Simulate Constitutional AI training epochs
        for _epoch in 0..self.config.epochs {
            let mut epoch_violations = 0;
            let mut epoch_loss = 0.0;

            let num_batches =
                (self.feedback_data.len() + self.config.batch_size - 1) / self.config.batch_size;

            for batch_idx in 0..num_batches {
                let start_idx = batch_idx * self.config.batch_size;
                let end_idx =
                    std::cmp::min(start_idx + self.config.batch_size, self.feedback_data.len());
                let batch = &self.feedback_data[start_idx..end_idx];

                // Simulate Constitutional AI update
                let (violations, const_loss) = self.compute_constitutional_update(batch)?;
                epoch_violations += violations;
                epoch_loss += const_loss;
            }

            total_violations += epoch_violations;
            total_constitutional_loss += epoch_loss / num_batches as f32;
        }

        // Update metrics with realistic values
        self.metrics.constitutional_violations = Some(total_violations / self.config.epochs);
        self.metrics.phase = RLHFPhase::Constitutional;

        Ok(self.metrics.clone())
    }

    /// Compute Constitutional AI update for a batch
    fn compute_constitutional_update(&self, batch: &[HumanFeedback]) -> Result<(usize, f32)> {
        let mut total_violations = 0;
        let mut total_loss = 0.0;

        for feedback in batch {
            // Check each constitutional principle
            let mut response_violations = 0;
            let mut principle_loss = 0.0;

            for principle in &self.config.constitutional_principles {
                let violation_score =
                    self.evaluate_principle_violation(&feedback.response, principle);

                // If violation score is above threshold, count as violation
                if violation_score > 0.5 {
                    response_violations += 1;
                }

                // Constitutional loss based on violation severity
                principle_loss += violation_score * principle.weight;
            }

            total_violations += response_violations;
            total_loss += principle_loss;
        }

        let avg_loss = total_loss / batch.len() as f32;

        Ok((total_violations, avg_loss))
    }

    /// Evaluate how much a response violates a constitutional principle
    fn evaluate_principle_violation(
        &self,
        response: &str,
        principle: &ConstitutionalPrinciple,
    ) -> f32 {
        // Simple heuristic for principle violation detection
        match principle.name.as_str() {
            "harmlessness" => {
                // Check for potentially harmful content indicators
                let harmful_indicators = ["violence", "hate", "harmful", "dangerous", "illegal"];
                let violations = harmful_indicators
                    .iter()
                    .filter(|&indicator| response.to_lowercase().contains(indicator))
                    .count();
                (violations as f32 / harmful_indicators.len() as f32).min(1.0)
            },
            "helpfulness" => {
                // Check for unhelpful response indicators
                let unhelpful_indicators = ["don't know", "can't help", "no idea", "unclear"];
                let violations = unhelpful_indicators
                    .iter()
                    .filter(|&indicator| response.to_lowercase().contains(indicator))
                    .count();
                (violations as f32 / unhelpful_indicators.len() as f32).min(1.0)
            },
            "honesty" => {
                // Check for dishonesty indicators
                let dishonest_indicators = ["fake", "false", "lie", "untrue", "misleading"];
                let violations = dishonest_indicators
                    .iter()
                    .filter(|&indicator| response.to_lowercase().contains(indicator))
                    .count();
                (violations as f32 / dishonest_indicators.len() as f32).min(1.0)
            },
            _ => {
                // Generic principle evaluation based on response quality
                let quality_score = self.simulate_reward_prediction(response);
                1.0 - quality_score // Higher quality means lower violation
            },
        }
    }

    /// Evaluate model performance
    pub async fn evaluate(&self) -> Result<HashMap<String, f32>> {
        let mut eval_metrics = HashMap::new();

        // Basic evaluation metrics
        eval_metrics.insert("avg_reward".to_string(), self.metrics.avg_reward);
        eval_metrics.insert("kl_divergence".to_string(), self.metrics.kl_divergence);

        if let Some(policy_loss) = self.metrics.policy_loss {
            eval_metrics.insert("policy_loss".to_string(), policy_loss);
        }

        if let Some(reward_accuracy) = self.metrics.reward_accuracy {
            eval_metrics.insert("reward_accuracy".to_string(), reward_accuracy);
        }

        Ok(eval_metrics)
    }

    /// Save trainer state
    pub fn save_state(&self, path: &str) -> Result<()> {
        use std::fs::File;
        use std::io::Write;

        // Create trainer state struct for serialization
        let state = TrainerState {
            config: self.config.clone(),
            metrics: self.metrics.clone(),
            feedback_data_count: self.feedback_data.len(),
            preference_pairs_count: self.preference_pairs.len(),
        };

        // Serialize and save state
        let serialized = serde_json::to_string_pretty(&state).map_err(|e| {
            invalid_config(
                format!("Failed to serialize trainer state: {}", e),
                "save_state",
            )
        })?;

        let mut file = File::create(path).map_err(|e| {
            invalid_config(format!("Failed to create state file: {}", e), "save_state")
        })?;

        file.write_all(serialized.as_bytes()).map_err(|e| {
            invalid_config(format!("Failed to write state file: {}", e), "save_state")
        })?;

        Ok(())
    }

    /// Load trainer state
    pub fn load_state(&mut self, path: &str) -> Result<()> {
        use std::fs::File;
        use std::io::Read;

        let mut file = File::open(path).map_err(|e| {
            invalid_config(format!("Failed to open state file: {}", e), "load_state")
        })?;

        let mut contents = String::new();
        file.read_to_string(&mut contents).map_err(|e| {
            invalid_config(format!("Failed to read state file: {}", e), "load_state")
        })?;

        let state: TrainerState = serde_json::from_str(&contents).map_err(|e| {
            invalid_config(
                format!("Failed to deserialize trainer state: {}", e),
                "load_state",
            )
        })?;

        // Restore state
        self.config = state.config;
        self.metrics = state.metrics;

        // Note: Actual feedback data and preference pairs are not saved for privacy/size reasons
        // In a real implementation, these might be saved separately or referenced by ID

        Ok(())
    }
}

/// Serializable trainer state for save/load operations
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
struct TrainerState {
    config: RLHFTrainerConfig,
    metrics: RLHFMetrics,
    feedback_data_count: usize,
    preference_pairs_count: usize,
}

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

    #[test]
    fn test_trainer_creation() {
        let config = RLHFTrainerConfig::default();
        let trainer = RLHFTrainer::new(config);

        assert_eq!(trainer.config.phase, RLHFPhase::SFT);
        assert_eq!(trainer.feedback_data.len(), 0);
        assert_eq!(trainer.preference_pairs.len(), 0);
    }

    #[test]
    fn test_add_feedback() {
        let config = RLHFTrainerConfig::default();
        let mut trainer = RLHFTrainer::new(config);

        let feedback = HumanFeedback {
            id: "test".to_string(),
            prompt: "Test prompt".to_string(),
            response: "Test response".to_string(),
            rating: 4.0,
            feedback_text: None,
            timestamp: Utc::now(),
            annotator_id: None,
            metadata: HashMap::new(),
        };

        trainer.add_feedback(feedback);
        assert_eq!(trainer.feedback_data.len(), 1);
    }

    #[test]
    fn test_phase_update() {
        let config = RLHFTrainerConfig::default();
        let mut trainer = RLHFTrainer::new(config);

        trainer.set_phase(RLHFPhase::PPO);
        assert_eq!(trainer.config.phase, RLHFPhase::PPO);
        assert_eq!(trainer.metrics.phase, RLHFPhase::PPO);
    }
}
