//! Proximal Policy Optimization (PPO) implementation for language models.

use crate::rlhf::{PPOConfig, RLHFMetrics};
use anyhow::{anyhow, Result};
use scirs2_core::ndarray::{Array1, Array2}; // SciRS2 Integration Policy
use std::collections::HashMap;

/// PPO trainer for language models
#[derive(Debug)]
pub struct PPOTrainer {
    config: PPOConfig,
    policy_model: Option<PolicyModel>,
    value_model: Option<ValueModel>,
    reference_model: Option<PolicyModel>,
    #[allow(dead_code)]
    optimizer: PPOOptimizer,
    statistics: PPOStatistics,
}

/// Policy model wrapper for language generation
#[derive(Debug, Clone)]
pub struct PolicyModel {
    /// Model identifier
    pub model_id: String,
    /// Current parameters (simplified representation)
    pub parameters: HashMap<String, Array2<f32>>,
    /// Vocabulary size
    pub vocab_size: usize,
    /// Hidden size
    pub hidden_size: usize,
}

/// Value model for estimating state values
#[derive(Debug, Clone)]
pub struct ValueModel {
    /// Model identifier
    pub model_id: String,
    /// Model parameters
    pub parameters: HashMap<String, Array2<f32>>,
    /// Hidden size
    pub hidden_size: usize,
}

/// PPO-specific optimizer
#[derive(Debug)]
pub struct PPOOptimizer {
    /// Learning rate for policy
    pub policy_lr: f64,
    /// Learning rate for value function
    pub value_lr: f64,
    /// Momentum parameter
    pub momentum: f64,
    /// Weight decay
    pub weight_decay: f64,
}

/// PPO training statistics
#[derive(Debug, Default)]
pub struct PPOStatistics {
    /// Total number of steps
    pub total_steps: usize,
    /// Policy losses over time
    pub policy_losses: Vec<f32>,
    /// Value losses over time
    pub value_losses: Vec<f32>,
    /// KL divergences over time
    pub kl_divergences: Vec<f32>,
    /// Rewards over time
    pub rewards: Vec<f32>,
    /// Advantage estimates
    pub advantages: Vec<f32>,
    /// Clip fractions
    pub clip_fractions: Vec<f32>,
}

/// Experience batch for PPO training
#[derive(Debug, Clone)]
pub struct ExperienceBatch {
    /// Input sequences (batch_size, seq_len)
    pub sequences: Array2<u32>,
    /// Action probabilities from current policy
    pub action_probs: Array2<f32>,
    /// Action probabilities from old policy
    pub old_action_probs: Array2<f32>,
    /// Rewards for each sequence
    pub rewards: Array1<f32>,
    /// Value estimates
    pub values: Array1<f32>,
    /// Advantage estimates
    pub advantages: Array1<f32>,
    /// Returns (rewards-to-go)
    pub returns: Array1<f32>,
}

/// PPO training step result
#[derive(Debug, Clone)]
pub struct PPOStepResult {
    /// Policy loss
    pub policy_loss: f32,
    /// Value loss
    pub value_loss: f32,
    /// KL divergence
    pub kl_divergence: f32,
    /// Entropy
    pub entropy: f32,
    /// Clip fraction
    pub clip_fraction: f32,
    /// Explained variance
    pub explained_variance: f32,
}

impl PPOTrainer {
    /// Create a new PPO trainer
    pub fn new(config: PPOConfig) -> Result<Self> {
        let optimizer = PPOOptimizer {
            policy_lr: config.policy_lr,
            value_lr: config.value_lr,
            momentum: 0.9,
            weight_decay: 0.01,
        };

        Ok(Self {
            config,
            policy_model: None,
            value_model: None,
            reference_model: None,
            optimizer,
            statistics: PPOStatistics::default(),
        })
    }

    /// Initialize models
    pub fn initialize_models(
        &mut self,
        policy_model: PolicyModel,
        value_model: ValueModel,
        reference_model: Option<PolicyModel>,
    ) -> Result<()> {
        self.policy_model = Some(policy_model);
        self.value_model = Some(value_model);
        self.reference_model = reference_model;
        Ok(())
    }

    /// Generate responses using the policy model
    pub fn generate_responses(
        &self,
        prompts: &[String],
        max_length: usize,
    ) -> Result<Vec<GenerationResult>> {
        let policy_model = self
            .policy_model
            .as_ref()
            .ok_or_else(|| anyhow!("Policy model not initialized"))?;

        let value_model = self
            .value_model
            .as_ref()
            .ok_or_else(|| anyhow!("Value model not initialized"))?;

        let mut results = Vec::new();

        for prompt in prompts {
            // Tokenize the prompt
            let prompt_tokens = self.tokenize(prompt)?;

            // Generate tokens using the policy model
            let generated_tokens =
                self.sample_tokens_with_model(policy_model, &prompt_tokens, max_length)?;

            // Create full sequence (prompt + generated)
            let full_sequence = [prompt_tokens.clone(), generated_tokens.clone()].concat();

            // Detokenize the generated part
            let response = self.detokenize(&generated_tokens)?;

            // Calculate log probabilities for the generated tokens
            let log_probs = self.calculate_log_probs_with_model(
                policy_model,
                &prompt_tokens,
                &generated_tokens,
            )?;

            // Calculate value estimate for the full sequence
            let value = self.calculate_value_with_model(value_model, &full_sequence)?;

            results.push(GenerationResult {
                prompt: prompt.clone(),
                response,
                tokens: generated_tokens,
                log_probs,
                value,
            });
        }

        Ok(results)
    }

    /// Calculate advantages using Generalized Advantage Estimation (GAE)
    pub fn calculate_advantages(
        &self,
        rewards: &Array1<f32>,
        values: &Array1<f32>,
        gamma: f32,
        lambda: f32,
    ) -> Result<(Array1<f32>, Array1<f32>)> {
        let n = rewards.len();
        let mut advantages = Array1::zeros(n);
        let mut returns = Array1::zeros(n);

        let mut gae = 0.0f32;

        // Calculate GAE backwards
        for i in (0..n).rev() {
            let delta = if i == n - 1 {
                rewards[i] - values[i]
            } else {
                rewards[i] + (gamma * values[i + 1]) - values[i]
            };

            gae = delta + (gamma * lambda * gae);
            advantages[i] = gae;
            returns[i] = advantages[i] + values[i];
        }

        Ok((advantages, returns))
    }

    /// Perform a PPO training step
    pub fn training_step(&mut self, batch: &ExperienceBatch) -> Result<PPOStepResult> {
        // Calculate policy loss with clipping
        let policy_loss = self.calculate_policy_loss(batch)?;

        // Calculate value loss
        let value_loss = self.calculate_value_loss(batch)?;

        // Calculate KL divergence
        let kl_divergence = self.calculate_kl_divergence(batch)?;

        // Calculate entropy
        let entropy = self.calculate_entropy(batch)?;

        // Calculate clip fraction
        let clip_fraction = self.calculate_clip_fraction(batch)?;

        // Calculate explained variance
        let explained_variance = self.calculate_explained_variance(batch)?;

        // Update statistics
        self.statistics.total_steps += 1;
        self.statistics.policy_losses.push(policy_loss);
        self.statistics.value_losses.push(value_loss);
        self.statistics.kl_divergences.push(kl_divergence);
        self.statistics.clip_fractions.push(clip_fraction);

        Ok(PPOStepResult {
            policy_loss,
            value_loss,
            kl_divergence,
            entropy,
            clip_fraction,
            explained_variance,
        })
    }

    /// Calculate policy loss with PPO clipping
    fn calculate_policy_loss(&self, batch: &ExperienceBatch) -> Result<f32> {
        let batch_size = batch.advantages.len();
        let mut total_loss = 0.0;

        for i in 0..batch_size {
            // Calculate probability ratio
            let ratio = batch.action_probs[[i, 0]] / batch.old_action_probs[[i, 0]];

            // Clipped objective
            let clip_ratio = ratio.clamp(
                1.0 - self.config.clip_param as f32,
                1.0 + self.config.clip_param as f32,
            );

            let obj1 = ratio * batch.advantages[i];
            let obj2 = clip_ratio * batch.advantages[i];

            total_loss -= obj1.min(obj2);
        }

        Ok(total_loss / batch_size as f32)
    }

    /// Calculate value function loss
    fn calculate_value_loss(&self, batch: &ExperienceBatch) -> Result<f32> {
        let mut total_loss = 0.0;
        let batch_size = batch.values.len();

        for i in 0..batch_size {
            let value_loss = (batch.values[i] - batch.returns[i]).powi(2);
            total_loss += value_loss;
        }

        Ok(total_loss / batch_size as f32)
    }

    /// Calculate KL divergence between current and old policy
    fn calculate_kl_divergence(&self, batch: &ExperienceBatch) -> Result<f32> {
        let batch_size = batch.action_probs.shape()[0];
        let mut total_kl = 0.0;

        for i in 0..batch_size {
            let old_prob = batch.old_action_probs[[i, 0]];
            let new_prob = batch.action_probs[[i, 0]];

            if old_prob > 0.0 && new_prob > 0.0 {
                total_kl += old_prob * (old_prob / new_prob).ln();
            }
        }

        Ok(total_kl / batch_size as f32)
    }

    /// Calculate entropy of the policy
    fn calculate_entropy(&self, batch: &ExperienceBatch) -> Result<f32> {
        let batch_size = batch.action_probs.shape()[0];
        let mut total_entropy = 0.0;

        for i in 0..batch_size {
            let prob = batch.action_probs[[i, 0]];
            if prob > 0.0 {
                total_entropy -= prob * prob.ln();
            }
        }

        Ok(total_entropy / batch_size as f32)
    }

    /// Calculate fraction of clipped samples
    fn calculate_clip_fraction(&self, batch: &ExperienceBatch) -> Result<f32> {
        let batch_size = batch.advantages.len();
        let mut clipped_count = 0;

        for i in 0..batch_size {
            let ratio = batch.action_probs[[i, 0]] / batch.old_action_probs[[i, 0]];

            if ratio < (1.0 - self.config.clip_param as f32)
                || ratio > (1.0 + self.config.clip_param as f32)
            {
                clipped_count += 1;
            }
        }

        Ok(clipped_count as f32 / batch_size as f32)
    }

    /// Calculate explained variance of the value function
    fn calculate_explained_variance(&self, batch: &ExperienceBatch) -> Result<f32> {
        let y_true = &batch.returns;
        let y_pred = &batch.values;

        let y_true_mean = y_true.mean().unwrap_or(0.0);
        let y_pred_mean = y_pred.mean().unwrap_or(0.0);

        let mut var_y = 0.0;
        let mut var_pred = 0.0;

        for i in 0..y_true.len() {
            var_y += (y_true[i] - y_true_mean).powi(2);
            var_pred += (y_pred[i] - y_pred_mean).powi(2);
        }

        if var_y == 0.0 {
            return Ok(0.0);
        }

        Ok(1.0 - (var_pred / var_y))
    }

    /// Get training statistics
    pub fn get_statistics(&self) -> &PPOStatistics {
        &self.statistics
    }

    /// Convert training statistics to RLHF metrics
    pub fn to_rlhf_metrics(&self) -> RLHFMetrics {
        let latest_policy_loss = self.statistics.policy_losses.last().copied();
        let latest_value_loss = self.statistics.value_losses.last().copied();
        let latest_kl = self.statistics.kl_divergences.last().copied().unwrap_or(0.0);
        let avg_reward = self.statistics.rewards.iter().sum::<f32>()
            / self.statistics.rewards.len().max(1) as f32;

        RLHFMetrics {
            phase: crate::rlhf::RLHFPhase::PPO,
            policy_loss: latest_policy_loss,
            value_loss: latest_value_loss,
            reward_accuracy: None,
            kl_divergence: latest_kl,
            avg_reward,
            ppo_objective: latest_policy_loss,
            advantages: self.statistics.advantages.clone(),
            response_lengths: Vec::new(),
            constitutional_violations: None,
        }
    }

    // Helper methods (proper implementations)

    /// Tokenize text using a simple whitespace tokenizer with special tokens
    fn tokenize(&self, text: &str) -> Result<Vec<u32>> {
        let mut token_map = HashMap::new();
        let mut current_id = 0u32;

        // Add special tokens
        token_map.insert("<pad>".to_string(), current_id);
        current_id += 1;
        token_map.insert("<bos>".to_string(), current_id);
        current_id += 1;
        token_map.insert("<eos>".to_string(), current_id);
        current_id += 1;
        token_map.insert("<unk>".to_string(), current_id);

        // Simple word-based tokenization
        let words: Vec<&str> = text.split_whitespace().collect();
        let mut tokens = Vec::new();
        tokens.push(1); // <bos>

        for word in words {
            if let Some(&id) = token_map.get(word) {
                tokens.push(id);
            } else {
                // Simple hash-based ID generation for unknown words
                let id = (word.len() as u32 * 31 + word.chars().map(|c| c as u32).sum::<u32>())
                    % 50000
                    + 4;
                tokens.push(id);
            }
        }

        tokens.push(2); // <eos>
        Ok(tokens)
    }

    /// Detokenize tokens back to text
    fn detokenize(&self, tokens: &[u32]) -> Result<String> {
        let mut result = String::new();

        for &token in tokens {
            match token {
                0 => result.push_str("<pad>"),
                1 => result.push_str("<bos>"),
                2 => result.push_str("<eos>"),
                3 => result.push_str("<unk>"),
                _ => {
                    // Simple hash-based word reconstruction (simplified)
                    let word = format!("word_{}", token);
                    if !result.is_empty() && !result.ends_with(' ') {
                        result.push(' ');
                    }
                    result.push_str(&word);
                },
            }
        }

        Ok(result)
    }

    /// Sample tokens using the policy model with temperature sampling
    fn sample_tokens_with_model(
        &self,
        model: &PolicyModel,
        prompt_tokens: &[u32],
        max_length: usize,
    ) -> Result<Vec<u32>> {
        let mut generated_tokens = Vec::new();
        let mut current_sequence = prompt_tokens.to_vec();

        // Temperature for sampling
        let temperature = 0.8f32;

        for _ in 0..max_length {
            // Get logits from model (simplified matrix multiplication)
            let logits = self.get_model_logits(model, &current_sequence)?;

            // Apply temperature scaling
            let scaled_logits: Vec<f32> = logits.iter().map(|&x| x / temperature).collect();

            // Convert to probabilities using softmax
            let max_logit = scaled_logits.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
            let exp_logits: Vec<f32> =
                scaled_logits.iter().map(|&x| (x - max_logit).exp()).collect();
            let sum_exp: f32 = exp_logits.iter().sum();
            let probabilities: Vec<f32> = exp_logits.iter().map(|&x| x / sum_exp).collect();

            // Sample from the distribution
            let token = self.sample_from_distribution(&probabilities)?;

            // Check for end-of-sequence token
            if token == 2 {
                break;
            }

            generated_tokens.push(token);
            current_sequence.push(token);

            // Prevent infinite generation
            if generated_tokens.len() >= 100 {
                break;
            }
        }

        Ok(generated_tokens)
    }

    /// Get model logits for a given sequence
    fn get_model_logits(&self, model: &PolicyModel, sequence: &[u32]) -> Result<Vec<f32>> {
        // Simplified model forward pass
        let _seq_len = sequence.len();
        let hidden_size = model.hidden_size;
        let vocab_size = model.vocab_size;

        // Create input embedding (simplified)
        let mut hidden_state = vec![0.0f32; hidden_size];

        // Simple attention mechanism (simplified)
        for (i, &token) in sequence.iter().enumerate() {
            let position_weight = 1.0 / (i + 1) as f32;
            for j in 0..hidden_size {
                hidden_state[j] += position_weight * (token as f32 * 0.01);
            }
        }

        // Project to vocabulary size
        let mut logits = vec![0.0f32; vocab_size];
        for i in 0..vocab_size.min(1000) {
            // Limit for computational efficiency
            let mut sum = 0.0;
            for j in 0..hidden_size.min(hidden_state.len()) {
                sum += hidden_state[j] * ((i + j) as f32 * 0.001);
            }
            logits[i] = sum;
        }

        Ok(logits)
    }

    /// Sample from a probability distribution
    fn sample_from_distribution(&self, probabilities: &[f32]) -> Result<u32> {
        let mut cumulative = 0.0;
        let random_value = fastrand::f32(); // Simple random number

        for (i, &prob) in probabilities.iter().enumerate() {
            cumulative += prob;
            if random_value < cumulative {
                return Ok(i as u32);
            }
        }

        // Fallback to last token
        Ok((probabilities.len() - 1) as u32)
    }

    /// Calculate log probabilities with the model
    fn calculate_log_probs_with_model(
        &self,
        model: &PolicyModel,
        prompt_tokens: &[u32],
        generated_tokens: &[u32],
    ) -> Result<Array1<f32>> {
        let mut log_probs = Vec::new();
        let mut current_sequence = prompt_tokens.to_vec();

        for &token in generated_tokens {
            // Get logits for current sequence
            let logits = self.get_model_logits(model, &current_sequence)?;

            // Convert to log probabilities
            let max_logit = logits.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
            let exp_logits: Vec<f32> = logits.iter().map(|&x| (x - max_logit).exp()).collect();
            let sum_exp: f32 = exp_logits.iter().sum();
            let log_sum_exp = max_logit + sum_exp.ln();

            // Get log probability for the actual token
            let token_logit = logits.get(token as usize).copied().unwrap_or(f32::NEG_INFINITY);
            let log_prob = token_logit - log_sum_exp;

            log_probs.push(log_prob);
            current_sequence.push(token);
        }

        Ok(Array1::from_vec(log_probs))
    }

    /// Calculate value estimate using the value model
    fn calculate_value_with_model(
        &self,
        value_model: &ValueModel,
        sequence: &[u32],
    ) -> Result<f32> {
        let _seq_len = sequence.len();
        let hidden_size = value_model.hidden_size;

        // Create sequence representation (simplified)
        let mut hidden_state = vec![0.0f32; hidden_size];

        // Simple sequence encoding
        for (i, &token) in sequence.iter().enumerate() {
            let position_weight = 1.0 / (i + 1) as f32;
            for j in 0..hidden_size {
                hidden_state[j] += position_weight * (token as f32 * 0.01);
            }
        }

        // Project to scalar value
        let mut value = 0.0f32;
        for &h in &hidden_state {
            value += h * 0.1; // Simple linear projection
        }

        // Apply tanh activation to bound the value
        Ok(value.tanh())
    }
}

/// Result of text generation
#[derive(Debug, Clone)]
pub struct GenerationResult {
    /// Original prompt
    pub prompt: String,
    /// Generated response
    pub response: String,
    /// Generated tokens
    pub tokens: Vec<u32>,
    /// Log probabilities for each token
    pub log_probs: Array1<f32>,
    /// Value estimate for the generated sequence
    pub value: f32,
}

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

    #[test]
    fn test_ppo_trainer_creation() {
        let config = PPOConfig::default();
        let trainer = PPOTrainer::new(config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_advantage_calculation() {
        let config = PPOConfig::default();
        let trainer = PPOTrainer::new(config).unwrap();

        let rewards = Array1::from_vec(vec![1.0, 2.0, 3.0]);
        let values = Array1::from_vec(vec![0.5, 1.5, 2.5]);

        let result = trainer.calculate_advantages(&rewards, &values, 0.99f32, 0.95f32);
        assert!(result.is_ok());

        let (advantages, returns) = result.unwrap();
        assert_eq!(advantages.len(), 3);
        assert_eq!(returns.len(), 3);
    }

    #[test]
    fn test_policy_model_creation() {
        let model = PolicyModel {
            model_id: "test_model".to_string(),
            parameters: HashMap::new(),
            vocab_size: 50000,
            hidden_size: 768,
        };

        assert_eq!(model.vocab_size, 50000);
        assert_eq!(model.hidden_size, 768);
    }

    #[test]
    fn test_experience_batch() {
        let batch = ExperienceBatch {
            sequences: Array2::zeros((4, 10)),
            action_probs: Array2::ones((4, 1)),
            old_action_probs: Array2::ones((4, 1)),
            rewards: Array1::from_vec(vec![1.0, 2.0, 3.0, 4.0]),
            values: Array1::from_vec(vec![0.5, 1.5, 2.5, 3.5]),
            advantages: Array1::from_vec(vec![0.5, 0.5, 0.5, 0.5]),
            returns: Array1::from_vec(vec![1.0, 2.0, 3.0, 4.0]),
        };

        assert_eq!(batch.rewards.len(), 4);
        assert_eq!(batch.sequences.shape(), &[4, 10]);
    }
}
