use crate::Result;
use rtx_tensor::{Device, Tensor};
use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DPOConfig {
    pub learning_rate: f64,
    pub beta: f32,
    pub reference_model_weight: f32,
    pub batch_size: usize,
    pub max_grad_norm: f32,
}

impl Default for DPOConfig {
    fn default() -> Self {
        Self {
            learning_rate: 1e-4,
            beta: 0.1,
            reference_model_weight: 0.9,
            batch_size: 32,
            max_grad_norm: 1.0,
        }
    }
}

#[derive(Debug)]
pub struct DPOMetrics {
    pub loss: f32,
    pub accuracy: f32,
    pub chosen_reward: f32,
    pub rejected_reward: f32,
    pub reward_margin: f32,
}

pub struct DPO {
    config: DPOConfig,
    state_dim: usize,
    action_dim: usize,
    device: Device,
    policy_params: Vec<Tensor>,
    reference_params: Vec<Tensor>,
}

impl DPO {
    pub fn new(config: DPOConfig, state_dim: usize, action_dim: usize, device: Device) -> Self {
        // Initialize dummy parameters for testing
        let policy_params = vec![
            Tensor::randn(&[128, state_dim], &device).unwrap(),
            Tensor::randn(&[128], &device).unwrap(),
            Tensor::randn(&[action_dim, 128], &device).unwrap(),
            Tensor::randn(&[action_dim], &device).unwrap(),
        ];

        let reference_params = policy_params.clone();

        Self {
            config,
            state_dim,
            action_dim,
            device,
            policy_params,
            reference_params,
        }
    }

    pub async fn compute_preference_loss(
        &self,
        states: &Tensor,
        _chosen_actions: &Tensor,
        _rejected_actions: &Tensor,
    ) -> Result<DPOMetrics> {
        let batch_size = states.shape().dims()[0];

        // Compute log probabilities for chosen and rejected actions
        let _chosen_log_probs = Tensor::randn(&[batch_size], &self.device)? * -1.0;
        let _rejected_log_probs = Tensor::randn(&[batch_size], &self.device)? * -2.0;

        // Reference model log probabilities
        let _ref_chosen_log_probs = Tensor::randn(&[batch_size], &self.device)? * -1.5;
        let _ref_rejected_log_probs = Tensor::randn(&[batch_size], &self.device)? * -1.8;

        // DPO loss computation
        let chosen_rewards: Tensor = Tensor::randn(&[batch_size], &self.device)?;
        let rejected_rewards: Tensor = Tensor::randn(&[batch_size], &self.device)?;

        // Simplified for RED phase
        let logits: Tensor = Tensor::randn(&[batch_size], &self.device)?;
        let sigmoid_logits = logits.sigmoid()?;
        let log_sigmoid = sigmoid_logits.log()?;
        let mean_log = log_sigmoid.mean(&[], false)?;
        let loss = mean_log.neg()?;

        // Compute metrics
        // Simplified for RED phase
        let accuracy = Tensor::full(&[1], 0.8, &self.device)?;
        let chosen_reward = chosen_rewards.mean(&[], false)?;
        let rejected_reward = rejected_rewards.mean(&[], false)?;
        let reward_margin = (&chosen_reward - &rejected_reward)?;

        Ok(DPOMetrics {
            loss: loss.item()?,
            accuracy: accuracy.item()?,
            chosen_reward: chosen_reward.item()?,
            rejected_reward: rejected_reward.item()?,
            reward_margin: reward_margin.item()?,
        })
    }

    pub async fn update(
        &mut self,
        states: &Tensor,
        chosen_actions: &Tensor,
        rejected_actions: &Tensor,
    ) -> Result<DPOMetrics> {
        let metrics = self
            .compute_preference_loss(states, chosen_actions, rejected_actions)
            .await?;

        // In real implementation, we'd perform gradient updates here

        Ok(metrics)
    }
}