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

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SACConfig {
    pub learning_rate: f64,
    pub gamma: f32,
    pub tau: f32,
    pub alpha: f32,
    pub target_update_interval: usize,
    pub automatic_entropy_tuning: bool,
    pub target_entropy: Option<f32>,
    pub replay_buffer_size: usize,
    pub batch_size: usize,
}

impl Default for SACConfig {
    fn default() -> Self {
        Self {
            learning_rate: 3e-4,
            gamma: 0.99,
            tau: 0.005,
            alpha: 0.2,
            target_update_interval: 1,
            automatic_entropy_tuning: true,
            target_entropy: None,
            replay_buffer_size: 1_000_000,
            batch_size: 256,
        }
    }
}

#[derive(Debug)]
pub struct SACMetrics {
    pub actor_loss: f32,
    pub critic1_loss: f32,
    pub critic2_loss: f32,
    pub temperature_loss: f32,
    pub alpha: f32,
    pub target_entropy: f32,
}

pub struct SAC {
    config: SACConfig,
    state_dim: usize,
    action_dim: usize,
    hidden_dim: usize,
    device: Device,
    // Neural network parameters - dummy implementation for testing
    actor_params: Vec<Tensor>,
    critic1_params: Vec<Tensor>,
    critic2_params: Vec<Tensor>,
    target_critic1_params: Vec<Tensor>,
    target_critic2_params: Vec<Tensor>,
    log_alpha: Tensor,
}

impl SAC {
    pub fn new(
        config: SACConfig,
        state_dim: usize,
        action_dim: usize,
        hidden_dim: usize,
        device: Device,
    ) -> Self {
        // Initialize dummy parameters for testing
        let actor_params = vec![
            Tensor::randn(&[hidden_dim, state_dim], &device).unwrap(),
            Tensor::randn(&[hidden_dim], &device).unwrap(),
            Tensor::randn(&[action_dim * 2, hidden_dim], &device).unwrap(), // mean and log_std
            Tensor::randn(&[action_dim * 2], &device).unwrap(),
        ];

        let critic1_params = vec![
            Tensor::randn(&[hidden_dim, state_dim + action_dim], &device).unwrap(),
            Tensor::randn(&[hidden_dim], &device).unwrap(),
            Tensor::randn(&[1, hidden_dim], &device).unwrap(),
            Tensor::randn(&[1], &device).unwrap(),
        ];

        let critic2_params = vec![
            Tensor::randn(&[hidden_dim, state_dim + action_dim], &device).unwrap(),
            Tensor::randn(&[hidden_dim], &device).unwrap(),
            Tensor::randn(&[1, hidden_dim], &device).unwrap(),
            Tensor::randn(&[1], &device).unwrap(),
        ];

        let target_critic1_params = critic1_params.clone();
        let target_critic2_params = critic2_params.clone();

        let log_alpha = Tensor::zeros([1], &device).unwrap();

        Self {
            config,
            state_dim,
            action_dim,
            hidden_dim,
            device,
            actor_params,
            critic1_params,
            critic2_params,
            target_critic1_params,
            target_critic2_params,
            log_alpha,
        }
    }

    pub fn state_dim(&self) -> usize {
        self.state_dim
    }

    pub fn action_dim(&self) -> usize {
        self.action_dim
    }

    pub async fn actor_forward(
        &self,
        states: &Tensor,
        deterministic: bool,
    ) -> Result<(Tensor, Tensor)> {
        let batch_size = states.shape().dims()[0];

        if deterministic {
            // Deterministic action (mean of policy) - simplified for testing
            let actions = Tensor::randn(&[batch_size, self.action_dim], &self.device)?;
            let log_probs = Tensor::zeros([batch_size], &self.device)?;
            Ok((actions, log_probs))
        } else {
            // Stochastic action with reparameterization trick - simplified for testing
            let actions = Tensor::randn(&[batch_size, self.action_dim], &self.device)?;
            let log_probs = Tensor::full(&[batch_size], -2.0, &self.device)?; // Dummy negative log probs
            Ok((actions, log_probs))
        }
    }

    pub async fn critic_forward(
        &self,
        states: &Tensor,
        _actions: &Tensor,
        _critic_id: usize,
    ) -> Result<Tensor> {
        let batch_size = states.shape().dims()[0];

        // Dummy Q-value computation
        let q_values = Tensor::randn(&[batch_size], &self.device)?;
        Ok(q_values)
    }

    pub async fn target_critic_forward(
        &self,
        states: &Tensor,
        actions: &Tensor,
        critic_id: usize,
    ) -> Result<Tensor> {
        // Same as critic_forward but using target parameters
        self.critic_forward(states, actions, critic_id).await
    }

    pub async fn compute_actor_loss(&self, states: &Tensor) -> Result<Tensor> {
        let (actions, _log_probs) = self.actor_forward(states, false).await?;

        let q1_values = self.critic_forward(states, &actions, 0).await?;
        let _q2_values = self.critic_forward(states, &actions, 1).await?;
        // Simplified for RED phase
        let _min_q_values = q1_values;

        let _alpha: f32 = self.log_alpha.exp()?.item()?;
        // Simplified for RED phase
        let actor_loss = Tensor::full(&[1], 0.1, &self.device)?;

        Ok(actor_loss)
    }

    pub async fn compute_critic_loss(
        &self,
        states: &Tensor,
        actions: &Tensor,
        rewards: &Tensor,
        next_states: &Tensor,
        _dones: &Tensor,
    ) -> Result<(Tensor, Tensor)> {
        // Current Q-values
        let _q1_values = self.critic_forward(states, actions, 0).await?;
        let _q2_values = self.critic_forward(states, actions, 1).await?;

        // Target Q-values
        let (next_actions, _next_log_probs) = self.actor_forward(next_states, false).await?;
        let target_q1 = self
            .target_critic_forward(next_states, &next_actions, 0)
            .await?;
        let _target_q2 = self
            .target_critic_forward(next_states, &next_actions, 1)
            .await?;
        // Simplified for RED phase - just use one of the Q values
        let min_target_q = target_q1;

        let _alpha: f32 = self.log_alpha.exp()?.item()?;
        // Simplified for RED phase
        let _next_v = min_target_q;

        // Simplified for RED phase
        let _target_q = rewards.clone();

        // Simplified for RED phase
        let critic1_loss = Tensor::full(&[1], 0.1, &self.device)?;
        let critic2_loss = Tensor::full(&[1], 0.1, &self.device)?;

        Ok((critic1_loss, critic2_loss))
    }

    pub async fn compute_temperature_loss(&self, log_probs: &Tensor) -> Result<Tensor> {
        if !self.config.automatic_entropy_tuning {
            return Ok(Tensor::zeros([1], &self.device)?);
        }

        let target_entropy = self
            .config
            .target_entropy
            .unwrap_or(-(self.action_dim as f32));
        let _target_entropy_tensor =
            Tensor::full(&[log_probs.shape().dims()[0]], target_entropy, &self.device)?;
        // Simplified for RED phase
        let temperature_loss = Tensor::full(&[1], 0.01, &self.device)?;

        Ok(temperature_loss)
    }

    pub async fn get_target_critic_params(&self) -> Result<Vec<f32>> {
        // Dummy implementation for testing
        Ok(vec![1.0, 2.0, 3.0, 4.0])
    }

    pub async fn update(
        &mut self,
        states: &Tensor,
        actions: &Tensor,
        rewards: &Tensor,
        next_states: &Tensor,
        dones: &Tensor,
    ) -> Result<SACMetrics> {
        // Compute losses
        let actor_loss = self.compute_actor_loss(states).await?;
        let (critic1_loss, critic2_loss) = self
            .compute_critic_loss(states, actions, rewards, next_states, dones)
            .await?;

        let (_, log_probs) = self.actor_forward(states, false).await?;
        let temperature_loss = self.compute_temperature_loss(&log_probs).await?;

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

        // Soft update target networks (dummy implementation)
        // target = tau * current + (1 - tau) * target

        let alpha: f32 = self.log_alpha.exp()?.item()?;
        let target_entropy = self
            .config
            .target_entropy
            .unwrap_or(-(self.action_dim as f32));

        Ok(SACMetrics {
            actor_loss: actor_loss.item()?,
            critic1_loss: critic1_loss.item()?,
            critic2_loss: critic2_loss.item()?,
            temperature_loss: temperature_loss.item()?,
            alpha,
            target_entropy,
        })
    }
}