//! RSSM (Recurrent State Space Model) implementation.
//!
//! Based on DreamerV3 architecture for world modeling.

use embodied_shared::{Observation, Transition, WorldModelConfig};

/// RSSM World Model.
#[derive(Debug)]
pub struct RSSM {
    /// Observation embedding dimension.
    obs_embed_dim: usize,
    /// Action embedding dimension.
    action_embed_dim: usize,
    /// Deterministic state dimension.
    deter_dim: usize,
    /// Stochastic state dimension.
    stoch_dim: usize,
    /// Number of stochastic classes.
    stoch_classes: usize,
    /// Hidden dimension.
    hidden_dim: usize,
    /// Encoder weights (simplified).
    encoder_weights: Vec<f64>,
    /// Dynamics weights.
    dynamics_weights: Vec<f64>,
    /// Decoder weights.
    decoder_weights: Vec<f64>,
    /// Reward predictor weights.
    reward_weights: Vec<f64>,
    /// Done predictor weights.
    done_weights: Vec<f64>,
    /// RNG state.
    rng_state: u64,
}

impl RSSM {
    /// Create a new RSSM model.
    pub fn new(config: &WorldModelConfig) -> Self {
        let encoder_size = config.obs_embed_dim * config.hidden_dim;
        let dynamics_size =
            (config.deter_dim + config.stoch_dim * config.stoch_classes + config.action_embed_dim)
                * config.hidden_dim;
        let decoder_size =
            (config.deter_dim + config.stoch_dim * config.stoch_classes) * config.hidden_dim;
        let reward_size = config.hidden_dim * 64;
        let done_size = config.hidden_dim * 32;

        let mut rssm = Self {
            obs_embed_dim: config.obs_embed_dim,
            action_embed_dim: config.action_embed_dim,
            deter_dim: config.deter_dim,
            stoch_dim: config.stoch_dim,
            stoch_classes: config.stoch_classes,
            hidden_dim: config.hidden_dim,
            encoder_weights: vec![0.0; encoder_size],
            dynamics_weights: vec![0.0; dynamics_size],
            decoder_weights: vec![0.0; decoder_size],
            reward_weights: vec![0.0; reward_size],
            done_weights: vec![0.0; done_size],
            rng_state: 42,
        };

        rssm.initialize_weights();
        rssm
    }

    /// Initialize weights with Xavier initialization.
    fn initialize_weights(&mut self) {
        let init_weights = |weights: &mut [f64], rng: &mut u64, fan_in: usize| {
            let scale = (2.0 / fan_in as f64).sqrt();
            for w in weights.iter_mut() {
                *w = Self::random_normal(rng) * scale;
            }
        };

        init_weights(
            &mut self.encoder_weights,
            &mut self.rng_state,
            self.obs_embed_dim,
        );
        init_weights(
            &mut self.dynamics_weights,
            &mut self.rng_state,
            self.deter_dim,
        );
        init_weights(
            &mut self.decoder_weights,
            &mut self.rng_state,
            self.deter_dim,
        );
        init_weights(
            &mut self.reward_weights,
            &mut self.rng_state,
            self.hidden_dim,
        );
        init_weights(&mut self.done_weights, &mut self.rng_state, self.hidden_dim);
    }

    /// Random number generator.
    fn random(rng: &mut u64) -> f64 {
        *rng = rng
            .wrapping_mul(6364136223846793005)
            .wrapping_add(1442695040888963407);
        (*rng >> 11) as f64 / (1u64 << 53) as f64
    }

    /// Random normal using Box-Muller.
    fn random_normal(rng: &mut u64) -> f64 {
        let u1 = Self::random(rng) + 1e-10;
        let u2 = Self::random(rng);
        (-2.0 * u1.ln()).sqrt() * (2.0 * std::f64::consts::PI * u2).cos()
    }

    /// Encode observation to state.
    pub fn encode_observation(&self, obs: &Observation) -> Vec<f64> {
        let state_dim = self.deter_dim + self.stoch_dim * self.stoch_classes;
        let mut state = vec![0.0; state_dim];

        // Simple encoding: use joint positions and velocities
        for (i, &pos) in obs.joint_positions.iter().enumerate() {
            let idx = i % state_dim;
            state[idx] += pos as f64 * 0.1;
        }

        for (i, &vel) in obs.joint_velocities.iter().enumerate() {
            let idx = (i + obs.joint_positions.len()) % state_dim;
            state[idx] += vel as f64 * 0.01;
        }

        // Apply encoder weights
        for (i, s) in state.iter_mut().enumerate() {
            let weight_idx = i % self.encoder_weights.len();
            *s = (*s * self.encoder_weights[weight_idx]).tanh();
        }

        state
    }

    /// Imagine one step forward.
    pub fn imagine_step(&self, state: &[f64], action: &[f64]) -> (Vec<f64>, f64, f64) {
        let state_dim = state.len();
        let mut next_state = vec![0.0; state_dim];

        // GRU-style update
        for i in 0..state_dim {
            let action_idx = i % action.len();
            let weight_idx = i % self.dynamics_weights.len();

            // Reset gate
            let reset =
                (state[i] * self.dynamics_weights[weight_idx] + action[action_idx] * 0.1).tanh();

            // Update gate
            let update = ((state[i] + action[action_idx] * 0.1)
                * self.dynamics_weights[(weight_idx + 1) % self.dynamics_weights.len()])
            .sigmoid();

            // Candidate
            let candidate = (reset * state[i]
                + action[action_idx] * 0.2
                + self.dynamics_weights[(weight_idx + 2) % self.dynamics_weights.len()])
            .tanh();

            next_state[i] = (1.0 - update) * state[i] + update * candidate;
        }

        // Predict reward
        let reward = self.predict_reward(&next_state);

        // Predict done probability
        let done_logit = self.predict_done(&next_state);
        let done_prob = done_logit.sigmoid();

        (next_state, reward, done_prob)
    }

    /// Predict reward from state.
    fn predict_reward(&self, state: &[f64]) -> f64 {
        let mut reward = 0.0;

        for (i, &s) in state.iter().enumerate() {
            let weight_idx = i % self.reward_weights.len();
            reward += s * self.reward_weights[weight_idx];
        }

        reward.tanh() // Bounded reward
    }

    /// Predict done from state.
    fn predict_done(&self, state: &[f64]) -> f64 {
        let mut logit = 0.0;

        for (i, &s) in state.iter().enumerate() {
            let weight_idx = i % self.done_weights.len();
            logit += s * self.done_weights[weight_idx];
        }

        logit * 0.01 // Small logit, mostly not done
    }

    /// Train on a batch of sequences.
    pub fn train_step(&mut self, batch: &[Vec<Transition>], learning_rate: f64) -> f64 {
        let mut total_loss = 0.0;

        for sequence in batch {
            let loss = self.train_sequence(sequence, learning_rate);
            total_loss += loss;
        }

        total_loss / batch.len() as f64
    }

    /// Train on a single sequence.
    fn train_sequence(&mut self, sequence: &[Transition], learning_rate: f64) -> f64 {
        let mut loss = 0.0;

        for trans in sequence {
            // Encode current observation
            let state = self.encode_observation(&trans.observation);

            // Encode action
            let action: Vec<f64> = trans.action.joint_positions.as_ref().map_or_else(
                || vec![0.0; self.action_embed_dim],
                |p| p.iter().map(|&x| x as f64).collect(),
            );

            // Imagine next state
            let (pred_next, pred_reward, pred_done) = self.imagine_step(&state, &action);

            // Encode actual next observation
            let actual_next = self.encode_observation(&trans.next_observation);

            // Compute losses
            let state_loss: f64 = pred_next
                .iter()
                .zip(actual_next.iter())
                .map(|(p, a)| (p - a).powi(2))
                .sum::<f64>()
                / pred_next.len() as f64;

            let reward_loss = (pred_reward - trans.reward as f64).powi(2);

            let done_target = if trans.done { 1.0 } else { 0.0 };
            let done_loss = -done_target * pred_done.ln().max(-10.0)
                - (1.0 - done_target) * (1.0 - pred_done).ln().max(-10.0);

            loss += state_loss + reward_loss * 0.5 + done_loss * 0.1;

            // Simplified gradient update (in real implementation, use autograd)
            self.update_weights(learning_rate * 0.01);
        }

        loss / sequence.len() as f64
    }

    /// Simplified weight update.
    fn update_weights(&mut self, lr: f64) {
        // Add small random perturbation as simplified training
        for w in &mut self.encoder_weights {
            *w += lr * Self::random_normal(&mut self.rng_state) * 0.001;
        }
        for w in &mut self.dynamics_weights {
            *w += lr * Self::random_normal(&mut self.rng_state) * 0.001;
        }
    }

    /// Get state dimension.
    #[must_use]
    pub fn state_dim(&self) -> usize {
        self.deter_dim + self.stoch_dim * self.stoch_classes
    }

    /// Get number of parameters.
    #[must_use]
    pub fn num_parameters(&self) -> usize {
        self.encoder_weights.len()
            + self.dynamics_weights.len()
            + self.decoder_weights.len()
            + self.reward_weights.len()
            + self.done_weights.len()
    }
}

/// Sigmoid activation.
trait Sigmoid {
    fn sigmoid(self) -> Self;
}

impl Sigmoid for f64 {
    fn sigmoid(self) -> Self {
        1.0 / (1.0 + (-self).exp())
    }
}

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

    #[test]
    fn test_rssm_creation() {
        let config = sample_world_model_config();
        let rssm = RSSM::new(&config);

        assert_eq!(rssm.deter_dim, config.deter_dim);
        assert_eq!(rssm.stoch_dim, config.stoch_dim);
    }

    #[test]
    fn test_encode_observation() {
        let config = sample_world_model_config();
        let rssm = RSSM::new(&config);
        let obs = embodied_shared::sample_observation();

        let state = rssm.encode_observation(&obs);
        let expected_dim = config.deter_dim + config.stoch_dim * config.stoch_classes;
        assert_eq!(state.len(), expected_dim);
    }

    #[test]
    fn test_imagine_step() {
        let config = sample_world_model_config();
        let rssm = RSSM::new(&config);
        let obs = embodied_shared::sample_observation();

        let state = rssm.encode_observation(&obs);
        let action = vec![0.1; 7];

        let (next_state, reward, done) = rssm.imagine_step(&state, &action);

        assert_eq!(next_state.len(), state.len());
        assert!(reward.is_finite());
        assert!(done >= 0.0 && done <= 1.0);
    }

    #[test]
    fn test_train_sequence() {
        let config = sample_world_model_config();
        let mut rssm = RSSM::new(&config);

        let sequence = vec![
            embodied_shared::sample_transition(),
            embodied_shared::sample_transition(),
        ];

        let loss = rssm.train_sequence(&sequence, 1e-4);
        assert!(loss.is_finite());
    }

    #[test]
    fn test_state_dim() {
        let config = sample_world_model_config();
        let rssm = RSSM::new(&config);

        let expected = config.deter_dim + config.stoch_dim * config.stoch_classes;
        assert_eq!(rssm.state_dim(), expected);
    }

    #[test]
    fn test_num_parameters() {
        let config = sample_world_model_config();
        let rssm = RSSM::new(&config);
        assert!(rssm.num_parameters() > 0);
    }
}