//! Diffusion Transformer (DiT) implementation for video generation.

use worldgen_shared::ModelConfig;

/// Diffusion Transformer.
#[derive(Debug)]
pub struct DiffusionTransformer {
    /// Hidden dimension.
    hidden_dim: usize,
    /// Number of layers.
    num_layers: usize,
    /// Number of attention heads.
    #[allow(dead_code)]
    num_heads: usize,
    /// Patch size.
    #[allow(dead_code)]
    patch_size: usize,
    /// Weights (simplified).
    weights: Vec<f64>,
    /// Use temporal attention.
    temporal_attention: bool,
    /// RNG state.
    rng_state: u64,
}

impl DiffusionTransformer {
    /// Create a new DiT model.
    pub fn new(config: &ModelConfig) -> Self {
        let num_params = Self::calculate_params(config);
        let mut model = Self {
            hidden_dim: config.hidden_dim,
            num_layers: config.num_layers,
            num_heads: config.num_heads,
            patch_size: config.patch_size,
            weights: vec![0.0; num_params],
            temporal_attention: config.temporal_attention,
            rng_state: 42,
        };
        model.initialize_weights();
        model
    }

    /// Calculate number of parameters.
    fn calculate_params(config: &ModelConfig) -> usize {
        let d = config.hidden_dim;
        let l = config.num_layers;

        // Embedding + transformer layers + output projection
        let embedding = d * 1024; // Patch embedding
        let transformer = l * (4 * d * d + 2 * d); // MLP + attention
        let output = d * 4; // Output projection

        embedding + transformer + output
    }

    /// Initialize weights.
    fn initialize_weights(&mut self) {
        let scale = (self.hidden_dim as f64).sqrt().recip();
        let num_weights = self.weights.len();
        let init_values: Vec<f64> = (0..num_weights)
            .map(|_| self.random_normal() * scale)
            .collect();
        for (weight, value) in self.weights.iter_mut().zip(init_values) {
            *weight = value;
        }
    }

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

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

    /// Forward pass: predict noise given noisy latents, timestep, and text embedding.
    pub fn forward(&self, latents: &[f64], timestep: f64, text_embedding: &[f64]) -> Vec<f64> {
        let mut output = vec![0.0; latents.len()];

        // Timestep embedding
        let t_embed = self.timestep_embedding(timestep);

        // Process each latent
        for (i, &latent) in latents.iter().enumerate() {
            // Combine latent, timestep, and text embedding
            let t_idx = i % t_embed.len();
            let txt_idx = i % text_embedding.len();

            let combined = latent + t_embed[t_idx] * 0.1 + text_embedding[txt_idx] * 0.1;

            // Apply transformer layers (simplified)
            let mut hidden = combined;
            for layer in 0..self.num_layers {
                let weight_idx = (i + layer) % self.weights.len();

                // Attention (simplified as weighted sum)
                hidden = hidden * self.weights[weight_idx] + hidden;

                // MLP
                hidden = hidden.tanh() * self.weights[(weight_idx + 1) % self.weights.len()];

                // Temporal attention (if enabled)
                if self.temporal_attention {
                    hidden *= 1.0 + 0.01 * (i as f64).sin();
                }
            }

            // Output projection
            output[i] = hidden.tanh();
        }

        output
    }

    /// Create timestep embedding.
    fn timestep_embedding(&self, timestep: f64) -> Vec<f64> {
        let dim = self.hidden_dim;
        let mut embedding = vec![0.0; dim];

        for i in 0..dim / 2 {
            let freq = (-((i as f64) / (dim as f64 / 2.0) * 10.0)).exp();
            embedding[i] = (timestep * freq).sin();
            embedding[i + dim / 2] = (timestep * freq).cos();
        }

        embedding
    }

    /// Get hidden dimension.
    #[must_use]
    pub fn hidden_dim(&self) -> usize {
        self.hidden_dim
    }

    /// Get number of parameters.
    #[must_use]
    pub fn num_parameters(&self) -> usize {
        self.weights.len()
    }
}

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

    #[test]
    fn test_dit_creation() {
        let config = sample_model_config();
        let dit = DiffusionTransformer::new(&config);

        assert_eq!(dit.hidden_dim, config.hidden_dim);
        assert_eq!(dit.num_layers, config.num_layers);
    }

    #[test]
    fn test_forward() {
        let config = sample_model_config();
        let dit = DiffusionTransformer::new(&config);

        let latents = vec![0.5; 64];
        let text_embedding = vec![0.1; config.hidden_dim];
        let timestep = 0.5;

        let output = dit.forward(&latents, timestep, &text_embedding);
        assert_eq!(output.len(), latents.len());

        // Check output is bounded
        for &val in &output {
            assert!(val >= -1.0 && val <= 1.0);
        }
    }

    #[test]
    fn test_timestep_embedding() {
        let config = sample_model_config();
        let dit = DiffusionTransformer::new(&config);

        let embedding = dit.timestep_embedding(0.5);
        assert_eq!(embedding.len(), config.hidden_dim);
    }

    #[test]
    fn test_num_parameters() {
        let config = sample_model_config();
        let dit = DiffusionTransformer::new(&config);
        assert!(dit.num_parameters() > 0);
    }
}