//! `LLaMA` Attention Mechanisms and Components (Simplified Stub)
//!
//! This is a simplified stub version to get compilation working.
//! TODO: Implement full `LLaMA` attention with rotary embeddings and grouped-query attention.

use crate::architectures::TransformerConfig;
use crate::tensor_bridge::TensorBridge;
use crate::{Result, TransformerError};
use rtx_tensor::{Device, Tensor};
use serde::{Deserialize, Serialize};

/// LLaMA-specific configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LLaMAConfig {
    /// Base transformer configuration
    pub base: TransformerConfig,
    /// Vocabulary size
    pub vocab_size: usize,
    /// Hidden dimension
    pub hidden_size: usize,
    /// Feed-forward intermediate dimension
    pub intermediate_size: usize,
    /// Number of transformer layers
    pub num_hidden_layers: usize,
    /// Number of attention heads
    pub num_attention_heads: usize,
    /// Number of key-value heads (for grouped-query attention)
    pub num_key_value_heads: usize,
    /// Maximum sequence length
    pub max_position_embeddings: usize,
    /// `RMSNorm` epsilon
    pub rms_norm_eps: f64,
    /// Initializer range for weights
    pub initializer_range: f64,
    /// Use cache for generation
    pub use_cache: bool,
    /// Pad token ID
    pub pad_token_id: i64,
    /// BOS token ID
    pub bos_token_id: i64,
    /// EOS token ID
    pub eos_token_id: i64,
    /// Pretraining type
    pub pretraining_tp: usize,
    /// Tie word embeddings
    pub tie_word_embeddings: bool,
    /// Rope scaling configuration
    pub rope_scaling: Option<RopeScaling>,
    /// Rope theta parameter
    pub rope_theta: f64,
    /// Attention bias
    pub attention_bias: bool,
    /// MLP bias
    pub mlp_bias: bool,
}

/// `RoPE` (Rotary Position Embedding) scaling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RopeScaling {
    /// Scaling type ("linear" or "dynamic")
    pub scaling_type: String,
    /// Scaling factor
    pub factor: f64,
}

impl Default for LLaMAConfig {
    fn default() -> Self {
        Self {
            base: TransformerConfig::default(),
            vocab_size: 32000,
            hidden_size: 4096,
            intermediate_size: 11008,
            num_hidden_layers: 32,
            num_attention_heads: 32,
            num_key_value_heads: 32, // Standard multi-head attention
            max_position_embeddings: 2048,
            rms_norm_eps: 1e-6,
            initializer_range: 0.02,
            use_cache: true,
            pad_token_id: -1,
            bos_token_id: 1,
            eos_token_id: 2,
            pretraining_tp: 1,
            tie_word_embeddings: false,
            rope_scaling: None,
            rope_theta: 10000.0,
            attention_bias: false,
            mlp_bias: false,
        }
    }
}

impl LLaMAConfig {
    /// Create `LLaMA` 7B configuration
    #[must_use]
    pub fn llama_7b() -> Self {
        Self::default()
    }

    /// Create `LLaMA` 13B configuration
    #[must_use]
    pub fn llama_13b() -> Self {
        let mut config = Self::default();
        config.hidden_size = 5120;
        config.intermediate_size = 13824;
        config.num_hidden_layers = 40;
        config.num_attention_heads = 40;
        config.num_key_value_heads = 40;
        config
    }

    /// Create `LLaMA` 30B configuration
    #[must_use]
    pub fn llama_30b() -> Self {
        let mut config = Self::default();
        config.hidden_size = 6656;
        config.intermediate_size = 17920;
        config.num_hidden_layers = 60;
        config.num_attention_heads = 52;
        config.num_key_value_heads = 52;
        config
    }

    /// Create `LLaMA` 65B configuration
    #[must_use]
    pub fn llama_65b() -> Self {
        let mut config = Self::default();
        config.hidden_size = 8192;
        config.intermediate_size = 22016;
        config.num_hidden_layers = 80;
        config.num_attention_heads = 64;
        config.num_key_value_heads = 64;
        config
    }

    /// Create Code Llama configuration
    pub fn code_llama(base_size: &str) -> Result<Self> {
        let mut config = match base_size {
            "7b" => Self::llama_7b(),
            "13b" => Self::llama_13b(),
            "34b" => {
                let mut config = Self::default();
                config.hidden_size = 8192;
                config.intermediate_size = 22016;
                config.num_hidden_layers = 48;
                config.num_attention_heads = 64;
                config.num_key_value_heads = 8; // Grouped-query attention
                config.max_position_embeddings = 16384; // Extended context
                config
            }
            _ => {
                return Err(TransformerError::config(format!(
                    "Unsupported Code Llama size: {base_size}"
                )));
            }
        };

        // Code Llama specific adjustments
        config.vocab_size = 32016; // Extended vocabulary
        config.rope_theta = 1000000.0; // Extended RoPE

        Ok(config)
    }

    /// Validate configuration parameters
    pub fn validate(&self) -> Result<()> {
        if self.vocab_size == 0 {
            return Err(TransformerError::Generic(
                "vocab_size must be positive".to_string(),
            ));
        }
        if self.hidden_size == 0 {
            return Err(TransformerError::Generic(
                "hidden_size must be positive".to_string(),
            ));
        }
        if self.num_hidden_layers == 0 {
            return Err(TransformerError::Generic(
                "num_hidden_layers must be positive".to_string(),
            ));
        }
        if self.num_attention_heads == 0 {
            return Err(TransformerError::Generic(
                "num_attention_heads must be positive".to_string(),
            ));
        }
        if !self.hidden_size.is_multiple_of(self.num_attention_heads) {
            return Err(TransformerError::Generic(
                "hidden_size must be divisible by num_attention_heads".to_string(),
            ));
        }
        Ok(())
    }
}

/// Rotary Position Embedding (`RoPE`) implementation
#[derive(Debug)]
pub struct RotaryPositionEmbedding {
    dim: usize,
    max_seq_len: usize,
    theta: f64,
    device: Device,
    // Precomputed frequency tensors for efficiency
    cos_cached: Option<Tensor>,
    sin_cached: Option<Tensor>,
}

impl RotaryPositionEmbedding {
    /// Create new `RoPE` layer
    pub fn new(dim: usize, max_seq_len: usize, theta: f64, device: &Device) -> Result<Self> {
        let mut rope = Self {
            dim,
            max_seq_len,
            theta,
            device: device.clone(),
            cos_cached: None,
            sin_cached: None,
        };

        // Precompute cos/sin values for efficiency
        rope.precompute_freqs(max_seq_len)?;
        Ok(rope)
    }

    /// Precompute frequency tensors
    fn precompute_freqs(&mut self, seq_len: usize) -> Result<()> {
        // Create frequency tensor: theta^(-2k/dim) for k = 0, 1, ..., dim//2-1
        let half_dim = self.dim / 2;
        let freqs: Vec<f32> = (0..half_dim)
            .map(|i| 1.0 / self.theta.powf(2.0 * i as f64 / self.dim as f64) as f32)
            .collect();

        let freq_tensor = Tensor::from_vec(freqs, &[half_dim], &self.device)?;

        // Create position tensor: [0, 1, 2, ..., seq_len-1]
        let positions: Vec<f32> = (0..seq_len).map(|i| i as f32).collect();
        let pos_tensor = Tensor::from_vec(positions, &[seq_len], &self.device)?;

        // Compute outer product: pos[i] * freq[j] for all i, j
        let pos_expanded = pos_tensor.unsqueeze(1)?; // [seq_len, 1]
        let freq_expanded = freq_tensor.unsqueeze(0)?; // [1, half_dim]
        let angles = pos_expanded.matmul(&freq_expanded)?; // [seq_len, half_dim]

        // Compute cos and sin
        self.cos_cached = Some(angles.cos()?);
        self.sin_cached = Some(angles.sin()?);

        Ok(())
    }

    /// Apply rotary position encoding to queries and keys
    pub fn forward(&self, q: &Tensor, k: &Tensor, seq_len: usize) -> Result<(Tensor, Tensor)> {
        if seq_len > self.max_seq_len {
            return Err(TransformerError::architecture(format!(
                "Sequence length {} exceeds maximum {}",
                seq_len, self.max_seq_len
            )));
        }

        // Get cached cos/sin values
        let cos = self.cos_cached.as_ref().ok_or_else(|| {
            TransformerError::architecture("RoPE frequencies not precomputed".to_string())
        })?;
        let sin = self.sin_cached.as_ref().ok_or_else(|| {
            TransformerError::architecture("RoPE frequencies not precomputed".to_string())
        })?;

        // Apply rotary position encoding
        let q_rot = self.apply_rotary_pos_emb(q, cos, sin, seq_len)?;
        let k_rot = self.apply_rotary_pos_emb(k, cos, sin, seq_len)?;

        Ok((q_rot, k_rot))
    }

    /// Apply rotary position embedding to a tensor
    fn apply_rotary_pos_emb(
        &self,
        x: &Tensor,
        cos: &Tensor,
        sin: &Tensor,
        seq_len: usize,
    ) -> Result<Tensor> {
        // Extract shape: [batch_size, seq_len, num_heads, head_dim]
        let shape = x.shape();
        let batch_size = shape[0];
        let input_seq_len = shape[1];
        let num_heads = shape[2];
        let head_dim = shape[3];

        let actual_seq_len = input_seq_len.min(seq_len);

        // Reshape x to [batch_size, seq_len, num_heads, head_dim/2, 2]
        let half_dim = head_dim / 2;
        let x_reshaped = x.view([batch_size, actual_seq_len, num_heads, half_dim, 2])?;

        // Split into x1 and x2 (real and imaginary parts)
        let x1 = x_reshaped.slice(4, 0, 1)?; // [..., 0]
        let x2 = x_reshaped.slice(4, 1, 2)?; // [..., 1]

        // Get cos/sin for the sequence length
        let cos_seq = cos.slice(0, 0, actual_seq_len)?; // [actual_seq_len, half_dim]
        let sin_seq = sin.slice(0, 0, actual_seq_len)?; // [actual_seq_len, half_dim]

        // Expand cos/sin to match tensor dimensions
        // [actual_seq_len, half_dim] -> [1, actual_seq_len, 1, half_dim, 1]
        let cos_expanded = cos_seq.view([1, actual_seq_len, 1, half_dim, 1])?;
        let sin_expanded = sin_seq.view([1, actual_seq_len, 1, half_dim, 1])?;

        // Apply rotation:
        // x_rot1 = x1 * cos - x2 * sin
        // x_rot2 = x1 * sin + x2 * cos
        let x_rot1 = x1.mul(&cos_expanded)?.sub(&x2.mul(&sin_expanded)?)?;
        let x_rot2 = x1.mul(&sin_expanded)?.add(&x2.mul(&cos_expanded)?)?;

        // Concatenate rotated parts
        let x_rot = Tensor::cat(&[x_rot1, x_rot2], 4)?;

        // Reshape back to original shape
        x_rot
            .view([batch_size, actual_seq_len, num_heads, head_dim])
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }
}

/// `LLaMA` Multi-Head Attention with `RoPE` and GQA
#[derive(Debug)]
pub struct LLaMAAttention {
    num_heads: usize,
    num_key_value_heads: usize,
    head_dim: usize,
    hidden_size: usize,
    rope: RotaryPositionEmbedding,
    // Linear projection weights
    q_proj: Tensor,
    k_proj: Tensor,
    v_proj: Tensor,
    o_proj: Tensor,
    device: Device,
}

impl LLaMAAttention {
    /// Create new `LLaMA` attention layer
    pub fn new(config: &LLaMAConfig, device: &Device) -> Result<Self> {
        let head_dim = config.hidden_size / config.num_attention_heads;

        let rope = RotaryPositionEmbedding::new(
            head_dim,
            config.max_position_embeddings,
            config.rope_theta,
            device,
        )?;

        // Initialize projection weights
        let scale = (config.hidden_size as f32).recip().sqrt();

        // Query projection: [hidden_size, num_heads * head_dim]
        let q_proj = Tensor::randn(
            &[config.hidden_size, config.num_attention_heads * head_dim],
            device,
        )?
        .mul_scalar(scale)?;

        // Key and Value projections for GQA: [hidden_size, num_kv_heads * head_dim]
        let k_proj = Tensor::randn(
            &[config.hidden_size, config.num_key_value_heads * head_dim],
            device,
        )?
        .mul_scalar(scale)?;
        let v_proj = Tensor::randn(
            &[config.hidden_size, config.num_key_value_heads * head_dim],
            device,
        )?
        .mul_scalar(scale)?;

        // Output projection: [num_heads * head_dim, hidden_size]
        let o_proj = Tensor::randn(
            &[config.num_attention_heads * head_dim, config.hidden_size],
            device,
        )?
        .mul_scalar(scale)?;

        Ok(Self {
            num_heads: config.num_attention_heads,
            num_key_value_heads: config.num_key_value_heads,
            head_dim,
            hidden_size: config.hidden_size,
            rope,
            q_proj,
            k_proj,
            v_proj,
            o_proj,
            device: device.clone(),
        })
    }

    /// Forward pass of attention
    pub fn forward(&self, hidden_states: &Tensor, position_ids: Option<&Tensor>) -> Result<Tensor> {
        let batch_size = hidden_states.shape()[0];
        let seq_len = hidden_states.shape()[1];

        // 1. Linear projections
        let q = self.compute_queries(hidden_states)?;
        let k = self.compute_keys(hidden_states)?;
        let v = self.compute_values(hidden_states)?;

        // Reshape for multi-head attention
        // Q: [batch, seq, num_heads, head_dim]
        let q = q.view([batch_size, seq_len, self.num_heads, self.head_dim])?;

        // K, V: [batch, seq, num_kv_heads, head_dim]
        let k = k.view([batch_size, seq_len, self.num_key_value_heads, self.head_dim])?;
        let v = v.view([batch_size, seq_len, self.num_key_value_heads, self.head_dim])?;

        // 2. Apply rotary position encoding
        let (q, k) = self.rope.forward(&q, &k, seq_len)?;

        // 3. Expand K, V for grouped-query attention if needed
        let (k, v) = if self.num_key_value_heads < self.num_heads {
            let repeat_factor = self.num_heads / self.num_key_value_heads;
            let k = self.repeat_kv(&k, repeat_factor)?;
            let v = self.repeat_kv(&v, repeat_factor)?;
            (k, v)
        } else {
            (k, v)
        };

        // 4. Compute scaled dot-product attention
        let attention_output = self.scaled_dot_product_attention(&q, &k, &v)?;

        // 5. Reshape and apply output projection
        let attention_output =
            attention_output.view([batch_size, seq_len, self.num_heads * self.head_dim])?;
        let output = attention_output.matmul(&self.o_proj)?;

        Ok(output)
    }

    /// Compute query projections
    fn compute_queries(&self, hidden_states: &Tensor) -> Result<Tensor> {
        hidden_states
            .matmul(&self.q_proj)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }

    /// Compute key projections
    fn compute_keys(&self, hidden_states: &Tensor) -> Result<Tensor> {
        hidden_states
            .matmul(&self.k_proj)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }

    /// Compute value projections
    fn compute_values(&self, hidden_states: &Tensor) -> Result<Tensor> {
        hidden_states
            .matmul(&self.v_proj)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }

    /// Repeat key/value tensors for grouped-query attention
    fn repeat_kv(&self, tensor: &Tensor, repeat_factor: usize) -> Result<Tensor> {
        if repeat_factor == 1 {
            return Ok(tensor.clone());
        }

        let shape = tensor.shape();
        let batch_size = shape[0];
        let seq_len = shape[1];
        let num_kv_heads = shape[2];
        let head_dim = shape[3];

        // Expand tensor by repeating along the head dimension
        // [batch, seq, num_kv_heads, head_dim] -> [batch, seq, num_kv_heads, repeat_factor, head_dim]
        let expanded = tensor.unsqueeze(3)?; // [batch, seq, num_kv_heads, 1, head_dim]
        let repeated =
            expanded.expand(&[batch_size, seq_len, num_kv_heads, repeat_factor, head_dim])?;

        // Reshape to [batch, seq, num_kv_heads * repeat_factor, head_dim]
        repeated
            .view([batch_size, seq_len, num_kv_heads * repeat_factor, head_dim])
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }

    /// Compute scaled dot-product attention
    fn scaled_dot_product_attention(&self, q: &Tensor, k: &Tensor, v: &Tensor) -> Result<Tensor> {
        let scale = (self.head_dim as f32).recip().sqrt();

        // Transpose for attention computation: [batch, seq, heads, dim] -> [batch, heads, seq, dim]
        let q = q.transpose(1, 2)?;
        let k = k.transpose(1, 2)?;
        let v = v.transpose(1, 2)?;

        // Compute attention scores: Q @ K^T / sqrt(d_k)
        let k_transposed = k.transpose(-2, -1)?;
        let attention_scores = q.matmul(&k_transposed)?.mul_scalar(scale)?;

        // Apply causal mask (lower triangular)
        let seq_len = attention_scores.shape()[2];
        let causal_mask = self.create_causal_mask(seq_len)?;
        let masked_scores = attention_scores.add(&causal_mask)?;

        // Apply softmax
        let attention_probs = masked_scores.softmax(-1)?;

        // Apply attention to values: attention_probs @ V
        let attention_output = attention_probs.matmul(&v)?;

        // Transpose back: [batch, heads, seq, dim] -> [batch, seq, heads, dim]
        attention_output
            .transpose(1, 2)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }

    /// Create causal attention mask
    fn create_causal_mask(&self, seq_len: usize) -> Result<Tensor> {
        // Create a lower triangular mask filled with 0s and -inf
        let mut mask_data = Vec::with_capacity(seq_len * seq_len);

        for i in 0..seq_len {
            for j in 0..seq_len {
                if j <= i {
                    mask_data.push(0.0f32); // Allow attention
                } else {
                    mask_data.push(f32::NEG_INFINITY); // Mask future positions
                }
            }
        }

        Tensor::from_vec(mask_data, &[seq_len, seq_len], &self.device)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }
}

/// `RMSNorm` layer for `LLaMA`
#[derive(Debug)]
pub struct RMSNorm {
    hidden_size: usize,
    eps: f64,
    weight: Tensor,
}

impl RMSNorm {
    /// Create new `RMSNorm` layer
    pub fn new(hidden_size: usize, eps: f64, device: &Device) -> Result<Self> {
        // Initialize weight to ones
        let weight = Tensor::ones([hidden_size], device)?;

        Ok(Self {
            hidden_size,
            eps,
            weight,
        })
    }

    /// Get the weight tensor (for parameter access)
    #[must_use]
    pub fn weight(&self) -> &Tensor {
        &self.weight
    }

    /// Forward pass of `RMSNorm`
    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
        // RMSNorm: x / sqrt(mean(x^2) + eps) * weight

        // Compute x^2
        let x_squared = hidden_states.mul(hidden_states)?;

        // Compute mean along last dimension (hidden_size)
        let mean_squared = x_squared.mean_dim(-1, true)?; // Keep dim for broadcasting

        // Add epsilon for numerical stability
        let variance = mean_squared.add_scalar(self.eps as f32)?;

        // Compute 1 / sqrt(variance)
        let inv_std = variance.rsqrt()?;

        // Normalize
        let normalized = hidden_states.mul(&inv_std)?;

        // Apply learnable weight
        normalized
            .mul(&self.weight)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }
}

/// `SwiGLU` activation function for `LLaMA` MLP
#[derive(Debug)]
pub struct SwiGLU {
    gate_proj: Tensor, // W_gate: [hidden_size, intermediate_size]
    up_proj: Tensor,   // W_up: [hidden_size, intermediate_size]
    down_proj: Tensor, // W_down: [intermediate_size, hidden_size]
}

impl SwiGLU {
    /// Create new `SwiGLU` layer
    pub fn new(config: &LLaMAConfig, device: &Device) -> Result<Self> {
        let scale = (config.hidden_size as f32).recip().sqrt();

        // Initialize projection weights
        let gate_proj = Tensor::randn(&[config.hidden_size, config.intermediate_size], device)?
            .mul_scalar(scale)?;
        let up_proj = Tensor::randn(&[config.hidden_size, config.intermediate_size], device)?
            .mul_scalar(scale)?;
        let down_proj = Tensor::randn(&[config.intermediate_size, config.hidden_size], device)?
            .mul_scalar(scale)?;

        Ok(Self {
            gate_proj,
            up_proj,
            down_proj,
        })
    }

    /// Forward pass of `SwiGLU`
    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
        // SwiGLU(x) = Swish(xW_gate) * (xW_up) @ W_down
        // where Swish(x) = x * sigmoid(x)

        // Gate projection and activation
        let gate_output = hidden_states.matmul(&self.gate_proj)?;
        let gate_activated = self.swish(&gate_output)?;

        // Up projection (no activation)
        let up_output = hidden_states.matmul(&self.up_proj)?;

        // Element-wise multiplication (gating)
        let gated = gate_activated.mul(&up_output)?;

        // Down projection
        gated
            .matmul(&self.down_proj)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }

    /// Swish activation function: x * sigmoid(x)
    fn swish(&self, x: &Tensor) -> Result<Tensor> {
        // Swish(x) = x * sigmoid(x) = x * (1 / (1 + exp(-x)))
        let sigmoid = x.sigmoid()?;
        x.mul(&sigmoid)
            .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }
}

/// `LLaMA` MLP layer
#[derive(Debug)]
pub struct LLaMAMLP {
    swiglu: SwiGLU,
    // TODO: Add down projection
}

impl LLaMAMLP {
    /// Create new `LLaMA` MLP layer
    pub fn new(config: &LLaMAConfig, device: &Device) -> Result<Self> {
        let swiglu = SwiGLU::new(config, device)?;

        Ok(Self { swiglu })
    }

    /// Forward pass of MLP
    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
        // TODO: Implement proper MLP computation
        // For now, just pass through SwiGLU
        self.swiglu.forward(hidden_states)
    }
}

/// Complete `LLaMA` transformer layer
#[derive(Debug)]
pub struct LLaMADecoderLayer {
    self_attn: LLaMAAttention,
    mlp: LLaMAMLP,
    input_layernorm: RMSNorm,
    post_attention_layernorm: RMSNorm,
}

impl LLaMADecoderLayer {
    /// Create new `LLaMA` decoder layer
    pub fn new(config: &LLaMAConfig, device: &Device) -> Result<Self> {
        let self_attn = LLaMAAttention::new(config, device)?;
        let mlp = LLaMAMLP::new(config, device)?;
        let input_layernorm = RMSNorm::new(config.hidden_size, config.rms_norm_eps, device)?;
        let post_attention_layernorm =
            RMSNorm::new(config.hidden_size, config.rms_norm_eps, device)?;

        Ok(Self {
            self_attn,
            mlp,
            input_layernorm,
            post_attention_layernorm,
        })
    }

    /// Forward pass of decoder layer
    pub fn forward(
        &self,
        hidden_states: &Tensor,
        attention_mask: Option<&Tensor>,
    ) -> Result<Tensor> {
        // Pre-attention norm
        let normed_hidden_states = self.input_layernorm.forward(hidden_states)?;

        // Self-attention with residual connection
        let attn_output = self
            .self_attn
            .forward(&normed_hidden_states, attention_mask)?;
        let hidden_states = hidden_states.add(&attn_output)?;

        // Pre-MLP norm
        let normed_hidden_states = self.post_attention_layernorm.forward(&hidden_states)?;

        // MLP with residual connection
        let mlp_output = self.mlp.forward(&normed_hidden_states)?;
        let hidden_states = hidden_states.add(&mlp_output)?;

        Ok(hidden_states)
    }
}

/// Complete `LLaMA` model
#[derive(Debug)]
pub struct LLaMAModel {
    config: LLaMAConfig,
    layers: Vec<LLaMADecoderLayer>,
    norm: RMSNorm,
    // TODO: Add embed_tokens and lm_head
}

impl LLaMAModel {
    /// Create new `LLaMA` model
    pub fn new(config: LLaMAConfig, device: &Device) -> Result<Self> {
        let mut layers = Vec::new();
        for _ in 0..config.num_hidden_layers {
            layers.push(LLaMADecoderLayer::new(&config, device)?);
        }

        let norm = RMSNorm::new(config.hidden_size, config.rms_norm_eps, device)?;

        Ok(Self {
            config,
            layers,
            norm,
        })
    }

    /// Forward pass of `LLaMA` model
    pub fn forward(&self, input_ids: &Tensor, _attention_mask: Option<&Tensor>) -> Result<Tensor> {
        // TODO: Implement proper forward pass with:
        // 1. Token embeddings
        // 2. All transformer layers
        // 3. Final normalization
        // 4. Language modeling head

        // For now, just return a dummy tensor
        let device = Device::cuda(0).unwrap_or_default();
        Tensor::zeros(
            vec![1, input_ids.shape().dims()[1], self.config.hidden_size],
            &device,
        )
        .map_err(|e| TransformerError::tensor_op(e.to_string()))
    }
}