#![allow(dead_code)]

use super::QuantizedLayer;
use candle_core::{DType, Result, Tensor};

pub struct AwqLinear {
    pub qweight: Tensor,
    pub qzeros: Tensor,
    pub scales: Tensor,
    pub g_idx: Option<Tensor>,
    pub bias: Option<Tensor>,
}

impl AwqLinear {
    pub fn new(qweight: Tensor, qzeros: Tensor, scales: Tensor, bias: Option<Tensor>) -> Self {
        Self {
            qweight,
            qzeros,
            scales,
            g_idx: None,
            bias,
        }
    }
}

impl QuantizedLayer for AwqLinear {
    fn forward(&self, x: &Tensor) -> Result<Tensor> {
        // AWQ logic typically involves unpacking 4-bit weights into a higher precision
        // (like F16) during the forward pass, using the pre-computed scales and zeros.

        // This is a high-level representation of the compute path.
        let weight_f16 = self.unpack_weights()?;
        let res = x.matmul(&weight_f16)?;

        if let Some(bias) = &self.bias {
            res.broadcast_add(bias)
        } else {
            Ok(res)
        }
    }

    fn unpack_weights(&self) -> Result<Tensor> {
        // Mock unpacking 4-bit to F16
        self.qweight.to_dtype(DType::F16)
    }
}
