//! 2D Convolutional layer implementation.
//!
//! This module provides a Conv2d layer for convolutional operations.

#![allow(unused_variables)] // Conv2D layer

use crate::errors::{Result, TrustformersError};
use crate::tensor::Tensor;
use crate::traits::Layer;
use serde::{Deserialize, Serialize};

/// 2D Convolutional layer
///
/// Applies a 2D convolution over an input tensor.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Conv2d {
    pub in_channels: usize,
    pub out_channels: usize,
    pub kernel_size: (usize, usize),
    pub stride: (usize, usize),
    pub padding: (usize, usize),
    pub dilation: (usize, usize),
    pub groups: usize,
    pub bias: bool,
    #[serde(skip)]
    pub weight: Option<Tensor>,
    #[serde(skip)]
    pub bias_term: Option<Tensor>,
}

impl Conv2d {
    /// Create a new Conv2d layer
    ///
    /// # Arguments
    ///
    /// * `in_channels` - Number of input channels
    /// * `out_channels` - Number of output channels
    /// * `kernel_size` - Size of the convolutional kernel
    /// * `stride` - Stride of the convolution
    /// * `padding` - Padding applied to the input
    /// * `bias` - Whether to include a bias term
    pub fn new(
        in_channels: usize,
        out_channels: usize,
        kernel_size: (usize, usize),
        stride: (usize, usize),
        padding: (usize, usize),
        bias: bool,
    ) -> Result<Self> {
        Ok(Self {
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation: (1, 1),
            groups: 1,
            bias,
            weight: None,
            bias_term: None,
        })
    }

    /// Create a Conv2d layer with simple kernel size (same for width and height)
    pub fn new_simple(
        in_channels: usize,
        out_channels: usize,
        kernel_size: usize,
        bias: bool,
    ) -> Result<Self> {
        Self::new(
            in_channels,
            out_channels,
            (kernel_size, kernel_size),
            (1, 1),
            (0, 0),
            bias,
        )
    }

    /// Initialize weights for the layer
    pub fn init_weights(&mut self, weight: Tensor, bias: Option<Tensor>) -> Result<()> {
        self.weight = Some(weight);
        self.bias_term = bias;
        Ok(())
    }
}

impl Layer for Conv2d {
    type Input = Tensor;
    type Output = Tensor;

    fn forward(&self, input: Self::Input) -> Result<Self::Output> {
        // Stub implementation - returns error for now
        Err(TrustformersError::tensor_op_error(
            "Conv2d forward pass not yet implemented. This is a stub for compilation.",
            "Conv2d::forward",
        ))
    }
}
