// CNN layer shader - uses modular convolution snippets
// Supports multi-pass rendering with residual connections
// DO NOT EDIT - Generated by train_cnn.py

@group(0) @binding(0) var smplr: sampler;
@group(0) @binding(1) var txt: texture_2d<f32>;

#include "common_uniforms"
#include "cnn_activation"
#include "cnn_conv3x3"
#include "cnn_weights_generated"

struct CNNLayerParams {
    layer_index: i32,
    blend_amount: f32,
    _pad: vec2<f32>,
};

@group(0) @binding(2) var<uniform> uniforms: CommonUniforms;
@group(0) @binding(3) var<uniform> params: CNNLayerParams;
@group(0) @binding(4) var original_input: texture_2d<f32>;

@vertex fn vs_main(@builtin(vertex_index) i: u32) -> @builtin(position) vec4<f32> {
    var pos = array<vec2<f32>, 3>(
        vec2<f32>(-1.0, -1.0), vec2<f32>(3.0, -1.0), vec2<f32>(-1.0, 3.0)
    );
    return vec4<f32>(pos[i], 0.0, 1.0);
}

@fragment fn fs_main(@builtin(position) p: vec4<f32>) -> @location(0) vec4<f32> {
    let uv = p.xy / uniforms.resolution;
    let input = textureSample(txt, smplr, uv);
    let original = textureSample(original_input, smplr, uv);
    var result = vec4<f32>(0.0);

    // Layer 0 uses coordinate-aware convolution
    if (params.layer_index == 0) {
        result = cnn_conv3x3_with_coord(txt, smplr, uv, uniforms.resolution,
                                        rgba_weights_layer0, coord_weights_layer0, bias_layer0);
        result = cnn_tanh(result);
    }
    else if (params.layer_index == 1) {
        result = cnn_conv3x3(txt, smplr, uv, uniforms.resolution,
                                   weights_layer1, bias_layer1);
        result = cnn_tanh(result);
    }
    else if (params.layer_index == 2) {
        result = cnn_conv3x3(txt, smplr, uv, uniforms.resolution,
                                   weights_layer2, bias_layer2);
    }
    else {
        result = input;
    }

    return mix(original, result, params.blend_amount);
}