#pragma once

#include "../clip-graph.h"

/*
 * IMPORTANT: The mtmd module does NOT accept pull requests that are fully or predominantly AI-generated.
 * We encourage human contributors to ensure the quality and reliability of the codebase.
 */

struct clip_graph_siglip : clip_graph {
    clip_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_gemma4v : clip_graph {
    clip_graph_gemma4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
    ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
    bool support_batch() const override { return true; }
};

struct clip_graph_gemma4uv : clip_graph {
    clip_graph_gemma4uv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_pixtral : clip_graph {
    clip_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_qwen2vl : clip_graph {
    clip_graph_qwen2vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
    ggml_tensor * build_inp_with_temporal_merge();
};

struct clip_graph_qwen3vl : clip_graph_qwen2vl {
    clip_graph_qwen3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph_qwen2vl(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_mimovl : clip_graph {
    clip_graph_mimovl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
    // Force F32 mat-mul accumulation to avoid F16 overflow in the FFN down-proj
    // when the mmproj is stored in F16 (the source weights are BF16; downcasting
    // to F16 reduces dynamic range below the SwiGLU output magnitude on the last few layers).
    ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
};

struct clip_graph_step3vl : clip_graph {
    clip_graph_step3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_youtuvl : clip_graph {
    clip_graph_youtuvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_yasa2 : clip_graph {
    clip_graph_yasa2(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;

    ggml_tensor * layer_norm_channels(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b, float eps = 1e-6f);
    ggml_tensor * convnext_grn(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b);
};

struct clip_graph_minicpmv : clip_graph {
    clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_minicpmv4_6 : clip_graph {
    clip_graph_minicpmv4_6(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_internvl : clip_graph {
    clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_nemotron_v2_vl : clip_graph {
    clip_graph_nemotron_v2_vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_llama4 : clip_graph {
    clip_graph_llama4(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_kimivl : clip_graph {
    clip_graph_kimivl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_paddleocr : clip_graph {
    clip_graph_paddleocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_dotsocr : clip_graph {
    clip_graph_dotsocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_cogvlm : clip_graph {
    clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_llava : clip_graph {
    clip_graph_llava(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_whisper_enc : clip_graph {
    clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_deepseekocr : clip_graph {
    clip_graph_deepseekocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
    ggml_tensor * build_sam(ggml_tensor * inp); // build the SAM model
};

struct clip_graph_deepseekocr2 : clip_graph_deepseekocr {
    clip_graph_deepseekocr2(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph_deepseekocr(ctx, img) {}
    ggml_cgraph * build() override; // reuses build_sam() from base
};

struct clip_graph_conformer : clip_graph {
    clip_graph_conformer(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_granite_speech : clip_graph {
    clip_graph_granite_speech(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_gemma4a : clip_graph {
    clip_graph_gemma4a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
    ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
};

struct clip_graph_gemma4ua : clip_graph {
    clip_graph_gemma4ua(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_glm4v : clip_graph {
    clip_graph_glm4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_hunyuanvl : clip_graph {
    clip_graph_hunyuanvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_mobilenetv5 : clip_graph {
    clip_graph_mobilenetv5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;

    ggml_tensor * rms_norm_2d(
        ggml_tensor * inp,
        ggml_tensor * weight,
        float eps = 1e-6f);

    ggml_tensor* pad_same_2d(
        ggml_tensor* inp,
        int kernel_h,
        int kernel_w,
        int stride_h,
        int stride_w,
        int dilation_h = 1,
        int dilation_w = 1);

    ggml_tensor * build_edge_residual(
        ggml_tensor * inp,
        const mobilenetv5_block & block,
        int stride);

    ggml_tensor * build_inverted_residual(
        ggml_tensor * inp,
        const mobilenetv5_block & block,
        int stride);

    ggml_tensor * build_mobilenet_attn(
        ggml_tensor * inp,
        const mobilenetv5_block & block);
};

struct clip_graph_qwen3a : clip_graph {
    clip_graph_qwen3a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_kimik25 : clip_graph {
    clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;

    ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode);
};

struct clip_graph_exaone4_5 : clip_graph {
    clip_graph_exaone4_5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
};

struct clip_graph_granite4_vision : clip_graph {
    clip_graph_granite4_vision(clip_ctx * ctx, const clip_image_f32 & img)
        : clip_graph(ctx, img),
          add_newline(img.add_newline) {}

    ggml_cgraph * build() override;

private:
    // The graph is per-tile since only batch-size 1 is supported in clip. As
    // such, this value is set at construct time based on the tile that will be
    // encoded, then used during build to determine how to handle newlines.
    const bool add_newline;

    ggml_tensor * gather(ggml_tensor * src, const std::string & name, int idx_len);
    ggml_tensor * interp_down(ggml_tensor * src, int side, int new_side);
    ggml_tensor * build_block(const qf_block & blk, ggml_tensor * h, int bid,
                              int spatial_offset, int image_side, int window_side,
                              int query_side, float qformer_eps);

    ggml_tensor * build_newline_row(ggml_context * ctx0);
    ggml_tensor * append_rowwise_newlines(ggml_context * ctx0, ggml_tensor * tile_output);
};
