#pragma once

#include "interop/torch.h"
#include "SanaModel.h"
#include "Serialization.h"
#include "debug.h"
#include "module.h"

class QuantizedSanaModel : public ModuleWrapper<SanaModel> {
public:
    void init(pybind11::dict config, std::vector<int> pag_layers, bool use_fp4, bool bf16, int8_t deviceId) {
        spdlog::info("Initializing QuantizedSanaModel");
        SanaConfig cfg{
            .num_layers = config["num_layers"].cast<int>(),
            .num_attention_heads = config["num_attention_heads"].cast<int>(),
            .attention_head_dim = config["attention_head_dim"].cast<int>(),
            .num_cross_attention_heads = config["num_cross_attention_heads"].cast<int>(),
            .expand_ratio = config["mlp_ratio"].cast<double>(),
            .pag_layers = pag_layers,
            .use_fp4 = use_fp4,
        };
        net = std::make_unique<SanaModel>(cfg, bf16 ? Tensor::BF16 : Tensor::FP16, Device::cuda((int)deviceId));
    }

    torch::Tensor forward(
        torch::Tensor hidden_states, 
        torch::Tensor encoder_hidden_states, 
        torch::Tensor timestep, 
        torch::Tensor cu_seqlens_img, 
        torch::Tensor cu_seqlens_txt, 
        int H, 
        int W,
        bool pag, 
        bool cfg) 
    {
        checkModel();

        spdlog::debug("QuantizedSanaModel forward");

        hidden_states = hidden_states.contiguous();
        encoder_hidden_states = encoder_hidden_states.contiguous();
        timestep = timestep.contiguous();
        cu_seqlens_img = cu_seqlens_img.contiguous();
        cu_seqlens_txt = cu_seqlens_txt.contiguous();

        Tensor result = net->forward(
            from_torch(hidden_states),
            from_torch(encoder_hidden_states),
            from_torch(timestep),
            from_torch(cu_seqlens_img),
            from_torch(cu_seqlens_txt),
            H, W,
            pag, cfg
        );

        torch::Tensor output = to_torch(result);
        // Tensor::synchronizeDevice();

        return output;
    }

    torch::Tensor forward_layer(
        int64_t idx,
        torch::Tensor hidden_states, 
        torch::Tensor encoder_hidden_states, 
        torch::Tensor timestep, 
        torch::Tensor cu_seqlens_img, 
        torch::Tensor cu_seqlens_txt, 
        int H, 
        int W,
        bool pag, 
        bool cfg) 
    {
        checkModel();

        spdlog::debug("QuantizedSanaModel forward_layer {}", idx);

        hidden_states = hidden_states.contiguous();
        encoder_hidden_states = encoder_hidden_states.contiguous();
        timestep = timestep.contiguous();
        cu_seqlens_img = cu_seqlens_img.contiguous();
        cu_seqlens_txt = cu_seqlens_txt.contiguous();

        Tensor result = net->transformer_blocks.at(idx)->forward(
            from_torch(hidden_states),
            from_torch(encoder_hidden_states),
            from_torch(timestep),
            from_torch(cu_seqlens_img),
            from_torch(cu_seqlens_txt),
            H, W,
            pag, cfg
        );

        torch::Tensor output = to_torch(result);
        // Tensor::synchronizeDevice();

        return output;
    }
};