using namespace TMVA::Experimental;

TString pythonSrc = "\
import torch\n\
import torch.nn as nn\n\
\n\
model = nn.Sequential(\n\
           nn.Linear(32,16),\n\
           nn.ReLU(),\n\
           nn.Linear(16,8),\n\
           nn.ReLU()\n\
           )\n\
\n\
criterion = nn.MSELoss()\n\
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)\n\
\n\
x=torch.randn(2,32)\n\
y=torch.randn(2,8)\n\
\n\
for i in range(500):\n\
    y_pred = model(x)\n\
    loss = criterion(y_pred,y)\n\
    optimizer.zero_grad()\n\
    loss.backward()\n\
    optimizer.step()\n\
\n\
model.eval()\n\
m = torch.jit.script(model)\n\
torch.jit.save(m,'PyTorchModel.pt')\n";

TMVA::PyMethodBase::PyInitialize();

TMacro m;
m.AddLine(pythonSrc);
m.SaveSource("make_pytorch_model.py");
gSystem->Exec(TMVA::Python_Executable() + " make_pytorch_model.py");

std::vector<size_t> inputTensorShapeSequential{2,32};
std::vector<std::vector<size_t>> inputShapesSequential{inputTensorShapeSequential};

SOFIE::RModel model = SOFIE::PyTorch::Parse("PyTorchModel.pt",inputShapesSequential);

model.Generate();
model.OutputGenerated("PyTorchModel.hxx");

std::cout<<"\n\n";
model.PrintRequiredInputTensors();

std::cout<<"\n\n";
model.PrintInitializedTensors();

std::cout<<"\n\n";
model.PrintIntermediateTensors();

std::cout<<"\n\nTensor \"0weight\" already exist: "<<std::boolalpha<<model.CheckIfTensorAlreadyExist("0weight")<<"\n\n";
std::vector<size_t> tensorShape = model.GetTensorShape("0weight");
std::cout<<"Shape of tensor \"0weight\": ";
for(auto& it:tensorShape){
    std::cout<<it<<",";
}
std::cout<<"\n\nData type of tensor \"0weight\": ";
SOFIE::ETensorType tensorType = model.GetTensorType("0weight");
std::cout<<SOFIE::ConvertTypeToString(tensorType);

std::cout<<"\n\n";
model.PrintGenerated();