# PyTorch has to be imported before ROOT to avoid crashes because of clashing
# std::regexp symbols that are exported by cppyy.
# See also: https://github.com/wlav/cppyy/issues/227
import torch

from ROOT import TMVA, TFile, TString, gROOT
from array import array
from subprocess import call
from os.path import isfile


# Setup TMVA
TMVA.Tools.Instance()
TMVA.PyMethodBase.PyInitialize()
reader = TMVA.Reader("Color:!Silent")


# Load data
fname = str(gROOT.GetTutorialDir()) + "/machine_learning/data/tmva_class_example.root"
data = TFile.Open(fname)
signal = data.Get('TreeS')
background = data.Get('TreeB')

branches = {}
for branch in signal.GetListOfBranches():
    branchName = branch.GetName()
    branches[branchName] = array('f', [-999])
    reader.AddVariable(branchName, branches[branchName])
    signal.SetBranchAddress(branchName, branches[branchName])
    background.SetBranchAddress(branchName, branches[branchName])


# Define predict function
def predict(model, test_X, batch_size=32):
    # Set to eval mode
    model.eval()

    test_dataset = torch.utils.data.TensorDataset(torch.Tensor(test_X))
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

    predictions = []
    with torch.no_grad():
        for i, data in enumerate(test_loader):
            X = data[0]
            outputs = model(X)
            predictions.append(outputs)
        preds = torch.cat(predictions)

    return preds.numpy()


load_model_custom_objects = {"optimizer": None, "criterion": None, "train_func": None, "predict_func": predict}


# Book methods
reader.BookMVA('PyTorch', TString('dataset/weights/TMVAClassification_PyTorch.weights.xml'))


# Print some example classifications
print('Some signal example classifications:')
for i in range(20):
    signal.GetEntry(i)
    print(reader.EvaluateMVA('PyTorch'))
print('')

print('Some background example classifications:')
for i in range(20):
    background.GetEntry(i)
    print(reader.EvaluateMVA('PyTorch'))