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from pkgutil import get_data

import pytest

import brevitas.onnx as bo
import numpy as np
import onnx
import onnx.numpy_helper as nph
import torch

import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import RemoveStaticGraphInputs
from finn.transformation.infer_shapes import InferShapes
from finn.util.basic import make_build_dir
from finn.util.test import get_test_model_trained

export_onnx_path = make_build_dir("test_brevitas_fc_")

# act bits
@pytest.mark.parametrize("abits", [1, 2])
# weight bits
@pytest.mark.parametrize("wbits", [1, 2])
# network topology / size
@pytest.mark.parametrize("size", ["TFC", "SFC", "LFC"])
def test_brevitas_fc_onnx_export_and_exec(size, wbits, abits):
    if size == "LFC" and wbits == 2 and abits == 2:
        pytest.skip("No LFC-w2a2 present at the moment")
    if wbits > abits:
        pytest.skip("No wbits > abits cases at the moment")
    nname = "%s_%dW%dA" % (size, wbits, abits)
    finn_onnx = export_onnx_path + "/%s.onnx" % nname
    fc = get_test_model_trained(size, wbits, abits)
    bo.export_finn_onnx(fc, (1, 1, 28, 28), finn_onnx)
    model = ModelWrapper(finn_onnx)
    model = model.transform(InferShapes())
    model = model.transform(FoldConstants())
    model = model.transform(RemoveStaticGraphInputs())
    assert len(model.graph.input) == 1
    assert len(model.graph.output) == 1
    # load one of the test vectors
    raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
    input_tensor = onnx.load_tensor_from_string(raw_i)
    # run using FINN-based execution
    input_dict = {"0": nph.to_array(input_tensor)}
    output_dict = oxe.execute_onnx(model, input_dict)
    produced = output_dict[list(output_dict.keys())[0]]
    # run using PyTorch/Brevitas
    input_tensor = torch.from_numpy(nph.to_array(input_tensor)).float()
    assert input_tensor.shape == (1, 1, 28, 28)
    # do forward pass in PyTorch/Brevitas
    expected = fc.forward(input_tensor).detach().numpy()
    assert np.isclose(produced, expected, atol=1e-3).all()