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import pytest

import numpy as np
import onnx  # noqa
import os
import torch
from brevitas.core.quant import QuantType
from brevitas.core.scaling import ScalingImplType
from brevitas.export import export_finn_onnx, export_qonnx
from brevitas.nn import QuantReLU
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.transformation.infer_shapes import InferShapes
from qonnx.util.cleanup import cleanup as qonnx_cleanup

import finn.core.onnx_exec as oxe
from finn.transformation.qonnx.convert_qonnx_to_finn import ConvertQONNXtoFINN

export_onnx_path = "test_brevitas_relu_act_export.onnx"


@pytest.mark.brevitas_export
@pytest.mark.parametrize("abits", [2, 4, 8])
@pytest.mark.parametrize("ishape", [(1, 15), (1, 32, 1, 1)])
@pytest.mark.parametrize(
    "scaling_impl_type", [ScalingImplType.CONST]  # , ScalingImplType.PARAMETER]
)
@pytest.mark.parametrize("scaling_per_output_channel", [True, False])
@pytest.mark.parametrize("per_channel_broadcastable_shape", [None, (1, 32, 1, 1)])
@pytest.mark.parametrize("QONNX_export", [False, True])
def test_brevitas_act_export_relu(
    abits,
    ishape,
    scaling_impl_type,
    scaling_per_output_channel,
    per_channel_broadcastable_shape,
    QONNX_export,
):

    b_act = QuantReLU(
        bit_width=abits,
        max_val=6.0,
        scaling_impl_type=scaling_impl_type,
        quant_type=QuantType.INT,
        scaling_per_output_channel=scaling_per_output_channel,
        per_channel_broadcastable_shape=per_channel_broadcastable_shape,
    )
    if QONNX_export:
        m_path = export_onnx_path
        export_qonnx(b_act, torch.randn(ishape), m_path)
        qonnx_cleanup(m_path, out_file=m_path)
        model = ModelWrapper(m_path)
        model = model.transform(ConvertQONNXtoFINN())
        model.save(m_path)
    else:
        export_finn_onnx(b_act, torch.randn(ishape), export_onnx_path)
    model = ModelWrapper(export_onnx_path)
    model = model.transform(InferShapes())
    inp_tensor = np.random.uniform(low=-1.0, high=6.0, size=ishape).astype(np.float32)
    idict = {model.graph.input[0].name: inp_tensor}
    odict = oxe.execute_onnx(model, idict, True)
    produced = odict[model.graph.output[0].name]
    inp_tensor = torch.from_numpy(inp_tensor).float()
    b_act.eval()
    expected = b_act.forward(inp_tensor).detach().numpy()

    assert np.isclose(produced, expected, atol=1e-3).all()
    os.remove(export_onnx_path)