# Copyright (c) 2021, Xilinx
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# * Neither the name of FINN nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

import pytest

import numpy as np
import os
import torch
from brevitas.core.quant import QuantType
from brevitas.export import export_qonnx
from brevitas.nn import QuantLinear
from qonnx.core.datatype import DataType
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.transformation.infer_shapes import InferShapes
from qonnx.util.basic import gen_finn_dt_tensor
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
from finn.util.basic import make_build_dir


@pytest.mark.brevitas_export
@pytest.mark.parametrize("bias", [False, True])
@pytest.mark.parametrize("out_features", [4])
@pytest.mark.parametrize("in_features", [3])
@pytest.mark.parametrize("w_bits", [4])
@pytest.mark.parametrize("i_dtype", [DataType["UINT4"]])
def test_brevitas_qlinear(bias, out_features, in_features, w_bits, i_dtype):
    i_shape = (1, in_features)
    w_shape = (out_features, in_features)
    b_linear = QuantLinear(
        out_features=out_features,
        in_features=in_features,
        bias=bias,
        bias_quant_type=QuantType.FP,
        weight_bit_width=w_bits,
        weight_quant_type=QuantType.INT,
        weight_scaling_per_output_channel=True,
    )
    weight_tensor_fp = np.random.uniform(low=-1.0, high=1.0, size=w_shape).astype(np.float32)
    b_linear.weight.data = torch.from_numpy(weight_tensor_fp)
    b_linear.eval()
    build_dir = make_build_dir(prefix="test_brevitas_qlinear")
    m_path = os.path.join(build_dir, "test_brevitas_qlinear.onnx")
    export_qonnx(b_linear, torch.randn(i_shape), m_path)
    qonnx_cleanup(m_path, out_file=m_path)
    model = ModelWrapper(m_path)
    model = model.transform(ConvertQONNXtoFINN())
    model = model.transform(InferShapes())
    inp_tensor = gen_finn_dt_tensor(i_dtype, i_shape)
    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()
    expected = b_linear.forward(inp_tensor).detach().numpy()

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