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import pytest
import os
import numpy as np
import torch
import brevitas.onnx as bo
from brevitas.nn import QuantLinear
from brevitas.core.quant import QuantType
from finn.core.modelwrapper import ModelWrapper
from finn.core.datatype import DataType
import finn.core.onnx_exec as oxe
from finn.transformation.infer_shapes import InferShapes
from finn.util.basic import gen_finn_dt_tensor

export_onnx_path = "test_brevitas_qlinear.onnx"


@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):
    if bias:
        pytest.xfail("bias export bug")
    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()
    bo.export_finn_onnx(b_linear, i_shape, export_onnx_path)
    model = ModelWrapper(export_onnx_path)
    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()
    os.remove(export_onnx_path)