# Copyright (c) 2021, Xilinx
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import pkg_resources as pk

import pytest

import brevitas.export.onnx.generic as b_onnx
import brevitas.onnx as bo
import numpy as np
import onnx
import onnx.numpy_helper as nph
import torch
from pkgutil import get_data
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.transformation.fold_constants import FoldConstants
from qonnx.transformation.general import GiveUniqueNodeNames, RemoveStaticGraphInputs
from qonnx.transformation.infer_shapes import InferShapes
from qonnx.util.cleanup import cleanup
from tempfile import TemporaryDirectory

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


def get_brev_model_and_sample_inputs(model_name, wbits, abits):
    if "FC" in model_name:
        in_shape = (1, 1, 28, 28)
        raw_i = get_data("qonnx.data", "onnx/mnist-conv/test_data_set_0/input_0.pb")
        input_tensor = onnx.load_tensor_from_string(raw_i)
        input_tensor = nph.to_array(input_tensor)
        brev_model = get_test_model_trained(model_name, wbits, abits)
    elif model_name == "CNV":
        in_shape = (1, 3, 32, 32)
        fn = pk.resource_filename(
            "finn.qnn-data", "cifar10/cifar10-test-data-class3.npz"
        )
        input_tensor = np.load(fn)["arr_0"].astype(np.float32)
        input_tensor = input_tensor / 255
        brev_model = get_test_model_trained(model_name, wbits, abits)
    elif model_name == "mobilenet":
        in_shape = (1, 3, 224, 224)
        np.random.seed(42)
        input_tensor = np.random.normal(size=in_shape).astype(dtype=np.float32)
        brev_model = get_test_model_trained(model_name, 4, 4)
    else:
        raise RuntimeError(f"The model with the name {model_name} is not supported.")

    return brev_model, in_shape, input_tensor


def analysis_testing_for_no_quant_nodes(model):
    # Test that all Quant nodes have been converted to MultiThreshold nodes
    # or folded into tensor initializers.

    for op_type in ["BinaryQuant", "Quant", "Trunc"]:
        q_count = len(model.get_nodes_by_op_type(op_type))
        if q_count > 0:
            raise ValueError(f"There should be no {op_type} nodes left in the graph.")

    return dict()


@pytest.mark.transform
# This test currently takes about 4 min and 20 seconds
@pytest.mark.parametrize("abits", [1, 2])
@pytest.mark.parametrize("wbits", [1, 2])
@pytest.mark.parametrize("model_name", ["TFC", "SFC", "LFC", "CNV", "mobilenet"])
def test_QONNX_to_FINN(model_name, wbits, abits):
    if model_name == "mobilenet":
        pytest.xfail("MobileNet test is temporarily excluded from QONNX testing.")

    if wbits > abits:
        pytest.skip("No wbits > abits cases at the moment")
    if model_name == "LFC" and wbits == 2 and abits == 2:
        pytest.skip("No LFC-w2a2 present at the moment")
    if model_name == "mobilenet" and (wbits != 2 or abits != 2):
        pytest.skip("Mobilenet only runs at W2A2, though it's technically W4A4.")

    # Get test config and model
    ATOL = 1e-7
    brev_model, in_shape, input_tensor = get_brev_model_and_sample_inputs(
        model_name, wbits, abits
    )
    temp_dir = TemporaryDirectory()
    qonnx_base_path = temp_dir.name + "/qonnx_{}.onnx"
    finn_base_path = temp_dir.name + "/finn_{}.onnx"

    # Get Brevitas output
    torch_input_tensor = torch.from_numpy(input_tensor).float()
    brev_output = brev_model.forward(torch_input_tensor).detach().numpy()

    # Get "clean" FINN model and it's output
    _ = bo.export_finn_onnx(brev_model, in_shape, finn_base_path.format("raw"))
    model = ModelWrapper(finn_base_path.format("raw"))
    model = model.transform(GiveUniqueNodeNames())
    model = model.transform(InferShapes())
    model = model.transform(FoldConstants())
    model = model.transform(RemoveStaticGraphInputs())
    model.save(finn_base_path.format("clean"))

    model = ModelWrapper(finn_base_path.format("clean"))
    input_dict = {model.graph.input[0].name: input_tensor}
    output_dict = oxe.execute_onnx(model, input_dict, False)
    finn_export_output = output_dict[model.graph.output[0].name]
    # This test always fails on MobileNet for some reason
    if model_name != "mobilenet":
        assert np.isclose(
            brev_output, finn_export_output, atol=ATOL
        ).all(), "The output of the Brevitas model and the FINN model should match."

    # Get the equivalent QONNX model
    b_onnx.function.DOMAIN_STRING = "qonnx.custom_op.general"
    _ = b_onnx.manager.BrevitasONNXManager.export(
        brev_model, in_shape, qonnx_base_path.format("raw")
    )
    cleanup(qonnx_base_path.format("raw"), out_file=qonnx_base_path.format("clean"))

    # Compare output
    model = ModelWrapper(qonnx_base_path.format("clean"))
    input_dict = {model.graph.input[0].name: input_tensor}
    output_dict = oxe.execute_onnx(model, input_dict, False)
    qonnx_export_output = output_dict[model.graph.output[0].name]
    assert np.isclose(
        brev_output, qonnx_export_output, atol=ATOL
    ).all(), "The output of the Brevitas model and the QONNX model should match."
    # This test always fails on MobileNet for some reason
    if model_name != "mobilenet":
        assert np.isclose(
            qonnx_export_output, finn_export_output, atol=ATOL
        ).all(), "The output of the FINN model and the QONNX model should match."

    # Run QONNX to FINN conversion
    model = ModelWrapper(qonnx_base_path.format("clean"))
    model = model.transform(ConvertQONNXtoFINN())
    model.save(qonnx_base_path.format("whole_trafo"))

    # Compare output
    model = ModelWrapper(qonnx_base_path.format("whole_trafo"))
    input_dict = {model.graph.input[0].name: input_tensor}
    output_dict = oxe.execute_onnx(model, input_dict, False)
    test_output = output_dict[model.graph.output[0].name]
    assert np.isclose(test_output, finn_export_output, atol=ATOL).all(), (
        "The output of the FINN model "
        "and the QONNX -> FINN converted model should match."
    )

    # Run analysis passes on the converted model
    model = ModelWrapper(qonnx_base_path.format("whole_trafo"))
    _ = model.analysis(analysis_testing_for_no_quant_nodes)

    temp_dir.cleanup()