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import pytest

import json
import shutil
from brevitas.export.onnx.generic.manager import BrevitasONNXManager
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.custom_op.registry import getCustomOp

import finn.builder.build_dataflow as build
import finn.builder.build_dataflow_config as build_cfg
from finn.util.basic import make_build_dir
from finn.util.test import get_trained_network_and_ishape


def fetch_test_model(topology, wbits=2, abits=2):
    tmp_output_dir = make_build_dir("build_fifosizing_%s_" % topology)
    (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
    chkpt_name = tmp_output_dir + "/model.onnx"
    BrevitasONNXManager.export(model, ishape, chkpt_name)
    return tmp_output_dir


@pytest.mark.slow
@pytest.mark.vivado
@pytest.mark.parametrize(
    "method", ["largefifo_rtlsim_python", "largefifo_rtlsim_cpp", "characterize"]
)
@pytest.mark.parametrize("topology", ["tfc"])
def test_fifosizing_linear(method, topology):
    force_python_rtlsim = "python" in method
    method_key = "largefifo_rtlsim" if "largefifo_rtlsim" in method else "characterize"
    tmp_output_dir = fetch_test_model(topology)
    cfg = build_cfg.DataflowBuildConfig(
        output_dir=tmp_output_dir,
        auto_fifo_depths=True,
        auto_fifo_strategy=method_key,
        target_fps=10000 if topology == "tfc" else 1000,
        force_python_rtlsim=force_python_rtlsim,
        synth_clk_period_ns=10.0,
        board="Pynq-Z1",
        rtlsim_batch_size=100,
        shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
        generate_outputs=[
            build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
            build_cfg.DataflowOutputType.STITCHED_IP,
            build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
        ],
        default_mem_mode=build_cfg.ComputeEngineMemMode.DECOUPLED,
    )
    build.build_dataflow_cfg(tmp_output_dir + "/model.onnx", cfg)
    with open(tmp_output_dir + "/report/estimate_network_performance.json") as f:
        est_data = json.load(f)
    with open(tmp_output_dir + "/report/rtlsim_performance.json") as f:
        sim_data = json.load(f)
    assert (
        float(sim_data["throughput[images/s]"])
        / float(est_data["estimated_throughput_fps"])
        > 0.9
    )
    # now run the same build using the generated folding and FIFO config
    tmp_output_dir_cmp = fetch_test_model(topology)
    cfg_cmp = cfg
    cfg_cmp.output_dir = tmp_output_dir_cmp
    cfg_cmp.auto_fifo_depths = False
    cfg_cmp.target_fps = None
    cfg_cmp.generate_outputs = [build_cfg.DataflowOutputType.STITCHED_IP]
    cfg_cmp.folding_config_file = tmp_output_dir + "/final_hw_config.json"
    build.build_dataflow_cfg(tmp_output_dir_cmp + "/model.onnx", cfg_cmp)

    model0 = ModelWrapper(
        tmp_output_dir + "/intermediate_models/step_create_stitched_ip.onnx"
    )
    model1 = ModelWrapper(
        tmp_output_dir_cmp + "/intermediate_models/step_create_stitched_ip.onnx"
    )

    assert len(model0.graph.node) == len(model1.graph.node)
    for i in range(len(model0.graph.node)):
        node0 = model0.graph.node[i]
        node1 = model1.graph.node[i]
        assert node0.op_type == node1.op_type
        if node0.op_type == "StreamingFIFO":
            node0_inst = getCustomOp(node0)
            node1_inst = getCustomOp(node1)
            assert node0_inst.get_nodeattr("depth") == node1_inst.get_nodeattr("depth")

    shutil.rmtree(tmp_output_dir)
    shutil.rmtree(tmp_output_dir_cmp)