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import os
from pkgutil import get_data

import pytest

import numpy as np

# as of Feb'20 there is a bug that segfaults ONNX shape inference if we
# import pytorch before onnx, so we make sure to import onnx first
import onnx  # NOQA
import onnx.numpy_helper as nph

import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
import finn.transformation.streamline.absorb as absorb
from finn.core.modelwrapper import ModelWrapper
from finn.core.onnx_exec import execute_onnx
from finn.core.throughput_test import throughput_test
from finn.custom_op.registry import getCustomOp
from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
from finn.transformation.fpgadataflow.create_stitched_ip import CreateStitchedIP
from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.create_dataflow_partition import (
    CreateDataflowPartition,
)
from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
from finn.transformation.fpgadataflow.insert_tlastmarker import InsertTLastMarker
from finn.transformation.fpgadataflow.insert_fifo import InsertFIFO
from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
from finn.transformation.fpgadataflow.make_pynq_driver import MakePYNQDriver
from finn.transformation.fpgadataflow.make_pynq_proj import MakePYNQProject
from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
    ReplaceVerilogRelPaths,
)
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.transformation.fpgadataflow.synth_pynq_proj import SynthPYNQProject
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline import Streamline
from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
from finn.util.basic import pynq_part_map
from finn.util.test import get_test_model_trained
from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim

build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
test_pynq_board = os.getenv("PYNQ_BOARD", default="Pynq-Z1")
test_fpga_part = pynq_part_map[test_pynq_board]
target_clk_ns = 5
mem_mode = "decoupled"


def test_end2end_tfc_w1a1_export():
    import brevitas.onnx as bo

    tfc = get_test_model_trained("TFC", 1, 1)
    bo.export_finn_onnx(
        tfc, (1, 1, 28, 28), build_dir + "/end2end_tfc_w1a1_export.onnx"
    )


def test_end2end_tfc_w1a1_import_and_tidy():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_export.onnx")
    model = model.transform(InferShapes())
    model = model.transform(FoldConstants())
    model = model.transform(GiveUniqueNodeNames())
    model = model.transform(GiveReadableTensorNames())
    model = model.transform(InferDataTypes())
    model.save(build_dir + "/end2end_tfc_w1a1_tidy.onnx")


def test_end2end_tfc_w1a1_streamline():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_tidy.onnx")
    model = model.transform(Streamline())
    model.save(build_dir + "/end2end_tfc_w1a1_streamlined.onnx")


def test_end2end_tfc_w1a1_convert_to_hls_layers():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_streamlined.onnx")
    model = model.transform(ConvertBipolarMatMulToXnorPopcount())
    model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
    model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
    model = model.transform(RoundAndClipThresholds())
    model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
    model.save(build_dir + "/end2end_tfc_w1a1_hls_layers.onnx")


def test_end2end_tfc_w1a1_create_dataflow_partition():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_hls_layers.onnx")
    parent_model = model.transform(CreateDataflowPartition())
    parent_model.save(build_dir + "/end2end_tfc_w1a1_dataflow_parent.onnx")
    sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
    sdp_node = getCustomOp(sdp_node)
    dataflow_model_filename = sdp_node.get_nodeattr("model")
    dataflow_model = ModelWrapper(dataflow_model_filename)
    dataflow_model.save(build_dir + "/end2end_tfc_w1a1_dataflow_model.onnx")


def test_end2end_tfc_w1a1_fold_and_tlastmarker():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_dataflow_model.onnx")
    fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
    # (PE, SIMD, in_fifo_depth, out_fifo_depth, ramstyle) for each layer
    config = [
        (16, 49, 16, 64, "block"),
        (8, 8, 64, 64, "auto"),
        (8, 8, 64, 64, "auto"),
        (10, 8, 64, 10, "distributed"),
    ]
    for fcl, (pe, simd, ififo, ofifo, ramstyle) in zip(fc_layers, config):
        fcl_inst = getCustomOp(fcl)
        fcl_inst.set_nodeattr("PE", pe)
        fcl_inst.set_nodeattr("SIMD", simd)
        fcl_inst.set_nodeattr("inFIFODepth", ififo)
        fcl_inst.set_nodeattr("outFIFODepth", ofifo)
        fcl_inst.set_nodeattr("ram_style", ramstyle)
    model = model.transform(InsertDWC())
    model = model.transform(InsertFIFO())
    model = model.transform(InsertTLastMarker())
    model = model.transform(GiveUniqueNodeNames())
    model = model.transform(AnnotateResources("estimate"))
    model.save(build_dir + "/end2end_tfc_w1a1_folded.onnx")


def test_end2end_tfc_w1a1_gen_hls_ip():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_folded.onnx")
    model = model.transform(PrepareIP(test_fpga_part, target_clk_ns))
    model = model.transform(HLSSynthIP())
    model = model.transform(AnnotateResources("hls"))
    model.save(build_dir + "/end2end_tfc_w1a1_ipgen.onnx")


def test_end2end_tfc_w1a1_ip_stitch():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_ipgen.onnx")
    model = model.transform(ReplaceVerilogRelPaths())
    model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns))
    model.save(build_dir + "/end2end_tfc_w1a1_ipstitch.onnx")


def test_end2end_tfc_w1a1_verify_dataflow_part():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_ipstitch.onnx")
    x = np.zeros((1, 784), dtype=np.float32)
    inp_name = model.graph.input[0].name
    out_name = model.graph.output[0].name
    inp_dict = {inp_name: x}
    # cppsim
    model = model.transform(PrepareCppSim())
    model = model.transform(CompileCppSim())
    model = model.transform(SetExecMode("cppsim"))
    model.save(build_dir + "/end2end_tfc_w1a1_ipstitch_cppsim.onnx")
    ret_cppsim = execute_onnx(model, inp_dict, True)
    res_cppsim = ret_cppsim[out_name]
    # node-by-node rtlsim
    model = model.transform(SetExecMode("rtlsim"))
    model = model.transform(PrepareRTLSim())
    model.save(build_dir + "/end2end_tfc_w1a1_ipstitch_nodebynode_rtlsim.onnx")
    ret_rtlsim_nodebynode = execute_onnx(model, inp_dict, True)
    res_rtlsim_nodebynode = ret_rtlsim_nodebynode[out_name]
    # whole-network (ip-stitched) rtlsim
    model.set_metadata_prop("exec_mode", "rtlsim")
    model.save(build_dir + "/end2end_tfc_w1a1_ipstitch_whole_rtlsim.onnx")
    ret_rtlsim_whole = execute_onnx(model, inp_dict, True)
    res_rtlsim_whole = ret_rtlsim_whole[out_name]
    assert np.isclose(res_cppsim, res_rtlsim_nodebynode).all()
    assert np.isclose(res_cppsim, res_rtlsim_whole).all()


def test_end2end_tfc_w1a1_verify_all():
    # use the streamlined model as the "golden" model for right answers
    golden = ModelWrapper(build_dir + "/end2end_tfc_w1a1_streamlined.onnx")
    iname = golden.graph.input[0].name
    oname = golden.graph.output[0].name
    raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
    input_tensor = onnx.load_tensor_from_string(raw_i)
    x = nph.to_array(input_tensor)
    # x = np.zeros(ishape, dtype=np.float32)
    ret_golden = execute_onnx(golden, {iname: x}, True)
    y_golden = ret_golden[oname]
    # set up parent+child graph to test
    # we'll use models from the previous step as the child model
    parent_model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_dataflow_parent.onnx")
    iname = parent_model.graph.input[0].name
    oname = parent_model.graph.output[0].name
    # produce results with cppsim
    sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
    sdp_node = getCustomOp(sdp_node)
    sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a1_ipstitch_cppsim.onnx")
    ret_cppsim = execute_onnx(parent_model, {iname: x}, True)
    y_cppsim = ret_cppsim[oname]
    # produce results with node-by-node rtlsim
    sdp_node.set_nodeattr(
        "model", build_dir + "/end2end_tfc_w1a1_ipstitch_nodebynode_rtlsim.onnx"
    )
    ret_nodebynode_rtlsim = execute_onnx(parent_model, {iname: x}, True)
    y_nodebynode_rtlsim = ret_nodebynode_rtlsim[oname]
    # produce results with whole-network (stitched ip) rtlsim
    sdp_node.set_nodeattr(
        "model", build_dir + "/end2end_tfc_w1a1_ipstitch_whole_rtlsim.onnx"
    )
    ret_whole_rtlsim = execute_onnx(parent_model, {iname: x}, True)
    y_whole_rtlsim = ret_whole_rtlsim[oname]
    assert np.isclose(y_golden, y_cppsim).all()
    assert np.isclose(y_golden, y_nodebynode_rtlsim).all()
    assert np.isclose(y_golden, y_whole_rtlsim).all()


def test_end2end_tfc_w1a1_make_pynq_proj():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_ipstitch.onnx")
    model = model.transform(MakePYNQProject(test_pynq_board))
    model.save(build_dir + "/end2end_tfc_w1a1_pynq_project.onnx")


def test_end2end_tfc_w1a1_synth_pynq_project():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_pynq_project.onnx")
    model = model.transform(SynthPYNQProject())
    model = model.transform(AnnotateResources("synth"))
    model.save(build_dir + "/end2end_tfc_w1a1_synth.onnx")


def test_end2end_tfc_w1a1_make_driver():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_synth.onnx")
    model = model.transform(MakePYNQDriver())
    model.save(build_dir + "/end2end_tfc_w1a1_pynq_driver.onnx")


def test_end2end_tfc_w1a1_deploy_on_pynq():
    model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_pynq_driver.onnx")
    try:
        ip = os.environ["PYNQ_IP"]  # no fault for this one; skip if not defined
        if ip == "":
            pytest.skip("PYNQ board IP address not specified")
        username = os.getenv("PYNQ_USERNAME", "xilinx")
        password = os.getenv("PYNQ_PASSWORD", "xilinx")
        port = os.getenv("PYNQ_PORT", 22)
        target_dir = os.getenv("PYNQ_TARGET_DIR", "/home/xilinx/finn")
        model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))
        # save the model to be able to link it to the parent
        model.save(build_dir + "/end2end_tfc_w1a1_pynq_deploy.onnx")
    except KeyError:
        pytest.skip("PYNQ board IP address not specified")


def test_end2end_tfc_w1a1_run_on_pynq():
    # use the streamlined model as the "golden" model for right answers
    golden = ModelWrapper(build_dir + "/end2end_tfc_w1a1_streamlined.onnx")
    iname = golden.graph.input[0].name
    oname = golden.graph.output[0].name
    raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
    input_tensor = onnx.load_tensor_from_string(raw_i)
    x = nph.to_array(input_tensor)
    # x = np.zeros(ishape, dtype=np.float32)
    # run using FINN-based execution
    ret_golden = execute_onnx(golden, {iname: x}, True)
    y_golden = ret_golden[oname]
    # set up parent+child graph to test
    # we'll use models from the previous step as the child model
    parent_model = ModelWrapper(build_dir + "/end2end_tfc_w1a1_dataflow_parent.onnx")
    iname = parent_model.graph.input[0].name
    oname = parent_model.graph.output[0].name
    try:
        ip = os.environ["PYNQ_IP"]  # NOQA
        if ip == "":
            pytest.skip("PYNQ board IP address not specified")
        # produce results with cppsim
        sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
        sdp_node = getCustomOp(sdp_node)
        sdp_node.set_nodeattr("model", build_dir + "/end2end_tfc_w1a1_pynq_deploy.onnx")
        ret = execute_onnx(parent_model, {iname: x}, True)
        y = ret[oname]
        assert np.isclose(y, y_golden).all()
        child_model = ModelWrapper(sdp_node.get_nodeattr("model"))
        res = throughput_test(child_model)
        assert res is not None

    except KeyError:
        pytest.skip("PYNQ board IP address not specified")