# Copyright (c) 2021, Xilinx
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import torch
from brevitas.nn import QuantLinear, QuantReLU
from brevitas.quant_tensor import QuantTensor
import torch.nn as nn
import numpy as np
from brevitas.core.quant import QuantType
from brevitas.nn import QuantIdentity
import brevitas.onnx as bo
from finn.core.modelwrapper import ModelWrapper
from finn.core.datatype import DataType
import finn.builder.build_dataflow as build
import finn.builder.build_dataflow_config as build_cfg
import os
import shutil
from finn.util.test import get_build_env, load_test_checkpoint_or_skip
import pytest
from finn.util.basic import make_build_dir
import pkg_resources as pk
import json
import wget
import subprocess

target_clk_ns = 10
build_kind = "zynq"
build_dir = os.environ["FINN_BUILD_DIR"]


def get_checkpoint_name(step):
    if step == "build":
        # checkpoint for build step is an entire dir
        return build_dir + "/end2end_cybsecmlp_build"
    else:
        # other checkpoints are onnx files
        return build_dir + "/end2end_cybsecmlp_%s.onnx" % (step)


class CybSecMLPForExport(nn.Module):
    def __init__(self, my_pretrained_model):
        super(CybSecMLPForExport, self).__init__()
        self.pretrained = my_pretrained_model
        self.qnt_output = QuantIdentity(
            quant_type=QuantType.BINARY, bit_width=1, min_val=-1.0, max_val=1.0
        )

    def forward(self, x):
        # assume x contains bipolar {-1,1} elems
        # shift from {-1,1} -> {0,1} since that is the
        # input range for the trained network
        x = (x + torch.tensor([1.0])) / 2.0
        out_original = self.pretrained(x)
        out_final = self.qnt_output(out_original)  # output as {-1,1}
        return out_final


def test_end2end_cybsec_mlp_export():
    assets_dir = pk.resource_filename("finn.qnn-data", "cybsec-mlp/")
    # load up trained net in Brevitas
    input_size = 593
    hidden1 = 64
    hidden2 = 64
    hidden3 = 64
    weight_bit_width = 2
    act_bit_width = 2
    num_classes = 1
    model = nn.Sequential(
        QuantLinear(input_size, hidden1, bias=True, weight_bit_width=weight_bit_width),
        nn.BatchNorm1d(hidden1),
        nn.Dropout(0.5),
        QuantReLU(bit_width=act_bit_width),
        QuantLinear(hidden1, hidden2, bias=True, weight_bit_width=weight_bit_width),
        nn.BatchNorm1d(hidden2),
        nn.Dropout(0.5),
        QuantReLU(bit_width=act_bit_width),
        QuantLinear(hidden2, hidden3, bias=True, weight_bit_width=weight_bit_width),
        nn.BatchNorm1d(hidden3),
        nn.Dropout(0.5),
        QuantReLU(bit_width=act_bit_width),
        QuantLinear(hidden3, num_classes, bias=True, weight_bit_width=weight_bit_width),
    )
    trained_state_dict = torch.load(assets_dir + "/state_dict.pth")[
        "models_state_dict"
    ][0]
    model.load_state_dict(trained_state_dict, strict=False)
    W_orig = model[0].weight.data.detach().numpy()
    # pad the second (593-sized) dimensions with 7 zeroes at the end
    W_new = np.pad(W_orig, [(0, 0), (0, 7)])
    model[0].weight.data = torch.from_numpy(W_new)
    model_for_export = CybSecMLPForExport(model)
    export_onnx_path = get_checkpoint_name("export")
    input_shape = (1, 600)
    # create a QuantTensor instance to mark the input as bipolar during export
    input_a = np.random.randint(0, 1, size=input_shape).astype(np.float32)
    input_a = 2 * input_a - 1
    scale = 1.0
    input_t = torch.from_numpy(input_a * scale)
    input_qt = QuantTensor(
        input_t, scale=torch.tensor(scale), bit_width=torch.tensor(1.0), signed=True
    )

    bo.export_finn_onnx(
        model_for_export, export_path=export_onnx_path, input_t=input_qt
    )
    assert os.path.isfile(export_onnx_path)
    # fix input datatype
    finn_model = ModelWrapper(export_onnx_path)
    finnonnx_in_tensor_name = finn_model.graph.input[0].name
    assert tuple(finn_model.get_tensor_shape(finnonnx_in_tensor_name)) == (1, 600)
    # verify a few exported ops
    assert finn_model.graph.node[1].op_type == "Add"
    assert finn_model.graph.node[2].op_type == "Div"
    assert finn_model.graph.node[3].op_type == "MatMul"
    assert finn_model.graph.node[-1].op_type == "MultiThreshold"
    # verify datatypes on some tensors
    assert finn_model.get_tensor_datatype(finnonnx_in_tensor_name) == DataType.BIPOLAR
    first_matmul_w_name = finn_model.graph.node[3].input[1]
    assert finn_model.get_tensor_datatype(first_matmul_w_name) == DataType.INT2


@pytest.mark.slow
@pytest.mark.vivado
def test_end2end_cybsec_mlp_build():
    model_file = get_checkpoint_name("export")
    load_test_checkpoint_or_skip(model_file)
    build_env = get_build_env(build_kind, target_clk_ns)
    output_dir = make_build_dir("test_end2end_cybsec_mlp_build")

    cfg = build.DataflowBuildConfig(
        output_dir=output_dir,
        target_fps=1000000,
        synth_clk_period_ns=target_clk_ns,
        board=build_env["board"],
        shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
        generate_outputs=[
            build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
            build_cfg.DataflowOutputType.BITFILE,
            build_cfg.DataflowOutputType.PYNQ_DRIVER,
            build_cfg.DataflowOutputType.DEPLOYMENT_PACKAGE,
        ],
    )
    build.build_dataflow_cfg(model_file, cfg)
    # check the generated files
    assert os.path.isfile(output_dir + "/time_per_step.json")
    assert os.path.isfile(output_dir + "/final_hw_config.json")
    assert os.path.isfile(output_dir + "/driver/driver.py")
    est_cycles_report = output_dir + "/report/estimate_layer_cycles.json"
    assert os.path.isfile(est_cycles_report)
    est_res_report = output_dir + "/report/estimate_layer_resources.json"
    assert os.path.isfile(est_res_report)
    assert os.path.isfile(output_dir + "/report/estimate_network_performance.json")
    assert os.path.isfile(output_dir + "/bitfile/finn-accel.bit")
    assert os.path.isfile(output_dir + "/bitfile/finn-accel.hwh")
    assert os.path.isfile(output_dir + "/report/post_synth_resources.xml")
    assert os.path.isfile(output_dir + "/report/post_route_timing.rpt")
    # examine the report contents
    with open(est_cycles_report, "r") as f:
        est_cycles_dict = json.load(f)
        assert est_cycles_dict["StreamingFCLayer_Batch_0"] == 80
        assert est_cycles_dict["StreamingFCLayer_Batch_1"] == 64
    with open(est_res_report, "r") as f:
        est_res_dict = json.load(f)
        assert est_res_dict["total"]["LUT"] == 11360.0
        assert est_res_dict["total"]["BRAM_18K"] == 36.0
    shutil.copytree(output_dir + "/deploy", get_checkpoint_name("build"))


def test_end2end_cybsec_mlp_run_on_hw():
    build_env = get_build_env(build_kind, target_clk_ns)
    assets_dir = pk.resource_filename("finn.qnn-data", "cybsec-mlp/")
    deploy_dir = get_checkpoint_name("build")
    if not os.path.isdir(deploy_dir):
        pytest.skip(deploy_dir + " not found from previous test step, skipping")
    driver_dir = deploy_dir + "/driver"
    assert os.path.isdir(driver_dir)
    # put all assets into driver dir
    shutil.copy(assets_dir + "/validate-unsw-nb15.py", driver_dir)
    # put a copy of binarized dataset into driver dir
    dataset_url = (
        "https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1"
    )
    dataset_local = driver_dir + "/unsw_nb15_binarized.npz"
    if not os.path.isfile(dataset_local):
        wget.download(dataset_url, out=dataset_local)
    assert os.path.isfile(dataset_local)
    # create a shell script for running validation: 10 batches x 10 imgs
    with open(driver_dir + "/validate.sh", "w") as f:
        f.write(
            """#!/bin/bash
cd %s/driver
echo %s | sudo -S python3.6 validate-unsw-nb15.py --batchsize=10 --limit_batches=10
        """
            % (
                build_env["target_dir"] + "/end2end_cybsecmlp_build",
                build_env["password"],
            )
        )
    # set up rsync command
    remote_target = "%s@%s:%s" % (
        build_env["username"],
        build_env["ip"],
        build_env["target_dir"],
    )
    rsync_res = subprocess.run(["rsync", "-avz", deploy_dir, remote_target])
    assert rsync_res.returncode == 0
    remote_verif_cmd = [
        "ssh",
        "%s@%s" % (build_env["username"], build_env["ip"]),
        "sh",
        build_env["target_dir"] + "/end2end_cybsecmlp_build/driver/validate.sh",
    ]
    verif_res = subprocess.run(
        remote_verif_cmd,
        stdout=subprocess.PIPE,
        universal_newlines=True,
        input=build_env["password"],
    )
    assert verif_res.returncode == 0
    log_output = verif_res.stdout.split("\n")
    assert log_output[-3] == "batch 10 / 10 : total OK 93 NOK 7"
    assert log_output[-2] == "Final accuracy: 93.000000"