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"""Benchmarking Relay VM using models from MXNet."""
import numpy as np

import tvm
from tvm import te
from tvm.contrib import graph_runtime
from tvm import relay
from tvm.runtime import container
from tvm.runtime import vm as vm_rt
from tvm.relay import testing
from tvm.relay import vm


def benchmark_execution(mod,
                        params,
                        measure=True,
                        data_shape=(1, 3, 224, 224),
                        out_shape=(1, 1000),
                        dtype='float32',
                        model="unknown"):
    def get_graph_runtime_output(mod, data, params, target, ctx,
                                 dtype='float32', number=2, repeat=20):
        with relay.build_config(opt_level=3):
            graph, lib, params = relay.build(mod, target, params=params)

        m = graph_runtime.create(graph, lib, ctx)
        # set inputs
        m.set_input("data", data)
        m.set_input(**params)
        m.run()
        out = m.get_output(0, tvm.nd.empty(out_shape, dtype))

        if measure:
            print("Evaluate graph runtime inference cost of {} on "
                  "{}".format(model, repr(ctx)))
            ftimer = m.module.time_evaluator("run", ctx, number=1, repeat=20)
            # Measure in millisecond.
            prof_res = np.array(ftimer().results) * 1000
            print("Mean graph runtime inference time (std dev): %.2f ms (%.2f ms)" %
                  (np.mean(prof_res), np.std(prof_res)))

        return out.asnumpy()

    def get_vm_output(mod, data, params, target, ctx, dtype='float32',
                      number=2, repeat=20):
        with relay.build_config(opt_level=3):
            exe = vm.compile(mod, target, params=params)
            rly_vm = vm_rt.VirtualMachine(exe)
            rly_vm.init(ctx)
            result = rly_vm.run(data)

        if measure:
            print("Evaluate vm inference cost of {} on {}".format(model,
                                                                  repr(ctx)))
            ftimer = rly_vm.mod.time_evaluator("invoke", ctx, number=number,
                                               repeat=repeat)
            # Measure in millisecond.
            prof_res = np.array(ftimer("main", data).results) * 1000
            print("Mean vm inference time (std dev): %.2f ms (%.2f ms)" %
                  (np.mean(prof_res), np.std(prof_res)))

        return result.asnumpy().astype(dtype)

    # random input
    data = np.random.uniform(size=data_shape).astype(dtype)
    target = "llvm"
    ctx = tvm.cpu(0)

    tvm_out = get_graph_runtime_output(mod, tvm.nd.array(data.astype(dtype)),
                                       params, target, ctx, dtype)
    vm_out = get_vm_output(mod, tvm.nd.array(data.astype(dtype)), params,
                           target, ctx, dtype)
    tvm.testing.assert_allclose(vm_out, tvm_out, rtol=1e-5, atol=1e-5)


def test_mlp():
    image_shape = (1, 1, 28, 28)
    mod, params = testing.mlp.get_workload(1)
    benchmark_execution(mod, params, data_shape=image_shape, out_shape=(1, 10),
                       model="mlp")


def test_vgg():
    for n in [11, 16]:
        mod, params = testing.vgg.get_workload(1, num_layers=n)
        model = "vgg" + str(n)
        benchmark_execution(mod, params, model=model)


def test_resnet():
    for n in [18, 50]:
        mod, params = testing.resnet.get_workload(batch_size=1, num_layers=n)
        model = "resnet" + str(n)
        benchmark_execution(mod, params, model=model)


def test_squeezenet():
    for version in ['1.0', '1.1']:
        mod, params = testing.squeezenet.get_workload(version=version)
        model = "squeezenet" + version
        benchmark_execution(mod, params, model=model)


def test_inception_v3():
    image_shape = (3, 299, 299)
    mod, params = testing.inception_v3.get_workload(image_shape=image_shape)
    benchmark_execution(mod, params, data_shape=(1, 3, 299, 299),
                        model="inception_v3")


def test_dqn():
    image_shape = (1, 4, 84, 84)
    mod, params = testing.dqn.get_workload(
        batch_size=1, image_shape=image_shape)
    benchmark_execution(mod, params, data_shape=image_shape, out_shape=(1, 18))


def test_dcgan():
    image_shape = (1, 100)
    mod, params = testing.dcgan.get_workload(batch_size=1)
    benchmark_execution(mod, params, data_shape=image_shape, out_shape=(1, 3, 64, 64))


def test_mobilenet():
    mod, params = testing.mobilenet.get_workload(batch_size=1)
    benchmark_execution(mod, params, model="mobilenet")

# TODO: enable when the low building performance (several minutes) fixed.
def test_mobilenet_nhwc():
    image_shape = (1, 224, 224, 3)
    mod, params = testing.mobilenet.get_workload(batch_size=1,
                                                 image_shape=image_shape[1:],
                                                 layout='NHWC')
    benchmark_execution(mod, params, measure=False, data_shape=image_shape)

def test_densenet():
    mod, params = testing.densenet.get_workload(batch_size=1)
    benchmark_execution(mod, params, model="densenet")


if __name__ == '__main__':
    test_resnet()
    test_vgg()
    test_squeezenet()
    test_mobilenet()
    test_densenet()
    test_inception_v3()
    test_mlp()
    test_dqn()
    test_dcgan()
