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from collections import namedtuple
import tvm
from tvm import te
from tvm import relay
from tvm.relay import quantize as qtz
import mxnet as mx
from mxnet import gluon
import logging
import os

logging.basicConfig(level=logging.INFO)

Config = namedtuple('Config', ['model', 'nbit_input',  'dtype_input', 'nbit_output', 'dtype_output', 'global_scale', 'expected_acc'])


def get_val_data(model_name,
                 rec_val,
                 batch_size,
                 num_workers=4):
    rec_val = os.path.expanduser(rec_val)
    mean_rgb = [123.68, 116.779, 103.939]
    std_rgb = [58.393, 57.12, 57.375]
    def batch_fn(batch, ctx):
        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
        return data, label

    img_size = 299 if model_name == 'inceptionv3' else 224
    val_data = mx.io.ImageRecordIter(
        path_imgrec         = rec_val,
        preprocess_threads  = num_workers,
        shuffle             = False,
        batch_size          = batch_size,
        resize              = 256,
        data_shape          = (3, img_size, img_size),
        mean_r              = mean_rgb[0],
        mean_g              = mean_rgb[1],
        mean_b              = mean_rgb[2],
        std_r               = std_rgb[0],
        std_g               = std_rgb[1],
        std_b               = std_rgb[2],
    )
    return val_data, batch_fn


def get_model(model_name, batch_size, qconfig, target=None, original=False, simulated=False):
    gluon_model = gluon.model_zoo.vision.get_model(model_name, pretrained=True)
    img_size = 299 if model_name == 'inceptionv3' else 224
    data_shape = (batch_size, 3, img_size, img_size)
    mod, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
    net = mod['main']

    with relay.build_config(opt_level=3):
        qfunc = relay.quantize.prerequisite_optimize(net, params=params)
    logging.debug('original')
    logging.debug(qfunc.astext(show_meta_data=False))
    if original:
        return qfunc

    with qconfig:
        logging.debug('current quantize config')
        logging.debug(qtz.current_qconfig())
        qfunc = qtz.quantize(qfunc)
        logging.debug('after quantize')
        logging.debug(qfunc.astext(show_meta_data=False))
    return qfunc


def eval_acc(model, dataset, batch_fn, target=tvm.target.cuda(), ctx=tvm.gpu(), log_interval=100):
    with relay.build_config(opt_level=3):
        graph, lib, params = relay.build(model, target)
    # create runtime module
    m = tvm.contrib.graph_runtime.create(graph, lib, ctx)
    m.set_input(**params)

    # setup evaluaiton metric
    dataset.reset()
    batch_size = dataset.batch_size
    acc_top1 = mx.metric.Accuracy()
    acc_top5 = mx.metric.TopKAccuracy(5)
    acc_top1.reset()
    acc_top5.reset()
    # Execute
    for i, batch in enumerate(dataset):
        data, label = batch_fn(batch, [mx.cpu(0)])
        m.run(data=data[0].asnumpy())
        out_arr = m.get_output(0)
        acc_top1.update(label, [mx.nd.array(out_arr.asnumpy())])
        acc_top5.update(label, [mx.nd.array(out_arr.asnumpy())])

        if not (i + 1) % log_interval:
            _, top1 = acc_top1.get()
            _, top5 = acc_top5.get()
            nsamples = (i + 1) * batch_size
            logging.info('[%d samples] validation: acc-top1=%f acc-top5=%f', nsamples, top1, top5)
    logging.info('[final] validation: acc-top1=%f acc-top5=%f', top1, top5)
    return top1

def test_quantize_acc(cfg, rec_val):
    qconfig = qtz.qconfig(skip_conv_layers=[0],
                          nbit_input=cfg.nbit_input,
                          nbit_weight=cfg.nbit_input,
                          global_scale=cfg.global_scale,
                          dtype_input=cfg.dtype_input,
                          dtype_weight=cfg.dtype_input,
                          dtype_activation=cfg.dtype_output,
                          debug_enabled_ops=None)

    model = get_model(cfg.model, 32, qconfig, tvm.target.cuda())
    val_data, batch_fn = get_val_data(cfg.model, rec_val=rec_val, batch_size=32)

    acc = eval_acc(model, val_data, batch_fn)
    assert acc > cfg.expected_acc
    return acc


if __name__ == "__main__":
    #TODO(for user): replace the line with the path to imagenet validation dataset
    rec_val = "/scratch/tqchen/imagenet/val.rec"

    results = []
    configs = [
        Config('mobilenetv2_1.0', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=4.0, expected_acc=0.666),

        Config('resnet18_v1', nbit_input=8, dtype_input='int8', nbit_output=16, dtype_output='int16', global_scale=8.0, expected_acc=0.692),
        Config('resnet18_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.692),
        Config('resnet34_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.733),
        Config('resnet50_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.747),
        Config('resnet101_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.756),
        # TODO: need to fix accuracy
        # Config('mobilenetv2_1.0', nbit_input=8, dtype_input='int8', nbit_output=16, dtype_output='int16', global_scale=4.0),
    ]

    for config in configs:
        acc = test_quantize_acc(config, rec_val)
        results.append((config, acc))
    for res in results:
        print(res)
