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import argparse
import logging
import os

import tensorflow as tf
import keras
from keras.callbacks import EarlyStopping, TensorBoard
from keras.datasets import cifar10
from keras.optimizers import SGD, Adadelta, Adagrad, Adam, Adamax, RMSprop
from keras.utils import multi_gpu_model, to_categorical
import keras.backend.tensorflow_backend as KTF

import nni
from nni.algorithms.hpo.networkmorphism_tuner.graph import json_to_graph

# set the logger format
log_format = "%(asctime)s %(message)s"
logging.basicConfig(
    filename="networkmorphism.log",
    filemode="a",
    level=logging.INFO,
    format=log_format,
    datefmt="%m/%d %I:%M:%S %p",
)
# set the logger format
logger = logging.getLogger("cifar10-network-morphism-keras")


# restrict gpu usage background
config = tf.ConfigProto()
# pylint: disable=E1101,W0603
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)

KTF.set_session(sess)


def get_args():
    """ get args from command line
    """
    parser = argparse.ArgumentParser("cifar10")
    parser.add_argument("--batch_size", type=int, default=128, help="batch size")
    parser.add_argument("--optimizer", type=str, default="SGD", help="optimizer")
    parser.add_argument("--epochs", type=int, default=200, help="epoch limit")
    parser.add_argument(
        "--learning_rate", type=float, default=0.001, help="learning rate"
    )
    parser.add_argument(
        "--weight_decay",
        type=float,
        default=1e-5,
        help="weight decay of the learning rate",
    )
    return parser.parse_args()


trainloader = None
testloader = None
net = None
args = get_args()
TENSORBOARD_DIR = os.environ["NNI_OUTPUT_DIR"]


def build_graph_from_json(ir_model_json):
    """build model from json representation
    """
    graph = json_to_graph(ir_model_json)
    logging.debug(graph.operation_history)
    model = graph.produce_keras_model()
    return model


def parse_rev_args(receive_msg):
    """ parse reveive msgs to global variable
    """
    global trainloader
    global testloader
    global net

    # Loading Data
    logger.debug("Preparing data..")

    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    y_train = to_categorical(y_train, 10)
    y_test = to_categorical(y_test, 10)
    x_train = x_train.astype("float32")
    x_test = x_test.astype("float32")
    x_train /= 255.0
    x_test /= 255.0
    trainloader = (x_train, y_train)
    testloader = (x_test, y_test)

    # Model
    logger.debug("Building model..")
    net = build_graph_from_json(receive_msg)

    # parallel model
    try:
        available_devices = os.environ["HIP_VISIBLE_DEVICES"]
        gpus = len(available_devices.split(","))
        if gpus > 1:
            net = multi_gpu_model(net, gpus)
    except KeyError:
        logger.debug("parallel model not support in this config settings")

    if args.optimizer == "SGD":
        optimizer = SGD(lr=args.learning_rate, momentum=0.9, decay=args.weight_decay)
    if args.optimizer == "Adadelta":
        optimizer = Adadelta(lr=args.learning_rate, decay=args.weight_decay)
    if args.optimizer == "Adagrad":
        optimizer = Adagrad(lr=args.learning_rate, decay=args.weight_decay)
    if args.optimizer == "Adam":
        optimizer = Adam(lr=args.learning_rate, decay=args.weight_decay)
    if args.optimizer == "Adamax":
        optimizer = Adamax(lr=args.learning_rate, decay=args.weight_decay)
    if args.optimizer == "RMSprop":
        optimizer = RMSprop(lr=args.learning_rate, decay=args.weight_decay)

    # Compile the model
    net.compile(
        loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"]
    )
    return 0


class SendMetrics(keras.callbacks.Callback):
    """
    Keras callback to send metrics to NNI framework
    """

    def on_epoch_end(self, epoch, logs=None):
        """
        Run on end of each epoch
        """
        if logs is None:
            logs = dict()
        logger.debug(logs)
        # TensorFlow 2.0 API reference claims the key is `val_acc`, but in fact it's `val_accuracy`
        if 'val_acc' in logs:
            nni.report_intermediate_result(logs['val_acc'])
        else:
            nni.report_intermediate_result(logs['val_accuracy'])


# Training
def train_eval():
    """ train and eval the model
    """

    global trainloader
    global testloader
    global net

    (x_train, y_train) = trainloader
    (x_test, y_test) = testloader

    # train procedure
    net.fit(
        x=x_train,
        y=y_train,
        batch_size=args.batch_size,
        validation_data=(x_test, y_test),
        epochs=args.epochs,
        shuffle=True,
        callbacks=[
            SendMetrics(),
            EarlyStopping(min_delta=0.001, patience=10),
            TensorBoard(log_dir=TENSORBOARD_DIR),
        ],
    )

    # trial report final acc to tuner
    _, acc = net.evaluate(x_test, y_test)
    logger.debug("Final result is: %.3f", acc)
    nni.report_final_result(acc)


if __name__ == "__main__":
    try:
        # trial get next parameter from network morphism tuner
        RCV_CONFIG = nni.get_next_parameter()
        logger.debug(RCV_CONFIG)
        parse_rev_args(RCV_CONFIG)
        train_eval()
    except Exception as exception:
        logger.exception(exception)
        raise
