#  Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.

import numpy as np
import os
from functools import partial
import logging
import time
import paddle
import paddle.fluid as fluid
import argparse
import network
import reader

logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger("fluid")
logger.setLevel(logging.INFO)


def parse_args():
    parser = argparse.ArgumentParser("gnn")
    parser.add_argument(
        '--train_path', type=str, default='./data/diginetica/train.txt', help='dir of training data')
    parser.add_argument(
        '--config_path', type=str, default='./data/diginetica/config.txt', help='dir of config')
    parser.add_argument(
        '--model_path', type=str, default='./saved_model', help="path of model parameters")
    parser.add_argument(
        '--epoch_num', type=int, default=30, help='number of epochs to train for')
    parser.add_argument(
        '--batch_size', type=int, default=100, help='input batch size')
    parser.add_argument(
        '--hidden_size', type=int, default=100, help='hidden state size')
    parser.add_argument(
        '--l2', type=float, default=1e-5, help='l2 penalty')
    parser.add_argument(
        '--lr', type=float, default=0.001, help='learning rate')
    parser.add_argument(
        '--step', type=int, default=1, help='gnn propogation steps')
    parser.add_argument(
        '--lr_dc', type=float, default=0.1, help='learning rate decay rate')
    parser.add_argument(
        '--lr_dc_step', type=int, default=3, help='the number of steps after which the learning rate decay')
    parser.add_argument(
        '--use_cuda', type=int, default=0, help='whether to use gpu')
    parser.add_argument(
        '--use_parallel', type=int, default=1, help='whether to use parallel executor')
    parser.add_argument(
        '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
    return parser.parse_args()


def train():
    args = parse_args()

    if args.enable_ce:
        SEED = 102
        fluid.default_main_program().random_seed = SEED
        fluid.default_startup_program().random_seed = SEED

    batch_size = args.batch_size
    items_num = reader.read_config(args.config_path)
    loss, acc = network.network(batch_size, items_num, args.hidden_size,
                                args.step)

    data_reader = reader.Data(args.train_path, True)
    logger.info("load data complete")

    use_cuda = True if args.use_cuda else False
    use_parallel = True if args.use_parallel else False
    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

    exe = fluid.Executor(place)
    step_per_epoch = data_reader.length // batch_size
    optimizer = fluid.optimizer.Adam(
        learning_rate=fluid.layers.exponential_decay(
            learning_rate=args.lr,
            decay_steps=step_per_epoch * args.lr_dc_step,
            decay_rate=args.lr_dc),
        regularization=fluid.regularizer.L2DecayRegularizer(
            regularization_coeff=args.l2))
    optimizer.minimize(loss)

    exe.run(fluid.default_startup_program())

    all_vocab = fluid.global_scope().var("all_vocab").get_tensor()
    all_vocab.set(
        np.arange(1, items_num).astype("int64").reshape((-1, 1)), place)

    feed_list = [
        "items", "seq_index", "last_index", "adj_in", "adj_out", "mask", "label"
    ]
    feeder = fluid.DataFeeder(feed_list=feed_list, place=place)

    if use_parallel:
        train_exe = fluid.ParallelExecutor(
            use_cuda=use_cuda, loss_name=loss.name)
    else:
        train_exe = exe

    logger.info("begin train")

    total_time = []
    ce_info = []
    start_time = time.time()
    loss_sum = 0.0
    acc_sum = 0.0
    global_step = 0
    PRINT_STEP = 500
    for i in range(args.epoch_num):
        epoch_sum = []
        for data in data_reader.reader(batch_size, batch_size * 20, True):
            res = train_exe.run(feed=feeder.feed(data),
                                fetch_list=[loss.name, acc.name])
            loss_sum += res[0]
            acc_sum += res[1]
            epoch_sum.append(res[0])
            global_step += 1
            if global_step % PRINT_STEP == 0:
                ce_info.append([loss_sum / PRINT_STEP, acc_sum / PRINT_STEP])
                total_time.append(time.time() - start_time)
                logger.info("global_step: %d, loss: %.4lf, train_acc: %.4lf" % (
                    global_step, loss_sum / PRINT_STEP, acc_sum / PRINT_STEP))
                loss_sum = 0.0
                acc_sum = 0.0
                start_time = time.time()
        logger.info("epoch loss: %.4lf" % (np.mean(epoch_sum)))
        save_dir = args.model_path + "/epoch_" + str(i)
        fetch_vars = [loss, acc]
        fluid.io.save_inference_model(save_dir, feed_list, fetch_vars, exe)
        logger.info("model saved in " + save_dir)

    # only for ce
    if args.enable_ce:
        gpu_num = get_cards(args)
        ce_loss = 0
        ce_acc = 0
        ce_time = 0
        try:
            ce_loss = ce_info[-1][0]
            ce_acc = ce_info[-1][1]
            ce_time = total_time[-1]
        except:
            print("ce info error")
        print("kpis\teach_pass_duration_card%s\t%s" %
                    (gpu_num, ce_time))
        print("kpis\ttrain_loss_card%s\t%f" %
                    (gpu_num, ce_loss))
        print("kpis\ttrain_acc_card%s\t%f" %
                    (gpu_num, ce_acc))


def get_cards(args):
    num = 0
    cards = os.environ.get('CUDA_VISIBLE_DEVICES')
    num = len(cards.split(","))
    return num


if __name__ == "__main__":
    train()