# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================

"""Train retinanet and get checkpoint files."""

import os
import ast
import time
import mindspore
import mindspore.nn as nn
from mindspore import  Tensor
from mindspore.communication.management import init, get_rank
from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor, Callback
from mindspore.train import Model
from mindspore import ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.common import set_seed
from src.retinanet import retinanetWithLossCell, TrainingWrapper, retinanet50, resnet50
from src.dataset import create_retinanet_dataset
from src.lr_schedule import get_lr
from src.init_params import init_net_param, filter_checkpoint_parameter
from src.model_utils.config import config
from src.model_utils.moxing_adapter import moxing_wrapper
from src.model_utils.device_adapter import get_device_id, get_device_num

set_seed(1)


class Monitor(Callback):
    """
    Monitor loss and time.

    Args:
        lr_init (numpy array): train lr

    Returns:
        None

    Examples:
        >>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
    """

    def __init__(self, lr_init=None):
        super(Monitor, self).__init__()
        self.lr_init = lr_init
        self.lr_init_len = len(lr_init)

    def step_end(self, run_context):
        cb_params = run_context.original_args()
        print("lr:[{:8.6f}]".format(self.lr_init[cb_params.cur_step_num - 1]), flush=True)


def modelarts_pre_process():
    '''modelarts pre process function.'''

    def unzip(zip_file, save_dir):
        import zipfile
        s_time = time.time()
        if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
            zip_isexist = zipfile.is_zipfile(zip_file)
            if zip_isexist:
                fz = zipfile.ZipFile(zip_file, 'r')
                data_num = len(fz.namelist())
                print("Extract Start...")
                print("unzip file num: {}".format(data_num))
                data_print = int(data_num / 100) if data_num > 100 else 1
                i = 0
                for file in fz.namelist():
                    if i % data_print == 0:
                        print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
                    i += 1
                    fz.extract(file, save_dir)
                print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
                                                     int(int(time.time() - s_time) % 60)))
                print("Extract Done.")
            else:
                print("This is not zip.")
        else:
            print("Zip has been extracted.")

    if config.need_modelarts_dataset_unzip:
        zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
        save_dir_1 = os.path.join(config.data_path)

        sync_lock = "/tmp/unzip_sync.lock"

        # Each server contains 8 devices as most.
        if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
            print("Zip file path: ", zip_file_1)
            print("Unzip file save dir: ", save_dir_1)
            unzip(zip_file_1, save_dir_1)
            print("===Finish extract data synchronization===")
            try:
                os.mknod(sync_lock)
            except IOError:
                pass

        while True:
            if os.path.exists(sync_lock):
                break
            time.sleep(1)

        print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))


def set_graph_kernel_context(device_target):
    if device_target == "GPU":
        # Enable graph kernel for default model ssd300 on GPU back-end.
        mindspore.set_context(enable_graph_kernel=True,
                            graph_kernel_flags="--enable_parallel_fusion --enable_expand_ops=Conv2D")


@moxing_wrapper(pre_process=modelarts_pre_process)
def main():
    config.lr_init = ast.literal_eval(config.lr_init)
    config.lr_end_rate = ast.literal_eval(config.lr_end_rate)
    device_id = get_device_id()
    mindspore.set_context(mode=0, device_target=config.device_target)
    if config.device_target == "Ascend":
        if mindspore.get_context("mode") == 1:
            mindspore.set_context(mempool_block_size="31GB")
    elif config.device_target == "GPU":
        set_graph_kernel_context(config.device_target)
    elif config.device_target == "CPU":
        device_id = 0
        config.distribute = False
    else:
        raise ValueError(f"device_target support ['Ascend', 'GPU', 'CPU'], but get {config.device_target}")
    if config.distribute:
        init()
        device_num = get_device_num()
        rank = get_rank()
        mindspore.reset_auto_parallel_context()
        mindspore.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
    else:
        rank = 0
        device_num = 1
        mindspore.set_context(device_id=device_id)

    mindrecord_file = os.path.join(config.mindrecord_dir, "retinanet.mindrecord0")

    loss_scale = float(config.loss_scale)

    # When create MindDataset, using the fitst mindrecord file, such as retinanet.mindrecord0.
    dataset = create_retinanet_dataset(mindrecord_file, repeat_num=1,
                                       num_parallel_workers=config.workers,
                                       batch_size=config.batch_size, device_num=device_num, rank=rank)

    dataset_size = dataset.get_dataset_size()
    print("Create dataset done!")

    backbone = resnet50(config.num_classes)
    retinanet = retinanet50(backbone, config)
    net = retinanetWithLossCell(retinanet, config)
    init_net_param(net)
    if hasattr(config, "finetune") and config.finetune:
        init_net_param(net, initialize_mode='XavierUniform')
    else:
        init_net_param(net)

    if config.pre_trained:
        if config.pre_trained_epoch_size <= 0:
            raise KeyError("pre_trained_epoch_size must be greater than 0.")
        param_dict = load_checkpoint(config.pre_trained)
        if config.filter_weight:
            filter_checkpoint_parameter(param_dict)
        load_param_into_net(net, param_dict)

    lr = Tensor(get_lr(global_step=0,
                       lr_init=config.lr_init, lr_end=config.lr_end_rate * config.lr, lr_max=config.lr,
                       warmup_epochs1=config.warmup_epochs1, warmup_epochs2=config.warmup_epochs2,
                       warmup_epochs3=config.warmup_epochs3, warmup_epochs4=config.warmup_epochs4,
                       warmup_epochs5=config.warmup_epochs5, total_epochs=config.epoch_size,
                       steps_per_epoch=dataset_size))
    opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr,
                      config.momentum, config.weight_decay, loss_scale)
    net = TrainingWrapper(net, opt, loss_scale)
    model = Model(net)
    print("Start train retinanet, the first epoch will be slower because of the graph compilation.")
    cb = [TimeMonitor(), LossMonitor()]
    cb += [Monitor(lr_init=lr.asnumpy())]
    config_ck = CheckpointConfig(save_checkpoint_steps=dataset_size * config.save_checkpoint_epochs,
                                 keep_checkpoint_max=config.keep_checkpoint_max)
    ckpt_cb = ModelCheckpoint(prefix="retinanet", directory=config.save_checkpoint_path, config=config_ck)
    if config.distribute:
        if rank == 0:
            cb += [ckpt_cb]
        model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)
    else:
        cb += [ckpt_cb]
        model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)


if __name__ == '__main__':
    main()