# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 random
import argparse

import paddle
import numpy as np

from medicalseg.cvlibs import manager, Config
from medicalseg.utils import get_sys_env, logger
from medicalseg.core import train
import nnunet


def parse_args():
    parser = argparse.ArgumentParser(description='Model training')
    # params of training
    parser.add_argument(
        "--config", dest="cfg", help="The config file.", default=None, type=str)
    parser.add_argument(
        '--iters',
        dest='iters',
        help='iters for training',
        type=int,
        default=None)
    parser.add_argument(
        '--batch_size',
        dest='batch_size',
        help='Mini batch size of one gpu or cpu',
        type=int,
        default=None)
    parser.add_argument(
        '--learning_rate',
        dest='learning_rate',
        help='Learning rate',
        type=float,
        default=None)
    parser.add_argument(
        '--save_interval',
        dest='save_interval',
        help='How many iters to save a model snapshot once during training.',
        type=int,
        default=1000)
    parser.add_argument(
        '--resume_model',
        dest='resume_model',
        help='The path of resume model',
        type=str,
        default=None)
    parser.add_argument(
        '--save_dir',
        dest='save_dir',
        help='The directory for saving the model snapshot',
        type=str,
        default='./output')
    parser.add_argument(
        '--keep_checkpoint_max',
        dest='keep_checkpoint_max',
        help='Maximum number of checkpoints to save',
        type=int,
        default=5)
    parser.add_argument(
        '--num_workers',
        dest='num_workers',
        help='Num workers for data loader',
        type=int,
        default=0)
    parser.add_argument(
        '--do_eval',
        dest='do_eval',
        help='Eval while training',
        action='store_true')
    parser.add_argument(
        '--log_iters',
        dest='log_iters',
        help='Display logging information at every log_iters',
        default=100,
        type=int)
    parser.add_argument(
        '--use_vdl',
        dest='use_vdl',
        help='Whether to record the data to VisualDL during training',
        action='store_true')
    parser.add_argument(
        '--seed',
        dest='seed',
        help='Set the random seed during training.',
        default=None,
        type=int)
    parser.add_argument(
        '--data_format',
        dest='data_format',
        help='Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".',
        type=str,
        default='NCHW')
    parser.add_argument(
        '--profiler_options',
        type=str,
        default=None,
        help='The option of train profiler. If profiler_options is not None, the train ' \
            'profiler is enabled. Refer to the medseg/utils/train_profiler.py for details.'
    )
    parser.add_argument(
        '--nnunet', dest='nnunet', help='nnunet training', action='store_true')
    parser.add_argument(
        "--precision",
        default="fp32",
        type=str,
        choices=["fp32", "fp16"],
        help="Use AMP (Auto mixed precision) if precision='fp16'. If precision='fp32', the training is normal."
    )

    parser.add_argument('--sw_num', default=None, type=int, help='sw_num')

    parser.add_argument(
        '--is_save_data', default=True, type=eval, help='warmup')

    parser.add_argument(
        '--has_dataset_json', default=True, type=eval, help='has_dataset_json')
    return parser.parse_args()


def main(args):

    if args.seed is not None:
        paddle.seed(args.seed)
        np.random.seed(args.seed)
        random.seed(args.seed)

    env_info = get_sys_env()
    info = ['{}: {}'.format(k, v) for k, v in env_info.items()]
    info = '\n'.join(['', format('Environment Information', '-^48s')] + info +
                     ['-' * 48])
    logger.info(info)

    place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
        'GPUs used'] else 'cpu'

    paddle.set_device(place)
    if not args.cfg:
        raise RuntimeError('No configuration file specified.')

    cfg = Config(
        args.cfg,
        learning_rate=args.learning_rate,
        iters=args.iters,
        batch_size=args.batch_size)

    # Only support for the DeepLabv3+ model
    if args.data_format == 'NHWC':
        if cfg.dic['model']['type'] != 'DeepLabV3P':
            raise ValueError(
                'The "NHWC" data format only support the DeepLabV3P model!')
        cfg.dic['model']['data_format'] = args.data_format
        cfg.dic['model']['backbone']['data_format'] = args.data_format
        loss_len = len(cfg.dic['loss']['types'])
        for i in range(loss_len):
            cfg.dic['loss']['types'][i]['data_format'] = args.data_format

    train_dataset = cfg.train_dataset
    if train_dataset is None:
        raise RuntimeError(
            'The training dataset is not specified in the configuration file.')
    elif len(train_dataset) == 0:
        raise ValueError(
            'The length of train_dataset is 0. Please check if your dataset is valid'
        )
    val_dataset = cfg.val_dataset if args.do_eval else None
    losses = cfg.loss

    msg = '\n---------------Config Information---------------\n'
    msg += str(cfg)
    msg += '------------------------------------------------'
    logger.info(msg)

    if args.nnunet:
        nnunet.core.train(
            cfg.model,
            train_dataset,
            val_dataset=val_dataset,
            optimizer=cfg.optimizer,
            save_dir=args.save_dir,
            iters=cfg.iters,
            batch_size=cfg.batch_size,
            resume_model=args.resume_model,
            save_interval=args.save_interval,
            log_iters=args.log_iters,
            num_workers=args.num_workers,
            use_vdl=args.use_vdl,
            losses=losses,
            keep_checkpoint_max=args.keep_checkpoint_max,
            precision=args.precision,
            profiler_options=args.profiler_options,
            to_static_training=cfg.to_static_training)
    else:
        train(
            cfg.model,
            train_dataset,
            val_dataset=val_dataset,
            optimizer=cfg.optimizer,
            save_dir=args.save_dir,
            iters=cfg.iters,
            batch_size=cfg.batch_size,
            resume_model=args.resume_model,
            save_interval=args.save_interval,
            log_iters=args.log_iters,
            num_workers=args.num_workers,
            use_vdl=args.use_vdl,
            losses=losses,
            keep_checkpoint_max=args.keep_checkpoint_max,
            profiler_options=args.profiler_options,
            to_static_training=cfg.to_static_training,
            sw_num=args.sw_num,
            is_save_data=args.is_save_data,
            has_dataset_json=args.has_dataset_json)


if __name__ == '__main__':
    args = parse_args()
    main(args)
