# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import warnings

import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist, set_random_seed
from mmcv.utils import get_git_hash

from mmaction import __version__
from mmaction.apis import train_model
from mmaction.datasets import build_dataset
from mmaction.models import build_model
from mmaction.utils import collect_env, get_root_logger, register_module_hooks

from mmaction.models.model_da.model_da import model_analysis


def parse_args():
    parser = argparse.ArgumentParser(description='Train a recognizer')
    parser.add_argument('config', help='train config file path')
    parser.add_argument('--work-dir', help='the dir to save logs and models')

    # resume_from  loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint
    # it is usually used for resuming the training process that is interrupted accidentally
    # load-from   only loads the model weights and the training epoch starts from 0. it is usually used for finetuning
    parser.add_argument(
        '--resume-from', help='the checkpoint file to resume from')
    parser.add_argument(
        '--validate',
        action='store_true',
        help='whether to evaluate the checkpoint during training') # evaluate the model on the validation set
    parser.add_argument(
        '--test-last',
        action='store_true',
        help='whether to test the checkpoint after training')
    parser.add_argument(
        '--test-best',
        action='store_true',
        help=('whether to test the best checkpoint (if applicable) after '
              'training'))
    group_gpus = parser.add_mutually_exclusive_group()
    group_gpus.add_argument(
        '--gpus',
        type=int,
        help='number of gpus to use '
        '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--gpu-ids',
        type=int,
        nargs='+',
        help='ids of gpus to use '
        '(only applicable to non-distributed training)')
    parser.add_argument('--seed', type=int, default=None, help='random seed')
    parser.add_argument(
        '--deterministic',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        default={},
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. For example, '
        "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')  # items for distributed job initialization launcher.  if set to None, it will test in a non-distributed mode
    parser.add_argument('--local_rank', type=int, default=0)
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)

    return args


def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)

    cfg.merge_from_dict(args.cfg_options)

    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True

    # work_dir is determined in this priority:
    # CLI > config file > default (base filename)
    if args.work_dir is not None:
        # update configs according to CLI args if args.work_dir is not None
        cfg.work_dir = args.work_dir
    elif cfg.get('work_dir', None) is None:
        # use config filename as default work_dir if cfg.work_dir is None
        cfg.work_dir = osp.join('./work_dirs',
                                osp.splitext(osp.basename(args.config))[0])
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids
    else:
        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)
        _, world_size = get_dist_info()
        cfg.gpu_ids = range(world_size)

    # The flag is used to determine whether it is omnisource training
    cfg.setdefault('omnisource', False)

    # The flag is used to register module's hooks
    cfg.setdefault('module_hooks', [])

    # create work_dir
    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
    # dump config
    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
    # init logger before other steps
    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)

    # init the meta dict to record some important information such as
    # environment info and seed, which will be logged
    meta = dict()
    # log env info
    env_info_dict = collect_env()
    env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
    dash_line = '-' * 60 + '\n'
    logger.info('Environment info:\n' + dash_line + env_info + '\n' +
                dash_line)
    meta['env_info'] = env_info

    # log some basic info
    logger.info(f'Distributed training: {distributed}')
    logger.info(f'Config: {cfg.pretty_text}')

    # set random seeds
    if args.seed is not None:
        logger.info(f'Set random seed to {args.seed}, '
                    f'deterministic: {args.deterministic}')
        set_random_seed(args.seed, deterministic=args.deterministic)
    cfg.seed = args.seed
    meta['seed'] = args.seed
    meta['config_name'] = osp.basename(args.config)
    meta['work_dir'] = osp.basename(cfg.work_dir.rstrip('/\\'))

    model = build_model(
        cfg.model,
        train_cfg=cfg.get('train_cfg'),
        test_cfg=cfg.get('test_cfg'))

    model_analysis(model, logger)


    if len(cfg.module_hooks) > 0:
        register_module_hooks(model, cfg.module_hooks)

    if cfg.omnisource:
        # If omnisource flag is set, cfg.data.train should be a list
        assert isinstance(cfg.data.train, list)
        datasets = [build_dataset(dataset) for dataset in cfg.data.train]
    else:
        datasets = [build_dataset(cfg.data.train)]

    if len(cfg.workflow) == 2:
        # For simplicity, omnisource is not compatible with val workflow,
        # we recommend you to use `--validate`
        assert not cfg.omnisource
        if args.validate:
            warnings.warn('val workflow is duplicated with `--validate`, '
                          'it is recommended to use `--validate`. see '
                          'https://github.com/open-mmlab/mmaction2/pull/123')
        val_dataset = copy.deepcopy(cfg.data.val)
        datasets.append(build_dataset(val_dataset))
    if cfg.checkpoint_config is not None:
        # save mmaction version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmaction_version=__version__ + get_git_hash(digits=7),
            config=cfg.pretty_text)

    test_option = dict(test_last=args.test_last, test_best=args.test_best)

    # cfg = update_config(cfg)

    train_model(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        test=test_option,
        timestamp=timestamp,
        meta=meta)


if __name__ == '__main__':
    main()
