# Copyright (c) OpenMMLab. All rights reserved.
try:
    import mmseg
except (ImportError, ModuleNotFoundError):
    mmseg = None

if mmseg is None:
    raise RuntimeError('mmseg is not installed')

import argparse
import copy
import os
import os.path as osp
import time
import warnings

import mmcv
import torch
import torch.distributed as dist
from mmcv.cnn.utils import revert_sync_batchnorm
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import Config, DictAction, get_git_hash
from mmseg import __version__
from mmseg.datasets import build_dataset
from mmseg.utils import collect_env, get_root_logger

# Differences from mmdetection
from mmrazor.apis import init_random_seed, set_random_seed, train_mmseg_model
from mmrazor.models.builder import build_algorithm
from mmrazor.utils import setup_multi_processes


def parse_args():
    parser = argparse.ArgumentParser(description='Train a segmentor')
    parser.add_argument('config', help='train config file path')
    parser.add_argument('--work-dir', help='the dir to save logs and models')
    parser.add_argument(
        '--load-from', help='the checkpoint file to load weights from')
    parser.add_argument(
        '--resume-from', help='the checkpoint file to resume from')
    parser.add_argument(
        '--auto-resume',
        action='store_true',
        help='resume from the latest checkpoint automatically')
    parser.add_argument(
        '--no-validate',
        action='store_true',
        help='whether not to evaluate the checkpoint during training')
    group_gpus = parser.add_mutually_exclusive_group()
    group_gpus.add_argument(
        '--gpus',
        type=int,
        help='(Deprecated, please use --gpu-id) number of gpus to use '
        '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--gpu-ids',
        type=int,
        nargs='+',
        help='(Deprecated, please use --gpu-id) ids of gpus to use '
        '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--gpu-id',
        type=int,
        default=0,
        help='id of gpu to use '
        '(only applicable to non-distributed training)')
    parser.add_argument('--seed', type=int, default=None, help='random seed')
    parser.add_argument(
        '--diff_seed',
        action='store_true',
        help='Whether or not set different seeds for different ranks')
    parser.add_argument(
        '--deterministic',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    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)

    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    # set multi-process settings
    setup_multi_processes(cfg)

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

    # work_dir is determined in this priority: CLI > segment in file > 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.load_from is not None:
        cfg.load_from = args.load_from
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.auto_resume = args.auto_resume
    if args.gpus is not None:
        cfg.gpu_ids = range(1)
        warnings.warn('`--gpus` is deprecated because we only support '
                      'single GPU mode in non-distributed training. '
                      'Use `gpus=1` now.')
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids[0:1]
        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
                      'Because we only support single GPU mode in '
                      'non-distributed training. Use the first GPU '
                      'in `gpu_ids` now.')
    if args.gpus is None and args.gpu_ids is None:
        cfg.gpu_ids = [args.gpu_id]

    # 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)
        # gpu_ids is used to calculate iter when resuming checkpoint
        _, world_size = get_dist_info()
        cfg.gpu_ids = range(world_size)

    # 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 the 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:\n{cfg.pretty_text}')

    # set random seeds
    seed = init_random_seed(args.seed)
    seed = seed + dist.get_rank() if args.diff_seed else seed
    logger.info(f'Set random seed to {args.seed}, deterministic: '
                f'{args.deterministic}')
    set_random_seed(args.seed, deterministic=args.deterministic)
    cfg.seed = seed
    meta['seed'] = args.seed
    meta['exp_name'] = osp.basename(args.config)

    # Difference from mmsegmentation
    # replace `model` to `algorithm`
    algorithm = build_algorithm(cfg.algorithm)
    algorithm.init_weights()

    # SyncBN is not support for DP
    if not distributed:
        warnings.warn(
            'SyncBN is only supported with DDP. To be compatible with DP, '
            'we convert SyncBN to BN. Please use dist_train.sh which can '
            'avoid this error.')
        algorithm = revert_sync_batchnorm(algorithm)

    logger.info(algorithm)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        val_dataset = copy.deepcopy(cfg.data.val)
        val_dataset.pipeline = cfg.data.train.pipeline
        datasets.append(build_dataset(val_dataset))
    if cfg.checkpoint_config is not None:
        # save mmseg version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
            config=cfg.pretty_text,
            CLASSES=datasets[0].CLASSES,
            PALETTE=datasets[0].PALETTE)
    # add an attribute for visualization convenience
    # Difference from mmsegmentation
    # replace `model` to `algorithm`
    algorithm.CLASSES = datasets[0].CLASSES
    # passing checkpoint meta for saving best checkpoint
    meta.update(cfg.checkpoint_config.meta)
    train_mmseg_model(
        # Difference from mmsegmentation
        # replace `model` to `algorithm`
        algorithm,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta)


if __name__ == '__main__':
    main()