# BSD 3-Clause License
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# Copyright 2021 Huawei Technologies Co., Ltd
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# ============================================================================

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

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

from mmseg import __version__
from mmseg.apis import set_random_seed, train_segmentor
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.utils import collect_env, get_root_logger


def str2bool(v):
    if isinstance(v, bool):
        return v
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')

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(
        '--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='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 ')
    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(
        '--options', nargs='+', action=DictAction, help='custom options')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument('--apex_opt_level', default='O1', type=str,
                        help='For apex mixed precision training'
                             'O0 for FP32 training, O1 for mixed precision training.'
                             'For further detail, see https://github.com/NVIDIA/apex/tree/master/examples/imagenet'
                        )
    # loss_scale_value == -1 means None/dynamic
    parser.add_argument('--loss_scale_value',
                        default=128,
                        type=int,
                        help='set loss scale value.')

    parser.add_argument(
        '--use_amp',
        type=str2bool,
        nargs='?',
        const=True,
        default=None,
        help='use nvidia apex amp ?'
    )
    parser.add_argument(
        '--sys_fp_16',
        type=str2bool,
        nargs='?',
        const=True,
        default=None,
        help='use sys fp16 ?'
    )
    parser.add_argument(
        '--use_npu',
        type=str2bool,
        nargs='?',
        const=True,
        default=None,
        help='use huawei npu ?'
    )
    parser.add_argument(
        '--total_iters',
        type=int,
        nargs='?',
        const=True,
        default=-1,
        help='set train iters !'
    )
    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()
    if args.resume_from is not None:
        args.work_dir = '/'.join(args.resume_from.split('/')[:-1])
    cfg = Config.fromfile(args.config)
    
    print("use npu:", args.use_npu)

    if args.options is not None:
        cfg.merge_from_dict(args.options)
    # set cudnn_benchmark
    # if cfg.get('cudnn_benchmark', False):
    #     torch.backends.cudnn.benchmark = True
    
    # npu 服务器设置
    if args.use_npu is not None:
        cfg.use_npu = args.use_npu
    elif cfg.get('use_npu',None) is None:
        cfg.use_npu = False
    if cfg.use_npu:
        cfg.dist_params.backend = 'hccl'
    else:
        cfg.dist_params.backend = 'nccl'
        os.environ["NCCL_DEBUG"] = "INFO"

    # 添加从命令行控制amp的方法
    if args.use_amp is not None:
        cfg.use_amp = args.use_amp
    elif cfg.get('use_amp',None) is None:
        cfg.use_amp = False
    
    if args.sys_fp_16 is not None:
        cfg.sys_fp_16 = args.sys_fp_16
    elif cfg.get('sys_fp_16',None) is None:
        cfg.sys_fp_16 = False

    if args.total_iters > 0:
        cfg.total_iters = args.total_iters

    print("work_dir", args.work_dir)
    cfg.apex_opt_level = args.apex_opt_level
    cfg.loss_scale_value = args.loss_scale_value
    if cfg.loss_scale_value == -1:
        cfg.loss_scale_value = None
    
    # 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
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids
        CALCULATE_DEVICE='npu:'+str(cfg.gpu_ids[0])
        torch.npu.set_device(CALCULATE_DEVICE)
    else:
        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
    
    print(cfg.gpu_ids)
    print(len(cfg.gpu_ids))

    # 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.gpu_ids, cfg.use_npu,  **cfg.dist_params)
    # 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
    if args.seed is not None:
        logger.info(f'Set random seed to {args.seed}, deterministic: '
                    f'{args.deterministic}')
        set_random_seed(args.seed, args.use_npu, deterministic=args.deterministic)
        cfg.seed = args.seed
    meta['seed'] = args.seed
    meta['exp_name'] = osp.basename(args.config)

    model = build_segmentor(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
    logger.info(model)

    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
    model.CLASSES = datasets[0].CLASSES
    train_segmentor(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta)


if __name__ == '__main__':
    main()