# Copyright 2020 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.
# ============================================================================
import argparse
import copy
import os
import os.path as osp
import time

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

from mmpose.apis import train_model
from mmpose.datasets import build_dataset
from mmpose.models import build_posenet
from mmpose.utils import collect_env, get_root_logger

__version__ = '0.13.0'

def parse_args():
    parser = argparse.ArgumentParser(description='Train a pose model')
    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(
        '--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 '
        '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--device',
        type=str,
        help='device'
        '(only applicable to non-distributed training)')
    parser.add_argument('--seed', type=int, default=1, 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','pytorch-npu'],
        default='none',
        help='job launcher')
    parser.add_argument('--world_size', type=int, default=1)
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument(
        '--autoscale-lr',
        action='store_true',
        help='automatically scale lr with the number of gpus')
    
    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 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.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)
    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 8
        
    ####
    ####    
    world_size=args.world_size
    ####
    ####
    
    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher,world_size, **cfg.dist_params)

    # create work_dir
    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
    # 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}, '
                    f'deterministic: {args.deterministic}')
        set_random_seed(args.seed, deterministic=args.deterministic,gpu_npu=cfg.gpu_npu)
    cfg.seed = args.seed
    meta['seed'] = args.seed
    meta['rank'] = int(os.environ['LOCAL_RANK'])
    meta['world_size'] = world_size
    meta['batch_size'] = cfg.data['samples_per_gpu']
    print("batch_size:",meta['batch_size'])
    
    model = build_posenet(cfg.model)
    
    datasets = [build_dataset(cfg.data.train)]
    if not distributed:
      if cfg.gpu_npu=='gpu':
          meta['dev']='gpu'
          cfg.device='cuda:0'
          model.cuda()
      else:
          meta['dev']='npu'
          cfg.device='npu:0'
          torch.npu.set_device(cfg.device)
          model.npu()
          print("use_gpu")
    else:
      if cfg.gpu_npu=='gpu':
          meta['dev']='gpu'
          model.cuda()
      else:
          meta['dev']='npu'
          model.npu()
          print("use_npu")
    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 mmpose version, config file content
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmpose_version=__version__ + get_git_hash(digits=7),
            config=cfg.pretty_text,
        )
    train_model(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta)


if __name__ == '__main__':
    main()