# 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 os
import os.path as osp
import warnings

import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint,set_random_seed

from mmpose.apis import multi_gpu_test, single_gpu_test
from mmpose.datasets import build_dataloader, build_dataset
from mmpose.models import build_posenet

try:
    from mmcv.runner import wrap_fp16_model
except ImportError:
    warnings.warn('auto_fp16 from mmpose will be deprecated from v0.15.0'
                  'Please install mmcv>=1.1.4')
    from mmpose.core import wrap_fp16_model


def parse_args():
    parser = argparse.ArgumentParser(description='mmpose test model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument('--out', help='output result file')
    parser.add_argument(
        '--fuse-conv-bn',
        action='store_true',
        help='Whether to fuse conv and bn, this will slightly increase'
        'the inference speed')
    parser.add_argument(
        '--eval',
        default=None,
        nargs='+',
        help='evaluation metric, which depends on the dataset,'
        ' e.g., "mAP" for MSCOCO')
    parser.add_argument(
        '--gpu_collect',
        default=False,
        help='whether to use gpu to collect results')
    parser.add_argument('--tmpdir', help='tmp dir for writing some results')
    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('--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 merge_configs(cfg1, cfg2):
    # Merge cfg2 into cfg1
    # Overwrite cfg1 if repeated, ignore if value is None.
    cfg1 = {} if cfg1 is None else cfg1.copy()
    cfg2 = {} if cfg2 is None else cfg2
    for k, v in cfg2.items():
        if v:
            cfg1[k] = v
    return cfg1


def main():
    args = parse_args()
    world_size=8
    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
    cfg.model.pretrained = None
    cfg.data.test.test_mode = True

    args.work_dir = osp.join('./work_dirs',
                             osp.splitext(osp.basename(args.config))[0])
    mmcv.mkdir_or_exist(osp.abspath(args.work_dir))

    # 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)
    
    # build the dataloader
    dataset = build_dataset(cfg.data.test, dict(test_mode=True))
    dataloader_setting = dict(
        samples_per_gpu=1,
        workers_per_gpu=cfg.data.get('workers_per_gpu', 1),
        dist=distributed,
        shuffle=False,
        drop_last=False)
    dataloader_setting = dict(dataloader_setting,
                              **cfg.data.get('test_dataloader', {}))
    data_loader = build_dataloader(dataset, **dataloader_setting)

    # build the model and load checkpoint
    model = build_posenet(cfg.model)
    if not distributed:
      if cfg.gpu_npu=='gpu':
          cfg.device='cuda:0'
          model.cuda()
      else:
          cfg.device='npu:0'
          torch.npu.set_device(cfg.device)
          model.npu()
          print("use_npu")
    else:
      if cfg.gpu_npu=='gpu':
          model.cuda()
      else:
          model.npu()
          print("use_npu")
    '''
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    '''
    load_checkpoint(model, args.checkpoint, map_location='cpu')
    '''
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)
    '''
    if not distributed:
        outputs = single_gpu_test(model, data_loader)
    else:
        model = MMDistributedDataParallel(
            model,
            device_ids=[int(os.environ['LOCAL_RANK'])],
            broadcast_buffers=False)
        outputs = multi_gpu_test(model, data_loader, osp.join('./work_dirs', '.eval_hook'),
                                 args.gpu_collect)
    
    rank, _ = get_dist_info()
    eval_config = cfg.get('evaluation', {})
    eval_config = merge_configs(eval_config, dict(metric=args.eval))

    if rank == 0:
        if args.out:
            print(f'\nwriting results to {args.out}')
            mmcv.dump(outputs, args.out)

        results = dataset.evaluate(outputs, args.work_dir, **eval_config)
        for k, v in sorted(results.items()):
            print(f'{k}: {v}')


if __name__ == '__main__':
    main()