# Copyright 2022 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.

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
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,
                         wrap_fp16_model)
from mmdet.apis import multi_gpu_test

from mmocr.apis.test import single_gpu_test
from mmocr.apis.utils import (disable_text_recog_aug_test,
                              replace_image_to_tensor)
from mmocr.datasets import build_dataloader, build_dataset
from mmocr.models import build_detector
from mmocr.utils import revert_sync_batchnorm, setup_multi_processes

def parse_args():
    parser = argparse.ArgumentParser(
        description='MMOCR test (and eval) a 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 in pickle format.')
    
    parser.add_argument(
        '--gpu-id',
        type=int,
        default=0,
        help='id of gpu to use '
        '(only applicable to non-distributed testing)')
    parser.add_argument(
        '--eval',
        type=str,
        nargs='+',
        help='The evaluation metrics. Options: \'hmean-ic13\', \'hmean-iou'
        '\' for text detection tasks, \'acc\' for text recognition tasks, and '
        '\'macro-f1\' for key information extraction tasks.')
    parser.add_argument(
        '--show-score-thr',
        type=float,
        default=0.3,
        help='Score threshold (default: 0.3).')
    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 the config file. If the value '
        'to be overwritten is a list, it should be of the form of either '
        'key="[a,b]" or key=a,b. The argument 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(
        '--options',
        nargs='+',
        action=DictAction,
        help='Custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be kwargs for dataset.evaluate() function (deprecate), '
        'change to --eval-options instead.')
    parser.add_argument(
        '--eval-options',
        nargs='+',
        action=DictAction,
        help='Custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be kwargs for dataset.evaluate() function.')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='Options for 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)

    if args.options and args.eval_options:
        raise ValueError(
            '--options and --eval-options cannot be both '
            'specified, --options is deprecated in favor of --eval-options.')
    if args.options:
        warnings.warn('--options is deprecated in favor of --eval-options.')
        args.eval_options = args.options
    return args
    
def main():
    args = parse_args()

    assert (
        args.out or args.eval or args.format_only or args.show
        or args.show_dir), (
            'Please specify at least one operation (save/eval/format/show the '
            'results / save the results) with the argument "--out", "--eval"'
            ', "--format-only", "--show" or "--show-dir".')

    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
        raise ValueError('The output file must be a pkl file.')

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    setup_multi_processes(cfg)

    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    if cfg.model.get('pretrained'):
        cfg.model.pretrained = None
    if cfg.model.get('neck'):
        if isinstance(cfg.model.neck, list):
            for neck_cfg in cfg.model.neck:
                if neck_cfg.get('rfp_backbone'):
                    if neck_cfg.rfp_backbone.get('pretrained'):
                        neck_cfg.rfp_backbone.pretrained = None
        elif cfg.model.neck.get('rfp_backbone'):
            if cfg.model.neck.rfp_backbone.get('pretrained'):
                cfg.model.neck.rfp_backbone.pretrained = None

    # in case the test dataset is concatenated
    samples_per_gpu = (cfg.data.get('test_dataloader', {})).get(
        'samples_per_gpu', cfg.data.get('samples_per_gpu', 1))
    if samples_per_gpu > 1:
        cfg = disable_text_recog_aug_test(cfg)
        cfg = replace_image_to_tensor(cfg)

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

    # build the dataloader
    cfg.data.test['datasets'][0]['ann_file'] = "mmocr/" + cfg.data.test['datasets'][0]['ann_file']
    cfg.data.test['datasets'][0]['img_prefix'] = "mmocr/" + cfg.data.test['datasets'][0]['img_prefix']
    dataset = build_dataset(cfg.data.test, dict(test_mode=True))
    # step 1: give default values and override (if exist) from cfg.data
    loader_cfg = {
        **dict(seed=cfg.get('seed'), drop_last=False, dist=distributed),
        **({} if torch.__version__ != 'parrots' else dict(
               prefetch_num=2,
               pin_memory=False,
           )),
        **dict((k, cfg.data[k]) for k in [
                   'workers_per_gpu',
                   'seed',
                   'prefetch_num',
                   'pin_memory',
                   'persistent_workers',
               ] if k in cfg.data)
    }
    test_loader_cfg = {
        **loader_cfg,
        **dict(shuffle=False, drop_last=False),
        **cfg.data.get('test_dataloader', {}),
        **dict(samples_per_gpu=samples_per_gpu)
    }

    data_loader = build_dataloader(dataset, **test_loader_cfg)

    # build the model and load checkpoint
    cfg.model.train_cfg = None
    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
    model = revert_sync_batchnorm(model)
    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 not distributed:
        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
        is_kie = cfg.model.type in ['SDMGR']
    else:
        model = MMDistributedDataParallel(
            model.cuda(),
            device_ids=[torch.cuda.current_device()],
            broadcast_buffers=False)

    rank, _ = get_dist_info()
    if rank == 0:
        if args.out:
            print(f'\nwriting results to {args.out}')
            mmcv.dump(outputs, args.out)
        kwargs = {} if args.eval_options is None else args.eval_options
        if args.eval:
            eval_kwargs = cfg.get('evaluation', {}).copy()
            # hard-code way to remove EvalHook args
            for key in [
                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
                    'rule'
            ]:
                eval_kwargs.pop(key, None)
            eval_kwargs.update(dict(metric=args.eval, **kwargs))
            output_file_path = './boundary_results.txt'
            boundary_results = []
            with open(output_file_path, 'r') as ff:
                temp = ff.readlines()
                for item in temp: 
                    tmp = eval(item)
                    boundary_results.append(tmp)
            
            print(dataset.evaluate(boundary_results, **eval_kwargs))


if __name__ == '__main__':
    main()