#!/usr/bin/env python
# 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


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(
        '--fuse-conv-bn',
        action='store_true',
        help='Whether to fuse conv and bn, this will slightly increase'
        'the inference speed.')
    parser.add_argument(
        '--format-only',
        action='store_true',
        help='Format the output results without performing evaluation. It is'
        'useful when you want to format the results to a specific format and '
        'submit them to the test server.')
    parser.add_argument(
        '--eval',
        type=str,
        nargs='+',
        help='The evaluation metrics, which depends on the dataset, e.g.,'
        '"bbox", "seg", "proposal" for COCO, and "mAP", "recall" for'
        'PASCAL VOC.')
    parser.add_argument('--show', action='store_true', help='Show results.')
    parser.add_argument(
        '--show-dir', help='Directory where the output images will be saved.')
    parser.add_argument(
        '--show-score-thr',
        type=float,
        default=0.3,
        help='Score threshold (default: 0.3).')
    parser.add_argument(
        '--gpu-collect',
        action='store_true',
        help='Whether to use gpu to collect results.')
    parser.add_argument(
        '--tmpdir',
        help='The tmp directory used for collecting results from multiple '
        'workers, available when gpu-collect is not specified.')
    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.eval and args.format_only:
        raise ValueError('--eval and --format_only cannot be both specified.')

    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)
    # import modules from string list.
    if cfg.get('custom_imports', None):
        from mmcv.utils import import_modules_from_strings
        import_modules_from_strings(**cfg['custom_imports'])
    # 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':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # build the dataloader
    dataset = build_dataset(cfg.data.test, dict(test_mode=True))

    # # ------------- self test -------------
    # dataset.datasets[0].run_check()
    # # ------------- self test ---------------


    # 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 isinstance(args.eval, list):
        args.eval = args.eval[0]
    print(f'metric:{args.eval}')
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)

    if not distributed:
        model = MMDataParallel(model, device_ids=[0])
        is_kie = cfg.model.type in ['SDMGR']
        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
                                  is_kie, args.show_score_thr)
    else:
        model = MMDistributedDataParallel(
            model.cuda(),
            device_ids=[torch.cuda.current_device()],
            broadcast_buffers=False)
        outputs = multi_gpu_test(model, data_loader, args.tmpdir,
                                 args.gpu_collect)

    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.format_only:
            dataset.format_results(outputs, **kwargs)


        # # self test eval
        # from mmocr.core import convert_vie_res
        # ocr_dict = dataset.datasets[0].ocr_dict
        # rev_ocr_dict = dataset.datasets[0].rev_ocr_dict
        # rev_entity_dict = dataset.datasets[0].rev_entity_dict
        # rec_preds = []
        # kie_preds = []
        # for result in outputs:
        #     rec_results, kie_results = convert_vie_res(result, len(result['boundary_result']),
        #                                                ocr_dict, rev_ocr_dict,
        #                                                rev_entity_dict,
        #                                                auto_reg=True)
        #     rec_preds.append(rec_results)
        #     kie_preds.append(kie_results)
        # max_iter = len(rec_preds[0][0])
        # iters_rec_preds = []
        # if kie_preds[0]:
        #     iters_kie_preds = []
        # else:
        #     iters_kie_preds = None
        # for iter_ in range(max_iter):
        #     # list[list[transcript]], 1st list -> image, 2rd list -> instances
        #     rec_transcripts = []
        #     kie_transcripts = []
        #     for rec_pred, kie_pred in zip(rec_preds, kie_preds):
        #         # list[transcript]
        #         rec_transcripts.append([x[iter_][0] for x in rec_pred])
        #         if kie_pred:
        #             kie_transcripts.append([x[iter_][0] for x in kie_pred])
        #     iters_rec_preds.append(rec_transcripts)
        #     if kie_preds[0]:
        #         iters_kie_preds.append(kie_transcripts)
        # import ipdb
        # ipdb.set_trace()


        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))
            print(dataset.evaluate(outputs, **eval_kwargs))


if __name__ == '__main__':
    main()
