# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp

import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                         wrap_fp16_model)
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

from mmdet import datasets
from mmdet.apis import multi_gpu_test, set_random_seed, single_gpu_test
from mmdet.core import eval_map
from mmdet.datasets import build_dataloader, build_dataset
from mmdet.models import build_detector
from tools.analysis_tools.robustness_eval import get_results


def coco_eval_with_return(result_files,
                          result_types,
                          coco,
                          max_dets=(100, 300, 1000)):
    for res_type in result_types:
        assert res_type in ['proposal', 'bbox', 'segm', 'keypoints']

    if mmcv.is_str(coco):
        coco = COCO(coco)
    assert isinstance(coco, COCO)

    eval_results = {}
    for res_type in result_types:
        result_file = result_files[res_type]
        assert result_file.endswith('.json')

        coco_dets = coco.loadRes(result_file)
        img_ids = coco.getImgIds()
        iou_type = 'bbox' if res_type == 'proposal' else res_type
        cocoEval = COCOeval(coco, coco_dets, iou_type)
        cocoEval.params.imgIds = img_ids
        if res_type == 'proposal':
            cocoEval.params.useCats = 0
            cocoEval.params.maxDets = list(max_dets)
        cocoEval.evaluate()
        cocoEval.accumulate()
        cocoEval.summarize()
        if res_type == 'segm' or res_type == 'bbox':
            metric_names = [
                'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10',
                'AR100', 'ARs', 'ARm', 'ARl'
            ]
            eval_results[res_type] = {
                metric_names[i]: cocoEval.stats[i]
                for i in range(len(metric_names))
            }
        else:
            eval_results[res_type] = cocoEval.stats

    return eval_results


def voc_eval_with_return(result_file,
                         dataset,
                         iou_thr=0.5,
                         logger='print',
                         only_ap=True):
    det_results = mmcv.load(result_file)
    annotations = [dataset.get_ann_info(i) for i in range(len(dataset))]
    if hasattr(dataset, 'year') and dataset.year == 2007:
        dataset_name = 'voc07'
    else:
        dataset_name = dataset.CLASSES
    mean_ap, eval_results = eval_map(
        det_results,
        annotations,
        scale_ranges=None,
        iou_thr=iou_thr,
        dataset=dataset_name,
        logger=logger)

    if only_ap:
        eval_results = [{
            'ap': eval_results[i]['ap']
        } for i in range(len(eval_results))]

    return mean_ap, eval_results


def parse_args():
    parser = argparse.ArgumentParser(description='MMDet test detector')
    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(
        '--corruptions',
        type=str,
        nargs='+',
        default='benchmark',
        choices=[
            'all', 'benchmark', 'noise', 'blur', 'weather', 'digital',
            'holdout', 'None', 'gaussian_noise', 'shot_noise', 'impulse_noise',
            'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow',
            'frost', 'fog', 'brightness', 'contrast', 'elastic_transform',
            'pixelate', 'jpeg_compression', 'speckle_noise', 'gaussian_blur',
            'spatter', 'saturate'
        ],
        help='corruptions')
    parser.add_argument(
        '--severities',
        type=int,
        nargs='+',
        default=[0, 1, 2, 3, 4, 5],
        help='corruption severity levels')
    parser.add_argument(
        '--eval',
        type=str,
        nargs='+',
        choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'],
        help='eval types')
    parser.add_argument(
        '--iou-thr',
        type=float,
        default=0.5,
        help='IoU threshold for pascal voc evaluation')
    parser.add_argument(
        '--summaries',
        type=bool,
        default=False,
        help='Print summaries for every corruption and severity')
    parser.add_argument(
        '--workers', type=int, default=32, help='workers per gpu')
    parser.add_argument('--show', action='store_true', help='show results')
    parser.add_argument(
        '--show-dir', help='directory where painted images will be saved')
    parser.add_argument(
        '--show-score-thr',
        type=float,
        default=0.3,
        help='score threshold (default: 0.3)')
    parser.add_argument('--tmpdir', help='tmp dir for writing some results')
    parser.add_argument('--seed', type=int, default=None, help='random seed')
    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(
        '--final-prints',
        type=str,
        nargs='+',
        choices=['P', 'mPC', 'rPC'],
        default='mPC',
        help='corruption benchmark metric to print at the end')
    parser.add_argument(
        '--final-prints-aggregate',
        type=str,
        choices=['all', 'benchmark'],
        default='benchmark',
        help='aggregate all results or only those for benchmark corruptions')
    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 config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It 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.')
    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()

    assert args.out or args.show or args.show_dir, \
        ('Please specify at least one operation (save or show the results) '
         'with the argument "--out", "--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 = mmcv.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
    if args.workers == 0:
        args.workers = cfg.data.workers_per_gpu

    # 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)

    # set random seeds
    if args.seed is not None:
        set_random_seed(args.seed)

    if 'all' in args.corruptions:
        corruptions = [
            'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
            'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
            'brightness', 'contrast', 'elastic_transform', 'pixelate',
            'jpeg_compression', 'speckle_noise', 'gaussian_blur', 'spatter',
            'saturate'
        ]
    elif 'benchmark' in args.corruptions:
        corruptions = [
            'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
            'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
            'brightness', 'contrast', 'elastic_transform', 'pixelate',
            'jpeg_compression'
        ]
    elif 'noise' in args.corruptions:
        corruptions = ['gaussian_noise', 'shot_noise', 'impulse_noise']
    elif 'blur' in args.corruptions:
        corruptions = [
            'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur'
        ]
    elif 'weather' in args.corruptions:
        corruptions = ['snow', 'frost', 'fog', 'brightness']
    elif 'digital' in args.corruptions:
        corruptions = [
            'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression'
        ]
    elif 'holdout' in args.corruptions:
        corruptions = ['speckle_noise', 'gaussian_blur', 'spatter', 'saturate']
    elif 'None' in args.corruptions:
        corruptions = ['None']
        args.severities = [0]
    else:
        corruptions = args.corruptions

    rank, _ = get_dist_info()
    aggregated_results = {}
    for corr_i, corruption in enumerate(corruptions):
        aggregated_results[corruption] = {}
        for sev_i, corruption_severity in enumerate(args.severities):
            # evaluate severity 0 (= no corruption) only once
            if corr_i > 0 and corruption_severity == 0:
                aggregated_results[corruption][0] = \
                    aggregated_results[corruptions[0]][0]
                continue

            test_data_cfg = copy.deepcopy(cfg.data.test)
            # assign corruption and severity
            if corruption_severity > 0:
                corruption_trans = dict(
                    type='Corrupt',
                    corruption=corruption,
                    severity=corruption_severity)
                # TODO: hard coded "1", we assume that the first step is
                # loading images, which needs to be fixed in the future
                test_data_cfg['pipeline'].insert(1, corruption_trans)

            # print info
            print(f'\nTesting {corruption} at severity {corruption_severity}')

            # build the dataloader
            # TODO: support multiple images per gpu
            #       (only minor changes are needed)
            dataset = build_dataset(test_data_cfg)
            data_loader = build_dataloader(
                dataset,
                samples_per_gpu=1,
                workers_per_gpu=args.workers,
                dist=distributed,
                shuffle=False)

            # build the model and load checkpoint
            cfg.model.train_cfg = None
            model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
            fp16_cfg = cfg.get('fp16', None)
            if fp16_cfg is not None:
                wrap_fp16_model(model)
            checkpoint = load_checkpoint(
                model, args.checkpoint, map_location='cpu')
            # old versions did not save class info in checkpoints,
            # this walkaround is for backward compatibility
            if 'CLASSES' in checkpoint.get('meta', {}):
                model.CLASSES = checkpoint['meta']['CLASSES']
            else:
                model.CLASSES = dataset.CLASSES

            if not distributed:
                model = MMDataParallel(model, device_ids=[0])
                show_dir = args.show_dir
                if show_dir is not None:
                    show_dir = osp.join(show_dir, corruption)
                    show_dir = osp.join(show_dir, str(corruption_severity))
                    if not osp.exists(show_dir):
                        osp.makedirs(show_dir)
                outputs = single_gpu_test(model, data_loader, args.show,
                                          show_dir, 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)

            if args.out and rank == 0:
                eval_results_filename = (
                    osp.splitext(args.out)[0] + '_results' +
                    osp.splitext(args.out)[1])
                mmcv.dump(outputs, args.out)
                eval_types = args.eval
                if cfg.dataset_type == 'VOCDataset':
                    if eval_types:
                        for eval_type in eval_types:
                            if eval_type == 'bbox':
                                test_dataset = mmcv.runner.obj_from_dict(
                                    cfg.data.test, datasets)
                                logger = 'print' if args.summaries else None
                                mean_ap, eval_results = \
                                    voc_eval_with_return(
                                        args.out, test_dataset,
                                        args.iou_thr, logger)
                                aggregated_results[corruption][
                                    corruption_severity] = eval_results
                            else:
                                print('\nOnly "bbox" evaluation \
                                is supported for pascal voc')
                else:
                    if eval_types:
                        print(f'Starting evaluate {" and ".join(eval_types)}')
                        if eval_types == ['proposal_fast']:
                            result_file = args.out
                        else:
                            if not isinstance(outputs[0], dict):
                                result_files = dataset.results2json(
                                    outputs, args.out)
                            else:
                                for name in outputs[0]:
                                    print(f'\nEvaluating {name}')
                                    outputs_ = [out[name] for out in outputs]
                                    result_file = args.out
                                    + f'.{name}'
                                    result_files = dataset.results2json(
                                        outputs_, result_file)
                        eval_results = coco_eval_with_return(
                            result_files, eval_types, dataset.coco)
                        aggregated_results[corruption][
                            corruption_severity] = eval_results
                    else:
                        print('\nNo task was selected for evaluation;'
                              '\nUse --eval to select a task')

                # save results after each evaluation
                mmcv.dump(aggregated_results, eval_results_filename)

    if rank == 0:
        # print final results
        print('\nAggregated results:')
        prints = args.final_prints
        aggregate = args.final_prints_aggregate

        if cfg.dataset_type == 'VOCDataset':
            get_results(
                eval_results_filename,
                dataset='voc',
                prints=prints,
                aggregate=aggregate)
        else:
            get_results(
                eval_results_filename,
                dataset='coco',
                prints=prints,
                aggregate=aggregate)


if __name__ == '__main__':
    main()