import argparse
import os
import os.path as osp
import shutil
import tempfile

import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from robustness_eval import get_results

from mmdet import datasets
from mmdet.apis import init_dist, set_random_seed
from mmdet.core import (eval_map, fast_eval_recall, results2json,
                        wrap_fp16_model)
from mmdet.datasets import build_dataloader, build_dataset
from mmdet.models import build_detector


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', 'proposal_fast', 'bbox', 'segm', 'keypoints'
        ]

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

    if result_types == ['proposal_fast']:
        ar = fast_eval_recall(result_files, coco, np.array(max_dets))
        for i, num in enumerate(max_dets):
            print('AR@{}\t= {:.4f}'.format(num, ar[i]))
        return

    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,
                         print_summary=True,
                         only_ap=True):
    det_results = mmcv.load(result_file)
    gt_bboxes = []
    gt_labels = []
    gt_ignore = []
    for i in range(len(dataset)):
        ann = dataset.get_ann_info(i)
        bboxes = ann['bboxes']
        labels = ann['labels']
        if 'bboxes_ignore' in ann:
            ignore = np.concatenate([
                np.zeros(bboxes.shape[0], dtype=np.bool),
                np.ones(ann['bboxes_ignore'].shape[0], dtype=np.bool)
            ])
            gt_ignore.append(ignore)
            bboxes = np.vstack([bboxes, ann['bboxes_ignore']])
            labels = np.concatenate([labels, ann['labels_ignore']])
        gt_bboxes.append(bboxes)
        gt_labels.append(labels)
    if not gt_ignore:
        gt_ignore = gt_ignore
    if hasattr(dataset, 'year') and dataset.year == 2007:
        dataset_name = 'voc07'
    else:
        dataset_name = dataset.CLASSES
    mean_ap, eval_results = eval_map(
        det_results,
        gt_bboxes,
        gt_labels,
        gt_ignore=gt_ignore,
        scale_ranges=None,
        iou_thr=iou_thr,
        dataset=dataset_name,
        print_summary=print_summary)

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

    return mean_ap, eval_results


def single_gpu_test(model, data_loader, show=False):
    model.eval()
    results = []
    dataset = data_loader.dataset
    prog_bar = mmcv.ProgressBar(len(dataset))
    for i, data in enumerate(data_loader):
        with torch.no_grad():
            result = model(return_loss=False, rescale=not show, **data)
        results.append(result)

        if show:
            model.module.show_result(data, result, dataset.img_norm_cfg)

        batch_size = data['img'][0].size(0)
        for _ in range(batch_size):
            prog_bar.update()
    return results


def multi_gpu_test(model, data_loader, tmpdir=None):
    model.eval()
    results = []
    dataset = data_loader.dataset
    rank, world_size = get_dist_info()
    if rank == 0:
        prog_bar = mmcv.ProgressBar(len(dataset))
    for i, data in enumerate(data_loader):
        with torch.no_grad():
            result = model(return_loss=False, rescale=True, **data)
        results.append(result)

        if rank == 0:
            batch_size = data['img'][0].size(0)
            for _ in range(batch_size * world_size):
                prog_bar.update()

    # collect results from all ranks
    results = collect_results(results, len(dataset), tmpdir)

    return results


def collect_results(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full((MAX_LEN, ),
                                32,
                                dtype=torch.uint8,
                                device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_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('--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')
    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, \
        ('Please specify at least one operation (save or show the results) '
         'with the argument "--out" or "--show"')

    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)
    # 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

    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

            # assign corruption and severity
            if corruption_severity == 0:
                # evaluate without corruptions for severity = 0
                cfg.data.test['corruption'] = None
                cfg.data.test['corruption_severity'] = 0
            else:
                cfg.data.test['corruption'] = corruption
                cfg.data.test['corruption_severity'] = corruption_severity

            # print info
            print('\nTesting {} at severity {}'.format(corruption,
                                                       corruption_severity))

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

            # build the model and load checkpoint
            model = build_detector(
                cfg.model, train_cfg=None, test_cfg=cfg.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['meta']:
                model.CLASSES = checkpoint['meta']['CLASSES']
            else:
                model.CLASSES = dataset.CLASSES

            if not distributed:
                model = MMDataParallel(model, device_ids=[0])
                outputs = single_gpu_test(model, data_loader, args.show)
            else:
                model = MMDistributedDataParallel(model.cuda())
                outputs = multi_gpu_test(model, data_loader, args.tmpdir)

            rank, _ = get_dist_info()
            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)
                                mean_ap, eval_results = \
                                    voc_eval_with_return(
                                        args.out, test_dataset,
                                        args.iou_thr, args.summaries)
                                aggregated_results[corruption][
                                    corruption_severity] = eval_results
                            else:
                                print('\nOnly "bbox" evaluation \
                                is supported for pascal voc')
                else:
                    if eval_types:
                        print('Starting evaluate {}'.format(
                            ' and '.join(eval_types)))
                        if eval_types == ['proposal_fast']:
                            result_file = args.out
                        else:
                            if not isinstance(outputs[0], dict):
                                result_files = results2json(
                                    dataset, outputs, args.out)
                            else:
                                for name in outputs[0]:
                                    print('\nEvaluating {}'.format(name))
                                    outputs_ = [out[name] for out in outputs]
                                    result_file = args.out
                                    + '.{}'.format(name)
                                    result_files = results2json(
                                        dataset, 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)

    # print filan 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()
