import torch

from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
from mmdet.models import DETECTORS
from .mvx_two_stage import MVXTwoStageDetector
from .bevf_faster_rcnn import BEVF_FasterRCNN


@DETECTORS.register_module()
class BEVF_CenterPoint(BEVF_FasterRCNN):
    """Base class of Multi-modality VoxelNet."""

    def __init__(self,
                **kwargs
                 ):
        super(BEVF_CenterPoint,
              self).__init__(
                            **kwargs
                             )

    def extract_pts_feat(self, pts, img_feats, img_metas):
        """Extract features of points."""
        # if not self.with_pts_bbox:
        if not self.with_pts_backbone:
            return None
        voxels, num_points, coors = self.voxelize(pts)

        voxel_features = self.pts_voxel_encoder(voxels, num_points, coors)
        batch_size = coors[-1, 0] + 1
        x = self.pts_middle_encoder(voxel_features, coors, batch_size)
        x = self.pts_backbone(x)
        if self.with_pts_neck:
            x = self.pts_neck(x)
        return x

    def forward_pts_train(self,
                          pts_feats,
                          gt_bboxes_3d,
                          gt_labels_3d,
                          img_metas,
                          gt_bboxes_ignore=None):
        """Forward function for point cloud branch.

        Args:
            pts_feats (list[torch.Tensor]): Features of point cloud branch
            gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
                boxes for each sample.
            gt_labels_3d (list[torch.Tensor]): Ground truth labels for
                boxes of each sampole
            img_metas (list[dict]): Meta information of samples.
            gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
                boxes to be ignored. Defaults to None.

        Returns:
            dict: Losses of each branch.
        """
        outs = self.pts_bbox_head(pts_feats)
        loss_inputs = [gt_bboxes_3d, gt_labels_3d, outs]
        losses = self.pts_bbox_head.loss(*loss_inputs)
        return losses

    def simple_test_pts(self, x, img_metas, rescale=False):
        """Test function of point cloud branch."""
        outs = self.pts_bbox_head(x)
        bbox_list = self.pts_bbox_head.get_bboxes(
            outs, img_metas, rescale=rescale)
        bbox_results = [
            bbox3d2result(bboxes, scores, labels)
            for bboxes, scores, labels in bbox_list
        ]
        return bbox_results

    def aug_test_pts(self, feats, img_metas, rescale=False):
        """Test function of point cloud branch with augmentaiton.

        The function implementation process is as follows:

            - step 1: map features back for double-flip augmentation.
            - step 2: merge all features and generate boxes.
            - step 3: map boxes back for scale augmentation.
            - step 4: merge results.

        Args:
            feats (list[torch.Tensor]): Feature of point cloud.
            img_metas (list[dict]): Meta information of samples.
            rescale (bool): Whether to rescale bboxes. Default: False.

        Returns:
            dict: Returned bboxes consists of the following keys:

                - boxes_3d (:obj:`LiDARInstance3DBoxes`): Predicted bboxes.
                - scores_3d (torch.Tensor): Scores of predicted boxes.
                - labels_3d (torch.Tensor): Labels of predicted boxes.
        """
        # only support aug_test for one sample
        outs_list = []
        for x, img_meta in zip(feats, img_metas):
            outs = self.pts_bbox_head(x)
            # merge augmented outputs before decoding bboxes
            for task_id, out in enumerate(outs):
                for key in out[0].keys():
                    if img_meta[0]['pcd_horizontal_flip']:
                        outs[task_id][0][key] = torch.flip(
                            outs[task_id][0][key], dims=[2])
                        if key == 'reg':
                            outs[task_id][0][key][:, 1, ...] = 1 - outs[
                                task_id][0][key][:, 1, ...]
                        elif key == 'rot':
                            outs[task_id][0][
                                key][:, 1,
                                     ...] = -outs[task_id][0][key][:, 1, ...]
                        elif key == 'vel':
                            outs[task_id][0][
                                key][:, 1,
                                     ...] = -outs[task_id][0][key][:, 1, ...]
                    if img_meta[0]['pcd_vertical_flip']:
                        outs[task_id][0][key] = torch.flip(
                            outs[task_id][0][key], dims=[3])
                        if key == 'reg':
                            outs[task_id][0][key][:, 0, ...] = 1 - outs[
                                task_id][0][key][:, 0, ...]
                        elif key == 'rot':
                            outs[task_id][0][
                                key][:, 0,
                                     ...] = -outs[task_id][0][key][:, 0, ...]
                        elif key == 'vel':
                            outs[task_id][0][
                                key][:, 0,
                                     ...] = -outs[task_id][0][key][:, 0, ...]

            outs_list.append(outs)

        preds_dicts = dict()
        scale_img_metas = []

        # concat outputs sharing the same pcd_scale_factor
        for i, (img_meta, outs) in enumerate(zip(img_metas, outs_list)):
            pcd_scale_factor = img_meta[0]['pcd_scale_factor']
            if pcd_scale_factor not in preds_dicts.keys():
                preds_dicts[pcd_scale_factor] = outs
                scale_img_metas.append(img_meta)
            else:
                for task_id, out in enumerate(outs):
                    for key in out[0].keys():
                        preds_dicts[pcd_scale_factor][task_id][0][key] += out[
                            0][key]

        aug_bboxes = []

        for pcd_scale_factor, preds_dict in preds_dicts.items():
            for task_id, pred_dict in enumerate(preds_dict):
                # merge outputs with different flips before decoding bboxes
                for key in pred_dict[0].keys():
                    preds_dict[task_id][0][key] /= len(outs_list) / len(
                        preds_dicts.keys())
            bbox_list = self.pts_bbox_head.get_bboxes(
                preds_dict, img_metas[0], rescale=rescale)
            bbox_list = [
                dict(boxes_3d=bboxes, scores_3d=scores, labels_3d=labels)
                for bboxes, scores, labels in bbox_list
            ]
            aug_bboxes.append(bbox_list[0])

        if len(preds_dicts.keys()) > 1:
            # merge outputs with different scales after decoding bboxes
            merged_bboxes = merge_aug_bboxes_3d(aug_bboxes, scale_img_metas,
                                                self.pts_bbox_head.test_cfg)
            return merged_bboxes
        else:
            for key in bbox_list[0].keys():
                bbox_list[0][key] = bbox_list[0][key].to('cpu')
            return bbox_list[0]

    def aug_test(self, points, img_metas, imgs=None, rescale=False):
        """Test function with augmentaiton."""
        img_feats, pts_feats = self.extract_feats(points, img_metas, imgs)
        bbox_list = dict()
        if pts_feats and self.with_pts_bbox:
            pts_bbox = self.aug_test_pts(pts_feats, img_metas, rescale)
            bbox_list.update(pts_bbox=pts_bbox)
        return [bbox_list]
