# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor

from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import (BaseBoxes, HorizontalBoxes, bbox_rescale,
                                   get_box_tensor)
from .base_bbox_coder import BaseBBoxCoder


@TASK_UTILS.register_module()
class BucketingBBoxCoder(BaseBBoxCoder):
    """Bucketing BBox Coder for Side-Aware Boundary Localization (SABL).

    Boundary Localization with Bucketing and Bucketing Guided Rescoring
    are implemented here.

    Please refer to https://arxiv.org/abs/1912.04260 for more details.

    Args:
        num_buckets (int): Number of buckets.
        scale_factor (int): Scale factor of proposals to generate buckets.
        offset_topk (int): Topk buckets are used to generate
             bucket fine regression targets. Defaults to 2.
        offset_upperbound (float): Offset upperbound to generate
             bucket fine regression targets.
             To avoid too large offset displacements. Defaults to 1.0.
        cls_ignore_neighbor (bool): Ignore second nearest bucket or Not.
             Defaults to True.
        clip_border (bool, optional): Whether clip the objects outside the
            border of the image. Defaults to True.
    """

    def __init__(self,
                 num_buckets: int,
                 scale_factor: int,
                 offset_topk: int = 2,
                 offset_upperbound: float = 1.0,
                 cls_ignore_neighbor: bool = True,
                 clip_border: bool = True,
                 **kwargs) -> None:
        super().__init__(**kwargs)
        self.num_buckets = num_buckets
        self.scale_factor = scale_factor
        self.offset_topk = offset_topk
        self.offset_upperbound = offset_upperbound
        self.cls_ignore_neighbor = cls_ignore_neighbor
        self.clip_border = clip_border

    def encode(self, bboxes: Union[Tensor, BaseBoxes],
               gt_bboxes: Union[Tensor, BaseBoxes]) -> Tuple[Tensor]:
        """Get bucketing estimation and fine regression targets during
        training.

        Args:
            bboxes (torch.Tensor or :obj:`BaseBoxes`): source boxes,
                e.g., object proposals.
            gt_bboxes (torch.Tensor or :obj:`BaseBoxes`): target of the
                transformation, e.g., ground truth boxes.

        Returns:
           encoded_bboxes(tuple[Tensor]): bucketing estimation
            and fine regression targets and weights
        """
        bboxes = get_box_tensor(bboxes)
        gt_bboxes = get_box_tensor(gt_bboxes)
        assert bboxes.size(0) == gt_bboxes.size(0)
        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
        encoded_bboxes = bbox2bucket(bboxes, gt_bboxes, self.num_buckets,
                                     self.scale_factor, self.offset_topk,
                                     self.offset_upperbound,
                                     self.cls_ignore_neighbor)
        return encoded_bboxes

    def decode(
        self,
        bboxes: Union[Tensor, BaseBoxes],
        pred_bboxes: Tensor,
        max_shape: Optional[Tuple[int]] = None
    ) -> Tuple[Union[Tensor, BaseBoxes], Tensor]:
        """Apply transformation `pred_bboxes` to `boxes`.
        Args:
            boxes (torch.Tensor or :obj:`BaseBoxes`): Basic boxes.
            pred_bboxes (torch.Tensor): Predictions for bucketing estimation
                and fine regression
            max_shape (tuple[int], optional): Maximum shape of boxes.
                Defaults to None.

        Returns:
            Union[torch.Tensor, :obj:`BaseBoxes`]: Decoded boxes.
        """
        bboxes = get_box_tensor(bboxes)
        assert len(pred_bboxes) == 2
        cls_preds, offset_preds = pred_bboxes
        assert cls_preds.size(0) == bboxes.size(0) and offset_preds.size(
            0) == bboxes.size(0)
        bboxes, loc_confidence = bucket2bbox(bboxes, cls_preds, offset_preds,
                                             self.num_buckets,
                                             self.scale_factor, max_shape,
                                             self.clip_border)
        if self.use_box_type:
            bboxes = HorizontalBoxes(bboxes, clone=False)
        return bboxes, loc_confidence


def generat_buckets(proposals: Tensor,
                    num_buckets: int,
                    scale_factor: float = 1.0) -> Tuple[Tensor]:
    """Generate buckets w.r.t bucket number and scale factor of proposals.

    Args:
        proposals (Tensor): Shape (n, 4)
        num_buckets (int): Number of buckets.
        scale_factor (float): Scale factor to rescale proposals.

    Returns:
        tuple[Tensor]: (bucket_w, bucket_h, l_buckets, r_buckets,
         t_buckets, d_buckets)

            - bucket_w: Width of buckets on x-axis. Shape (n, ).
            - bucket_h: Height of buckets on y-axis. Shape (n, ).
            - l_buckets: Left buckets. Shape (n, ceil(side_num/2)).
            - r_buckets: Right buckets. Shape (n, ceil(side_num/2)).
            - t_buckets: Top buckets. Shape (n, ceil(side_num/2)).
            - d_buckets: Down buckets. Shape (n, ceil(side_num/2)).
    """
    proposals = bbox_rescale(proposals, scale_factor)

    # number of buckets in each side
    side_num = int(np.ceil(num_buckets / 2.0))
    pw = proposals[..., 2] - proposals[..., 0]
    ph = proposals[..., 3] - proposals[..., 1]
    px1 = proposals[..., 0]
    py1 = proposals[..., 1]
    px2 = proposals[..., 2]
    py2 = proposals[..., 3]

    bucket_w = pw / num_buckets
    bucket_h = ph / num_buckets

    # left buckets
    l_buckets = px1[:, None] + (0.5 + torch.arange(
        0, side_num).to(proposals).float())[None, :] * bucket_w[:, None]
    # right buckets
    r_buckets = px2[:, None] - (0.5 + torch.arange(
        0, side_num).to(proposals).float())[None, :] * bucket_w[:, None]
    # top buckets
    t_buckets = py1[:, None] + (0.5 + torch.arange(
        0, side_num).to(proposals).float())[None, :] * bucket_h[:, None]
    # down buckets
    d_buckets = py2[:, None] - (0.5 + torch.arange(
        0, side_num).to(proposals).float())[None, :] * bucket_h[:, None]
    return bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets


def bbox2bucket(proposals: Tensor,
                gt: Tensor,
                num_buckets: int,
                scale_factor: float,
                offset_topk: int = 2,
                offset_upperbound: float = 1.0,
                cls_ignore_neighbor: bool = True) -> Tuple[Tensor]:
    """Generate buckets estimation and fine regression targets.

    Args:
        proposals (Tensor): Shape (n, 4)
        gt (Tensor): Shape (n, 4)
        num_buckets (int): Number of buckets.
        scale_factor (float): Scale factor to rescale proposals.
        offset_topk (int): Topk buckets are used to generate
             bucket fine regression targets. Defaults to 2.
        offset_upperbound (float): Offset allowance to generate
             bucket fine regression targets.
             To avoid too large offset displacements. Defaults to 1.0.
        cls_ignore_neighbor (bool): Ignore second nearest bucket or Not.
             Defaults to True.

    Returns:
        tuple[Tensor]: (offsets, offsets_weights, bucket_labels, cls_weights).

            - offsets: Fine regression targets. \
                Shape (n, num_buckets*2).
            - offsets_weights: Fine regression weights. \
                Shape (n, num_buckets*2).
            - bucket_labels: Bucketing estimation labels. \
                Shape (n, num_buckets*2).
            - cls_weights: Bucketing estimation weights. \
                Shape (n, num_buckets*2).
    """
    assert proposals.size() == gt.size()

    # generate buckets
    proposals = proposals.float()
    gt = gt.float()
    (bucket_w, bucket_h, l_buckets, r_buckets, t_buckets,
     d_buckets) = generat_buckets(proposals, num_buckets, scale_factor)

    gx1 = gt[..., 0]
    gy1 = gt[..., 1]
    gx2 = gt[..., 2]
    gy2 = gt[..., 3]

    # generate offset targets and weights
    # offsets from buckets to gts
    l_offsets = (l_buckets - gx1[:, None]) / bucket_w[:, None]
    r_offsets = (r_buckets - gx2[:, None]) / bucket_w[:, None]
    t_offsets = (t_buckets - gy1[:, None]) / bucket_h[:, None]
    d_offsets = (d_buckets - gy2[:, None]) / bucket_h[:, None]

    # select top-k nearest buckets
    l_topk, l_label = l_offsets.abs().topk(
        offset_topk, dim=1, largest=False, sorted=True)
    r_topk, r_label = r_offsets.abs().topk(
        offset_topk, dim=1, largest=False, sorted=True)
    t_topk, t_label = t_offsets.abs().topk(
        offset_topk, dim=1, largest=False, sorted=True)
    d_topk, d_label = d_offsets.abs().topk(
        offset_topk, dim=1, largest=False, sorted=True)

    offset_l_weights = l_offsets.new_zeros(l_offsets.size())
    offset_r_weights = r_offsets.new_zeros(r_offsets.size())
    offset_t_weights = t_offsets.new_zeros(t_offsets.size())
    offset_d_weights = d_offsets.new_zeros(d_offsets.size())
    inds = torch.arange(0, proposals.size(0)).to(proposals).long()

    # generate offset weights of top-k nearest buckets
    for k in range(offset_topk):
        if k >= 1:
            offset_l_weights[inds, l_label[:,
                                           k]] = (l_topk[:, k] <
                                                  offset_upperbound).float()
            offset_r_weights[inds, r_label[:,
                                           k]] = (r_topk[:, k] <
                                                  offset_upperbound).float()
            offset_t_weights[inds, t_label[:,
                                           k]] = (t_topk[:, k] <
                                                  offset_upperbound).float()
            offset_d_weights[inds, d_label[:,
                                           k]] = (d_topk[:, k] <
                                                  offset_upperbound).float()
        else:
            offset_l_weights[inds, l_label[:, k]] = 1.0
            offset_r_weights[inds, r_label[:, k]] = 1.0
            offset_t_weights[inds, t_label[:, k]] = 1.0
            offset_d_weights[inds, d_label[:, k]] = 1.0

    offsets = torch.cat([l_offsets, r_offsets, t_offsets, d_offsets], dim=-1)
    offsets_weights = torch.cat([
        offset_l_weights, offset_r_weights, offset_t_weights, offset_d_weights
    ],
                                dim=-1)

    # generate bucket labels and weight
    side_num = int(np.ceil(num_buckets / 2.0))
    labels = torch.stack(
        [l_label[:, 0], r_label[:, 0], t_label[:, 0], d_label[:, 0]], dim=-1)

    batch_size = labels.size(0)
    bucket_labels = F.one_hot(labels.view(-1), side_num).view(batch_size,
                                                              -1).float()
    bucket_cls_l_weights = (l_offsets.abs() < 1).float()
    bucket_cls_r_weights = (r_offsets.abs() < 1).float()
    bucket_cls_t_weights = (t_offsets.abs() < 1).float()
    bucket_cls_d_weights = (d_offsets.abs() < 1).float()
    bucket_cls_weights = torch.cat([
        bucket_cls_l_weights, bucket_cls_r_weights, bucket_cls_t_weights,
        bucket_cls_d_weights
    ],
                                   dim=-1)
    # ignore second nearest buckets for cls if necessary
    if cls_ignore_neighbor:
        bucket_cls_weights = (~((bucket_cls_weights == 1) &
                                (bucket_labels == 0))).float()
    else:
        bucket_cls_weights[:] = 1.0
    return offsets, offsets_weights, bucket_labels, bucket_cls_weights


def bucket2bbox(proposals: Tensor,
                cls_preds: Tensor,
                offset_preds: Tensor,
                num_buckets: int,
                scale_factor: float = 1.0,
                max_shape: Optional[Union[Sequence[int], Tensor,
                                          Sequence[Sequence[int]]]] = None,
                clip_border: bool = True) -> Tuple[Tensor]:
    """Apply bucketing estimation (cls preds) and fine regression (offset
    preds) to generate det bboxes.

    Args:
        proposals (Tensor): Boxes to be transformed. Shape (n, 4)
        cls_preds (Tensor): bucketing estimation. Shape (n, num_buckets*2).
        offset_preds (Tensor): fine regression. Shape (n, num_buckets*2).
        num_buckets (int): Number of buckets.
        scale_factor (float): Scale factor to rescale proposals.
        max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W)
        clip_border (bool, optional): Whether clip the objects outside the
            border of the image. Defaults to True.

    Returns:
        tuple[Tensor]: (bboxes, loc_confidence).

            - bboxes: predicted bboxes. Shape (n, 4)
            - loc_confidence: localization confidence of predicted bboxes.
                Shape (n,).
    """

    side_num = int(np.ceil(num_buckets / 2.0))
    cls_preds = cls_preds.view(-1, side_num)
    offset_preds = offset_preds.view(-1, side_num)

    scores = F.softmax(cls_preds, dim=1)
    score_topk, score_label = scores.topk(2, dim=1, largest=True, sorted=True)

    rescaled_proposals = bbox_rescale(proposals, scale_factor)

    pw = rescaled_proposals[..., 2] - rescaled_proposals[..., 0]
    ph = rescaled_proposals[..., 3] - rescaled_proposals[..., 1]
    px1 = rescaled_proposals[..., 0]
    py1 = rescaled_proposals[..., 1]
    px2 = rescaled_proposals[..., 2]
    py2 = rescaled_proposals[..., 3]

    bucket_w = pw / num_buckets
    bucket_h = ph / num_buckets

    score_inds_l = score_label[0::4, 0]
    score_inds_r = score_label[1::4, 0]
    score_inds_t = score_label[2::4, 0]
    score_inds_d = score_label[3::4, 0]
    l_buckets = px1 + (0.5 + score_inds_l.float()) * bucket_w
    r_buckets = px2 - (0.5 + score_inds_r.float()) * bucket_w
    t_buckets = py1 + (0.5 + score_inds_t.float()) * bucket_h
    d_buckets = py2 - (0.5 + score_inds_d.float()) * bucket_h

    offsets = offset_preds.view(-1, 4, side_num)
    inds = torch.arange(proposals.size(0)).to(proposals).long()
    l_offsets = offsets[:, 0, :][inds, score_inds_l]
    r_offsets = offsets[:, 1, :][inds, score_inds_r]
    t_offsets = offsets[:, 2, :][inds, score_inds_t]
    d_offsets = offsets[:, 3, :][inds, score_inds_d]

    x1 = l_buckets - l_offsets * bucket_w
    x2 = r_buckets - r_offsets * bucket_w
    y1 = t_buckets - t_offsets * bucket_h
    y2 = d_buckets - d_offsets * bucket_h

    if clip_border and max_shape is not None:
        x1 = x1.clamp(min=0, max=max_shape[1] - 1)
        y1 = y1.clamp(min=0, max=max_shape[0] - 1)
        x2 = x2.clamp(min=0, max=max_shape[1] - 1)
        y2 = y2.clamp(min=0, max=max_shape[0] - 1)
    bboxes = torch.cat([x1[:, None], y1[:, None], x2[:, None], y2[:, None]],
                       dim=-1)

    # bucketing guided rescoring
    loc_confidence = score_topk[:, 0]
    top2_neighbor_inds = (score_label[:, 0] - score_label[:, 1]).abs() == 1
    loc_confidence += score_topk[:, 1] * top2_neighbor_inds.float()
    loc_confidence = loc_confidence.view(-1, 4).mean(dim=1)

    return bboxes, loc_confidence
