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

import numpy as np
import torch
from mmcv.ops import box_iou_rotated, points_in_boxes_all, points_in_boxes_part
from torch import Tensor

from mmdet3d.structures.points import BasePoints
from .utils import limit_period


class BaseInstance3DBoxes:
    """Base class for 3D Boxes.

    Note:
        The box is bottom centered, i.e. the relative position of origin in the
        box is (0.5, 0.5, 0).

    Args:
        tensor (Tensor or np.ndarray or Sequence[Sequence[float]]): The boxes
            data with shape (N, box_dim).
        box_dim (int): Number of the dimension of a box. Each row is
            (x, y, z, x_size, y_size, z_size, yaw). Defaults to 7.
        with_yaw (bool): Whether the box is with yaw rotation. If False, the
            value of yaw will be set to 0 as minmax boxes. Defaults to True.
        origin (Tuple[float]): Relative position of the box origin.
            Defaults to (0.5, 0.5, 0). This will guide the box be converted to
            (0.5, 0.5, 0) mode.

    Attributes:
        tensor (Tensor): Float matrix with shape (N, box_dim).
        box_dim (int): Integer indicating the dimension of a box. Each row is
            (x, y, z, x_size, y_size, z_size, yaw, ...).
        with_yaw (bool): If True, the value of yaw will be set to 0 as minmax
            boxes.
    """

    def __init__(
        self,
        tensor: Union[Tensor, np.ndarray, Sequence[Sequence[float]]],
        box_dim: int = 7,
        with_yaw: bool = True,
        origin: Tuple[float, float, float] = (0.5, 0.5, 0)
    ) -> None:
        if isinstance(tensor, Tensor):
            device = tensor.device
        else:
            device = torch.device('cpu')
        tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
        if tensor.numel() == 0:
            # Use reshape, so we don't end up creating a new tensor that does
            # not depend on the inputs (and consequently confuses jit)
            tensor = tensor.reshape((-1, box_dim))
        assert tensor.dim() == 2 and tensor.size(-1) == box_dim, \
            ('The box dimension must be 2 and the length of the last '
             f'dimension must be {box_dim}, but got boxes with shape '
             f'{tensor.shape}.')

        if tensor.shape[-1] == 6:
            # If the dimension of boxes is 6, we expand box_dim by padding 0 as
            # a fake yaw and set with_yaw to False
            assert box_dim == 6
            fake_rot = tensor.new_zeros(tensor.shape[0], 1)
            tensor = torch.cat((tensor, fake_rot), dim=-1)
            self.box_dim = box_dim + 1
            self.with_yaw = False
        else:
            self.box_dim = box_dim
            self.with_yaw = with_yaw
        self.tensor = tensor.clone()

        if origin != (0.5, 0.5, 0):
            dst = self.tensor.new_tensor((0.5, 0.5, 0))
            src = self.tensor.new_tensor(origin)
            self.tensor[:, :3] += self.tensor[:, 3:6] * (dst - src)

    @property
    def volume(self) -> Tensor:
        """Tensor: A vector with volume of each box in shape (N, )."""
        return self.tensor[:, 3] * self.tensor[:, 4] * self.tensor[:, 5]

    @property
    def dims(self) -> Tensor:
        """Tensor: Size dimensions of each box in shape (N, 3)."""
        return self.tensor[:, 3:6]

    @property
    def yaw(self) -> Tensor:
        """Tensor: A vector with yaw of each box in shape (N, )."""
        return self.tensor[:, 6]

    @property
    def height(self) -> Tensor:
        """Tensor: A vector with height of each box in shape (N, )."""
        return self.tensor[:, 5]

    @property
    def top_height(self) -> Tensor:
        """Tensor: A vector with top height of each box in shape (N, )."""
        return self.bottom_height + self.height

    @property
    def bottom_height(self) -> Tensor:
        """Tensor: A vector with bottom height of each box in shape (N, )."""
        return self.tensor[:, 2]

    @property
    def center(self) -> Tensor:
        """Calculate the center of all the boxes.

        Note:
            In MMDetection3D's convention, the bottom center is usually taken
            as the default center.

            The relative position of the centers in different kinds of boxes
            are different, e.g., the relative center of a boxes is
            (0.5, 1.0, 0.5) in camera and (0.5, 0.5, 0) in lidar. It is
            recommended to use ``bottom_center`` or ``gravity_center`` for
            clearer usage.

        Returns:
            Tensor: A tensor with center of each box in shape (N, 3).
        """
        return self.bottom_center

    @property
    def bottom_center(self) -> Tensor:
        """Tensor: A tensor with center of each box in shape (N, 3)."""
        return self.tensor[:, :3]

    @property
    def gravity_center(self) -> Tensor:
        """Tensor: A tensor with center of each box in shape (N, 3)."""
        bottom_center = self.bottom_center
        gravity_center = torch.zeros_like(bottom_center)
        gravity_center[:, :2] = bottom_center[:, :2]
        gravity_center[:, 2] = bottom_center[:, 2] + self.tensor[:, 5] * 0.5
        return gravity_center

    @property
    def corners(self) -> Tensor:
        """Tensor: A tensor with 8 corners of each box in shape (N, 8, 3)."""
        pass

    @property
    def bev(self) -> Tensor:
        """Tensor: 2D BEV box of each box with rotation in XYWHR format, in
        shape (N, 5)."""
        return self.tensor[:, [0, 1, 3, 4, 6]]

    @property
    def nearest_bev(self) -> Tensor:
        """Tensor: A tensor of 2D BEV box of each box without rotation."""
        # Obtain BEV boxes with rotation in XYWHR format
        bev_rotated_boxes = self.bev
        # convert the rotation to a valid range
        rotations = bev_rotated_boxes[:, -1]
        normed_rotations = torch.abs(limit_period(rotations, 0.5, np.pi))

        # find the center of boxes
        conditions = (normed_rotations > np.pi / 4)[..., None]
        bboxes_xywh = torch.where(conditions, bev_rotated_boxes[:,
                                                                [0, 1, 3, 2]],
                                  bev_rotated_boxes[:, :4])

        centers = bboxes_xywh[:, :2]
        dims = bboxes_xywh[:, 2:]
        bev_boxes = torch.cat([centers - dims / 2, centers + dims / 2], dim=-1)
        return bev_boxes

    def in_range_bev(
            self, box_range: Union[Tensor, np.ndarray,
                                   Sequence[float]]) -> Tensor:
        """Check whether the boxes are in the given range.

        Args:
            box_range (Tensor or np.ndarray or Sequence[float]): The range of
                box in order of (x_min, y_min, x_max, y_max).

        Note:
            The original implementation of SECOND checks whether boxes in a
            range by checking whether the points are in a convex polygon, we
            reduce the burden for simpler cases.

        Returns:
            Tensor: A binary vector indicating whether each box is inside the
            reference range.
        """
        in_range_flags = ((self.bev[:, 0] > box_range[0])
                          & (self.bev[:, 1] > box_range[1])
                          & (self.bev[:, 0] < box_range[2])
                          & (self.bev[:, 1] < box_range[3]))
        return in_range_flags

    @abstractmethod
    def rotate(
        self,
        angle: Union[Tensor, np.ndarray, float],
        points: Optional[Union[Tensor, np.ndarray, BasePoints]] = None
    ) -> Union[Tuple[Tensor, Tensor], Tuple[np.ndarray, np.ndarray], Tuple[
            BasePoints, Tensor], None]:
        """Rotate boxes with points (optional) with the given angle or rotation
        matrix.

        Args:
            angle (Tensor or np.ndarray or float): Rotation angle or rotation
                matrix.
            points (Tensor or np.ndarray or :obj:`BasePoints`, optional):
                Points to rotate. Defaults to None.

        Returns:
            tuple or None: When ``points`` is None, the function returns None,
            otherwise it returns the rotated points and the rotation matrix
            ``rot_mat_T``.
        """
        pass

    @abstractmethod
    def flip(
        self,
        bev_direction: str = 'horizontal',
        points: Optional[Union[Tensor, np.ndarray, BasePoints]] = None
    ) -> Union[Tensor, np.ndarray, BasePoints, None]:
        """Flip the boxes in BEV along given BEV direction.

        Args:
            bev_direction (str): Direction by which to flip. Can be chosen from
                'horizontal' and 'vertical'. Defaults to 'horizontal'.
            points (Tensor or np.ndarray or :obj:`BasePoints`, optional):
                Points to flip. Defaults to None.

        Returns:
            Tensor or np.ndarray or :obj:`BasePoints` or None: When ``points``
            is None, the function returns None, otherwise it returns the
            flipped points.
        """
        pass

    def translate(self, trans_vector: Union[Tensor, np.ndarray]) -> None:
        """Translate boxes with the given translation vector.

        Args:
            trans_vector (Tensor or np.ndarray): Translation vector of size
                1x3.
        """
        if not isinstance(trans_vector, Tensor):
            trans_vector = self.tensor.new_tensor(trans_vector)
        self.tensor[:, :3] += trans_vector

    def in_range_3d(
            self, box_range: Union[Tensor, np.ndarray,
                                   Sequence[float]]) -> Tensor:
        """Check whether the boxes are in the given range.

        Args:
            box_range (Tensor or np.ndarray or Sequence[float]): The range of
                box (x_min, y_min, z_min, x_max, y_max, z_max).

        Note:
            In the original implementation of SECOND, checking whether a box in
            the range checks whether the points are in a convex polygon, we try
            to reduce the burden for simpler cases.

        Returns:
            Tensor: A binary vector indicating whether each point is inside the
            reference range.
        """
        in_range_flags = ((self.tensor[:, 0] > box_range[0])
                          & (self.tensor[:, 1] > box_range[1])
                          & (self.tensor[:, 2] > box_range[2])
                          & (self.tensor[:, 0] < box_range[3])
                          & (self.tensor[:, 1] < box_range[4])
                          & (self.tensor[:, 2] < box_range[5]))
        return in_range_flags

    @abstractmethod
    def convert_to(self,
                   dst: int,
                   rt_mat: Optional[Union[Tensor, np.ndarray]] = None,
                   correct_yaw: bool = False) -> 'BaseInstance3DBoxes':
        """Convert self to ``dst`` mode.

        Args:
            dst (int): The target Box mode.
            rt_mat (Tensor or np.ndarray, optional): The rotation and
                translation matrix between different coordinates.
                Defaults to None. The conversion from ``src`` coordinates to
                ``dst`` coordinates usually comes along the change of sensors,
                e.g., from camera to LiDAR. This requires a transformation
                matrix.
            correct_yaw (bool): Whether to convert the yaw angle to the target
                coordinate. Defaults to False.

        Returns:
            :obj:`BaseInstance3DBoxes`: The converted box of the same type in
            the ``dst`` mode.
        """
        pass

    def scale(self, scale_factor: float) -> None:
        """Scale the box with horizontal and vertical scaling factors.

        Args:
            scale_factors (float): Scale factors to scale the boxes.
        """
        self.tensor[:, :6] *= scale_factor
        self.tensor[:, 7:] *= scale_factor  # velocity

    def limit_yaw(self, offset: float = 0.5, period: float = np.pi) -> None:
        """Limit the yaw to a given period and offset.

        Args:
            offset (float): The offset of the yaw. Defaults to 0.5.
            period (float): The expected period. Defaults to np.pi.
        """
        self.tensor[:, 6] = limit_period(self.tensor[:, 6], offset, period)

    def nonempty(self, threshold: float = 0.0) -> Tensor:
        """Find boxes that are non-empty.

        A box is considered empty if either of its side is no larger than
        threshold.

        Args:
            threshold (float): The threshold of minimal sizes. Defaults to 0.0.

        Returns:
            Tensor: A binary vector which represents whether each box is empty
            (False) or non-empty (True).
        """
        box = self.tensor
        size_x = box[..., 3]
        size_y = box[..., 4]
        size_z = box[..., 5]
        keep = ((size_x > threshold)
                & (size_y > threshold) & (size_z > threshold))
        return keep

    def __getitem__(
            self, item: Union[int, slice, np.ndarray,
                              Tensor]) -> 'BaseInstance3DBoxes':
        """
        Args:
            item (int or slice or np.ndarray or Tensor): Index of boxes.

        Note:
            The following usage are allowed:

            1. `new_boxes = boxes[3]`: Return a `Boxes` that contains only one
               box.
            2. `new_boxes = boxes[2:10]`: Return a slice of boxes.
            3. `new_boxes = boxes[vector]`: Where vector is a
               torch.BoolTensor with `length = len(boxes)`. Nonzero elements in
               the vector will be selected.

            Note that the returned Boxes might share storage with this Boxes,
            subject to PyTorch's indexing semantics.

        Returns:
            :obj:`BaseInstance3DBoxes`: A new object of
            :class:`BaseInstance3DBoxes` after indexing.
        """
        original_type = type(self)
        if isinstance(item, int):
            return original_type(
                self.tensor[item].view(1, -1),
                box_dim=self.box_dim,
                with_yaw=self.with_yaw)
        b = self.tensor[item]
        assert b.dim() == 2, \
            f'Indexing on Boxes with {item} failed to return a matrix!'
        return original_type(b, box_dim=self.box_dim, with_yaw=self.with_yaw)

    def __len__(self) -> int:
        """int: Number of boxes in the current object."""
        return self.tensor.shape[0]

    def __repr__(self) -> str:
        """str: Return a string that describes the object."""
        return self.__class__.__name__ + '(\n    ' + str(self.tensor) + ')'

    @classmethod
    def cat(cls, boxes_list: Sequence['BaseInstance3DBoxes']
            ) -> 'BaseInstance3DBoxes':
        """Concatenate a list of Boxes into a single Boxes.

        Args:
            boxes_list (Sequence[:obj:`BaseInstance3DBoxes`]): List of boxes.

        Returns:
            :obj:`BaseInstance3DBoxes`: The concatenated boxes.
        """
        assert isinstance(boxes_list, (list, tuple))
        if len(boxes_list) == 0:
            return cls(torch.empty(0))
        assert all(isinstance(box, cls) for box in boxes_list)

        # use torch.cat (v.s. layers.cat)
        # so the returned boxes never share storage with input
        cat_boxes = cls(
            torch.cat([b.tensor for b in boxes_list], dim=0),
            box_dim=boxes_list[0].box_dim,
            with_yaw=boxes_list[0].with_yaw)
        return cat_boxes

    def to(self, device: Union[str, torch.device], *args,
           **kwargs) -> 'BaseInstance3DBoxes':
        """Convert current boxes to a specific device.

        Args:
            device (str or :obj:`torch.device`): The name of the device.

        Returns:
            :obj:`BaseInstance3DBoxes`: A new boxes object on the specific
                device.
        """
        original_type = type(self)
        return original_type(
            self.tensor.to(device, *args, **kwargs),
            box_dim=self.box_dim,
            with_yaw=self.with_yaw)

    def clone(self) -> 'BaseInstance3DBoxes':
        """Clone the boxes.

        Returns:
            :obj:`BaseInstance3DBoxes`: Box object with the same properties as
            self.
        """
        original_type = type(self)
        return original_type(
            self.tensor.clone(), box_dim=self.box_dim, with_yaw=self.with_yaw)

    @property
    def device(self) -> torch.device:
        """torch.device: The device of the boxes are on."""
        return self.tensor.device

    def __iter__(self) -> Iterator[Tensor]:
        """Yield a box as a Tensor at a time.

        Returns:
            Iterator[Tensor]: A box of shape (box_dim, ).
        """
        yield from self.tensor

    @classmethod
    def height_overlaps(cls, boxes1: 'BaseInstance3DBoxes',
                        boxes2: 'BaseInstance3DBoxes') -> Tensor:
        """Calculate height overlaps of two boxes.

        Note:
            This function calculates the height overlaps between ``boxes1`` and
            ``boxes2``, ``boxes1`` and ``boxes2`` should be in the same type.

        Args:
            boxes1 (:obj:`BaseInstance3DBoxes`): Boxes 1 contain N boxes.
            boxes2 (:obj:`BaseInstance3DBoxes`): Boxes 2 contain M boxes.

        Returns:
            Tensor: Calculated height overlap of the boxes.
        """
        assert isinstance(boxes1, BaseInstance3DBoxes)
        assert isinstance(boxes2, BaseInstance3DBoxes)
        assert type(boxes1) == type(boxes2), \
            '"boxes1" and "boxes2" should be in the same type, ' \
            f'but got {type(boxes1)} and {type(boxes2)}.'

        boxes1_top_height = boxes1.top_height.view(-1, 1)
        boxes1_bottom_height = boxes1.bottom_height.view(-1, 1)
        boxes2_top_height = boxes2.top_height.view(1, -1)
        boxes2_bottom_height = boxes2.bottom_height.view(1, -1)

        heighest_of_bottom = torch.max(boxes1_bottom_height,
                                       boxes2_bottom_height)
        lowest_of_top = torch.min(boxes1_top_height, boxes2_top_height)
        overlaps_h = torch.clamp(lowest_of_top - heighest_of_bottom, min=0)
        return overlaps_h

    @classmethod
    def overlaps(cls,
                 boxes1: 'BaseInstance3DBoxes',
                 boxes2: 'BaseInstance3DBoxes',
                 mode: str = 'iou') -> Tensor:
        """Calculate 3D overlaps of two boxes.

        Note:
            This function calculates the overlaps between ``boxes1`` and
            ``boxes2``, ``boxes1`` and ``boxes2`` should be in the same type.

        Args:
            boxes1 (:obj:`BaseInstance3DBoxes`): Boxes 1 contain N boxes.
            boxes2 (:obj:`BaseInstance3DBoxes`): Boxes 2 contain M boxes.
            mode (str): Mode of iou calculation. Defaults to 'iou'.

        Returns:
            Tensor: Calculated 3D overlap of the boxes.
        """
        assert isinstance(boxes1, BaseInstance3DBoxes)
        assert isinstance(boxes2, BaseInstance3DBoxes)
        assert type(boxes1) == type(boxes2), \
            '"boxes1" and "boxes2" should be in the same type, ' \
            f'but got {type(boxes1)} and {type(boxes2)}.'

        assert mode in ['iou', 'iof']

        rows = len(boxes1)
        cols = len(boxes2)
        if rows * cols == 0:
            return boxes1.tensor.new(rows, cols)

        # height overlap
        overlaps_h = cls.height_overlaps(boxes1, boxes2)

        # Restrict the min values of W and H to avoid memory overflow in
        # ``box_iou_rotated``.
        boxes1_bev, boxes2_bev = boxes1.bev, boxes2.bev
        boxes1_bev[:, 2:4] = boxes1_bev[:, 2:4].clamp(min=1e-4)
        boxes2_bev[:, 2:4] = boxes2_bev[:, 2:4].clamp(min=1e-4)

        # bev overlap
        iou2d = box_iou_rotated(boxes1_bev, boxes2_bev)
        areas1 = (boxes1_bev[:, 2] * boxes1_bev[:, 3]).unsqueeze(1).expand(
            rows, cols)
        areas2 = (boxes2_bev[:, 2] * boxes2_bev[:, 3]).unsqueeze(0).expand(
            rows, cols)
        overlaps_bev = iou2d * (areas1 + areas2) / (1 + iou2d)

        # 3d overlaps
        overlaps_3d = overlaps_bev.to(boxes1.device) * overlaps_h

        volume1 = boxes1.volume.view(-1, 1)
        volume2 = boxes2.volume.view(1, -1)

        if mode == 'iou':
            # the clamp func is used to avoid division of 0
            iou3d = overlaps_3d / torch.clamp(
                volume1 + volume2 - overlaps_3d, min=1e-8)
        else:
            iou3d = overlaps_3d / torch.clamp(volume1, min=1e-8)

        return iou3d

    def new_box(
        self, data: Union[Tensor, np.ndarray, Sequence[Sequence[float]]]
    ) -> 'BaseInstance3DBoxes':
        """Create a new box object with data.

        The new box and its tensor has the similar properties as self and
        self.tensor, respectively.

        Args:
            data (Tensor or np.ndarray or Sequence[Sequence[float]]): Data to
                be copied.

        Returns:
            :obj:`BaseInstance3DBoxes`: A new bbox object with ``data``, the
            object's other properties are similar to ``self``.
        """
        new_tensor = self.tensor.new_tensor(data) \
            if not isinstance(data, Tensor) else data.to(self.device)
        original_type = type(self)
        return original_type(
            new_tensor, box_dim=self.box_dim, with_yaw=self.with_yaw)

    def points_in_boxes_part(
            self,
            points: Tensor,
            boxes_override: Optional[Tensor] = None) -> Tensor:
        """Find the box in which each point is.

        Args:
            points (Tensor): Points in shape (1, M, 3) or (M, 3), 3 dimensions
                are (x, y, z) in LiDAR or depth coordinate.
            boxes_override (Tensor, optional): Boxes to override `self.tensor`.
                Defaults to None.

        Note:
            If a point is enclosed by multiple boxes, the index of the first
            box will be returned.

        Returns:
            Tensor: The index of the first box that each point is in with shape
            (M, ). Default value is -1 (if the point is not enclosed by any
            box).
        """
        if boxes_override is not None:
            boxes = boxes_override
        else:
            boxes = self.tensor

        points_clone = points.clone()[..., :3]
        if points_clone.dim() == 2:
            points_clone = points_clone.unsqueeze(0)
        else:
            assert points_clone.dim() == 3 and points_clone.shape[0] == 1

        boxes = boxes.to(points_clone.device).unsqueeze(0)
        box_idx = points_in_boxes_part(points_clone, boxes)

        return box_idx.squeeze(0)

    def points_in_boxes_all(self,
                            points: Tensor,
                            boxes_override: Optional[Tensor] = None) -> Tensor:
        """Find all boxes in which each point is.

        Args:
            points (Tensor): Points in shape (1, M, 3) or (M, 3), 3 dimensions
                are (x, y, z) in LiDAR or depth coordinate.
            boxes_override (Tensor, optional): Boxes to override `self.tensor`.
                Defaults to None.

        Returns:
            Tensor: A tensor indicating whether a point is in a box with shape
            (M, T). T is the number of boxes. Denote this tensor as A, it the
            m^th point is in the t^th box, then `A[m, t] == 1`, otherwise
            `A[m, t] == 0`.
        """
        if boxes_override is not None:
            boxes = boxes_override
        else:
            boxes = self.tensor

        points_clone = points.clone()[..., :3]
        if points_clone.dim() == 2:
            points_clone = points_clone.unsqueeze(0)
        else:
            assert points_clone.dim() == 3 and points_clone.shape[0] == 1

        boxes = boxes.to(points_clone.device).unsqueeze(0)
        box_idxs_of_pts = points_in_boxes_all(points_clone, boxes)

        return box_idxs_of_pts.squeeze(0)

    def points_in_boxes(self,
                        points: Tensor,
                        boxes_override: Optional[Tensor] = None) -> Tensor:
        warnings.warn('DeprecationWarning: points_in_boxes is a deprecated '
                      'method, please consider using points_in_boxes_part.')
        return self.points_in_boxes_part(points, boxes_override)

    def points_in_boxes_batch(
            self,
            points: Tensor,
            boxes_override: Optional[Tensor] = None) -> Tensor:
        warnings.warn('DeprecationWarning: points_in_boxes_batch is a '
                      'deprecated method, please consider using '
                      'points_in_boxes_all.')
        return self.points_in_boxes_all(points, boxes_override)
