# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Function

from ..utils import ext_loader

ext_module = ext_loader.load_ext(
    '_ext',
    ['dynamic_point_to_voxel_forward', 'dynamic_point_to_voxel_backward'])


class _DynamicScatter(Function):

    @staticmethod
    def forward(ctx, feats, coors, reduce_type='max'):
        """convert kitti points(N, >=3) to voxels.

        Args:
            feats (torch.Tensor): [N, C]. Points features to be reduced
                into voxels.
            coors (torch.Tensor): [N, ndim]. Corresponding voxel coordinates
                (specifically multi-dim voxel index) of each points.
            reduce_type (str, optional): Reduce op. support 'max', 'sum' and
                'mean'. Default: 'max'.

        Returns:
            tuple[torch.Tensor]: A tuple contains two elements. The first one
            is the voxel features with shape [M, C] which are respectively
            reduced from input features that share the same voxel coordinates.
            The second is voxel coordinates with shape [M, ndim].
        """
        results = ext_module.dynamic_point_to_voxel_forward(
            feats, coors, reduce_type)
        (voxel_feats, voxel_coors, point2voxel_map,
         voxel_points_count) = results
        ctx.reduce_type = reduce_type
        ctx.save_for_backward(feats, voxel_feats, point2voxel_map,
                              voxel_points_count)
        ctx.mark_non_differentiable(voxel_coors)
        return voxel_feats, voxel_coors

    @staticmethod
    def backward(ctx, grad_voxel_feats, grad_voxel_coors=None):
        (feats, voxel_feats, point2voxel_map,
         voxel_points_count) = ctx.saved_tensors
        grad_feats = torch.zeros_like(feats)
        # TODO: whether to use index put or use cuda_backward
        # To use index put, need point to voxel index
        ext_module.dynamic_point_to_voxel_backward(
            grad_feats, grad_voxel_feats.contiguous(), feats, voxel_feats,
            point2voxel_map, voxel_points_count, ctx.reduce_type)
        return grad_feats, None, None


dynamic_scatter = _DynamicScatter.apply


class DynamicScatter(nn.Module):
    """Scatters points into voxels, used in the voxel encoder with dynamic
    voxelization.

    Note:
        The CPU and GPU implementation get the same output, but have numerical
        difference after summation and division (e.g., 5e-7).

    Args:
        voxel_size (list): list [x, y, z] size of three dimension.
        point_cloud_range (list): The coordinate range of points, [x_min,
            y_min, z_min, x_max, y_max, z_max].
        average_points (bool): whether to use avg pooling to scatter points
            into voxel.
    """

    def __init__(self, voxel_size, point_cloud_range, average_points: bool):
        super().__init__()

        self.voxel_size = voxel_size
        self.point_cloud_range = point_cloud_range
        self.average_points = average_points

    def forward_single(self, points, coors):
        """Scatters points into voxels.

        Args:
            points (torch.Tensor): Points to be reduced into voxels.
            coors (torch.Tensor): Corresponding voxel coordinates (specifically
                multi-dim voxel index) of each points.

        Returns:
            tuple[torch.Tensor]: A tuple contains two elements. The first one
            is the voxel features with shape [M, C] which are respectively
            reduced from input features that share the same voxel coordinates.
            The second is voxel coordinates with shape [M, ndim].
        """
        reduce = 'mean' if self.average_points else 'max'
        return dynamic_scatter(points.contiguous(), coors.contiguous(), reduce)

    def forward(self, points, coors):
        """Scatters points/features into voxels.

        Args:
            points (torch.Tensor): Points to be reduced into voxels.
            coors (torch.Tensor): Corresponding voxel coordinates (specifically
                multi-dim voxel index) of each points.

        Returns:
            tuple[torch.Tensor]: A tuple contains two elements. The first one
            is the voxel features with shape [M, C] which are respectively
            reduced from input features that share the same voxel coordinates.
            The second is voxel coordinates with shape [M, ndim].
        """
        if coors.size(-1) == 3:
            return self.forward_single(points, coors)
        else:
            batch_size = coors[-1, 0] + 1
            voxels, voxel_coors = [], []
            for i in range(batch_size):
                inds = torch.where(coors[:, 0] == i)
                voxel, voxel_coor = self.forward_single(
                    points[inds], coors[inds][:, 1:])
                coor_pad = F.pad(voxel_coor, (1, 0), mode='constant', value=i)
                voxel_coors.append(coor_pad)
                voxels.append(voxel)
            features = torch.cat(voxels, dim=0)
            feature_coors = torch.cat(voxel_coors, dim=0)

            return features, feature_coors

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += 'voxel_size=' + str(self.voxel_size)
        s += ', point_cloud_range=' + str(self.point_cloud_range)
        s += ', average_points=' + str(self.average_points)
        s += ')'
        return s
