import torch
from torch import nn as nn
from torch.autograd import Function
from typing import Tuple

from ..ball_query import ball_query
from ..knn import knn
from . import group_points_ext


class QueryAndGroup(nn.Module):
    """Query and Group.

    Groups with a ball query of radius

    Args:
        max_radius (float | None): The maximum radius of the balls.
            If None is given, we will use kNN sampling instead of ball query.
        sample_num (int): Maximum number of features to gather in the ball.
        min_radius (float): The minimum radius of the balls.
        use_xyz (bool): Whether to use xyz.
            Default: True.
        return_grouped_xyz (bool): Whether to return grouped xyz.
            Default: False.
        normalize_xyz (bool): Whether to normalize xyz.
            Default: False.
        uniform_sample (bool): Whether to sample uniformly.
            Default: False
        return_unique_cnt (bool): Whether to return the count of
            unique samples.
            Default: False.
        return_grouped_idx (bool): Whether to return grouped idx.
            Default: False.
    """

    def __init__(
        self,
        max_radius,
        sample_num,
        min_radius=0,
        use_xyz=True,
        return_grouped_xyz=False,
        normalize_xyz=False,
        uniform_sample=False,
        return_unique_cnt=False,
        return_grouped_idx=False,
    ):
        super(QueryAndGroup, self).__init__()
        self.max_radius = max_radius
        self.min_radius = min_radius
        self.sample_num = sample_num
        self.use_xyz = use_xyz
        self.return_grouped_xyz = return_grouped_xyz
        self.normalize_xyz = normalize_xyz
        self.uniform_sample = uniform_sample
        self.return_unique_cnt = return_unique_cnt
        self.return_grouped_idx = return_grouped_idx
        if self.return_unique_cnt:
            assert self.uniform_sample, (
                "uniform_sample should be True when " "returning the count of unique samples"
            )
        if self.max_radius is None:
            assert not self.normalize_xyz, "can not normalize grouped xyz when max_radius is None"

    def forward(self, points_xyz, center_xyz, features=None):
        """forward.

        Args:
            points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
            center_xyz (Tensor): (B, npoint, 3) Centriods.
            features (Tensor): (B, C, N) Descriptors of the features.

        Return：
            Tensor: (B, 3 + C, npoint, sample_num) Grouped feature.
        """
        # if self.max_radius is None, we will perform kNN instead of ball query
        # idx is of shape [B, npoint, sample_num]
        if self.max_radius is None:
            idx = knn(self.sample_num, points_xyz, center_xyz, False)
            idx = idx.transpose(1, 2).contiguous()
        else:
            idx = ball_query(
                self.min_radius, self.max_radius, self.sample_num, points_xyz, center_xyz
            )

        if self.uniform_sample:
            unique_cnt = torch.zeros((idx.shape[0], idx.shape[1]))
            for i_batch in range(idx.shape[0]):
                for i_region in range(idx.shape[1]):
                    unique_ind = torch.unique(idx[i_batch, i_region, :])
                    num_unique = unique_ind.shape[0]
                    unique_cnt[i_batch, i_region] = num_unique
                    sample_ind = torch.randint(
                        0, num_unique, (self.sample_num - num_unique,), dtype=torch.long
                    )
                    all_ind = torch.cat((unique_ind, unique_ind[sample_ind]))
                    idx[i_batch, i_region, :] = all_ind

        xyz_trans = points_xyz.transpose(1, 2).contiguous()
        # (B, 3, npoint, sample_num)
        grouped_xyz = grouping_operation(xyz_trans, idx)
        grouped_xyz_diff = grouped_xyz - center_xyz.transpose(1, 2).unsqueeze(
            -1
        )  # relative offsets
        if self.normalize_xyz:
            grouped_xyz_diff /= self.max_radius

        if features is not None:
            grouped_features = grouping_operation(features, idx)
            if self.use_xyz:
                # (B, C + 3, npoint, sample_num)
                new_features = torch.cat([grouped_xyz_diff, grouped_features], dim=1)
            else:
                new_features = grouped_features
        else:
            assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
            new_features = grouped_xyz_diff

        ret = [new_features]
        if self.return_grouped_xyz:
            ret.append(grouped_xyz)
        if self.return_unique_cnt:
            ret.append(unique_cnt)
        if self.return_grouped_idx:
            ret.append(idx)
        if len(ret) == 1:
            return ret[0]
        else:
            return tuple(ret)


class GroupAll(nn.Module):
    """Group All.

    Group xyz with feature.

    Args:
        use_xyz (bool): Whether to use xyz.
    """

    def __init__(self, use_xyz: bool = True):
        super().__init__()
        self.use_xyz = use_xyz

    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None):
        """forward.

        Args:
            xyz (Tensor): (B, N, 3) xyz coordinates of the features.
            new_xyz (Tensor): Ignored.
            features (Tensor): (B, C, N) features to group.

        Return:
            Tensor: (B, C + 3, 1, N) Grouped feature.
        """
        grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
        if features is not None:
            grouped_features = features.unsqueeze(2)
            if self.use_xyz:
                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, 3 + C, 1, N)
            else:
                new_features = grouped_features
        else:
            new_features = grouped_xyz

        return new_features


class GroupingOperation(Function):
    """Grouping Operation.

    Group feature with given index.
    """

    @staticmethod
    def forward(ctx, features: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
        """forward.

        Args:
            features (Tensor): (B, C, N) tensor of features to group.
            indices (Tensor): (B, npoint, nsample) the indicies of
                features to group with.

        Returns:
            Tensor: (B, C, npoint, nsample) Grouped features.
        """
        assert features.is_contiguous()
        assert indices.is_contiguous()

        B, nfeatures, nsample = indices.size()
        _, C, N = features.size()
        output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)

        group_points_ext.forward(B, C, N, nfeatures, nsample, features, indices, output)

        ctx.for_backwards = (indices, N)
        return output

    @staticmethod
    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """backward.

        Args:
            grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients
                of the output from forward.

        Returns:
            Tensor: (B, C, N) gradient of the features.
        """
        idx, N = ctx.for_backwards

        B, C, npoint, nsample = grad_out.size()
        grad_features = torch.cuda.FloatTensor(B, C, N).zero_()

        grad_out_data = grad_out.data.contiguous()
        group_points_ext.backward(B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data)
        return grad_features, None


grouping_operation = GroupingOperation.apply