"""Adopted from https://github.com/WangYueFt/dgcnn/blob/master/pytorch/model.py"""
import torch
import torch.nn as nn
import torch.nn.functional as F


def knn(x, k):
    inner = -2 * torch.matmul(x.transpose(2, 1), x)
    xx = torch.sum(x ** 2, dim=1, keepdim=True)
    pairwise_distance = -xx - inner - xx.transpose(2, 1)

    idx = pairwise_distance.topk(k=k, dim=-1)[1]  # (batch_size, num_points, k)
    return idx


def get_graph_feature(x, k=20, idx=None):
    batch_size = x.size(0)
    num_points = x.size(2)
    x = x.view(batch_size, -1, num_points)
    if idx is None:
        idx = knn(x, k=k)  # (batch_size, num_points, k)
    device = torch.device('cuda')

    idx_base = torch.arange(
        0, batch_size, device=device).view(-1, 1, 1) * num_points

    idx = idx + idx_base

    idx = idx.view(-1)

    _, num_dims, _ = x.size()

    x = x.transpose(2, 1).contiguous()
    feature = x.view(batch_size * num_points, -1)[idx, :]
    feature = feature.view(batch_size, num_points, k, num_dims)
    x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)

    feature = torch.cat((feature - x, x), dim=3).permute(0, 3, 1, 2)

    return feature


class DGCNN(nn.Module):
    def __init__(self, emb_dims=1024, k=20, output_channels=40, use_bn=True):
        super(DGCNN, self).__init__()
        self.k = k
        self.emb_dims = emb_dims

        if use_bn:
            self.bn1 = nn.BatchNorm2d(64)
            self.bn2 = nn.BatchNorm2d(64)
            self.bn3 = nn.BatchNorm2d(128)
            self.bn4 = nn.BatchNorm2d(256)
            self.bn5 = nn.BatchNorm1d(self.emb_dims)

            self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=False),
                                       self.bn1,
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv2 = nn.Sequential(nn.Conv2d(64 * 2, 64, kernel_size=1, bias=False),
                                       self.bn2,
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv3 = nn.Sequential(nn.Conv2d(64 * 2, 128, kernel_size=1, bias=False),
                                       self.bn3,
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv4 = nn.Sequential(nn.Conv2d(128 * 2, 256, kernel_size=1, bias=False),
                                       self.bn4,
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv5 = nn.Sequential(nn.Conv1d(512, self.emb_dims, kernel_size=1, bias=False),
                                       self.bn5,
                                       nn.LeakyReLU(negative_slope=0.2))
            self.bn6 = nn.BatchNorm1d(512)
            self.bn7 = nn.BatchNorm1d(256)
            self.linear1 = nn.Sequential(
                nn.Linear(self.emb_dims * 2, 512, bias=False),
                self.bn6
            )
            self.linear2 = nn.Sequential(
                nn.Linear(512, 256),
                self.bn7
            )
        else:
            self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=False),
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv2 = nn.Sequential(nn.Conv2d(64 * 2, 64, kernel_size=1, bias=False),
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv3 = nn.Sequential(nn.Conv2d(64 * 2, 128, kernel_size=1, bias=False),
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv4 = nn.Sequential(nn.Conv2d(128 * 2, 256, kernel_size=1, bias=False),
                                       nn.LeakyReLU(negative_slope=0.2))
            self.conv5 = nn.Sequential(nn.Conv1d(512, self.emb_dims, kernel_size=1, bias=False),
                                       nn.LeakyReLU(negative_slope=0.2))
            self.linear1 = nn.Linear(self.emb_dims * 2, 512, bias=False)
            self.linear2 = nn.Linear(512, 256)

        self.dp1 = nn.Dropout(p=0.5)
        self.dp2 = nn.Dropout(p=0.5)
        self.linear3 = nn.Linear(256, output_channels)

    def forward(self, x):
        batch_size = x.size(0)
        x = get_graph_feature(x, k=self.k)
        x = self.conv1(x)
        x1 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x1, k=self.k)
        x = self.conv2(x)
        x2 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x2, k=self.k)
        x = self.conv3(x)
        x3 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x3, k=self.k)
        x = self.conv4(x)
        x4 = x.max(dim=-1, keepdim=False)[0]

        x = torch.cat((x1, x2, x3, x4), dim=1)

        x = self.conv5(x)
        x1 = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
        x2 = F.adaptive_avg_pool1d(x, 1).view(batch_size, -1)
        x = torch.cat((x1, x2), 1)

        x = F.leaky_relu(self.linear1(x), negative_slope=0.2)
        x = self.dp1(x)
        x = F.leaky_relu(self.linear2(x), negative_slope=0.2)
        x = self.dp2(x)
        x = self.linear3(x)
        return x
