import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url

from .utils import pytorch_utils as pt_utils
from .utils.pointnet2_modules import PointnetFPModule, PointnetSAModuleMSG


__all__ = ['Pointnet2MSGSeg', 'pointnet2_msg_seg']


class Pointnet2MSGSeg(nn.Module):
    r"""
        PointNet2 with multi-scale grouping
        Semantic segmentation network that uses feature propogation layers

        Parameters
        ----------
        num_classes: int
            Number of semantics classes to predict over -- size of softmax classifier that run for each point
        input_channels: int = 6
            Number of input channels in the feature descriptor for each point.  If the point cloud is Nx9, this
            value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors
        use_xyz: bool = True
            Whether or not to use the xyz position of a point as a feature
    """

    def __init__(self, num_classes=9, input_channels=6, use_xyz=True):
        super().__init__()

        self.SA_modules = nn.ModuleList()
        c_in = input_channels
        self.SA_modules.append(
            PointnetSAModuleMSG(
                npoint=1024,
                radii=[0.05, 0.1],
                nsamples=[16, 32],
                mlps=[[c_in, 16, 16, 32], [c_in, 32, 32, 64]],
                use_xyz=use_xyz,
            )
        )
        c_out_0 = 32 + 64

        c_in = c_out_0
        self.SA_modules.append(
            PointnetSAModuleMSG(
                npoint=256,
                radii=[0.1, 0.2],
                nsamples=[16, 32],
                mlps=[[c_in, 64, 64, 128], [c_in, 64, 96, 128]],
                use_xyz=use_xyz,
            )
        )
        c_out_1 = 128 + 128

        c_in = c_out_1
        self.SA_modules.append(
            PointnetSAModuleMSG(
                npoint=64,
                radii=[0.2, 0.4],
                nsamples=[16, 32],
                mlps=[[c_in, 128, 196, 256], [c_in, 128, 196, 256]],
                use_xyz=use_xyz,
            )
        )
        c_out_2 = 256 + 256

        c_in = c_out_2
        self.SA_modules.append(
            PointnetSAModuleMSG(
                npoint=16,
                radii=[0.4, 0.8],
                nsamples=[16, 32],
                mlps=[[c_in, 256, 256, 512], [c_in, 256, 384, 512]],
                use_xyz=use_xyz,
            )
        )
        c_out_3 = 512 + 512

        self.FP_modules = nn.ModuleList()
        self.FP_modules.append(PointnetFPModule(mlp=[256 + input_channels, 128, 128]))
        self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_0, 256, 256]))
        self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_1, 512, 512]))
        self.FP_modules.append(PointnetFPModule(mlp=[c_out_3 + c_out_2, 512, 512]))

        self.FC_layer = (
            pt_utils.Seq(128)
            .conv1d(128, bn=True)
            .dropout()
            .conv1d(num_classes, activation=None)
        )

    def _break_up_pc(self, pc):
        xyz = pc[..., 0:3].contiguous()
        features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None

        return xyz, features

    def forward(self, pointcloud):
        # type: (Pointnet2MSGSeg, torch.cuda.FloatTensor) -> pt_utils.Seq
        r"""
            Forward pass of the network

            Parameters
            ----------
            pointcloud: Variable(torch.cuda.FloatTensor)
                (B, N, 3 + input_channels) tensor
                Point cloud to run predicts on
                Each point in the point-cloud MUST
                be formated as (x, y, z, features...)
        """
        xyz, features = self._break_up_pc(pointcloud)

        l_xyz, l_features = [xyz], [features]
        for i in range(len(self.SA_modules)):
            li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i])
            l_xyz.append(li_xyz)
            l_features.append(li_features)

        for i in range(-1, -(len(self.FP_modules) + 1), -1):
            l_features[i - 1] = self.FP_modules[i](
                l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i]
            )

        return self.FC_layer(l_features[0]).transpose(1, 2).contiguous()


def pointnet2_msg_seg(pretrained=False, progress=True, **kwargs):
    model = Pointnet2MSGSeg(**kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url('',
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model

