import torch
import torch.nn as nn
from .. import SparseTensor
from torchsparse.utils import make_ntuple


def sparseconv3d_func(input: SparseTensor, weight: torch.Tensor, kernel_size: int, stride: int = 1, dilation: int = 1, padding: int = 0, bias: torch.Tensor = None, training: bool = True):
    if 'torchsparse' not in globals():
        import torchsparse
    stride = make_ntuple(stride, ndim=3)
    kernel_size = make_ntuple(kernel_size, ndim=3)
    _padding = make_ntuple(padding, 3)
    padding = ()
    for i in range(3):
        if kernel_size[i] % 2 == 1 and stride[i] == 1:
            padding += ((kernel_size[i] - 1) // 2,)
        else:
            padding += (_padding[i],)
    out = torchsparse.nn.functional.conv3d(input.data, weight, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias, training=training)
    spatial_range = out.spatial_range
    new_shape = [input.shape[0], weight.shape[1]]
    out = SparseTensor(out, shape=torch.Size(new_shape), layout=input.layout if all(s == 1 for s in stride) else None)
    out._spatial_cache = input._spatial_cache
    out._scale = tuple([s * stride for s, stride in zip(input._scale, stride)])
    out.data.spatial_range = spatial_range
    return out

class SparseConv3d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, bias=True, indice_key=None):
        super(SparseConv3d, self).__init__()
        if 'torchsparse' not in globals():
            import torchsparse
        self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias)

    def forward(self, x: SparseTensor) -> SparseTensor:
        out = self.conv(x.data)

        spatial_range = out.spatial_range

        new_shape = [x.shape[0], self.conv.out_channels]
        out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
        out._spatial_cache = x._spatial_cache
        out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)])

        out.data.spatial_range = spatial_range

        return out


class SparseInverseConv3d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
        super(SparseInverseConv3d, self).__init__()
        if 'torchsparse' not in globals():
            import torchsparse
        self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True)

    def forward(self, x: SparseTensor) -> SparseTensor:
        out = self.conv(x.data)        

        new_shape = [x.shape[0], self.conv.out_channels]
        out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
        out._spatial_cache = x._spatial_cache
        out._scale = tuple([s // stride for s, stride in zip(x._scale, self.conv.stride)])
        
        return out