from typing import *
import torch
import torch.nn as nn
from .. import SparseTensor

__all__ = [
    'SparseDownsample',
    'SparseUpsample',
]


class SparseDownsample(nn.Module):
    """
    Downsample a sparse tensor by a factor of `factor`.
    Implemented as average pooling.
    """
    def __init__(self, factor: int, mode: Literal['mean', 'max'] = 'mean'):
        super(SparseDownsample, self).__init__()
        self.factor = factor
        self.mode = mode
        assert self.mode in ['mean', 'max'], f'Invalid mode: {self.mode}'

    def forward(self, x: SparseTensor) -> SparseTensor:
        cache = x.get_spatial_cache(f'downsample_{self.factor}')
        if cache is None:
            DIM = x.coords.shape[-1] - 1

            coord = list(x.coords.unbind(dim=-1))
            for i in range(DIM):
                coord[i+1] = coord[i+1] // self.factor

            MAX = [(s + self.factor - 1) // self.factor for s in x.spatial_shape]
            OFFSET = torch.cumprod(torch.tensor(MAX[::-1]), 0).tolist()[::-1] + [1]
            code = sum([c * o for c, o in zip(coord, OFFSET)])
            code, idx = code.unique(return_inverse=True)

            new_coords = torch.stack(
                [code // OFFSET[0]] +
                [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)],
                dim=-1
            )
        else:
            new_coords, idx = cache
            
        new_feats = torch.scatter_reduce(
            torch.zeros(new_coords.shape[0], x.feats.shape[1], device=x.feats.device, dtype=x.feats.dtype),
            dim=0,
            index=idx.unsqueeze(1).expand(-1, x.feats.shape[1]),
            src=x.feats,
            reduce=self.mode,
            include_self=False,
        )
        out = SparseTensor(new_feats, new_coords, x._shape)
        out._scale = tuple([s * self.factor for s in x._scale])
        out._spatial_cache = x._spatial_cache
        
        if cache is None:
            x.register_spatial_cache(f'downsample_{self.factor}', (new_coords, idx))
            out.register_spatial_cache(f'upsample_{self.factor}', (x.coords, idx))
            out.register_spatial_cache(f'shape', torch.Size(MAX))
            if self.training:
                subidx = x.coords[:, 1:] % self.factor
                subidx = sum([subidx[..., i] * self.factor ** i for i in range(DIM)])
                subdivision = torch.zeros((new_coords.shape[0], self.factor ** DIM), device=x.device, dtype=torch.bool)
                subdivision[idx, subidx] = True
                out.register_spatial_cache(f'subdivision', subdivision)

        return out


class SparseUpsample(nn.Module):
    """
    Upsample a sparse tensor by a factor of `factor`.
    Implemented as nearest neighbor interpolation.
    """
    def __init__(
        self, factor: int
    ):
        super(SparseUpsample, self).__init__()
        self.factor = factor

    def forward(self, x: SparseTensor, subdivision: Optional[SparseTensor] = None) -> SparseTensor:
        DIM = x.coords.shape[-1] - 1

        cache = x.get_spatial_cache(f'upsample_{self.factor}')
        if cache is None:
            if subdivision is None:
                raise ValueError('Cache not found. Provide subdivision tensor or pair SparseUpsample with SparseDownsample.')
            else:
                sub = subdivision.feats
                N_leaf = sub.sum(dim=-1)
                subidx = sub.nonzero()[:, -1]
                new_coords = x.coords.clone().detach()
                new_coords[:, 1:] *= self.factor
                new_coords = torch.repeat_interleave(new_coords, N_leaf, dim=0, output_size=subidx.shape[0])
                for i in range(DIM):
                    new_coords[:, i+1] += subidx // self.factor ** i % self.factor
                idx = torch.repeat_interleave(torch.arange(x.coords.shape[0], device=x.device), N_leaf, dim=0, output_size=subidx.shape[0])
        else:
            new_coords, idx = cache
            
        new_feats = x.feats[idx]
        out = SparseTensor(new_feats, new_coords, x._shape)
        out._scale = tuple([s / self.factor for s in x._scale])
        if cache is not None:           # only keep cache when subdiv following it
            out._spatial_cache = x._spatial_cache
        
        return out