"""MLX implementation of the TRELLIS-lineage sparse module.

Microsoft's TRELLIS.2 sparse stack has been inherited, near-verbatim, by a growing
family of 3D generation models — LATO.2 and TencentARC's Pixal3D among them. All of
them hard-require spconv or torchsparse, neither of which has a Metal build, and that
single dependency is what keeps the whole lineage off Apple Silicon.

The blocker is one operation: submanifold 3x3x3 convolution. Every SparseConv3d in
these models is constructed `stride=1, padding=None`, which spconv dispatches to
SubMConv3d. Implement that in MLX and the rest is ordinary linear/norm/attention work.

Packaged separately from any one model so each port depends on a tested core rather
than vendoring its own copy.
"""

from .conv import SubMConv3d, build_indice_map
from .tensor import SparseTensor, VarLenTensor, downsample, subdivide, upsample
from .ops import (
    LayerNorm32,
    SparseFeedForwardNet,
    SparseGELU,
    SparseGroupNorm32,
    SparseLinear,
    SparseMultiHeadAttention,
    SparseResBlock,
    SparseSiLU,
    SparseTransformerBlock,
    SparseTransformerCrossBlock,
)

__all__ = [
    "SparseTensor", "VarLenTensor", "subdivide", "downsample", "upsample",
    "SubMConv3d", "build_indice_map",
    "SparseLinear", "LayerNorm32", "SparseGroupNorm32", "SparseSiLU", "SparseGELU",
    "SparseResBlock", "SparseFeedForwardNet", "SparseMultiHeadAttention",
    "SparseTransformerBlock", "SparseTransformerCrossBlock",
]
__version__ = "0.1.0"