from typing import Optional, Tuple

import numpy as np
import torch
import torch.nn as nn
from jaxtyping import Float, Integer
from torch import Tensor

from .mesh import Mesh


class IsosurfaceHelper(nn.Module):
    points_range: Tuple[float, float] = (0, 1)

    @property
    def grid_vertices(self) -> Float[Tensor, "N 3"]:
        raise NotImplementedError

    @property
    def requires_instance_per_batch(self) -> bool:
        return False


class MarchingTetrahedraHelper(IsosurfaceHelper):
    def __init__(self, resolution: int, tets_path: str):
        super().__init__()
        self.resolution = resolution
        self.tets_path = tets_path

        self.triangle_table: Float[Tensor, "..."]
        self.register_buffer(
            "triangle_table",
            torch.as_tensor(
                [
                    [-1, -1, -1, -1, -1, -1],
                    [1, 0, 2, -1, -1, -1],
                    [4, 0, 3, -1, -1, -1],
                    [1, 4, 2, 1, 3, 4],
                    [3, 1, 5, -1, -1, -1],
                    [2, 3, 0, 2, 5, 3],
                    [1, 4, 0, 1, 5, 4],
                    [4, 2, 5, -1, -1, -1],
                    [4, 5, 2, -1, -1, -1],
                    [4, 1, 0, 4, 5, 1],
                    [3, 2, 0, 3, 5, 2],
                    [1, 3, 5, -1, -1, -1],
                    [4, 1, 2, 4, 3, 1],
                    [3, 0, 4, -1, -1, -1],
                    [2, 0, 1, -1, -1, -1],
                    [-1, -1, -1, -1, -1, -1],
                ],
                dtype=torch.long,
            ),
            persistent=False,
        )
        self.num_triangles_table: Integer[Tensor, "..."]
        self.register_buffer(
            "num_triangles_table",
            torch.as_tensor(
                [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long
            ),
            persistent=False,
        )
        self.base_tet_edges: Integer[Tensor, "..."]
        self.register_buffer(
            "base_tet_edges",
            torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),
            persistent=False,
        )

        tets = np.load(self.tets_path)
        self._grid_vertices: Float[Tensor, "..."]
        self.register_buffer(
            "_grid_vertices",
            torch.from_numpy(tets["vertices"]).float(),
            persistent=False,
        )
        self.indices: Integer[Tensor, "..."]
        self.register_buffer(
            "indices", torch.from_numpy(tets["indices"]).long(), persistent=False
        )

        self._all_edges: Optional[Integer[Tensor, "Ne 2"]] = None

        center_indices, boundary_indices = self.get_center_boundary_index(
            self._grid_vertices
        )
        self.center_indices: Integer[Tensor, "..."]
        self.register_buffer("center_indices", center_indices, persistent=False)
        self.boundary_indices: Integer[Tensor, "..."]
        self.register_buffer("boundary_indices", boundary_indices, persistent=False)

    def get_center_boundary_index(self, verts):
        magn = torch.sum(verts**2, dim=-1)

        center_idx = torch.argmin(magn)
        boundary_neg = verts == verts.max()
        boundary_pos = verts == verts.min()

        boundary = torch.bitwise_or(boundary_pos, boundary_neg)
        boundary = torch.sum(boundary.float(), dim=-1)

        boundary_idx = torch.nonzero(boundary)
        return center_idx, boundary_idx.squeeze(dim=-1)

    def normalize_grid_deformation(
        self, grid_vertex_offsets: Float[Tensor, "Nv 3"]
    ) -> Float[Tensor, "Nv 3"]:
        return (
            (self.points_range[1] - self.points_range[0])
            / self.resolution  # half tet size is approximately 1 / self.resolution
            * torch.tanh(grid_vertex_offsets)
        )  # FIXME: hard-coded activation

    @property
    def grid_vertices(self) -> Float[Tensor, "Nv 3"]:
        return self._grid_vertices

    @property
    def all_edges(self) -> Integer[Tensor, "Ne 2"]:
        if self._all_edges is None:
            # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)
            edges = torch.tensor(
                [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],
                dtype=torch.long,
                device=self.indices.device,
            )
            _all_edges = self.indices[:, edges].reshape(-1, 2)
            _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]
            _all_edges = torch.unique(_all_edges_sorted, dim=0)
            self._all_edges = _all_edges
        return self._all_edges

    def sort_edges(self, edges_ex2):
        with torch.no_grad():
            order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()
            order = order.unsqueeze(dim=1)

            a = torch.gather(input=edges_ex2, index=order, dim=1)
            b = torch.gather(input=edges_ex2, index=1 - order, dim=1)

        return torch.stack([a, b], -1)

    def _forward(self, pos_nx3, sdf_n, tet_fx4):
        with torch.no_grad():
            occ_n = sdf_n > 0
            occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
            occ_sum = torch.sum(occ_fx4, -1)
            valid_tets = (occ_sum > 0) & (occ_sum < 4)
            occ_sum = occ_sum[valid_tets]

            # find all vertices
            all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)
            all_edges = self.sort_edges(all_edges)
            unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)

            unique_edges = unique_edges.long()
            mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
            mapping = (
                torch.ones(
                    (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device
                )
                * -1
            )
            mapping[mask_edges] = torch.arange(
                mask_edges.sum(), dtype=torch.long, device=pos_nx3.device
            )
            idx_map = mapping[idx_map]  # map edges to verts

            interp_v = unique_edges[mask_edges]
        edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
        edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
        edges_to_interp_sdf[:, -1] *= -1

        denominator = edges_to_interp_sdf.sum(1, keepdim=True)

        edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
        verts = (edges_to_interp * edges_to_interp_sdf).sum(1)

        idx_map = idx_map.reshape(-1, 6)

        v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))
        tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
        num_triangles = self.num_triangles_table[tetindex]

        # Generate triangle indices
        faces = torch.cat(
            (
                torch.gather(
                    input=idx_map[num_triangles == 1],
                    dim=1,
                    index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],
                ).reshape(-1, 3),
                torch.gather(
                    input=idx_map[num_triangles == 2],
                    dim=1,
                    index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],
                ).reshape(-1, 3),
            ),
            dim=0,
        )

        return verts, faces

    def forward(
        self,
        level: Float[Tensor, "N3 1"],
        deformation: Optional[Float[Tensor, "N3 3"]] = None,
    ) -> Mesh:
        if deformation is not None:
            grid_vertices = self.grid_vertices + self.normalize_grid_deformation(
                deformation
            )
        else:
            grid_vertices = self.grid_vertices

        v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)

        mesh = Mesh(
            v_pos=v_pos,
            t_pos_idx=t_pos_idx,
            # extras
            grid_vertices=grid_vertices,
            tet_edges=self.all_edges,
            grid_level=level,
            grid_deformation=deformation,
        )

        return mesh