content: import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  import craftsman
  from craftsman.utils.typing import *
  
  
  def dot(x, y):
      return torch.sum(x * y, -1, keepdim=True)
  
  
  class Mesh:
      def __init__(
          self, v_pos: Float[Tensor, "Nv 3"], t_pos_idx: Integer[Tensor, "Nf 3"], **kwargs
      ) -> None:
          self.v_pos: Float[Tensor, "Nv 3"] = v_pos
          self.t_pos_idx: Integer[Tensor, "Nf 3"] = t_pos_idx
          self._v_nrm: Optional[Float[Tensor, "Nv 3"]] = None
          self._v_tng: Optional[Float[Tensor, "Nv 3"]] = None
          self._v_tex: Optional[Float[Tensor, "Nt 3"]] = None
          self._t_tex_idx: Optional[Float[Tensor, "Nf 3"]] = None
          self._v_rgb: Optional[Float[Tensor, "Nv 3"]] = None
          self._edges: Optional[Integer[Tensor, "Ne 2"]] = None
          self.extras: Dict[str, Any] = {}
          for k, v in kwargs.items():
              self.add_extra(k, v)
  
      def add_extra(self, k, v):
          self.extras[k] = v
  
      def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]):
          if self.requires_grad:
              craftsman.debug("Mesh is differentiable, not removing outliers")
              return self
  
          # use trimesh to first split the mesh into connected components
          # then remove the components with less than n_face_threshold faces
          import trimesh
  
          # construct a trimesh object
          mesh = trimesh.Trimesh(
              vertices=self.v_pos.detach().cpu().numpy(),
              faces=self.t_pos_idx.detach().cpu().numpy(),
          )
  
          # split the mesh into connected components
          components = mesh.split(only_watertight=False)
          # log the number of faces in each component
          craftsman.debug(
              "Mesh has {} components, with faces: {}".format(
                  len(components), [c.faces.shape[0] for c in components]
              )
          )
  
          n_faces_threshold: int
          if isinstance(outlier_n_faces_threshold, float):
              # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold
              n_faces_threshold = int(
                  max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold
              )
          else:
              # set the threshold directly to outlier_n_faces_threshold
              n_faces_threshold = outlier_n_faces_threshold
  
          # log the threshold
          craftsman.debug(
              "Removing components with less than {} faces".format(n_faces_threshold)
          )
  
          # remove the components with less than n_face_threshold faces
          components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]
  
          # log the number of faces in each component after removing outliers
          craftsman.debug(
              "Mesh has {} components after removing outliers, with faces: {}".format(
                  len(components), [c.faces.shape[0] for c in components]
              )
          )
          # merge the components
          mesh = trimesh.util.concatenate(components)
  
          # convert back to our mesh format
          v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)
          t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)
  
          clean_mesh = Mesh(v_pos, t_pos_idx)
          # keep the extras unchanged
  
          if len(self.extras) > 0:
              clean_mesh.extras = self.extras
              craftsman.debug(
                  f"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}"
              )
          return clean_mesh
  
      @property
      def requires_grad(self):
          return self.v_pos.requires_grad
  
      @property
      def v_nrm(self):
          if self._v_nrm is None:
              self._v_nrm = self._compute_vertex_normal()
          return self._v_nrm
  
      @property
      def v_tng(self):
          if self._v_tng is None:
              self._v_tng = self._compute_vertex_tangent()
          return self._v_tng
  
      @property
      def v_tex(self):
          if self._v_tex is None:
              self._v_tex, self._t_tex_idx = self._unwrap_uv()
          return self._v_tex
  
      @property
      def t_tex_idx(self):
          if self._t_tex_idx is None:
              self._v_tex, self._t_tex_idx = self._unwrap_uv()
          return self._t_tex_idx
  
      @property
      def v_rgb(self):
          return self._v_rgb
  
      @property
      def edges(self):
          if self._edges is None:
              self._edges = self._compute_edges()
          return self._edges
  
      def _compute_vertex_normal(self):
          i0 = self.t_pos_idx[:, 0]
          i1 = self.t_pos_idx[:, 1]
          i2 = self.t_pos_idx[:, 2]
  
          v0 = self.v_pos[i0, :]
          v1 = self.v_pos[i1, :]
          v2 = self.v_pos[i2, :]
  
          face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
  
          # Splat face normals to vertices
          v_nrm = torch.zeros_like(self.v_pos)
          v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
          v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
          v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
  
          # Normalize, replace zero (degenerated) normals with some default value
          v_nrm = torch.where(
              dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
          )
          v_nrm = F.normalize(v_nrm, dim=1)
  
          if torch.is_anomaly_enabled():
              assert torch.all(torch.isfinite(v_nrm))
  
          return v_nrm
  
      def _compute_vertex_tangent(self):
          vn_idx = [None] * 3
          pos = [None] * 3
          tex = [None] * 3
          for i in range(0, 3):
              pos[i] = self.v_pos[self.t_pos_idx[:, i]]
              tex[i] = self.v_tex[self.t_tex_idx[:, i]]
              # t_nrm_idx is always the same as t_pos_idx
              vn_idx[i] = self.t_pos_idx[:, i]
  
          tangents = torch.zeros_like(self.v_nrm)
          tansum = torch.zeros_like(self.v_nrm)
  
          # Compute tangent space for each triangle
          uve1 = tex[1] - tex[0]
          uve2 = tex[2] - tex[0]
          pe1 = pos[1] - pos[0]
          pe2 = pos[2] - pos[0]
  
          nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]
          denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]
  
          # Avoid division by zero for degenerated texture coordinates
          tang = nom / torch.where(
              denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)
          )
  
          # Update all 3 vertices
          for i in range(0, 3):
              idx = vn_idx[i][:, None].repeat(1, 3)
              tangents.scatter_add_(0, idx, tang)  # tangents[n_i] = tangents[n_i] + tang
              tansum.scatter_add_(
                  0, idx, torch.ones_like(tang)
              )  # tansum[n_i] = tansum[n_i] + 1
          tangents = tangents / tansum
  
          # Normalize and make sure tangent is perpendicular to normal
          tangents = F.normalize(tangents, dim=1)
          tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)
  
          if torch.is_anomaly_enabled():
              assert torch.all(torch.isfinite(tangents))
  
          return tangents
  
      def _unwrap_uv(
          self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}
      ):
          craftsman.info("Using xatlas to perform UV unwrapping, may take a while ...")
  
          import xatlas
  
          atlas = xatlas.Atlas()
          atlas.add_mesh(
              self.v_pos.detach().cpu().numpy(),
              self.t_pos_idx.cpu().numpy(),
          )
          co = xatlas.ChartOptions()
          po = xatlas.PackOptions()
          for k, v in xatlas_chart_options.items():
              setattr(co, k, v)
          for k, v in xatlas_pack_options.items():
              setattr(po, k, v)
              
          setattr(co, 'max_cost', 2.0)
          setattr(po, 'resolution', 4096)
          
          atlas.generate(co, po, verbose=True)
          vmapping, indices, uvs = atlas.get_mesh(0)
          vmapping = (
              torch.from_numpy(
                  vmapping.astype(np.uint64, casting="same_kind").view(np.int64)
              )
              .to(self.v_pos.device)
              .long()
          )
          uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()
          indices = (
              torch.from_numpy(
                  indices.astype(np.uint64, casting="same_kind").view(np.int64)
              )
              .to(self.v_pos.device)
              .long()
          )
          return uvs, indices
  
      def unwrap_uv(
          self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}
      ):
          self._v_tex, self._t_tex_idx = self._unwrap_uv(
              xatlas_chart_options, xatlas_pack_options
          )
  
      def set_vertex_color(self, v_rgb):
          assert v_rgb.shape[0] == self.v_pos.shape[0]
          self._v_rgb = v_rgb
  
      def _compute_edges(self):
          # Compute edges
          edges = torch.cat(
              [
                  self.t_pos_idx[:, [0, 1]],
                  self.t_pos_idx[:, [1, 2]],
                  self.t_pos_idx[:, [2, 0]],
              ],
              dim=0,
          )
          edges = edges.sort()[0]
          edges = torch.unique(edges, dim=0)
          return edges
  
      def normal_consistency(self) -> Float[Tensor, ""]:
          edge_nrm: Float[Tensor, "Ne 2 3"] = self.v_nrm[self.edges]
          nc = (
              1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)
          ).mean()
          return nc
  
      def _laplacian_uniform(self):
          # from stable-dreamfusion
          # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224
          verts, faces = self.v_pos, self.t_pos_idx
  
          V = verts.shape[0]
          F = faces.shape[0]
  
          # Neighbor indices
          ii = faces[:, [1, 2, 0]].flatten()
          jj = faces[:, [2, 0, 1]].flatten()
          adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(
              dim=1
          )
          adj_values = torch.ones(adj.shape[1]).to(verts)
  
          # Diagonal indices
          diag_idx = adj[0]
  
          # Build the sparse matrix
          idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)
          values = torch.cat((-adj_values, adj_values))
  
          # The coalesce operation sums the duplicate indices, resulting in the
          # correct diagonal
          return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()
  
      def laplacian(self) -> Float[Tensor, ""]:
          with torch.no_grad():
              L = self._laplacian_uniform()
          loss = L.mm(self.v_pos)
          loss = loss.norm(dim=1)
          loss = loss.mean()
          return loss
          
  class IsosurfaceHelper(nn.Module):
      points_range: Tuple[float, float] = (0, 1)
  
      @property
      def grid_vertices(self) -> Float[Tensor, "N 3"]:
          raise NotImplementedError
  
  
  class MarchingCubeCPUHelper(IsosurfaceHelper):
      def __init__(self, resolution: int) -> None:
          super().__init__()
          self.resolution = resolution
          import mcubes
  
          self.mc_func: Callable = mcubes.marching_cubes
          self._grid_vertices: Optional[Float[Tensor, "N3 3"]] = None
          self._dummy: Float[Tensor, "..."]
          self.register_buffer(
              "_dummy", torch.zeros(0, dtype=torch.float32), persistent=False
          )
  
      @property
      def grid_vertices(self) -> Float[Tensor, "N3 3"]:
          if self._grid_vertices is None:
              # keep the vertices on CPU so that we can support very large resolution
              x, y, z = (
                  torch.linspace(*self.points_range, self.resolution),
                  torch.linspace(*self.points_range, self.resolution),
                  torch.linspace(*self.points_range, self.resolution),
              )
              x, y, z = torch.meshgrid(x, y, z, indexing="ij")
              verts = torch.cat(
                  [x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1
              ).reshape(-1, 3)
              self._grid_vertices = verts
          return self._grid_vertices
  
      def forward(
          self,
          level: Float[Tensor, "N3 1"],
          deformation: Optional[Float[Tensor, "N3 3"]] = None,
      ) -> Mesh:
          if deformation is not None:
              craftsman.warn(
                  f"{self.__class__.__name__} does not support deformation. Ignoring."
              )
          level = -level.view(self.resolution, self.resolution, self.resolution)
          v_pos, t_pos_idx = self.mc_func(
              level.detach().cpu().numpy(), 0.0
          )  # transform to numpy
          v_pos, t_pos_idx = (
              torch.from_numpy(v_pos).float().to(self._dummy.device),
              torch.from_numpy(t_pos_idx.astype(np.int64)).long().to(self._dummy.device),
          )  # transform back to torch tensor on CUDA
          v_pos = v_pos / (self.resolution - 1.0)
          return Mesh(v_pos=v_pos, t_pos_idx=t_pos_idx)
  
  
  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
  
      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
  