"""
Delaunay meshing + SDF labeling + surface extraction.
Pure Python using scipy.spatial.Delaunay instead of CGAL.
"""
import numpy as np
import torch


def compute_circumsphere_centers(tetrahedra):
    """
    tetrahedra: (n, 4, 3)
    Returns: (n, 3) circumcenters.
    """
    A = tetrahedra[:, 0, :]
    B = tetrahedra[:, 1, :]
    C = tetrahedra[:, 2, :]
    D = tetrahedra[:, 3, :]

    x1, y1, z1 = A[:, 0:1], A[:, 1:2], A[:, 2:3]
    x2, y2, z2 = B[:, 0:1], B[:, 1:2], B[:, 2:3]
    x3, y3, z3 = C[:, 0:1], C[:, 1:2], C[:, 2:3]
    x4, y4, z4 = D[:, 0:1], D[:, 1:2], D[:, 2:3]

    A_matrix = np.stack([
        np.concatenate([x2 - x1, y2 - y1, z2 - z1], axis=-1),
        np.concatenate([x3 - x1, y3 - y1, z3 - z1], axis=-1),
        np.concatenate([x4 - x1, y4 - y1, z4 - z1], axis=-1),
    ], axis=1)  # (n, 3, 3)

    b_vector = 0.5 * np.concatenate([
        x2 ** 2 - x1 ** 2 + y2 ** 2 - y1 ** 2 + z2 ** 2 - z1 ** 2,
        x3 ** 2 - x1 ** 2 + y3 ** 2 - y1 ** 2 + z3 ** 2 - z1 ** 2,
        x4 ** 2 - x1 ** 2 + y4 ** 2 - y1 ** 2 + z4 ** 2 - z1 ** 2,
    ], axis=-1)  # (n, 3)

    center = np.linalg.solve(A_matrix, b_vector)
    return torch.from_numpy(center).float()


def random_sampling_tetra(cell_vertex, k_samples):
    """
    Random barycentric samples inside each tetrahedron.
    cell_vertex: (n, 4, 3)
    Returns: (n, k, 3)
    """
    n = cell_vertex.shape[0]
    random = np.random.rand(k_samples, 4).astype(np.float32)
    random = random / (random.sum(axis=1, keepdims=True) + 1e-8)
    random = torch.from_numpy(random).float()
    random_samples = cell_vertex.unsqueeze(1) * random.view(1, k_samples, 4, 1)
    random_samples = random_samples.sum(dim=2)
    return random_samples


def labeling(sdf_network, queries, sdf_threshold=0.0, device='cpu', batch_size=10000):
    """
    Query SDF and label tetrahedra.
    queries: (n, k, 3)
    Returns: labels (n,) int, 0=outside, 1=inside.
    """
    n, k, _ = queries.shape
    queries_flat = queries.view(-1, 3).to(device)
    sdf_vals = []
    with torch.no_grad():
        for i in range(0, len(queries_flat), batch_size):
            batch = queries_flat[i:i + batch_size]
            s = sdf_network.sdf(batch).cpu()
            sdf_vals.append(s)
    sdf = torch.cat(sdf_vals, dim=0).view(n, k, 1)

    ref = torch.where(sdf >= sdf_threshold, 1.0, 0.0)
    ref_sum = ref.mean(dim=1)  # (n, 1)
    labels = torch.where(ref_sum >= 0.45, 1, 0).squeeze(-1)
    return labels


def relabeling(labels, infinite_cell_id, cell_adj):
    """
    Relabel using adjacency consistency.
    labels: (n,) int
    infinite_cell_id: set of infinite cell indices
    cell_adj: (n, 4) neighbor indices
    """
    labels = labels.clone()
    adj_labels = labels[cell_adj]  # (n, 4)
    adj_labels_sum = adj_labels.sum(dim=-1, keepdim=True)
    inside = torch.where((adj_labels_sum == 0) | (adj_labels_sum == 1))
    outside = torch.where((adj_labels_sum == 3) | (adj_labels_sum == 4))
    labels[inside] = 0
    labels[outside] = 1
    for idx in infinite_cell_id:
        labels[idx] = 0
    return labels


def create_mesh_from_delaunay(points, labels, delaunay):
    """
    Extract surface mesh from labeled Delaunay tetrahedra.
    Returns: vertices (m, 3), faces (p, 3)
    """
    # facets between cells with different labels form the surface
    # Build adjacency from Delaunay.neighbors
    simplices = delaunay.simplices  # (n, 4)
    neighbors = delaunay.neighbors   # (n, 4)

    faces = []
    for i in range(len(simplices)):
        for j in range(4):
            ni = neighbors[i, j]
            if ni == -1:
                # boundary facet
                if labels[i] == 1:
                    f = np.delete(simplices[i], j)
                    faces.append(f)
            elif labels[i] != labels[ni] and i < ni:
                # shared facet between inside and outside
                f = np.delete(simplices[i], j)
                faces.append(f)

    if len(faces) == 0:
        return points, np.zeros((0, 3), dtype=np.int32)

    faces = np.array(faces)
    return points, faces


def delaunay_meshing(points, sdf_network, sdf_threshold=0.0, k_samples=21, device='cpu'):
    """
    Full pipeline: Delaunay -> sample -> label -> extract mesh.

    Args:
        points: (N, 3) numpy array or tensor of generated vertices
        sdf_network: trained SDFNetwork
        sdf_threshold: surface level
        k_samples: random samples per tetrahedron
        device: torch device
    Returns:
        vertices, faces as numpy arrays
    """
    from scipy.spatial import Delaunay

    if torch.is_tensor(points):
        points_np = points.detach().cpu().numpy()
    else:
        points_np = np.asarray(points, dtype=np.float32)

    print("Building Delaunay triangulation...")
    delaunay = Delaunay(points_np)
    simplices = delaunay.simplices  # (n_tets, 4)

    # Identify infinite cells (any vertex == -1 in neighbor means boundary)
    # scipy Delaunay marks boundary neighbors as -1
    n_tets = len(simplices)
    infinite_cell_id = set()
    for i in range(n_tets):
        if np.any(delaunay.neighbors[i] == -1):
            infinite_cell_id.add(i)

    # Compute circumcenters for constraint sampling
    cell_vertex = torch.from_numpy(points_np[simplices]).float()  # (n_tets, 4, 3)
    ball_centers = cell_vertex.mean(dim=1, keepdim=True)  # (n_tets, 1, 3)

    try:
        c_centers = compute_circumsphere_centers(cell_vertex.numpy()).unsqueeze(1)  # (n_tets, 1, 3)
        use_cc = True
    except Exception:
        c_centers = None
        use_cc = False

    samples = random_sampling_tetra(cell_vertex, k_samples)
    samples = torch.cat([samples, ball_centers], dim=1)  # (n_tets, k+1, 3)
    if use_cc:
        samples = torch.cat([samples, c_centers], dim=1)

    print(f"Labeling {n_tets} cells with SDF queries...")
    labels = labeling(sdf_network, samples, sdf_threshold=sdf_threshold, device=device)

    # Build adjacency for relabeling
    neighbors_t = torch.from_numpy(delaunay.neighbors).long()
    neighbors_t = torch.where(neighbors_t < 0, torch.tensor(0), neighbors_t)  # dummy for -1
    labels = relabeling(labels, infinite_cell_id, neighbors_t)

    vertices, faces = create_mesh_from_delaunay(points_np, labels, delaunay)
    print(f"Mesh: {len(vertices)} vertices, {len(faces)} faces")
    return vertices, faces


def add_mid_vertices(vertices, faces):
    """
    Fix non-manifold edges by adding midpoint vertices.
    Simple pure-Python version.
    """
    import collections
    edges = []
    for f in faces:
        edges.append(tuple(sorted([f[0], f[1]])))
        edges.append(tuple(sorted([f[1], f[2]])))
        edges.append(tuple(sorted([f[2], f[0]])))

    edge_count = collections.Counter(edges)
    bad_edges = [e for e, c in edge_count.items() if c != 2]

    if len(bad_edges) == 0:
        return vertices, faces

    vert_list = list(vertices)
    face_list = list(faces)

    for e in bad_edges:
        v0, v1 = e
        mid = (vert_list[v0] + vert_list[v1]) * 0.5
        mid_idx = len(vert_list)
        vert_list.append(mid)

        new_faces = []
        for f in face_list:
            ef = [tuple(sorted([f[0], f[1]])),
                  tuple(sorted([f[1], f[2]])),
                  tuple(sorted([f[2], f[0]]))]
            if e in ef:
                # split this face along the edge
                other = [fv for fv in f if fv not in e][0]
                f1 = np.array([v0, mid_idx, other])
                f2 = np.array([mid_idx, v1, other])
                new_faces.append(f1)
                new_faces.append(f2)
            else:
                new_faces.append(f)
        face_list = new_faces

    return np.array(vert_list), np.array(face_list)