"""
SE(3)-invariant feature extraction for interface graphs.
Node and edge features used by the Q_theta scorer.
"""

import os
import sys
import numpy as np

# Ensure utils is importable (for both direct and package imports)
_CODE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if _CODE_DIR not in sys.path:
    sys.path.insert(0, _CODE_DIR)

from utils.pdb_utils import (
    rbf_encode, compute_backbone_frames, compute_torsion_angles,
    get_aa_indices, compute_chi_angles, get_cb_positions, NUM_AA
)

# Feature dimensions
# one-hot AA (21) + backbone torsions (6) + chi1 sin/cos (2) + chi2 sin/cos (2) + chain indicator (1) = 32
NODE_DIM = NUM_AA + 6 + 4 + 1  # = 32
EDGE_DIM = 16 + 3 + 9 + 8 + 1  # RBF dist (16) + direction (3) + rel rotation (9) + seq sep (8) + same chain (1) = 37

MAX_SEQ_SEP = 32  # bins for sequence separation


def seq_sep_encode(sep, n_bins=8, max_sep=MAX_SEQ_SEP):
    """Bin-encode sequence separation."""
    bins = np.linspace(-max_sep, max_sep, n_bins + 1)
    sep_clipped = np.clip(sep, -max_sep, max_sep)
    encoded = np.zeros(n_bins, dtype=np.float32)
    bin_idx = np.digitize(sep_clipped, bins) - 1
    bin_idx = np.clip(bin_idx, 0, n_bins - 1)
    encoded[bin_idx] = 1.0
    return encoded


def extract_node_features(residues, coords, mask, torsion_angles, chi_angles, chain_id):
    """
    Compute per-residue node features.

    Args:
        residues: list of Bio.PDB residues
        coords: [N, 4, 3] backbone coords
        mask: [N] bool
        torsion_angles: [N, 6] sin/cos of phi, psi, omega
        chi_angles: [N, 4] sin/cos of chi1, chi2
        chain_id: 0 = receptor, 1 = binder

    Returns:
        node_feats: [N, NODE_DIM]  (NODE_DIM = 32)
    """
    N = len(residues)
    aa_idx = get_aa_indices(residues)

    # One-hot amino acid
    aa_onehot = np.zeros((N, NUM_AA), dtype=np.float32)
    for i in range(N):
        if mask[i]:
            aa_onehot[i, aa_idx[i]] = 1.0

    # Chain indicator
    chain_feat = np.full((N, 1), chain_id, dtype=np.float32)

    # Concatenate
    node_feats = np.concatenate([
        aa_onehot,          # [N, 21]
        torsion_angles,     # [N, 6]
        chi_angles,         # [N, 4]
        chain_feat,         # [N, 1]
    ], axis=-1)

    return node_feats  # [N, 32]


def extract_edge_features(coords_i, frames_i, coords_j, frames_j,
                          seq_idx_i, seq_idx_j, chain_i, chain_j, mask_i, mask_j):
    """
    Compute SE(3)-invariant edge features between residue sets i and j.
    Vectorized over all pairs.

    Args:
        coords_i: [N_i, 4, 3] backbone coords of set i (full interface)
        frames_i: (origins_i [N_i, 3], rotations_i [N_i, 3, 3])
        coords_j: [N_j, 4, 3]
        frames_j: (origins_j [N_j, 3], rotations_j [N_j, 3, 3])
        seq_idx_i: [N_i] integer sequence indices (for sequence separation)
        seq_idx_j: [N_j] integer sequence indices
        chain_i: int (0 or 1)
        chain_j: int (0 or 1)
        mask_i: [N_i] bool
        mask_j: [N_j] bool

    Returns:
        edge_feats: [N_i, N_j, EDGE_DIM]
    """
    N_i, N_j = len(coords_i), len(coords_j)
    origins_i, rotations_i = frames_i
    origins_j, rotations_j = frames_j

    ca_i = origins_i  # [N_i, 3]
    ca_j = origins_j  # [N_j, 3]

    # --- Distance features ---
    diff = ca_j[None, :, :] - ca_i[:, None, :]  # [N_i, N_j, 3]
    dist = np.sqrt((diff ** 2).sum(axis=-1))     # [N_i, N_j]
    dist_rbf = rbf_encode(dist, d_min=0., d_max=20., n_bins=16)  # [N_i, N_j, 16]

    # --- Direction in local frame of i ---
    # unit vector from i to j in global frame
    unit_diff = diff / (dist[..., None] + 1e-8)  # [N_i, N_j, 3]
    # rotate by R_i^T to get local direction
    # rotations_i: [N_i, 3, 3], unit_diff: [N_i, N_j, 3]
    # local_dir[i,j] = R_i^T @ (ca_j - ca_i) / dist
    local_dir = np.einsum('ikl,ijl->ijk', rotations_i, unit_diff)  # [N_i, N_j, 3]

    # --- Relative rotation: R_i^T R_j ---
    # rotations_i: [N_i, 3, 3], rotations_j: [N_j, 3, 3]
    # rel_rot[i,j] = R_i^T @ R_j -> [N_i, N_j, 3, 3] -> flatten to [N_i, N_j, 9]
    rel_rot = np.einsum('ikl,jlm->ijkm', rotations_i, rotations_j)  # [N_i, N_j, 3, 3]
    rel_rot_flat = rel_rot.reshape(N_i, N_j, 9)  # [N_i, N_j, 9]

    # --- Sequence separation ---
    sep = seq_idx_j[None, :] - seq_idx_i[:, None]  # [N_i, N_j]
    # Encode each pair (loop over all; use vectorized bin assignment)
    sep_flat = sep.reshape(-1)
    sep_enc = np.array([seq_sep_encode(s) for s in sep_flat])  # [N_i*N_j, 8]
    sep_enc = sep_enc.reshape(N_i, N_j, 8)

    # Cross-chain pairs get sep=0 by convention if different chains
    if chain_i != chain_j:
        sep_enc[:] = 0.0

    # --- Same chain indicator ---
    same_chain = float(chain_i == chain_j)
    same_chain_feat = np.full((N_i, N_j, 1), same_chain, dtype=np.float32)

    # --- Concatenate ---
    edge_feats = np.concatenate([
        dist_rbf,       # [N_i, N_j, 16]
        local_dir,      # [N_i, N_j, 3]
        rel_rot_flat,   # [N_i, N_j, 9]
        sep_enc,        # [N_i, N_j, 8]
        same_chain_feat # [N_i, N_j, 1]
    ], axis=-1)         # [N_i, N_j, 37]

    # Zero out edges involving masked residues
    edge_feats[~mask_i, :, :] = 0.0
    edge_feats[:, ~mask_j, :] = 0.0

    return edge_feats.astype(np.float32)


def build_interface_graph(rec_residues, rec_coords, rec_mask,
                          binder_residues, binder_coords, binder_mask,
                          rec_interface_mask, binder_interface_mask,
                          max_nodes: int = 128):
    """
    Build a joint interface graph combining receptor and binder interface residues.

    Returns a dict with:
        node_feats: [N_total, NODE_DIM]
        edge_feats: [N_total, N_total, EDGE_DIM]
        node_mask: [N_total] bool
        n_rec: int (number of receptor interface nodes)
        n_binder: int (number of binder interface nodes)
    """
    # Select interface residues
    rec_iface_idx = np.where(rec_interface_mask)[0]
    binder_iface_idx = np.where(binder_interface_mask)[0]

    # Truncate if too many
    if len(rec_iface_idx) > max_nodes // 2:
        rec_iface_idx = rec_iface_idx[:max_nodes // 2]
    if len(binder_iface_idx) > max_nodes // 2:
        binder_iface_idx = binder_iface_idx[:max_nodes // 2]

    n_rec = len(rec_iface_idx)
    n_binder = len(binder_iface_idx)
    n_total = n_rec + n_binder

    if n_total == 0:
        return None

    # Extract coords for interface residues
    rec_iface_coords = rec_coords[rec_iface_idx]    # [n_rec, 4, 3]
    binder_iface_coords = binder_coords[binder_iface_idx]  # [n_binder, 4, 3]
    rec_iface_mask = rec_mask[rec_iface_idx]
    binder_iface_mask = binder_mask[binder_iface_idx]

    # Compute backbone frames
    rec_origins, rec_rotations = compute_backbone_frames(rec_iface_coords, rec_iface_mask)
    binder_origins, binder_rotations = compute_backbone_frames(binder_iface_coords, binder_iface_mask)

    # Compute torsion angles
    # We need full-chain coords for proper phi/psi computation, but use local approximation here
    rec_torsion = compute_torsion_angles(rec_iface_coords, rec_iface_mask)
    binder_torsion = compute_torsion_angles(binder_iface_coords, binder_iface_mask)

    # Extract residues
    rec_iface_residues = [rec_residues[i] for i in rec_iface_idx]
    binder_iface_residues = [binder_residues[i] for i in binder_iface_idx]

    # Compute sidechain chi1/chi2 angles
    rec_chi = compute_chi_angles(rec_iface_residues, rec_iface_mask)
    binder_chi = compute_chi_angles(binder_iface_residues, binder_iface_mask)

    # Node features
    rec_node_feats = extract_node_features(
        rec_iface_residues, rec_iface_coords, rec_iface_mask, rec_torsion, rec_chi, chain_id=0
    )  # [n_rec, NODE_DIM]
    binder_node_feats = extract_node_features(
        binder_iface_residues, binder_iface_coords, binder_iface_mask, binder_torsion, binder_chi, chain_id=1
    )  # [n_binder, NODE_DIM]

    node_feats = np.concatenate([rec_node_feats, binder_node_feats], axis=0)  # [N, NODE_DIM]
    node_mask = np.concatenate([rec_iface_mask, binder_iface_mask], axis=0)

    # Edge features (4 blocks: RR, RB, BR, BB)
    all_coords = np.concatenate([rec_iface_coords, binder_iface_coords], axis=0)
    all_mask = node_mask
    all_origins = np.concatenate([rec_origins, binder_origins], axis=0)
    all_rotations = np.concatenate([rec_rotations, binder_rotations], axis=0)
    all_seq_idx = np.concatenate([rec_iface_idx, binder_iface_idx + len(rec_residues)], axis=0)
    all_chain = np.array([0] * n_rec + [1] * n_binder, dtype=np.int32)

    # Compute full NxN edge features
    frames_all = (all_origins, all_rotations)
    edge_feats = extract_edge_features(
        all_coords, frames_all,
        all_coords, frames_all,
        all_seq_idx, all_seq_idx,
        -1, -1,  # chain handled via all_chain array below
        all_mask, all_mask
    )  # [N, N, EDGE_DIM]

    # Patch same_chain feature (last dim) using actual chain IDs
    same_chain_feat = (all_chain[:, None] == all_chain[None, :]).astype(np.float32)
    edge_feats[:, :, -1] = same_chain_feat

    return {
        'node_feats': node_feats.astype(np.float32),    # [N, NODE_DIM]
        'edge_feats': edge_feats.astype(np.float32),    # [N, N, EDGE_DIM]
        'node_mask': node_mask,                          # [N]
        'n_rec': n_rec,
        'n_binder': n_binder,
        'rec_iface_idx': rec_iface_idx,                  # [n_rec] original residue indices
        'binder_iface_idx': binder_iface_idx,            # [n_binder] original residue indices
    }