"""
Convert HumanML3D data (SMPL-based .npy format) into our unified representation.

HumanML3D stores motions as:
- new_joints/XXXXXX.npy: [T, 22, 3] joint positions (SMPL 22-joint skeleton)
- new_joint_vecs/XXXXXX.npy: [T, 263] rotation-invariant features
- texts/XXXXXX.txt: text descriptions (multiple per motion)

We convert to:
- SkeletonGraph (fixed SMPL-22 topology)
- Motion dict with positions, velocities, and text annotations
"""

import numpy as np
from pathlib import Path
from typing import Optional

from .skeleton_graph import SkeletonGraph


# SMPL 22-joint skeleton definition
SMPL_22_JOINT_NAMES = [
    'pelvis',           # 0
    'left_hip',         # 1
    'right_hip',        # 2
    'spine1',           # 3
    'left_knee',        # 4
    'right_knee',       # 5
    'spine2',           # 6
    'left_ankle',       # 7
    'right_ankle',      # 8
    'spine3',           # 9
    'left_foot',        # 10
    'right_foot',       # 11
    'neck',             # 12
    'left_collar',      # 13
    'right_collar',     # 14
    'head',             # 15
    'left_shoulder',    # 16
    'right_shoulder',   # 17
    'left_elbow',       # 18
    'right_elbow',      # 19
    'left_wrist',       # 20
    'right_wrist',      # 21
]

SMPL_22_PARENTS = [
    -1,  # 0 pelvis (root)
    0,   # 1 left_hip -> pelvis
    0,   # 2 right_hip -> pelvis
    0,   # 3 spine1 -> pelvis
    1,   # 4 left_knee -> left_hip
    2,   # 5 right_knee -> right_hip
    3,   # 6 spine2 -> spine1
    4,   # 7 left_ankle -> left_knee
    5,   # 8 right_ankle -> right_knee
    6,   # 9 spine3 -> spine2
    7,   # 10 left_foot -> left_ankle
    8,   # 11 right_foot -> right_ankle
    9,   # 12 neck -> spine3
    9,   # 13 left_collar -> spine3
    9,   # 14 right_collar -> spine3
    12,  # 15 head -> neck
    13,  # 16 left_shoulder -> left_collar
    14,  # 17 right_shoulder -> right_collar
    16,  # 18 left_elbow -> left_shoulder
    17,  # 19 right_elbow -> right_shoulder
    18,  # 20 left_wrist -> left_elbow
    19,  # 21 right_wrist -> right_elbow
]


def get_smpl22_skeleton(rest_pose: Optional[np.ndarray] = None) -> SkeletonGraph:
    """
    Get the SMPL 22-joint skeleton graph.

    Args:
        rest_pose: [22, 3] rest-pose joint positions. If None, uses default T-pose offsets.

    Returns:
        SkeletonGraph for SMPL-22.
    """
    if rest_pose is None:
        # Default T-pose offsets (approximate, from HumanML3D average)
        rest_pose = np.array([
            [0.0, 0.0, 0.0],       # pelvis
            [0.08, -0.05, 0.0],     # left_hip
            [-0.08, -0.05, 0.0],    # right_hip
            [0.0, 0.1, 0.0],        # spine1
            [0.0, -0.4, 0.0],       # left_knee
            [0.0, -0.4, 0.0],       # right_knee
            [0.0, 0.15, 0.0],       # spine2
            [0.0, -0.4, 0.0],       # left_ankle
            [0.0, -0.4, 0.0],       # right_ankle
            [0.0, 0.15, 0.0],       # spine3
            [0.0, -0.05, 0.1],      # left_foot
            [0.0, -0.05, 0.1],      # right_foot
            [0.0, 0.12, 0.0],       # neck
            [0.05, 0.0, 0.0],       # left_collar
            [-0.05, 0.0, 0.0],      # right_collar
            [0.0, 0.12, 0.0],       # head
            [0.15, 0.0, 0.0],       # left_shoulder
            [-0.15, 0.0, 0.0],      # right_shoulder
            [0.25, 0.0, 0.0],       # left_elbow
            [-0.25, 0.0, 0.0],      # right_elbow
            [0.25, 0.0, 0.0],       # left_wrist
            [-0.25, 0.0, 0.0],      # right_wrist
        ], dtype=np.float32)

    # Compute offsets from parent
    offsets = np.zeros_like(rest_pose)
    for j in range(len(SMPL_22_PARENTS)):
        p = SMPL_22_PARENTS[j]
        if p >= 0:
            offsets[j] = rest_pose[j] - rest_pose[p]
        else:
            offsets[j] = rest_pose[j]

    return SkeletonGraph(
        joint_names=SMPL_22_JOINT_NAMES,
        parent_indices=SMPL_22_PARENTS,
        rest_offsets=offsets,
    )


def load_humanml3d_motion(
    motion_id: str,
    data_dir: str | Path,
) -> dict:
    """
    Load a single HumanML3D motion sample.

    Args:
        motion_id: e.g., '000000'
        data_dir: path to HumanML3D directory

    Returns:
        dict with keys:
        - 'joint_positions': [T, 22, 3] global joint positions
        - 'joint_vecs': [T, 263] rotation-invariant features (if available)
        - 'texts': list of text descriptions
        - 'motion_id': str
    """
    data_dir = Path(data_dir)

    # Load joint positions
    joints_path = data_dir / 'new_joints' / f'{motion_id}.npy'
    joint_positions = np.load(joints_path)  # [T, 22, 3]

    # Load joint vectors (rotation-invariant features) if available
    vecs_path = data_dir / 'new_joint_vecs' / f'{motion_id}.npy'
    joint_vecs = None
    if vecs_path.exists():
        joint_vecs = np.load(vecs_path)  # [T, 263]

    # Load text descriptions
    text_path = data_dir / 'texts' / f'{motion_id}.txt'
    texts = []
    if text_path.exists():
        with open(text_path, 'r') as f:
            for line in f:
                line = line.strip()
                if line:
                    # Format: "text#token1 token2#start#end"
                    parts = line.split('#')
                    if parts:
                        texts.append(parts[0].strip())

    return {
        'joint_positions': joint_positions.astype(np.float32),
        'joint_vecs': joint_vecs,
        'texts': texts,
        'motion_id': motion_id,
    }


def compute_motion_features(
    joint_positions: np.ndarray,
    skeleton: SkeletonGraph,
    fps: float = 20.0,
) -> dict:
    """
    Compute motion features from joint positions for TopoSlots (Scheme C).

    Scheme C:
      - Slot tokens: per-joint [local_pos(3) + velocity(3)] = 6D (cross-skeleton compatible)
      - Decoder GT: per-joint rotations via FK supervision (skeleton-specific)
      - Root trajectory: separate track
      - Foot contact: auxiliary loss

    Args:
        joint_positions: [T, J, 3] global joint positions
        skeleton: SkeletonGraph
        fps: frames per second

    Returns:
        dict with:
        - 'root_position': [T, 3]
        - 'root_velocity': [T, 3]
        - 'local_positions': [T, J, 3] root-relative joint positions
        - 'velocities': [T, J, 3] joint velocities
        - 'accelerations': [T, J, 3] joint accelerations
        - 'bone_lengths': [T, J] per-frame bone lengths
        - 'foot_contact': [T, 4] 4-channel (l_heel, l_toe, r_heel, r_toe)
    """
    T, J, _ = joint_positions.shape

    # Root position (joint 0)
    root_pos = joint_positions[:, 0, :]  # [T, 3]

    # Local positions (relative to root)
    local_pos = joint_positions - root_pos[:, None, :]  # [T, J, 3]

    # Velocities (finite difference)
    vel = np.zeros_like(joint_positions)
    vel[1:] = (joint_positions[1:] - joint_positions[:-1]) * fps
    vel[0] = vel[1]

    root_vel = vel[:, 0, :]  # [T, 3]

    # Accelerations (finite difference of velocity)
    acc = np.zeros_like(vel)
    acc[1:] = (vel[1:] - vel[:-1]) * fps
    acc[0] = acc[1]

    # Bone lengths per frame
    bone_lengths = np.zeros((T, J), dtype=np.float32)
    for j in range(J):
        p = skeleton.parent_indices[j]
        if p >= 0:
            bone_lengths[:, j] = np.linalg.norm(
                joint_positions[:, j] - joint_positions[:, p], axis=-1
            )

    # Foot contact: 4-channel detection via velocity + height
    foot_contact = _detect_foot_contact(joint_positions, vel, skeleton)

    return {
        'root_position': root_pos,
        'root_velocity': root_vel,
        'local_positions': local_pos,
        'velocities': vel,
        'accelerations': acc,
        'bone_lengths': bone_lengths,
        'foot_contact': foot_contact,
    }


def _detect_foot_contact(
    positions: np.ndarray,
    velocities: np.ndarray,
    skeleton: SkeletonGraph,
    vel_thresh: float = None,
) -> np.ndarray:
    """
    Detect 4-channel foot contact: [l_heel, l_toe, r_heel, r_toe].

    Auto-adapts thresholds based on data scale (meters vs centimeters).
    """
    T = positions.shape[0]
    foot_contact = np.zeros((T, 4), dtype=np.float32)

    # Auto-detect scale for thresholds
    body_height = positions[0, :, 1].max() - positions[0, :, 1].min()
    if body_height < 0.01:
        return foot_contact  # degenerate
    # Velocity threshold proportional to body height
    # ~0.5 m/s for 1.7m human → 0.3 * body_height
    if vel_thresh is None:
        vel_thresh = 0.3 * body_height
    height_margin = 0.03 * body_height  # ~5cm for 1.7m human

    names_lower = [n.lower() for n in skeleton.joint_names]

    # Find foot-related joints with broader matching
    joint_map = {
        'l_heel': None, 'l_toe': None,
        'r_heel': None, 'r_toe': None,
    }
    for j, n in enumerate(names_lower):
        is_left = 'left' in n or n.startswith('l_') or n.startswith('l ') or 'leftfoot' in n.replace(' ', '')
        is_right = 'right' in n or n.startswith('r_') or n.startswith('r ') or 'rightfoot' in n.replace(' ', '')
        is_ankle = 'ankle' in n or 'heel' in n
        is_foot = 'foot' in n or 'toe' in n

        if is_left and is_ankle and joint_map['l_heel'] is None:
            joint_map['l_heel'] = j
        elif is_left and is_foot and joint_map['l_toe'] is None:
            joint_map['l_toe'] = j
        elif is_right and is_ankle and joint_map['r_heel'] is None:
            joint_map['r_heel'] = j
        elif is_right and is_foot and joint_map['r_toe'] is None:
            joint_map['r_toe'] = j

    channels = ['l_heel', 'l_toe', 'r_heel', 'r_toe']
    for ch_idx, ch_name in enumerate(channels):
        jidx = joint_map[ch_name]
        if jidx is None:
            continue
        jvel = np.linalg.norm(velocities[:, jidx, :], axis=-1)
        jheight = positions[:, jidx, 1]
        height_thresh = np.percentile(jheight, 10) + height_margin
        foot_contact[:, ch_idx] = (
            (jvel < vel_thresh) & (jheight < height_thresh)
        ).astype(np.float32)

    return foot_contact


def extract_rotations_from_263d(joint_vecs: np.ndarray) -> dict:
    """
    Extract structured features from HumanML3D 263D vector.

    Layout (22-joint SMPL):
        [0:1]    root angular velocity (y-axis)
        [1:3]    root linear velocity (xz)
        [3:4]    root height (y)
        [4:67]   joint positions relative to root  (21 × 3 = 63)
        [67:193]  joint 6D continuous rotations      (21 × 6 = 126)
        [193:259] joint velocities                   (22 × 3 = 66)
        [259:263] foot contact (4 channels)

    Returns:
        dict with:
        - 'root_angular_vel': [T, 1]
        - 'root_linear_vel': [T, 2]
        - 'root_height': [T, 1]
        - 'ric_positions': [T, 21, 3]
        - 'local_rotations_6d': [T, 21, 6]
        - 'joint_velocities': [T, 22, 3]
        - 'foot_contact_4ch': [T, 4]
    """
    T = joint_vecs.shape[0]
    return {
        'root_angular_vel': joint_vecs[:, 0:1],
        'root_linear_vel': joint_vecs[:, 1:3],
        'root_height': joint_vecs[:, 3:4],
        'ric_positions': joint_vecs[:, 4:67].reshape(T, 21, 3),
        'local_rotations_6d': joint_vecs[:, 67:193].reshape(T, 21, 6),
        'joint_velocities': joint_vecs[:, 193:259].reshape(T, 22, 3),
        'foot_contact_4ch': joint_vecs[:, 259:263],
    }


def load_humanml3d_split(
    data_dir: str | Path,
    split: str = 'train',
) -> list[str]:
    """Load motion IDs for a data split."""
    data_dir = Path(data_dir)
    split_file = data_dir / f'{split}.txt'

    if not split_file.exists():
        raise FileNotFoundError(f"Split file not found: {split_file}")

    motion_ids = []
    with open(split_file, 'r') as f:
        for line in f:
            line = line.strip()
            if line:
                motion_ids.append(line)

    return motion_ids