"""
Full skeleton audit: naming + structural features for all 79 skeletons.

Checks:
1. Canonical name quality (empty, duplicate, semantic sense)
2. Side tag correctness (L/R balance, missed sides)
3. Symmetry pair validity (matched pairs have similar bone lengths/depths)
4. Topology structure (tree depth, branching factor, connected)
5. Geodesic distance matrix sanity
6. Rest pose geometry (bone lengths, height, spread)
7. Cross-dataset consistency for shared canonical names
"""

import sys
import os
from pathlib import Path
import numpy as np
from collections import Counter, defaultdict

project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))

from src.data.skeleton_graph import SkeletonGraph

RESULT_DIR = project_root / 'logs' / 'data_fix_20260318'

# Severity levels
ERRORS = []    # Must fix
WARNINGS = []  # Should review
INFO = []      # FYI


def log_error(dataset, msg):
    ERRORS.append(f'[ERROR] {dataset}: {msg}')

def log_warn(dataset, msg):
    WARNINGS.append(f'[WARN]  {dataset}: {msg}')

def log_info(dataset, msg):
    INFO.append(f'[INFO]  {dataset}: {msg}')


def audit_skeleton(dataset_id: str, skel_data: dict, motion_sample: dict = None):
    """Full audit of one skeleton."""
    sg = SkeletonGraph.from_dict(skel_data)
    J = sg.num_joints
    canon = [str(n) for n in skel_data.get('canonical_names', [])]
    raw = [str(n) for n in skel_data['joint_names']]

    results = {'dataset': dataset_id, 'num_joints': J, 'issues': []}

    # ===== 1. Canonical Name Quality =====
    if not canon:
        log_error(dataset_id, 'No canonical_names field in skeleton.npz')
        results['issues'].append('no_canonical')
    else:
        # Empty names
        empty = [i for i, c in enumerate(canon) if not c.strip()]
        if empty:
            log_error(dataset_id, f'{len(empty)} empty canonical names at indices {empty[:5]}')

        # Duplicate canonical names (within same skeleton)
        dupes = {c: [i for i, x in enumerate(canon) if x == c] for c in set(canon)}
        dupes = {c: idxs for c, idxs in dupes.items() if len(idxs) > 1}
        if dupes:
            for c, idxs in list(dupes.items())[:3]:
                raw_names = [raw[i] for i in idxs]
                log_warn(dataset_id, f'Duplicate canonical "{c}" for raw: {raw_names}')

        # Names that are just numbers or single chars
        bad_names = [c for c in canon if len(c) <= 2 and not c.isalpha()]
        if bad_names:
            log_warn(dataset_id, f'Very short canonical names: {bad_names[:5]}')

        # Prefix residue (bip01, bn_, jt_, mixamorig still present)
        prefix_residue = [c for c in canon if any(p in c.lower() for p in ['bip01', 'bn_', 'jt_', 'mixamorig', 'npc_'])]
        if prefix_residue:
            log_warn(dataset_id, f'Prefix residue in canonical: {prefix_residue[:5]}')

    # ===== 2. Side Tag Correctness =====
    n_left = sum(1 for t in sg.side_tags if t == 'left')
    n_right = sum(1 for t in sg.side_tags if t == 'right')
    n_center = sum(1 for t in sg.side_tags if t == 'center')

    if n_left != n_right and J > 5:  # small skeletons may be asymmetric
        log_warn(dataset_id, f'L/R imbalance: L={n_left} R={n_right} C={n_center}')

    # Check if canonical names agree with side tags
    if canon:
        for i, (c, t) in enumerate(zip(canon, sg.side_tags)):
            if 'left' in c and t != 'left':
                log_warn(dataset_id, f'Joint {i} "{raw[i]}" canonical="{c}" but side_tag="{t}"')
            elif 'right' in c and t != 'right':
                log_warn(dataset_id, f'Joint {i} "{raw[i]}" canonical="{c}" but side_tag="{t}"')

    # ===== 3. Symmetry Pair Validation =====
    for i, j in sg.symmetry_pairs:
        # Check bone lengths match (should be ~equal for symmetric joints)
        bl_i = sg.bone_lengths[i]
        bl_j = sg.bone_lengths[j]
        if bl_i > 0.01 and bl_j > 0.01:
            ratio = max(bl_i, bl_j) / min(bl_i, bl_j)
            if ratio > 1.5:
                log_warn(dataset_id, f'Sym pair ({raw[i]}, {raw[j]}) bone length mismatch: {bl_i:.3f} vs {bl_j:.3f}')

        # Check depths match
        if sg.depths[i] != sg.depths[j]:
            log_warn(dataset_id, f'Sym pair ({raw[i]}, {raw[j]}) depth mismatch: {sg.depths[i]} vs {sg.depths[j]}')

    # ===== 4. Topology Structure =====
    max_depth = sg.depths.max()
    max_degree = sg.degrees.max()
    leaf_count = sum(1 for d in sg.degrees if d == 0)
    root_count = sum(1 for p in sg.parent_indices if p == -1)

    if root_count != 1:
        log_error(dataset_id, f'Expected 1 root, found {root_count}')

    if max_depth > 20:
        log_warn(dataset_id, f'Very deep tree: max_depth={max_depth}')

    # Check connectivity (all joints reachable from root)
    reachable = set()
    queue = [i for i, p in enumerate(sg.parent_indices) if p == -1]
    while queue:
        curr = queue.pop(0)
        reachable.add(curr)
        for j in range(J):
            if sg.parent_indices[j] == curr and j not in reachable:
                queue.append(j)
    if len(reachable) != J:
        log_error(dataset_id, f'Disconnected: only {len(reachable)}/{J} joints reachable from root')

    # ===== 5. Geodesic Distance Sanity =====
    geo = sg.geodesic_dist
    if geo.shape != (J, J):
        log_error(dataset_id, f'Geodesic distance shape mismatch: {geo.shape} vs ({J},{J})')
    else:
        # Should be symmetric
        asym = np.abs(geo - geo.T).max()
        if asym > 0.01:
            log_error(dataset_id, f'Geodesic distance not symmetric: max asymmetry={asym}')

        # Diagonal should be 0
        diag_max = np.diag(geo).max()
        if diag_max > 0.01:
            log_error(dataset_id, f'Geodesic distance diagonal non-zero: max={diag_max}')

        # Max distance should be reasonable
        max_geo = geo.max()
        if max_geo > J:
            log_warn(dataset_id, f'Geodesic max={max_geo} exceeds num_joints={J}')

        # No unreachable pairs (distance should be < J+1)
        unreachable = (geo >= J + 1).sum()
        if unreachable > 0:
            log_error(dataset_id, f'{unreachable} unreachable joint pairs in geodesic matrix')

    # ===== 6. Rest Pose Geometry =====
    offsets = sg.rest_offsets
    bone_lengths = sg.bone_lengths

    # Zero-length bones (excluding root)
    zero_bones = sum(1 for i, bl in enumerate(bone_lengths) if bl < 1e-6 and sg.parent_indices[i] >= 0)
    if zero_bones > 0:
        log_info(dataset_id, f'{zero_bones} zero-length bones')

    # Very long bones (> 10x median)
    nonzero_bl = bone_lengths[bone_lengths > 1e-6]
    if len(nonzero_bl) > 0:
        median_bl = np.median(nonzero_bl)
        long_bones = [(raw[i], bl) for i, bl in enumerate(bone_lengths)
                      if bl > 10 * median_bl and sg.parent_indices[i] >= 0]
        if long_bones:
            log_warn(dataset_id, f'Unusually long bones (>10x median={median_bl:.4f}): {long_bones[:3]}')

    # ===== 7. Motion Data Spot Check =====
    if motion_sample is not None:
        T = int(motion_sample['num_frames'])
        jp = motion_sample['joint_positions'][:T]
        lp = motion_sample['local_positions'][:T]
        vel = motion_sample['velocities'][:T]

        # Check joint count matches
        if jp.shape[1] != J:
            log_error(dataset_id, f'Motion joints={jp.shape[1]} != skeleton joints={J}')

        # Check for NaN/Inf
        for key in ['joint_positions', 'local_positions', 'velocities']:
            arr = motion_sample[key][:T]
            if np.any(np.isnan(arr)):
                log_error(dataset_id, f'NaN in motion {key}')
            if np.any(np.isinf(arr)):
                log_error(dataset_id, f'Inf in motion {key}')

        # Height sanity
        height = jp[0, :, 1].max() - jp[0, :, 1].min()
        if height < 0.05:
            log_warn(dataset_id, f'Very small body height: {height:.4f}m')
        elif height > 10:
            log_warn(dataset_id, f'Very large body height: {height:.2f}m (scale issue?)')

        # Velocity sanity
        vel_max = np.linalg.norm(vel, axis=-1).max()
        if vel_max > 50:
            log_warn(dataset_id, f'Very high velocity: max={vel_max:.1f} m/s')

    # Collect summary
    results['n_left'] = n_left
    results['n_right'] = n_right
    results['n_center'] = n_center
    results['n_sym'] = len(sg.symmetry_pairs)
    results['max_depth'] = int(max_depth)
    results['max_degree'] = int(max_degree)
    results['leaf_count'] = leaf_count
    results['n_canonical'] = len(canon)
    results['n_dupe_canonical'] = sum(len(v) - 1 for v in dupes.values()) if canon and dupes else 0

    return results


def main():
    all_results = []

    # Audit human datasets
    human_datasets = ['humanml3d', 'lafan1', '100style', 'bandai_namco', 'cmu_mocap', 'mixamo']
    for ds in human_datasets:
        ds_path = project_root / 'data' / 'processed' / ds
        skel_data = dict(np.load(ds_path / 'skeleton.npz', allow_pickle=True))

        # Load a motion sample
        motions_dir = ds_path / 'motions'
        files = sorted(os.listdir(motions_dir))
        motion_sample = dict(np.load(motions_dir / files[0], allow_pickle=True)) if files else None

        r = audit_skeleton(ds, skel_data, motion_sample)
        all_results.append(r)

    # Audit Zoo species
    zoo_path = project_root / 'data' / 'processed' / 'truebones_zoo'
    skel_dir = zoo_path / 'skeletons'
    motions_dir = zoo_path / 'motions'

    # Build species → motion mapping
    species_motions = {}
    for f in sorted(os.listdir(motions_dir))[:200]:
        d = dict(np.load(motions_dir / f, allow_pickle=True))
        sp = str(d.get('species', ''))
        if sp and sp not in species_motions:
            species_motions[sp] = d

    for skel_file in sorted(skel_dir.glob('*.npz')):
        species = skel_file.stem
        skel_data = dict(np.load(skel_file, allow_pickle=True))
        motion_sample = species_motions.get(species)
        r = audit_skeleton(f'zoo/{species}', skel_data, motion_sample)
        all_results.append(r)

    # ===== Cross-dataset canonical consistency =====
    # For human datasets, check that same canonical name → similar tree depth
    canonical_depths = defaultdict(list)
    for ds in human_datasets:
        skel_data = dict(np.load(project_root / 'data' / 'processed' / ds / 'skeleton.npz', allow_pickle=True))
        sg = SkeletonGraph.from_dict(skel_data)
        canon = [str(n) for n in skel_data.get('canonical_names', [])]
        for c, d in zip(canon, sg.depths):
            canonical_depths[c].append((ds, int(d)))

    for c, entries in canonical_depths.items():
        depths = [d for _, d in entries]
        if len(set(depths)) > 1 and max(depths) - min(depths) > 2:
            sources = [(ds, d) for ds, d in entries]
            log_warn('cross-dataset', f'Canonical "{c}" has depth variance: {sources}')

    # ===== Write Report =====
    report_path = RESULT_DIR / 'skeleton_audit_report.md'
    with open(report_path, 'w') as f:
        f.write('# Skeleton Audit Report\n\n')
        f.write(f'**Date**: 2026-03-18\n')
        f.write(f'**Datasets**: {len(human_datasets)} human + {len(list(skel_dir.glob("*.npz")))} zoo species\n\n')

        # Summary table
        f.write('## Summary\n\n')
        f.write(f'| Dataset | J | L | R | C | Sym | Depth | Leaves | Canon | Dupes |\n')
        f.write(f'|---------|:-:|:-:|:-:|:-:|:---:|:-----:|:------:|:-----:|:-----:|\n')
        for r in all_results:
            f.write(f'| {r["dataset"]:20s} | {r["num_joints"]:3d} | {r["n_left"]:2d} | {r["n_right"]:2d} | '
                    f'{r["n_center"]:2d} | {r["n_sym"]:2d} | {r["max_depth"]:2d} | {r["leaf_count"]:2d} | '
                    f'{r["n_canonical"]:3d} | {r["n_dupe_canonical"]:2d} |\n')

        # Issues
        f.write(f'\n## Errors ({len(ERRORS)})\n\n')
        if ERRORS:
            for e in ERRORS:
                f.write(f'- {e}\n')
        else:
            f.write('None.\n')

        f.write(f'\n## Warnings ({len(WARNINGS)})\n\n')
        if WARNINGS:
            for w in WARNINGS:
                f.write(f'- {w}\n')
        else:
            f.write('None.\n')

        f.write(f'\n## Info ({len(INFO)})\n\n')
        if INFO:
            for i in INFO:
                f.write(f'- {i}\n')
        else:
            f.write('None.\n')

    print(f'Audit complete: {len(ERRORS)} errors, {len(WARNINGS)} warnings, {len(INFO)} info')
    print(f'Report: {report_path}')

    # Print to console too
    if ERRORS:
        print(f'\n=== ERRORS ({len(ERRORS)}) ===')
        for e in ERRORS:
            print(f'  {e}')
    if WARNINGS:
        print(f'\n=== WARNINGS ({len(WARNINGS)}) ===')
        for w in WARNINGS[:30]:
            print(f'  {w}')
        if len(WARNINGS) > 30:
            print(f'  ... and {len(WARNINGS) - 30} more')


if __name__ == '__main__':
    main()