#!/usr/bin/env python3
"""Check release integrity, cohorts, camera arrays, and reference statistics."""
import argparse
from collections import Counter
import hashlib
import json
from pathlib import Path

import numpy as np


def check(condition, message):
    if not condition:
        raise ValueError(message)


def main():
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument('--root', type=Path, default=Path(__file__).resolve().parents[1])
    ap.add_argument('--require-images', action='store_true')
    args = ap.parse_args()
    root = args.root
    for line in (root / 'checksums.sha256').read_text().splitlines():
        expected, relative = line.split('  ', 1)
        path = root / relative
        check(path.is_file(), f'Missing release file: {relative}')
        check(hashlib.sha256(path.read_bytes()).hexdigest() == expected, f'Checksum mismatch: {relative}')

    def rows(name):
        return [json.loads(s) for s in (root / name).read_text().splitlines()]

    t2v, i2v = rows('data/t2v/test.jsonl'), rows('data/i2v/test.jsonl')
    sources = rows('sources/i2v_images.jsonl')
    check(len(t2v) == len({r['scene_id'] for r in t2v}) == 100, 'T2V scene count or uniqueness mismatch')
    check(len(i2v) == len({r['sample_id'] for r in i2v}) == 750, 'I2V sample count or uniqueness mismatch')
    check(len(sources) == len({r['scene_id'] for r in i2v}) == 150, 'I2V scene count mismatch')
    check(Counter(r['scene_type'] for r in sources) == {'indoor': 50, 'outdoor': 100}, 'Incorrect scene mix')
    for row in t2v + i2v:
        check(isinstance(row['seed'], str) and row['seed'].isdigit(), 'Seed must be a decimal string')
        check(0 <= int(row['seed']) < 2**63, 'Seed out of range')
    for row in t2v:
        check(len(row['prompts']) == 4 and all(row['prompts']), 'Incomplete T2V prompt schedule')
        check(row['prompt_start_seconds'] == [0.0, 3.6, 6.2, 11.2], 'T2V prompt timing mismatch')
    cohorts = {}
    for row in i2v:
        cohorts.setdefault(row['setting'], set()).add(row['scene_id'])
        with np.load(root / row['trajectory_path'], allow_pickle=False) as data:
            p, k = data['poses'], data['intrinsics']
            check(p.shape == (row['pose_samples'], 4, 4), 'Pose shape mismatch')
            check(k.shape == (row['intrinsics_samples'], 4), 'Intrinsics shape mismatch')
            check(np.isfinite(p).all() and np.isfinite(k).all(), 'Non-finite camera values')
            check(np.allclose(k, k[0]), 'Expected constant camera intrinsics')
            check(np.allclose(p[0], p[-1], atol=1e-5), 'Trajectory does not return to its origin')
            check(np.allclose(p[:, 3], [0, 0, 0, 1]), 'Invalid homogeneous pose')
            r = p[:, :3, :3]
            check(np.allclose(r.transpose(0, 2, 1) @ r, np.eye(3), atol=1e-5), 'Invalid rotation matrix')
            check(np.allclose(np.linalg.det(r), 1, atol=1e-5), 'Invalid rotation determinant')
            moving = np.max(np.linalg.norm(p[:, :3, 3] - p[0, :3, 3], axis=1)) > 1e-5
            check(bool(moving) == row['translation'], 'Translation flag disagrees with camera path')
    all_scenes = {r['scene_id'] for r in sources}
    outdoors = {r['scene_id'] for r in sources if r['scene_type'] == 'outdoor'}
    for angle in [90, 180, 360]:
        check(cohorts[f'rotation_{angle}'] == all_scenes, 'Rotation cohort mismatch')
        check(cohorts[f'translation_{angle}'] == outdoors, 'Translation cohort mismatch')
    pairs = rows('evaluation/t2v_reference_pairs.jsonl')
    check(len(pairs) == 600, 'Missing reference annotations')
    check(len({(p['scene_id'], p['method'], p['comparison_budget_chunks']) for p in pairs}) == 600,
          'Duplicate reference annotations')
    for p in pairs:
        check(0 <= p['departure_frame'] < p['revisit_frame'] < 379, 'Reference frame out of range')
    summary = json.loads((root / 'evaluation/t2v_reference_summary.json').read_text())
    for s in summary:
        subset = [p for p in pairs if p['method'] == s['method'] and p['comparison_budget_chunks'] == s['comparison_budget_chunks']]
        check(len(subset) == s['n'] == 100, 'Reference cohort mismatch')
        for key in ['clip', 'lpips_alex']:
            check(np.isclose(np.median([p[key] for p in subset]), s[f'median_{key}']), 'Reference summary mismatch')
    prepared = 0
    from PIL import Image
    for row in sources:
        path = root / row['image_path']
        if args.require_images:
            check(path.is_file(), f'Missing conditioning image: {path}')
        if path.is_file():
            image = Image.open(path).convert('RGB')
            check(image.size == (832, 480), 'Conditioning image dimensions differ')
            check(hashlib.sha256(image.tobytes()).hexdigest() == row['rgb_sha256'], 'Conditioning image differs')
            prepared += 1
    print(json.dumps(dict(status='passed', t2v_samples=100, i2v_scenes=150, i2v_samples=750,
                          trajectory_files=750, t2v_reference_pairs=600, prepared_images=prepared), indent=2))


if __name__ == '__main__':
    main()