"""Validate the frozen 40-case input and decode every sampled frame; no API calls."""
import hashlib
import json
import sys
from pathlib import Path

import cv2
import openai

sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
import benchmark_gpt6_astra as runner


def main():
    output = Path('gpt6-reassemble40/results/api-preflight')
    config = json.loads((output / 'config.json').read_text())
    previous = json.loads(Path('gpt6-30/results/openrouter/config.json').read_text())
    assert {k: v for k, v in config.items() if k != 'benchmark'} == {
        k: v for k, v in previous.items() if k != 'benchmark'
    }, 'Inference configuration differs from the original run'
    assert hashlib.sha256(runner.SYSTEM_PROMPT.encode()).hexdigest() == previous['prompt_sha256']
    records = []
    paths = sorted(Path('gpt6-reassemble40/benchmark/cases').glob('case_*'))
    assert [p.name for p in paths] == [f'case_{n:03d}' for n in range(31, 71)]
    for path in paths:
        instruction, metadata, videos = runner.load_case(path)
        assert len(videos) == 3
        timestamps, fps = runner.choose_timestamps(metadata, len(videos), 2, 1, 30, 900)
        hashes = {}
        for video in videos:
            digest = hashlib.sha256(video.read_bytes()).hexdigest()
            assert digest == metadata['videos'][video.stem]['sha256'], str(video)
            hashes[video.name] = digest
        content, count = runner.make_input(path.name, instruction, metadata, videos, timestamps, 'high', 85)
        assert sum(item['type'] == 'input_image' for item in content) == count
        records.append({'case_id': path.name, 'image_count': count,
                        'sampling_fps': fps, 'timestamps_s': timestamps,
                        'video_sha256': hashes,
                        'instruction_sha256': hashlib.sha256(instruction.encode()).hexdigest()})
        print(f'{path.name}: {count} images decoded, video hashes verified', flush=True)
    report = {'checked_at': runner.utc_now(), 'case_count': len(records),
              'image_count': sum(r['image_count'] for r in records),
              'config_matches_previous_except_benchmark_name': True,
              'openai_sdk_version': openai.__version__, 'opencv_version': cv2.__version__,
              'api_calls': 0, 'ground_truth_read': False, 'cases': records}
    runner.atomic_write_json(output / 'preflight.json', report)


if __name__ == '__main__':
    main()