import json, os, hashlib, logging
from pathlib import Path

log = logging.getLogger(__name__)

# --- real data source: security ---
TV_DATASET = 'UCF101'
HF_CANDIDATES = []
IMAGE_FIELD = None
TEXT_FIELD = None
LABEL_FIELD = 'label'
PROMPT_TEMPLATE = 'a surveillance scene showing {label}'
DATASET_URL = 'https://www.crcv.ucf.edu/projects/real-world/'

def fetch_real_samples(max_samples=5000, cache_dir='./_cache'):
    # 本地没有数据时自动下载真实公开数据集: torchvision -> HuggingFace -> 手动说明
    out = []
    if TV_DATASET is not None:
        try:
            import torchvision
            ctor = getattr(torchvision.datasets, TV_DATASET)
            try:
                ds = ctor(root=cache_dir, split='train', download=True)
            except TypeError:
                try:
                    ds = ctor(root=cache_dir, train=True, download=True)
                except TypeError:
                    ds = ctor(root=cache_dir, download=True)
            classes = getattr(ds, 'classes', None)
            os.makedirs(os.path.join(cache_dir, 'tv'), exist_ok=True)
            for i, item in enumerate(ds):
                if len(out) >= max_samples:
                    break
                img, label = item[0], item[1]
                name = classes[label] if classes else str(label)
                p = os.path.join(cache_dir, 'tv', str(i) + '.png')
                try:
                    img.save(p)
                except Exception:
                    continue
                out.append({'image': p, 'text': PROMPT_TEMPLATE.format(label=name)})
            if out:
                return out
        except Exception as e:
            print('torchvision load failed:', e)
    for repo in HF_CANDIDATES:
        try:
            from datasets import load_dataset
            try:
                ds = load_dataset(repo, split='train', streaming=True)
            except Exception:
                ds = load_dataset(repo, split='train')
            img_dir = os.path.join(cache_dir, 'hf_images')
            os.makedirs(img_dir, exist_ok=True)
            for i, ex in enumerate(ds):
                if len(out) >= max_samples:
                    break
                txt = None
                if TEXT_FIELD is not None and TEXT_FIELD in ex:
                    v = ex[TEXT_FIELD]
                    txt = v if isinstance(v, str) else ' '.join(map(str, v if isinstance(v, (list, tuple)) else [v]))
                if txt is None and LABEL_FIELD in ex:
                    txt = PROMPT_TEMPLATE.format(label=ex[LABEL_FIELD])
                if txt is None:
                    continue
                out.append({'text': txt})
            if out:
                return out
        except Exception as e:
            print('HF load failed for', repo, ':', e)
    print('Automatic download failed. Please get the data manually from:')
    print('   ' + DATASET_URL)
    return out


def load(data_dir):
    # 加载原始数据
    files = list(Path(data_dir).glob('*.jsonl'))
    if not files:
        files = list(Path(data_dir).glob('*.json'))
    samples = []
    for f in files:
        with open(f) as fp:
            if f.suffix == '.json':
                d = json.load(fp)
                samples.extend(d if isinstance(d, list) else [d])
            else:
                samples.extend(json.loads(l) for l in fp if l.strip())
    return samples

def filter_quality(samples, min_score=0.5):
    # 质量过滤
    results = []
    for s in samples:
        text = s.get("security", s.get("text", ""))
        if len(text.split()) >= 3:
            results.append(s)
    return results

def dedup(samples):
    seen = set()
    out = []
    for s in samples:
        fp = s.get("image", s.get("audio", ""))
        if fp and os.path.exists(fp):
            h = hashlib.md5(open(fp, 'rb').read()).hexdigest()
            if h in seen:
                continue
            seen.add(h)
        out.append(s)
    return out

def preprocess(samples, out_dir, img_size=224):
    
    os.makedirs(out_dir, exist_ok=True)
    processed = []
    for s in samples:
        item = {}
        if "image" in s:
            try:
                from PIL import Image as IM
                img = IM.open(s["image"]).convert("RGB")
                img = img.resize((img_size, img_size))
                p = os.path.join(out_dir, os.path.basename(s["image"]))
                img.save(p, "JPEG", quality=95)
                item["image"] = p
            except Exception:
                continue
        text = s.get("security", s.get("text", ""))
        item["text"] = text
        item["domain"] = "security"
        processed.append(item)
    return processed

def save_jsonl(data, path):
    with open(path, 'w') as f:
        for d in data:
            f.write(json.dumps(d, ensure_ascii=False) + '\n')

def main():
    import sys
    data_dir = sys.argv[1] if len(sys.argv) > 1 else './data'
    out = sys.argv[2] if len(sys.argv) > 2 else './output'
    samples = load(data_dir) or fetch_real_samples()
    samples = filter_quality(samples)
    samples = dedup(samples)
    result = preprocess(samples, out)
    save_jsonl(result, os.path.join(out, 'dataset.jsonl'))
    print(f'Done: {len(result)} samples')

if __name__ == '__main__':
    main()