import os
import argparse
import torch
from torchvision import transforms
from torch.utils.data import DataLoader
from PIL import Image
from dataset.t2i_control import T2IControlCode 
from tqdm import tqdm




def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--code_path', type=str, required=True, help='根目录，包含 code/control/image/caption_emb 等文件夹')
    parser.add_argument('--code_path2', type=str, default=None, help='第二组数据路径 (可选)')
    parser.add_argument('--image_size', type=int, default=512)
    parser.add_argument('--downsample_size', type=int, default=8)
    parser.add_argument('--condition_type', type=str, default='seg', choices=['seg', 'depth', 'canny', 'hed', 'lineart'], help='控制类型')
    parser.add_argument('--get_image', action='store_true', help='是否返回 image')
    parser.add_argument('--get_prompt', action='store_true', help='是否返回 prompt')
    parser.add_argument('--get_label', action='store_true', help='是否返回 label')
    parser.add_argument('--max_show', type=int, default=5, help='最多显示多少条样本')
    return parser.parse_args()

def main():
    args = get_args()

    dataset = T2IControlCode(args)
    print(f"\n📦 数据集大小: {len(dataset)}")

    loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=2, collate_fn=dataset.collate_fn)

    for i, batch in enumerate(tqdm(loader)):
        print(f"\n🟡 Sample #{i}")
        print(f" - code shape: {batch['code'].shape}")
        print(f" - control shape: {batch['control'].shape}")
        print(f" - caption_emb shape: {batch['caption_emb'].shape}")
        print(f" - attention mask shape: {batch['attn_mask'].shape}")
        print(f" - valid: {batch['valid'].item()}")

        if args.get_image:
            print(f" - image shape: {batch['image'].shape}")
        if args.get_prompt:
            print(f" - prompt: {batch['prompt']}")
        if args.get_label:
            print(f" - label shape: {batch['label'].shape}")

        if i + 1 >= args.max_show:
            break

if __name__ == "__main__":
    main()