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# from huggingface_hub import list_repo_files, hf_hub_download
# import os
# # Optional: choose your loader
# USE_SAFETENSORS = True
# if USE_SAFETENSORS:
# from safetensors.torch import load_file as model_loader
# # Step 1: List all files in the dataset repo
# repo_id = "MatchLab/PointMapVerse"
# all_files = list_repo_files(repo_id=repo_id, repo_type="dataset")
# # Step 2: Automatically detect all subfolders (first-level only)
# subfolders = set(f.split('/')[0] for f in all_files if '/' in f)
# print(f"Detected subfolders: {subfolders}")
# # Step 3: Collect target files (e.g., only .safetensors inside subfolders)
# target_files = [f for f in all_files if f.split('/')[0] in ['light_arkitscenes']]
# print(f"Found {len(target_files)} .safetensors files in subfolders.")
# for file_path in target_files:
# print(f"Caching: {file_path}")
# cached_file = hf_hub_download(
# repo_id=repo_id,
# filename=file_path,
# repo_type="dataset",
# local_files_only=False,
# resume_download = True
# )
# # Optional: Load into memory
# data = model_loader(cached_file)
# print(data['point_map'].shape)
# print(f"Loaded: {file_path}, keys: {list(data.keys())}")
# # import os
# # import glob
# # from safetensors.torch import load_file
# # repo_id = "MatchLab/PointMapVerse"
# # # Step 1: Download & cache the dataset snapshot
# # from huggingface_hub import snapshot_download
# # local_dir = snapshot_download(
# # repo_id=repo_id,
# # repo_type="dataset",
# # allow_patterns=["light_scannet/*", "light_3rscan/*", "light_arkitscenes/*"], # include just these subfolders
# # )
# # print(f"Local dataset directory: {local_dir}")
# # # Step 2: Find all .safetensors files inside the target subfolders
# # file_paths = glob.glob(os.path.join(local_dir, "light_*", "*.safetensors"))
# # print(f"Found {len(file_paths)} .safetensors files")
# from huggingface_hub import hf_hub_download
# repo_id = "MatchLab/PointMapVerse"
# subfolders = ["light_scannet", "light_3rscan", "light_arkitscenes"]
# all_files = []
# for sub in subfolders:
# for fname in filenames:
# try:
# cached_path = hf_hub_download(
# repo_id=repo_id,
# repo_type="dataset",
# filename=f"{sub}/{fname}",
# local_files_only=False, # set True if you already downloaded and cached
# resume_download=True,
# )
# all_files.append(cached_path)
# except Exception as e:
# print(f"⚠️ Could not download {sub}/{fname}: {e}")
# print(f"Downloaded {len(all_files)} files")
# # Step 2: Load the files
# for path in all_files:
# data = load_file(path) # dict-like object
# print(
# f"Loaded {os.path.basename(path)}: "
# f"keys={list(data.keys())}, "
# f"point_map shape={data['point_map'].shape}"
# )
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