# import os
# import json
# import torch
# from huggingface_hub import list_repo_files, hf_hub_download
# from safetensors.torch import load_file

# REPO_ID = "MatchLab/PointMapVerse"
# SUBFOLDER = "light_arkitscenes"


# def pointmap_to_points(pm: torch.Tensor, max_points: int = 4096) -> torch.Tensor:
#     """
#     Convert [H, W, 3] point map to [N, 3] points, with subsampling.
#     """
#     pm = pm.float()
#     pts = pm.reshape(-1, 3)
#     pts = pts[pts.norm(dim=-1) > 0]  # remove zero points
#     if pts.shape[0] > max_points:
#         pts = pts[torch.randperm(pts.shape[0])[:max_points]]
#     return pts


# def chamfer_distance(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
#     """
#     Symmetric Chamfer distance between two point sets a, b: [N,3], [M,3]
#     """
#     d = torch.cdist(a, b, p=2)
#     return d.min(dim=1).values.mean() + d.min(dim=0).values.mean()


# def compute_chamfer_rankings_for_scene(scene_path: str, repo_id: str):
#     """
#     For a single safetensors scene file, compute sorted Chamfer rankings
#     for each view.
#     Returns a list of dicts for JSON.
#     """
#     local_path = hf_hub_download(repo_id, scene_path, repo_type="dataset")
#     data = load_file(local_path)
#     point_maps = data["point_map"]  # [V, H, W, 3]
#     num_views = point_maps.shape[0]

#     # Precompute point clouds per view
#     pcs = [pointmap_to_points(point_maps[v]) for v in range(num_views)]

#     scene_id = os.path.basename(scene_path).split(".")[0]  # e.g. scene0000_01

#     results = []
#     for i in range(num_views):
#         pts_i = pcs[i]
#         dists = []
#         for j in range(num_views):
#             if i == j:
#                 continue
#             d = chamfer_distance(pts_i, pcs[j]).item()
#             dists.append((j, d))

#         # sort by Chamfer distance
#         dists_sorted = sorted(dists, key=lambda x: x[1])
#         sorted_views = [j for (j, _) in dists_sorted]

#         item = {
#                 "scene_id": scene_id,
#                 "cur_view": i,
#                 "sorted_views": sorted_views,
#             }
#         results.append(
#             item
#         )
        
#         print(item)
#     return results


# def load_all_scenes_and_dump_json(
#     repo_id: str = REPO_ID,
#     subfolder: str = SUBFOLDER,
#     output_json: str = "light_arkitscenes_chamfer_rankings.json",
# ):
#     print("📂 Listing files…")
#     files = list_repo_files(repo_id, repo_type="dataset")

#     # Filter for safetensors inside subfolder/
#     scene_files = [f for f in files if f.startswith(subfolder) and f.endswith(".safetensors")]
#     print(f"🔍 Found {len(scene_files)} scenes")

#     all_entries = []

#     for fname in sorted(scene_files):
#         print(f"➡️ Processing scene: {fname}")
#         scene_entries = compute_chamfer_rankings_for_scene(fname, repo_id)
#         all_entries.extend(scene_entries)

#     print(f"💾 Saving JSON to: {output_json}")
#     with open(output_json, "w") as f:
#         json.dump(all_entries, f, indent=2)

#     print("✅ Finished computing Chamfer rankings for all scenes")
#     return all_entries


# if __name__ == "__main__":
#     load_all_scenes_and_dump_json(REPO_ID, SUBFOLDER)

# from transformers import AutoModel, AutoTokenizer
# text_encoder = AutoModel.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True)
# tokenizer =  AutoTokenizer.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True)

# from safetensors.torch import load_file
# from safetensors.torch import load
# from huggingface_hub import hf_hub_download

# def load_safetensor_from_hf(repo_id, filename, repo_type="dataset"):
#     if filename.startswith('cc3m'):
#         with open(filename, "rb") as f:
#             return load(f.read())
#     else:
#         cached_path = hf_hub_download(
#             repo_id=repo_id,
#             filename=filename,
#             repo_type=repo_type,
#             local_files_only=True
#         )
#         return load_file(cached_path)

from huggingface_hub import snapshot_download
from pathlib import Path

# ---- Config ----
REPO_ID = "MatchLab/ScenePointv2"   # e.g. "openai/light_lsun"
SUBFOLDER = "light_lsun"
LOCAL_DIR = "./light_lsun"

# ---- Download ----
snapshot_download(
    repo_id=REPO_ID,
    repo_type="dataset",
    local_dir=LOCAL_DIR,
    allow_patterns=[f"{SUBFOLDER}/**"],
    local_dir_use_symlinks=False,
)

print(f"✅ Downloaded {SUBFOLDER} to {Path(LOCAL_DIR).resolve()}")