from huggingface_hub import HfApi, HfFolder, Repository, create_repo, upload_file
import os

# 登录到 Hugging Face
from huggingface_hub import login
login()

# 创建或指定现有的 Repository
repo_name = "xxx-ckpt"
username = "LTT"
repo_id = f"{username}/{repo_name}"

# 创建仓库（如果它不存在）
create_repo(repo_id, exist_ok=True)

# 文件夹
# 上传整个文件夹
def upload_folder(folder_path, repo_id):
    """
    递归上传文件夹及其内容到 Hugging Face 仓库。
    """
    for root, _, files in os.walk(folder_path):
        for file in files:
            # 文件完整路径
            full_file_path = os.path.join(root, file)
            # 相对于文件夹的相对路径（保留文件夹结构）
            relative_path = os.path.relpath(full_file_path, folder_path)

            # 上传文件到仓库
            print(f"Uploading {relative_path}...")
            upload_file(
                path_or_fileobj=full_file_path,
                path_in_repo=relative_path,
                repo_id=repo_id
            )
            print(f"Uploaded {relative_path} successfully.")


# 上传模型文件
model_path = "checkpoint/zero123++/flexgen_19w.ckpt"
upload_file(path_or_fileobj=model_path, path_in_repo="flexgen_19w.ckpt", repo_id=repo_id)

# # 上传数据文件
# data_path = "/hpc2hdd/home/jlin695/data/env_map/data/env_mipmap_large.tar.gz"
# upload_file(path_or_fileobj=data_path, path_in_repo="env_mipmap_large.tar.gz", repo_id=repo_id)

# # 上传数据文件
# data_path = "/hpc2hdd/home/jlin695/data/env_map/data/env_map_light_large.tar.gz"
# upload_file(path_or_fileobj=data_path, path_in_repo="env_map_light_large.tar.gz", repo_id=repo_id)

# # 定义要上传的文件夹路径
# folder_path = "checkpoint/flux_lora"

# # 调用上传文件夹的函数
# upload_folder(folder_path, repo_id)

# print("模型和数据文件已上传到 Hugging Face。")