""" Utiliy functions to load pre-trained models more easily """
import os
import pkg_resources
from omegaconf import OmegaConf

import torch
from huggingface_hub import hf_hub_download

from mvdream.ldm.util import instantiate_from_config


PRETRAINED_MODELS = {
    "sd-v2.1-base-4view": {
        "config": "sd-v2-base.yaml",
        "repo_id": "MVDream/MVDream",
        "filename": "sd-v2.1-base-4view.pt",
    },
    "sd-v1.5-4view": {
        "config": "sd-v1.yaml",
        "repo_id": "MVDream/MVDream",
        "filename": "sd-v1.5-4view.pt",
    },
}


def get_config_file(config_path):
    cfg_file = pkg_resources.resource_filename("mvdream", os.path.join("configs", config_path))
    if not os.path.exists(cfg_file):
        raise RuntimeError(f"Config {config_path} not available!")
    return cfg_file


def build_model(model_name, ckpt_path=None, cache_dir=None):
    if model_name not in PRETRAINED_MODELS:
        raise RuntimeError(
            f"Model name {model_name} is not a pre-trained model. Available models are:\n- "
            + "\n- ".join(PRETRAINED_MODELS.keys())
        )
    model_info = PRETRAINED_MODELS[model_name]

    # Instiantiate the model
    print(f"Loading model from config: {model_info['config']}")
    config_file = get_config_file(model_info["config"])
    config = OmegaConf.load(config_file)
    model = instantiate_from_config(config.model)

    # Load pre-trained checkpoint from huggingface
    if not ckpt_path:
        ckpt_path = hf_hub_download(
            repo_id=model_info["repo_id"],
            filename=model_info["filename"],
            cache_dir=cache_dir,
        )
        print(f"Loading model from cache file: {ckpt_path}")
    model.load_state_dict(torch.load(ckpt_path, map_location="cpu"))
    return model
