import json

from .factory import create_model_from_config
from .utils import load_ckpt_state_dict

from huggingface_hub import hf_hub_download
import torch

def get_pretrained_model(name: str):
    
    model_config_path = hf_hub_download(name, filename="config.json", repo_type='model')

    with open(model_config_path) as f:
        model_config = json.load(f)

    model = create_model_from_config(model_config)

    # Try to download the model.safetensors file first, if it doesn't exist, download the model.ckpt file
    try:
        model_ckpt_path = hf_hub_download(name, filename="model.safetensors", repo_type='model')
    except Exception as e:
        model_ckpt_path = hf_hub_download(name, filename="model.ckpt", repo_type='model')

    # Load state dict with strict=False to ignore missing keys
    state_dict = load_ckpt_state_dict(model_ckpt_path)
    model.load_state_dict(state_dict, strict=False)
    
    # Initialize missing position_ids if needed
    if hasattr(model.conditioner.conditioners.video_prompt.visual_encoder_model.vision_model.embeddings, 'num_positions'):
        num_positions = model.conditioner.conditioners.video_prompt.visual_encoder_model.vision_model.embeddings.num_positions
        model.conditioner.conditioners.video_prompt.visual_encoder_model.vision_model.embeddings.position_ids = torch.arange(0, num_positions, dtype=torch.long)

    return model, model_config