from src.models.scGPT.model import TransformerModel
from src.models.perturbation.model import Model as FlowModel
from src.models.perturbation.model import TimedTransformer
from src.models.origin.model import model as OriginModel
import torch

def instantiate_model(model_type: str, **kwargs):

    if model_type == 'origin':
        if kwargs['fusion_method'] == 'differential_transformer':
            layers = 8
        elif kwargs['fusion_method'] == 'differential_perceiver':
            layers = 4
        else:
            layers = 8
        d_model = kwargs.get('d_model', 512)
        ntoken = kwargs.get('ntoken', 6000)
        d_hid = int(4.0 * d_model)
        return OriginModel(
            ntoken=ntoken,
            d_model=d_model,
            d_hid=d_hid,
            nlayers=layers,
            fusion_method=kwargs['fusion_method'],
            perturbation_function=kwargs['perturbation_function'],
            mask_path=kwargs['mask_path'],
        )
    else:
        raise ValueError(f"Invalid model type: {model_type}")
    
if __name__ == "__main__":
    model = instantiate_model("punet128")
    x = torch.randn(32,  128, 128)
    t = torch.randn(32)
    out = model( x,t)
    print(out.shape)