import importlib

__attributes = {
    'SparseStructureEncoder': 'sparse_structure_vae',
    'SparseStructureDecoder': 'sparse_structure_vae',
    'SparseStructureFlowModel': 'sparse_structure_flow',
    'SLatEncoder': 'structured_latent_vae',
    'SLatGaussianDecoder': 'structured_latent_vae',
    'SLatMeshDecoder': 'structured_latent_vae',
    'SLatFlowModel': 'structured_latent_flow',
    'ModulatedMultiViewCond': 'sparse_structure_flow',
}

__submodules = []

__all__ = list(__attributes.keys()) + __submodules

def __getattr__(name):
    if name not in globals():
        if name in __attributes:
            module_name = __attributes[name]
            module = importlib.import_module(f".{module_name}", __name__)
            globals()[name] = getattr(module, name)
        elif name in __submodules:
            module = importlib.import_module(f".{name}", __name__)
            globals()[name] = module
        else:
            raise AttributeError(f"module {__name__} has no attribute {name}")
    return globals()[name]


def from_pretrained(path: str, **kwargs):
    """
    Load a model from a pretrained checkpoint.

    Args:
        path: The path to the checkpoint. Can be either local path or a Hugging Face model name.
              NOTE: config file and model file should take the name f'{path}.json' and f'{path}.safetensors' respectively.
        **kwargs: Additional arguments for the model constructor.
    """
    import os
    import json
    from safetensors.torch import load_file
    is_local = os.path.exists(f"{path}.json") and os.path.exists(f"{path}.safetensors")

    if is_local:
        config_file = f"{path}.json"
        model_file = f"{path}.safetensors"
    else:
        from huggingface_hub import hf_hub_download
        path_parts = path.split('/')
        repo_id = f'{path_parts[0]}/{path_parts[1]}'
        model_name = '/'.join(path_parts[2:])
        config_file = hf_hub_download(repo_id, f"{model_name}.json")
        model_file = hf_hub_download(repo_id, f"{model_name}.safetensors")

    with open(config_file, 'r') as f:
        config = json.load(f)
    model = __getattr__(config['name'])(**config['args'], **kwargs)
    model.load_state_dict(load_file(model_file), strict=False)

    return model

def save_finetuned_model(model, output_dir: str):
    """
    Save a fine-tuned model's state_dict as safetensors with a timestamp.

    Args:
        model: The model to be saved.
        output_dir: The directory where the model's state_dict will be saved.
                    The file will be saved as f'{output_dir}/{timestamp}.safetensors'.
    """
    from safetensors.torch import save_file
    import os
    from datetime import datetime

    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    save_file(model.state_dict(), f"{output_dir}/{timestamp}.safetensors")

# For Pylance
if __name__ == '__main__':
    from .sparse_structure_vae import SparseStructureEncoder, SparseStructureDecoder
    from .sparse_structure_flow import SparseStructureFlowModel, ModulatedMultiViewCond
    from .structured_latent_vae import SLatEncoder, SLatGaussianDecoder, SLatMeshDecoder
    from .structured_latent_flow import SLatFlowModel