def convert_state_dict(unet_state_dict):
    """
    Args:
    Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.
        unet_model (torch.nn.Module): The original U-Net model. unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet
        model to match keys with.

    Returns:
        OrderedDict: The converted state dictionary.
    """
    # Example of renaming logic (this will vary based on your model's architecture)
    converted_state_dict = {}
    for key in unet_state_dict:
        new_key = key
        for pattern, new_pattern in MAPPING.items():
            new_key = new_key.replace(pattern, new_pattern)

        for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items():
            has_matched = False
            if fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*") and not has_matched:
                star = int(new_key.split(dyn_pattern.split(".")[0])[-1].split(".")[1])

                if isinstance(dyn_new_pattern, tuple):
                    new_star = star + dyn_new_pattern[-1]
                    dyn_new_pattern = dyn_new_pattern[0]
                else:
                    new_star = star

                pattern = dyn_pattern.replace("*", str(star))
                new_pattern = dyn_new_pattern.replace("*", str(new_star))

                new_key = new_key.replace(pattern, new_pattern)
                has_matched = True

        converted_state_dict[new_key] = unet_state_dict[key]

    return converted_state_dict

    Args:
    Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.
        unet_model (torch.nn.Module): The original U-Net model. unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet
        model to match keys with.

    Returns:
        OrderedDict: The converted state dictionary.
    
src/diffusers/pipelines/kandinsky3/convert_kandinsky3_unet.py