def _dispatch_accelerate_model(
        self,
        device_map: str,
        max_memory: Optional[int] = None,
        offload_folder: Optional[str] = None,
        offload_index: Optional[int] = None,
    ) -> None:
        """
        Optional re-dispatch the model and attach new hooks to the model in case the model has been loaded with
        accelerate (i.e. with `device_map=xxx`)

        Args:
            device_map (`str` or `Dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*):
                A map that specifies where each submodule should go. It doesn't need to be refined to each
                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
                same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank
                like `1`) on which the model will be allocated, the device map will map the entire model to this
                device. Passing `device_map = 0` means put the whole model on GPU 0.

                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
            max_memory (`Dict`, *optional*):
                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
                GPU and the available CPU RAM if unset.
            offload_folder (`str` or `os.PathLike`, *optional*):
                If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
            offload_index (`int`, *optional*):
                The offload_index argument to be passed to `accelerate.dispatch_model` method.
        """
        dispatch_model_kwargs = {}
        # Safety checker for previous `accelerate` versions
        # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
        if "offload_index" in inspect.signature(dispatch_model).parameters:
            dispatch_model_kwargs["offload_index"] = offload_index

        no_split_module_classes = self._no_split_modules

        if device_map != "sequential":
            max_memory = get_balanced_memory(
                self,
                max_memory=max_memory,
                no_split_module_classes=no_split_module_classes,
                low_zero=(device_map == "balanced_low_0"),
            )
        if isinstance(device_map, str):
            device_map = infer_auto_device_map(
                self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
            )
        dispatch_model(
            self,
            device_map=device_map,
            offload_dir=offload_folder,
            **dispatch_model_kwargs,
        )

        Optional re-dispatch the model and attach new hooks to the model in case the model has been loaded with
        accelerate (i.e. with `device_map=xxx`)

        Args:
            device_map (`str` or `Dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*):
                A map that specifies where each submodule should go. It doesn't need to be refined to each
                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
                same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank
                like `1`) on which the model will be allocated, the device map will map the entire model to this
                device. Passing `device_map = 0` means put the whole model on GPU 0.

                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
            max_memory (`Dict`, *optional*):
                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
                GPU and the available CPU RAM if unset.
            offload_folder (`str` or `os.PathLike`, *optional*):
                If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
            offload_index (`int`, *optional*):
                The offload_index argument to be passed to `accelerate.dispatch_model` method.
        
dpkd/transformers/src/transformers/integrations/peft.py