def input_processing(func, config, **kwargs):
    """
    Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input
    has to be named accordingly to the parameters name, i.e. `input_ids = tf.keras.Input(shape=(128,), dtype='int32',
    name="input_ids")` otherwise the order of the tensors will not be guaranteed during the training.

    Args:
        func (`callable`):
            The callable function of the TensorFlow model.
        config ([`PretrainedConfig`]):
            The config of the running model.
        **kwargs:
            The inputs of the model.

    Returns:
        Two lists, one for the missing layers, and another one for the unexpected layers.
    """
    signature = dict(inspect.signature(func).parameters)
    has_kwargs = bool(signature.pop("kwargs", None))
    signature.pop("self", None)
    parameter_names = list(signature.keys())
    main_input_name = parameter_names[0]
    main_input = kwargs.pop(main_input_name, None)
    output = {}
    allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict, np.ndarray)

    if "inputs" in kwargs["kwargs_call"]:
        warnings.warn(
            "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.",
            FutureWarning,
        )

        output["input_ids"] = kwargs["kwargs_call"].pop("inputs")

    if "decoder_cached_states" in kwargs["kwargs_call"]:
        warnings.warn(
            "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use"
            " `past_key_values` instead.",
            FutureWarning,
        )
        output["past_key_values"] = kwargs["kwargs_call"].pop("decoder_cached_states")

    if "past" in kwargs["kwargs_call"] and "past_key_values" in parameter_names:
        warnings.warn(
            "The `past` argument is deprecated and will be removed in a future version, use `past_key_values`"
            " instead.",
            FutureWarning,
        )
        kwargs["past_key_values"] = kwargs["kwargs_call"].pop("past")
    elif "past_key_values" in kwargs["kwargs_call"] and "past" in parameter_names:
        kwargs["past"] = kwargs["kwargs_call"].pop("past_key_values")

    if has_kwargs:
        output["kwargs"] = kwargs.pop("kwargs_call", {})
    else:
        if len(kwargs["kwargs_call"]) > 0:
            raise ValueError(
                "The following keyword arguments are not supported by this model:"
                f" {list(kwargs['kwargs_call'].keys())}."
            )
        kwargs.pop("kwargs_call")

    for k, v in kwargs.items():
        if isinstance(v, allowed_types) or tf.is_tensor(v) or v is None:
            output[k] = v
        else:
            raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")

    if isinstance(main_input, (tuple, list)):
        for i, input in enumerate(main_input):
            # EagerTensors don't allow to use the .name property so we check for a real Tensor
            if is_tf_symbolic_tensor(input):
                # Tensor names have always the pattern `name:id` then we check only the
                # `name` part
                tensor_name = input.name.split(":")[0]

                if tensor_name in parameter_names:
                    output[tensor_name] = input
                else:
                    output[parameter_names[i]] = input
            elif isinstance(input, allowed_types) or input is None:
                output[parameter_names[i]] = input
            else:
                raise ValueError(
                    f"Data of type {type(input)} is not allowed only {allowed_types} is accepted for"
                    f" {parameter_names[i]}."
                )
    elif isinstance(main_input, Mapping):
        if "inputs" in main_input:
            warnings.warn(
                "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids`"
                " instead.",
                FutureWarning,
            )

            output["input_ids"] = main_input.pop("inputs")

        if "decoder_cached_states" in main_input:
            warnings.warn(
                "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use"
                " `past_key_values` instead.",
                FutureWarning,
            )
            output["past_key_values"] = main_input.pop("decoder_cached_states")

        for k, v in dict(main_input).items():
            if isinstance(v, allowed_types) or v is None:
                output[k] = v
            elif k not in parameter_names and "args" not in parameter_names:
                logger.warning(
                    f"The parameter {k} does not belongs to the parameter list {parameter_names} and will be ignored."
                )
                continue
            else:
                raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
    else:
        if tf.is_tensor(main_input) or main_input is None:
            output[main_input_name] = main_input
        else:
            raise ValueError(
                f"Data of type {type(main_input)} is not allowed only {allowed_types} is accepted for"
                f" {main_input_name}."
            )

    # Populates any unspecified argument with their default value, according to the signature.
    for name in parameter_names:
        if name not in list(output.keys()) and name != "args":
            output[name] = kwargs.pop(name, signature[name].default)

    # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
    # So to respect the proper output we have to add this exception
    if "args" in output:
        if output["args"] is not None and is_tf_symbolic_tensor(output["args"]):
            tensor_name = output["args"].name.split(":")[0]
            output[tensor_name] = output["args"]
        else:
            # `args` in this case is always the first parameter, then `input_ids`
            output["input_ids"] = output["args"]

        del output["args"]

    if "kwargs" in output:
        del output["kwargs"]

    cast_output = {}
    for key, val in output.items():
        if isinstance(val, tf.Tensor) and val.dtype == tf.int64:
            cast_output[key] = tf.cast(val, tf.int32)
        elif isinstance(val, np.ndarray) and val.dtype == np.int64:
            cast_output[key] = val.astype(np.int32)
        else:
            cast_output[key] = val

    output = cast_output
    del cast_output

    if config is not None:
        boolean_dict = {
            k: v
            for k, v in output.items()
            if k in ["return_dict", "output_attentions", "output_hidden_states", "use_cache"]
        }

        output.update(
            booleans_processing(
                config=config,
                **boolean_dict,
            )
        )

    return output

    Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input
    has to be named accordingly to the parameters name, i.e. `input_ids = tf.keras.Input(shape=(128,), dtype='int32',
    name="input_ids")` otherwise the order of the tensors will not be guaranteed during the training.

    Args:
        func (`callable`):
            The callable function of the TensorFlow model.
        config ([`PretrainedConfig`]):
            The config of the running model.
        **kwargs:
            The inputs of the model.

    Returns:
        Two lists, one for the missing layers, and another one for the unexpected layers.
    
dpkd/transformers/src/transformers/modeling_tf_utils.py