def generate_dummy_inputs(
        self,
        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin", "ImageProcessingMixin"],
        batch_size: int = -1,
        seq_length: int = -1,
        num_choices: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
        num_channels: int = 3,
        image_width: int = 40,
        image_height: int = 40,
        sampling_rate: int = 22050,
        time_duration: float = 5.0,
        frequency: int = 220,
        tokenizer: "PreTrainedTokenizerBase" = None,
    ) -> Mapping[str, Any]:
        """
        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]):
                The preprocessor associated with this model configuration.
            batch_size (`int`, *optional*, defaults to -1):
                The batch size to export the model for (-1 means dynamic axis).
            num_choices (`int`, *optional*, defaults to -1):
                The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
            seq_length (`int`, *optional*, defaults to -1):
                The sequence length to export the model for (-1 means dynamic axis).
            is_pair (`bool`, *optional*, defaults to `False`):
                Indicate if the input is a pair (sentence 1, sentence 2)
            framework (`TensorType`, *optional*, defaults to `None`):
                The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
            num_channels (`int`, *optional*, defaults to 3):
                The number of channels of the generated images.
            image_width (`int`, *optional*, defaults to 40):
                The width of the generated images.
            image_height (`int`, *optional*, defaults to 40):
                The height of the generated images.
            sampling_rate (`int`, *optional* defaults to 22050)
                The sampling rate for audio data generation.
            time_duration (`float`, *optional* defaults to 5.0)
                Total seconds of sampling for audio data generation.
            frequency (`int`, *optional* defaults to 220)
                The desired natural frequency of generated audio.

        Returns:
            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
        """
        from ..feature_extraction_utils import FeatureExtractionMixin
        from ..image_processing_utils import ImageProcessingMixin
        from ..tokenization_utils_base import PreTrainedTokenizerBase

        if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None:
            raise ValueError("You cannot provide both a tokenizer and a preprocessor to generate dummy inputs.")
        if tokenizer is not None:
            warnings.warn(
                "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use"
                " `preprocessor` instead.",
                FutureWarning,
            )
            logger.warning("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.")
            preprocessor = tokenizer
        if isinstance(preprocessor, PreTrainedTokenizerBase):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(
                batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
            )
            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            token_to_add = preprocessor.num_special_tokens_to_add(is_pair)
            seq_length = compute_effective_axis_dimension(
                seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
            )
            # Generate dummy inputs according to compute batch and sequence
            input_token = (
                preprocessor.unk_token
                if (preprocessor.unk_token is not None and len(preprocessor.unk_token) > 0)
                else "0"
            )
            dummy_input = [" ".join([input_token]) * seq_length] * batch_size
            if self.task == "multiple-choice":
                # If dynamic axis (-1) we forward with a fixed dimension of 4 candidate answers to avoid optimizations
                # made by ONNX
                num_choices = compute_effective_axis_dimension(
                    num_choices, fixed_dimension=OnnxConfig.default_fixed_num_choices, num_token_to_add=0
                )
                dummy_input = dummy_input * num_choices
                # The shape of the tokenized inputs values is [batch_size * num_choices, seq_length]
                tokenized_input = preprocessor(dummy_input, text_pair=dummy_input)
                # Unflatten the tokenized inputs values expanding it to the shape [batch_size, num_choices, seq_length]
                for k, v in tokenized_input.items():
                    tokenized_input[k] = [v[i : i + num_choices] for i in range(0, len(v), num_choices)]
                return dict(tokenized_input.convert_to_tensors(tensor_type=framework))
            return dict(preprocessor(dummy_input, return_tensors=framework))
        elif isinstance(preprocessor, ImageProcessingMixin):
            if preprocessor.model_input_names[0] != "pixel_values":
                raise ValueError(
                    f"The `preprocessor` is an image processor ({preprocessor.__class__.__name__}) and expects"
                    f' `model_input_names[0]` to be "pixel_values", but got {preprocessor.model_input_names[0]}'
                )
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
            return dict(preprocessor(images=dummy_input, return_tensors=framework))
        elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
            return dict(preprocessor(images=dummy_input, return_tensors=framework))
        elif (
            isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "input_features"
        ):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_audio(batch_size, sampling_rate, time_duration, frequency)
            return dict(preprocessor(dummy_input, return_tensors=framework))
        else:
            raise ValueError(
                "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."
            )

        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]):
                The preprocessor associated with this model configuration.
            batch_size (`int`, *optional*, defaults to -1):
                The batch size to export the model for (-1 means dynamic axis).
            num_choices (`int`, *optional*, defaults to -1):
                The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
            seq_length (`int`, *optional*, defaults to -1):
                The sequence length to export the model for (-1 means dynamic axis).
            is_pair (`bool`, *optional*, defaults to `False`):
                Indicate if the input is a pair (sentence 1, sentence 2)
            framework (`TensorType`, *optional*, defaults to `None`):
                The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
            num_channels (`int`, *optional*, defaults to 3):
                The number of channels of the generated images.
            image_width (`int`, *optional*, defaults to 40):
                The width of the generated images.
            image_height (`int`, *optional*, defaults to 40):
                The height of the generated images.
            sampling_rate (`int`, *optional* defaults to 22050)
                The sampling rate for audio data generation.
            time_duration (`float`, *optional* defaults to 5.0)
                Total seconds of sampling for audio data generation.
            frequency (`int`, *optional* defaults to 220)
                The desired natural frequency of generated audio.

        Returns:
            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
        
dpkd/transformers/src/transformers/onnx/config.py