vllm.transformers_utils.processors.bagel ¶

class BagelProcessor(ProcessorMixin):
    """
    Constructs a BAGEL processor which wraps a
    SigLIP image processor and a Qwen2 tokenizer.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "SiglipImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __call__(
        self,
        text: TextInput
        | PreTokenizedInput
        | list[TextInput]
        | list[PreTokenizedInput] = None,
        images: ImageInput = None,
        **kwargs,
    ):
        """
        Main method to prepare for the model one or several sequences(s) and image(s).
        """
        if images is not None:
            # Process images with the image processor
            # Ensure return_tensors is set to "pt" for PyTorch tensors
            image_kwargs = {**kwargs}
            if "return_tensors" not in image_kwargs:
                image_kwargs["return_tensors"] = "pt"
            pixel_values = self.image_processor(images, **image_kwargs)
        else:
            pixel_values = None

        text_inputs = self.tokenizer(text, **kwargs) if text is not None else None

        if pixel_values is not None and text_inputs is not None:
            text_inputs["pixel_values"] = pixel_values["pixel_values"]
            return text_inputs
        elif pixel_values is not None:
            return pixel_values
        else:
            return text_inputs

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's batch_decode.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's decode.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))