src/transformers/models/clip/processing_clip.py/CLIPProcessor/__call__
class CLIPProcessor:    def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the
        :obj:`text` and :obj:`kwargs` arguments to CLIPTokenizer's :meth:`~transformers.CLIPTokenizer.__call__` if
        :obj:`text` is not :obj:`None` to encode the text. To prepare the image(s), this method forwards the
        :obj:`images` and :obj:`kwrags` arguments to CLIPFeatureExtractor's
        :meth:`~transformers.CLIPFeatureExtractor.__call__` if :obj:`images` is not :obj:`None`. Please refer to the
        doctsring of the above two methods for more information.

        Args:
            text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                :obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            images (:obj:`PIL.Image.Image`, :obj:`np.ndarray`, :obj:`torch.Tensor`, :obj:`List[PIL.Image.Image]`, :obj:`List[np.ndarray]`, :obj:`List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                number of channels, H and W are image height and width.

            return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
                If set, will return tensors of a particular framework. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return NumPy :obj:`np.ndarray` objects.
                * :obj:`'jax'`: Return JAX :obj:`jnp.ndarray` objects.

        Returns:
            :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when :obj:`text` is not :obj:`None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              :obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names` and if
              :obj:`text` is not :obj:`None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when :obj:`images` is not :obj:`None`.
        """

        if text is None and images is None:
            raise ValueError("You have to specify either text or images. Both cannot be none.")

        if text is not None:
            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)

        if images is not None:
            image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)

        if text is not None and images is not None:
            encoding["pixel_values"] = image_features.pixel_values
            return encoding
        elif text is not None:
            return encoding
        else:
            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)