src/transformers/models/layoutlmv2/processing_layoutlmv2.py/LayoutLMv2Processor/__call__
class LayoutLMv2Processor:    def __call__(
        self,
        images,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs
    ) -> BatchEncoding:
        """
        This method first forwards the :obj:`images` argument to
        :meth:`~transformers.LayoutLMv2FeatureExtractor.__call__`. In case :class:`~LayoutLMv2FeatureExtractor` was
        initialized with :obj:`apply_ocr` set to ``True``, it passes the obtained words and bounding boxes along with
        the additional arguments to :meth:`~transformers.LayoutLMv2Tokenizer.__call__` and returns the output, together
        with resized :obj:`images`. In case :class:`~LayoutLMv2FeatureExtractor` was initialized with :obj:`apply_ocr`
        set to ``False``, it passes the words (:obj:`text`/:obj:`text_pair`) and :obj:`boxes` specified by the user
        along with the additional arguments to :meth:`~transformers.LayoutLMv2Tokenizer.__call__` and returns the
        output, together with resized :obj:`images`.

        Please refer to the docstring of the above two methods for more information.
        """
        # verify input
        if self.feature_extractor.apply_ocr and (boxes is not None):
            raise ValueError(
                "You cannot provide bounding boxes "
                "if you initialized the feature extractor with apply_ocr set to True."
            )

        if self.feature_extractor.apply_ocr and (word_labels is not None):
            raise ValueError(
                "You cannot provide word labels "
                "if you initialized the feature extractor with apply_ocr set to True."
            )

        # first, apply the feature extractor
        features = self.feature_extractor(images=images, return_tensors=return_tensors)

        # second, apply the tokenizer
        if text is not None and self.feature_extractor.apply_ocr and text_pair is None:
            if isinstance(text, str):
                text = [text]  # add batch dimension (as the feature extractor always adds a batch dimension)
            text_pair = features["words"]

        encoded_inputs = self.tokenizer(
            text=text if text is not None else features["words"],
            text_pair=text_pair if text_pair is not None else None,
            boxes=boxes if boxes is not None else features["boxes"],
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            return_tensors=return_tensors,
            **kwargs,
        )

        # add pixel values
        encoded_inputs["image"] = features.pop("pixel_values")

        return encoded_inputs