# LayoutXLM ## Overview LayoutXLM was proposed in [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://huggingface.co/papers/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei. It's a multilingual extension of the [LayoutLMv2 model](https://huggingface.co/papers/2012.14740) trained on 53 languages. The abstract from the paper is the following: *Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUN dataset.* This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm). ## Usage tips and examples One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so: ```python from transformers import LayoutLMv2Model model = LayoutLMv2Model.from_pretrained("microsoft/layoutxlm-base") ``` Note that LayoutXLM has its own tokenizer, based on [LayoutXLMTokenizer](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer)/[LayoutXLMTokenizerFast](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizerFast). You can initialize it as follows: ```python from transformers import LayoutXLMTokenizer tokenizer = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base") ``` Similar to LayoutLMv2, you can use [LayoutXLMProcessor](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMProcessor) (which internally applies [LayoutLMv2ImageProcessor](/docs/transformers/v4.57.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ImageProcessor) and [LayoutXLMTokenizer](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer)/[LayoutXLMTokenizerFast](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizerFast) in sequence) to prepare all data for the model. As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to [LayoutLMv2's documentation page](layoutlmv2) for all tips, code examples and notebooks. ## LayoutXLMTokenizer[[transformers.LayoutXLMTokenizer]] #### transformers.LayoutXLMTokenizer[[transformers.LayoutXLMTokenizer]] [Source](https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/layoutxlm/tokenization_layoutxlm.py#L148) Adapted from [RobertaTokenizer](/docs/transformers/v4.57.1/en/model_doc/roberta#transformers.RobertaTokenizer) and [XLNetTokenizer](/docs/transformers/v4.57.1/en/model_doc/xlnet#transformers.XLNetTokenizer). Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. __call__transformers.LayoutXLMTokenizer.__call__https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/layoutxlm/tokenization_layoutxlm.py#L439[{"name": "text", "val": ": typing.Union[str, list[str], list[list[str]]]"}, {"name": "text_pair", "val": ": typing.Union[list[str], list[list[str]], NoneType] = None"}, {"name": "boxes", "val": ": typing.Union[list[list[int]], list[list[list[int]]], NoneType] = None"}, {"name": "word_labels", "val": ": typing.Union[list[int], list[list[int]], NoneType] = None"}, {"name": "add_special_tokens", "val": ": bool = True"}, {"name": "padding", "val": ": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False"}, {"name": "truncation", "val": ": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None"}, {"name": "max_length", "val": ": typing.Optional[int] = None"}, {"name": "stride", "val": ": int = 0"}, {"name": "pad_to_multiple_of", "val": ": typing.Optional[int] = None"}, {"name": "padding_side", "val": ": typing.Optional[str] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}, {"name": "return_token_type_ids", "val": ": typing.Optional[bool] = None"}, {"name": "return_attention_mask", "val": ": typing.Optional[bool] = None"}, {"name": "return_overflowing_tokens", "val": ": bool = False"}, {"name": "return_special_tokens_mask", "val": ": bool = False"}, {"name": "return_offsets_mapping", "val": ": bool = False"}, {"name": "return_length", "val": ": bool = False"}, {"name": "verbose", "val": ": bool = True"}, {"name": "**kwargs", "val": ""}]- **text** (`str`, `list[str]`, `list[list[str]]`) -- The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (words of a single example or questions of a batch of examples) or a list of list of strings (batch of words). - **text_pair** (`list[str]`, `list[list[str]]`) -- The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). - **boxes** (`list[list[int]]`, `list[list[list[int]]]`) -- Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale. - **word_labels** (`list[int]`, `list[list[int]]`, *optional*) -- Word-level integer labels (for token classification tasks such as FUNSD, CORD). - **add_special_tokens** (`bool`, *optional*, defaults to `True`) -- Whether or not to encode the sequences with the special tokens relative to their model. - **padding** (`bool`, `str` or [PaddingStrategy](/docs/transformers/v4.57.1/en/internal/file_utils#transformers.utils.PaddingStrategy), *optional*, defaults to `False`) -- Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). - **truncation** (`bool`, `str` or [TruncationStrategy](/docs/transformers/v4.57.1/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy), *optional*, defaults to `False`) -- Activates and controls truncation. Accepts the following values: - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). - **max_length** (`int`, *optional*) -- Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. - **stride** (`int`, *optional*, defaults to 0) -- If set to a number along with `max_length`, the overflowing tokens returned when `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. - **pad_to_multiple_of** (`int`, *optional*) -- If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). - **return_tensors** (`str` or [TensorType](/docs/transformers/v4.57.1/en/internal/file_utils#transformers.TensorType), *optional*) -- If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. - **return_token_type_ids** (`bool`, *optional*) -- Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are token type IDs?](../glossary#token-type-ids) - **return_attention_mask** (`bool`, *optional*) -- Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) - **return_overflowing_tokens** (`bool`, *optional*, defaults to `False`) -- Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead of returning overflowing tokens. - **return_special_tokens_mask** (`bool`, *optional*, defaults to `False`) -- Whether or not to return special tokens mask information. - **return_offsets_mapping** (`bool`, *optional*, defaults to `False`) -- Whether or not to return `(char_start, char_end)` for each token. This is only available on fast tokenizers inheriting from [PreTrainedTokenizerFast](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast), if using Python's tokenizer, this method will raise `NotImplementedError`. - **return_length** (`bool`, *optional*, defaults to `False`) -- Whether or not to return the lengths of the encoded inputs. - **verbose** (`bool`, *optional*, defaults to `True`) -- Whether or not to print more information and warnings. - ****kwargs** -- passed to the `self.tokenize()` method0[BatchEncoding](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.BatchEncoding)A [BatchEncoding](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.BatchEncoding) with the following fields: - **input_ids** -- List of token ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **bbox** -- List of bounding boxes to be fed to a model. - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or if *"token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified). - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and `return_overflowing_tokens=True`). - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and `return_overflowing_tokens=True`). - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). - **length** -- The length of the inputs (when `return_length=True`). Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels. **Parameters:** vocab_file (`str`) : Path to the vocabulary file. bos_token (`str`, *optional*, defaults to `""`) : The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. eos_token (`str`, *optional*, defaults to `""`) : The end of sequence token. When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. sep_token (`str`, *optional*, defaults to `""`) : The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `""`) : The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `""`) : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `""`) : The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `""`) : The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. cls_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`) : The bounding box to use for the special [CLS] token. sep_token_box (`list[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`) : The bounding box to use for the special [SEP] token. pad_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`) : The bounding box to use for the special [PAD] token. pad_token_label (`int`, *optional*, defaults to -100) : The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's CrossEntropyLoss. only_label_first_subword (`bool`, *optional*, defaults to `True`) : Whether or not to only label the first subword, in case word labels are provided. sp_model_kwargs (`dict`, *optional*) : Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size = 7.5` (Volta). - **return_tensors** (`str` or [TensorType](/docs/transformers/v4.57.1/en/internal/file_utils#transformers.TensorType), *optional*) -- If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. - **return_token_type_ids** (`bool`, *optional*) -- Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are token type IDs?](../glossary#token-type-ids) - **return_attention_mask** (`bool`, *optional*) -- Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) - **return_overflowing_tokens** (`bool`, *optional*, defaults to `False`) -- Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead of returning overflowing tokens. - **return_special_tokens_mask** (`bool`, *optional*, defaults to `False`) -- Whether or not to return special tokens mask information. - **return_offsets_mapping** (`bool`, *optional*, defaults to `False`) -- Whether or not to return `(char_start, char_end)` for each token. This is only available on fast tokenizers inheriting from [PreTrainedTokenizerFast](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast), if using Python's tokenizer, this method will raise `NotImplementedError`. - **return_length** (`bool`, *optional*, defaults to `False`) -- Whether or not to return the lengths of the encoded inputs. - **verbose** (`bool`, *optional*, defaults to `True`) -- Whether or not to print more information and warnings. - ****kwargs** -- passed to the `self.tokenize()` method0[BatchEncoding](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.BatchEncoding)A [BatchEncoding](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.BatchEncoding) with the following fields: - **input_ids** -- List of token ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **bbox** -- List of bounding boxes to be fed to a model. - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or if *"token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified). - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and `return_overflowing_tokens=True`). - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and `return_overflowing_tokens=True`). - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). - **length** -- The length of the inputs (when `return_length=True`). Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels. **Parameters:** vocab_file (`str`) : Path to the vocabulary file. bos_token (`str`, *optional*, defaults to `""`) : The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. eos_token (`str`, *optional*, defaults to `""`) : The end of sequence token. When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. sep_token (`str`, *optional*, defaults to `""`) : The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `""`) : The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `""`) : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `""`) : The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `""`) : The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. cls_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`) : The bounding box to use for the special [CLS] token. sep_token_box (`list[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`) : The bounding box to use for the special [SEP] token. pad_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`) : The bounding box to use for the special [PAD] token. pad_token_label (`int`, *optional*, defaults to -100) : The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's CrossEntropyLoss. only_label_first_subword (`bool`, *optional*, defaults to `True`) : Whether or not to only label the first subword, in case word labels are provided. additional_special_tokens (`list[str]`, *optional*, defaults to `["NOTUSED", "NOTUSED"]`) : Additional special tokens used by the tokenizer. **Returns:** `[BatchEncoding](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.BatchEncoding)` A [BatchEncoding](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.BatchEncoding) with the following fields: - **input_ids** -- List of token ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **bbox** -- List of bounding boxes to be fed to a model. - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or if *"token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified). - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and `return_overflowing_tokens=True`). - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and `return_overflowing_tokens=True`). - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). - **length** -- The length of the inputs (when `return_length=True`). ## LayoutXLMProcessor[[transformers.LayoutXLMProcessor]] #### transformers.LayoutXLMProcessor[[transformers.LayoutXLMProcessor]] [Source](https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/layoutxlm/processing_layoutxlm.py#L27) Constructs a LayoutXLM processor which combines a LayoutXLM image processor and a LayoutXLM tokenizer into a single processor. [LayoutXLMProcessor](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMProcessor) offers all the functionalities you need to prepare data for the model. It first uses [LayoutLMv2ImageProcessor](/docs/transformers/v4.57.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ImageProcessor) to resize document images to a fixed size, and optionally applies OCR to get words and normalized bounding boxes. These are then provided to [LayoutXLMTokenizer](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer) or [LayoutXLMTokenizerFast](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizerFast), which turns the words and bounding boxes into token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned into token-level `labels` for token classification tasks (such as FUNSD, CORD). __call__transformers.LayoutXLMProcessor.__call__https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/layoutxlm/processing_layoutxlm.py#L64[{"name": "images", "val": ""}, {"name": "text", "val": ": typing.Union[str, list[str], list[list[str]]] = None"}, {"name": "text_pair", "val": ": typing.Union[list[str], list[list[str]], NoneType] = None"}, {"name": "boxes", "val": ": typing.Union[list[list[int]], list[list[list[int]]], NoneType] = None"}, {"name": "word_labels", "val": ": typing.Union[list[int], list[list[int]], NoneType] = None"}, {"name": "add_special_tokens", "val": ": bool = True"}, {"name": "padding", "val": ": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False"}, {"name": "truncation", "val": ": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None"}, {"name": "max_length", "val": ": typing.Optional[int] = None"}, {"name": "stride", "val": ": int = 0"}, {"name": "pad_to_multiple_of", "val": ": typing.Optional[int] = None"}, {"name": "return_token_type_ids", "val": ": typing.Optional[bool] = None"}, {"name": "return_attention_mask", "val": ": typing.Optional[bool] = None"}, {"name": "return_overflowing_tokens", "val": ": bool = False"}, {"name": "return_special_tokens_mask", "val": ": bool = False"}, {"name": "return_offsets_mapping", "val": ": bool = False"}, {"name": "return_length", "val": ": bool = False"}, {"name": "verbose", "val": ": bool = True"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}, {"name": "**kwargs", "val": ""}] This method first forwards the `images` argument to `~LayoutLMv2ImagePrpcessor.__call__`. In case `LayoutLMv2ImagePrpcessor` was initialized with `apply_ocr` set to `True`, it passes the obtained words and bounding boxes along with the additional arguments to [__call__()](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer.__call__) and returns the output, together with resized `images`. In case `LayoutLMv2ImagePrpcessor` was initialized with `apply_ocr` set to `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional arguments to [__call__()](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer.__call__) and returns the output, together with resized `images``. Please refer to the docstring of the above two methods for more information. **Parameters:** image_processor (`LayoutLMv2ImageProcessor`, *optional*) : An instance of [LayoutLMv2ImageProcessor](/docs/transformers/v4.57.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ImageProcessor). The image processor is a required input. tokenizer (`LayoutXLMTokenizer` or `LayoutXLMTokenizerFast`, *optional*) : An instance of [LayoutXLMTokenizer](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer) or [LayoutXLMTokenizerFast](/docs/transformers/v4.57.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizerFast). The tokenizer is a required input.