U 9%et@sdZddlZddlZddlmZddlmZmZmZm Z m Z ddl Z ddl mZmZmZddlmZmZddlmZmZmZmZmZmZmZdd lmZeeZ d d d d Z!dddddddZ"dddZ#dZ$eddZ%ddZ&GdddeZ'dS)z Tokenization class for MarkupLM.N) lru_cache)DictListOptionalTupleUnion)PaddingStrategy TensorTypeadd_end_docstrings) AddedTokenPreTrainedTokenizer)ENCODE_KWARGS_DOCSTRING BatchEncoding EncodedInputPreTokenizedInput TextInput TextInputPairTruncationStrategy)loggingz vocab.jsonz merges.txtztokenizer.json) vocab_file merges_fileZtokenizer_filezFhttps://huggingface.co/microsoft/markuplm-base/resolve/main/vocab.jsonzGhttps://huggingface.co/microsoft/markuplm-large/resolve/main/vocab.json)zmicrosoft/markuplm-basezmicrosoft/markuplm-largezFhttps://huggingface.co/microsoft/markuplm-base/resolve/main/merges.txtzGhttps://huggingface.co/microsoft/markuplm-large/resolve/main/merges.txt)rriaT 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 [`~file_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 [`~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 [`~file_utils.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. cCstttdtddtttdtddtttdtdd}|dd}d }td D],}||krf|||d ||d7}qfd d |D}tt||S) a3 Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. !~¡¬®ÿNrcSsg|] }t|qS)chr).0nr r q/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/markuplm/tokenization_markuplm.py sz$bytes_to_unicode..)listrangeordappenddictzip)bscsr#br r r$bytes_to_unicodems L    r/cCs6t}|d}|ddD]}|||f|}q|S)z Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). rrN)setadd)wordpairsZ prev_charcharr r r$ get_pairss r5cseZdZdZeZeZeZ dRfdd Z ddZ e ddZ ddZddZddZddZddZddZdSeeeeed!d"d#ZdTd$d%ZdUeeeeeeed&d'd(ZdVeeeeeeed)d*d+ZdWeeeeeeed,d-d.ZdXeeeeeeeed/fd0d1 ZdYeeeeeeed&d2d3Z e!e"e#dZe$e%e&ee%ee&fee$e&ee&fe$eeeeeeefee$eeeeefee$eee'fe$eee(feeeeeee$ee)feeeeeeeeee*d5d6d7Z+e!e"e#d[e$ee%ee,ee&feeeeeeee$eeeeefee$eee'fe$eee(feeeeeee$ee)feeeeeeeeee*d8d9d:Z-d d d d e'j.e(j/d d4d d d d ddddd fe$ee%ee,ee&feeeeeeeeeeee'e(eeeeeee$ee)feeeeeeeeee*d;dd?d@Z1e!e"d\e$e%e&fee&eeeeeeeee$eee'fe$eee(feeeeeee$ee)feeeeeeeeeeed5dAdBZ2e!e"e#d]e$e%e&fee&eeeeeeeee$eee'fe$eee(feeeeeee$ee)feeeeeeeeee*d5dCdDZ3d d d d e'j.e(j/d d4d d d d ddddd fe$e%e&fee&eeeeeeeee'e(eeeeeee$ee)feeeeeeeeee*dEdFdGZ4e!e"e#d^e$e%e&fee&eeeeeeeee$eee'fe$eee(feeeeeee$ee)feeeeeeeeeee*dHdIdJZ5d_eeeeeeeeeeeeeeeeeeeeeeee$ee(feeeeeeeefdL dMdNZ6d e'j.d d fe$e7ee8fe*feee'eeeee9dOdPdQZ:Z;S)`MarkupLMTokenizera Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. 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. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space). replaceF2Tc s$t|trt|dddn|}t|tr4t|dddn|}t|trPt|dddn|}t|trlt|dddn|}t| trt| dddn| } t| trt| dddn| } t| trt| dddn| } t|dd}t||_W5QRX||_dd|jD|_ ||_ t |_ dd|j D|_ t|dd}|d d d }W5QRXd d |D}tt|tt||_i|_| |_td|_| |_||_||_t|j|_|jd |_|jg|j|_|jg|j|_ t!j"f||||||| ||| | | | ||||d|||_#||_$dS)NF)lstriprstripTutf-8encodingcSsi|]\}}||qSr r r"kvr r r$ sz.MarkupLMTokenizer.__init__..cSsi|]\}}||qSr r rFr r r$rIs rcSsg|]}t|qSr )tuplesplit)r"merger r r$r%sz.MarkupLMTokenizer.__init__..zJ's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+)rr tags_dicterrors bos_token eos_token unk_token sep_token cls_token pad_token mask_tokenadd_prefix_space max_depth max_width pad_widthpad_token_labelonly_label_first_subword)% isinstancestrr openjsonloadencoderrOitemsdecoderrPr/ byte_encoder byte_decoderreadrMr*r+r'len bpe_rankscacherXrecompilepatrYrZr[ unk_tag_id pad_tag_idpad_xpath_tags_seqpad_xpath_subs_seqsuper__init__r\r])selfrrrOrPrQrRrTrUrSrVrWrXrYrZr[r\r]kwargsZ vocab_handleZ merges_handleZ bpe_merges __class__r r$rtsf    zMarkupLMTokenizer.__init__c Csg}g}|d}|D]p}|s$q|d}|d}t|dkrJdnt|ddd}||j||j|t|j |q|d|j }|d|j }||j g|j t|7}||j g|j t|7}||fS)z Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of tag IDs and corresponding subscripts, taking into account max depth. /[rrNrK) rMstripriintr)rOgetrominrZrYrpr[) ruxpathxpath_tags_listxpath_subs_listZ xpath_unitsunitZ name_subsZtag_namesubr r r$ get_xpath_seqs  $zMarkupLMTokenizer.get_xpath_seqcCs t|jSN)rircrur r r$ vocab_size7szMarkupLMTokenizer.vocab_sizecCs|j}||j|Sr)rccopyupdateZadded_tokens_encoder)ruZvocabr r r$ get_vocab;s  zMarkupLMTokenizer.get_vocabc sd|jkrj|St|}t|}|s,|St|fddd}|jkrNqL|\}}g}d}|t|kr"z|||} Wn,tk r|||dYq"YnX|||| | }|||kr |t|dkr ||d|kr | |||d7}q^| |||d7}q^t|}|}t|dkrBqLq,t|}q,d |}|j|<|S)Ncsj|tdS)Ninf)rjr}floatpairrr r$Jz'MarkupLMTokenizer.bpe..keyrr ) rkrLr5r~rjriindex ValueErrorextendr)join) rutokenr2r3ZbigramfirstsecondZnew_wordijr rr$bpe@sB    2     zMarkupLMTokenizer.bpecsZg}tj|D]B}dfdd|dD}|dd|dDq|S)zTokenize a string.c3s|]}j|VqdSr)rf)r"r.rr r$ nsz.MarkupLMTokenizer._tokenize..rCcss|] }|VqdSrr )r"Z bpe_tokenr r r$rqsr)rlfindallrnrencoderrrM)rutext bpe_tokensrr rr$ _tokenizejs"zMarkupLMTokenizer._tokenizecCs|j||j|jS)z0Converts a token (str) in an id using the vocab.)rcr}rS)rurr r r$_convert_token_to_idtsz&MarkupLMTokenizer._convert_token_to_idcCs |j|S)z=Converts an index (integer) in a token (str) using the vocab.)rer})rurr r r$_convert_id_to_tokenxsz&MarkupLMTokenizer._convert_id_to_tokencs:tdd|}tfdd|Djdjd}|S)z:Converts a sequence of tokens (string) in a single string.z_MarkupLM now does not support generative tasks, decoding is experimental and subject to change.rcsg|]}j|qSr )rg)r"crr r$r%sz>MarkupLMTokenizer.convert_tokens_to_string..rC)rP)loggerwarningr bytearraydecoderP)rutokensrr rr$convert_tokens_to_string|s  "z*MarkupLMTokenizer.convert_tokens_to_stringN)save_directoryfilename_prefixreturnc Cs(tj|s"td|ddStj||r6|dndtd}tj||rX|dndtd}t|ddd $}|t j |j d d d d dW5QRXd}t|ddd j}|dt |j dddD]B\}} || krtd|d| }|d|d|d7}qW5QRX||fS)NzVocabulary path (z) should be a directory-rrrwrCrDrTF)indent sort_keys ensure_asciirJrz#version: 0.2 cSs|dS)Nrr )kvr r r$rrz3MarkupLMTokenizer.save_vocabulary..rzSaving vocabulary to zZ: BPE merge indices are not consecutive. Please check that the tokenizer is not corrupted!rr)ospathisdirrerrorrVOCAB_FILES_NAMESr`writeradumpsrcsortedrjrdr) rurrrZ merge_filefrwriterrZ token_indexr r r$save_vocabularys2 (  z!MarkupLMTokenizer.save_vocabularycKs>|d|j}|s|r6t|dkr6|ds6d|}||fS)NrXrr)poprXriisspace)rurZis_split_into_wordsrvrXr r r$prepare_for_tokenizations z*MarkupLMTokenizer.prepare_for_tokenization) token_ids_0 token_ids_1rcCs@|dkr|jg||jgS|jg}|jg}|||||S)a Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format: - single sequence: ` X ` - pair of sequences: ` A B ` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. N) cls_token_id sep_token_id)rurrclssepr r r$ build_inputs_with_special_tokenss z2MarkupLMTokenizer.build_inputs_with_special_tokens) xpath_tags_0 xpath_tags_1rcCs4|jg}t|dkr |||S|||||SNr)rqri)rurrpadr r r$$build_xpath_tags_with_special_tokenss  z6MarkupLMTokenizer.build_xpath_tags_with_special_tokens) xpath_subs_0 xpath_subs_1rcCs4|jg}t|dkr |||S|||||Sr)rrri)rurrrr r r$$build_xpath_subs_with_special_tokenss  z6MarkupLMTokenizer.build_xpath_subs_with_special_tokens)rralready_has_special_tokensrcsh|rtj||ddS|dkr8dgdgt|dgSdgdgt|ddgdgt|dgS)a Args: Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. T)rrrNrr)rsget_special_tokens_maskri)rurrrrwr r$rsz)MarkupLMTokenizer.get_special_tokens_maskcCsL|jg}|jg}|dkr.t|||dgSt|||||dgS)a Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. Nr)rrri)rurrrrr r r$$create_token_type_ids_from_sequencess z6MarkupLMTokenizer.create_token_type_ids_from_sequencesr)r text_pairxpaths node_labelsadd_special_tokenspadding truncation max_lengthstridepad_to_multiple_ofreturn_tensorsreturn_token_type_idsreturn_attention_maskreturn_overflowing_tokensreturn_special_tokens_maskreturn_offsets_mapping return_lengthverbosercKsdd}|dk r8||s tdt|ttfsNtdnt|ttfsNtd|dk rft|ttf}n$t|ttfo|ot|dttf}|dkr|n|}|dk std|rt|t|kstdt||D] \}}t|t|kstd qnt|t|kstd |r|dk rNt|t|krNtd t|d t|d |dk rftt||n|}t|dk }|jf||||||||| | | | | |||||d |S|j f||||||||| | | | | |||||d|SdS)a Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with node-level xpaths and optional labels. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (nodes of a single example or questions of a batch of examples) or a list of list of strings (batch of nodes). text_pair (`List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). xpaths (`List[List[int]]`, `List[List[List[int]]]`): Node-level xpaths. node_labels (`List[int]`, `List[List[int]]`, *optional*): Node-level integer labels (for token classification tasks). cSst|trdSt|ttfrxt|dkr,dSt|dtr>dSt|dttfrrt|ddkppt|ddtSdSndSdS)NTrF)r^r_r&rLri)tr r r$_is_valid_text_input*s  "z8MarkupLMTokenizer.__call__.._is_valid_text_inputNzStext input must of type `str` (single example) or `List[str]` (batch of examples). zwNodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]` (batch of pretokenized examples).rz%You must provide corresponding xpathszAYou must provide nodes and xpaths for an equal amount of examplesz2You must provide as many nodes as there are xpathszbatch length of `text`: z- does not match batch length of `text_pair`: .)batch_text_or_text_pairsis_pairrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr) rr^r&rLAssertionErrorrir+boolbatch_encode_plus encode_plus)rurrrrrrrrrrrrrrrrrrrvrZ is_batchedZnodesZ nodes_examplexpaths_examplerrr r r$__call__s*$ zMarkupLMTokenizer.__call__)rrrrrrrrrrrrrrrrrrrcKsZ|jf|||| |d|\}}}}|jf||||||||| | | | | |||||d|S)Nrrrrr)rrrrrpadding_strategytruncation_strategyrrrrrrrrrrr)"_get_padding_truncation_strategies_batch_encode_plus)rurrrrrrrrrrrrrrrrrrrvrrr r r$rs> z#MarkupLMTokenizer.batch_encode_plus)rrrrrrrrrrrrrrrrrrrcKs@|r td|j||||||||| | | | |||| |d}t|S)Nzreturn_offset_mapping is not available when using Python tokenizers. To use this feature, change your tokenizer to one deriving from transformers.PreTrainedTokenizerFast.)rrrrrrrrrrrrrrrrr)NotImplementedError_batch_prepare_for_modelr)rurrrrrrrrrrrrrrrrrrrv batch_outputsr r r$rs0z$MarkupLMTokenizer._batch_encode_plus)rrrrrrrrrrrrrrrrrcCsi}tt||D]\}}|\}}|j|r2|dn||r@|dnd||dk rT||nd|tjj|j|| dd| |||dd|d}|D]&\}}||krg||<|||qq|j||j|| | d}t || d}|S)a Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Args: batch_ids_pairs: list of tokenized input ids or input ids pairs rrNF)rrrrrrrrrrrrrprepend_batch_axisr)rrrr) tensor_type) enumerater+prepare_for_modelr DO_NOT_PADvaluerdr)rr)rurrrrrrrrrrrrrrrrrridxZexampleZbatch_text_or_text_pairroutputsrrr r r$rsH z*MarkupLMTokenizer._batch_prepare_for_modelcKs>|jf||||||||| | | | | |||||d|}|dS)Nr input_ids)r)rurrrrrrrrrrrrrrrrrrrvencoded_inputsr r r$rJs.zMarkupLMTokenizer.encodecKsZ|jf|||| |d|\}}}}|jf||||||||| | | | | |||||d|S)aj Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated, `__call__` should be used instead. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a list of list of strings (nodes of a batch of examples). r)rrrrrrrrrrrrrrrrrr)r _encode_plus)rurrrrrrrrrrrrrrrrrrrvrrr r r$rys>$ zMarkupLMTokenizer.encode_plus)rrrrrrrrrrrrrrrrrrrcKs>|r td|j||||||j|j|| | | d| | ||||dS)Na return_offset_mapping is not available when using Python tokenizers. To use this feature, change your tokenizer to one deriving from transformers.PreTrainedTokenizerFast. More information on available tokenizers at https://github.com/huggingface/transformers/pull/2674T)rrrrrrrrrrrrrrrrrr)rrr)rurrrrrrrrrrrrrrrrrrrvr r r$rs0zMarkupLMTokenizer._encode_plus)rrrrrrrrrrrrrrrrrrrrc4 sjf|||| |d|\}}}}g}g}g}g}g}g}g}|dkrp|dkrt||D]`\}}t|dkrrq\|} || |\}!}"||!gt| ||"gt| q\nt|||D]\}}}#t|dkrq̈|} || |\}!}"||!gt| ||"gt| jrX||#gjgt| dq||#gt| qn|}fddtt|D}fddtt|D}t||D]f\}}t|dkrҐq|} || |\}!}"||!gt| ||"gt| q |}$|r: |nd}%|rb|t j krb|%dk rbt dt |%dk }&t|$}'|&rt|%nd}(|'|(|rj|&d nd})g}*g}+g},g}-|t jkr |r |)|kr j|$|||%||||)||| d \ }$}}}%}}}}*}+},}-| r|st d | dkr2d jk} | dkrFd jk} i}.|r||*|.d<|+|.d<|,|.d<|-|.d<|)||.d<|rΈ|$|%}/|$|%}0||}1||}2|r*jg|jg}n\|&r|$|%n|$}/dgt|$|&rdgt|%ng}0|&r||n|}1|&r&||n|}2|/|.d<|1|.d<|2|.d<| rP|0|.d <|r|rn|$|%|.d<ndgt|/|.d<|r||.d<|.d|||tjks| rȈj|.||j| | d}.|rt|.d|.d<t|.| |d}3|3S)a Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. Node-level `xpaths` are turned into token-level `xpath_tags_seq` and `xpath_subs_seq`. If provided, node-level `node_labels` are turned into token-level `labels`. The node label is used for the first token of the node, while remaining tokens are labeled with -100, such that they will be ignored by the loss function. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a list of list of strings (nodes of a batch of examples). rNrcsg|] }jqSr )rqr"_rr r$r%Lsz7MarkupLMTokenizer.prepare_for_model..csg|] }jqSr )rrrrr r$r%MszNot possible to return overflowing tokens for pair of sequences with the `longest_first`. Please select another truncation strategy than `longest_first`, for instance `only_second` or `only_first`.rr) xpath_tags_seqxpath_subs_seqpair_idspair_xpath_tags_seqpair_xpath_subs_seqlabelsnum_tokens_to_removerrzAsking to return token_type_ids while setting add_special_tokens to False results in an undefined behavior. Please set add_special_tokens to True or set return_token_type_ids to None.token_type_idsattention_maskoverflowing_tokensoverflowing_xpath_tags_seqoverflowing_xpath_subs_seqoverflowing_labelsZnum_truncated_tokensrr r special_tokens_maskr)rrrrlength)rr)rr+ritokenizerrr]r\r'Zconvert_tokens_to_idsr LONGEST_FIRSTrrZnum_special_tokens_to_addDO_NOT_TRUNCATEtruncate_sequencesmodel_input_namesrrrrrZ&_eventual_warn_about_too_long_sequencer rrrr)4rurrrrrrrrrrrrrrrrrrrrvrrrZ pair_tokensr r r rrr2rZ word_tokensrrlabelidsr rZlen_idsZ len_pair_idsZ total_lenrrrrrsequencerZxpath_tags_idsZxpath_subs_idsrr rr$rs$,        "                &z#MarkupLMTokenizer.prepare_for_model longest_first) rr r r r rrrrrrc  Cs|dkr||||||gggf St| ts0t| } g} g} g} g}| tjks`| tjkr"|dkr"t||krtt|| |}|| d} || d} || d} |d| }|d| }|d| }|d| }n= 7.5` (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) Nrrrrightrr r rrleftzInvalid padding strategy:) rr ZLONGESTrirZ padding_sideZpad_token_type_idrqrrr\Z pad_token_idrr_) rurrrrrZrequired_inputZneeds_to_be_padded differencer r r$_pad]sf                     zMarkupLMTokenizer._pad)r7r8r9r9r8r:r;r<Fr=r>r?r@T)N)F)N)N)N)NF)N)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFTF)NNNNrr!r)<__name__ __module__ __qualname____doc__rZvocab_files_namesPRETRAINED_VOCAB_FILES_MAPZpretrained_vocab_files_map&PRETRAINED_POSITIONAL_EMBEDDINGS_SIZESZmax_model_input_sizesrtrpropertyrrrrrrrr_rrrrrr|rrrrrrr r0MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRINGrrrr rr rrrrrrrrrrrrrrrr*r& __classcell__r r rwr$r6s@6R *                A 7F    .    E   4    j       r6)(r*rar functoolsrtypingrrrrrregexrlZ file_utilsr r r Ztokenization_utilsr r Ztokenization_utils_baserrrrrrrutilsrZ get_loggerr'rrr+r,r.r/r5r6r r r r$s4 $    1