U 9%e@s:dZddlZddlZddlZddlZddlmZmZmZm Z m Z ddl m Z m Z mZmZddlmZmZmZmZmZmZddlmZmZmZmZeeZdd iZdd d d iZ d d d Z!ddiddid Z"dZ#dZ$ddZ%ddZ&e'(dde)ej*DZ+ddZ,Gddde Z-Gddde.Z/Gddde.Z0dS) z"Tokenization class for LayoutLMv2.N)DictListOptionalTupleUnion)PreTrainedTokenizer _is_control_is_punctuation_is_whitespace) BatchEncoding EncodedInputPreTokenizedInput TextInput TextInputPairTruncationStrategy)PaddingStrategy TensorTypeadd_end_docstringslogging vocab_filez vocab.txtzOhttps://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/vocab.txtzPhttps://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/vocab.txt)z!microsoft/layoutlmv2-base-uncasedz"microsoft/layoutlmv2-large-uncased do_lower_caseTaU 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. a 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`], 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()` method Return: [`BatchEncoding`]: A [`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`). c CsRt}t|ddd}|}W5QRXt|D]\}}|d}|||<q2|S)z*Loads a vocabulary file into a dictionary.rutf-8encoding ) collections OrderedDictopen readlines enumeraterstrip)rvocabreadertokensindextokenr)u/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/layoutlmv2/tokenization_layoutlmv2.py load_vocabs  r+cCs|}|sgS|}|S)z@Runs basic whitespace cleaning and splitting on a piece of text.)stripsplit)textr&r)r)r*whitespace_tokenizes r/ccs&|]}tt|dr|VqdS)PN) unicodedatacategorychr startswith).0ir)r)r* sr7cCszg}g}ttt|D]D\}}|||dkr|||t||kr||||q|rr|d|dfSdSdS)Nr)Nr)r"rangelenappend)Zmylistpatternmatchesindicesidxr6r)r)r* subfinders(  r?cseZdZdZeZeZeZ e Z ddddddddd d d d gd d d d gd d d d gd dddd dfe e eed fdd ZeddZeddZddZddZddZddZddZdGee e ee ee ddd ZdHee e ee eee d"fd#d$ ZdIee e ee ee dd%d&ZdJee eeed'd(d)Zee e!dKe"e#e$ee#ee$fe e"e$ee$fe"eee eeee fe e"ee eee fee"eee%fe"eee&fe e e e e e e"ee'fe ee eeeeeee(d*d+d,Z)ee e!dLe"ee#ee*ee$fee eeee e e"ee eee fee"eee%fe"eee&fe e e e e e e"ee'fe ee eeeeeee(d-d.d/Z+dddde%j,e&j-dd ddddd!d!d!d!dfe"ee#ee*ee$fee eeee e eee ee%e&e e e e e e e"ee'fe ee eeeeeee(d0d1d2Z.ee e!dddde%j,e&j-dd ddddd!d!d!dfee eee e eee ee%e&e e e e e e ee ee eeeeee(d3d4d5Z/ee dMe"e#e$fe e$e eee e ee ee"eee%fe"eee&fe e e e e e e"ee'fe ee eeeeeeee d*d6d7Z0ee e!dNe"e#e$fe e$e eee e ee ee"eee%fe"eee&fe e e e e e e"ee'fe ee eeeeeee(d*d8d9Z1dddde%j,e&j-dd ddddd!d!d!d!dfe"e#e$fe e$e eee e ee ee%e&e e e e e e e"ee'fe ee eeeeeee(d:d;d<Z2ee e!dOe"e#e$fe e$e eee e ee ee"eee%fe"eee&fe e e e e e e"ee'fe ee eeeeeeee(d=d>d?Z3dPee eee e ee e eee e ee e e"ee&fe eee ee ee fdA dBdCZ4de%j,ddfe"e5ee6fe(fe e e%e e e ee7dDdEdFZ8Z9S)QLayoutLMv2Tokenizera Construct a LayoutLMv2 tokenizer. Based on WordPiece. [`LayoutLMv2Tokenizer`] can be used to turn words, word-level bounding boxes and optional word labels to token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and optional `labels` (for token classification). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. [`LayoutLMv2Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the word-level bounding boxes into token-level bounding boxes. TNz[UNK]z[SEP]z[PAD]z[CLS]z[MASK]riir)model_max_lengthadditional_special_tokensc stj|std|dt||_tdd|jD|_ ||_ |r^t ||||d|_ t |jt|d|_| |_| |_| |_| |_||_tjf|||||||| | | | | |||||d|dS)Nz&Can't find a vocabulary file at path 'z'. To load the vocabulary from a Google pretrained model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`cSsg|]\}}||fqSr)r))r5tokidsr)r)r* sz0LayoutLMv2Tokenizer.__init__..)r never_splittokenize_chinese_chars strip_accents)r$ unk_token)rdo_basic_tokenizerFrI sep_token pad_token cls_token mask_token cls_token_box sep_token_box pad_token_boxpad_token_labelonly_label_first_subwordrGrHrArB)ospathisfile ValueErrorr+r$rritems ids_to_tokensrJBasicTokenizerbasic_tokenizerWordpieceTokenizerstrwordpiece_tokenizerrOrPrQrRrSsuper__init__)selfrrrJrFrIrKrLrMrNrOrPrQrRrSrGrHrArBkwargs __class__r)r*r`sR   zLayoutLMv2Tokenizer.__init__cCs|jjSN)r[rrar)r)r*r#sz!LayoutLMv2Tokenizer.do_lower_casecCs t|jSre)r9r$rfr)r)r* vocab_size'szLayoutLMv2Tokenizer.vocab_sizecCst|jf|jSre)dictr$Zadded_tokens_encoderrfr)r)r* get_vocab+szLayoutLMv2Tokenizer.get_vocabcCs\g}|jrL|jj||jdD],}||jjkr8||q||j|7}qn |j|}|S)N)rF)rJr[tokenizeZall_special_tokensrFr:r^)rar. split_tokensr(r)r)r* _tokenize.s   zLayoutLMv2Tokenizer._tokenizecCs|j||j|jS)z0Converts a token (str) in an id using the vocab.)r$getrI)rar(r)r)r*_convert_token_to_id;sz(LayoutLMv2Tokenizer._convert_token_to_idcCs|j||jS)z=Converts an index (integer) in a token (str) using the vocab.)rYrmrI)rar'r)r)r*_convert_id_to_token?sz(LayoutLMv2Tokenizer._convert_id_to_tokencCsd|dd}|S)z:Converts a sequence of tokens (string) in a single string. z ##)joinreplacer,)rar&Z out_stringr)r)r*convert_tokens_to_stringCsz,LayoutLMv2Tokenizer.convert_tokens_to_string) token_ids_0 token_ids_1returncCs@|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 BERT sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` 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)rarurvclssepr)r)r* build_inputs_with_special_tokensHs z4LayoutLMv2Tokenizer.build_inputs_with_special_tokensF)rurvalready_has_special_tokensrwcsf|rtj||ddS|dk rLdgdgt|dgdgt|dgSdgdgt|dgS)a 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. Args: 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)rurvr}Nr)r_get_special_tokens_maskr9)rarurvr}rcr)r*ras.z+LayoutLMv2Tokenizer.get_special_tokens_maskcCsV|jg}|jg}|dkr.t|||dgSt|||dgt||dgS)a Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). 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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s). Nrr~)ryrxr9)rarurvr{rzr)r)r*$create_token_type_ids_from_sequences}s z8LayoutLMv2Tokenizer.create_token_type_ids_from_sequences)save_directoryfilename_prefixrwc Csd}tj|r4tj||r$|dndtd}n|r@|dnd|}t|dddZ}t|jdd d D]<\}}||krt d |d |}| |d |d7}qnW5QRX|fS)Nr-rqrwrrcSs|dS)Nr~r))kvr)r)r*z5LayoutLMv2Tokenizer.save_vocabulary..)keyzSaving vocabulary to z\: vocabulary indices are not consecutive. Please check that the vocabulary is not corrupted!rr~) rTrUisdirrrVOCAB_FILES_NAMESr sortedr$rXloggerwarningwrite)rarrr'rwriterr(Z token_indexr)r)r*save_vocabularys"  z#LayoutLMv2Tokenizer.save_vocabulary)r. text_pairboxes word_labelsadd_special_tokenspadding truncation max_lengthstridepad_to_multiple_ofreturn_tensorsreturn_token_type_idsreturn_attention_maskreturn_overflowing_tokensreturn_special_tokens_maskreturn_offsets_mapping return_lengthverboserwcKsdd}|dk r8||s tdt|ttfsNtdnt|ttfsNtd|dk rft|ttf}n$t|ttfo|ot|dttf}|dkr|n|}|dkrtd|rt|t|krtdt||D] \}}t|t|krtd qnt|t|krtd |r|dk rNt|t|krNtd t|d t|d |dk rftt||n|}t|dk }|jf||||||||| | | | | |||||d |S|jf||||||||| | | | | |||||d|SdS)a3 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. 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 (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). cSst|trdSt|ttfrxt|dkr,dSt|dtr>dSt|dttfrrt|ddkppt|ddtSdSndSdS)NTrF) isinstancer]listtupler9)tr)r)r*_is_valid_text_inputs  "z:LayoutLMv2Tokenizer.__call__.._is_valid_text_inputNzStext input must of type `str` (single example) or `List[str]` (batch of examples). zwWords must be of type `List[str]` (single pretokenized example), or `List[List[str]]` (batch of pretokenized examples).rz-You must provide corresponding bounding boxesz@You must provide words and boxes for an equal amount of examplesz:You must provide as many words as there are bounding boxeszbatch length of `text`: z- does not match batch length of `text_pair`: .)batch_text_or_text_pairsis_pairrrrrrrrrrrrrrrrrr.rrrrrrrrrrrrrrrrr) rWrrrr9zipboolbatch_encode_plus encode_plus)rar.rrrrrrrrrrrrrrrrrrbrZ is_batchedwordsZ words_example boxes_examplerrr)r)r*__call__s*$  zLayoutLMv2Tokenizer.__call__)rrrrrrrrrrrrrrrrrrrwcKsZ|jf|||| |d|\}}}}|jf||||||||| | | | | |||||d|S)Nrrrrr)rrrrrpadding_strategytruncation_strategyrrrrrrrrrrr)"_get_padding_truncation_strategies_batch_encode_plus)rarrrrrrrrrrrrrrrrrrrbrrr)r)r*r=s> z%LayoutLMv2Tokenizer.batch_encode_plus)rrrrrrrrrrrrrrrrrrrwcKs@|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 )rarrrrrrrrrrrrrrrrrrrb batch_outputsr)r)r*rxs0z&LayoutLMv2Tokenizer._batch_encode_plus)rrrrrrrrrrrrrrrrrwcCsi}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 rr~NF)rrrrrrrrrrrrrprepend_batch_axisr)rrrr) tensor_type) r"rprepare_for_modelr DO_NOT_PADvaluerXr:padr )rarrrrrrrrrrrrrrrrrrr>ZexampleZbatch_text_or_text_pairroutputsrrr)r)r*rsH z,LayoutLMv2Tokenizer._batch_prepare_for_modelcKs>|jf||||||||| | | | | |||||d|}|dS)Nr input_ids)r)rar.rrrrrrrrrrrrrrrrrrbencoded_inputsr)r)r*encodes.zLayoutLMv2Tokenizer.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 (words of a single example) or a list of list of strings (words of a batch of examples). r)r.rrrrrrrrrrrrrrrrr)r _encode_plus)rar.rrrrrrrrrrrrrrrrrrbrrr)r)r*r%s>$ zLayoutLMv2Tokenizer.encode_plus)r.rrrrrrrrrrrrrrrrrrwcKs>|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)r.rrrrrrrrrrrrrrrrr)rrr)rar.rrrrrrrrrrrrrrrrrrbr)r)r*rhs0z LayoutLMv2Tokenizer._encode_plus)r.rrrrrrrrrrrrrrrrrrrwc- s8jf|||| |d|\}}}}g}g}g}g}g}|dkr$|dkrt||D]>\}}t|dkrjqT|}||||gt|qTnt|||D]~\}}}t|dkrq|}||||gt|jr ||gjgt|dq||gt|qnt|}fddtt|D}t||D]D\}}t|dkrlqR|}||||gt|qR|} |r|nd}!|r|t j kr|!dk rt dt |!dk }"t| }#|"rt|!nd}$|#|$|rj |"dnd}%g}&g}'g}(|t jkrt|rt|%|krtj| ||!|||%||| d \} }}!}}}&}'}(| r|st d | dkrd jk} | dkrd jk} i})|r|&|)d <|'|)d<|(|)d<|%||)d<|r>| |!}*| |!}+jg|jg}|r"|jg}|rvjg|jg}n8|"rL| |!n| }*dgt| |"rpdgt|!ng}+|*|)d<|||)d<| r|+|)d <|r|r| |!|)d<ndgt|*|)d<|r||)d<|)d|||tjks| rj|)||j| | d})|r&t|)d|)d<t|)| |d},|,S)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. Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into token-level `labels`. The word label is used for the first token of the word, 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 (words of a single example) or a list of list of strings (words of a batch of examples). rNr~csg|] }jqSr))rQ)r5_rfr)r*rEsz9LayoutLMv2Tokenizer.prepare_for_model..zNot 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`.r)pair)pair_idspair_token_boxeslabelsnum_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_token_boxesoverflowing_labelsZnum_truncated_tokensrbboxspecial_tokens_maskr)rrrrlength)rr)rrr9rjextendrSrRr8Zconvert_tokens_to_idsr LONGEST_FIRSTrWrZnum_special_tokens_to_addDO_NOT_TRUNCATEtruncate_sequencesmodel_input_namesr|rrOrPrZ&_eventual_warn_about_too_long_sequencerrrrr )-rar.rrrrrrrrrrrrrrrrrrrbrrr&Z pair_tokens token_boxesrrwordboxZ word_tokenslabelrDrrZlen_idsZ len_pair_idsZ total_lenrrrrsequencerrr)rfr*rs,        "               & z%LayoutLMv2Tokenizer.prepare_for_model longest_first) rDrrrrrrrrwc  Cs@|dkr|||||gggfSt|ts.t|}g} g} g} |tjksZ|tjkr |dkr t||krtt|||} || d} || d} || d} |d| }|d| }|d| }n:d|dt|d} |tjkr| d|d} t| n |tjkrtdtjj d t |D]`}|dksVt|t|kr||dd }|dd }|dd }n|dd }|dd }q6n|tj kr,|dk r,t||kr tt|||} || d} || d} |d| }|d| }n"td|d t|d |d |||||| | | fS)a Truncates a sequence pair in-place following the strategy. Args: ids (`List[int]`): Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. token_boxes (`List[List[int]]`): Bounding boxes of the first sequence. pair_ids (`List[int]`, *optional*): Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. pair_token_boxes (`List[List[int]]`, *optional*): Bounding boxes of the second sequence. labels (`List[int]`, *optional*): Labels of the first sequence (for token classification tasks). num_tokens_to_remove (`int`, *optional*, defaults to 0): Number of tokens to remove using the truncation strategy. truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): The strategy to follow for truncation. Can be: - `'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. - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). stride (`int`, *optional*, defaults to 0): If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens. Returns: `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair of sequences (or a batch of pairs) is provided. rNzWe need to remove z; to truncate the input but the first sequence has a length z. z/Please select another truncation strategy than z0, for instance 'longest_first' or 'only_second'.zmBe aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'zg' truncation strategy. So the returned list will always be empty even if some tokens have been removed.z< to truncate the input but the second sequence has a length z1. Please select another truncation strategy than z/, for instance 'longest_first' or 'only_first'.) rrZ ONLY_FIRSTrr9minrerrorrrr8Z ONLY_SECOND)rarDrrrrrrrrrrZ window_len error_msgrr)r)r*rosp5         z&LayoutLMv2Tokenizer.truncate_sequences)rrrrrrwc CsJ|dkrd|jk}||jd}|tjkr2t|}|dk r^|dk r^||dkr^||d|}|tjkort||k}|rd|krdgt||d<|rF|t|}|jdkrj|r|ddg||d<d|kr|d|jg||d<d|kr |d|jg||d<d|kr.|d|jg||d<d |krN|d dg||d <||j g|||jd<n|jd kr4|rdg||d|d<d|kr|jg||d|d<d|kr|jg||d|d<d|kr|jg||d|d<d |krdg||d |d <|j g||||jd<nt d t |j|S) a? Pad encoded inputs (on left/right and up to predefined length or max length in the batch) Args: encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability `>= 7.5` (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) Nrrr~rightrrrrleftzInvalid padding strategy:) rrZLONGESTr9rZ padding_sideZpad_token_type_idrQrRZ pad_token_idrWr]) rarrrrrZrequired_inputZneeds_to_be_padded differencer)r)r*_padsR                zLayoutLMv2Tokenizer._pad)N)NF)N)N)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFT)NNNTFNNrNNNNFFFFTF)NNNrrr):__name__ __module__ __qualname____doc__rZvocab_files_namesPRETRAINED_VOCAB_FILES_MAPZpretrained_vocab_files_map&PRETRAINED_POSITIONAL_EMBEDDINGS_SIZESZmax_model_input_sizesPRETRAINED_INIT_CONFIGURATIONZpretrained_init_configurationintrrr]r`propertyrrgrirlrnrortr|rrrrrr"LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING2LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRINGrrrrrrr rrrrrrrrrrrrrr rhr __classcell__r)r)rcr*r@s.     B            A 7F    .    E   4    W     r@c@sNeZdZdZdddZdddZdd Zdd d Zd d ZddZ ddZ dS)rZa Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. never_split (`Iterable`, *optional*): Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True` tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). do_split_on_punc (`bool`, *optional*, defaults to `True`): In some instances we want to skip the basic punctuation splitting so that later tokenization can capture the full context of the words, such as contractions. TNcCs2|dkr g}||_t||_||_||_||_dSre)rsetrFrGrHdo_split_on_punc)rarrFrGrHrr)r)r*r`Ys zBasicTokenizer.__init__cCs|r|jt|n|j}||}|jr4||}td|}t|}g}|D]R}||kr|j r| }|j dk r| |}n|j r| |}| |||qPtd|}|S)aj Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. Args: never_split (`List[str]`, *optional*) Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of token not to split. NFCFrp)rFunionr _clean_textrG_tokenize_chinese_charsr1 normalizer/rlowerrH_run_strip_accentsr_run_split_on_puncrr)rar.rFZunicode_normalized_textZ orig_tokensrkr( output_tokensr)r)r*rjis$       zBasicTokenizer.tokenizecCsBtd|}g}|D]"}t|}|dkr,q||qd|S)z$Strips accents from a piece of text.ZNFDZMnrq)r1rr2r:rr)rar.outputcharcatr)r)r*rs   z!BasicTokenizer._run_strip_accentscCs|jr|dk r||kr|gSt|}d}d}g}|t|kr||}t|r^||gd}n |rl|gd}|d||d7}q0dd|DS) z&Splits punctuation on a piece of text.NrTFrr~cSsg|]}d|qS)rq)rr)r5xr)r)r*rEsz5BasicTokenizer._run_split_on_punc..)rrr9r r:)rar.rFcharsr6Zstart_new_wordrrr)r)r*rs"    z!BasicTokenizer._run_split_on_punccCsTg}|D]@}t|}||r>|d|||dq||qd|S)z)Adds whitespace around any CJK character.rprq)ord_is_chinese_charr:rrrar.rrcpr)r)r*rs     z&BasicTokenizer._tokenize_chinese_charscCs|dkr|dks|dkr |dks|dkr0|dks|dkr@|dks|d krP|d ks|d kr`|d ks|d krp|dks|dkr|dkrdSdS)z6Checks whether CP is the codepoint of a CJK character.iNii4iMiiߦii?i@ii iiiiiTFr))rarr)r)r*rsD  zBasicTokenizer._is_chinese_charcCsXg}|D]D}t|}|dks|dkst|r.qt|rB|dq||qd|S)zBPerforms invalid character removal and whitespace cleanup on text.rirprq)rr r r:rrrr)r)r*rs  zBasicTokenizer._clean_text)TNTNT)N)N) rrrrr`rjrrrrrr)r)r)r*rZBs  &  rZc@s"eZdZdZdddZddZdS) r\zRuns WordPiece tokenization.dcCs||_||_||_dSre)r$rImax_input_chars_per_word)rar$rIrr)r)r*r`szWordpieceTokenizer.__init__c Csg}t|D]}t|}t||jkr4||jq d}d}g}|t|krt|}d} ||krd|||} |dkrd| } | |jkr| } q|d8}qX| dkrd}q|| |}q@|r||jq ||q |S)a Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`. Args: text: A single token or whitespace separated tokens. This should have already been passed through *BasicTokenizer*. Returns: A list of wordpiece tokens. FrNrqz##r~T) r/rr9rr:rIrrr$r) rar.rr(rZis_badstartZ sub_tokensendZ cur_substrsubstrr)r)r*rjs:       zWordpieceTokenizer.tokenizeN)r)rrrrr`rjr)r)r)r*r\s r\)1rrrTsysr1typingrrrrrZtokenization_utilsrr r r Ztokenization_utils_baser r rrrrutilsrrrrZ get_loggerrrrrrrrrr+r/rhfromkeysr8 maxunicodetabler?r@objectrZr\r)r)r)r*sN   1:   |#