o Zh?K@sdZddlZddlZddlZddlZddlmZmZmZm Z ddl m Z ddl m Z e eZddd d Zd d Zd dZddZGddde ZdgZdS)zTokenization classes for FSMT.N)DictListOptionalTuple)PreTrainedTokenizer)loggingzvocab-src.jsonzvocab-tgt.jsonz merges.txt)src_vocab_filetgt_vocab_file merges_filecCs6t}|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) rN)setadd)wordpairsZ prev_charcharrY/var/www/auris/lib/python3.10/site-packages/transformers/models/fsmt/tokenization_fsmt.py get_pairs$s rcCs|dd}tdd|}|dd}|dd}|dd}|d d }|d d }|d d }|dd}|dd}|dd}|dd}|dd}|dd}|dd}|dd}|dd}|dd}|dd}|d d!}|d"d#}|d$d%}|d&d'}|d(d)}|d*d+}|d,d-}td.d|}|d/d0}|d1d2}|d3d4}|d5d6}|d7d8}|d9d:}|d;d<}|d=d>}|d?d@}|S)Azz Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl u，,u。\s*z. u、u”"u“u∶:u：u？?u《u》u）)u！!u（(u；;u１1u」u「u０0u３3u２2u５5u６6u９9u７7u８8u４4u．\s*u～~u’'u…z...u━-u〈u【[u】]u％%)replaceresub)textrrrreplace_unicode_punct1sJ                                  r3cCs8g}|D]}t|}|drq||qd|S)zw Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl C) unicodedatacategory startswithappendjoin)r2outputrcatrrrremove_non_printing_char\s    r=c seZdZdZeZddgZ         d=fd d Zd ee e ffd dZ e d e fddZ ddZddZddZddZe ddZe ddZddZdd Zd!d"Zd>d$d%Zd&d'Zd(d)Zd*d+Z d?d,ee d-eee d ee fd.d/Z d@d,ee d-eee d0ed ee ffd1d2 Z d?d,ee d-eee d ee fd3d4Z d?d5e d6ee d e!e fd7d8Z"d9d:Z#d;d<Z$Z%S)A FSMTTokenizera Construct an FAIRSEQ Transformer tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following: - Moses preprocessing and tokenization. - Normalizing all inputs text. - The arguments `special_tokens` and the function `set_special_tokens`, can be used to add additional symbols (like "__classify__") to a vocabulary. - The argument `langs` defines a pair of languages. 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: langs (`List[str]`, *optional*): A list of two languages to translate from and to, for instance `["en", "ru"]`. src_vocab_file (`str`, *optional*): File containing the vocabulary for the source language. tgt_vocab_file (`st`, *optional*): File containing the vocabulary for the target language. merges_file (`str`, *optional*): File containing the merges. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the input when tokenizing. 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. 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`. 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. pad_token (`str`, *optional*, defaults to `""`): The token used for padding, for example when batching sequences of different lengths. Z input_idsZattention_maskNFc  szddl} Wn tytdw| |_||_||_||_||_i|_i|_i|_ |r9t |dkr9|\|_ |_ nt d|dt|dd} t| |_Wdn1sXwYt|dd} t| }dd |D|_Wdn1s}wYt|dd}|d dd }Wdn1swYd d |D}tt|tt ||_i|_tjd||||||||| d | dS)NrnYou need to install sacremoses to use XLMTokenizer. See https://pypi.org/project/sacremoses/ for installation.zFarg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got zw. Usually that means that tokenizer can't find a mapping for the given model path in and other maps of this tokenizer.utf-8encodingcSi|]\}}||qSrr.0kvrrr z*FSMTTokenizer.__init__.. cSs g|] }t|ddqS)NrD)tuplesplit)rJmergerrr  z*FSMTTokenizer.__init__..) langsr r r do_lower_case unk_token bos_token sep_token pad_tokenr) sacremoses ImportErrorsmr r r rWcache_moses_punct_normalizercache_moses_tokenizercache_moses_detokenizerlensrc_langtgt_lang ValueErroropenjsonloadencoderitemsdecoderreadrRdictziprange bpe_rankscachesuper__init__)selfrVr r r rWrXrYrZr[kwargsr\Zsrc_vocab_handleZtgt_vocab_handle tgt_vocabZ merges_handleZmerges __class__rrrss\     zFSMTTokenizer.__init__returncCs|SN) get_src_vocabrtrrr get_vocabszFSMTTokenizer.get_vocabcCs|jSrz)src_vocab_sizer|rrr vocab_sizeszFSMTTokenizer.vocab_sizecC2||jvr|jj|d}||j|<|j||SNlang)r_r^ZMosesPunctNormalizer normalize)rtr2rZpunct_normalizerrrrmoses_punct_norm  zFSMTTokenizer.moses_punct_normcCs:||jvr|jj|d}||j|<|j|j|ddddS)NrTF)Zaggressive_dash_splitsZ return_strescape)r`r^ZMosesTokenizertokenize)rtr2rZmoses_tokenizerrrrmoses_tokenizes   zFSMTTokenizer.moses_tokenizecCrr)rar^ZMosesDetokenizerZ detokenize)rttokensrZmoses_detokenizerrrrmoses_detokenizerzFSMTTokenizer.moses_detokenizecCs t|}|||}t|}|Srz)r3rr=)rtr2rrrrmoses_pipelines zFSMTTokenizer.moses_pipelinecC t|jSrz)rbrir|rrrr~ zFSMTTokenizer.src_vocab_sizecCrrz)rbrkr|rrrtgt_vocab_size rzFSMTTokenizer.tgt_vocab_sizecCt|jfi|jSrz)rmriZadded_tokens_encoderr|rrrr{zFSMTTokenizer.get_src_vocabcCrrz)rmrkZadded_tokens_decoderr|rrr get_tgt_vocabrzFSMTTokenizer.get_tgt_vocabc s~t|dd|ddf}|jvrj|St|}|s#|dS t|fddd}|jvr4ny|\}}g}d}|t|krz|||} Wnty\|||dYn?w|||| | }|||kr|t|dkr||d|kr| |||d 7}n | |||d7}|t|ksBt|}|}t|dkrnt|}q$d |}|d krd }|j|<|S) NrPTcsj|tdS)Ninf)rpgetfloat)pairr|rr sz#FSMTTokenizer.bpe..keyrr rD z z ) rQrqrminrprbindexreextendr9r:) rttokenrrZbigramfirstsecondZnew_wordijrr|rbpesN     ,     zFSMTTokenizer.bpeencCsn|j}|jr |}|r|}n|j||d}|j||d}g}|D]}|r4|t||dq#|S)av Tokenize a string given language code using Moses. Details of tokenization: - [sacremoses](https://github.com/alvations/sacremoses): port of Moses - Install with `pip install sacremoses` Args: - lang: ISO language code (default = 'en') (string). Languages should belong of the model supported languages. However, we don't enforce it. - bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False) (bool). If True, we only apply BPE. Returns: List of tokens. rr) rcrWlowerrRrrrlistr)rtr2rZbypass_tokenizerZ split_tokensrrrr _tokenizeBs zFSMTTokenizer._tokenizecCs|j||j|jS)z0Converts a token (str) in an id using the vocab.)rirrX)rtrrrr_convert_token_to_idisz"FSMTTokenizer._convert_token_to_idcCs|j||jS)z=Converts an index (integer) in a token (str) using the vocab.)rkrrX)rtrrrr_convert_id_to_tokenmsz"FSMTTokenizer._convert_id_to_tokencCs.dd|D}d|}|||j}|S)z:Converts a sequence of tokens (string) in a single string.cSs g|] }|ddddqS)rr5r)r/)rJtrrrrTurUz:FSMTTokenizer.convert_tokens_to_string..r5)r:rRrrd)rtrr2rrrconvert_tokens_to_stringqsz&FSMTTokenizer.convert_tokens_to_string token_ids_0 token_ids_1cCs(|jg}|dur ||S||||S)a Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A FAIRSEQ Transformer 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) sep_token_idrtrrseprrr build_inputs_with_special_tokens{sz.FSMTTokenizer.build_inputs_with_special_tokensalready_has_special_tokenscsZ|r tj||ddS|dur#dgt|dgdgt|dgSdgt|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)rrrNrr )rrget_special_tokens_maskrb)rtrrrrwrrrs(z%FSMTTokenizer.get_special_tokens_maskcCsF|jg}|durt||dgSt||dgt||dgS)a Create a mask from the two sequences passed to be used in a sequence-pair classification task. A FAIRSEQ Transformer 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). Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An FAIRSEQ_TRANSFORMER sequence pair mask has the following format: Nrr )rrbrrrr$create_token_type_ids_from_sequencess$z2FSMTTokenizer.create_token_type_ids_from_sequencessave_directoryfilename_prefixc Cstj|std|ddStj||r|dndtd}tj||r,|dndtd}tj||r=|dndtd}t|dd d }|t j |j d d d ddWdn1sewYt|dd d  }dd|j D}|t j |d d d ddWdn1swYd}t|dd d 5} t|j dddD]!\} } || krtd|d| }| d| d|d7}qWdn1swY|||fS)NzVocabulary path (z) should be a directoryr)r5r r r wrErFrDTF)indent sort_keys ensure_asciirOcSrHrrrIrrrrMrNz1FSMTTokenizer.save_vocabulary..rcSs|dS)Nr r)kvrrrrsz/FSMTTokenizer.save_vocabulary..rzSaving vocabulary to zZ: BPE merge indices are not consecutive. Please check that the tokenizer is not corrupted!rr )ospathisdirloggererrorr:VOCAB_FILES_NAMESrfwritergdumpsrirkrjsortedrpwarning) rtrrr r r frvrwriterZ bpe_tokensZ token_indexrrrsave_vocabularys@     zFSMTTokenizer.save_vocabularycCs|j}d|d<|S)Nr^)__dict__copy)rtstaterrr __getstate__s zFSMTTokenizer.__getstate__cCs4||_zddl}Wn tytdw||_dS)NrrC)rr\r]r^)rtdr\rrr __setstate__s   zFSMTTokenizer.__setstate__) NNNNFr?r@rArB)rFrz)NF)&__name__ __module__ __qualname____doc__rZvocab_files_namesZmodel_input_namesrsrstrintr}propertyrrrrrr~rr{rrrrrrrrrboolrrrrrr __classcell__rrrwrr>rsz-C   ,'      !$r>)rrgrr0r6typingrrrrZtokenization_utilsrutilsrZ get_loggerrrrrr3r=r>__all__rrrrs*    +