U 4AfJZ@sdZddlZddlZddlZddlmZmZmZddlm Z m Z m Z m Z ddl mZddlmZmZeeZdd iZd d Zd d ZGddde ZGdddZGdddZdS)zTokenization classes for REALM.N)ListOptionalTuple)PreTrainedTokenizer _is_control_is_punctuation_is_whitespace) BatchEncoding)PaddingStrategylogging vocab_filez vocab.txtc CsRt}t|ddd}|}W5QRXt|D]\}}|d}|||<q2|S)z*Loads a vocabulary file into a dictionary.rutf-8encoding ) collections OrderedDictopen readlines enumeraterstrip)r vocabreadertokensindextokenr[/tmp/pip-unpacked-wheel-zw5xktn0/transformers/models/deprecated/realm/tokenization_realm.py load_vocab s  r cCs|}|sgS|}|S)z@Runs basic whitespace cleaning and splitting on a piece of text.)stripsplit)textrrrrwhitespace_tokenize+s r$c seZdZdZeZd'fd d Zed d Zed dZ ddZ ddZ ddZ ddZ ddZfddZd(eeeeeeedddZd)eeeeeeeedfd d! Zd*eeeeeeedd"d#Zd+eeeeed$d%d&ZZS),RealmTokenizera| Construct a REALM tokenizer. [`RealmTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting and wordpiece. 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`): File containing the vocabulary. do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. do_basic_tokenize (`bool`, *optional*, defaults to `True`): Whether or not to do basic tokenization before WordPiece. never_split (`Iterable`, *optional*): Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True` unk_token (`str`, *optional*, defaults to `"[UNK]"`): 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. sep_token (`str`, *optional*, defaults to `"[SEP]"`): 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 `"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. cls_token (`str`, *optional*, defaults to `"[CLS]"`): 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. mask_token (`str`, *optional*, defaults to `"[MASK]"`): 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. 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). TN[UNK][SEP][PAD][CLS][MASK]c 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 = RealmTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`cSsg|]\}}||fqSrr).0tokidsrrr xsz+RealmTokenizer.__init__..) do_lower_case never_splittokenize_chinese_chars strip_accents)r unk_token) r/do_basic_tokenizer0r3 sep_token pad_token cls_token mask_tokenr1r2)ospathisfile ValueErrorr rrritems ids_to_tokensr4BasicTokenizerbasic_tokenizerWordpieceTokenizerstrwordpiece_tokenizersuper__init__) selfr r/r4r0r3r5r6r7r8r1r2kwargs __class__rrrEcs:    zRealmTokenizer.__init__cCs|jjSN)r@r/rFrrrr/szRealmTokenizer.do_lower_casecCs t|jSrJ)lenrrKrrr vocab_sizeszRealmTokenizer.vocab_sizecCst|jf|jSrJ)dictrZadded_tokens_encoderrKrrr get_vocabszRealmTokenizer.get_vocabcCs\g}|jrL|jj||jdD],}||jjkr8||q||j|7}qn |j|}|S)N)r0)r4r@tokenizeZall_special_tokensr0appendrC)rFr# split_tokensrrrr _tokenizes   zRealmTokenizer._tokenizecCs|j||j|jS)z0Converts a token (str) in an id using the vocab.)rgetr3)rFrrrr_convert_token_to_idsz#RealmTokenizer._convert_token_to_idcCs|j||jS)z=Converts an index (integer) in a token (str) using the vocab.)r>rTr3)rFrrrr_convert_id_to_tokensz#RealmTokenizer._convert_id_to_tokencCsd|dd}|S)z:Converts a sequence of tokens (string) in a single string. z ##)joinreplacer!)rFrZ out_stringrrrconvert_tokens_to_stringsz'RealmTokenizer.convert_tokens_to_stringc stj|d<|}|dd}|dd}gggd}t|D]\}}|dk rT||} nd} tj|| fddi|} | d} | d} | d} | dk r|d| | dk r|d| | dk r:|d| q:d d |D}t ||d S) a Encode a batch of text or text pair. This method is similar to regular __call__ method but has the following differences: 1. Handle additional num_candidate axis. (batch_size, num_candidates, text) 2. Always pad the sequences to *max_length*. 3. Must specify *max_length* in order to stack packs of candidates into a batch. - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: text (`List[List[str]]`): The batch of sequences to be encoded. Each sequence must be in this format: (batch_size, num_candidates, text). text_pair (`List[List[str]]`, *optional*): The batch of sequences to be encoded. Each sequence must be in this format: (batch_size, num_candidates, text). **kwargs: Keyword arguments of the __call__ method. Returns: [`BatchEncoding`]: Encoded text or text pair. Example: ```python >>> from transformers import RealmTokenizer >>> # batch_size = 2, num_candidates = 2 >>> text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]] >>> tokenizer = RealmTokenizer.from_pretrained("google/realm-cc-news-pretrained-encoder") >>> tokenized_text = tokenizer.batch_encode_candidates(text, max_length=10, return_tensors="pt") ```paddingZ text_pairNreturn_tensors) input_idsattention_masktoken_type_idsr^r_r`cSs"i|]\}}t|dkr||qS)r)rL)r+keyitemrrr s z:RealmTokenizer.batch_encode_candidates..)Z tensor_type) r Z MAX_LENGTHpoprrD__call__rTrQr=r )rFr#rGZ batch_textZbatch_text_pairr]Z output_dataidxZcandidate_textZcandidate_text_pairZencoded_candidatesZencoded_input_idsZencoded_attention_maskZencoded_token_type_idsrHrrbatch_encode_candidatess0&       z&RealmTokenizer.batch_encode_candidates) 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 REALM 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)rFrhriclsseprrr build_inputs_with_special_tokenss z/RealmTokenizer.build_inputs_with_special_tokensF)rhrialready_has_special_tokensrjcsf|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)rhrirpNr)rDget_special_tokens_maskrL)rFrhrirprHrrrrs.z&RealmTokenizer.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 REALM 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). Nrrq)rlrkrL)rFrhrirnrmrrr$create_token_type_ids_from_sequences3s z3RealmTokenizer.create_token_type_ids_from_sequences)save_directoryfilename_prefixrjc Csd}tj|r4tj||r$|dndtd}n|r@|dnd|}t|dddZ}t|jdd d D]<\}}||krt d |d |}| |d |d7}qnW5QRX|fS)Nr-rXr wrrcSs|dS)Nrqr)kvrrrYz0RealmTokenizer.save_vocabulary..)razSaving vocabulary to z\: vocabulary indices are not consecutive. Please check that the vocabulary is not corrupted!rrq) r9r:isdirrYVOCAB_FILES_NAMESrsortedrr=loggerwarningwrite)rFrtrurr writerrZ token_indexrrrsave_vocabularyPs"  zRealmTokenizer.save_vocabulary) TTNr&r'r(r)r*TN)N)NF)N)N)__name__ __module__ __qualname____doc__r|Zvocab_files_namesrEpropertyr/rMrOrSrUrVr[rgrintrroboolrrrsrBrr __classcell__rrrHrr%4sX,-    J     r%c@sNeZdZdZdddZdddZdd Zdd d Zd d ZddZ ddZ dS)r?a 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). TNcCs,|dkr g}||_t||_||_||_dSrJ)r/setr0r1r2)rFr/r0r1r2rrrrEys  zBasicTokenizer.__init__cCs|r|jt|n|j}||}|jr4||}t|}g}|D]R}||kr|jrt|}|j dk r| |}n|j r| |}| | ||qDtd |}|S)a Basic Tokenization of a piece of text. Split on "white spaces" only, 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. FrW)r0unionr _clean_textr1_tokenize_chinese_charsr$r/lowerr2_run_strip_accentsextend_run_split_on_puncrY)rFr#r0Z orig_tokensrRr output_tokensrrrrPs"      zBasicTokenizer.tokenizecCsBtd|}g}|D]"}t|}|dkr,q||qd|S)z$Strips accents from a piece of text.ZNFDZMnrX) unicodedata normalizecategoryrQrY)rFr#outputcharcatrrrrs   z!BasicTokenizer._run_strip_accentscCs|dk r||kr|gSt|}d}d}g}|t|kr||}t|rX||gd}n |rf|gd}|d||d7}q*dd|DS) z&Splits punctuation on a piece of text.NrTFrqcSsg|]}d|qS)rX)rY)r+xrrrr.sz5BasicTokenizer._run_split_on_punc..)listrLrrQ)rFr#r0charsiZstart_new_wordrrrrrrs"    z!BasicTokenizer._run_split_on_punccCsTg}|D]@}t|}||r>|d|||dq||qd|S)z)Adds whitespace around any CJK character.rWrX)ord_is_chinese_charrQrYrFr#rrcprrrrs     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)rFrrrrrsD  zBasicTokenizer._is_chinese_charcCsXg}|D]D}t|}|dks|dkst|r.qt|rB|dq||qd|S)zBPerforms invalid character removal and whitespace cleanup on text.rirWrX)rrr rQrYrrrrrs  zBasicTokenizer._clean_text)TNTN)N)N) rrrrrErPrrrrrrrrrr?es  %  r?c@s"eZdZdZdddZddZdS) rAzRuns WordPiece tokenization.dcCs||_||_||_dSrJ)rr3max_input_chars_per_word)rFrr3rrrrrEszWordpieceTokenizer.__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. FrNrXz##rqT) r$rrLrrQr3rYrr) rFr#rrrZis_badstartZ sub_tokensendZ cur_substrsubstrrrrrPs:       zWordpieceTokenizer.tokenizeN)r)rrrrrErPrrrrrAs rA)rrr9rtypingrrrZtokenization_utilsrrrr Ztokenization_utils_baser utilsr r Z get_loggerrr~r|r r$r%r?rArrrrs"    3