o ߥi:@sdZddlZddlZddlmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZeZd d d Zd d id did Zd diZdejiZdddiiZd diZdddiiZGddde ZddZddZGdddeZdS)zTokenization classes for OFA.N)ListOptionalTuple)PreTrainedTokenizer) BartTokenizer)BasicTokenizerWordpieceTokenizer)logging) ModelFilez vocab.jsonz merges.txt) vocab_file merges_filezofa-basez7https://huggingface.co/ofa-base/resolve/main/vocab.jsonz7https://huggingface.co/ofa-base/resolve/main/merges.txtir zbert-base-chinesez?https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt do_lower_caseTc@seZdZdZeZeZeZ dS) OFATokenizera Construct a OFA tokenizer. [`~OFATokenizer`] is identical to [`BartTokenizer`] and runs end-to-end tokenization: punctuation splitting and wordpiece. Refer to superclass [`BartTokenizer`] for usage examples and documentation concerning parameters. N) __name__ __module__ __qualname____doc__VOCAB_FILES_NAMESvocab_files_namesPRETRAINED_VOCAB_FILES_MAPpretrained_vocab_files_map&PRETRAINED_POSITIONAL_EMBEDDINGS_SIZESmax_model_input_sizesrrf/home/ubuntu/.local/lib/python3.10/site-packages/modelscope/models/multi_modal/ofa/tokenization_ofa.pyrFs  rcCsft}t|ddd }|}Wdn1swYt|D] \}}|d}|||<q#|S)z*Loads a vocabulary file into a dictionary.rutf-8encodingN ) collections OrderedDictopen readlines enumeraterstrip)r vocabreadertokensindextokenrrr load_vocabUs   r+cCs|}|sgS|}|S)z@Runs basic whitespace cleaning and splitting on a piece of text.)stripsplit)textr(rrrwhitespace_tokenize`s r/c s4eZdZdZeZeZeZ e Z            d(fd d Z e d d Ze d dZddZddZddZddZddZ d)deedeeedeefddZ  d*deedeeededeeffd d! Z d)deedeeedeefd"d#Z d)d$ed%eedeefd&d'ZZS)+OFATokenizerZHa Construct a OFA tokenizer. Based on 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` 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. 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). TNc stj|std|dt||_tjd|||||| ||| | | | d |t dd|j D|_ ||_ |rEt ||| | d|_t|j|jd|_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)`) r do_basic_tokenize never_split bos_token eos_token unk_token sep_token cls_token pad_token mask_tokentokenize_chinese_chars strip_accentscSsg|]\}}||fqSrr).0tokidsrrr s z+OFATokenizerZH.__init__..)r r7r?r@)r&r:r)ospathisfile ValueErrorr+r&super__init__r r!items ids_to_tokensr6rbasic_tokenizerrr:wordpiece_tokenizer)selfr r r6r7r8r9r;r<r:r=r>r?r@kwargs __class__rrrJsF       zOFATokenizerZH.__init__cCs|jjSN)rMr rOrrrr szOFATokenizerZH.do_lower_casecCs t|jSrS)lenr&rTrrr vocab_sizes zOFATokenizerZH.vocab_sizecCst|jfi|jSrS)dictr&added_tokens_encoderrTrrr get_vocabszOFATokenizerZH.get_vocabcCs^g}|jr'|jj||jdD]}||jjvr||q||j|7}q|S|j|}|S)N)r7)r6rMtokenizeall_special_tokensr7appendrN)rOr. split_tokensr*rrr _tokenizes   zOFATokenizerZH._tokenizecCs|j||j|jS)z0Converts a token (str) in an id using the vocab.)r&getr:)rOr*rrr_convert_token_to_idsz#OFATokenizerZH._convert_token_to_idcCs|j||jS)z=Converts an index (integer) in a token (str) using the vocab.)rLr_r:)rOr)rrr_convert_id_to_tokensz#OFATokenizerZH._convert_id_to_tokencCsd|dd}|S)z:Converts a sequence of tokens (string) in a single string. z ##)joinreplacer,)rOr( out_stringrrrconvert_tokens_to_stringsz'OFATokenizerZH.convert_tokens_to_string token_ids_0 token_ids_1returncCs@|dur|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)rOrhriclsseprrr build_inputs_with_special_tokenss z/OFATokenizerZH.build_inputs_with_special_tokensFalready_has_special_tokenscsf|r tj||ddS|dur&dgdgt|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)rIget_special_tokens_maskrU)rOrhrirprQrrrrs z&OFATokenizerZH.get_special_tokens_maskcCsV|jg}|jg}|durt|||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). Nrrq)rlrkrU)rOrhrirnrmrrr$create_token_type_ids_from_sequences?sz3OFATokenizerZH.create_token_type_ids_from_sequencessave_directoryfilename_prefixcCsd}tj|rtj||r|dndtd}n |r |dnd|}t|ddd4}t|jdd d D]\}}||krJt d |d |}| |d |d7}q7Wd|fS1sbwY|fS)Nr-rcr wrrcSs|dS)Nrqr)kvrrrlsz0OFATokenizerZH.save_vocabulary..)keyzSaving vocabulary to z\: vocabulary indices are not consecutive. Please check that the vocabulary is not corrupted!rrq) rErFisdirrdrr"sortedr&rKloggerwarningwrite)rOrtrur)r writerr* token_indexrrrsave_vocabulary^s<      zOFATokenizerZH.save_vocabulary) TTNr1r2r2r1r3r4r5TNrS)NF) rrrrVOCAB_FILES_NAMES_ZHrPRETRAINED_VOCAB_FILES_MAP_ZHr PRETRAINED_INIT_CONFIGURATION_ZHpretrained_init_configuration)PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES_ZHrrJpropertyr rVrYr^r`rargrintrroboolrrrsstrrr __classcell__rrrQrr0is~<3     #  !r0) rr rEtypingrrr transformersr*transformers.models.bart.tokenization_bartr*transformers.models.bert.tokenization_bertrrtransformers.utilsr modelscope.utils.constantr get_loggerr}rrr VOCAB_FILErrrrrr+r/r0rrrrsD