o iA@sddlZddlZddlZddlmZddlmZddlmZm Z m Z ddl Z ddl m Z ddlmZddlmZeeZd d d d d dZdZeddGddde Zdedeeefde jfddZdeddfddZdede eeffddZdgZdS)N)Path)copyfile)AnyOptionalUnion)PreTrainedTokenizer)logging)requiresz source.spmz target.spmz vocab.jsonztarget_vocab.jsonztokenizer_config.json) source_spm target_spmvocabtarget_vocab_filetokenizer_config_fileu▁) sentencepiece)backendsc seZdZdZeZddgZ         dDd eee e fd dffd d Z ddZ de d e fddZ ddZde fddZde d ee fddZded e fddZfddZfdd Zd!ee d e fd"d#ZdEd eefd$d%Zd&d'Zd(d)Zed efd*d+ZdEd,e d-ee d ee fd.d/Zd efd0d1Zd2d3Zd4d5Z d efd6d7Z!d8ed dfd9d:Z"d;d<Z#d=d>Z$ dFd?ed@eedAe%d eefdBdCZ&Z'S)GMarianTokenizeraB Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). 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: source_spm (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary for the source language. target_spm (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary for the target language. source_lang (`str`, *optional*): A string representing the source language. target_lang (`str`, *optional*): A string representing the target language. 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. eos_token (`str`, *optional*, defaults to `""`): The end of sequence token. pad_token (`str`, *optional*, defaults to `""`): The token used for padding, for example when batching sequences of different lengths. model_max_length (`int`, *optional*, defaults to 512): The maximum sentence length the model accepts. additional_special_tokens (`list[str]`, *optional*, defaults to `["", ""]`): Additional special tokens used by the tokenizer. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Examples: ```python >>> from transformers import MarianForCausalLM, MarianTokenizer >>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de") >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de") >>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."] >>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional >>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True) >>> outputs = model(**inputs) # should work ``` input_idsattention_maskNFsp_model_kwargsreturnc  s.| durin| |_t|sJd|| |_t||_t||jvr)tdt| |jvs2J| rHt||_dd|j D|_ g|_ ndd|j D|_ dd|jD|_ ||_ ||_ ||g|_t||j|_t||j|_|j|_|j|_|tjd ||||| | |j|| d | dS) Nzcannot find spm source z token must be in the vocabcSi|]\}}||qSr.0kvrrk/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/transformers/models/marian/tokenization_marian.py z,MarianTokenizer.__init__..cSrrrrrrr!r"r#cSs$g|]}|dr|dr|qS)>><<) startswithendswith)rrrrr! s$z,MarianTokenizer.__init__..) source_lang target_lang unk_token eos_token pad_tokenmodel_max_lengthrrseparate_vocabsr)rrexistsr/ load_jsonencoderstrKeyErrortarget_encoderitemsdecodersupported_language_codesr)r* spm_filesload_spm spm_source spm_target current_spmcurrent_encoder_setup_normalizersuper__init__)selfr r r rr)r*r+r,r-r.rr/kwargs __class__rr!rAksD    zMarianTokenizer.__init__c CsNzddlm}||jj|_WdSttfy&tddd|_YdSw)Nr)MosesPunctNormalizerz$Recommended: pip install sacremoses.cSs|SNr)xrrr!sz3MarianTokenizer._setup_normalizer..) sacremosesrFr) normalizepunc_normalizer ImportErrorFileNotFoundErrorwarningswarn)rBrFrrr!r?s  z!MarianTokenizer._setup_normalizerrHcCs|r||SdS)zHCover moses empty string edge case. 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Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). use_source_tokenizer (`bool`, *optional*, defaults to `False`): Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence problems). kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `list[str]`: The list of decoded sentences. )r@ batch_decode)rB sequencesrCrDrr!rdszMarianTokenizer.batch_decodec rc)a Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, list[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). use_source_tokenizer (`bool`, *optional*, defaults to `False`): Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence problems). kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. )r@decode)rB token_idsrCrDrr!rfszMarianTokenizer.decodetokenscCsv|jr|jn|j}g}d}|D]}||jvr$||||d7}g}q||q|||7}|td}|S)zQUses source spm if _decode_use_source_tokenizer is True, and target spm otherwiserQ ) _decode_use_source_tokenizerr;r<all_special_tokens decode_piecesrYreplaceSPIECE_UNDERLINEstrip)rBrhsp_modelcurrent_sub_tokens out_stringrSrrr!convert_tokens_to_strings   z(MarianTokenizer.convert_tokens_to_stringcCs$|dur ||jgS|||jgS)z=Build model inputs from a sequence by appending eos_token_id.N) eos_token_id)rB token_ids_0 token_ids_1rrr! build_inputs_with_special_tokens s z0MarianTokenizer.build_inputs_with_special_tokenscCs|j|_|j|_dSrG)r;r=r2r>rBrrr!_switch_to_input_modes z%MarianTokenizer._switch_to_input_modecCs|j|_|jr |j|_dSdSrG)r<r=r/r5r>rxrrr!_switch_to_target_modes z&MarianTokenizer._switch_to_target_modecCs t|jSrG)lenr2rxrrr! vocab_sizes zMarianTokenizer.vocab_sizesave_directoryfilename_prefixc Cstj|std|ddSg}|jrOtj||r |dndtd}tj||r1|dndtd}t|j |t|j || || |ntj||rY|dndtd}t|j || |t tdtdg|j |j|jgD]Z\}}} tj||r|dnd|} tj|tj| krtj|rt|| | | q|tj|st| d } | } | | Wdn1swY| | q|t|S) NzVocabulary path (z) should be a directory-rQr rr r wb)ospathisdirloggererrorr/joinVOCAB_FILES_NAMES save_jsonr2r5rYzipr9r;r<abspathisfileropenserialized_model_protowritetuple) rBr}r~ saved_filesout_src_vocab_fileout_tgt_vocab_fileout_vocab_filespm_save_filename spm_orig_path spm_model spm_save_pathficontent_spiece_modelrrr!save_vocabularysR        $      zMarianTokenizer.save_vocabularycCs|SrG) get_src_vocabrxrrr! get_vocabLszMarianTokenizer.get_vocabcCt|jfi|jSrG)dictr2added_tokens_encoderrxrrr!rOzMarianTokenizer.get_src_vocabcCrrG)rr5added_tokens_decoderrxrrr! get_tgt_vocabRrzMarianTokenizer.get_tgt_vocabcCs"|j}|tgd|S)N)r;r<r=rLr)__dict__copyupdaterfromkeys)rBstaterrr! __getstate__Us  zMarianTokenizer.__getstate__dcsF|_tds i_fddjD\__j_dS)Nrc3s|] }t|jVqdSrG)r:r)rfrxrr! csz/MarianTokenizer.__setstate__..)rhasattrrr9r;r<r=r?)rBrrrxr! __setstate__\s   zMarianTokenizer.__setstate__cOsdS)zJust EOSr)rBargsrCrrr!num_special_tokens_to_addgsz)MarianTokenizer.num_special_tokens_to_addcs(t|j|jfdd|DS)Ncsg|] }|vr dndqS)rrr)rrHall_special_idsrr!r(nsz7MarianTokenizer._special_token_mask..)setrremove unk_token_id)rBseqrrr!_special_token_maskks  z#MarianTokenizer._special_token_maskrurvalready_has_special_tokenscCs:|r||S|dur||dgS|||dgS)zCGet list where entries are [1] if a token is [eos] or [pad] else 0.Nr)r)rBrurvrrrr!get_special_tokens_maskps  z'MarianTokenizer.get_special_tokens_mask) NNNrrrrNFrG)NF)(__name__ __module__ __qualname____doc__rvocab_files_namesmodel_input_namesrrr3rrAr?rKrTr\listr`intrbrdrfrsrwryrzpropertyr|rrrrrrrrrboolr __classcell__rrrDr!r,sd: >    - rrrrcCstjdi|}|||S)Nr)rSentencePieceProcessorLoad)rrspmrrr!r:|s r:cCs@t|d}tj||ddWddS1swYdS)NwrW)indent)rjsondump)datarrrrr!rs "rcCs8t|d }t|WdS1swYdS)Nr)rrload)rrrrr!r1s $r1) rrrOpathlibrshutilrtypingrrrrtokenization_utilsrutilsr utils.import_utilsr get_loggerrrrrnrr3rrr:rrr1__all__rrrr!s4         Q