a hM@sdZddlZddlZddlZddlmZddlmZmZm Z ddl Z ddl m Z ddlmZddlmZerxdd lmZdd lmZdd lmZeeZd d iZdZeddGdddeZdgZdS)z Tokenization class for model T5.N)copyfile) TYPE_CHECKINGAnyOptional)import_protobuf)PreTrainedTokenizer) AddedToken) TextInput)logging)requires vocab_filez spiece.modelu▁) sentencepiece)backendscsveZdZdZeZddgZd:eee e fdd fd d Z d;ddZ e ddZeddZddZdeeeeeeed d#d$Zd%d&Zd'd(Zd)ee d*fd+d, Zed-d.Zd/d0Zd1d2Z d3d4Z!d5d6Z"d?e ee e#e d7d8d9Z$Z%S)@ T5Tokenizera Construct a T5 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: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. 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`. 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. extra_ids (`int`, *optional*, defaults to 100): Add a number of extra ids added to the vocabulary for use as sentinels. These tokens are accessible as "" where "{%d}" is a number between 0 and extra_ids-1. These tokens can be retrieved by calling get_sentinel_tokens method and token ids can be by calling get_sentinel_token_ids method additional_special_tokens (`list[str]`, *optional*): 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. legacy (`bool`, *optional*): Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622 and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple example: - `legacy=True`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True) >>> tokenizer.encode("Hello .") [8774, 32099, 3, 5, 1] ``` - `legacy=False`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False) >>> tokenizer.encode("Hello .") # the extra space `[3]` is no longer here [8774, 32099, 5, 1] ``` Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. Attributes: sp_model (`SentencePieceProcessor`): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). Z input_idsZattention_maskdNT)sp_model_kwargsreturnc st|trt|ddn|}t|tr0t|ddn|}t|trJt|ddn|}|durZin||_||_||_tjfi|j|_|j ||durdd|D} t | dkr|ddt |D7}n*|dkr|t | krt d|d |d nd dt |D} | }i|_ t t | D]:} td | d dddddd|j t |jd|| <q|durxtd|jdd}||_|| dd|_| |_tjf||||||j|| d| dS)NT)specialcSsg|]}dt|vr|qS) z(T5Tokenizer.__init__..cSsg|]}d|dqSr>rrirrrrrrzBoth extra_ids (z!) and additional_special_tokens (zk) are provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids tokenscSsg|]}d|dqSr!rr#rrrrrrr"F)Z single_wordlstriprstripr normalizedz2You are using the default legacy behaviour of the a_. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 from_slow) eos_token unk_token pad_token extra_idsadditional_special_tokensrlegacyadd_prefix_space) isinstancerr rr Z _extra_idsspmSentencePieceProcessorsp_modelLoadlenrange ValueErrorZ_added_tokens_decoderloggerZ warning_once __class__r.get_spm_processorpopr/super__init__) selfr r)r*r+r,r-rr.r/kwargsZ extra_tokensr$r9rrr=sV   "   zT5Tokenizer.__init__FcCstjfi|j}|js|r,||j|St|jdd}|}td|j j d}|j |}| }d|_|j||}||Wdn1s0Y|S)NrbzThe new behaviour of z (with `self.legacy = False`)F)r1r2rr.r4r openreadrr9__name__Z ModelProtoZ FromStringZNormalizerSpecZadd_dummy_prefixnormalizer_specZ MergeFromZSerializeToStringZLoadFromSerializedProto)r>r( tokenizerfr3Z model_pb2modelrErrrr:s    (zT5Tokenizer.get_spm_processorc CsZ|tjvrVtj|}|dur(||kr(|S|durVtd|d|d|d|d t|S)NzGThis tokenizer was incorrectly instantiated with a model max length of z which will be corrected in Transformers v5. For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`. - Be aware that you SHOULD NOT rely on z( automatically truncating your input to zM when padding/encoding. - If you want to encode/pad to sequences longer than z you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding. - To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.)rZmax_model_input_sizeswarningswarn FutureWarning)Zpretrained_model_name_or_pathZmax_model_lengthZinit_max_model_lengthZdeprecated_max_model_lengthrrr!_eventually_correct_t5_max_lengths$    z-T5Tokenizer._eventually_correct_t5_max_lengthcCs |jSN)r3Zget_piece_sizer>rrr vocab_sizeszT5Tokenizer.vocab_sizecs(fddtjD}|j|S)Ncsi|]}||qSr)Zconvert_ids_to_tokensr#rNrr rz)T5Tokenizer.get_vocab..)r6rOupdateZadded_tokens_encoder)r>ZvocabrrNr get_vocabs zT5Tokenizer.get_vocab) token_ids_0 token_ids_1already_has_special_tokensrcsZ|rtj||ddS|dur2dgt|dgSdgt|dgdgt|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)rSrTrUNrr )r<get_special_tokens_maskr5)r>rSrTrUr@rrrVsz#T5Tokenizer.get_special_tokens_maskcCstttdd|jS)NcSsttd|duS)Nz)boolresearch)rrrrrz1T5Tokenizer.get_sentinel_tokens..)listsetfilterr-rNrrrget_sentinel_tokensszT5Tokenizer.get_sentinel_tokenscsfddDS)Ncsg|]}|qSr)Zconvert_tokens_to_ids)rtokenrNrrrrz6T5Tokenizer.get_sentinel_token_ids..)r^rNrrNrget_sentinel_token_idssz"T5Tokenizer.get_sentinel_token_ids) token_idsrcCsBt|dkr2|d|jkr2td|jd|S||jgSdS)z.Do not add eos again if user already added it.rzThis sequence already has zQ. In future versions this behavior may lead to duplicated eos tokens being added.N)r5 eos_token_idrIrJr))r>rarrr_add_eos_if_not_presents  z#T5Tokenizer._add_eos_if_not_present)rSrTrcCs<|jg}|dur"t||dgSt||||dgS)a Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make use of token type ids, therefore a list of zeros is returned. 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 zeros. Nr)rcr5)r>rSrTZeosrrr$create_token_type_ids_from_sequences'sz0T5Tokenizer.create_token_type_ids_from_sequencescCs,||}|dur|S||}||SdS)a Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A sequence has the following format: - single sequence: `X ` - pair of sequences: `A B ` Args: token_ids_0 (`list[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`list[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. N)rd)r>rSrTrrr build_inputs_with_special_tokens=s   z,T5Tokenizer.build_inputs_with_special_tokenscCs|j}d|d<|S)Nr3)__dict__copy)r>staterrr __getstate__Ws zT5Tokenizer.__getstate__cCs<||_t|dsi|_tjfi|j|_|j|jdS)Nr)rghasattrrr1r2r3r4r )r>drrr __setstate__\s  zT5Tokenizer.__setstate__r )textrc s|jst|dkr&tj|fi|S|td}|jr@t|}tj|fi|}t|dkr|dtkr|d|jvr|dd}|S)z Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the first token is special. r r N)r.r5r<tokenizereplaceSPIECE_UNDERLINEr/all_special_tokensr>rnr?tokensr@rrrpfs & zT5Tokenizer.tokenizecCst|jt|jSrM)r5r3encoderr*rNrrrunk_token_lengthxszT5Tokenizer.unk_token_lengthcKsZ|js|tdfs$|jj|tdS|jj|j|td}t||jkrV||jdS|S)u( Returns a tokenized string. We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. 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