eTh~7SSKJrJrJrJrJr SSKJrJrJ r J r J r SSK J r SSKJr SSKJr SSKJr SSKJr \\\4r"SS 5rg ) )DictListOptionalTupleUnion) AddedToken EncodeInputEncoding InputSequence Tokenizer)Decoder)Model) Normalizer) PreTokenizer) PostProcessorcB\rSrSrSJS\4SjjrSrS\S\4Sjr SKS \S\ \ \44S jjr S\ \\ 44S jrSKS \S\4S jjrSLS \\ S\\S\\S\\S\\ S\\4 SjjrSr\S\\4Sj5rSMS\S\\S\\ 4SjjrSr\S\\4Sj5rS\\\ \ 4S\4SjrS\\\ \ 4S\4SjrS \ S\ 4S!jrSNS \S"\\S#\S$\S\4 S%jjr SOS&\\!S#\S$\S\\4S'jjr"SKS(\\S)\\S\ 4S*jjr#SKS+\\\S)\\S\ 4S,jjr$S-\ S\\4S.jr%S/\S\\ 4S0jr&SJS1\ S2\\ 4S3jjr'SKS4\ S5\4S6jjr(SPS5\4S7jjr)SQS8\S"\\S$\S\4S9jjr*\S\+4S:j5r,\,RZS;\+4S<j5r,\S\.4S=j5r/\/RZS>\.4S?j5r/\S\04S@j5r1\1RZSA\04SBj5r1\S\24SCj5r3\3RZSD\24SEj5r3\S\44SFj5r5\5RZSG\44SHj5r5SIr6g)R BaseTokenizerN tokenizerc2XlUbX lg0UlgN) _tokenizer _parameters)selfr parameterss a/var/www/auris/envauris/lib/python3.13/site-packages/tokenizers/implementations/base_tokenizer.py__init__BaseTokenizer.__init__s#)3)?:RcSRURR5SRSURR 5555$)Nz!Tokenizer(vocabulary_size={}, {})z, c3H# UHupUS-[U5-v M g7f)=N)str).0kvs r )BaseTokenizer.__repr__..s!L3K41a#gA&3Ks ")formatrget_vocab_sizejoinritemsrs r__repr__BaseTokenizer.__repr__sF299 OO * * , IIL43C3C3I3I3KL L  ris_pairreturnc8URRU5$)z Return the number of special tokens that would be added for single/pair sentences. :param is_pair: Boolean indicating if the input would be a single sentence or a pair :return: )rnum_special_tokens_to_add)rr0s rr3'BaseTokenizer.num_special_tokens_to_adds 88AArwith_added_tokensc4URRUS9$)zReturns the vocabulary Args: with_added_tokens: boolean: Whether to include the added tokens in the vocabulary Returns: The vocabulary r5)r get_vocabrr5s rr8BaseTokenizer.get_vocab!s((;L(MMrc6URR5$)zdReturns the added reverse vocabulary Returns: The added vocabulary mapping ints to AddedTokens )rget_added_tokens_decoderr-s rr<&BaseTokenizer.get_added_tokens_decoder-s 7799rc4URRUS9$)zReturn the size of vocabulary, with or without added tokens. Args: with_added_tokens: (`optional`) bool: Whether to count in added special tokens or not Returns: Size of vocabulary r7)rr*r9s rr*BaseTokenizer.get_vocab_size5s--@Q-RRr directionpad_to_multiple_ofpad_id pad_type_id pad_tokenlengthc >URRUUUUUUS9$)a/Change the padding strategy Args: direction: (`optional`) str: Can be one of: `right` or `left` pad_to_multiple_of: (`optional`) unsigned int: If specified, the padding length should always snap to the next multiple of the given value. For example if we were going to pad with a length of 250 but `pad_to_multiple_of=8` then we will pad to 256. pad_id: (`optional`) unsigned int: The indice to be used when padding pad_type_id: (`optional`) unsigned int: The type indice to be used when padding pad_token: (`optional`) str: The pad token to be used when padding length: (`optional`) unsigned int: If specified, the length at which to pad. If not specified we pad using the size of the longest sequence in a batch )r@rArBrCrDrE)renable_padding)rr@rArBrCrDrEs rrGBaseTokenizer.enable_paddingAs3B--1# .  rc6URR5$)zDisable padding)r no_paddingr-s rrJBaseTokenizer.no_paddingks))++rc.URR$)zGet the current padding parameters Returns: None if padding is disabled, a dict with the currently set parameters if the padding is enabled. )rpaddingr-s rrMBaseTokenizer.paddingos&&&r max_lengthstridestrategyc6URRXUS9$)afChange the truncation options Args: max_length: unsigned int: The maximum length at which to truncate stride: (`optional`) unsigned int: The length of the previous first sequence to be included in the overflowing sequence strategy: (`optional`) str: Can be one of `longest_first`, `only_first` or `only_second` )rPrQ)renable_truncation)rrOrPrQs rrSBaseTokenizer.enable_truncationys00U]0^^rc6URR5$)zDisable truncation)r no_truncationr-s rrVBaseTokenizer.no_truncations,,..rc.URR$)zGet the current truncation parameters Returns: None if truncation is disabled, a dict with the current truncation parameters if truncation is enabled )r truncationr-s rrYBaseTokenizer.truncations)))rtokensc8URRU5$)aAdd the given tokens to the vocabulary Args: tokens: List[Union[str, AddedToken]]: A list of tokens to add to the vocabulary. Each token can either be a string, or an instance of AddedToken Returns: The number of tokens that were added to the vocabulary )r add_tokens)rr[s rr]BaseTokenizer.add_tokenss))&11rspecial_tokensc8URRU5$)aAdd the given special tokens to the vocabulary, and treat them as special tokens. The special tokens will never be processed by the model, and will be removed while decoding. Args: tokens: List[Union[str, AddedToken]]: A list of special tokens to add to the vocabulary. Each token can either be a string, or an instance of AddedToken Returns: The number of tokens that were added to the vocabulary )radd_special_tokens)rr_s rra BaseTokenizer.add_special_tokenss11.AArsequencec8URRU5$)z}Normalize the given sequence Args: sequence: str: The sequence to normalize Returns: The normalized string )r normalize)rrcs rreBaseTokenizer.normalizes((22rpairis_pretokenizedracVUc [S5eURRXX45$)aEncode the given sequence and pair. This method can process raw text sequences as well as already pre-tokenized sequences. Args: sequence: InputSequence: The sequence we want to encode. This sequence can be either raw text or pre-tokenized, according to the `is_pretokenized` argument: - If `is_pretokenized=False`: `InputSequence` is expected to be `str` - If `is_pretokenized=True`: `InputSequence` is expected to be `Union[List[str], Tuple[str]]` is_pretokenized: bool: Whether the input is already pre-tokenized. add_special_tokens: bool: Whether to add the special tokens while encoding. Returns: An Encoding z"encode: `sequence` can't be `None`) ValueErrorrencode)rrcrgrhras rrkBaseTokenizer.encodes-8  AB B%%hoZZrinputscVUc [S5eURRXU5$)aiEncode the given inputs. This method accept both raw text sequences as well as already pre-tokenized sequences. Args: inputs: List[EncodeInput]: A list of single sequences or pair sequences to encode. Each `EncodeInput` is expected to be of the following form: `Union[InputSequence, Tuple[InputSequence, InputSequence]]` Each `InputSequence` can either be raw text or pre-tokenized, according to the `is_pretokenized` argument: - If `is_pretokenized=False`: `InputSequence` is expected to be `str` - If `is_pretokenized=True`: `InputSequence` is expected to be `Union[List[str], Tuple[str]]` is_pretokenized: bool: Whether the input is already pre-tokenized. add_special_tokens: bool: Whether to add the special tokens while encoding. Returns: A list of Encoding z&encode_batch: `inputs` can't be `None`)rjr encode_batch)rrmrhras rroBaseTokenizer.encode_batchs.@ >EF F++FEWXXridsskip_special_tokenscPUc [S5eURRXS9$)aDecode the given list of ids to a string sequence Args: ids: List[unsigned int]: A list of ids to be decoded skip_special_tokens: (`optional`) boolean: Whether to remove all the special tokens from the output string Returns: The decoded string z6None input is not valid. Should be a list of integers.rr)rjrdecode)rrqrrs rruBaseTokenizer.decodes- ;UV V%%c%SSr sequencescPUc [S5eURRXS9$)a<Decode the list of sequences to a list of string sequences Args: sequences: List[List[unsigned int]]: A list of sequence of ids to be decoded skip_special_tokens: (`optional`) boolean: Whether to remove all the special tokens from the output strings Returns: A list of decoded strings z N:$sJ*?: S S S$+,0 !%&#* $( C=( %SM(  ( c] ( C= (  ( T,'$''_C_#_V^_bVc_ /*HTN** 2eCO&rs;55RR'#-2/ S/T*T*r