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Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding.
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:
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
bos_token (`str`, *optional*, defaults to `"[BOS]"`):
The BOS token (reserved in the vocab, but not actually used).
eos_token (`str`, *optional*, defaults to `"[EOS]"`):
The end of sequence token (reserved in the vocab, but not actually used).
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`.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The MASK token, useful for masked language modeling.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The CLS token (reserved in the vocab, but not actually used).
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from two sequences.
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mask_token cls_token sep_tokenmodel_max_length )
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|S N)ranger" chrr% updateadded_tokens_encoder)r( vocabitokens r+ get_vocabz PerceiverTokenizer.get_vocabd sW t++, 8AFE t 7 77E%L 8 T../ r, c | j S r. )r" )r( s r+
vocab_sizez PerceiverTokenizer.vocab_sizel s ###r, token_ids_0 token_ids_1already_has_special_tokensc |rt | ||d S |dgdgt | z z dgz S dgdgt | z z dgz dgt | z z dgz S )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)r9 r: r; r r )r& get_special_tokens_maskr$ )r( r9 r: r; r* s r+ r= z*PerceiverTokenizer.get_special_tokens_maskp s $ &72 '[]a 3
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks. A 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.
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}t | }}|S c c}w )zPTake as input a string and return a list of strings (tokens) for words/sub-wordsutf-8)encoder0 )r( rB r4 tokenss r+ _tokenizez PerceiverTokenizer._tokenize s, "&++g"67Q#a&77
8s *c n t | dk7 r| j }|S t | | j z }|S )z0Converts a token (str) in an id using the vocab.r )r$ unk_token_idordr% )r( r5 token_ids r+ _convert_token_to_idz'PerceiverTokenizer._convert_token_to_id s: u:?((H 5zD$<$< $ $ sxU 9U3;DI3FUkoU
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