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g)z!Tokenization class for Perceiver. )
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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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vocab_size PerceiverTokenizer.vocab_sizei s ###r* token_ids_0 token_ids_1already_has_special_tokensc > U( a [ TU ] XSS9$ Uc S/S/[ U5 - - S/- $ S/S/[ U5 - - S/- S/[ U5 - - S/- $ )ad
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= *PerceiverTokenizer.get_special_tokens_maskm 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.
) cls_token_id sep_token_id)r% r9 r: s r( build_inputs_with_special_tokens3PerceiverTokenizer.build_inputs_with_special_tokens sb & %%&48I8I7JJ J%%&48I8I7JJ[X\`\m\m[nn nr* textc d UR S5 Vs/ s H n[ U5 PM nnU$ s snf )zPTake as input a string and return a list of strings (tokens) for words/sub-wordsutf-8)encoder. )r% rD r2 tokenss r( _tokenize PerceiverTokenizer._tokenize s/ "&++g"67"6Q#a&"67
8s -c p [ U5 S:w a U R nU$ [ U5 U R - nU$ )z0Converts a token (str) in an id using the vocab.r )r! unk_token_idordr" )r% r3 token_ids r( _convert_token_to_id'PerceiverTokenizer._convert_token_to_id s: u:?((H 5zD$<$< $ $ puU 9U379t3CUhlU
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