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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.
input_idsattention_mask[PAD][BOS][EOS][MASK][CLS][SEP] returnNc
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mask_token cls_token sep_tokenmodel_max_length )
isinstancestrr _utf_vocab_size_added_tokens_decoderlen_num_special_tokenssuper__init__) selfr r r r r r r kwargs __class__r q/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/transformers/models/perceiver/tokenization_perceiver.pyr( ; s4
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}t|}|| j ||< q|| j |S N)ranger# chrr& updateadded_tokens_encoder)r) vocabitokenr r r- get_vocabd s z PerceiverTokenizer.get_vocabc C s | j S r. )r# )r) r r r-
vocab_sizel s z PerceiverTokenizer.vocab_sizeF token_ids_0 token_ids_1already_has_special_tokensc sf |r t j||ddS |du r dgdgt| dg S dgdgt| dg dgt| dg 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)r8 r9 r: Nr r )r' get_special_tokens_maskr% )r) r8 r9 r: r+ r r- r; p s .z*PerceiverTokenizer.get_special_tokens_maskc C s< |du r| j g| | jg S | j g| | jg | | jg S )af
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.
N) cls_token_id sep_token_id)r) r8 r9 r r r- build_inputs_with_special_tokens s z3PerceiverTokenizer.build_inputs_with_special_tokenstextc C s dd | dD }|S )zPTake as input a string and return a list of strings (tokens) for words/sub-wordsc S s g | ]}t |qS r )r0 ).0r4 r r r-
s z0PerceiverTokenizer._tokenize..utf-8)encode)r) r? tokensr r r- _tokenize s z PerceiverTokenizer._tokenizec C s( t |dkr | j}|S t|| j }|S )z0Converts a token (str) in an id using the vocab.r )r% unk_token_idordr& )r) r5 token_idr r r- _convert_token_to_id s
z'PerceiverTokenizer._convert_token_to_idc C s t || j }|S )z=Converts an index (integer) in a token (str) using the vocab.)r0 r& )r) indexr5 r r r- _convert_id_to_token s z'PerceiverTokenizer._convert_id_to_tokenc C sP d}|D ]}|| j v rt|d}ntt|g}||7 }q|jddd}|S )z:Converts a sequence of tokens (string) in a single string. rB replace)errors)r2 r" rC bytesrG decode)r) rD bstringr5
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z+PerceiverTokenizer.convert_tokens_to_stringsave_directoryfilename_prefixc C s dS )Nr r )r) rU rV r r r- save_vocabulary s z"PerceiverTokenizer.save_vocabulary)r
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