Source code for transformers.models.byt5.tokenization_byt5

# coding=utf-8
# Copyright 2021 T5 Authors and HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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""" Tokenization class for model ByT5."""

import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging

logger = logging.get_logger(__name__)

[docs]class ByT5Tokenizer(PreTrainedTokenizer): """ Construct a ByT5 tokenizer. ByT5 simply uses raw bytes utf-8 encoding. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`): The end of sequence token. .. note:: 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 :obj:`sep_token`. unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`): 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 (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`): The token used for padding, for example when batching sequences of different lengths. extra_ids (:obj:`int`, `optional`, defaults to 100): Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary like in ByT5 preprocessing see `here <>`__). additional_special_tokens (:obj:`List[str]`, `optional`): Additional special tokens used by the tokenizer. """ model_input_names = ["input_ids", "attention_mask"] def __init__( self, eos_token="</s>", unk_token="<unk>", pad_token="<pad>", extra_ids=125, additional_special_tokens=None, **kwargs ) -> None: # Add extra_ids to the special token list if extra_ids > 0 and additional_special_tokens is None: additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)] elif extra_ids > 0 and additional_special_tokens is not None: # Check that we have the right number of extra_id special tokens extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens))) if extra_tokens != extra_ids: raise ValueError( f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to ByT5Tokenizer. " "In this case the additional_special_tokens must include the extra_ids tokens" ) pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token super().__init__( eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, extra_ids=extra_ids, additional_special_tokens=additional_special_tokens, **kwargs, ) self._extra_ids = extra_ids self._utf_vocab_size = 2 ** 8 # utf is 8 bits # define special tokens dict self.special_tokens_encoder: Dict[int, str] = { self.pad_token: 0, self.eos_token: 1, self.unk_token: 2, } self._num_special_tokens = len(self.special_tokens_encoder) n = len(additional_special_tokens) for i, token in enumerate(additional_special_tokens): self.special_tokens_encoder[token] = self.vocab_size + i - n self.special_tokens_decoder: Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()} @property def vocab_size(self): return self._utf_vocab_size + self._num_special_tokens + self._extra_ids def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ 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 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the token list is already formatted with special tokens for the model. Returns: :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) # normal case: some special tokens if token_ids_1 is None: return ([0] * len(token_ids_0)) + [1] return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]: """Do not add eos again if user already added it.""" if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added." ) return token_ids else: return token_ids + [self.eos_token_id] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. ByT5 does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of zeros. """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ 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 </s>`` - pair of sequences: ``A </s> B </s>`` Args: token_ids_0 (:obj:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. """ token_ids_0 = self._add_eos_if_not_present(token_ids_0) if token_ids_1 is None: return token_ids_0 else: token_ids_1 = self._add_eos_if_not_present(token_ids_1) return token_ids_0 + token_ids_1 def _tokenize(self, text: str) -> List[str]: """Take as input a string and return a list of strings (tokens) for words/sub-words""" tokens = [chr(i) for i in text.encode("utf-8")] return tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token in self.special_tokens_encoder: token_id = self.special_tokens_encoder[token] elif token in self.added_tokens_encoder: token_id = self.added_tokens_encoder[token] elif len(token) != 1: token_id = self.unk_token_id else: token_id = ord(token) + self._num_special_tokens return token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.special_tokens_decoder: token = self.special_tokens_decoder[index] else: token = chr(index - self._num_special_tokens) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" bstring = b"" for token in tokens: if token in self.special_tokens_decoder: tok_string = self.special_tokens_decoder[token].encode("utf-8") elif token in self.added_tokens_decoder: tok_string = self.special_tokens_decoder[token].encode("utf-8") elif token in self.special_tokens_encoder: tok_string = token.encode("utf-8") elif token in self.added_tokens_encoder: tok_string = token.encode("utf-8") else: tok_string = bytes([ord(token)]) bstring += tok_string string = bstring.decode("utf-8") return string # ByT5Tokenizer has no vocab file def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: return ()