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| # 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. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ Tokenization class for model ByT5.""" | |
| import warnings | |
| from typing import List, Optional, Tuple | |
| from ...tokenization_utils import AddedToken, PreTrainedTokenizer | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| class ByT5Tokenizer(PreTrainedTokenizer): | |
| """ | |
| Construct a ByT5 tokenizer. ByT5 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: | |
| eos_token (`str`, *optional*, defaults to `"</s>"`): | |
| The end of sequence token. | |
| <Tip> | |
| 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`. | |
| </Tip> | |
| unk_token (`str`, *optional*, defaults to `"<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 (`str`, *optional*, defaults to `"<pad>"`): | |
| The token used for padding, for example when batching sequences of different lengths. | |
| extra_ids (`int`, *optional*, defaults to 125): | |
| 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](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)). | |
| additional_special_tokens (`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 and len(additional_special_tokens) > 0: | |
| # 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=True, rstrip=True) if isinstance(pad_token, str) else pad_token | |
| # we force left and right stripping for backward compatibility. The byt5tests depend on this. | |
| eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token | |
| unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token | |
| # unk token needs to be in the vocab with correct index | |
| self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token} | |
| self.offset = len(self._added_tokens_decoder) | |
| self._utf_vocab_size = 2**8 # utf is 8 bits | |
| super().__init__( | |
| eos_token=eos_token, | |
| unk_token=unk_token, | |
| pad_token=pad_token, | |
| extra_ids=0, | |
| additional_special_tokens=additional_special_tokens, # TODO extra ids are not used :sweatywmile: | |
| **kwargs, | |
| ) | |
| def vocab_size(self): | |
| return self._utf_vocab_size | |
| def get_vocab(self): | |
| vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)} | |
| vocab.update(self.added_tokens_encoder) | |
| return vocab | |
| 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 (`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. | |
| """ | |
| 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 (`List[int]`): | |
| List of IDs. | |
| token_ids_1 (`List[int]`, *optional*): | |
| Optional second list of IDs for sequence pairs. | |
| Returns: | |
| `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 (`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. | |
| """ | |
| 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 len(token) != 1: | |
| token_id = None | |
| else: | |
| token_id = ord(token) + self.offset | |
| return token_id | |
| def _convert_id_to_token(self, index): | |
| """Converts an index (integer) in a token (str) using the vocab.""" | |
| token = chr(index - self.offset) | |
| 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.added_tokens_decoder: | |
| tok_string = self.added_tokens_decoder[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", errors="ignore") | |
| return string | |
| # ByT5Tokenizer has no vocab file | |
| def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: | |
| return () | |