235 lines
9.8 KiB
Python
235 lines
9.8 KiB
Python
# coding=utf-8
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# Copyright 2021 T5 Authors and HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Tokenization class for model ByT5."""
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import warnings
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from typing import List, Optional, Tuple
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class ByT5Tokenizer(PreTrainedTokenizer):
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"""
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Construct a ByT5 tokenizer. ByT5 simply uses raw bytes utf-8 encoding.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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extra_ids (`int`, *optional*, defaults to 125):
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Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
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accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
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indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
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like in ByT5 preprocessing see
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[here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
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additional_special_tokens (`List[str]`, *optional*):
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Additional special tokens used by the tokenizer.
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"""
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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eos_token="</s>",
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unk_token="<unk>",
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pad_token="<pad>",
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extra_ids=125,
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additional_special_tokens=None,
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**kwargs,
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) -> None:
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# Add extra_ids to the special token list
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if extra_ids > 0 and additional_special_tokens is None:
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additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
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elif extra_ids > 0 and additional_special_tokens is not None and len(additional_special_tokens) > 0:
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# Check that we have the right number of extra_id special tokens
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extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
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if extra_tokens != extra_ids:
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raise ValueError(
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f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
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" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
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" extra_ids tokens"
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)
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pad_token = AddedToken(pad_token, lstrip=True, rstrip=True) if isinstance(pad_token, str) else pad_token
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# we force left and right stripping for backward compatibility. The byt5tests depend on this.
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eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token
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# unk token needs to be in the vocab with correct index
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self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token}
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self.offset = len(self._added_tokens_decoder)
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self._utf_vocab_size = 2**8 # utf is 8 bits
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super().__init__(
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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extra_ids=0,
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additional_special_tokens=additional_special_tokens, # TODO extra ids are not used :sweatywmile:
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**kwargs,
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)
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@property
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def vocab_size(self):
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return self._utf_vocab_size
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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# normal case: some special tokens
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if token_ids_1 is None:
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return ([0] * len(token_ids_0)) + [1]
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return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
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"""Do not add eos again if user already added it."""
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if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
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warnings.warn(
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f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
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" eos tokens being added."
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)
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return token_ids
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else:
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return token_ids + [self.eos_token_id]
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. ByT5 does not
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make use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of zeros.
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"""
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eos = [self.eos_token_id]
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if token_ids_1 is None:
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return len(token_ids_0 + eos) * [0]
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A sequence has the following format:
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- single sequence: `X </s>`
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- pair of sequences: `A </s> B </s>`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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token_ids_0 = self._add_eos_if_not_present(token_ids_0)
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if token_ids_1 is None:
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return token_ids_0
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else:
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token_ids_1 = self._add_eos_if_not_present(token_ids_1)
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return token_ids_0 + token_ids_1
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def _tokenize(self, text: str) -> List[str]:
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"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
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tokens = [chr(i) for i in text.encode("utf-8")]
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return tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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if len(token) != 1:
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token_id = None
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else:
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token_id = ord(token) + self.offset
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return token_id
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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token = chr(index - self.offset)
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return token
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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bstring = b""
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for token in tokens:
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if token in self.added_tokens_decoder:
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tok_string = self.added_tokens_decoder[token].encode("utf-8")
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elif token in self.added_tokens_encoder:
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tok_string = token.encode("utf-8")
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else:
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tok_string = bytes([ord(token)])
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bstring += tok_string
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string = bstring.decode("utf-8", errors="ignore")
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return string
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# ByT5Tokenizer has no vocab file
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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return ()
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