# coding=utf-8
# Copyright Google AI and The HuggingFace Inc. team. All rights reserved.
#
# 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 classes for CANINE."""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"nielsr/canine-s": 2048,
}
# Unicode defines 1,114,112 total “codepoints”
UNICODE_VOCAB_SIZE = 1114112
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
PAD = 0
CLS = 0xE000
SEP = 0xE001
BOS = 0xE002
MASK = 0xE003
RESERVED = 0xE004
# Maps special codepoints to human-readable names.
SPECIAL_CODEPOINTS: Dict[int, str] = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
SPECIAL_CODEPOINTS_BY_NAME: Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
[docs]class CanineTokenizer(PreTrainedTokenizer):
r"""
Construct a CANINE tokenizer (i.e. a character splitter). It turns text into a sequence of characters, and then
converts each character into its Unicode code point.
:class:`~transformers.CanineTokenizer` inherits from :class:`~transformers.PreTrainedTokenizer`.
Refer to superclass :class:`~transformers.PreTrainedTokenizer` for usage examples and documentation concerning
parameters.
Args:
model_max_length (:obj:`int`, `optional`, defaults to 2048):
The maximum sentence length the model accepts.
"""
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
bos_token=chr(CLS),
eos_token=chr(SEP),
sep_token=chr(SEP),
cls_token=chr(CLS),
pad_token=chr(PAD),
mask_token=chr(MASK),
add_prefix_space=False,
model_max_length=2048,
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
model_max_length=model_max_length,
**kwargs,
)
# Creates a mapping for looking up the IDs of special symbols.
self._special_codepoints: Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
self._special_codepoints[name] = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
self._special_codepoint_strings: Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
self._unicode_vocab_size = UNICODE_VOCAB_SIZE
self._num_special_tokens = len(self._special_codepoints)
@property
def vocab_size(self) -> int:
return self._unicode_vocab_size
def _tokenize(self, text: str) -> List[str]:
"""Tokenize a string (i.e. perform character splitting)."""
return list(text)
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (i.e. a Unicode character) in an id (i.e. its integer Unicode code point value)."""
try:
return ord(token)
except TypeError:
raise ValueError(f"invalid token: '{token}'")
def _convert_id_to_token(self, index: int) -> str:
"""
Converts a Unicode code point (integer) in a token (str). In case it's a special code point, convert to
human-readable format.
"""
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(index)
except TypeError:
raise ValueError(f"invalid id: {index}")
def convert_tokens_to_string(self, tokens):
return "".join(tokens)
[docs] 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
)
result = [1] + ([0] * len(token_ids_0)) + [1]
if token_ids_1 is not None:
result += ([0] * len(token_ids_1)) + [1]
return result
[docs] 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. A CANINE
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
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 `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
result = len(cls + token_ids_0 + sep) * [0]
if token_ids_1 is not None:
result += len(token_ids_1 + sep) * [1]
return result
# CanineTokenizer has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
return ()