119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright 2025 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 class for Dia."""
|
||
|
|
|
||
|
|
from typing import Optional
|
||
|
|
|
||
|
|
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
|
||
|
|
from ...utils import logging
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
class DiaTokenizer(PreTrainedTokenizer):
|
||
|
|
"""
|
||
|
|
Construct a Dia tokenizer. Dia simply uses raw bytes utf-8 encoding except for special tokens `[S1]` and `[S2]`.
|
||
|
|
|
||
|
|
This tokenizer inherits from [`PreTrainedTokenizerFast`] 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.
|
||
|
|
unk_token (`str`, *optional*, defaults to `"<pad>"`):
|
||
|
|
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.
|
||
|
|
max_length (`int`, *optional*, defaults to 1024):
|
||
|
|
The maximum length of the sequences when encoding. Sequences longer than this will be truncated.
|
||
|
|
offset (`int`, *optional*, defaults to 0):
|
||
|
|
The offset of the tokenizer.
|
||
|
|
"""
|
||
|
|
|
||
|
|
model_input_names = ["input_ids", "attention_mask"]
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
pad_token: Optional[str] = "<pad>",
|
||
|
|
unk_token: Optional[str] = "<pad>",
|
||
|
|
max_length: Optional[int] = 1024,
|
||
|
|
offset: int = 0,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
# We have no eos/bos tokens but allow padding -- no l/r strip as we treat them as tokens as well
|
||
|
|
pad_token = AddedToken(pad_token) if isinstance(pad_token, str) else pad_token
|
||
|
|
unk_token = AddedToken(unk_token) if isinstance(unk_token, str) else unk_token
|
||
|
|
|
||
|
|
self._utf_vocab_size = 2**8 # utf is 8 bits
|
||
|
|
self._added_tokens_decoder = {0: pad_token, 1: AddedToken("[S1]"), 2: AddedToken("[S2]")}
|
||
|
|
self.offset = offset
|
||
|
|
super().__init__(
|
||
|
|
unk_token=unk_token,
|
||
|
|
pad_token=pad_token,
|
||
|
|
max_length=max_length,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
@property
|
||
|
|
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 _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: list[str]) -> str:
|
||
|
|
"""Converts a sequence of tokens (string) in a single string."""
|
||
|
|
bstring = b""
|
||
|
|
for token in tokens:
|
||
|
|
if token in self.added_tokens_decoder:
|
||
|
|
added_token_obj = self.added_tokens_decoder[token]
|
||
|
|
tok_string = str(added_token_obj).encode("utf-8")
|
||
|
|
elif token in self.added_tokens_encoder:
|
||
|
|
tok_string = token.encode("utf-8")
|
||
|
|
else:
|
||
|
|
tok_string = token.encode("utf-8") # Assume general string token
|
||
|
|
bstring += tok_string
|
||
|
|
string = bstring.decode("utf-8", errors="ignore")
|
||
|
|
return string
|
||
|
|
|
||
|
|
# No vocab file
|
||
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
|
||
|
|
return ()
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = ["DiaTokenizer"]
|