ast_t5_base / tokenization_ast_t5.py
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import os
import re
from shutil import copyfile
from typing import List, Optional
import tiktoken
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging
from .fairseq_dictionary import Dictionary
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"dict_path": "dict.txt"
}
GPT4ENC = tiktoken.encoding_for_model("gpt-4")
class GPT4Dictionary:
def __init__(self):
self.vocab = {}
self.words = {}
self.vocab_cnt = 4
for i in range(GPT4ENC.n_vocab):
try:
w = GPT4ENC.decode_single_token_bytes(i)
self.vocab[w] = self.vocab_cnt
self.words[self.vocab_cnt] = w
self.vocab_cnt += 1
except KeyError:
pass
self.eos_index = 2
self.words[2] = b"</s>"
self.sentinel_start = self.vocab_cnt
for i in range(1000):
self.words[self.sentinel_start + i] = f"<sen{i:03d}>".encode("utf-8")
def index(self, w):
assert w in self.vocab
return self.vocab[w]
def __getitem__(self, i):
if i in self.words:
return self.words[i]
else:
return b""
class ASTT5Tokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
dict_path,
n_sentinel_tokens=0,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
**kwargs
) -> None:
self.dict_path = dict_path
self.tik_dict = GPT4Dictionary()
self.fs_dict = Dictionary.load(dict_path)
self.fs_dict_sentinel_start = len(self.fs_dict)
for i in range(n_sentinel_tokens):
self.fs_dict.add_symbol(f'<sen{i:03d}>')
if "sep_token" in kwargs:
assert kwargs["sep_token"] == eos_token
kwargs.pop("sep_token")
if "cls_token" in kwargs:
assert kwargs["cls_token"] == bos_token
kwargs.pop("cls_token")
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
sep_token=eos_token,
cls_token=bos_token,
n_sentinel_tokens=n_sentinel_tokens,
**kwargs, )
@property
def vocab_size(self):
return len(self.fs_dict)
def get_vocab(self):
return self.fs_dict.indices
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
)
mask_0 = [(0 if w < self.fs_dict_sentinel_start else 1) for w in token_ids_0]
mask_1 = [(0 if w < self.fs_dict_sentinel_start else 1) for w in token_ids_1]
if token_ids_1 is None:
return mask_0 + [1]
return mask_0 + [1] + mask_1 + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
sep = [self.sep_token_id]
if token_ids_1 is None:
return len(token_ids_0 + sep) * [0]
return len(token_ids_0 + sep + token_ids_1 + sep) * [0]
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
if token_ids_1 is None:
return token_ids_0 + [self.sep_token_id]
sep = [self.sep_token_id]
return token_ids_0 + sep + token_ids_1 + sep
def _tokenize(self, text: str) -> List[str]:
parts = re.split(r"(<sen\d+>)", text)
tokenized = []
for part in parts:
if re.match(r"<sen\d+>", part):
tokenized.append(part)
else:
tokenized.extend(
[
self.fs_dict[self.tik_dict.index(w)]
for w in GPT4ENC.decode_tokens_bytes(GPT4ENC.encode_ordinary(part))
]
)
return tokenized
def _convert_token_to_id(self, token):
return self.fs_dict.index(token)
def _convert_id_to_token(self, index):
return self.fs_dict[index]
def convert_tokens_to_string(self, tokens):
token_bytes = b"".join([self.tik_dict[self.fs_dict.index(token)] for token in tokens])
return token_bytes.decode("utf-8", errors="ignore")
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_dict_path = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["dict_path"]
)
if os.path.abspath(self.dict_path) != os.path.abspath(out_dict_path):
copyfile(self.dict_path, out_dict_path)
logger.info(f"Copy from {self.dict_path} to {out_dict_path}")
return (out_dict_path,)