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import os
from shutil import copyfile
from typing import List, Optional
from omegaconf import DictConfig
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging
from .fairseq_dictionary import Dictionary
from .guoke_tokenizer import GuokeTokenizer
from .sentencepiece_bpe import SentencepieceBPE
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"sp_path": "sp.model",
"dict_path": "dict.txt"
}
class FairseqT5Tokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
sp_path,
dict_path,
lower,
n_sentinel_tokens=0,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
**kwargs
) -> None:
self.sp_path = sp_path
self.dict_path = dict_path
self.lower = lower
self.fs_tokenizer = GuokeTokenizer(
DictConfig(
dict(
lower=lower
)
)
)
self.fs_bpe = SentencepieceBPE(
dict(
sentencepiece_model=sp_path,
)
)
self.fs_dict = Dictionary.load(dict_path)
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,
lower=self.lower,
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
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_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]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + 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 [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def _tokenize(self, text: str) -> List[str]:
return self.fs_bpe.encode(self.fs_tokenizer.encode(text)).split(" ")
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):
return self.fs_bpe.decode(" ".join(tokens))
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_sp_path = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["sp_path"]
)
out_dict_path = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["dict_path"]
)
if os.path.abspath(self.sp_path) != os.path.abspath(out_sp_path):
copyfile(self.sp_path, out_sp_path)
logger.info(f"Copy from {self.sp_path} to {out_sp_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_sp_path, out_dict_path
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