|
import os |
|
from shutil import copyfile |
|
from typing import Any, Dict, List, Optional, Tuple |
|
|
|
import sentencepiece as spm |
|
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer |
|
from transformers.utils import logging |
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} |
|
|
|
PRETRAINED_VOCAB_FILES_MAP = { |
|
"vocab_file": {}, |
|
"tokenizer_file": {}, |
|
} |
|
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} |
|
|
|
|
|
class YiTokenizer(PreTrainedTokenizer): |
|
""" |
|
Construct a Yi tokenizer. Based on byte-level Byte-Pair-Encoding. |
|
|
|
Args: |
|
vocab_file (`str`): |
|
Path to the vocabulary file. |
|
""" |
|
|
|
vocab_files_names = VOCAB_FILES_NAMES |
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
|
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
|
model_input_names = ["input_ids", "attention_mask"] |
|
|
|
def __init__( |
|
self, |
|
vocab_file, |
|
unk_token="<unk>", |
|
bos_token="<|startoftext|>", |
|
eos_token="<|endoftext|>", |
|
pad_token="<unk>", |
|
sp_model_kwargs: Optional[Dict[str, Any]] = None, |
|
add_bos_token=True, |
|
add_eos_token=False, |
|
clean_up_tokenization_spaces=False, |
|
**kwargs, |
|
): |
|
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_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 |
|
) |
|
unk_token = ( |
|
AddedToken(unk_token, lstrip=False, rstrip=False) |
|
if isinstance(unk_token, str) |
|
else unk_token |
|
) |
|
pad_token = ( |
|
AddedToken(pad_token, lstrip=False, rstrip=False) |
|
if isinstance(pad_token, str) |
|
else pad_token |
|
) |
|
self.vocab_file = vocab_file |
|
self.add_bos_token = add_bos_token |
|
self.add_eos_token = add_eos_token |
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
|
self.sp_model.Load(vocab_file) |
|
super().__init__( |
|
bos_token=bos_token, |
|
eos_token=eos_token, |
|
unk_token=unk_token, |
|
pad_token=pad_token, |
|
add_bos_token=add_bos_token, |
|
add_eos_token=add_eos_token, |
|
sp_model_kwargs=self.sp_model_kwargs, |
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
|
**kwargs, |
|
) |
|
|
|
def __getstate__(self): |
|
state = self.__dict__.copy() |
|
state["sp_model"] = None |
|
return state |
|
|
|
def __setstate__(self, d): |
|
self.__dict__ = d |
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
|
self.sp_model.Load(self.vocab_file) |
|
|
|
@property |
|
def vocab_size(self): |
|
"""Returns vocab size""" |
|
return self.sp_model.get_piece_size() |
|
|
|
def get_vocab(self): |
|
"""Returns vocab as a dict""" |
|
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} |
|
vocab.update(self.added_tokens_encoder) |
|
return vocab |
|
|
|
def _tokenize(self, text): |
|
"""Returns a tokenized string.""" |
|
return self.sp_model.encode(text, out_type=str) |
|
|
|
def _convert_token_to_id(self, token): |
|
"""Converts a token (str) in an id using the vocab.""" |
|
return self.sp_model.piece_to_id(token) |
|
|
|
def _convert_id_to_token(self, index): |
|
"""Converts an index (integer) in a token (str) using the vocab.""" |
|
token = self.sp_model.IdToPiece(index) |
|
return token |
|
|
|
def convert_tokens_to_string(self, tokens): |
|
"""Converts a sequence of tokens (string) in a single string.""" |
|
current_sub_tokens = [] |
|
out_string = "" |
|
prev_is_special = False |
|
for i, token in enumerate(tokens): |
|
|
|
if token in self.all_special_tokens: |
|
if not prev_is_special and i != 0: |
|
out_string += " " |
|
out_string += self.sp_model.decode(current_sub_tokens) + token |
|
prev_is_special = True |
|
current_sub_tokens = [] |
|
else: |
|
current_sub_tokens.append(token) |
|
prev_is_special = False |
|
out_string += self.sp_model.decode(current_sub_tokens) |
|
return out_string |
|
|
|
def save_vocabulary( |
|
self, save_directory, filename_prefix: Optional[str] = None |
|
) -> Tuple[str]: |
|
""" |
|
Save the vocabulary and special tokens file to a directory. |
|
|
|
Args: |
|
save_directory (`str`): |
|
The directory in which to save the vocabulary. |
|
|
|
Returns: |
|
`Tuple(str)`: Paths to the files saved. |
|
""" |
|
if not os.path.isdir(save_directory): |
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
|
return |
|
out_vocab_file = os.path.join( |
|
save_directory, |
|
(filename_prefix + "-" if filename_prefix else "") |
|
+ VOCAB_FILES_NAMES["vocab_file"], |
|
) |
|
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath( |
|
out_vocab_file |
|
) and os.path.isfile(self.vocab_file): |
|
copyfile(self.vocab_file, out_vocab_file) |
|
elif not os.path.isfile(self.vocab_file): |
|
with open(out_vocab_file, "wb") as fi: |
|
content_spiece_model = self.sp_model.serialized_model_proto() |
|
fi.write(content_spiece_model) |
|
|
|
return (out_vocab_file,) |
|
|
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): |
|
bos_token_id = [self.bos_token_id] if self.add_bos_token else [] |
|
eos_token_id = [self.eos_token_id] if self.add_eos_token else [] |
|
|
|
output = bos_token_id + token_ids_0 + eos_token_id |
|
|
|
if token_ids_1 is not None: |
|
output = output + bos_token_id + token_ids_1 + eos_token_id |
|
|
|
return output |
|
|
|
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, |
|
) |
|
|
|
bos_token_id = [1] if self.add_bos_token else [] |
|
eos_token_id = [1] if self.add_eos_token else [] |
|
|
|
if token_ids_1 is None: |
|
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id |
|
return ( |
|
bos_token_id |
|
+ ([0] * len(token_ids_0)) |
|
+ eos_token_id |
|
+ bos_token_id |
|
+ ([0] * len(token_ids_1)) |
|
+ eos_token_id |
|
) |
|
|
|
def create_token_type_ids_from_sequences( |
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
|
) -> List[int]: |
|
""" |
|
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT |
|
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 token_ids_1 is None, only returns the first portion of the mask (0s). |
|
|
|
Args: |
|
token_ids_0 (`List[int]`): |
|
List of ids. |
|
token_ids_1 (`List[int]`, *optional*): |
|
Optional second list of IDs for sequence pairs. |
|
|
|
Returns: |
|
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). |
|
""" |
|
bos_token_id = [self.bos_token_id] if self.add_bos_token else [] |
|
eos_token_id = [self.eos_token_id] if self.add_eos_token else [] |
|
|
|
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id) |
|
|
|
if token_ids_1 is not None: |
|
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) |
|
|
|
return output |
|
|
