|
import os |
|
import json |
|
|
|
from typing import Dict, List, Optional, Union, Tuple |
|
|
|
from transformers.utils import logging |
|
from sentencepiece import SentencePieceProcessor |
|
from transformers.tokenization_utils import PreTrainedTokenizer |
|
|
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
SPIECE_UNDERLINE = "▁" |
|
|
|
SPECIAL_TAGS = { |
|
"_bt_", |
|
"_ft_", |
|
"asm_Beng", |
|
"awa_Deva", |
|
"ben_Beng", |
|
"bho_Deva", |
|
"brx_Deva", |
|
"doi_Deva", |
|
"eng_Latn", |
|
"gom_Deva", |
|
"gon_Deva", |
|
"guj_Gujr", |
|
"hin_Deva", |
|
"hne_Deva", |
|
"kan_Knda", |
|
"kas_Arab", |
|
"kas_Deva", |
|
"kha_Latn", |
|
"lus_Latn", |
|
"mag_Deva", |
|
"mai_Deva", |
|
"mal_Mlym", |
|
"mar_Deva", |
|
"mni_Beng", |
|
"mni_Mtei", |
|
"npi_Deva", |
|
"ory_Orya", |
|
"pan_Guru", |
|
"san_Deva", |
|
"sat_Olck", |
|
"snd_Arab", |
|
"snd_Deva", |
|
"tam_Taml", |
|
"tel_Telu", |
|
"urd_Arab", |
|
"unr_Deva", |
|
} |
|
|
|
VOCAB_FILES_NAMES = { |
|
"src_vocab_fp": "dict.SRC.json", |
|
"tgt_vocab_fp": "dict.TGT.json", |
|
"src_spm_fp": "model.SRC", |
|
"tgt_spm_fp": "model.TGT", |
|
} |
|
|
|
|
|
class IndicTransTokenizer(PreTrainedTokenizer): |
|
_added_tokens_encoder = {} |
|
_added_tokens_decoder = {} |
|
|
|
vocab_files_names = VOCAB_FILES_NAMES |
|
model_input_names = ["input_ids", "attention_mask"] |
|
|
|
def __init__( |
|
self, |
|
src_vocab_fp=None, |
|
tgt_vocab_fp=None, |
|
src_spm_fp=None, |
|
tgt_spm_fp=None, |
|
unk_token="<unk>", |
|
bos_token="<s>", |
|
eos_token="</s>", |
|
pad_token="<pad>", |
|
do_lower_case=False, |
|
**kwargs, |
|
): |
|
|
|
self.src = True |
|
|
|
self.src_vocab_fp = src_vocab_fp |
|
self.tgt_vocab_fp = tgt_vocab_fp |
|
self.src_spm_fp = src_spm_fp |
|
self.tgt_spm_fp = tgt_spm_fp |
|
|
|
self.unk_token = unk_token.content |
|
self.pad_token = pad_token.content |
|
self.eos_token = eos_token.content |
|
self.bos_token = bos_token.content |
|
|
|
self.encoder = self._load_json(self.src_vocab_fp) |
|
if self.unk_token not in self.encoder: |
|
raise KeyError("<unk> token must be in vocab") |
|
assert self.pad_token in self.encoder |
|
self.encoder_rev = {v: k for k, v in self.encoder.items()} |
|
|
|
self.decoder = self._load_json(self.tgt_vocab_fp) |
|
if self.unk_token not in self.encoder: |
|
raise KeyError("<unk> token must be in vocab") |
|
assert self.pad_token in self.encoder |
|
self.decoder_rev = {v: k for k, v in self.decoder.items()} |
|
|
|
|
|
self.src_spm = self._load_spm(self.src_spm_fp) |
|
self.tgt_spm = self._load_spm(self.tgt_spm_fp) |
|
|
|
self.current_spm = self.src_spm |
|
self.current_encoder = self.encoder |
|
self.current_encoder_rev = self.encoder_rev |
|
|
|
self.unk_token_id = self.encoder[self.unk_token] |
|
self.pad_token_id = self.encoder[self.pad_token] |
|
self.eos_token_id = self.encoder[self.eos_token] |
|
self.bos_token_id = self.encoder[self.bos_token] |
|
|
|
super().__init__( |
|
src_vocab_file=self.src_vocab_fp, |
|
tgt_vocab_file=self.src_vocab_fp, |
|
do_lower_case=do_lower_case, |
|
unk_token=unk_token, |
|
bos_token=bos_token, |
|
eos_token=eos_token, |
|
pad_token=pad_token, |
|
**kwargs, |
|
) |
|
|
|
def add_new_special_tags(self, new_tags: List[str]): |
|
SPECIAL_TAGS.update(new_tags) |
|
|
|
def _switch_to_input_mode(self): |
|
self.src = True |
|
self.padding_side = "left" |
|
self.current_spm = self.src_spm |
|
self.current_encoder = self.encoder |
|
self.current_encoder_rev = self.encoder_rev |
|
|
|
def _switch_to_target_mode(self): |
|
self.src = False |
|
self.padding_side = "right" |
|
self.current_spm = self.tgt_spm |
|
self.current_encoder = self.decoder |
|
self.current_encoder_rev = self.decoder_rev |
|
|
|
def _load_spm(self, path: str) -> SentencePieceProcessor: |
|
return SentencePieceProcessor(model_file=path) |
|
|
|
def _save_json(self, data, path: str) -> None: |
|
with open(path, "w", encoding="utf-8") as f: |
|
json.dump(data, f, indent=2) |
|
|
|
def _load_json(self, path: str) -> Union[Dict, List]: |
|
with open(path, "r", encoding="utf-8") as f: |
|
return json.load(f) |
|
|
|
def _split_tags(self, tokens: List[str]) -> Tuple[List[str], List[str]]: |
|
tags = [token for token in tokens if token in SPECIAL_TAGS] |
|
tokens = [token for token in tokens if token not in SPECIAL_TAGS] |
|
return tags, tokens |
|
|
|
def _split_pads(self, tokens: List[str]) -> Tuple[List[str], List[str]]: |
|
pads = [token for token in tokens if token == self.pad_token] |
|
tokens = [token for token in tokens if token != self.pad_token] |
|
return pads, tokens |
|
|
|
@property |
|
def src_vocab_size(self) -> int: |
|
return len(self.encoder) |
|
|
|
@property |
|
def tgt_vocab_size(self) -> int: |
|
return len(self.decoder) |
|
|
|
def get_src_vocab(self) -> Dict[str, int]: |
|
return dict(self.encoder, **self.added_tokens_encoder) |
|
|
|
def get_tgt_vocab(self) -> Dict[str, int]: |
|
return dict(self.decoder, **self.added_tokens_decoder) |
|
|
|
|
|
def get_vocab(self) -> Dict[str, int]: |
|
return self.get_src_vocab() |
|
|
|
|
|
@property |
|
def vocab_size(self) -> int: |
|
return self.src_vocab_size |
|
|
|
def _convert_token_to_id(self, token: str) -> int: |
|
"""Converts an token (str) into an index (integer) using the source/target vocabulary map.""" |
|
return self.current_encoder.get(token, self.current_encoder[self.unk_token]) |
|
|
|
def _convert_id_to_token(self, index: int) -> str: |
|
"""Converts an index (integer) into a token (str) using the source/target vocabulary map.""" |
|
return self.current_encoder_rev.get(index, self.unk_token) |
|
|
|
def convert_tokens_to_string(self, tokens: List[str]) -> str: |
|
"""Uses sentencepiece model for detokenization""" |
|
pads, tokens = self._split_pads(tokens) |
|
|
|
if self.src: |
|
|
|
tags, non_tags = self._split_tags(tokens) |
|
|
|
return ( |
|
" ".join(pads) |
|
+ " " |
|
+ " ".join(tags) |
|
+ " " |
|
+ "".join(non_tags).replace(SPIECE_UNDERLINE, " ").strip() |
|
) |
|
|
|
return ( |
|
"".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() |
|
+ " " |
|
+ " ".join(pads) |
|
) |
|
|
|
def _tokenize(self, text) -> List[str]: |
|
if self.src: |
|
tokens = text.split(" ") |
|
tags, non_tags = self._split_tags(tokens) |
|
text = " ".join(non_tags) |
|
tokens = self.current_spm.EncodeAsPieces(text) |
|
return tags + tokens |
|
else: |
|
return self.current_spm.EncodeAsPieces(text) |
|
|
|
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.eos_token_id] |
|
|
|
return token_ids_0 + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] |
|
|
|
def save_vocabulary( |
|
self, save_directory: str, filename_prefix: Optional[str] = None |
|
) -> Tuple[str]: |
|
if not os.path.isdir(save_directory): |
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
|
return |
|
|
|
src_spm_fp = os.path.join(save_directory, "model.SRC") |
|
tgt_spm_fp = os.path.join(save_directory, "model.TGT") |
|
src_vocab_fp = os.path.join(save_directory, "dict.SRC.json") |
|
tgt_vocab_fp = os.path.join(save_directory, "dict.TGT.json") |
|
|
|
self._save_json(self.encoder, src_vocab_fp) |
|
self._save_json(self.decoder, tgt_vocab_fp) |
|
|
|
with open(src_spm_fp, "wb") as f: |
|
f.write(self.src_spm.serialized_model_proto()) |
|
|
|
with open(tgt_spm_fp, "wb") as f: |
|
f.write(self.tgt_spm.serialized_model_proto()) |
|
|
|
return src_vocab_fp, tgt_vocab_fp, src_spm_fp, tgt_spm_fp |
