File size: 7,549 Bytes
a067af2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
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 = "▁"
SUPPORTED_LANGUAGES = [
    "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
        self.pad_token = pad_token
        self.eos_token = eos_token
        self.bos_token = bos_token

        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()}

        # load SentencePiece model for pre-processing
        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 _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)

    @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)

    # hack override
    def get_vocab(self) -> Dict[str, int]:
        return self.get_src_vocab()

    # hack override
    @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"""
        pad_tokens = [token for token in tokens if token == self.pad_token]
        tokens = [token for token in tokens if token != self.pad_token]
        if self.src:
            return (
                " ".join(pad_tokens)
                + " "
                + " ".join(tokens[:2])
                + " "
                + "".join(tokens[2:]).replace(SPIECE_UNDERLINE, " ").strip()
            )
        return (
            "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
            + " "
            + " ".join(pad_tokens)
        )

    def _tokenize(self, text) -> List[str]:
        if self.src:
            tokens = text.split(" ")
            tags = tokens[:2]
            text = " ".join(tokens[2:])
            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]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        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