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decodon-200M-euk

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Safetensors
decodon
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mohsennp commited on
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Upload tokenizer

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Files changed (4) hide show
  1. special_tokens_map.json +8 -0
  2. tokenization_decodon.py +321 -0
  3. tokenizer_config.json +59 -0
  4. vocab.json +1 -0
special_tokens_map.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "<CLS>",
3
+ "cls_token": "<CLS>",
4
+ "mask_token": "<MASK>",
5
+ "pad_token": "<PAD>",
6
+ "sep_token": "<SEP>",
7
+ "unk_token": "<UNK>"
8
+ }
tokenization_decodon.py ADDED
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1
+ import json
2
+ import os
3
+ import re
4
+ from transformers import PreTrainedTokenizer
5
+ from itertools import product
6
+
7
+ class DeCodonTokenizer(PreTrainedTokenizer):
8
+ """
9
+ DeCodonTokenizer Tokenizer: tokenize 3-mer codons into tokens
10
+ The input sequences are expected to be raw sequences of coding DNA/RNA sequences.
11
+ """
12
+
13
+ SUPPORTED_TYPES = ["dna", "rna"]
14
+
15
+ @staticmethod
16
+ def get_all_codons(seq_type="dna"):
17
+ """
18
+ Get all possible codons.
19
+ """
20
+ seq_type = seq_type.lower()
21
+ assert (
22
+ seq_type in DeCodonTokenizer.SUPPORTED_TYPES
23
+ ), f"seq_type should be either 'dna' or 'rna'. Got {seq_type}!"
24
+
25
+ if seq_type == "dna":
26
+ return ["".join(codon) for codon in product("ACGT", repeat=3)]
27
+ else:
28
+ return ["".join(codon) for codon in product("ACGU", repeat=3)]
29
+
30
+ def __init__(
31
+ self,
32
+ vocab_file=None,
33
+ cls_token="<CLS>",
34
+ bos_token="<CLS>",
35
+ sep_token="<SEP>",
36
+ unk_token="<UNK>",
37
+ pad_token="<PAD>",
38
+ mask_token="<MASK>",
39
+ seq_type="dna",
40
+ **kwargs,
41
+ ):
42
+ self.codons = self.get_all_codons(seq_type=seq_type)
43
+ self.seq_type = seq_type
44
+ self.special_tokens = [cls_token, sep_token, unk_token, pad_token, mask_token]
45
+ self.special_tokens = [str(token) for token in self.special_tokens]
46
+
47
+ if vocab_file is not None:
48
+ import json
49
+ with open(vocab_file, "r") as f:
50
+ self.encoder = json.load(f)
51
+ self.decoder = {i: k for k, i in self.encoder.items()}
52
+ else:
53
+ self.encoder = {k: i for i, k in enumerate(self.special_tokens + self.codons)}
54
+ self.decoder = {i: k for k, i in self.encoder.items()}
55
+
56
+ self.compiled_regex = re.compile(
57
+ "|".join(self.codons + self.special_tokens + [r"\S"])
58
+ )
59
+
60
+ super().__init__(
61
+ cls_token=cls_token,
62
+ bos_token=bos_token,
63
+ sep_token=sep_token,
64
+ unk_token=unk_token,
65
+ pad_token=pad_token,
66
+ mask_token=mask_token,
67
+ **kwargs,
68
+ )
69
+
70
+ self.aa_to_codon = {
71
+ "A": ["GCT", "GCC", "GCA", "GCG"],
72
+ "C": ["TGT", "TGC"],
73
+ "D": ["GAT", "GAC"],
74
+ "E": ["GAA", "GAG"],
75
+ "F": ["TTT", "TTC"],
76
+ "G": ["GGT", "GGC", "GGA", "GGG"],
77
+ "H": ["CAT", "CAC"],
78
+ "I": ["ATT", "ATC", "ATA"],
79
+ "K": ["AAA", "AAG"],
80
+ "L": ["TTA", "TTG", "CTT", "CTC", "CTA", "CTG"],
81
+ "M": ["ATG"],
82
+ "N": ["AAT", "AAC"],
83
+ "P": ["CCT", "CCC", "CCA", "CCG"],
84
+ "Q": ["CAA", "CAG"],
85
+ "R": ["CGT", "CGC", "CGA", "CGG", "AGA", "AGG"],
86
+ "S": ["TCT", "TCC", "TCA", "TCG", "AGT", "AGC"],
87
+ "T": ["ACT", "ACC", "ACA", "ACG"],
88
+ "V": ["GTT", "GTC", "GTA", "GTG"],
89
+ "W": ["TGG"],
90
+ "Y": ["TAT", "TAC"],
91
+ "*": ["TAA", "TAG", "TGA"],
92
+ }
93
+ self.codon_to_aa = {
94
+ codon: aa for aa, codons in self.aa_to_codon.items() for codon in codons
95
+ }
96
+
97
+ if seq_type == "rna":
98
+ self.aa_to_codon = {
99
+ k: [c.replace("T", "U") for c in v] for k, v in self.aa_to_codon.items()
100
+ }
101
+ self.codon_to_aa = {
102
+ k.replace("T", "U"): v for k, v in self.codon_to_aa.items()
103
+ }
104
+
105
+ self.amino_acids = list("ACDEFGHIKLMNPQRSTVWY")
106
+ self.encoder_aa = {
107
+ k: i for i, k in enumerate(self.special_tokens + self.amino_acids)
108
+ }
109
+ self.compiled_regex_aa = re.compile(
110
+ "|".join(self.amino_acids + self.special_tokens + [r"\S"])
111
+ )
112
+
113
+ self.token_type_mode = kwargs.get("token_type_mode", "regular")
114
+ self.build_token_type_encoder()
115
+
116
+ def set_organism_tokens(self, organism_tokens):
117
+ """
118
+ Add organism tokens to the tokenizer.
119
+ """
120
+ vocab_size = len(self.encoder)
121
+ for i, token in enumerate(organism_tokens):
122
+ self.encoder[token] = vocab_size + i
123
+ self.decoder[vocab_size + i] = token
124
+
125
+ self.organism_tokens = organism_tokens
126
+ self.compiled_regex = re.compile(
127
+ "|".join(self.codons + self.special_tokens + organism_tokens + [r"\S"])
128
+ )
129
+
130
+ @property
131
+ def vocab_size(self):
132
+ return len(self.encoder)
133
+
134
+ def build_token_type_encoder(self):
135
+ if self.token_type_mode == "aa":
136
+ # build a token type encoder for amino acids with codon ids as keys and amino acid ids as values
137
+ # CLS, SEP, UNK, MASK, PAD tokens are assigned to the same token type as zero
138
+ token_type_encoder = {}
139
+ for token, token_id in self.encoder.items():
140
+ if token in self.special_tokens:
141
+ token_type_encoder[token_id] = 0
142
+ elif token in self.codons:
143
+ aa = self.codon_to_aa[token]
144
+ token_type_encoder[token_id] = (
145
+ list(self.amino_acids + ["*"]).index(aa) + 1
146
+ )
147
+ else:
148
+ token_type_encoder[token_id] = len(self.amino_acids) + 2
149
+ elif self.token_type_mode == "regular":
150
+ # build a token type encoder for regular tokens
151
+ token_type_encoder = {token_id: 0 for token_id in self.encoder.values()}
152
+ elif self.token_type_mode == "regular_special":
153
+ # build a token type encoder for regular tokens with special tokens having a different but same token type
154
+ token_type_encoder = {
155
+ token_id: 0 if token in self.special_tokens else 1
156
+ for token, token_id in self.encoder.items()
157
+ }
158
+ else:
159
+ raise ValueError(f"Unknown token type mode: {self.token_type_mode}")
160
+
161
+ self.token_type_encoder = token_type_encoder
162
+
163
+ @property
164
+ def token_type_vocab_size(self):
165
+ return len(set(self.token_type_encoder.values())) + 1
166
+
167
+ def get_vocab(self):
168
+ return dict(self.encoder, **self.added_tokens_encoder)
169
+
170
+ def _tokenize(self, text):
171
+ """
172
+ Tokenize a string.
173
+ """
174
+ text = text.upper()
175
+ tokens = self.compiled_regex.findall(text)
176
+ return tokens
177
+
178
+ def _convert_token_to_id(self, token):
179
+ """
180
+ Converts a token (str) in an id using the vocab.
181
+ """
182
+ return self.encoder.get(token, self.encoder[self.unk_token])
183
+
184
+ def _convert_id_to_token(self, index):
185
+ """
186
+ Converts an index (integer) in a token (str) using the vocab.
187
+ """
188
+ return self.decoder.get(index, self.unk_token)
189
+
190
+ def convert_tokens_to_string(self, tokens):
191
+ """
192
+ Converts a sequence of tokens (string) in a single string.
193
+ """
194
+ return "".join(tokens)
195
+
196
+ def encode_aa(self, text):
197
+ """
198
+ Encode a DNA/RNA string using the amino acid vocab.
199
+ """
200
+ tokens = self._tokenize(text)
201
+ return [
202
+ self.encoder_aa.get(token, self.encoder_aa[self.unk_token])
203
+ for token in tokens
204
+ ]
205
+
206
+ def get_aa_vocab_size(self):
207
+ return len(self.encoder_aa)
208
+
209
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
210
+ """
211
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
212
+ adding special tokens.
213
+
214
+ This implementation does not add special tokens and this method should be overridden in a subclass.
215
+
216
+ Args:
217
+ token_ids_0 (`List[int]`): The first tokenized sequence.
218
+ token_ids_1 (`List[int]`, *optional*): The second tokenized sequence.
219
+
220
+ Returns:
221
+ `List[int]`: The model input with special tokens.
222
+ """
223
+ token_ids_0 = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
224
+ return token_ids_0
225
+
226
+ def get_special_tokens_mask(
227
+ self, token_ids_0, token_ids_1=None, already_has_special_tokens: bool = False
228
+ ):
229
+ """
230
+ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
231
+ special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
232
+
233
+ Args:
234
+ token_ids_0 (`List[int]`):
235
+ List of ids of the first sequence.
236
+ token_ids_1 (`List[int]`, *optional*):
237
+ List of ids of the second sequence.
238
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
239
+ Whether or not the token list is already formatted with special tokens for the model.
240
+
241
+ Returns:
242
+ A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
243
+ """
244
+ special_ids = [
245
+ self.eos_token_id,
246
+ self.pad_token_id,
247
+ self.mask_token_id,
248
+ self.sep_token_id,
249
+ self.cls_token_id,
250
+ ]
251
+
252
+ if already_has_special_tokens:
253
+ special_tokens_mask = [
254
+ 1 if idx in special_ids else 0 for idx in token_ids_0
255
+ ]
256
+ else:
257
+ special_tokens_mask = (
258
+ [1] + [1 if idx in special_ids else 0 for idx in token_ids_0] + [1]
259
+ )
260
+
261
+ return special_tokens_mask
262
+
263
+ def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
264
+ """
265
+ Create the token type IDs corresponding to the sequences passed. [What are token type
266
+ IDs?](../glossary#token-type-ids)
267
+
268
+ Should be overridden in a subclass if the model has a special way of building those.
269
+
270
+ Args:
271
+ token_ids_0 (`List[int]`): The first tokenized sequence.
272
+ token_ids_1 (`List[int]`, *optional*): The second tokenized sequence.
273
+
274
+ Returns:
275
+ `List[int]`: The token type ids.
276
+ """
277
+ # special_ids = [
278
+ # self.bos_token_id,
279
+ # self.eos_token_id,
280
+ # self.pad_token_id,
281
+ # self.mask_token_id,
282
+ # self.cls_token_id,
283
+ # self.sep_token_id,
284
+ # ]
285
+
286
+ # token_type_ids = [0] + [0 for idx in token_ids_0] + [0]
287
+
288
+ unk_type_id = len(set(self.token_type_encoder.values()))
289
+
290
+ token_type_ids = [
291
+ self.token_type_encoder.get(token_id, unk_type_id)
292
+ for token_id in token_ids_0
293
+ ]
294
+
295
+ return token_type_ids
296
+
297
+ def save_vocabulary(self, save_directory, filename_prefix=None):
298
+ """
299
+ Save only the vocabulary of the tokenizer (vocabulary + added tokens).
300
+
301
+ This method won't save the configuration and special token mappings of the tokenizer. Use
302
+ [`~PreTrainedTokenizerFast._save_pretrained`] to save the whole state of the tokenizer.
303
+
304
+ Args:
305
+ save_directory (`str`):
306
+ The directory in which to save the vocabulary.
307
+ filename_prefix (`str`, *optional*):
308
+ An optional prefix to add to the named of the saved files.
309
+
310
+ Returns:
311
+ `Tuple(str)`: Paths to the files saved.
312
+ """
313
+ if filename_prefix is None:
314
+ filename_prefix = ""
315
+
316
+ vocab_file = os.path.join(save_directory, filename_prefix + "vocab.json")
317
+
318
+ with open(vocab_file, "w") as f:
319
+ json.dump(self.encoder, f)
320
+
321
+ return (vocab_file,)
tokenizer_config.json ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<CLS>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<SEP>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "<UNK>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<PAD>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "4": {
36
+ "content": "<MASK>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "auto_map": {
45
+ "AutoTokenizer": [
46
+ "tokenization_decodon.DeCodonTokenizer",
47
+ null
48
+ ]
49
+ },
50
+ "bos_token": "<CLS>",
51
+ "clean_up_tokenization_spaces": true,
52
+ "cls_token": "<CLS>",
53
+ "mask_token": "<MASK>",
54
+ "model_max_length": 2048,
55
+ "pad_token": "<PAD>",
56
+ "sep_token": "<SEP>",
57
+ "tokenizer_class": "DeCodonTokenizer",
58
+ "unk_token": "<UNK>"
59
+ }
vocab.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"<CLS>": 0, "<SEP>": 1, "<UNK>": 2, "<PAD>": 3, "<MASK>": 4, "AAA": 5, "AAC": 6, "AAG": 7, "AAT": 8, "ACA": 9, "ACC": 10, "ACG": 11, "ACT": 12, "AGA": 13, "AGC": 14, "AGG": 15, "AGT": 16, "ATA": 17, "ATC": 18, "ATG": 19, "ATT": 20, "CAA": 21, "CAC": 22, "CAG": 23, "CAT": 24, "CCA": 25, "CCC": 26, "CCG": 27, "CCT": 28, "CGA": 29, "CGC": 30, "CGG": 31, "CGT": 32, "CTA": 33, "CTC": 34, "CTG": 35, "CTT": 36, "GAA": 37, "GAC": 38, "GAG": 39, "GAT": 40, "GCA": 41, "GCC": 42, "GCG": 43, "GCT": 44, "GGA": 45, "GGC": 46, "GGG": 47, "GGT": 48, "GTA": 49, "GTC": 50, "GTG": 51, "GTT": 52, "TAA": 53, "TAC": 54, "TAG": 55, "TAT": 56, "TCA": 57, "TCC": 58, "TCG": 59, "TCT": 60, "TGA": 61, "TGC": 62, "TGG": 63, "TGT": 64, "TTA": 65, "TTC": 66, "TTG": 67, "TTT": 68, "<1314>": 69, "<257758>": 70, "<208224>": 71, "<573>": 72, "<439334>": 73, "<1639>": 74, "<562>": 75, "<106648>": 76, "<287>": 77, "<28450>": 78, "<2104>": 79, "<600>": 80, "<727>": 81, "<549>": 82, "<2495445>": 83, "<1313>": 84, "<1680>": 85, "<1929291>": 86, "<2600065>": 87, "<2496766>": 88, "<1351>": 89, "<738>": 90, "<2702>": 91, "<210>": 92, "<741093>": 93, "<195>": 94, "<593905>": 95, "<485>": 96, "<283734>": 97, "<373>": 98, "<470>": 99, "<2781243>": 100, "<1311>": 101, "<273384>": 102, "<520>": 103, "<1307>": 104, "<458197>": 105, "<670>": 106, "<1280>": 107, "<1920116>": 108, "<1185650>": 109, "<74426>": 110, "<1915>": 111, "<347834>": 112, "<90370>": 113, "<487>": 114, "<587>": 115, "<1773>": 116, "<28901>": 117, "<158836>": 118, "<192955>": 119, "<1134687>": 120, "<29486>": 121, "<668>": 122, "<1282>": 123, "<2735528>": 124, "<1613>": 125, "<2944939>": 126, "<1329816>": 127, "<263>": 128, "<159>": 129, "<1203599>": 130, "<1522>": 131, "<2735914>": 132, "<220697>": 133, "<480>": 134, "<1408>": 135, "<59201>": 136, "<68213>": 137, "<666>": 138, "<624>": 139, "<1402>": 140, "<714>": 141, "<55601>": 142, "<1597>": 143, "<59823>": 144, "<1194708>": 145, "<1352>": 146, "<2077273>": 147, "<1502>": 148, 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