File size: 9,446 Bytes
9d452e1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 |
import os
import json
import torch
import numpy as np
from transformers import BatchEncoding
from typing import Dict, List, Tuple, Union
from sentencepiece import SentencePieceProcessor
_PATH = os.path.dirname(os.path.realpath(__file__))
class IndicTransTokenizer:
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>",
direction="indic-en",
model_max_length=256,
):
self.model_max_length = model_max_length
self.supported_langs = [
"asm_Beng",
"ben_Beng",
"brx_Deva",
"doi_Deva",
"eng_Latn",
"gom_Deva",
"guj_Gujr",
"hin_Deva",
"kan_Knda",
"kas_Arab",
"kas_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",
]
self.src_vocab_fp = (
src_vocab_fp
if (src_vocab_fp is not None)
else os.path.join(_PATH, direction, "dict.SRC.json")
)
self.tgt_vocab_fp = (
tgt_vocab_fp
if (tgt_vocab_fp is not None)
else os.path.join(_PATH, direction, "dict.TGT.json")
)
self.src_spm_fp = (
src_spm_fp
if (src_spm_fp is not None)
else os.path.join(_PATH, direction, "model.SRC")
)
self.tgt_spm_fp = (
tgt_spm_fp
if (tgt_spm_fp is not None)
else os.path.join(_PATH, direction, "model.TGT")
)
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)
def is_special_token(self, x: str):
return (x == self.pad_token) or (x == self.bos_token) or (x == self.eos_token)
def get_vocab_size(self, src: bool) -> int:
"""Returns the size of the vocabulary"""
return len(self.encoder) if src else len(self.decoder)
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 _convert_token_to_id(self, token: str, src: bool) -> int:
"""Converts an token (str) into an index (integer) using the source/target vocabulary map."""
return (
self.encoder.get(token, self.encoder[self.unk_token])
if src
else self.decoder.get(token, self.encoder[self.unk_token])
)
def _convert_id_to_token(self, index: int, src: bool) -> str:
"""Converts an index (integer) into a token (str) using the source/target vocabulary map."""
return (
self.encoder_rev.get(index, self.unk_token)
if src
else self.decoder_rev.get(index, self.unk_token)
)
def _convert_tokens_to_string(self, tokens: List[str], src: bool) -> str:
"""Uses sentencepiece model for detokenization"""
if src:
if tokens[0] in self.supported_langs and tokens[1] in self.supported_langs:
tokens = tokens[2:]
return " ".join(tokens)
else:
return " ".join(tokens)
def _remove_translation_tags(self, text: str) -> Tuple[List, str]:
"""Removes the translation tags before text normalization and tokenization."""
tokens = text.split(" ")
return tokens[:2], " ".join(tokens[2:])
def _tokenize_src_line(self, line: str) -> List[str]:
"""Tokenizes a source line."""
tags, text = self._remove_translation_tags(line)
tokens = self.src_spm.encode(text, out_type=str)
return tags + tokens
def _tokenize_tgt_line(self, line: str) -> List[str]:
"""Tokenizes a target line."""
return self.tgt_spm.encode(line, out_type=str)
def tokenize(self, text: str, src: bool) -> List[str]:
"""Tokenizes a string into tokens using the source/target vocabulary."""
return self._tokenize_src_line(text) if src else self._tokenize_tgt_line(text)
def batch_tokenize(self, batch: List[str], src: bool) -> List[List[str]]:
"""Tokenizes a list of strings into tokens using the source/target vocabulary."""
return [self.tokenize(line, src) for line in batch]
def _create_attention_mask(self, ids: List[int], max_seq_len: int) -> List[int]:
"""Creates a attention mask for the input sequence."""
return ([0] * (max_seq_len - len(ids))) + ([1] * (len(ids) + 1))
def _pad_batch(self, tokens: List[str], max_seq_len: int) -> List[str]:
"""Pads a batch of tokens and adds BOS/EOS tokens."""
return (
([self.pad_token] * (max_seq_len - len(tokens))) + tokens + [self.eos_token]
)
def _decode_line(self, ids: List[int], src: bool) -> List[str]:
return [self._convert_id_to_token(_id, src) for _id in ids]
def _encode_line(self, tokens: List[str], src: bool) -> List[int]:
return [self._convert_token_to_id(token, src) for token in tokens]
def _strip_special_tokens(self, tokens: List[str]) -> List[str]:
return [token for token in tokens if not self.is_special_token(token)]
def _single_input_preprocessing(
self, tokens: List[str], src: bool, max_seq_len: int
) -> Tuple[List[int], List[int], int]:
"""Tokenizes a string into tokens and also converts them into integers using source/target vocabulary map."""
attention_mask = self._create_attention_mask(tokens, max_seq_len)
padded_tokens = self._pad_batch(tokens, max_seq_len)
input_ids = self._encode_line(padded_tokens, src)
return input_ids, attention_mask
def _single_output_postprocessing(self, ids: List[int], src: bool) -> str:
"""Detokenizes a list of integer ids into a string using the source/target vocabulary."""
tokens = self._decode_line(ids, src)
tokens = self._strip_special_tokens(tokens)
return self._convert_tokens_to_string(tokens, src)
def __call__(
self,
batch: Union[list, str],
src: bool,
truncation: bool = False,
padding: str = "longest",
max_length: int = None,
return_tensors: str = "pt",
return_attention_mask: bool = True,
return_length: bool = False,
) -> BatchEncoding:
"""Tokenizes a string into tokens and also converts them into integers using source/target vocabulary map."""
assert padding in [
"longest",
"max_length",
], "padding should be either 'longest' or 'max_length'"
if not isinstance(batch, list):
raise TypeError(
f"batch must be a list, but current batch is of type {type(batch)}"
)
# tokenize the source sentences
batch = self.batch_tokenize(batch, src)
# truncate the sentences if needed
if truncation and max_length is not None:
batch = [ids[:max_length] for ids in batch]
lengths = [len(ids) for ids in batch]
max_seq_len = max(lengths) if padding == "longest" else max_length
input_ids, attention_mask = zip(
*[
self._single_input_preprocessing(
tokens=tokens, src=src, max_seq_len=max_seq_len
)
for tokens in batch
]
)
_data = {"input_ids": input_ids}
if return_attention_mask:
_data["attention_mask"] = attention_mask
if return_length:
_data["lengths"] = lengths
return BatchEncoding(_data, tensor_type=return_tensors)
def batch_decode(
self, batch: Union[list, torch.Tensor], src: bool
) -> List[List[str]]:
"""Detokenizes a list of integer ids or a tensor into a list of strings using the source/target vocabulary."""
if isinstance(batch, torch.Tensor):
batch = batch.detach().cpu().tolist()
return [self._single_output_postprocessing(ids=ids, src=src) for ids in batch]
|