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import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import sentencepiece
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"spm_file": "mitre_spm.model",
"tokenizer_config_file": "tokenizer_config.json",
}
# follow iso639-2
FAIRSEQ_LANGUAGE_CODES = ["en", "de", "nl", "sv", "da", "af", "fr", "es", "it", "pt", "ro", "ru", "cs", "pl", "bg", "uk", "id", "jv", "ms", "tl", "ja", "zh", "ko", "vi"]
# This is the tokenizer of MITRE.
# This code is modified from transformers.models.m2m_100.tokenization_m2m_100.M2M100Tokenizer
class MitreTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
prefix_tokens: List[int] = []
suffix_tokens: List[int] = []
def __init__(
self,
vocab_file,
spm_file,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
pad_token="<pad>",
unk_token="<unk>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
fairseq_language_code = FAIRSEQ_LANGUAGE_CODES
self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}
additional_special_tokens = kwargs.pop("additional_special_tokens", [])
for lang_code in fairseq_language_code:
token = self.get_lang_token(lang_code)
if token not in additional_special_tokens:
additional_special_tokens.append(token)
self.vocab_file = vocab_file
self.encoder = load_json(vocab_file)
self.decoder = {v: k for k, v in self.encoder.items()}
self.spm_file = spm_file
self.sp_model = load_spm(spm_file, self.sp_model_kwargs)
self.encoder_size = len(self.encoder)
self.lang_token_to_id = {
self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)
}
self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}
# default
self.tgt_lang = "en"
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
unk_token=unk_token,
pad_token=pad_token,
sp_model_kwargs=self.sp_model_kwargs,
additional_special_tokens=additional_special_tokens,
**kwargs,
)
@property
def vocab_size(self) -> int:
return len(self.encoder)
def get_vocab(self) -> 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: str) -> List[str]:
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(token, self.encoder[self.unk_token])
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the decoder."""
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(current_sub_tokens) + token
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
def __getstate__(self) -> Dict:
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d: Dict) -> None:
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
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 self.prefix_tokens + token_ids_0 + self.suffix_tokens
return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
def _switch_to_input_mode(self):
self.set_tgt_lang_special_tokens(self.tgt_lang)
def _switch_to_target_mode(self):
self.clear_lang_special_tokens()
def clear_lang_special_tokens(self) -> None:
self.prefix_tokens = []
self.suffix_tokens = [self.eos_token_id]
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
"""Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
lang_token = self.get_lang_token(tgt_lang)
self.cur_lang_id = self.lang_token_to_id[lang_token]
self.prefix_tokens = [self.cur_lang_id]
self.suffix_tokens = [self.eos_token_id]
def get_lang_token(self, lang: str) -> str:
return self.lang_code_to_token[lang]
def get_lang_id(self, lang: str) -> int:
lang_token = self.get_lang_token(lang)
return self.lang_token_to_id[lang_token]
def encode_source_tokens_to_input_ids(self, inputs, target_language="en"):
"""pads + target language id + source tokens id + eos id"""
self.tgt_lang = target_language
input_ids = self.__call__(inputs, add_special_tokens=True, padding_side='left', padding=True, return_attention_mask=False, return_tensors="pt")
return input_ids["input_ids"]
def encode_source_tokens_to_input_ids_with_different_tags(self, inputs_text, target_languages_list: list):
"""
'encode_source_tokens_to_input_ids' only supports a language tag,
but sevenral in a batch could have different language tags.
"""
self.tgt_lang = "en"
input_ids = self.__call__(inputs_text, add_special_tokens=True, padding_side='left', padding=True, return_attention_mask=False, return_tensors="pt")["input_ids"]
_, max_indices = torch.max(input_ids, dim=1)
input_ids[torch.arange(max_indices.shape[0]), max_indices] = torch.LongTensor([self.lang_token_to_id[self.get_lang_token(lang_code)] for lang_code in target_languages_list])
return input_ids
def encode_target_tokens_to_labels(self, inputs_text):
"""target tokens id + eos id + pads"""
input_ids = self.__call__(text_target=inputs_text, add_special_tokens=True, padding_side='right', padding=True, return_attention_mask=False, return_tensors="pt")
return input_ids["input_ids"]
def encode_target_tokens_to_input_ids(self, inputs_text):
"""eos id + target tokens id + pads, namely, left shifted"""
input_ids = self.__call__(text_target=inputs_text, add_special_tokens=False, padding_side='right', padding=True, return_attention_mask=False, return_tensors="pt")
labels_without_eos = input_ids["input_ids"]
return torch.cat((torch.full((labels_without_eos.size(0), 1), self.eos_token_id), labels_without_eos), dim=1)
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
spm.Load(str(path))
return spm
def load_json(path: str) -> Union[Dict, List]:
with open(path, "r") as f:
return json.load(f)
def save_json(data, path: str) -> None:
with open(path, "w") as f:
json.dump(data, f, indent=2)
MitreTokenizer.register_for_auto_class("AutoTokenizer") |