import json
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Dict, List, Optional, Tuple, Union
import sentencepiece
from .file_utils import add_start_docstrings
from .tokenization_utils import BatchEncoding, PreTrainedTokenizer
from .tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
vocab_files_names = {
"source_spm": "source.spm",
"target_spm": "target.spm",
"vocab": "vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
}
# Example URL https://s3.amazonaws.com/models.huggingface.co/bert/Helsinki-NLP/opus-mt-en-de/vocab.json
[docs]class MarianTokenizer(PreTrainedTokenizer):
"""Sentencepiece tokenizer for marian. Source and target languages have different SPM models.
The logic is use the relevant source_spm or target_spm to encode txt as pieces, then look up each piece in a
vocab dictionary.
Examples::
>>> from transformers import MarianTokenizer
>>> tok = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-de')
>>> src_texts = [ "I am a small frog.", "Tom asked his teacher for advice."]
>>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional
>>> batch_enc: BatchEncoding = tok.prepare_seq2seq_batch(src_texts, tgt_texts=tgt_texts)
>>> # keys [input_ids, attention_mask, labels].
>>> # model(**batch) should work
"""
vocab_files_names = vocab_files_names
model_input_names = ["attention_mask"]
language_code_re = re.compile(">>.+<<") # type: re.Pattern
def __init__(
self,
vocab,
source_spm,
target_spm,
source_lang=None,
target_lang=None,
unk_token="<unk>",
eos_token="</s>",
pad_token="<pad>",
model_max_length=512,
**kwargs
):
super().__init__(
# bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id
model_max_length=model_max_length,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
**kwargs,
)
assert Path(source_spm).exists(), f"cannot find spm source {source_spm}"
self.encoder = load_json(vocab)
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 = {v: k for k, v in self.encoder.items()}
self.source_lang = source_lang
self.target_lang = target_lang
self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]
self.spm_files = [source_spm, target_spm]
# load SentencePiece model for pre-processing
self.spm_source = load_spm(source_spm)
self.spm_target = load_spm(target_spm)
self.current_spm = self.spm_source
# Multilingual target side: default to using first supported language code.
self._setup_normalizer()
def _setup_normalizer(self):
try:
from sacremoses import MosesPunctNormalizer
self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize
except (ImportError, FileNotFoundError):
warnings.warn("Recommended: pip install sacremoses.")
self.punc_normalizer = lambda x: x
def normalize(self, x: str) -> str:
"""Cover moses empty string edge case. They return empty list for '' input!"""
return self.punc_normalizer(x) if x else ""
def _convert_token_to_id(self, token):
return self.encoder.get(token, self.encoder[self.unk_token])
def remove_language_code(self, text: str):
"""Remove language codes like <<fr>> before sentencepiece"""
match = self.language_code_re.match(text)
code: list = [match.group(0)] if match else []
return code, self.language_code_re.sub("", text)
def _tokenize(self, text: str) -> List[str]:
code, text = self.remove_language_code(text)
pieces = self.current_spm.EncodeAsPieces(text)
return code + pieces
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the encoder."""
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Uses target language sentencepiece model"""
return self.spm_target.DecodePieces(tokens)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""Build model inputs from a sequence by appending eos_token_id."""
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 + token_ids_1 + [self.eos_token_id]
[docs] @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
def prepare_seq2seq_batch(
self,
src_texts: List[str],
tgt_texts: Optional[List[str]] = None,
max_length: Optional[int] = None,
max_target_length: Optional[int] = None,
return_tensors: str = "pt",
truncation=True,
padding="longest",
**unused,
) -> BatchEncoding:
"""Prepare model inputs for translation. For best performance, translate one sentence at a time."""
if "" in src_texts:
raise ValueError(f"found empty string in src_texts: {src_texts}")
self.current_spm = self.spm_source
src_texts = [self.normalize(t) for t in src_texts] # this does not appear to do much
tokenizer_kwargs = dict(
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
truncation=truncation,
padding=padding,
)
model_inputs: BatchEncoding = self(src_texts, **tokenizer_kwargs)
if tgt_texts is None:
return model_inputs
if max_target_length is not None:
tokenizer_kwargs["max_length"] = max_target_length
self.current_spm = self.spm_target
model_inputs["labels"] = self(tgt_texts, **tokenizer_kwargs)["input_ids"]
self.current_spm = self.spm_source
return model_inputs
@property
def vocab_size(self) -> int:
return len(self.encoder)
def save_vocabulary(self, save_directory: str) -> Tuple[str]:
"""save vocab file to json and copy spm files from their original path."""
save_dir = Path(save_directory)
assert save_dir.is_dir(), f"{save_directory} should be a directory"
save_json(self.encoder, save_dir / self.vocab_files_names["vocab"])
for orig, f in zip(["source.spm", "target.spm"], self.spm_files):
dest_path = save_dir / Path(f).name
if not dest_path.exists():
copyfile(f, save_dir / orig)
return tuple(save_dir / f for f in self.vocab_files_names)
def get_vocab(self) -> Dict:
vocab = self.encoder.copy()
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self) -> Dict:
state = self.__dict__.copy()
state.update({k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer"]})
return state
def __setstate__(self, d: Dict) -> None:
self.__dict__ = d
self.spm_source, self.spm_target = (load_spm(f) for f in self.spm_files)
self.current_spm = self.spm_source
self._setup_normalizer()
def num_special_tokens_to_add(self, **unused):
"""Just EOS"""
return 1
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
if already_has_special_tokens:
return self._special_token_mask(token_ids_0)
elif token_ids_1 is None:
return self._special_token_mask(token_ids_0) + [1]
else:
return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
def load_spm(path: str) -> sentencepiece.SentencePieceProcessor:
spm = sentencepiece.SentencePieceProcessor()
spm.Load(path)
return spm
def save_json(data, path: str) -> None:
with open(path, "w") as f:
json.dump(data, f, indent=2)
def load_json(path: str) -> Union[Dict, List]:
with open(path, "r") as f:
return json.load(f)