Source code for transformers.tokenization_marian

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 .tokenization_utils import BatchEncoding, PreTrainedTokenizer

vocab_files_names = {
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
# Example URL

[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_translation_batch(src_texts, tgt_texts=tgt_texts) >>> # keys [input_ids, attention_mask, decoder_input_ids, decoder_attention_mask]. >>> # model(**batch) should work """ vocab_files_names = vocab_files_names model_input_names = ["attention_mask"] # actually attention_mask, decoder_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 = [] 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] def prepare_translation_batch( self, src_texts: List[str], tgt_texts: Optional[List[str]] = None, max_length: Optional[int] = None, pad_to_max_length: bool = True, return_tensors: str = "pt", truncation_strategy="only_first", padding="longest", ) -> BatchEncoding: """Prepare model inputs for translation. For best performance, translate one sentence at a time. Arguments: src_texts: list of src language texts tgt_texts: list of tgt language texts max_length: (None) defer to config (1024 for mbart-large-en-ro) pad_to_max_length: (bool) return_tensors: (str) default "pt" returns pytorch tensors, pass None to return lists. Returns: BatchEncoding: with keys [input_ids, attention_mask, decoder_input_ids, decoder_attention_mask] all shaped bs, seq_len. (BatchEncoding is a dict of string -> tensor or lists). If no tgt_text is specified, the only keys will be input_ids and attention_mask. """ 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, pad_to_max_length=pad_to_max_length, truncation_strategy=truncation_strategy, padding=padding, ) model_inputs: BatchEncoding = self(src_texts, **tokenizer_kwargs) if tgt_texts is None: return model_inputs self.current_spm = self.spm_target decoder_inputs: BatchEncoding = self(tgt_texts, **tokenizer_kwargs) for k, v in decoder_inputs.items(): model_inputs[f"decoder_{k}"] = v 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)