class NonAutoregressiveMachineTranslationAdapter(Adapter): __provider__ = 'narnmt' @classmethod def parameters(cls): parameters = super().parameters() parameters.update({ 'vocabulary_file': PathField(), 'merges_file': PathField(), 'output_name': StringField(optional=True, default=None), 'sos_symbol': StringField(optional=True, default='<s>'), 'eos_symbol': StringField(optional=True, default='</s>'), 'pad_symbol': StringField(optional=True, default='<pad>'), 'remove_extra_symbols': BoolField(optional=True, default=True) }) return parameters def configure(self): if isinstance(SentencePieceBPETokenizer, UnsupportedPackage): SentencePieceBPETokenizer.raise_error(self.__provider__) self.tokenizer = SentencePieceBPETokenizer( str(self.get_value_from_config('vocabulary_file')), str(self.get_value_from_config('merges_file'))) self.remove_extra_symbols = self.get_value_from_config( 'remove_extra_symbols') self.idx = {} for s in ['sos', 'eos', 'pad']: self.idx[s] = str(self.get_value_from_config(s + '_symbol')) self.output_name = self.get_value_from_config('output_name') if self.output_name is None: self.output_name = self.output_blob def process(self, raw, identifiers, frame_meta): raw_outputs = self._extract_predictions(raw, frame_meta) translation = raw_outputs[self.output_name] results = [] for identifier, tokens in zip(identifiers, translation): sentence = self.tokenizer.decode(tokens) if self.remove_extra_symbols: for s in self.idx.values(): sentence = sentence.replace(s, '') results.append( MachineTranslationPrediction(identifier, sentence.lstrip().split(' '))) return results
class TokenizerWrapper: def __init__(self, tok_type, unk_token, sep_token, cls_token, pad_token, mask_token): self.tok_type = tok_type if self.tok_type == 'bpe': self.tokenizer = ByteLevelBPETokenizer() elif self.tok_type == 'wordpiece': self.tokenizer = BertWordPieceTokenizer(unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token) elif self.tok_type == 'sentencepiece': self.tokenizer = SentencePieceBPETokenizer(unk_token=unk_token) def train(self, data_file, vocab_size, special_tokens): if self.tok_type in ['bpe', 'wordpiece', 'sentencepiece']: self.tokenizer.train([data_file], vocab_size=vocab_size, special_tokens=special_tokens) def tokenize(self, text): if self.tok_type in ['bpe', 'wordpiece', 'sentencepiece']: return self.tokenizer.encode(text).tokens elif self.tok_type == 'word': return nltk.tokenize.word_tokenize(text) elif self.tok_type == 'char': return [c for c in text] else: raise Exception('Unknown tokenizer: ' + self.tok_type) def decode(self, tokens, blank_token): if self.tok_type in ['bpe', 'wordpiece', 'sentencepiece']: ids = [self.tokenizer.token_to_id(t) for t in tokens] ids = [ i if i != None else self.tokenizer.token_to_id(blank_token) for i in ids ] return self.tokenizer.decode(ids, skip_special_tokens=False) elif self.tok_type == 'word': return ' '.join(tokens) elif self.tok_type == 'char': return ''.join(tokens) else: raise Exception('Unknown tokenizer: ' + self.tok_type)
class Tokenizer: """ Sentence tokenizer. Arguments: path (str): path to tokenizer's model folder. max_tokens (int): max tokens. """ def __init__(self, path, max_tokens): self.logger = log.getLogger("Tokenizer") self.logger.info("loading tokenizer") self.logger.info("path: " + path) self.logger.info("max_tokens: " + str(max_tokens)) self.tokenizer = SentencePieceBPETokenizer( os.path.join(path, "vocab.json"), os.path.join(path, "merges.txt") ) self.max_tokens = max_tokens self.idx = {} for s in ['</s>', '<s>', '<pad>']: self.idx[s] = self.tokenizer.token_to_id(s) def encode(self, sentence): """ Encode method for sentence. Arguments: sentence (str): sentence. Returns: tokens (np.array): encoded sentence in tokneized format. """ tokens = self.tokenizer.encode(sentence).ids return self._extend_tokens(tokens) def decode(self, tokens, remove_repeats=True): """ Decode method for tokens. Arguments: tokens (np.array): sentence in tokenized format. remove_repeats (bool): remove repeated words. Returns: sentence (str): output sentence. """ sentence = self.tokenizer.decode(tokens) for s in self.idx.keys(): sentence = sentence.replace(s, '') if remove_repeats: sentence = self._remove_repeats(sentence) return sentence.lstrip() def _extend_tokens(self, tokens): """ Extend tokens. Arguments: tokens (np.array): sentence in tokenized format. Returns: tokens (np.array): extended tokens. """ tokens = [self.idx['<s>']] + tokens + [self.idx['</s>']] pad_length = self.max_tokens - len(tokens) if pad_length > 0: tokens = tokens + [self.idx['<pad>']] * pad_length return tokens def _remove_repeats(self, sentence): """ Remove repeated words. Arguments: sentence (str): sentence. Returns: sentence (str): sentence in lowercase without repeated words. """ tokens = sentence.lower().split() return " ".join(key for key, _ in itertools.groupby(tokens))
class BPEVocabulary(Vocabulary): """ Represents a SentencePiece vocabulary for c2s. """ def __init__(self, args: Namespace): super().__init__() self.target_encoder = SentencePieceBPETokenizer( args.target_vocab, args.target_merges) self.subtoken_encoder = SentencePieceBPETokenizer( args.subtoken_vocab, args.subtoken_merges) # self.target_encoder.add_special_tokens( # [self.EOS_TOKEN, self.SOS_TOKEN, self.PAD_TOKEN] # ) # self.subtoken_encoder.add_special_tokens([self.EOS_TOKEN, self.PAD_TOKEN]) with open(args.node_dict, "rb") as f: self.node_to_index = pickle.load(f) self.index_to_node = {v: k for k, v in self.node_to_index.items()} def target_vocab_size(self): # print(self.target_encoder.num_special_tokens_to_add()) return self.target_encoder.get_vocab_size() + 4 def node_vocab_size(self): # print(self.target_encoder.num_special_tokens_to_add()) return len(self.node_to_index) + 2 def terminal_vocab_size(self): return self.subtoken_encoder.get_vocab_size() + 4 def add_special_target_token(self, token: str): self.target_encoder.add_special_tokens([token]) def add_special_terminal_token(self, token: str): self.subtoken_encoder.add_special_tokens([token]) def encode_node(self, token_or_tokens): if isinstance(token_or_tokens, str): return self.node_to_index.get(token_or_tokens, self.node_to_index[self.UNK_TOKEN]) else: return list(map(self.encode_node, token_or_tokens)) def decode_node(self, index_or_indices): if isinstance(index_or_indices, int): return self.index_to_node[index_or_indices] else: return list(map(self.decode_node, index_or_indices)) def encode_target(self, token_or_tokens): if isinstance(token_or_tokens, str): return self.target_encoder.token_to_id(token_or_tokens) else: return self.target_encoder.encode(" ".join(token_or_tokens)).ids def decode_target(self, index_or_indices): if isinstance(index_or_indices, int): return self.target_encoder.id_to_token(index_or_indices) else: return self.target_encoder.decode(index_or_indices) def encode_terminal(self, token_or_tokens): if isinstance(token_or_tokens, str): return self.subtoken_encoder.token_to_id(token_or_tokens) else: return self.subtoken_encoder.encode(" ".join(token_or_tokens)).ids def decode_terminal(self, index_or_indices): if isinstance(index_or_indices, int): return self.terminal_encoder.id_to_token(index_or_indices) else: return self.terminal_encoder.decode(index_or_indices)