Source code for transformers.models.fsmt.tokenization_fsmt

# coding=utf-8
# Copyright 2019 The Open AI Team Authors and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for FSMT."""

import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

import sacremoses as sm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging

logger = logging.get_logger(__name__)

    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",

    "src_vocab_file": {
        "stas/tiny-wmt19-en-de": ""
    "tgt_vocab_file": {
        "stas/tiny-wmt19-en-de": ""
    "merges_file": {"stas/tiny-wmt19-en-de": ""},

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"stas/tiny-wmt19-en-de": 1024}
    "stas/tiny-wmt19-en-de": {
        "langs": ["en", "de"],
        "model_max_length": 1024,
        "special_tokens_map_file": None,
        "full_tokenizer_file": None,

def get_pairs(word):
    Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs

def replace_unicode_punct(text):
    Port of
    text = text.replace(",", ",")
    text = re.sub(r"。\s*", ". ", text)
    text = text.replace("、", ",")
    text = text.replace("”", '"')
    text = text.replace("“", '"')
    text = text.replace("∶", ":")
    text = text.replace(":", ":")
    text = text.replace("?", "?")
    text = text.replace("《", '"')
    text = text.replace("》", '"')
    text = text.replace(")", ")")
    text = text.replace("!", "!")
    text = text.replace("(", "(")
    text = text.replace(";", ";")
    text = text.replace("1", "1")
    text = text.replace("」", '"')
    text = text.replace("「", '"')
    text = text.replace("0", "0")
    text = text.replace("3", "3")
    text = text.replace("2", "2")
    text = text.replace("5", "5")
    text = text.replace("6", "6")
    text = text.replace("9", "9")
    text = text.replace("7", "7")
    text = text.replace("8", "8")
    text = text.replace("4", "4")
    text = re.sub(r".\s*", ". ", text)
    text = text.replace("~", "~")
    text = text.replace("’", "'")
    text = text.replace("…", "...")
    text = text.replace("━", "-")
    text = text.replace("〈", "<")
    text = text.replace("〉", ">")
    text = text.replace("【", "[")
    text = text.replace("】", "]")
    text = text.replace("%", "%")
    return text

def remove_non_printing_char(text):
    Port of
    output = []
    for char in text:
        cat = unicodedata.category(char)
        if cat.startswith("C"):
    return "".join(output)

# Porting notes:
# this one is modeled after XLMTokenizer
# added:
# - src_vocab_file,
# - tgt_vocab_file,
# - langs,

[docs]class FSMTTokenizer(PreTrainedTokenizer): """ Construct an FAIRSEQ Transformer tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following: - Moses preprocessing and tokenization. - Normalizing all inputs text. - The arguments ``special_tokens`` and the function ``set_special_tokens``, can be used to add additional symbols (like "__classify__") to a vocabulary. - The argument :obj:`langs` defines a pair of languages. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: langs (:obj:`List[str]`): A list of two languages to translate from and to, for instance :obj:`["en", "ru"]`. src_vocab_file (:obj:`str`): File containing the vocabulary for the source language. tgt_vocab_file (:obj:`st`): File containing the vocabulary for the target language. merges_file (:obj:`str`): File containing the merges. do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to lowercase the input when tokenizing. unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. .. note:: When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the :obj:`cls_token`. sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`): The token used for padding, for example when batching sequences of different lengths. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, langs=None, src_vocab_file=None, tgt_vocab_file=None, merges_file=None, do_lower_case=False, unk_token="<unk>", bos_token="<s>", sep_token="</s>", pad_token="<pad>", **kwargs ): super().__init__( langs=langs, src_vocab_file=src_vocab_file, tgt_vocab_file=tgt_vocab_file, merges_file=merges_file, do_lower_case=do_lower_case, unk_token=unk_token, bos_token=bos_token, sep_token=sep_token, pad_token=pad_token, **kwargs, ) self.src_vocab_file = src_vocab_file self.tgt_vocab_file = tgt_vocab_file self.merges_file = merges_file self.do_lower_case = do_lower_case # cache of sm.MosesPunctNormalizer instance self.cache_moses_punct_normalizer = dict() # cache of sm.MosesTokenizer instance self.cache_moses_tokenizer = dict() self.cache_moses_detokenizer = dict() if langs and len(langs) == 2: self.src_lang, self.tgt_lang = langs else: raise ValueError( f"arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. " "Usually that means that tokenizer can't find a mapping for the given model path " "in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer." ) with open(src_vocab_file, encoding="utf-8") as src_vocab_handle: self.encoder = json.load(src_vocab_handle) with open(tgt_vocab_file, encoding="utf-8") as tgt_vocab_handle: tgt_vocab = json.load(tgt_vocab_handle) self.decoder = {v: k for k, v in tgt_vocab.items()} with open(merges_file, encoding="utf-8") as merges_handle: merges ="\n")[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} # hack override def get_vocab(self) -> Dict[str, int]: return self.get_src_vocab() # hack override @property def vocab_size(self) -> int: return self.src_vocab_size def moses_punct_norm(self, text, lang): if lang not in self.cache_moses_punct_normalizer: punct_normalizer = sm.MosesPunctNormalizer(lang=lang) self.cache_moses_punct_normalizer[lang] = punct_normalizer return self.cache_moses_punct_normalizer[lang].normalize(text) def moses_tokenize(self, text, lang): if lang not in self.cache_moses_tokenizer: moses_tokenizer = sm.MosesTokenizer(lang=lang) self.cache_moses_tokenizer[lang] = moses_tokenizer return self.cache_moses_tokenizer[lang].tokenize( text, aggressive_dash_splits=True, return_str=False, escape=True ) def moses_detokenize(self, tokens, lang): if lang not in self.cache_moses_tokenizer: moses_detokenizer = sm.MosesDetokenizer(lang=self.tgt_lang) self.cache_moses_detokenizer[lang] = moses_detokenizer return self.cache_moses_detokenizer[lang].detokenize(tokens) def moses_pipeline(self, text, lang): text = replace_unicode_punct(text) text = self.moses_punct_norm(text, lang) text = remove_non_printing_char(text) return text @property def src_vocab_size(self): return len(self.encoder) @property def tgt_vocab_size(self): return len(self.decoder) def get_src_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def get_tgt_vocab(self): return dict(self.decoder, **self.added_tokens_decoder) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + "</w>",) if token in self.cache: return self.cache[token] pairs = get_pairs(word) if not pairs: return token + "</w>" while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) if word == "\n </w>": word = "\n</w>" self.cache[token] = word return word def _tokenize(self, text, lang="en", bypass_tokenizer=False): """ Tokenize a string given language code using Moses. Details of tokenization: - [sacremoses]( port of Moses - Install with `pip install sacremoses` Args: - lang: ISO language code (default = 'en') (string). Languages should belong of the model supported languages. However, we don't enforce it. - bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False) (bool). If True, we only apply BPE. Returns: List of tokens. """ # ignore `lang` which is currently isn't explicitly passed in and always results in lang=en # if lang != self.src_lang: # raise ValueError(f"Expected lang={self.src_lang}, but got {lang}") lang = self.src_lang if self.do_lower_case: text = text.lower() if bypass_tokenizer: text = text.split() else: text = self.moses_pipeline(text, lang=lang) text = self.moses_tokenize(text, lang=lang) split_tokens = [] for token in text: if token: split_tokens.extend([t for t in self.bpe(token).split(" ")]) return split_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" 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.""" # remove BPE tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens] tokens = "".join(tokens).split() # detokenize text = self.moses_detokenize(tokens, self.tgt_lang) return text
[docs] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A FAIRSEQ Transformer sequence has the following format: - single sequence: ``<s> X </s>`` - pair of sequences: ``<s> A </s> B </s>`` Args: token_ids_0 (:obj:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. """ sep = [self.sep_token_id] # no bos used in fairseq if token_ids_1 is None: return token_ids_0 + sep return token_ids_0 + sep + token_ids_1 + sep
[docs] def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``prepare_for_model`` method. Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the token list is already formatted with special tokens for the model. Returns: :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) # no bos used in fairseq if token_ids_1 is not None: return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return ([0] * len(token_ids_0)) + [1]
[docs] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A FAIRSEQ Transformer sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given sequence(s). Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An FAIRSEQ_TRANSFORMER sequence pair mask has the following format: """ sep = [self.sep_token_id] # no bos used in fairseq if token_ids_1 is None: return len(token_ids_0 + sep) * [0] return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return src_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"] ) tgt_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"] ) merges_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(src_vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, ensure_ascii=False)) with open(tgt_vocab_file, "w", encoding="utf-8") as f: tgt_vocab = {v: k for k, v in self.decoder.items()} f.write(json.dumps(tgt_vocab, ensure_ascii=False)) index = 0 with open(merges_file, "w", encoding="utf-8") as writer: for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return src_vocab_file, tgt_vocab_file, merges_file