Source code for transformers.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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for FSMT."""


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

import sacremoses as sm

from .file_utils import add_start_docstrings
from .tokenization_utils import BatchEncoding, PreTrainedTokenizer
from .tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING


logger = logging.getLogger(__name__)

VOCAB_FILES_NAMES = {
    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",
}

PRETRAINED_VOCAB_FILES_MAP = {}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
PRETRAINED_INIT_CONFIGURATION = {}


def get_pairs(word):
    """
    Return set of symbol pairs in a word.
    word is represented as tuple of symbols (symbols being variable-length strings)
    """
    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 https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
    """
    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 https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
    """
    output = []
    for char in text:
        cat = unicodedata.category(char)
        if cat.startswith("C"):
            continue
        output.append(char)
    return "".join(output)


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


[docs]class FSMTTokenizer(PreTrainedTokenizer): """ BPE tokenizer for FSMT (fairseq transformer) See: https://github.com/pytorch/fairseq/tree/master/examples/wmt19 - Moses preprocessing & tokenization for most supported languages - (optionally) lower case & normalize all inputs text - argument ``special_tokens`` and function ``set_special_tokens``, can be used to add additional symbols \ (ex: "__classify__") to a vocabulary - `langs` defines a pair of languages This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users should refer to the superclass for more information regarding methods. Args: langs (:obj:`List[str]`): a list of two languages to translate from and to, e.g. ``["en", "ru"]``. src_vocab_file (:obj:`string`): Source language vocabulary file. tgt_vocab_file (:obj:`string`): Target language vocabulary file. merges_file (:obj:`string`): Merges file. do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to lowercase the input when tokenizing. unk_token (:obj:`string`, `optional`, defaults to "<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:`string`, `optional`, defaults to "<s>"): The beginning of sequence token that was used during pre-training. 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:`string`, `optional`, defaults to "</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:`string`, `optional`, defaults to "<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 def __init__( self, langs=None, src_vocab_file=None, tgt_vocab_file=None, merges_file=None, unk_token="<unk>", bos_token="<s>", sep_token="</s>", pad_token="<pad>", **kwargs ): super().__init__( 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 # 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 = merges_handle.read().split("\n")[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} # hack override
[docs] 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](https://github.com/alvations/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 tokenization_utils.py 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 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)
[docs] 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]: """ Retrieves 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`` methods. 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`): Set to True if 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: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formated with special tokens for the model." ) return list( map( lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0, ) ) # 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]: """ 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: :: 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 token_ids_1 is None, 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). """ 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] @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 type(src_texts) is not list: raise ValueError("src_texts is expected to be a list") if "" in src_texts: raise ValueError(f"found empty string in src_texts: {src_texts}") 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 model_inputs["labels"] = self(tgt_texts, **tokenizer_kwargs)["input_ids"] return model_inputs
[docs] def save_vocabulary(self, save_directory): """ Save the vocabulary and special tokens file to a directory. Args: save_directory (:obj:`str`): The directory in which to save the vocabulary. Returns: :obj:`Tuple(str)`: Paths to the files saved. """ if not os.path.isdir(save_directory): logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) return src_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES["src_vocab_file"]) tgt_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES["tgt_vocab_file"]) merges_file = os.path.join(save_directory, 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( "Saving vocabulary to {}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!".format(merges_file) ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return src_vocab_file, tgt_vocab_file, merges_file