tokenization_fsmt.py
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	# 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 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__)

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

	PRETRAINED_VOCAB_FILES_MAP = {
	"src_vocab_file": {
	"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json"
	},
	"tgt_vocab_file": {
	"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json"
	},
	"merges_file": {"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt"},
	}

	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"stas/tiny-wmt19-en-de": 1024}
	PRETRAINED_INIT_CONFIGURATION = {
	"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
	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,


	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 = 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
	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 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

	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

	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:
	if token_ids_1 is not None:
	raise ValueError(
	"You should not supply a second sequence if the provided sequence of "
	"ids is already formatted 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]

	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]

	def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
	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, (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(
	"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

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