line-distilbert-base-japanese-finetuned-aio_v2 / distilbert_japanese_tokenizer.py

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	# coding=utf-8

	# Copyright 2023 LINE Corporation.
	#
	# 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.

	# Almost copied from [transformers.BertJapaneseTokenizer](https://github.com/huggingface/transformers/blob/v4.26.1/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#)
	# This code is distributed under the Apache License 2.0.

	"""Tokenization classes."""


	import collections
	import copy
	import os
	import unicodedata
	from typing import Any, Dict, List, Optional, Tuple

	from transformers.tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
	from transformers.utils import is_sentencepiece_available, logging

	try:
	import sentencepiece as spm
	except ModuleNotFoundError as error:
	raise error.__class__(
	"The sentencepiece is not installed. "
	"See https://github.com/google/sentencepiece for installation."
	)



	logger = logging.get_logger(__name__)

	VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "spm_file": "spiece.model"}

	SPIECE_UNDERLINE = "▁"

	PRETRAINED_VOCAB_FILES_MAP = {
	"vocab_file": {
	"cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/vocab.txt",
	"cl-tohoku/bert-base-japanese-whole-word-masking": (
	"https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/vocab.txt"
	),
	"cl-tohoku/bert-base-japanese-char": (
	"https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/vocab.txt"
	),
	"cl-tohoku/bert-base-japanese-char-whole-word-masking": (
	"https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/vocab.txt"
	),
	}
	}

	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
	"cl-tohoku/bert-base-japanese": 512,
	"cl-tohoku/bert-base-japanese-whole-word-masking": 512,
	"cl-tohoku/bert-base-japanese-char": 512,
	"cl-tohoku/bert-base-japanese-char-whole-word-masking": 512,
	}

	PRETRAINED_INIT_CONFIGURATION = {
	"cl-tohoku/bert-base-japanese": {
	"do_lower_case": False,
	"word_tokenizer_type": "mecab",
	"subword_tokenizer_type": "wordpiece",
	},
	"cl-tohoku/bert-base-japanese-whole-word-masking": {
	"do_lower_case": False,
	"word_tokenizer_type": "mecab",
	"subword_tokenizer_type": "wordpiece",
	},
	"cl-tohoku/bert-base-japanese-char": {
	"do_lower_case": False,
	"word_tokenizer_type": "mecab",
	"subword_tokenizer_type": "character",
	},
	"cl-tohoku/bert-base-japanese-char-whole-word-masking": {
	"do_lower_case": False,
	"word_tokenizer_type": "mecab",
	"subword_tokenizer_type": "character",
	},
	}


	# Copied from transformers.models.bert.tokenization_bert.load_vocab
	def load_vocab(vocab_file):
	"""Loads a vocabulary file into a dictionary."""
	vocab = collections.OrderedDict()
	with open(vocab_file, "r", encoding="utf-8") as reader:
	tokens = reader.readlines()
	for index, token in enumerate(tokens):
	token = token.rstrip("\n")
	vocab[token] = index
	return vocab


	# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
	def whitespace_tokenize(text):
	"""Runs basic whitespace cleaning and splitting on a piece of text."""
	text = text.strip()
	if not text:
	return []
	tokens = text.split()
	return tokens


	class DistilBertJapaneseTokenizer(PreTrainedTokenizer):
	r"""
	Construct a BERT tokenizer for Japanese text.

	This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer
	to: this superclass for more information regarding those methods.

	Args:
	vocab_file (`str`):
	Path to a one-wordpiece-per-line vocabulary file.
	spm_file (`str`, optional):
	Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm or .model
	extension) that contains the vocabulary.
	do_lower_case (`bool`, optional, defaults to `True`):
	Whether to lower case the input. Only has an effect when do_basic_tokenize=True.
	do_word_tokenize (`bool`, optional, defaults to `True`):
	Whether to do word tokenization.
	do_subword_tokenize (`bool`, optional, defaults to `True`):
	Whether to do subword tokenization.
	word_tokenizer_type (`str`, optional, defaults to `"basic"`):
	Type of word tokenizer. Choose from ["basic", "mecab", "sudachi", "jumanpp"].
	subword_tokenizer_type (`str`, optional, defaults to `"wordpiece"`):
	Type of subword tokenizer. Choose from ["wordpiece", "character", "sentencepiece",].
	mecab_kwargs (`dict`, optional):
	Dictionary passed to the `MecabTokenizer` constructor.
	sudachi_kwargs (`dict`, optional):
	Dictionary passed to the `SudachiTokenizer` constructor.
	jumanpp_kwargs (`dict`, optional):
	Dictionary passed to the `JumanppTokenizer` constructor.
	"""

	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,
	vocab_file,
	spm_file=None,
	do_lower_case=False,
	do_word_tokenize=True,
	do_subword_tokenize=True,
	word_tokenizer_type="basic",
	subword_tokenizer_type="wordpiece",
	never_split=None,
	unk_token="[UNK]",
	sep_token="[SEP]",
	pad_token="[PAD]",
	cls_token="[CLS]",
	mask_token="[MASK]",
	mecab_kwargs=None,
	sudachi_kwargs=None,
	jumanpp_kwargs=None,
	**kwargs
	):
	super().__init__(
	spm_file=spm_file,
	unk_token=unk_token,
	sep_token=sep_token,
	pad_token=pad_token,
	cls_token=cls_token,
	mask_token=mask_token,
	do_lower_case=do_lower_case,
	do_word_tokenize=do_word_tokenize,
	do_subword_tokenize=do_subword_tokenize,
	word_tokenizer_type=word_tokenizer_type,
	subword_tokenizer_type=subword_tokenizer_type,
	never_split=never_split,
	mecab_kwargs=mecab_kwargs,
	sudachi_kwargs=sudachi_kwargs,
	jumanpp_kwargs=jumanpp_kwargs,
	**kwargs,
	)

	if subword_tokenizer_type == "sentencepiece":
	if not os.path.isfile(spm_file):
	raise ValueError(
	f"Can't find a vocabulary file at path '{spm_file}'. To load the vocabulary from a Google"
	" pretrained model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
	)
	self.spm_file = spm_file
	else:
	if not os.path.isfile(vocab_file):
	raise ValueError(
	f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google"
	" pretrained model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
	)
	self.vocab = load_vocab(vocab_file)
	self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])

	self.do_word_tokenize = do_word_tokenize
	self.word_tokenizer_type = word_tokenizer_type
	self.lower_case = do_lower_case
	self.never_split = never_split
	self.mecab_kwargs = copy.deepcopy(mecab_kwargs)
	self.sudachi_kwargs = copy.deepcopy(sudachi_kwargs)
	self.jumanpp_kwargs = copy.deepcopy(jumanpp_kwargs)
	if do_word_tokenize:
	if word_tokenizer_type == "basic":
	self.word_tokenizer = BasicTokenizer(
	do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=False
	)
	elif word_tokenizer_type == "mecab":
	self.word_tokenizer = MecabTokenizer(
	do_lower_case=do_lower_case, never_split=never_split, **(mecab_kwargs or {})
	)
	elif word_tokenizer_type == "sudachi":
	self.word_tokenizer = SudachiTokenizer(
	do_lower_case=do_lower_case, never_split=never_split, **(sudachi_kwargs or {})
	)
	elif word_tokenizer_type == "jumanpp":
	self.word_tokenizer = JumanppTokenizer(
	do_lower_case=do_lower_case, never_split=never_split, **(jumanpp_kwargs or {})
	)
	else:
	raise ValueError(f"Invalid word_tokenizer_type '{word_tokenizer_type}' is specified.")

	self.do_subword_tokenize = do_subword_tokenize
	self.subword_tokenizer_type = subword_tokenizer_type
	if do_subword_tokenize:
	if subword_tokenizer_type == "wordpiece":
	self.subword_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
	elif subword_tokenizer_type == "character":
	self.subword_tokenizer = CharacterTokenizer(vocab=self.vocab, unk_token=self.unk_token)
	elif subword_tokenizer_type == "sentencepiece":
	self.subword_tokenizer = SentencepieceTokenizer(vocab=self.spm_file, unk_token=self.unk_token)
	else:
	raise ValueError(f"Invalid subword_tokenizer_type '{subword_tokenizer_type}' is specified.")

	@property
	def do_lower_case(self):
	return self.lower_case

	def __getstate__(self):
	state = dict(self.__dict__)
	if self.word_tokenizer_type in ["mecab", "sudachi", "jumanpp"]:
	del state["word_tokenizer"]
	return state

	def __setstate__(self, state):
	self.__dict__ = state
	if self.word_tokenizer_type == "mecab":
	self.word_tokenizer = MecabTokenizer(
	do_lower_case=self.do_lower_case, never_split=self.never_split, **(self.mecab_kwargs or {})
	)
	elif self.word_tokenizer_type == "sudachi":
	self.word_tokenizer = SudachiTokenizer(
	do_lower_case=self.do_lower_case, never_split=self.never_split, **(self.sudachi_kwargs or {})
	)
	elif self.word_tokenizer_type == "jumanpp":
	self.word_tokenizer = JumanppTokenizer(
	do_lower_case=self.do_lower_case, never_split=self.never_split, **(self.jumanpp_kwargs or {})
	)

	def _tokenize(self, text):
	if self.do_word_tokenize:
	tokens = self.word_tokenizer.tokenize(text, never_split=self.all_special_tokens)
	else:
	tokens = [text]

	if self.do_subword_tokenize:
	split_tokens = [sub_token for token in tokens for sub_token in self.subword_tokenizer.tokenize(token)]
	else:
	split_tokens = tokens

	return split_tokens

	@property
	def vocab_size(self):
	if self.subword_tokenizer_type == "sentencepiece":
	return len(self.subword_tokenizer.sp_model)
	return len(self.vocab)

	def get_vocab(self):
	if self.subword_tokenizer_type == "sentencepiece":
	vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
	vocab.update(self.added_tokens_encoder)
	return vocab
	return dict(self.vocab, **self.added_tokens_encoder)

	def _convert_token_to_id(self, token):
	"""Converts a token (str) in an id using the vocab."""
	if self.subword_tokenizer_type == "sentencepiece":
	return self.subword_tokenizer.sp_model.PieceToId(token)
	return self.vocab.get(token, self.vocab.get(self.unk_token))

	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the vocab."""
	if self.subword_tokenizer_type == "sentencepiece":
	return self.subword_tokenizer.sp_model.IdToPiece(index)
	return self.ids_to_tokens.get(index, self.unk_token)

	def convert_tokens_to_string(self, tokens):
	"""Converts a sequence of tokens (string) in a single string."""
	if self.subword_tokenizer_type == "sentencepiece":
	return self.subword_tokenizer.sp_model.decode(tokens)
	out_string = " ".join(tokens).replace(" ##", "").strip()
	return out_string

	# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.build_inputs_with_special_tokens
	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 BERT sequence has the following format:

	- single sequence: `[CLS] X [SEP]`
	- pair of sequences: `[CLS] A [SEP] B [SEP]`

	Args:
	token_ids_0 (`List[int]`):
	List of IDs to which the special tokens will be added.
	token_ids_1 (`List[int]`, optional):
	Optional second list of IDs for sequence pairs.

	Returns:
	`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
	"""
	if token_ids_1 is None:
	return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
	cls = [self.cls_token_id]
	sep = [self.sep_token_id]
	return cls + token_ids_0 + sep + token_ids_1 + sep

	# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_special_tokens_mask
	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 (`List[int]`):
	List of IDs.
	token_ids_1 (`List[int]`, optional):
	Optional second list of IDs for sequence pairs.
	already_has_special_tokens (`bool`, optional, defaults to `False`):
	Whether or not the token list is already formatted with special tokens for the model.

	Returns:
	`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
	)

	if token_ids_1 is not None:
	return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
	return [1] + ([0] * len(token_ids_0)) + [1]

	# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.create_token_type_ids_from_sequences
	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 BERT 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`, this method only returns the first portion of the mask (0s).

	Args:
	token_ids_0 (`List[int]`):
	List of IDs.
	token_ids_1 (`List[int]`, optional):
	Optional second list of IDs for sequence pairs.

	Returns:
	`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
	"""
	sep = [self.sep_token_id]
	cls = [self.cls_token_id]
	if token_ids_1 is None:
	return len(cls + token_ids_0 + sep) * [0]
	return len(cls + 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 os.path.isdir(save_directory):
	if self.subword_tokenizer_type == "sentencepiece":
	vocab_file = os.path.join(
	save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["spm_file"]
	)
	else:
	vocab_file = os.path.join(
	save_directory,
	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"],
	)
	else:
	vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory

	if self.subword_tokenizer_type == "sentencepiece":
	with open(vocab_file, "wb") as writer:
	content_spiece_model = self.subword_tokenizer.sp_model.serialized_model_proto()
	writer.write(content_spiece_model)
	else:
	with open(vocab_file, "w", encoding="utf-8") as writer:
	index = 0
	for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
	if index != token_index:
	logger.warning(
	f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
	" Please check that the vocabulary is not corrupted!"
	)
	index = token_index
	writer.write(token + "\n")
	index += 1
	return (vocab_file,)


	class MecabTokenizer:
	"""Runs basic tokenization with MeCab morphological parser."""

	def __init__(
	self,
	do_lower_case=False,
	never_split=None,
	normalize_text=True,
	mecab_dic: Optional[str] = "unidic_lite",
	mecab_option: Optional[str] = None,
	):
	"""
	Constructs a MecabTokenizer.

	Args:
	do_lower_case: (optional) boolean (default True)
	Whether to lowercase the input.
	never_split: (optional) list of str
	Kept for backward compatibility purposes. Now implemented directly at the base class level (see
	[`PreTrainedTokenizer.tokenize`]) List of tokens not to split.
	normalize_text: (optional) boolean (default True)
	Whether to apply unicode normalization to text before tokenization.
	mecab_dic: (optional) string (default "unidic_lite")
	Name of dictionary to be used for MeCab initialization. If you are using a system-installed dictionary,
	set this option to `None` and modify mecab_option.
	mecab_option: (optional) string
	String passed to MeCab constructor.
	"""
	self.do_lower_case = do_lower_case
	self.never_split = never_split if never_split is not None else []
	self.normalize_text = normalize_text

	try:
	import fugashi
	except ModuleNotFoundError as error:
	raise error.__class__(
	"You need to install fugashi to use MecabTokenizer. "
	"See https://pypi.org/project/fugashi/ for installation."
	)

	mecab_option = mecab_option or ""

	if mecab_dic is not None:
	if mecab_dic == "unidic_lite":
	try:
	import unidic_lite
	except ModuleNotFoundError as error:
	raise error.__class__(
	"The unidic_lite dictionary is not installed. "
	"See https://github.com/polm/unidic-lite for installation."
	)

	dic_dir = unidic_lite.DICDIR
	else:
	raise ValueError("Invalid mecab_dic is specified.")

	mecabrc = os.path.join(dic_dir, "mecabrc")
	mecab_option = f'-d "{dic_dir}" -r "{mecabrc}" ' + mecab_option

	self.mecab = fugashi.GenericTagger(mecab_option)

	def tokenize(self, text, never_split=None, **kwargs):
	"""Tokenizes a piece of text."""
	if self.normalize_text:
	text = unicodedata.normalize("NFKC", text)

	never_split = self.never_split + (never_split if never_split is not None else [])
	tokens = []

	for word in self.mecab(text):
	token = word.surface

	if self.do_lower_case and token not in never_split:
	token = token.lower()

	tokens.append(token)

	return tokens


	class CharacterTokenizer:
	"""Runs Character tokenization."""

	def __init__(self, vocab, unk_token, normalize_text=True):
	"""
	Constructs a CharacterTokenizer.

	Args:
	vocab:
	Vocabulary object.
	unk_token: str
	A special symbol for out-of-vocabulary token.
	normalize_text: (`optional`) boolean (default True)
	Whether to apply unicode normalization to text before tokenization.
	"""
	self.vocab = vocab
	self.unk_token = unk_token
	self.normalize_text = normalize_text

	def tokenize(self, text):
	"""
	Tokenizes a piece of text into characters.

	For example, `input = "apple""` wil return as output `["a", "p", "p", "l", "e"]`.

	Args:
	text: A single token or whitespace separated tokens.
	This should have already been passed through BasicTokenizer.

	Returns:
	A list of characters.
	"""
	if self.normalize_text:
	text = unicodedata.normalize("NFKC", text)

	output_tokens = []
	for char in text:
	if char not in self.vocab:
	output_tokens.append(self.unk_token)
	continue

	output_tokens.append(char)

	return output_tokens


	# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
	class BasicTokenizer(object):
	"""
	Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).

	Args:
	do_lower_case (`bool`, optional, defaults to `True`):
	Whether or not to lowercase the input when tokenizing.
	never_split (`Iterable`, optional):
	Collection of tokens which will never be split during tokenization. Only has an effect when
	`do_basic_tokenize=True`
	tokenize_chinese_chars (`bool`, optional, defaults to `True`):
	Whether or not to tokenize Chinese characters.

	This should likely be deactivated for Japanese (see this
	[issue](https://github.com/huggingface/transformers/issues/328)).
	strip_accents (`bool`, optional):
	Whether or not to strip all accents. If this option is not specified, then it will be determined by the
	value for `lowercase` (as in the original BERT).
	"""

	def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
	if never_split is None:
	never_split = []
	self.do_lower_case = do_lower_case
	self.never_split = set(never_split)
	self.tokenize_chinese_chars = tokenize_chinese_chars
	self.strip_accents = strip_accents

	def tokenize(self, text, never_split=None):
	"""
	Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
	WordPieceTokenizer.

	Args:
	never_split (`List[str]`, optional)
	Kept for backward compatibility purposes. Now implemented directly at the base class level (see
	[`PreTrainedTokenizer.tokenize`]) List of token not to split.
	"""
	# union() returns a new set by concatenating the two sets.
	never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
	text = self._clean_text(text)

	# This was added on November 1st, 2018 for the multilingual and Chinese
	# models. This is also applied to the English models now, but it doesn't
	# matter since the English models were not trained on any Chinese data
	# and generally don't have any Chinese data in them (there are Chinese
	# characters in the vocabulary because Wikipedia does have some Chinese
	# words in the English Wikipedia.).
	if self.tokenize_chinese_chars:
	text = self._tokenize_chinese_chars(text)
	orig_tokens = whitespace_tokenize(text)
	split_tokens = []
	for token in orig_tokens:
	if token not in never_split:
	if self.do_lower_case:
	token = token.lower()
	if self.strip_accents is not False:
	token = self._run_strip_accents(token)
	elif self.strip_accents:
	token = self._run_strip_accents(token)
	split_tokens.extend(self._run_split_on_punc(token, never_split))

	output_tokens = whitespace_tokenize(" ".join(split_tokens))
	return output_tokens

	def _run_strip_accents(self, text):
	"""Strips accents from a piece of text."""
	text = unicodedata.normalize("NFD", text)
	output = []
	for char in text:
	cat = unicodedata.category(char)
	if cat == "Mn":
	continue
	output.append(char)
	return "".join(output)

	def _run_split_on_punc(self, text, never_split=None):
	"""Splits punctuation on a piece of text."""
	if never_split is not None and text in never_split:
	return [text]
	chars = list(text)
	i = 0
	start_new_word = True
	output = []
	while i < len(chars):
	char = chars[i]
	if _is_punctuation(char):
	output.append([char])
	start_new_word = True
	else:
	if start_new_word:
	output.append([])
	start_new_word = False
	output[-1].append(char)
	i += 1

	return ["".join(x) for x in output]

	def _tokenize_chinese_chars(self, text):
	"""Adds whitespace around any CJK character."""
	output = []
	for char in text:
	cp = ord(char)
	if self._is_chinese_char(cp):
	output.append(" ")
	output.append(char)
	output.append(" ")
	else:
	output.append(char)
	return "".join(output)

	def _is_chinese_char(self, cp):
	"""Checks whether CP is the codepoint of a CJK character."""
	# This defines a "chinese character" as anything in the CJK Unicode block:
	# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
	#
	# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
	# despite its name. The modern Korean Hangul alphabet is a different block,
	# as is Japanese Hiragana and Katakana. Those alphabets are used to write
	# space-separated words, so they are not treated specially and handled
	# like the all of the other languages.
	if (
	(cp >= 0x4E00 and cp <= 0x9FFF)
	or (cp >= 0x3400 and cp <= 0x4DBF) #
	or (cp >= 0x20000 and cp <= 0x2A6DF) #
	or (cp >= 0x2A700 and cp <= 0x2B73F) #
	or (cp >= 0x2B740 and cp <= 0x2B81F) #
	or (cp >= 0x2B820 and cp <= 0x2CEAF) #
	or (cp >= 0xF900 and cp <= 0xFAFF)
	or (cp >= 0x2F800 and cp <= 0x2FA1F) #
	): #
	return True

	return False

	def _clean_text(self, text):
	"""Performs invalid character removal and whitespace cleanup on text."""
	output = []
	for char in text:
	cp = ord(char)
	if cp == 0 or cp == 0xFFFD or _is_control(char):
	continue
	if _is_whitespace(char):
	output.append(" ")
	else:
	output.append(char)
	return "".join(output)


	# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
	class WordpieceTokenizer(object):
	"""Runs WordPiece tokenization."""

	def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
	self.vocab = vocab
	self.unk_token = unk_token
	self.max_input_chars_per_word = max_input_chars_per_word

	def tokenize(self, text):
	"""
	Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
	tokenization using the given vocabulary.

	For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.

	Args:
	text: A single token or whitespace separated tokens. This should have
	already been passed through BasicTokenizer.

	Returns:
	A list of wordpiece tokens.
	"""

	output_tokens = []
	for token in whitespace_tokenize(text):
	chars = list(token)
	if len(chars) > self.max_input_chars_per_word:
	output_tokens.append(self.unk_token)
	continue

	is_bad = False
	start = 0
	sub_tokens = []
	while start < len(chars):
	end = len(chars)
	cur_substr = None
	while start < end:
	substr = "".join(chars[start:end])
	if start > 0:
	substr = "##" + substr
	if substr in self.vocab:
	cur_substr = substr
	break
	end -= 1
	if cur_substr is None:
	is_bad = True
	break
	sub_tokens.append(cur_substr)
	start = end

	if is_bad:
	output_tokens.append(self.unk_token)
	else:
	output_tokens.extend(sub_tokens)
	return output_tokens


	class SentencepieceTokenizer(object):
	"""
	Runs sentencepiece tokenization. Based on transformers.models.albert.tokenization_albert.AlbertTokenizer.
	"""

	def __init__(
	self,
	vocab,
	unk_token,
	do_lower_case=False,
	remove_space=True,
	keep_accents=True,
	sp_model_kwargs: Optional[Dict[str, Any]] = None,
	):
	self.vocab = vocab
	self.unk_token = unk_token
	self.do_lower_case = do_lower_case
	self.remove_space = remove_space
	self.keep_accents = keep_accents

	self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
	self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
	self.sp_model.Load(self.vocab)

	def preprocess_text(self, inputs):
	if self.remove_space:
	outputs = " ".join(inputs.strip().split())
	else:
	outputs = inputs
	outputs = outputs.replace("``", '"').replace("''", '"')

	if not self.keep_accents:
	outputs = unicodedata.normalize("NFKD", outputs)
	outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
	if self.do_lower_case:
	outputs = outputs.lower()

	return outputs

	def tokenize(self, text):
	"""
	Tokenizes text by sentencepiece. Based on [SentencePiece](https://github.com/google/sentencepiece).
	Tokenization needs the given vocabulary.

	Args:
	text: A string needs to be tokenized.

	Returns:
	A list of sentencepiece tokens.
	"""
	text = self.preprocess_text(text)
	pieces = self.sp_model.encode(text, out_type=str)
	new_pieces = []
	for piece in pieces:
	if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
	cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
	if len(cur_pieces[0]) == 1:
	cur_pieces = cur_pieces[1:]
	else:
	cur_pieces[0] = cur_pieces[0][1:]
	cur_pieces.append(piece[-1])
	new_pieces.extend(cur_pieces)
	else:
	new_pieces.append(piece)

	return new_pieces