byt5-Korean-small / tokenizer.py

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	# coding=utf-8
	#
	# Everdoubling LLC.
	#
	# 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.
	#
	# The following code is modified from HuggingFace's ByT5 Tokenizer: transformers/models/byt5/tokenization_byt5.py
	#
	""" Tokenization class for model ByT5."""


	import warnings
	from typing import Dict, List, Optional, Tuple, Union

	from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
	from transformers.models.byt5.tokenization_byt5 import ByT5Tokenizer

	class ByT5KoreanTokenizer(PreTrainedTokenizer):
	"""
	Construct a ByT5Korean tokenizer.
	On top of ByT5's simple raw bytes utf-8 encoding, ByT5Korean adds extra tokens for Korean jamo.

	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:
	eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
	The end of sequence token.

	.. note::

	When building a sequence using special tokens, this is not the token that is used for the end of
	sequence. The token used is the :obj:`sep_token`.
	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.
	pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
	The token used for padding, for example when batching sequences of different lengths.
	extra_ids (:obj:`int`, `optional`, defaults to 100):
	Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
	accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
	indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
	like in ByT5 preprocessing see `here
	<https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
	additional_special_tokens (:obj:`List[str]`, `optional`):
	Additional special tokens used by the tokenizer.
	"""

	model_input_names = ["input_ids", "attention_mask"]

	def __init__(
	self,
	eos_token="</s>",
	unk_token="<unk>",
	pad_token="<pad>",
	extra_ids=57,
	additional_special_tokens=None,
	**kwargs
	) -> None:
	# Add extra_ids to the special token list
	if extra_ids > 0 and additional_special_tokens is None:
	additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
	elif extra_ids > 0 and additional_special_tokens is not None:
	# Check that we have the right number of extra_id special tokens
	extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
	if extra_tokens != extra_ids:
	raise ValueError(
	f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to ByT5Tokenizer. "
	"In this case the additional_special_tokens must include the extra_ids tokens"
	)

	pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
	eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
	unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token

	super().__init__(
	eos_token=eos_token,
	unk_token=unk_token,
	pad_token=pad_token,
	extra_ids=extra_ids,
	additional_special_tokens=additional_special_tokens,
	**kwargs,
	)

	self._extra_ids = extra_ids

	# Add the special tokens (including extra_ids)
	for token in self.all_special_tokens:
	self.tokens_trie.add(token)

	self._utf_vocab_size = 2 ** 8 # utf is 8 bits
	self._utf_vocab_size += 19 + 21 + 28 # korean jamo

	# define special tokens dict
	self.special_tokens_encoder: Dict[int, str] = {
	self.pad_token: 0,
	self.eos_token: 1,
	self.unk_token: 2,
	}
	self._num_special_tokens = len(self.special_tokens_encoder)
	n = len(additional_special_tokens)
	for i, token in enumerate(additional_special_tokens):
	self.special_tokens_encoder[token] = self.vocab_size + i - n
	self.special_tokens_decoder: Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}

	@property
	def vocab_size(self):
	return self._utf_vocab_size + self._num_special_tokens + self._extra_ids

	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
	)

	# normal case: some special tokens
	if token_ids_1 is None:
	return ([0] * len(token_ids_0)) + [1]
	return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

	def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
	"""Do not add eos again if user already added it."""
	if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
	warnings.warn(
	f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added."
	)
	return token_ids
	else:
	return token_ids + [self.eos_token_id]

	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. ByT5 does not
	make use of token type ids, therefore a list of zeros is returned.

	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 zeros.
	"""
	eos = [self.eos_token_id]

	if token_ids_1 is None:
	return len(token_ids_0 + eos) * [0]
	return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

	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 sequence has the following format:

	- single sequence: ``X </s>``
	- pair of sequences: ``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.
	"""
	token_ids_0 = self._add_eos_if_not_present(token_ids_0)
	if token_ids_1 is None:
	return token_ids_0
	else:
	token_ids_1 = self._add_eos_if_not_present(token_ids_1)
	return token_ids_0 + token_ids_1

	def _convert_char_to_tokens_Korean(self, c):
	o = ord(c)
	if 44032 <= o and o <= 55203: # 44032: 가, 55203: 힣
	o -= 44032
	return [chr(256 + (o // 588)), chr(256 + 19 + ((o % 588) // 28)), chr(256 + 19 + 21 + (o % 28))]
	return [chr(i) for i in c.encode("utf-8")]

	def _tokenize(self, text: str) -> List[str]:
	"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
	if text in self.all_special_tokens:
	return [text]
	# return [self.special_tokens_encoder[text]]
	# tokens = [chr(i) for i in text.encode("utf-8")]
	# return tokens
	return sum([self._convert_char_to_tokens_Korean(c) for c in text], [])

	def _convert_token_to_id(self, token):
	"""Converts a token (str) in an id using the vocab."""
	if token in self.special_tokens_encoder:
	token_id = self.special_tokens_encoder[token]
	elif token in self.added_tokens_encoder:
	token_id = self.added_tokens_encoder[token]
	# else:
	# token_id = token + self._num_special_tokens
	elif len(token) != 1:
	token_id = self.unk_token_id
	else:
	token_id = ord(token) + self._num_special_tokens
	return token_id

	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the vocab."""
	if index in self.special_tokens_decoder:
	token = self.special_tokens_decoder[index]
	else:
	token = chr(index - self._num_special_tokens)
	return token

	def convert_tokens_to_string(self, tokens):
	"""Converts a sequence of tokens (string) in a single string."""
	bstring = b""
	ids = [ord(t[0]) for t in tokens]
	for i in range(len(ids)-2):
	if 256 <= ids[i] and ids[i] < 256+19 and 256+19 <= ids[i+1] and ids[i+1] < 256+19+21 and 256+19+21 <= ids[i+2] and ids[i+2] < 256+19+21+28:
	tokens[i] = chr(44032 + (ids[i]-256)2128 + (ids[i+1]-256-19)*28 + (ids[i+2]-256-19-21))
	tokens[i+1] = None
	tokens[i+2] = None
	for token in tokens:
	if token == None:
	continue
	if token in self.special_tokens_decoder:
	tok_string = self.special_tokens_decoder[token].encode("utf-8")
	elif token in self.added_tokens_decoder:
	tok_string = self.special_tokens_decoder[token].encode("utf-8")
	elif token in self.special_tokens_encoder:
	tok_string = token.encode("utf-8")
	elif token in self.added_tokens_encoder:
	tok_string = token.encode("utf-8")
	else:
	if type(token) == str and ord(token) >= 256:
	tok_string = token.encode("utf-8")
	else:
	tok_string = bytes([ord(token) if type(token) == str else min(255, token)])
	bstring += tok_string
	string = bstring.decode("utf-8", errors="ignore")
	return string

	# ByT5KoreanTokenizer has no vocab file
	def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
	return ()


	if __name__ == "__main__":
	tokenizer = ByT5KoreanTokenizer()
	text = "This is a test <extra_id_0> of the 가나힣 안녕하세요 <extra_id_1>."
	tokenized_text = tokenizer.tokenize(text)
	print(tokenized_text)
	print(tokenizer(text))
	print(tokenizer.convert_tokens_to_ids(tokenized_text))
	print(tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids(tokenized_text)))
	print(tokenizer.convert_tokens_to_string(tokenized_text))