text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HugginFace 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.
  #
  #
  # 한국어 WordPiece 단위 BERT를 위한 Tokenization Class
  # 수정: joonho.lim
  # 일자: 2019-05-23
  #
  """Tokenization classes."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import unicodedata
  import os
  import logging
  
  from pytorch_transformers.file_utils import cached_path
  # from pytorch_pretrained_bert.file_utils import cached_path
  # from .file_utils import cached_path
  
  logger = logging.getLogger(__name__)
  
  PRETRAINED_VOCAB_ARCHIVE_MAP = {
  	'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
  	'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
  	'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
  	'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
  	'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
  	'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
  	'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
  }
  PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
  	'bert-base-uncased': 512,
  	'bert-large-uncased': 512,
  	'bert-base-cased': 512,
  	'bert-large-cased': 512,
  	'bert-base-multilingual-uncased': 512,
  	'bert-base-multilingual-cased': 512,
  	'bert-base-chinese': 512,
  }
  VOCAB_NAME = 'vocab.txt'
  
  
  def load_vocab(vocab_file):
  	"""Loads a vocabulary file into a dictionary."""
  	vocab = collections.OrderedDict()
  	index = 0
  	with open(vocab_file, "r", encoding="utf-8") as reader:
  		while True:
  			token = reader.readline()
  			if not token:
  				break
  			
  			### joonho.lim @ 2019-03-15
  			if token.find('n_iters=') == 0 or token.find('max_length=') == 0 :
  				continue
  			token = token.split('\t')[0]
  			
  			token = token.strip()
  			vocab[token] = index
  			index += 1
  	return vocab
  
  
  def whitespace_tokenize(text):
  	"""Runs basic whitespace cleaning and splitting on a peice of text."""
  	text = text.strip()
  	if not text:
  		return []
  	tokens = text.split()
  	return tokens
  
  
  class BertTokenizer(object):
  	"""Runs end-to-end tokenization: punctuation splitting + wordpiece"""
  
  	def __init__(self, vocab_file, do_lower_case=True, max_len=None,
  				 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
  		if not os.path.isfile(vocab_file):
  			raise ValueError(
  				"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
  				"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
  		self.vocab = load_vocab(vocab_file)
  		self.ids_to_tokens = collections.OrderedDict(
  			[(ids, tok) for tok, ids in self.vocab.items()])
  		self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
  											  never_split=never_split)
  		self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
  		self.max_len = max_len if max_len is not None else int(1e12)
  
  	def tokenize(self, text):
  		split_tokens = []
  		for token in self.basic_tokenizer.tokenize(text):
  			### joonho.lim @ 2019-03-15
  			token += '_'
  			for sub_token in self.wordpiece_tokenizer.tokenize(token):
  				split_tokens.append(sub_token)
  		return split_tokens
  
  	def convert_tokens_to_ids(self, tokens):
  		"""Converts a sequence of tokens into ids using the vocab."""
  		ids = []
  		for token in tokens:
  			ids.append(self.vocab[token])
  		if len(ids) > self.max_len:
  			raise ValueError(
  				"Token indices sequence length is longer than the specified maximum "
  				" sequence length for this BERT model ({} > {}). Running this"
  				" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
  			)
  		return ids
  
  	def convert_ids_to_tokens(self, ids):
  		"""Converts a sequence of ids in wordpiece tokens using the vocab."""
  		tokens = []
  		for i in ids:
  			tokens.append(self.ids_to_tokens[i])
  		return tokens
  
  	@classmethod
  	def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
  		"""
  		Instantiate a PreTrainedBertModel from a pre-trained model file.
  		Download and cache the pre-trained model file if needed.
  		"""
  		if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
  			vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
  		else:
  			vocab_file = pretrained_model_name
  		if os.path.isdir(vocab_file):
  			vocab_file = os.path.join(vocab_file, VOCAB_NAME)
  		# redirect to the cache, if necessary
  		try:
  			resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
  		except FileNotFoundError:
  			logger.error(
  				"Model name '{}' was not found in model name list ({}). "
  				"We assumed '{}' was a path or url but couldn't find any file "
  				"associated to this path or url.".format(
  					pretrained_model_name,
  					', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
  					vocab_file))
  			return None
  		if resolved_vocab_file == vocab_file:
  			logger.info("loading vocabulary file {}".format(vocab_file))
  		else:
  			logger.info("loading vocabulary file {} from cache at {}".format(
  				vocab_file, resolved_vocab_file))
  		if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
  			# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
  			# than the number of positional embeddings
  			max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
  			kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
  		# Instantiate tokenizer.
  		tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
  		return tokenizer
  
  
  class BasicTokenizer(object):
  	"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
  
  	def __init__(self,
  				 do_lower_case=True,
  				 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
  		"""Constructs a BasicTokenizer.
  
  		Args:
  		  do_lower_case: Whether to lower case the input.
  		"""
  		self.do_lower_case = do_lower_case
  		self.never_split = never_split
  
  	def tokenize(self, text):
  		"""Tokenizes a piece of text."""
  		text = self._clean_text(text)
  		### joonho.lim @ 2019-03-15
  		# # # 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.).
  		# # text = self._tokenize_chinese_chars(text)
  		orig_tokens = whitespace_tokenize(text)
  		split_tokens = []
  		for token in orig_tokens:
  			if self.do_lower_case and token not in self.never_split:
  				token = token.lower()
  				token = self._run_strip_accents(token)
  			split_tokens.extend(self._run_split_on_punc(token))
  
  		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):
  		"""Splits punctuation on a piece of text."""
  		if text in self.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)
  
  
  class WordpieceTokenizer(object):
  	"""Runs WordPiece tokenization."""
  
  	def __init__(self, vocab, unk_token="[UNK]", 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"
  		  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])
  					### joonho.lim @ 2019-03-15
  					# if start > 0:
  						# substr = "##" + substr
  					# print ( '[substr]\t%s\t%s\t%d\t%d' % ( substr, substr in self.vocab, start, end))
  					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
  
  
  def _is_whitespace(char):
  	"""Checks whether `chars` is a whitespace character."""
  	# \t, \n, and \r are technically contorl characters but we treat them
  	# as whitespace since they are generally considered as such.
  	if char == " " or char == "\t" or char == "\n" or char == "\r":
  		return True
  	cat = unicodedata.category(char)
  	if cat == "Zs":
  		return True
  	return False
  
  
  def _is_control(char):
  	"""Checks whether `chars` is a control character."""
  	# These are technically control characters but we count them as whitespace
  	# characters.
  	if char == "\t" or char == "\n" or char == "\r":
  		return False
  	cat = unicodedata.category(char)
  	if cat.startswith("C"):
  		return True
  	return False
  
  
  def _is_punctuation(char):
  	### joonho.lim @ 2019-03-15
  	return char == ' '
  	
  	# """Checks whether `chars` is a punctuation character."""
  	# cp = ord(char)
  	# # We treat all non-letter/number ASCII as punctuation.
  	# # Characters such as "^", "$", and "`" are not in the Unicode
  	# # Punctuation class but we treat them as punctuation anyways, for
  	# # consistency.
  	# if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
  			# (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
  		# return True
  	# cat = unicodedata.category(char)
  	# if cat.startswith("P"):
  		# return True
  	# return False
  