text: # coding=utf-8
  
  # /************************************************************************************
  # ***
  # ***    File Author: Dell, 2018-11-28 14:04:41
  # ***
  # ************************************************************************************/
  
  import collections
  import unicodedata
  import os
  import transformer.Constants as Constants
  
  class Vocab(object):
      """Vocab class."""
  
      def __init__(self, vocab_file, do_lower_case=True):
          """Init vocab class."""
          self.do_lower_case = do_lower_case
          self.vocab = self._load(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)
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
          self.vocab_size = len(self.vocab)
  
      def __str__(self):
          """Return vocab size."""
          return "vocab size: %d" % self.vocab_size
  
      def lower_case(self):
          """Check token do lower case."""
          return self.do_lower_case
  
      def size(self):
          """Vocab Size."""
          return self.vocab_size
  
      def checkoov(self, text):
          """Check out of vocab."""
          for token in self.basic_tokenizer.tokenize(text):
              if self.vocab.get(token) is None:
                  print("OOV: ", token)
  
      def split(self, text):
          """Split text via vocab."""
          split_tokens = []
          for token in self.basic_tokenizer.tokenize(text):
              for sub_token in self.wordpiece_tokenizer.tokenize(token):
                  split_tokens.append(sub_token)
          return split_tokens
  
      def indexs(self, tokens):
          """Converts a sequence of tokens into index list using the vocab."""
          ids = []
          for token in tokens:
              ids.append(self.index(token))
          return ids
  
      def tokens(self, ids):
          """Converts a sequence of ids in wordpiece tokens using the vocab."""
          tokens = []
          for i in ids:
              tokens.append(self.word(i))
          return tokens
  
      def index(self, w):
          return self.vocab.get(w, Constants.UNK)
  
      def word(self, i):
          return self.ids_to_tokens[i] if i >= 0 and i < self.vocab_size else Constants.UNK_WORD
  
      def _load(self, vocab_file):
          """Loads a vocabulary file into dictionary."""
  
          if not os.path.isfile(vocab_file):
              raise ValueError("Can't find a vocabulary file".format(vocab_file))
  
          vocab = collections.OrderedDict()
          index = 0
          with open(vocab_file, "r") as reader:
              while True:
                  token = convert_to_unicode(reader.readline())
                  if not token:
                      break
                  token = token.strip()
                  vocab[token] = index
                  index += 1
          return vocab
  
  
  def convert_to_unicode(text):
      """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
      if isinstance(text, str):
          return text
      elif isinstance(text, bytes):
          return text.decode("utf-8", "ignore")
      else:
          raise ValueError("Unsupported string type: %s" % (type(text)))
  
  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 BasicTokenizer(object):
      """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
  
      def __init__(self, do_lower_case=True):
          """Constructs a BasicTokenizer.
  
          Args:
            do_lower_case: Whether to lower case the input.
          """
          self.do_lower_case = do_lower_case
  
      def tokenize(self, text):
          """Tokenizes a piece of text."""
          text = convert_to_unicode(text)
          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.).
          text = self.split_chinese_chars(text)
          orig_tokens = whitespace_tokenize(text)
          split_tokens = []
          for token in orig_tokens:
              if self.do_lower_case:
                  token = token.lower()
                  token = self.strip_accents(token)
              split_tokens.extend(self.split_on_punc(token))
  
          output_tokens = whitespace_tokenize(" ".join(split_tokens))
          # pdb.set_trace()
          # text = 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'
          # (Pdb) print(output_tokens)
          # ['to', 'whom', 'did', 'the', 'virgin', 'mary', 'allegedly', 'appear', 'in', '1858', 'in', 'lourdes', 'france', '?']        
          return output_tokens
  
      def 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)
              # [Mn] Mark, Nonspacing
              if cat == "Mn":
                  continue
              output.append(char)
          return "".join(output)
  
      def is_punctuation(self, 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
  
      def split_on_punc(self, text):
          """Splits punctuation on a piece of text."""
          chars = list(text)
          i = 0
          start_new_word = True
          output = []
          while i < len(chars):
              char = chars[i]
              if self.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 split_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 is_control(self, 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_whitespace(self, 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 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 self.is_control(char):
                  continue
              if self.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.
          """
  
          text = convert_to_unicode(text)
  
          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
  
  # def test():
  #     vocab = Vocab("model/multilingual/vocab.txt")
  #     print(vocab)
  
  #     tokens = vocab.split("How many tickets are there in your hands? 您手里还有多少张旧船票？")
  #     indexs = vocab.indexs(tokens)
  
  #     print(tokens)
  #     print(indexs)
  #     print(vocab.tokens(indexs))
  
  #     print(vocab.word(Constants.PAD), vocab.word(Constants.MASK))
  #     print(vocab.index("xxxx"))
  