text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  #
  # Copyright (c) 2019 Baidu.com, Inc. All Rights Reserved
  #
  
  """
  File: main.py
  Author: liwei(liwei85@baidu.com)
  Date: 2019-12-01 16:21
  Desc: Tokenization classes.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import unicodedata
  import six
  import os
  import json
  import regex as re
  from functools import lru_cache
  
  
  def convert_to_unicode(text):
      """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
      if six.PY3:
          if isinstance(text, str):
              return text
          elif isinstance(text, bytes):
              try:
                  return text.decode("utf-8", "ignore")
              except:
                  return text.decode("gb18030", "ignore")
          else:
              raise ValueError("Unsupported string type: %s" % (type(text)))
      elif six.PY2:
          if isinstance(text, str):
              try:
                  return text.decode("utf-8", "ignore")
              except:
                  return text.decode("gb18030", "ignore")
          elif isinstance(text, unicode):
              return text
          else:
              raise ValueError("Unsupported string type: %s" % (type(text)))
      else:
          raise ValueError("Not running on Python2 or Python 3?")
  
  
  def printable_text(text):
      """Returns text encoded in a way suitable for print or `tf.logging`."""
  
      # These functions want `str` for both Python2 and Python3, but in one case
      # it's a Unicode string and in the other it's a byte string.
      if six.PY3:
          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)))
      elif six.PY2:
          if isinstance(text, str):
              return text
          elif isinstance(text, unicode):
              return text.encode("utf-8")
          else:
              raise ValueError("Unsupported string type: %s" % (type(text)))
      else:
          raise ValueError("Not running on Python2 or Python 3?")
  
  
  def load_vocab(vocab_file):
      """Loads a vocabulary file into a dictionary."""
      vocab = collections.OrderedDict()
      fin = open(vocab_file)
      for num, line in enumerate(fin):
          items = convert_to_unicode(line.strip()).split("\t")
          if len(items) > 2:
              break
          token = items[0]
          index = items[1] if len(items) == 2 else num
          token = token.strip()
          vocab[token] = int(index)
      return vocab
  
  
  def convert_by_vocab(vocab, items):
      """Converts a sequence of [tokens|ids] using the vocab."""
      output = []
      for item in items:
          output.append(vocab[item])
      return output
  
  
  def convert_tokens_to_ids(vocab, tokens):
      """convert tokens to vocab ids"""
      return convert_by_vocab(vocab, tokens)
  
  
  def convert_ids_to_tokens(inv_vocab, ids):
      """convert vocab ids to tokens"""
      return convert_by_vocab(inv_vocab, ids)
  
  
  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
  
  
  @lru_cache()
  def bytes_to_unicode():
      """
      Returns list of utf-8 byte and a corresponding list of unicode strings.
      The reversible bpe codes work on unicode strings.
      This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
      When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
      This is a signficant percentage of your normal, say, 32K bpe vocab.
      To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
      And avoids mapping to whitespace/control characters the bpe code barfs on.
      """
      bs = list(range(33, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 1))
      cs = bs[:]
      n = 0
      for b in range(2 ** 8):
          if b not in bs:
              bs.append(b)
              cs.append(2 ** 8 + n)
              n += 1
      cs = [chr(n) for n in cs]
      return dict(zip(bs, cs))
  
  
  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
  
  
  class Tokenizer(object):
      """RoBERTa Tokenizer"""
      def __init__(self, encoder, bpe_merges, errors='replace'):
          self.encoder = encoder
          self.decoder = {v: k for k, v in self.encoder.items()}
          self.errors = errors  # how to handle errors in decoding
          self.byte_encoder = bytes_to_unicode()
          self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
          self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
          self.cache = {}
  
          # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
          self.int_token = re.compile(r"^[0-9]+$")
  
      def bpe(self, token):
          """bpe tokenizing"""
          if token in self.cache:
              return self.cache[token]
          word = tuple(token)
          pairs = get_pairs(word)
  
          if not pairs:
              return token
  
          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)
                      new_word.extend(word[i:j])
                      i = j
                  except:
                      new_word.extend(word[i:])
                      break
  
                  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)
          self.cache[token] = word
          return word
  
      def encode(self, text):
          """bpe encoding"""
          bpe_tokens = []
          for token in re.findall(self.pat, text):
              token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
              bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
          return bpe_tokens
  
      def decode(self, tokens):
          """bpe decoding"""
          decoded_tokens = []
          for token in tokens:
              if self.int_token.match(token):
                  decoded_tokens.append(self.decoder[int(token)])
              else:
                  decoded_tokens.append(str(token))
          text = ''.join(decoded_tokens)
          text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
          return text
  
  
  class GptBpeTokenizer(object):
      """GptBpeTokenizer"""
      def __init__(self, vocab_file=None, encoder_json_file=None, vocab_bpe_file=None, do_lower_case=True):
          if vocab_file is None:
              vocab_file = "./bpe/gpt2_bpe/vocab.txt"
          if encoder_json_file is None:
              encoder_json_file = "./bpe/gpt2_bpe/encoder.json"
          if vocab_bpe_file is None:
              vocab_bpe_file = "./bpe/gpt2_bpe/vocab.bpe"
  
          with open(encoder_json_file, 'r') as f:
              encoder = json.load(f)
          with open(vocab_bpe_file, 'r', encoding="utf-8") as f:
              bpe_data = f.read()
  
          bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
          self.gptbpe_tokenizer = Tokenizer(encoder=encoder, bpe_merges=bpe_merges)
          self.vocab = load_vocab(vocab_file)
          self.inv_vocab = {v: k for k, v in self.vocab.items()}
  
          self.cls_token = '[CLS]'
          self.pad_token = '[PAD]'
          self.sep_token = '[SEP]'
          self.unk_token = '[UNK]'
          self.mask_token = '[MASK]'
  
          self.cls_token_id = self.vocab['[CLS]']
          self.pad_token_id = self.vocab['[PAD]']
          self.sep_token_id = self.vocab['[SEP]']
          self.unk_token_id = self.vocab['[UNK]']
          self.mask_token_id = self.vocab['[MASK]']
  
      def tokenize(self, text):
          """tokenize text to a list of tokens"""
          return [str(token) for token in self.gptbpe_tokenizer.encode(text)]
  
      def convert_tokens_to_ids(self, tokens):
          """convert tokens to vocab ids"""
          return convert_by_vocab(self.vocab, tokens)
  
      def convert_ids_to_tokens(self, ids):
          """convert vocab ids to tokens"""
          return convert_by_vocab(self.inv_vocab, ids)
  
      def vocab_size(self):
          """get the vocab size"""
          return len(self.vocab)
  
  
  class FullTokenizer(object):
      """Runs end-to-end tokenziation."""
  
      def __init__(self, vocab_file, do_lower_case=True):
          self.vocab = load_vocab(vocab_file)
          self.inv_vocab = {v: k for k, v in self.vocab.items()}
          self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
  
      def tokenize(self, text):
          """tokenize the text"""
          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 convert_tokens_to_ids(self, tokens):
          """convert tokens to vocab ids"""
          return convert_by_vocab(self.vocab, tokens)
  
      def convert_ids_to_tokens(self, ids):
          """convert vocab ids to tokens"""
          return convert_by_vocab(self.inv_vocab, ids)
  
  
  class CharTokenizer(object):
      """Runs end-to-end tokenziation."""
  
      def __init__(self, vocab_file, do_lower_case=True):
          self.vocab = load_vocab(vocab_file)
          self.inv_vocab = {v: k for k, v in self.vocab.items()}
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
  
      def tokenize(self, text):
          """tokenize the text"""
          split_tokens = []
          for token in text.lower().split(" "):
              for sub_token in self.wordpiece_tokenizer.tokenize(token):
                  split_tokens.append(sub_token)
  
          return split_tokens
  
      def convert_tokens_to_ids(self, tokens):
          """convert tokens to vocab ids"""
          return convert_by_vocab(self.vocab, tokens)
  
      def convert_ids_to_tokens(self, ids):
          """convert vocab ids to tokens"""
          return convert_by_vocab(self.inv_vocab, ids)
  
  
  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._tokenize_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._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."""
          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 tokenziation."""
  
      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 _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):
      """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 test_gpt_bpe_tokenizer():
      """test the tokenizer"""
      vocab_file = "./bpe/gpt2_bpe/vocab.txt"
      encoder_json_file = "./bpe/gpt2_bpe/encoder.json"
      vocab_bpe_file = "./bpe/gpt2_bpe/vocab.bpe"
      tokenizer = GptBpeTokenizer(vocab_file, encoder_json_file, vocab_bpe_file)
  
      # text = "Hello world!"
      text = "Want to contribute to PaddlePaddle/ERNIE?"
      print(text)
      text = convert_to_unicode(text)
      tokens = tokenizer.tokenize(text)
      # tokens = ["[UNK]", "[PAD]", "[CLS]", "[SEP]"]
      print(tokens)
      token_ids = tokenizer.convert_tokens_to_ids(tokens)
      print(token_ids)
      tokens = tokenizer.convert_ids_to_tokens(token_ids)
      print(tokens)
      tokens = tokenizer.gptbpe_tokenizer.decode(tokens)
      print(tokens)
      tokens = tokenizer.gptbpe_tokenizer.encode(text)
      print(tokens)
      tokens = tokenizer.gptbpe_tokenizer.decode(tokens)
      print(tokens)
  
  
  if __name__ == "__main__":
      test_gpt_bpe_tokenizer()
  