text: # coding=utf-8
  # Copyright 2018 Salesforce and The HuggingFace 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.
  """Tokenization classes for Salesforce CTRL."""
  
  
  import json
  import logging
  import os
  
  import regex as re
  
  from .tokenization_utils import PreTrainedTokenizer
  
  
  logger = logging.getLogger(__name__)
  
  VOCAB_FILES_NAMES = {
      "vocab_file": "vocab.json",
      "merges_file": "merges.txt",
  }
  
  PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
      "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
  }
  
  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "ctrl": 256,
  }
  
  CONTROL_CODES = {
      "Pregnancy": 168629,
      "Christianity": 7675,
      "Explain": 106423,
      "Fitness": 63440,
      "Saving": 63163,
      "Ask": 27171,
      "Ass": 95985,
      "Joke": 163509,
      "Questions": 45622,
      "Thoughts": 49605,
      "Retail": 52342,
      "Feminism": 164338,
      "Writing": 11992,
      "Atheism": 192263,
      "Netflix": 48616,
      "Computing": 39639,
      "Opinion": 43213,
      "Alone": 44967,
      "Funny": 58917,
      "Gaming": 40358,
      "Human": 4088,
      "India": 1331,
      "Joker": 77138,
      "Diet": 36206,
      "Legal": 11859,
      "Norman": 4939,
      "Tip": 72689,
      "Weight": 52343,
      "Movies": 46273,
      "Running": 23425,
      "Science": 2090,
      "Horror": 37793,
      "Confession": 60572,
      "Finance": 12250,
      "Politics": 16360,
      "Scary": 191985,
      "Support": 12654,
      "Technologies": 32516,
      "Teenage": 66160,
      "Event": 32769,
      "Learned": 67460,
      "Notion": 182770,
      "Wikipedia": 37583,
      "Books": 6665,
      "Extract": 76050,
      "Confessions": 102701,
      "Conspiracy": 75932,
      "Links": 63674,
      "Narcissus": 150425,
      "Relationship": 54766,
      "Relationships": 134796,
      "Reviews": 41671,
      "News": 4256,
      "Translation": 26820,
      "multilingual": 128406,
  }
  
  
  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
  
      pairs = set(pairs)
      return pairs
  
  
  class CTRLTokenizer(PreTrainedTokenizer):
      """
      CTRL BPE tokenizer. Peculiarities:
          - Byte-Pair-Encoding
      """
  
      vocab_files_names = VOCAB_FILES_NAMES
      pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
      max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      control_codes = CONTROL_CODES
  
      def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
          super(CTRLTokenizer, self).__init__(unk_token=unk_token, **kwargs)
          self.max_len_single_sentence = (
              self.max_len
          )  # no default special tokens - you can update this value if you add special tokens
          self.max_len_sentences_pair = (
              self.max_len
          )  # no default special tokens - you can update this value if you add special tokens
  
          with open(vocab_file, encoding="utf-8") as vocab_handle:
              self.encoder = json.load(vocab_handle)
          self.decoder = {v: k for k, v in self.encoder.items()}
          with open(merges_file, encoding="utf-8") as merges_handle:
              merges = merges_handle.read().split("\n")[1:-1]
          merges = [tuple(merge.split()) for merge in merges]
          self.bpe_ranks = dict(zip(merges, range(len(merges))))
          self.cache = {}
  
      @property
      def vocab_size(self):
          return len(self.encoder)
  
      def bpe(self, token):
          if token in self.cache:
              return self.cache[token]
          word = tuple(token)
          word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
          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)
                  except ValueError:
                      new_word.extend(word[i:])
                      break
                  else:
                      new_word.extend(word[i:j])
                      i = j
  
                  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)
          word = word[:-4]
          self.cache[token] = word
          return word
  
      def _tokenize(self, text):
          """ Tokenize a string.
          """
          split_tokens = []
  
          words = re.findall(r"\S+\n?", text)
  
          for token in words:
              split_tokens.extend([t for t in self.bpe(token).split(" ")])
          return split_tokens
  
      def _convert_token_to_id(self, token):
          """ Converts a token (str) in an id using the vocab. """
          return self.encoder.get(token, self.encoder.get(self.unk_token))
  
      def _convert_id_to_token(self, index):
          """Converts an index (integer) in a token (str) using the vocab."""
          return self.decoder.get(index, self.unk_token)
  
      def convert_tokens_to_string(self, tokens):
          """ Converts a sequence of tokens (string) in a single string. """
          out_string = " ".join(tokens).replace("@@ ", "").strip()
          return out_string
  
      def save_vocabulary(self, save_directory):
          """Save the tokenizer vocabulary and merge files to a directory."""
          if not os.path.isdir(save_directory):
              logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
              return
          vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES["vocab_file"])
          merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES["merges_file"])
  
          with open(vocab_file, "w", encoding="utf-8") as f:
              f.write(json.dumps(self.encoder, ensure_ascii=False))
  
          index = 0
          with open(merge_file, "w", encoding="utf-8") as writer:
              writer.write("#version: 0.2\n")
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
                  if index != token_index:
                      logger.warning(
                          "Saving vocabulary to {}: BPE merge indices are not consecutive."
                          " Please check that the tokenizer is not corrupted!".format(merge_file)
                      )
                      index = token_index
                  writer.write(" ".join(bpe_tokens) + "\n")
                  index += 1
  
          return vocab_file, merge_file
  
      # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
      #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
      #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
      #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
      #     return ''.join(tokens_generated_so_far)
  