text: # coding=utf-8
  # Copyright 2018 The Open AI Team Authors 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 OpenAI GPT."""
  
  
  import json
  import logging
  import os
  from functools import lru_cache
  
  import regex as re
  from tokenizers import ByteLevelBPETokenizer
  
  from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
  
  
  logger = logging.getLogger(__name__)
  
  VOCAB_FILES_NAMES = {
      "vocab_file": "vocab.json",
      "merges_file": "merges.txt",
  }
  
  PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {
          "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
          "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-vocab.json",
          "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-vocab.json",
          "gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-vocab.json",
          "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-vocab.json",
      },
      "merges_file": {
          "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
          "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-merges.txt",
          "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-merges.txt",
          "gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-merges.txt",
          "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-merges.txt",
      },
  }
  
  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "gpt2": 1024,
      "gpt2-medium": 1024,
      "gpt2-large": 1024,
      "gpt2-xl": 1024,
      "distilgpt2": 1024,
  }
  
  
  @lru_cache()
  def bytes_to_unicode():
      """
      Returns list of utf-8 byte and a mapping to unicode strings.
      We specifically avoids mapping to whitespace/control characters the bpe code barfs on.
  
      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.
      """
      bs = (
          list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 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 GPT2Tokenizer(PreTrainedTokenizer):
      """
      GPT-2 BPE tokenizer. Peculiarities:
          - Byte-level Byte-Pair-Encoding
          - Requires a space to start the input string => the encoding and tokenize methods should be called with the
            ``add_prefix_space`` flag set to ``True``.
            Otherwise, this tokenizer's ``encode``, ``decode``, and ``tokenize`` methods will not conserve
            the spaces at the beginning of a string: `tokenizer.decode(tokenizer.encode(" Hello")) = "Hello"`
      """
  
      vocab_files_names = VOCAB_FILES_NAMES
      pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
      max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  
      def __init__(
          self,
          vocab_file,
          merges_file,
          errors="replace",
          unk_token="<|endoftext|>",
          bos_token="<|endoftext|>",
          eos_token="<|endoftext|>",
          **kwargs
      ):
          super().__init__(bos_token=bos_token, eos_token=eos_token, 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()}
          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()}
          with open(merges_file, encoding="utf-8") as merges_handle:
              bpe_merges = merges_handle.read().split("\n")[1:-1]
          bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
          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+""")
  
      @property
      def vocab_size(self):
          return len(self.encoder)
  
      def get_vocab(self):
          return dict(self.encoder, **self.added_tokens_encoder)
  
      def bpe(self, token):
          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)
                  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)
          self.cache[token] = word
          return word
  
      def _tokenize(self, text):
          """ Tokenize a string. """
          bpe_tokens = []
          for token in re.findall(self.pat, text):
              token = "".join(
                  self.byte_encoder[b] for b in token.encode("utf-8")
              )  # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
          return bpe_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)
  
      def convert_tokens_to_string(self, tokens):
          """ Converts a sequence of tokens (string) in a single string. """
          text = "".join(tokens)
          text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
          return text
  
      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 prepare_for_tokenization(self, text, **kwargs):
          if "add_prefix_space" in kwargs and kwargs["add_prefix_space"]:
              return " " + text
          return text
  
  
  class GPT2TokenizerFast(PreTrainedTokenizerFast):
      vocab_files_names = VOCAB_FILES_NAMES
      pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
      max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  
      def __init__(
          self,
          vocab_file,
          merges_file,
          unk_token="<|endoftext|>",
          bos_token="<|endoftext|>",
          eos_token="<|endoftext|>",
          add_prefix_space=False,
          **kwargs
      ):
          super().__init__(
              ByteLevelBPETokenizer(vocab_file=vocab_file, merges_file=merges_file, add_prefix_space=add_prefix_space),
              bos_token=bos_token,
              eos_token=eos_token,
              unk_token=unk_token,
              **kwargs,
          )
  