text: # coding=utf-8
  # Copyright 2019 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 XLM."""
  
  
  import json
  import os
  import re
  import sys
  import unicodedata
  from typing import List, Optional, Tuple
  
  import sacremoses as sm
  
  from .tokenization_utils import PreTrainedTokenizer
  from .utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  VOCAB_FILES_NAMES = {
      "vocab_file": "vocab.json",
      "merges_file": "merges.txt",
  }
  
  PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {
          "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/vocab.json",
          "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/vocab.json",
          "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/vocab.json",
          "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/vocab.json",
          "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/vocab.json",
          "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/vocab.json",
          "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/vocab.json",
          "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/vocab.json",
          "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/vocab.json",
          "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/vocab.json",
      },
      "merges_file": {
          "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/merges.txt",
          "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/merges.txt",
          "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/merges.txt",
          "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/merges.txt",
          "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/merges.txt",
          "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/merges.txt",
          "xlm-clm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/merges.txt",
          "xlm-clm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/merges.txt",
          "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/merges.txt",
          "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/merges.txt",
      },
  }
  
  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "xlm-mlm-en-2048": 512,
      "xlm-mlm-ende-1024": 512,
      "xlm-mlm-enfr-1024": 512,
      "xlm-mlm-enro-1024": 512,
      "xlm-mlm-tlm-xnli15-1024": 512,
      "xlm-mlm-xnli15-1024": 512,
      "xlm-clm-enfr-1024": 512,
      "xlm-clm-ende-1024": 512,
      "xlm-mlm-17-1280": 512,
      "xlm-mlm-100-1280": 512,
  }
  
  PRETRAINED_INIT_CONFIGURATION = {
      "xlm-mlm-en-2048": {"do_lowercase_and_remove_accent": True},
      "xlm-mlm-ende-1024": {
          "do_lowercase_and_remove_accent": True,
          "id2lang": {0: "de", 1: "en"},
          "lang2id": {"de": 0, "en": 1},
      },
      "xlm-mlm-enfr-1024": {
          "do_lowercase_and_remove_accent": True,
          "id2lang": {0: "en", 1: "fr"},
          "lang2id": {"en": 0, "fr": 1},
      },
      "xlm-mlm-enro-1024": {
          "do_lowercase_and_remove_accent": True,
          "id2lang": {0: "en", 1: "ro"},
          "lang2id": {"en": 0, "ro": 1},
      },
      "xlm-mlm-tlm-xnli15-1024": {
          "do_lowercase_and_remove_accent": True,
          "id2lang": {
              0: "ar",
              1: "bg",
              2: "de",
              3: "el",
              4: "en",
              5: "es",
              6: "fr",
              7: "hi",
              8: "ru",
              9: "sw",
              10: "th",
              11: "tr",
              12: "ur",
              13: "vi",
              14: "zh",
          },
          "lang2id": {
              "ar": 0,
              "bg": 1,
              "de": 2,
              "el": 3,
              "en": 4,
              "es": 5,
              "fr": 6,
              "hi": 7,
              "ru": 8,
              "sw": 9,
              "th": 10,
              "tr": 11,
              "ur": 12,
              "vi": 13,
              "zh": 14,
          },
      },
      "xlm-mlm-xnli15-1024": {
          "do_lowercase_and_remove_accent": True,
          "id2lang": {
              0: "ar",
              1: "bg",
              2: "de",
              3: "el",
              4: "en",
              5: "es",
              6: "fr",
              7: "hi",
              8: "ru",
              9: "sw",
              10: "th",
              11: "tr",
              12: "ur",
              13: "vi",
              14: "zh",
          },
          "lang2id": {
              "ar": 0,
              "bg": 1,
              "de": 2,
              "el": 3,
              "en": 4,
              "es": 5,
              "fr": 6,
              "hi": 7,
              "ru": 8,
              "sw": 9,
              "th": 10,
              "tr": 11,
              "ur": 12,
              "vi": 13,
              "zh": 14,
          },
      },
      "xlm-clm-enfr-1024": {
          "do_lowercase_and_remove_accent": True,
          "id2lang": {0: "en", 1: "fr"},
          "lang2id": {"en": 0, "fr": 1},
      },
      "xlm-clm-ende-1024": {
          "do_lowercase_and_remove_accent": True,
          "id2lang": {0: "de", 1: "en"},
          "lang2id": {"de": 0, "en": 1},
      },
      "xlm-mlm-17-1280": {
          "do_lowercase_and_remove_accent": False,
          "id2lang": {
              0: "ar",
              1: "de",
              2: "en",
              3: "es",
              4: "fr",
              5: "hi",
              6: "it",
              7: "ja",
              8: "ko",
              9: "nl",
              10: "pl",
              11: "pt",
              12: "ru",
              13: "sv",
              14: "tr",
              15: "vi",
              16: "zh",
          },
          "lang2id": {
              "ar": 0,
              "de": 1,
              "en": 2,
              "es": 3,
              "fr": 4,
              "hi": 5,
              "it": 6,
              "ja": 7,
              "ko": 8,
              "nl": 9,
              "pl": 10,
              "pt": 11,
              "ru": 12,
              "sv": 13,
              "tr": 14,
              "vi": 15,
              "zh": 16,
          },
      },
      "xlm-mlm-100-1280": {
          "do_lowercase_and_remove_accent": False,
          "id2lang": {
              0: "af",
              1: "als",
              2: "am",
              3: "an",
              4: "ang",
              5: "ar",
              6: "arz",
              7: "ast",
              8: "az",
              9: "bar",
              10: "be",
              11: "bg",
              12: "bn",
              13: "br",
              14: "bs",
              15: "ca",
              16: "ceb",
              17: "ckb",
              18: "cs",
              19: "cy",
              20: "da",
              21: "de",
              22: "el",
              23: "en",
              24: "eo",
              25: "es",
              26: "et",
              27: "eu",
              28: "fa",
              29: "fi",
              30: "fr",
              31: "fy",
              32: "ga",
              33: "gan",
              34: "gl",
              35: "gu",
              36: "he",
              37: "hi",
              38: "hr",
              39: "hu",
              40: "hy",
              41: "ia",
              42: "id",
              43: "is",
              44: "it",
              45: "ja",
              46: "jv",
              47: "ka",
              48: "kk",
              49: "kn",
              50: "ko",
              51: "ku",
              52: "la",
              53: "lb",
              54: "lt",
              55: "lv",
              56: "mk",
              57: "ml",
              58: "mn",
              59: "mr",
              60: "ms",
              61: "my",
              62: "nds",
              63: "ne",
              64: "nl",
              65: "nn",
              66: "no",
              67: "oc",
              68: "pl",
              69: "pt",
              70: "ro",
              71: "ru",
              72: "scn",
              73: "sco",
              74: "sh",
              75: "si",
              76: "simple",
              77: "sk",
              78: "sl",
              79: "sq",
              80: "sr",
              81: "sv",
              82: "sw",
              83: "ta",
              84: "te",
              85: "th",
              86: "tl",
              87: "tr",
              88: "tt",
              89: "uk",
              90: "ur",
              91: "uz",
              92: "vi",
              93: "war",
              94: "wuu",
              95: "yi",
              96: "zh",
              97: "zh_classical",
              98: "zh_min_nan",
              99: "zh_yue",
          },
          "lang2id": {
              "af": 0,
              "als": 1,
              "am": 2,
              "an": 3,
              "ang": 4,
              "ar": 5,
              "arz": 6,
              "ast": 7,
              "az": 8,
              "bar": 9,
              "be": 10,
              "bg": 11,
              "bn": 12,
              "br": 13,
              "bs": 14,
              "ca": 15,
              "ceb": 16,
              "ckb": 17,
              "cs": 18,
              "cy": 19,
              "da": 20,
              "de": 21,
              "el": 22,
              "en": 23,
              "eo": 24,
              "es": 25,
              "et": 26,
              "eu": 27,
              "fa": 28,
              "fi": 29,
              "fr": 30,
              "fy": 31,
              "ga": 32,
              "gan": 33,
              "gl": 34,
              "gu": 35,
              "he": 36,
              "hi": 37,
              "hr": 38,
              "hu": 39,
              "hy": 40,
              "ia": 41,
              "id": 42,
              "is": 43,
              "it": 44,
              "ja": 45,
              "jv": 46,
              "ka": 47,
              "kk": 48,
              "kn": 49,
              "ko": 50,
              "ku": 51,
              "la": 52,
              "lb": 53,
              "lt": 54,
              "lv": 55,
              "mk": 56,
              "ml": 57,
              "mn": 58,
              "mr": 59,
              "ms": 60,
              "my": 61,
              "nds": 62,
              "ne": 63,
              "nl": 64,
              "nn": 65,
              "no": 66,
              "oc": 67,
              "pl": 68,
              "pt": 69,
              "ro": 70,
              "ru": 71,
              "scn": 72,
              "sco": 73,
              "sh": 74,
              "si": 75,
              "simple": 76,
              "sk": 77,
              "sl": 78,
              "sq": 79,
              "sr": 80,
              "sv": 81,
              "sw": 82,
              "ta": 83,
              "te": 84,
              "th": 85,
              "tl": 86,
              "tr": 87,
              "tt": 88,
              "uk": 89,
              "ur": 90,
              "uz": 91,
              "vi": 92,
              "war": 93,
              "wuu": 94,
              "yi": 95,
              "zh": 96,
              "zh_classical": 97,
              "zh_min_nan": 98,
              "zh_yue": 99,
          },
      },
  }
  
  
  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
  
  
  def lowercase_and_remove_accent(text):
      """
      Lowercase and strips accents from a piece of text based on
      https://github.com/facebookresearch/XLM/blob/master/tools/lowercase_and_remove_accent.py
      """
      text = " ".join(text)
      text = text.lower()
      text = unicodedata.normalize("NFD", text)
      output = []
      for char in text:
          cat = unicodedata.category(char)
          if cat == "Mn":
              continue
          output.append(char)
      return "".join(output).lower().split(" ")
  
  
  def replace_unicode_punct(text):
      """
      Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
      """
      text = text.replace("，", ",")
      text = re.sub(r"。\s*", ". ", text)
      text = text.replace("、", ",")
      text = text.replace("”", '"')
      text = text.replace("“", '"')
      text = text.replace("∶", ":")
      text = text.replace("：", ":")
      text = text.replace("？", "?")
      text = text.replace("《", '"')
      text = text.replace("》", '"')
      text = text.replace("）", ")")
      text = text.replace("！", "!")
      text = text.replace("（", "(")
      text = text.replace("；", ";")
      text = text.replace("１", "1")
      text = text.replace("」", '"')
      text = text.replace("「", '"')
      text = text.replace("０", "0")
      text = text.replace("３", "3")
      text = text.replace("２", "2")
      text = text.replace("５", "5")
      text = text.replace("６", "6")
      text = text.replace("９", "9")
      text = text.replace("７", "7")
      text = text.replace("８", "8")
      text = text.replace("４", "4")
      text = re.sub(r"．\s*", ". ", text)
      text = text.replace("～", "~")
      text = text.replace("’", "'")
      text = text.replace("…", "...")
      text = text.replace("━", "-")
      text = text.replace("〈", "<")
      text = text.replace("〉", ">")
      text = text.replace("【", "[")
      text = text.replace("】", "]")
      text = text.replace("％", "%")
      return text
  
  
  def remove_non_printing_char(text):
      """
      Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
      """
      output = []
      for char in text:
          cat = unicodedata.category(char)
          if cat.startswith("C"):
              continue
          output.append(char)
      return "".join(output)
  
  
  def romanian_preprocessing(text):
      """Sennrich's WMT16 scripts for Romanian preprocessing, used by model `xlm-mlm-enro-1024`"""
      # https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/normalise-romanian.py
      text = text.replace("\u015e", "\u0218").replace("\u015f", "\u0219")
      text = text.replace("\u0162", "\u021a").replace("\u0163", "\u021b")
      # https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/remove-diacritics.py
      text = text.replace("\u0218", "S").replace("\u0219", "s")  # s-comma
      text = text.replace("\u021a", "T").replace("\u021b", "t")  # t-comma
      text = text.replace("\u0102", "A").replace("\u0103", "a")
      text = text.replace("\u00C2", "A").replace("\u00E2", "a")
      text = text.replace("\u00CE", "I").replace("\u00EE", "i")
      return text
  
  
  class XLMTokenizer(PreTrainedTokenizer):
      """
      Construct an XLM tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following:
  
      - Moses preprocessing and tokenization for most supported languages.
      - Language specific tokenization for Chinese (Jieba), Japanese (KyTea) and Thai (PyThaiNLP).
      - Optionally lowercases and normalizes all inputs text.
      - The arguments ``special_tokens`` and the function ``set_special_tokens``, can be used to add additional symbols
        (like "__classify__") to a vocabulary.
      - The :obj:`lang2id` attribute maps the languages supported by the model with their IDs if provided (automatically
        set for pretrained vocabularies).
      - The :obj:`id2lang` attributes does reverse mapping if provided (automatically set for pretrained vocabularies).
  
      This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
      Users should refer to this superclass for more information regarding those methods.
  
      Args:
          vocab_file (:obj:`str`):
              Vocabulary file.
          merges_file (:obj:`str`):
              Merges file.
          unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
              The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
              token instead.
          bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
              The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  
              .. note::
  
                  When building a sequence using special tokens, this is not the token that is used for the beginning of
                  sequence. The token used is the :obj:`cls_token`.
          sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
              The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
              sequence classification or for a text and a question for question answering. It is also used as the last
              token of a sequence built with special tokens.
          pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
              The token used for padding, for example when batching sequences of different lengths.
          cls_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
              The classifier token which is used when doing sequence classification (classification of the whole sequence
              instead of per-token classification). It is the first token of the sequence when built with special tokens.
          mask_token (:obj:`str`, `optional`, defaults to :obj:`"<special1>"`):
              The token used for masking values. This is the token used when training this model with masked language
              modeling. This is the token which the model will try to predict.
          additional_special_tokens (:obj:`List[str]`, `optional`, defaults to :obj:`["<special0>","<special1>","<special2>","<special3>","<special4>","<special5>","<special6>","<special7>","<special8>","<special9>"]`):
              List of additional special tokens.
          lang2id (:obj:`Dict[str, int]`, `optional`):
              Dictionary mapping languages string identifiers to their IDs.
          id2lang (:obj:`Dict[int, str]`, `optional`):
              Dictionary mapping language IDs to their string identifiers.
          do_lowercase_and_remove_accent (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Whether to lowercase and remove accents when tokenizing.
      """
  
      vocab_files_names = VOCAB_FILES_NAMES
      pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
      pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
      max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  
      def __init__(
          self,
          vocab_file,
          merges_file,
          unk_token="<unk>",
          bos_token="<s>",
          sep_token="</s>",
          pad_token="<pad>",
          cls_token="</s>",
          mask_token="<special1>",
          additional_special_tokens=[
              "<special0>",
              "<special1>",
              "<special2>",
              "<special3>",
              "<special4>",
              "<special5>",
              "<special6>",
              "<special7>",
              "<special8>",
              "<special9>",
          ],
          lang2id=None,
          id2lang=None,
          do_lowercase_and_remove_accent=True,
          **kwargs
      ):
          super().__init__(
              unk_token=unk_token,
              bos_token=bos_token,
              sep_token=sep_token,
              pad_token=pad_token,
              cls_token=cls_token,
              mask_token=mask_token,
              additional_special_tokens=additional_special_tokens,
              lang2id=lang2id,
              id2lang=id2lang,
              do_lowercase_and_remove_accent=do_lowercase_and_remove_accent,
              **kwargs,
          )
  
          # cache of sm.MosesPunctNormalizer instance
          self.cache_moses_punct_normalizer = dict()
          # cache of sm.MosesTokenizer instance
          self.cache_moses_tokenizer = dict()
          self.lang_with_custom_tokenizer = set(["zh", "th", "ja"])
          # True for current supported model (v1.2.0), False for XLM-17 & 100
          self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent
          self.lang2id = lang2id
          self.id2lang = id2lang
          if lang2id is not None and id2lang is not None:
              assert len(lang2id) == len(id2lang)
  
          self.ja_word_tokenizer = None
          self.zh_word_tokenizer = None
  
          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]
          merges = [tuple(merge.split()[:2]) for merge in merges]
          self.bpe_ranks = dict(zip(merges, range(len(merges))))
          self.cache = {}
  
      @property
      def do_lower_case(self):
          return self.do_lowercase_and_remove_accent
  
      def moses_punct_norm(self, text, lang):
          if lang not in self.cache_moses_punct_normalizer:
              punct_normalizer = sm.MosesPunctNormalizer(lang=lang)
              self.cache_moses_punct_normalizer[lang] = punct_normalizer
          else:
              punct_normalizer = self.cache_moses_punct_normalizer[lang]
          return punct_normalizer.normalize(text)
  
      def moses_tokenize(self, text, lang):
          if lang not in self.cache_moses_tokenizer:
              moses_tokenizer = sm.MosesTokenizer(lang=lang)
              self.cache_moses_tokenizer[lang] = moses_tokenizer
          else:
              moses_tokenizer = self.cache_moses_tokenizer[lang]
          return moses_tokenizer.tokenize(text, return_str=False, escape=False)
  
      def moses_pipeline(self, text, lang):
          text = replace_unicode_punct(text)
          text = self.moses_punct_norm(text, lang)
          text = remove_non_printing_char(text)
          return text
  
      def ja_tokenize(self, text):
          if self.ja_word_tokenizer is None:
              try:
                  import Mykytea
  
                  self.ja_word_tokenizer = Mykytea.Mykytea(
                      "-model %s/local/share/kytea/model.bin" % os.path.expanduser("~")
                  )
              except (AttributeError, ImportError):
                  logger.error(
                      "Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper (https://github.com/chezou/Mykytea-python) with the following steps"
                  )
                  logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea")
                  logger.error("2. autoreconf -i")
                  logger.error("3. ./configure --prefix=$HOME/local")
                  logger.error("4. make && make install")
                  logger.error("5. pip install kytea")
                  raise
          return list(self.ja_word_tokenizer.getWS(text))
  
      @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):
          word = tuple(token[:-1]) + (token[-1] + "</w>",)
          if token in self.cache:
              return self.cache[token]
          pairs = get_pairs(word)
  
          if not pairs:
              return token + "</w>"
  
          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)
          if word == "\n  </w>":
              word = "\n</w>"
          self.cache[token] = word
          return word
  
      def _tokenize(self, text, lang="en", bypass_tokenizer=False):
          """
          Tokenize a string given language code. For Chinese, Japanese and Thai, we use a language specific
          tokenizerself. Otherwise, we use Moses.
  
          Details of tokenization:
  
              - [sacremoses](https://github.com/alvations/sacremoses): port of Moses
              - Install with `pip install sacremoses`
              - [pythainlp](https://github.com/PyThaiNLP/pythainlp): Thai tokenizer
              - Install with `pip install pythainlp`
              - [kytea](https://github.com/chezou/Mykytea-python): Japanese tokenizer, wrapper of
                [KyTea](https://github.com/neubig/kytea)
              - Install with the following steps:
  
              ::
  
                  git clone git@github.com:neubig/kytea.git && cd kytea
                  autoreconf -i
                  ./configure --prefix=$HOME/local
                  make && make install
                  pip install kytea
  
              - [jieba](https://github.com/fxsjy/jieba): Chinese tokenizer (*)
              - Install with `pip install jieba`
  
          (*) The original XLM used [Stanford
          Segmenter](https://nlp.stanford.edu/software/stanford-segmenter-2018-10-16.zip). However, the wrapper
          (`nltk.tokenize.stanford_segmenter`) is slow due to JVM overhead, and it will be deprecated. Jieba is a lot
          faster and pip-installable. Note there is some mismatch with the Stanford Segmenter. It should be fine if you
          fine-tune the model with Chinese supervisionself. If you want the same exact behaviour, use the original XLM
          [preprocessing script](https://github.com/facebookresearch/XLM/tree/master/tools) to tokenize the sentence
          externally, and set `bypass_tokenizer=True` to bypass the tokenizer.
  
          Args:
  
              - lang: ISO language code (default = 'en') (string). Languages should belong of the model supported
                languages. However, we don't enforce it.
              - bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False)
                (bool). If True, we only apply BPE.
  
          Returns:
              List of tokens.
          """
          if lang and self.lang2id and lang not in self.lang2id:
              logger.error(
                  "Supplied language code not found in lang2id mapping. Please check that your language is supported by the loaded pretrained model."
              )
          if bypass_tokenizer:
              text = text.split()
          elif lang not in self.lang_with_custom_tokenizer:
              text = self.moses_pipeline(text, lang=lang)
              # TODO: make sure we are using `xlm-mlm-enro-1024`, since XLM-100 doesn't have this step
              if lang == "ro":
                  text = romanian_preprocessing(text)
              text = self.moses_tokenize(text, lang=lang)
          elif lang == "th":
              text = self.moses_pipeline(text, lang=lang)
              try:
                  if "pythainlp" not in sys.modules:
                      from pythainlp.tokenize import word_tokenize as th_word_tokenize
                  else:
                      th_word_tokenize = sys.modules["pythainlp"].word_tokenize
              except (AttributeError, ImportError):
                  logger.error(
                      "Make sure you install PyThaiNLP (https://github.com/PyThaiNLP/pythainlp) with the following steps"
                  )
                  logger.error("1. pip install pythainlp")
                  raise
              text = th_word_tokenize(text)
          elif lang == "zh":
              try:
                  if "jieba" not in sys.modules:
                      import jieba
                  else:
                      jieba = sys.modules["jieba"]
              except (AttributeError, ImportError):
                  logger.error("Make sure you install Jieba (https://github.com/fxsjy/jieba) with the following steps")
                  logger.error("1. pip install jieba")
                  raise
              text = " ".join(jieba.cut(text))
              text = self.moses_pipeline(text, lang=lang)
              text = text.split()
          elif lang == "ja":
              text = self.moses_pipeline(text, lang=lang)
              text = self.ja_tokenize(text)
          else:
              raise ValueError("It should not reach here")
  
          if self.do_lowercase_and_remove_accent and not bypass_tokenizer:
              text = lowercase_and_remove_accent(text)
  
          split_tokens = []
          for token in text:
              if token:
                  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("</w>", " ").strip()
          return out_string
  
      def build_inputs_with_special_tokens(
          self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
      ) -> List[int]:
          """
          Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
          adding special tokens. An XLM sequence has the following format:
  
          - single sequence: ``<s> X </s>``
          - pair of sequences: ``<s> A </s> B </s>``
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs to which the special tokens will be added.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
  
          Returns:
              :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
  
          """
          bos = [self.bos_token_id]
          sep = [self.sep_token_id]
  
          if token_ids_1 is None:
              return bos + token_ids_0 + sep
          return bos + token_ids_0 + sep + token_ids_1 + sep
  
      def get_special_tokens_mask(
          self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
      ) -> List[int]:
          """
          Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
          special tokens using the tokenizer ``prepare_for_model`` method.
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
              already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Whether or not the token list is already formatted with special tokens for the model.
  
          Returns:
              :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
          """
  
          if already_has_special_tokens:
              if token_ids_1 is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model."
                  )
              return list(
                  map(
                      lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0,
                      token_ids_0,
                  )
              )
  
          if token_ids_1 is not None:
              return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
          return [1] + ([0] * len(token_ids_0)) + [1]
  
      def create_token_type_ids_from_sequences(
          self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
      ) -> List[int]:
          """
          Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence
          pair mask has the following format:
  
          ::
  
              0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
              | first sequence    | second sequence |
  
          If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
  
          Returns:
              :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
              sequence(s).
          """
          sep = [self.sep_token_id]
          cls = [self.cls_token_id]
          if token_ids_1 is None:
              return len(cls + token_ids_0 + sep) * [0]
          return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
  
      def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
          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, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
          )
          merge_file = os.path.join(
              save_directory, (filename_prefix + "-" if filename_prefix else "") + 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:
              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
  