text: # coding=utf-8
  # The MIT License (MIT)
  
  # Copyright (c) Microsoft Corporation
  
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  """Tokenization classes for UniLM."""
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import collections
  import logging
  import os
  import unicodedata
  from io import open
  
  from transformers.tokenization_bert import BertTokenizer, whitespace_tokenize
  
  logger = logging.getLogger(__name__)
  
  VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'}
  
  PRETRAINED_VOCAB_FILES_MAP = {
      'vocab_file':
      {
          'unilm-large-cased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm-large-cased-vocab.txt",
          'unilm-base-cased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm-base-cased-vocab.txt",
          'unilm1-large-cased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm1-large-cased-vocab.txt",
          'unilm1-base-cased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm1-base-cased-vocab.txt",
          'unilm1.2-base-uncased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm1.2-base-uncased-vocab.txt", 
          'unilm2-base-uncased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm2-base-uncased-vocab.txt", 
          'unilm2-large-uncased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm2-large-uncased-vocab.txt", 
          'unilm2-large-cased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/unilm2-large-cased-vocab.txt", 
      }
  }
  
  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      'unilm-large-cased': 512,
      'unilm-base-cased': 512,
      'unilm1-large-cased': 512,
      'unilm1-base-cased': 512,
      'unilm1.2-base-uncased': 512,
      'unilm2-base-uncased': 512,
      'unilm2-large-cased': 512,
      'unilm2-large-uncased': 512,
  }
  
  
  class UnilmTokenizer(BertTokenizer):
      r"""
      Constructs a UnilmTokenizer.
      :class:`~transformers.UnilmTokenizer` is identical to BertTokenizer and runs end-to-end tokenization: punctuation splitting + wordpiece
      Args:
          vocab_file: Path to a one-wordpiece-per-line vocabulary file
          do_lower_case: Whether to lower case the input. Only has an effect when do_wordpiece_only=False
          do_basic_tokenize: Whether to do basic tokenization before wordpiece.
          max_len: An artificial maximum length to truncate tokenized sequences to; Effective maximum length is always the
              minimum of this value (if specified) and the underlying BERT model's sequence length.
          never_split: List of tokens which will never be split during tokenization. Only has an effect when
              do_wordpiece_only=False
      """
  
      vocab_files_names = VOCAB_FILES_NAMES
      pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
      max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  
  
  class WhitespaceTokenizer(object):
      def tokenize(self, text):
          return whitespace_tokenize(text)
  