code: 
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  _SCREAMING_SNAKE_CASE    :				List[Any]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				List[Any]       =					{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
  
  _SCREAMING_SNAKE_CASE    :				int       =					{
      """vocab_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
      },
      """tokenizer_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
      },
  }
  
  class 						A__      (					UpperCAmelCase__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=True    ,      __snake_case=False    ,      __snake_case="<s>"    ,      __snake_case="</s>"    ,      __snake_case="<unk>"    ,      __snake_case="<sep>"    ,      __snake_case="<pad>"    ,      __snake_case="<cls>"    ,      __snake_case="<mask>"    ,      __snake_case=["<eop>", "<eod>"]    ,      **__snake_case    ,      ):
           snake_case					=       AddedToken(lowerCamelCase__    ,      lstrip=lowerCamelCase__    ,      rstrip=lowerCamelCase__					) if isinstance(lowerCamelCase__    ,      lowerCamelCase__					) else mask_token
  
           super().__init__(
               vocab_file=lowerCamelCase__    ,      tokenizer_file=lowerCamelCase__    ,      do_lower_case=lowerCamelCase__    ,      remove_space=lowerCamelCase__    ,      keep_accents=lowerCamelCase__    ,      bos_token=lowerCamelCase__    ,      eos_token=lowerCamelCase__    ,      unk_token=lowerCamelCase__    ,      sep_token=lowerCamelCase__    ,      pad_token=lowerCamelCase__    ,      cls_token=lowerCamelCase__    ,      mask_token=lowerCamelCase__    ,      additional_special_tokens=lowerCamelCase__    ,      **lowerCamelCase__    ,      )
  
           snake_case					=       3
           snake_case					=       do_lower_case
           snake_case					=       remove_space
           snake_case					=       keep_accents
           snake_case					=       vocab_file
           snake_case					=       False if not self.vocab_file else True
  
           try:
                import jieba
           except ModuleNotFoundError as error:
                raise error.__class__(
                    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                    '''See https://pypi.org/project/jieba/ for installation.'''					)
           snake_case					=       jieba
           snake_case					=       str.maketrans(''' \n'''    ,      '''\u2582\u2583'''					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case = None					):
           snake_case					=       [self.sep_token_id]
           snake_case					=       [self.cls_token_id]
           if token_ids_a is None:
                return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls
      def 	a_    ( self    ,      __snake_case    ,      __snake_case = None					):
           snake_case					=       [self.sep_token_id]
           snake_case					=       [2]
  
           if token_ids_a is None:
                return len(token_ids_a + sep					) * [0] + cls_segment_id
           return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id
      def 	a_    ( self    ,      __snake_case    ,      __snake_case = None					):
           if not self.can_save_slow_tokenizer:
                raise ValueError(
                    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                    '''tokenizer.'''					)
  
           if not os.path.isdir(lowerCamelCase__					):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                return
           snake_case					=       os.path.join(
               lowerCamelCase__    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
  
           if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCamelCase__					):
                copyfile(self.vocab_file    ,      lowerCamelCase__					)
  
           return (out_vocab_file,)
      def 	a_    ( self    ,      __snake_case    ,      *__snake_case    ,      **__snake_case					):
           snake_case					=       [
               " ".join([x.translate(self.translator					) for x in self.jieba.cut(lowerCamelCase__    ,      cut_all=lowerCamelCase__					)]					)
               for text in batch_text_or_text_pairs
           ]
           return super()._batch_encode_plus(lowerCamelCase__    ,      *lowerCamelCase__    ,      **lowerCamelCase__					)
  
  
  
  
      def 	a_    ( self    ,      *__snake_case    ,      **__snake_case					):
           snake_case					=       super()._decode(*lowerCamelCase__    ,      **lowerCamelCase__					)
           snake_case					=       text.replace(''' '''    ,      ''''''					).replace('''\u2582'''    ,      ''' '''					).replace('''\u2583'''    ,      '''\n'''					)
           return text
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  