code: 
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  __lowerCamelCase       :       int				   =			logging.get_logger(__name__)
  
  __lowerCamelCase       :       List[Any]				   =			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
  
  __lowerCamelCase       :       List[str]				   =			{
      "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_   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=False	,     __UpperCamelCase=True	,     __UpperCamelCase=False	,     __UpperCamelCase="<s>"	,     __UpperCamelCase="</s>"	,     __UpperCamelCase="<unk>"	,     __UpperCamelCase="<sep>"	,     __UpperCamelCase="<pad>"	,     __UpperCamelCase="<cls>"	,     __UpperCamelCase="<mask>"	,     __UpperCamelCase=["<eop>", "<eod>"]	,     **__UpperCamelCase	,     )->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							str   				=AddedToken(__UpperCamelCase	,     lstrip=__UpperCamelCase	,     rstrip=__UpperCamelCase							) if isinstance(__UpperCamelCase	,     __UpperCamelCase							) else mask_token
  
  						super().__init__(
  						    vocab_file=__UpperCamelCase	,     tokenizer_file=__UpperCamelCase	,     do_lower_case=__UpperCamelCase	,     remove_space=__UpperCamelCase	,     keep_accents=__UpperCamelCase	,     bos_token=__UpperCamelCase	,     eos_token=__UpperCamelCase	,     unk_token=__UpperCamelCase	,     sep_token=__UpperCamelCase	,     pad_token=__UpperCamelCase	,     cls_token=__UpperCamelCase	,     mask_token=__UpperCamelCase	,     additional_special_tokens=__UpperCamelCase	,     **__UpperCamelCase	,     )
  
  						A__     :							Any   				=3
  						A__     :							Optional[int]   				=do_lower_case
  						A__     :							Optional[Any]   				=remove_space
  						A__     :							Tuple   				=keep_accents
  						A__     :							str   				=vocab_file
  						A__     :							Tuple   				=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.'''							)
  						A__     :							List[Any]   				=jieba
  						A__     :							Optional[Any]   				=str.maketrans(''' \n'''	,     '''\u2582\u2583'''							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			List[int]:
  						'''simple docstring'''
  
  
  						A__     :							str   				=[self.sep_token_id]
  						A__     :							Optional[int]   				=[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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			List[int]:
  						'''simple docstring'''
  
  
  						A__     :							int   				=[self.sep_token_id]
  						A__     :							Optional[int]   				=[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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			Tuple[str]:
  						'''simple docstring'''
  
  
  						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(__UpperCamelCase							):
  										logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
  										return
  						A__     :							Union[str, Any]   				=os.path.join(
  						    __UpperCamelCase	,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
  
  						if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCamelCase							):
  										copyfile(self.vocab_file	,     __UpperCamelCase							)
  
  						return (out_vocab_file,)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=[
  						    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(__UpperCamelCase	,     cut_all=__UpperCamelCase							)]							)
  						    for text in batch_text_or_text_pairs
  						]
  						return super()._batch_encode_plus(__UpperCamelCase	,     *__UpperCamelCase	,     **__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							str   				=super()._decode(*__UpperCamelCase	,     **__UpperCamelCase							)
  						A__     :							int   				=text.replace(''' '''	,     ''''''							).replace('''\u2582'''	,     ''' '''							).replace('''\u2583'''	,     '''\n'''							)
  						return text
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  __lowerCamelCase       :       str				   =			{
      "configuration_align": [
          "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "AlignConfig",
          "AlignTextConfig",
          "AlignVisionConfig",
      ],
      "processing_align": ["AlignProcessor"],
  }
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Dict				   =			[
  						    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "AlignModel",
  						    "AlignPreTrainedModel",
  						    "AlignTextModel",
  						    "AlignVisionModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_align import (
  						    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    AlignConfig,
  						    AlignTextConfig,
  						    AlignVisionConfig,
  						)
  						from .processing_align import AlignProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_align import (
  												    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    AlignModel,
  												    AlignPreTrainedModel,
  												    AlignTextModel,
  												    AlignVisionModel,
  												)
  
  else:
  						import sys
  
  						__lowerCamelCase       :       List[str]				   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  