code: import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  a     :			List[Any]					    =						logging.get_logger(__name__)
  
  a     :			List[Any]					    =						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
  
  a     :			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 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__="<s>"					,	lowerCamelCase__="</s>"					,	lowerCamelCase__="<unk>"					,	lowerCamelCase__="<sep>"					,	lowerCamelCase__="<pad>"					,	lowerCamelCase__="<cls>"					,	lowerCamelCase__="<mask>"					,	lowerCamelCase__=["<eop>", "<eod>"]					,	**lowerCamelCase__					,	):
         lowerCAmelCase_:       Tuple         =  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__					,	)
  
         lowerCAmelCase_:       Tuple         =  3
         lowerCAmelCase_:       Tuple         =  do_lower_case
         lowerCAmelCase_:       Any         =  remove_space
         lowerCAmelCase_:       Union[str, Any]         =  keep_accents
         lowerCAmelCase_:       List[Any]         =  vocab_file
         lowerCAmelCase_:       List[Any]         =  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."   )
         lowerCAmelCase_:       Optional[int]         =  jieba
         lowerCAmelCase_:       Dict         =  str.maketrans(" \n"					,	"\u2582\u2583"   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
         lowerCAmelCase_:       Any         =  [self.sep_token_id]
         lowerCAmelCase_:       List[Any]         =  [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					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
         lowerCAmelCase_:       Optional[Any]         =  [self.sep_token_id]
         lowerCAmelCase_:       Tuple         =  [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					,	lowerCamelCase__					,	lowerCamelCase__ = 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
         lowerCAmelCase_:       int         =  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					,	lowerCamelCase__					,	*lowerCamelCase__					,	**lowerCamelCase__   ):
         lowerCAmelCase_:       List[str]         =  [
             " ".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					,	*lowerCamelCase__					,	**lowerCamelCase__   ):
         lowerCAmelCase_:       Any         =  super()._decode(*lowerCamelCase__					,	**lowerCamelCase__   )
         lowerCAmelCase_:       Tuple         =  text.replace(" "					,	""   ).replace("\u2582"					,	" "   ).replace("\u2583"					,	"\n"   )
         return text
style_context: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)