'''simple docstring'''

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


A_     :						str    							=logging.get_logger(__name__)

A_     :						Optional[int]    							={'''vocab_file''': '''spiece.model'''}

A_     :						Optional[int]    							={
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}


class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=False    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase="<s>"    ,     _lowerCamelCase="</s>"    ,     _lowerCamelCase="<unk>"    ,     _lowerCamelCase="<sep>"    ,     _lowerCamelCase="<pad>"    ,     _lowerCamelCase="<cls>"    ,     _lowerCamelCase="<mask>"    ,     _lowerCamelCase=["<eop>", "<eod>"]    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      AddedToken(_lowerCamelCase    ,     lstrip=_lowerCamelCase    ,     rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase    ,     _lowerCamelCase ) else mask_token

     lowerCAmelCase_			=      {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         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    ,     sp_model_kwargs=self.sp_model_kwargs    ,     **_lowerCamelCase    ,     )

     lowerCAmelCase_			=      3

     lowerCAmelCase_			=      do_lower_case
     lowerCAmelCase_			=      remove_space
     lowerCAmelCase_			=      keep_accents
     lowerCAmelCase_			=      vocab_file

     lowerCAmelCase_			=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(_lowerCamelCase )

     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_			=      jieba
     lowerCAmelCase_			=      str.maketrans(''' \n'''    ,     '''\u2582\u2583''' )





  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def 						UpperCAmelCase_	(	self ):
     return len(self.sp_model )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab





  def __getstate__(	self ):
     lowerCAmelCase_			=      self.__dict__.copy()
     lowerCAmelCase_			=      None
     return state





  def __setstate__(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      d

     # for backward compatibility
     if not hasattr(self    ,     '''sp_model_kwargs''' ):
        lowerCAmelCase_			=      {}

     lowerCAmelCase_			=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(self.vocab_file )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     if self.remove_space:
        lowerCAmelCase_			=      ''' '''.join(inputs.strip().split() )
     else:
        lowerCAmelCase_			=      inputs
     lowerCAmelCase_			=      outputs.replace('''``'''    ,     '''"''' ).replace('''\'\''''    ,     '''"''' )

     if not self.keep_accents:
        lowerCAmelCase_			=      unicodedata.normalize('''NFKD'''    ,     _lowerCamelCase )
        lowerCAmelCase_			=      ''''''.join([c for c in outputs if not unicodedata.combining(_lowerCamelCase )] )
     if self.do_lower_case:
        lowerCAmelCase_			=      outputs.lower()

     return outputs





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.preprocess_text(_lowerCamelCase )
     lowerCAmelCase_			=      self.sp_model.encode(_lowerCamelCase    ,     out_type=_lowerCamelCase )
     lowerCAmelCase_			=      []
     for piece in pieces:
        if len(_lowerCamelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
           lowerCAmelCase_			=      self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase    ,     '''''' ) )
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
              if len(cur_pieces[0] ) == 1:
                 lowerCAmelCase_			=      cur_pieces[1:]
              else:
                 lowerCAmelCase_			=      cur_pieces[0][1:]
           cur_pieces.append(piece[-1] )
           new_pieces.extend(_lowerCamelCase )
        else:
           new_pieces.append(_lowerCamelCase )

     return new_pieces





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return self.sp_model.PieceToId(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return self.sp_model.IdToPiece(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      ''''''.join(_lowerCamelCase ).replace(_lowerCamelCase    ,     ''' ''' ).strip()
     return out_string





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      [self.sep_token_id]
     lowerCAmelCase_			=      [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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False ):

     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowerCamelCase    ,     token_ids_a=_lowerCamelCase    ,     already_has_special_tokens=_lowerCamelCase )

     if token_ids_a is not None:
        return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1, 1]
     return ([0] * len(_lowerCamelCase )) + [1, 1]





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      [self.sep_token_id]
     lowerCAmelCase_			=      [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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     if not os.path.isdir(_lowerCamelCase ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
        return
     lowerCAmelCase_			=      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 ) and os.path.isfile(self.vocab_file ):
        copyfile(self.vocab_file    ,     _lowerCamelCase )
     elif not os.path.isfile(self.vocab_file ):
        with open(_lowerCamelCase    ,     '''wb''' ) as fi:
           lowerCAmelCase_			=      self.sp_model.serialized_model_proto()
           fi.write(_lowerCamelCase )

     return (out_vocab_file,)







  def 						UpperCAmelCase_	(	self    ,     *_lowerCamelCase    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      super()._decode(*_lowerCamelCase    ,     **_lowerCamelCase )
     lowerCAmelCase_			=      text.replace(''' '''    ,     '''''' ).replace('''\u2582'''    ,     ''' ''' ).replace('''\u2583'''    ,     '''\n''' )
     return text






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_     :						Optional[Any]    							={
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Dict    							=['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Optional[int]    							=[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 A_     :						List[str]    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





