
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_a   :      int       =				logging.get_logger(__name__)

_a   :      Any       =				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_a   :      Any       =				{
    """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   _UpperCAmelCase					(       _snake_case):
    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  :    Tuple            = AddedToken(snake_case_    ,     lstrip=snake_case_    ,     rstrip=snake_case_  ) if isinstance(snake_case_    ,     snake_case_  ) else mask_token

      super().__init__(
          vocab_file=snake_case_    ,     tokenizer_file=snake_case_    ,     do_lower_case=snake_case_    ,     remove_space=snake_case_    ,     keep_accents=snake_case_    ,     bos_token=snake_case_    ,     eos_token=snake_case_    ,     unk_token=snake_case_    ,     sep_token=snake_case_    ,     pad_token=snake_case_    ,     cls_token=snake_case_    ,     mask_token=snake_case_    ,     additional_special_tokens=snake_case_    ,     **snake_case_    ,     )

      _snake_case  :    Optional[int]            = 3
      _snake_case  :    Dict            = do_lower_case
      _snake_case  :    Optional[int]            = remove_space
      _snake_case  :    Optional[Any]            = keep_accents
      _snake_case  :    Dict            = vocab_file
      _snake_case  :    int            = 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  :    List[Any]            = jieba
      _snake_case  :    Union[str, Any]            = str.maketrans(" \n"    ,     "\u2582\u2583"  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    Any            = [self.sep_token_id]
      _snake_case  :    str            = [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    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    Optional[int]            = [self.sep_token_id]
      _snake_case  :    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        lowerCamelCase__						(     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(snake_case_  ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'  )
        return
      _snake_case  :    Any            = os.path.join(
          snake_case_    ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case_  ):
        copyfile(self.vocab_file    ,     snake_case_  )

      return (out_vocab_file,)
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    str            = [
          " ".join([x.translate(self.translator  ) for x in self.jieba.cut(snake_case_    ,     cut_all=snake_case_  )]  )
          for text in batch_text_or_text_pairs
      ]
      return super()._batch_encode_plus(snake_case_    ,     *snake_case_    ,     **snake_case_  )


    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    Any            = super()._decode(*snake_case_    ,     **snake_case_  )
      _snake_case  :    Any            = text.replace(" "    ,     ""  ).replace("\u2582"    ,     " "  ).replace("\u2583"    ,     "\n"  )
      return text








'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase       :		Dict		=				{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Union[str, Any]		=				[
       'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'HubertForCTC',
       'HubertForSequenceClassification',
       'HubertModel',
       'HubertPreTrainedModel',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Optional[int]		=				[
       'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFHubertForCTC',
       'TFHubertModel',
       'TFHubertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase       :		Optional[int]		=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


