


"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
       from .tokenization_xlnet import XLNetTokenizer
else:
       a_			=      None


a_			=      logging.get_logger(__name__)

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

a_			=      {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

a_			=      {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

a_			=      """▁"""

# Segments (not really needed)
a_			=      0
a_			=      1
a_			=      2
a_			=      3
a_			=      4






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					VOCAB_FILES_NAMES
 _lowerCamelCase				=					PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase				=					"""left"""
 _lowerCamelCase				=					XLNetTokenizer

 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 ,						):



     '''simple docstring'''




     __A   :						List[str]									=					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 ,						)

     __A   :						Dict									=					3
     __A   :						Optional[Any]									=					do_lower_case
     __A   :						Tuple									=					remove_space
     __A   :						Tuple									=					keep_accents
     __A   :						Optional[Any]									=					vocab_file
     __A   :						Optional[Any]									=					False if not self.vocab_file else True

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




     __A   :						List[Any]									=					[self.sep_token_id]
     __A   :						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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					[self.sep_token_id]
     __A   :						Any									=					[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  ):



     '''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(__lowerCamelCase  ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
         return
     __A   :						List[Any]									=					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,)











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




