





"""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_fnet import FNetTokenizer
else:
   lowerCamelCase_     :						List[str]						=					None

lowerCamelCase_     :						List[str]						=					logging.get_logger(__name__)
lowerCamelCase_     :						int						=					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase_     :						Optional[Any]						=					{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase_     :						int						=					{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

lowerCamelCase_     :						List[str]						=					'▁'

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Optional[int]	    =   VOCAB_FILES_NAMES
      lowercase_      : Tuple	    =   PRETRAINED_VOCAB_FILES_MAP
      lowercase_      : List[str]	    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase_      : List[str]	    =   ["""input_ids""", """token_type_ids"""]
      lowercase_      : Optional[Any]	    =   FNetTokenizer

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      snake_case_=False					,      snake_case_=True					,      snake_case_=True					,      snake_case_="<unk>"					,      snake_case_="[SEP]"					,      snake_case_="<pad>"					,      snake_case_="[CLS]"					,      snake_case_="[MASK]"					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       int										=  (
            AddedToken(snake_case_					,      lstrip=snake_case_					,      rstrip=snake_case_					,      normalized=snake_case_  )
            if isinstance(snake_case_					,      snake_case_  )
            else mask_token
        )

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

        A_  :       Dict										=  do_lower_case
        A_  :       str										=  remove_space
        A_  :       Tuple										=  keep_accents
        A_  :       Union[str, Any]										=  vocab_file
        A_  :       Any										=  False if not self.vocab_file else True

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None  ):

        """simple docstring"""

        A_  :       List[str]										=  [self.sep_token_id]
        A_  :       Dict										=  [self.cls_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  [self.sep_token_id]
        A_  :       Any										=  [self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep  ) * [0]
        return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None  ):

        """simple docstring"""

        if not os.path.isdir(snake_case_  ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
          return
        A_  :       Optional[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,)






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

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