

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase	=			{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	=			[
          'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Wav2Vec2ConformerForAudioFrameClassification',
          'Wav2Vec2ConformerForCTC',
          'Wav2Vec2ConformerForPreTraining',
          'Wav2Vec2ConformerForSequenceClassification',
          'Wav2Vec2ConformerForXVector',
          'Wav2Vec2ConformerModel',
          'Wav2Vec2ConformerPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          WavaVecaConformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca_conformer import (
                WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaConformerForAudioFrameClassification,
                WavaVecaConformerForCTC,
                WavaVecaConformerForPreTraining,
                WavaVecaConformerForSequenceClassification,
                WavaVecaConformerForXVector,
                WavaVecaConformerModel,
                WavaVecaConformerPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""




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

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


if is_sentencepiece_available():
      from .tokenization_reformer import ReformerTokenizer
else:
      __UpperCAmelCase	=			None


__UpperCAmelCase	=			logging.get_logger(__name__)


__UpperCAmelCase	=			'▁'

__UpperCAmelCase	=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

__UpperCAmelCase	=			{
    'google/reformer-crime-and-punishment': 52_4288,
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask"""]
  snake_case_			    =    ReformerTokenizer

  def __init__(					self						:     List[Any]   ,A						:     Dict=None   ,A						:     List[str]=None   ,A						:     Union[str, Any]="</s>"   ,A						:     List[str]="<unk>"   ,A						:     Tuple=[]   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   super().__init__(
       A   ,tokenizer_file=A   ,eos_token=A   ,unk_token=A   ,additional_special_tokens=A   ,**A   ,)

   UpperCAmelCase__   :					Tuple   				=   vocab_file
   UpperCAmelCase__   :					Optional[Any]   				=   False if not self.vocab_file else True




  def 				__lowercase    (					self						:     Any   ,A						:     str   ,A						:     Optional[str] = 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(A      ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
    return
   UpperCAmelCase__   :					Tuple   				=   os.path.join(
       A   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

   return (out_vocab_file,)





