





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

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


if is_sentencepiece_available():
   from .tokenization_xglm import XGLMTokenizer
else:
   A__:  Union[str, Any]       					=					None


A__:  Dict       					=					logging.get_logger(__name__)

A__:  str       					=					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A__:  Dict       					=					{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

A__:  Optional[Any]       					=					{
    '''facebook/xglm-564M''': 2048,
}




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   VOCAB_FILES_NAMES
      UpperCamelCase__       						=   PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__       						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__       						=   ["""input_ids""", """attention_mask"""]
      UpperCamelCase__       						=   XGLMTokenizer






      def __init__(						self:	str					,  __lowerCamelCase:	Optional[Any]=None					,  __lowerCamelCase:	Dict=None					,  __lowerCamelCase:	List[str]="<s>"					,  __lowerCamelCase:	Any="</s>"					,  __lowerCamelCase:	List[str]="</s>"					,  __lowerCamelCase:	Dict="<s>"					,  __lowerCamelCase:	Optional[int]="<unk>"					,  __lowerCamelCase:	str="<pad>"					,  **__lowerCamelCase:	List[Any]					,  ):
          '''simple docstring'''


          UpperCamelCase__:					str												=      7
          UpperCamelCase__:					int												=      [F"<madeupword{i}>" for i in range(self.num_madeup_words			)]

          UpperCamelCase__:					Union[str, Any]												=      kwargs.get("additional_special_tokens"					,  []			)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              __lowerCamelCase					,  tokenizer_file=__lowerCamelCase					,  bos_token=__lowerCamelCase					,  eos_token=__lowerCamelCase					,  sep_token=__lowerCamelCase					,  cls_token=__lowerCamelCase					,  unk_token=__lowerCamelCase					,  pad_token=__lowerCamelCase					,  **__lowerCamelCase					,  )

          UpperCamelCase__:					Dict												=      vocab_file
          UpperCamelCase__:					Union[str, Any]												=      False if not self.vocab_file else True






      def 	UpperCAmelCase_   (						self:	Dict					,  __lowerCamelCase:	List[int]					,  __lowerCamelCase:	Optional[List[int]] = None			):
          '''simple docstring'''


          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          UpperCamelCase__:					Any												=      [self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a






      def 	UpperCAmelCase_   (						self:	List[Any]					,  __lowerCamelCase:	List[int]					,  __lowerCamelCase:	Optional[List[int]] = None			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      [self.sep_token_id]

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






      def 	UpperCAmelCase_   (						self:	Union[str, Any]					,  __lowerCamelCase:	str					,  __lowerCamelCase:	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(__lowerCamelCase			):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory."			)
              return
          UpperCamelCase__:					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,)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__:  List[str]       					=					{
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:  int       					=					[
       '''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

   A__:  Dict       					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

