


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:
      _snake_case					=					None


_snake_case					=					logging.get_logger(__name__)


_snake_case					=					'''▁'''

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

_snake_case					=					{
    '''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'''
        )
    },
}

_snake_case					=					{
    '''google/reformer-crime-and-punishment''': 52_42_88,
}
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Dict							=		VOCAB_FILES_NAMES
   __A			:				Any							=		PRETRAINED_VOCAB_FILES_MAP
   __A			:				Union[str, Any]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A			:				Optional[Any]							=		["input_ids", "attention_mask"]
   __A			:				Optional[int]							=		ReformerTokenizer






   def __init__(		self						,    __A=None						,    __A=None						,    __A="</s>"						,    __A="<unk>"						,    __A=[]						,    **__A						,    ):






     """simple docstring"""





     super().__init__(
         __A						,    tokenizer_file=__A						,    eos_token=__A						,    unk_token=__A						,    additional_special_tokens=__A						,    **__A						,    )

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






   def _snake_case   (		self						,    __A						,    __A = 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
     lowerCamelCase			: str   =    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,)







from typing import TYPE_CHECKING

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


_snake_case					=					{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

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


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''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

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



