


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:
 lowerCamelCase		      = None


lowerCamelCase		      = logging.get_logger(__name__)


lowerCamelCase		      = """▁"""

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

lowerCamelCase		      = {
    """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"""
        )
    },
}

lowerCamelCase		      = {
    """google/reformer-crime-and-punishment""": 52_42_88,
}




class A						(     UpperCAmelCase__  ):
      UpperCamelCase__							:						Dict   =VOCAB_FILES_NAMES
      UpperCamelCase__							:						Optional[Any]   =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:						List[Any]   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:						str   =['input_ids', 'attention_mask']
      UpperCamelCase__							:						List[Any]   =ReformerTokenizer






      def __init__(      self		:  int    ,     lowercase_		:  int=None    ,     lowercase_		:  Dict=None    ,     lowercase_		:  Tuple="</s>"    ,     lowercase_		:  Union[str, Any]="<unk>"    ,     lowercase_		:  int=[]    ,     **lowercase_		:  Dict    ,     )      ->		Any:
             """simple docstring"""




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

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






      def  lowerCamelCase (      self		:  int    ,     lowercase_		:  Tuple    ,     lowercase_		:  Tuple = None				)      ->		Optional[int]:
             """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
             _lowerCamelCase :	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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase		      = {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = ['DebertaV2TokenizerFast']

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDebertaV2ForMaskedLM',
     'TFDebertaV2ForQuestionAnswering',
     'TFDebertaV2ForSequenceClassification',
     'TFDebertaV2ForTokenClassification',
     'TFDebertaV2Model',
     'TFDebertaV2PreTrainedModel',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DebertaV2ForMaskedLM',
     'DebertaV2ForMultipleChoice',
     'DebertaV2ForQuestionAnswering',
     'DebertaV2ForSequenceClassification',
     'DebertaV2ForTokenClassification',
     'DebertaV2Model',
     'DebertaV2PreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

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


