



from typing import TYPE_CHECKING

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


a_								=   {
    "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:
  a_								=   ["DebertaV2TokenizerFast"]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_								=   [
      "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:
  a_								=   [
      "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

  a_								=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


a_								=   logging.get_logger(__name__)

a_								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_								=   {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
    """tokenizer_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"""},
}

a_								=   {
    """openai-gpt""": 512,
}



class 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					VOCAB_FILES_NAMES
 lowerCAmelCase__       =					PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__       =					["""input_ids""", """attention_mask"""]
 lowerCAmelCase__       =					OpenAIGPTTokenizer




 def __init__(  self		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase="<unk>"		,			**__UpperCAmelCase			):



    '''simple docstring'''
    super().__init__(__UpperCAmelCase		,			__UpperCAmelCase		,			tokenizer_file=__UpperCAmelCase		,			unk_token=__UpperCAmelCase		,			**__UpperCAmelCase			)




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return True




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None			):



    '''simple docstring'''
    __lowerCamelCase  							=      self._tokenizer.model.save(__UpperCAmelCase		,			name=__UpperCAmelCase			)
    return tuple(__UpperCAmelCase			)






