from typing import TYPE_CHECKING

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


a_    					=       {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DebertaForMaskedLM',
       'DebertaForQuestionAnswering',
       'DebertaForSequenceClassification',
       'DebertaForTokenClassification',
       'DebertaModel',
       'DebertaPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFDebertaForMaskedLM',
       'TFDebertaForQuestionAnswering',
       'TFDebertaForSequenceClassification',
       'TFDebertaForTokenClassification',
       'TFDebertaModel',
       'TFDebertaPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

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




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)



