




from typing import TYPE_CHECKING

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


_lowerCamelCase			:	Any							 =     {
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase			:	Dict							 =     ["""MobileBertTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase			:	Any							 =     [
       """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MobileBertForMaskedLM""",
       """MobileBertForMultipleChoice""",
       """MobileBertForNextSentencePrediction""",
       """MobileBertForPreTraining""",
       """MobileBertForQuestionAnswering""",
       """MobileBertForSequenceClassification""",
       """MobileBertForTokenClassification""",
       """MobileBertLayer""",
       """MobileBertModel""",
       """MobileBertPreTrainedModel""",
       """load_tf_weights_in_mobilebert""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase			:	Optional[int]							 =     [
       """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFMobileBertForMaskedLM""",
       """TFMobileBertForMultipleChoice""",
       """TFMobileBertForNextSentencePrediction""",
       """TFMobileBertForPreTraining""",
       """TFMobileBertForQuestionAnswering""",
       """TFMobileBertForSequenceClassification""",
       """TFMobileBertForTokenClassification""",
       """TFMobileBertMainLayer""",
       """TFMobileBertModel""",
       """TFMobileBertPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_mobilebert import (
       MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileBertConfig,
       MobileBertOnnxConfig,
   )
   from .tokenization_mobilebert import MobileBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mobilebert_fast import MobileBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilebert import (
          MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertLayer,
          MobileBertModel,
          MobileBertPreTrainedModel,
          load_tf_weights_in_mobilebert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilebert import (
          TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertMainLayer,
          TFMobileBertModel,
          TFMobileBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase			:	List[str]							 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       return DownloadCommand(args.model				,       args.cache_dir				,       args.force				,       args.trust_remote_code		)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    @staticmethod
    def  SCREAMING_SNAKE_CASE							(    UpperCAmelCase__   :			ArgumentParser)		->Any:





           '''simple docstring'''

           A__   				=					parser.add_parser('''download''')
           download_parser.add_argument(
               '''--cache-dir'''      ,				type=UpperCAmelCase__      ,				default=UpperCAmelCase__      ,				help='''Path to location to store the models''')
           download_parser.add_argument(
               '''--force'''      ,				action='''store_true'''      ,				help='''Force the model to be download even if already in cache-dir''')
           download_parser.add_argument(
               '''--trust-remote-code'''      ,				action='''store_true'''      ,				help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''      ,				)
           download_parser.add_argument('''model'''      ,				type=UpperCAmelCase__      ,				help='''Name of the model to download''')
           download_parser.set_defaults(func=UpperCAmelCase__)


    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			bool      ,				UpperCAmelCase__   :			bool)		->int:





           '''simple docstring'''

           A__   				=					model
           A__   				=					cache
           A__   				=					force
           A__   				=					trust_remote_code





    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Union[str, Any]:





           '''simple docstring'''

           from ..models.auto import AutoModel, AutoTokenizer

           AutoModel.from_pretrained(
               self._model      ,				cache_dir=self._cache      ,				force_download=self._force      ,				trust_remote_code=self._trust_remote_code)
           AutoTokenizer.from_pretrained(
               self._model      ,				cache_dir=self._cache      ,				force_download=self._force      ,				trust_remote_code=self._trust_remote_code)


