


from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]    )     ->		int:

       """simple docstring"""

       return DownloadCommand(args.model  ,   args.cache_dir  ,   args.force  ,   args.trust_remote_code    )
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(	SCREAMING_SNAKE_CASE__    :	ArgumentParser					)		->      Tuple:
             a_ :		Tuple							=  parser.add_parser('download'					)
             download_parser.add_argument(
                 '--cache-dir'					,							type=SCREAMING_SNAKE_CASE__					,							default=SCREAMING_SNAKE_CASE__					,							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=SCREAMING_SNAKE_CASE__					,							help='Name of the model to download'					)
             download_parser.set_defaults(func=SCREAMING_SNAKE_CASE__					)
      def __init__(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	bool					,							SCREAMING_SNAKE_CASE__    :	bool					)		->      Tuple:
             a_ :		Dict							=  model
             a_ :		Tuple							=  cache
             a_ :		str							=  force
             a_ :		Optional[Any]							=  trust_remote_code

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      int:
             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					)



from typing import TYPE_CHECKING

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


UpperCAmelCase_				:			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:
   UpperCAmelCase_				:			Union[str, Any]   =	['MobileBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       '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:
   UpperCAmelCase_				:			Tuple   =	[
       '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

   UpperCAmelCase_				:			Tuple   =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


