

from typing import TYPE_CHECKING

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


_UpperCamelCase     			=     {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     			=     [
     '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ConvBertForMaskedLM''',
     '''ConvBertForMultipleChoice''',
     '''ConvBertForQuestionAnswering''',
     '''ConvBertForSequenceClassification''',
     '''ConvBertForTokenClassification''',
     '''ConvBertLayer''',
     '''ConvBertModel''',
     '''ConvBertPreTrainedModel''',
     '''load_tf_weights_in_convbert''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     			=     [
     '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFConvBertForMaskedLM''',
     '''TFConvBertForMultipleChoice''',
     '''TFConvBertForQuestionAnswering''',
     '''TFConvBertForSequenceClassification''',
     '''TFConvBertForTokenClassification''',
     '''TFConvBertLayer''',
     '''TFConvBertModel''',
     '''TFConvBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

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






from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




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




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							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      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[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   )

