


'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int       ):
					return DownloadCommand(args.model						,				args.cache_dir						,				args.force						,				args.trust_remote_code       )





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	@staticmethod
	def 				__UpperCAmelCase							(       __a							):

						'''simple docstring'''





						__a      :	Dict    =  parser.add_parser('download'							)
						download_parser.add_argument(
						    '--cache-dir'						,    type=__a						,    default=__a						,    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=__a						,    help='Name of the model to download'							)
						download_parser.set_defaults(func=__a							)




	def __init__(       self						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  model
						__a      :	List[str]    =  cache
						__a      :	Any    =  force
						__a      :	Optional[Any]    =  trust_remote_code





	def 				__UpperCAmelCase							(       self							):

						'''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							)







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



