
from typing import TYPE_CHECKING

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


A		: int								=     {
    '''configuration_mobilebert''': [
        '''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileBertConfig''',
        '''MobileBertOnnxConfig''',
    ],
    '''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		: int								=     ['''MobileBertTokenizerFast''']

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

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

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Optional[int]:
						return {key.lstrip("""-"""       ): value for key, value in zip(unknown_args[::2]		,   unknown_args[1::2]       )}




def 	UpperCamelCase__  (     )  ->     Dict:
						_lowercase					       =					ArgumentParser(
						    """HuggingFace Datasets CLI tool"""		,   usage="""datasets-cli <command> [<args>]"""		,   allow_abbrev=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					parser.add_subparsers(help="""datasets-cli command helpers"""       )
						set_verbosity_info()

						# Register commands
						ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						TestCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )

						# Parse args
						_lowercase       ,						_lowercase					       =					parser.parse_known_args()
						if not hasattr(SCREAMING_SNAKE_CASE_		,   """func"""       ):
												parser.print_help()
												exit(1       )
						_lowercase					       =					parse_unknown_args(SCREAMING_SNAKE_CASE_       )

						# Run
						_lowercase					       =					args.func(SCREAMING_SNAKE_CASE_		,   **SCREAMING_SNAKE_CASE_       )
						service.run()


if __name__ == "__main__":
		main()