



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 A    (							__UpperCamelCase       )    ->   List[str]:
			return {key.lstrip('-'       ): value for key, value in zip(unknown_args[::2]			,						unknown_args[1::2]       )}


def A    (							)    ->   Optional[int]:
			A__	  =     ArgumentParser(
			    'HuggingFace Datasets CLI tool'			,						usage='datasets-cli <command> [<args>]'			,						allow_abbrev=__UpperCamelCase       )
			A__	  =     parser.add_subparsers(help='datasets-cli command helpers'       )
			set_verbosity_info()

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

			# Parse args
			A__  ,			A__	  =     parser.parse_known_args()
			if not hasattr(__UpperCamelCase			,						'func'       ):
						parser.print_help()
						exit(1       )
			A__	  =     parse_unknown_args(__UpperCamelCase       )

			# Run
			A__	  =     args.func(__UpperCamelCase			,						**__UpperCamelCase       )
			service.run()


if __name__ == "__main__":
							main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




