
'''simple docstring'''







from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser







def        SCREAMING_SNAKE_CASE__			(     ) ->			Union[str, Any]:
	_snake_case     		=	ArgumentParser('Accelerate CLI tool'    ,       usage='accelerate <command> [<args>]'    ,       allow_abbrev=__A			)
	_snake_case     		=	parser.add_subparsers(help='accelerate command helpers'			)

	# Register commands
	get_config_parser(subparsers=__A			)
	env_command_parser(subparsers=__A			)
	launch_command_parser(subparsers=__A			)
	tpu_command_parser(subparsers=__A			)
	test_command_parser(subparsers=__A			)

	# Let's go
	_snake_case     		=	parser.parse_args()

	if not hasattr(__A    ,       'func'			):
		parser.print_help()
		exit(1			)

	# Run
	args.func(__A			)


if __name__ == "__main__":
					main()



'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase					:							str   =      {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Any   =      [
					    "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "NezhaForNextSentencePrediction",
					    "NezhaForMaskedLM",
					    "NezhaForPreTraining",
					    "NezhaForMultipleChoice",
					    "NezhaForQuestionAnswering",
					    "NezhaForSequenceClassification",
					    "NezhaForTokenClassification",
					    "NezhaModel",
					    "NezhaPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nezha import (
										    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NezhaForMaskedLM,
										    NezhaForMultipleChoice,
										    NezhaForNextSentencePrediction,
										    NezhaForPreTraining,
										    NezhaForQuestionAnswering,
										    NezhaForSequenceClassification,
										    NezhaForTokenClassification,
										    NezhaModel,
										    NezhaPreTrainedModel,
										)


else:
					import sys

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

