



from typing import TYPE_CHECKING

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


snake_case    :						str  =						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						['AlbertTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						['AlbertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						[
		    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AlbertForMaskedLM',
		    'AlbertForMultipleChoice',
		    'AlbertForPreTraining',
		    'AlbertForQuestionAnswering',
		    'AlbertForSequenceClassification',
		    'AlbertForTokenClassification',
		    'AlbertModel',
		    'AlbertPreTrainedModel',
		    'load_tf_weights_in_albert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Union[str, Any]  =						[
		    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAlbertForMaskedLM',
		    'TFAlbertForMultipleChoice',
		    'TFAlbertForPreTraining',
		    'TFAlbertForQuestionAnswering',
		    'TFAlbertForSequenceClassification',
		    'TFAlbertForTokenClassification',
		    'TFAlbertMainLayer',
		    'TFAlbertModel',
		    'TFAlbertPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						[
		    'FlaxAlbertForMaskedLM',
		    'FlaxAlbertForMultipleChoice',
		    'FlaxAlbertForPreTraining',
		    'FlaxAlbertForQuestionAnswering',
		    'FlaxAlbertForSequenceClassification',
		    'FlaxAlbertForTokenClassification',
		    'FlaxAlbertModel',
		    'FlaxAlbertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert import AlbertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert_fast import AlbertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_albert import (
				    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				    AlbertPreTrainedModel,
				    load_tf_weights_in_albert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_albert import (
				    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAlbertForMaskedLM,
				    TFAlbertForMultipleChoice,
				    TFAlbertForPreTraining,
				    TFAlbertForQuestionAnswering,
				    TFAlbertForSequenceClassification,
				    TFAlbertForTokenClassification,
				    TFAlbertMainLayer,
				    TFAlbertModel,
				    TFAlbertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				    FlaxAlbertPreTrainedModel,
				)
else:
		import sys

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




from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def 	snake_case__       (					)		->		List[Any]:







						"""simple docstring"""

						A__	:							List[Any]						    = ArgumentParser("Transformers CLI tool"							,     usage="transformers-cli <command> [<args>]"       )
						A__	:							int						    = parser.add_subparsers(help="transformers-cli command helpers"       )

						# Register commands
						ConvertCommand.register_subcommand(__lowercase       )
						DownloadCommand.register_subcommand(__lowercase       )
						EnvironmentCommand.register_subcommand(__lowercase       )
						RunCommand.register_subcommand(__lowercase       )
						ServeCommand.register_subcommand(__lowercase       )
						UserCommands.register_subcommand(__lowercase       )
						AddNewModelCommand.register_subcommand(__lowercase       )
						AddNewModelLikeCommand.register_subcommand(__lowercase       )
						LfsCommands.register_subcommand(__lowercase       )
						PTtoTFCommand.register_subcommand(__lowercase       )

						# Let's go
						A__	:							int						    = parser.parse_args()

						if not hasattr(__lowercase							,     "func"       ):
												parser.print_help()
												exit(1       )

						# Run
						A__	:							Any						    = args.func(__lowercase       )
						service.run()


if __name__ == "__main__":
		main()