





"""simple docstring"""




from typing import TYPE_CHECKING

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


a						: Tuple     	=			{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a						: List[Any]     	=			[
		    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MegaForCausalLM''',
		    '''MegaForMaskedLM''',
		    '''MegaForMultipleChoice''',
		    '''MegaForQuestionAnswering''',
		    '''MegaForSequenceClassification''',
		    '''MegaForTokenClassification''',
		    '''MegaModel''',
		    '''MegaPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

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









from typing import TYPE_CHECKING

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


UpperCAmelCase						:   Optional[int]			  =     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Union[str, Any]			  =     [
				    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''XLMForMultipleChoice''',
				    '''XLMForQuestionAnswering''',
				    '''XLMForQuestionAnsweringSimple''',
				    '''XLMForSequenceClassification''',
				    '''XLMForTokenClassification''',
				    '''XLMModel''',
				    '''XLMPreTrainedModel''',
				    '''XLMWithLMHeadModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Optional[Any]			  =     [
				    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFXLMForMultipleChoice''',
				    '''TFXLMForQuestionAnsweringSimple''',
				    '''TFXLMForSequenceClassification''',
				    '''TFXLMForTokenClassification''',
				    '''TFXLMMainLayer''',
				    '''TFXLMModel''',
				    '''TFXLMPreTrainedModel''',
				    '''TFXLMWithLMHeadModel''',
				]


if TYPE_CHECKING:
				from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
				from .tokenization_xlm import XLMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm import (
								    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMForMultipleChoice,
								    XLMForQuestionAnswering,
								    XLMForQuestionAnsweringSimple,
								    XLMForSequenceClassification,
								    XLMForTokenClassification,
								    XLMModel,
								    XLMPreTrainedModel,
								    XLMWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlm import (
								    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLMForMultipleChoice,
								    TFXLMForQuestionAnsweringSimple,
								    TFXLMForSequenceClassification,
								    TFXLMForTokenClassification,
								    TFXLMMainLayer,
								    TFXLMModel,
								    TFXLMPreTrainedModel,
								    TFXLMWithLMHeadModel,
								)

else:
				import sys

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




