





"""simple docstring"""




from typing import TYPE_CHECKING

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


a		:				Union[str, Any]  			=    {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		:				str  			=    ['''ConvBertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		:				Optional[Any]  			=    [
					    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ConvBertForMaskedLM''',
					    '''ConvBertForMultipleChoice''',
					    '''ConvBertForQuestionAnswering''',
					    '''ConvBertForSequenceClassification''',
					    '''ConvBertForTokenClassification''',
					    '''ConvBertLayer''',
					    '''ConvBertModel''',
					    '''ConvBertPreTrainedModel''',
					    '''load_tf_weights_in_convbert''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		:				List[str]  			=    [
					    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFConvBertForMaskedLM''',
					    '''TFConvBertForMultipleChoice''',
					    '''TFConvBertForQuestionAnswering''',
					    '''TFConvBertForSequenceClassification''',
					    '''TFConvBertForTokenClassification''',
					    '''TFConvBertLayer''',
					    '''TFConvBertModel''',
					    '''TFConvBertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

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









"""simple docstring"""




import baseaa






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->bytes:






	'''simple docstring'''






	return baseaa.aaaencode(string.encode("utf-8"      )      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					bytes      )				->str:






	'''simple docstring'''






	return baseaa.aaadecode(_lowercase      ).decode("utf-8"      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


