
from typing import TYPE_CHECKING

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


__A				={
    '''configuration_mobilebert''': [
        '''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileBertConfig''',
        '''MobileBertOnnxConfig''',
    ],
    '''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''MobileBertTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''MobileBertForMaskedLM''',
			    '''MobileBertForMultipleChoice''',
			    '''MobileBertForNextSentencePrediction''',
			    '''MobileBertForPreTraining''',
			    '''MobileBertForQuestionAnswering''',
			    '''MobileBertForSequenceClassification''',
			    '''MobileBertForTokenClassification''',
			    '''MobileBertLayer''',
			    '''MobileBertModel''',
			    '''MobileBertPreTrainedModel''',
			    '''load_tf_weights_in_mobilebert''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFMobileBertForMaskedLM''',
			    '''TFMobileBertForMultipleChoice''',
			    '''TFMobileBertForNextSentencePrediction''',
			    '''TFMobileBertForPreTraining''',
			    '''TFMobileBertForQuestionAnswering''',
			    '''TFMobileBertForSequenceClassification''',
			    '''TFMobileBertForTokenClassification''',
			    '''TFMobileBertMainLayer''',
			    '''TFMobileBertModel''',
			    '''TFMobileBertPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_mobilebert import (
			    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileBertConfig,
			    MobileBertOnnxConfig,
			)
			from .tokenization_mobilebert import MobileBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mobilebert_fast import MobileBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilebert import (
						    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileBertForMaskedLM,
						    MobileBertForMultipleChoice,
						    MobileBertForNextSentencePrediction,
						    MobileBertForPreTraining,
						    MobileBertForQuestionAnswering,
						    MobileBertForSequenceClassification,
						    MobileBertForTokenClassification,
						    MobileBertLayer,
						    MobileBertModel,
						    MobileBertPreTrainedModel,
						    load_tf_weights_in_mobilebert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_mobilebert import (
						    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFMobileBertForMaskedLM,
						    TFMobileBertForMultipleChoice,
						    TFMobileBertForNextSentencePrediction,
						    TFMobileBertForPreTraining,
						    TFMobileBertForQuestionAnswering,
						    TFMobileBertForSequenceClassification,
						    TFMobileBertForTokenClassification,
						    TFMobileBertMainLayer,
						    TFMobileBertModel,
						    TFMobileBertPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase	:						str       =	{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						str       =	[
	    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecAudioForAudioFrameClassification',
	    'Data2VecAudioForCTC',
	    'Data2VecAudioForSequenceClassification',
	    'Data2VecAudioForXVector',
	    'Data2VecAudioModel',
	    'Data2VecAudioPreTrainedModel',
	]
	__lowercase	:						Tuple       =	[
	    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecTextForCausalLM',
	    'Data2VecTextForMaskedLM',
	    'Data2VecTextForMultipleChoice',
	    'Data2VecTextForQuestionAnswering',
	    'Data2VecTextForSequenceClassification',
	    'Data2VecTextForTokenClassification',
	    'Data2VecTextModel',
	    'Data2VecTextPreTrainedModel',
	]
	__lowercase	:						Dict       =	[
	    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecVisionForImageClassification',
	    'Data2VecVisionForMaskedImageModeling',
	    'Data2VecVisionForSemanticSegmentation',
	    'Data2VecVisionModel',
	    'Data2VecVisionPreTrainedModel',
	]

if is_tf_available():
	__lowercase	:						Optional[Any]       =	[
	    'TFData2VecVisionForImageClassification',
	    'TFData2VecVisionForSemanticSegmentation',
	    'TFData2VecVisionModel',
	    'TFData2VecVisionPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						int       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



