


"""simple docstring"""






from typing import TYPE_CHECKING

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


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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase     =  [
	    '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:
	_lowerCamelCase     =  [
	    '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

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




"""simple docstring"""






from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					CustomTokenizer
				pass
