


from typing import TYPE_CHECKING

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


_lowerCAmelCase:						Optional[int]   				=       {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Dict   				=       [
	    '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:
	_lowerCAmelCase:						Union[str, Any]   				=       [
	    '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

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









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

