





from typing import TYPE_CHECKING

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


_a     :    Tuple         =						{
    '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     :    Union[str, Any]         =						['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[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

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




'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case				 =	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoForCausalLM''',
		    '''GPTNeoForQuestionAnswering''',
		    '''GPTNeoForSequenceClassification''',
		    '''GPTNeoForTokenClassification''',
		    '''GPTNeoModel''',
		    '''GPTNeoPreTrainedModel''',
		    '''load_tf_weights_in_gpt_neo''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxGPTNeoForCausalLM''',
		    '''FlaxGPTNeoModel''',
		    '''FlaxGPTNeoPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neo import (
				    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoForCausalLM,
				    GPTNeoForQuestionAnswering,
				    GPTNeoForSequenceClassification,
				    GPTNeoForTokenClassification,
				    GPTNeoModel,
				    GPTNeoPreTrainedModel,
				    load_tf_weights_in_gpt_neo,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
		import sys

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