






from typing import TYPE_CHECKING

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


_a   			=						{
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						["ImageGPTFeatureExtractor"]
		_a   			=						["ImageGPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ImageGPTForCausalImageModeling",
		    "ImageGPTForImageClassification",
		    "ImageGPTModel",
		    "ImageGPTPreTrainedModel",
		    "load_tf_weights_in_imagegpt",
		]


if TYPE_CHECKING:
		from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
				from .image_processing_imagegpt import ImageGPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_imagegpt import (
				    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ImageGPTForCausalImageModeling,
				    ImageGPTForImageClassification,
				    ImageGPTModel,
				    ImageGPTPreTrainedModel,
				    load_tf_weights_in_imagegpt,
				)

else:
		import sys

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   			=						{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

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