



from typing import TYPE_CHECKING

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


a			:     str     = {
    'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a			:     Dict     = ['DeformableDetrFeatureExtractor']
 a			:     Optional[int]     = ['DeformableDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a			:     List[str]     = [
     'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DeformableDetrForObjectDetection',
     'DeformableDetrModel',
     'DeformableDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
  from .image_processing_deformable_detr import DeformableDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deformable_detr import (
      DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeformableDetrForObjectDetection,
      DeformableDetrModel,
      DeformableDetrPreTrainedModel,
  )

else:
 import sys

 a			:     Dict     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











from typing import TYPE_CHECKING

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


a			:     Union[str, Any]     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Optional[int]     = [
	    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaubertForMultipleChoice',
	    'FlaubertForQuestionAnswering',
	    'FlaubertForQuestionAnsweringSimple',
	    'FlaubertForSequenceClassification',
	    'FlaubertForTokenClassification',
	    'FlaubertModel',
	    'FlaubertWithLMHeadModel',
	    'FlaubertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Dict     = [
	    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFFlaubertForMultipleChoice',
	    'TFFlaubertForQuestionAnsweringSimple',
	    'TFFlaubertForSequenceClassification',
	    'TFFlaubertForTokenClassification',
	    'TFFlaubertModel',
	    'TFFlaubertPreTrainedModel',
	    'TFFlaubertWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	a			:     Tuple     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






