

"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase  :      Union[str, Any]							      =		{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		['''BeitFeatureExtractor''']
							__UpperCamelCase  :      Optional[int]							      =		['''BeitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BeitForImageClassification''',
							    '''BeitForMaskedImageModeling''',
							    '''BeitForSemanticSegmentation''',
							    '''BeitModel''',
							    '''BeitPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		[
							    '''FlaxBeitForImageClassification''',
							    '''FlaxBeitForMaskedImageModeling''',
							    '''FlaxBeitModel''',
							    '''FlaxBeitPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_beit import BeitFeatureExtractor
														from .image_processing_beit import BeitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_beit import (
														    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BeitForImageClassification,
														    BeitForMaskedImageModeling,
														    BeitForSemanticSegmentation,
														    BeitModel,
														    BeitPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_beit import (
														    FlaxBeitForImageClassification,
														    FlaxBeitForMaskedImageModeling,
														    FlaxBeitModel,
														    FlaxBeitPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      Optional[Any]							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

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




