






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :      Tuple				=		{
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      List[str]				=		["""EfficientFormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      List[str]				=		[
					    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """EfficientFormerForImageClassification""",
					    """EfficientFormerForImageClassificationWithTeacher""",
					    """EfficientFormerModel""",
					    """EfficientFormerPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      Optional[int]				=		[
					    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFEfficientFormerForImageClassification""",
					    """TFEfficientFormerForImageClassificationWithTeacher""",
					    """TFEfficientFormerModel""",
					    """TFEfficientFormerPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_efficientformer import EfficientFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_efficientformer import (
										    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EfficientFormerForImageClassification,
										    EfficientFormerForImageClassificationWithTeacher,
										    EfficientFormerModel,
										    EfficientFormerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_efficientformer import (
										    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFEfficientFormerForImageClassification,
										    TFEfficientFormerForImageClassificationWithTeacher,
										    TFEfficientFormerModel,
										    TFEfficientFormerPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :      int				=		_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


UpperCAmelCase__							  =						{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

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

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "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__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


