Move out common backbone config param validation (#31144)

* Move out common validation

* Add missing backbone config arguments

src/transformers/models/conditional_detr/configuration_conditional_detr.py

@@ -22,6 +22,7 @@ from packaging import version
 from ...configuration_utils import PretrainedConfig
 from ...onnx import OnnxConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -179,17 +180,6 @@ class ConditionalDetrConfig(PretrainedConfig):
         focal_alpha=0.25,
         **kwargs,
     ):
-        if not use_timm_backbone and use_pretrained_backbone:
-            raise ValueError(
-                "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`"
-            )
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
-        if backbone_config is not None and use_timm_backbone:
-            raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")
-
         # We default to values which were previously hard-coded in the model. This enables configurability of the config
         # while keeping the default behavior the same.
         if use_timm_backbone and backbone_kwargs is None:
@@ -208,6 +198,14 @@ class ConditionalDetrConfig(PretrainedConfig):
                 config_class = CONFIG_MAPPING[backbone_model_type]
                 backbone_config = config_class.from_dict(backbone_config)
 
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
+
         self.use_timm_backbone = use_timm_backbone
         self.backbone_config = backbone_config
         self.num_channels = num_channels

src/transformers/models/deformable_detr/configuration_deformable_detr.py

@@ -16,6 +16,7 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -195,20 +196,6 @@ class DeformableDetrConfig(PretrainedConfig):
         disable_custom_kernels=False,
         **kwargs,
     ):
-        if not use_timm_backbone and use_pretrained_backbone:
-            raise ValueError(
-                "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`"
-            )
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
-        if backbone_config is not None and use_timm_backbone:
-            raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")
-
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
-
         # We default to values which were previously hard-coded in the model. This enables configurability of the config
         # while keeping the default behavior the same.
         if use_timm_backbone and backbone_kwargs is None:
@@ -227,6 +214,14 @@ class DeformableDetrConfig(PretrainedConfig):
                 config_class = CONFIG_MAPPING[backbone_model_type]
                 backbone_config = config_class.from_dict(backbone_config)
 
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
+
         self.use_timm_backbone = use_timm_backbone
         self.backbone_config = backbone_config
         self.num_channels = num_channels

src/transformers/models/depth_anything/configuration_depth_anything.py

@@ -18,6 +18,7 @@ import copy
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto.configuration_auto import CONFIG_MAPPING
 
 
@@ -44,6 +45,12 @@ class DepthAnythingConfig(PretrainedConfig):
             is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
         use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
             Whether to use pretrained weights for the backbone.
+        use_timm_backbone (`bool`, *optional*, defaults to `False`):
+            Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
+            API.
+        backbone_kwargs (`dict`, *optional*):
+            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
         patch_size (`int`, *optional*, defaults to 14):
             The size of the patches to extract from the backbone features.
         initializer_range (`float`, *optional*, defaults to 0.02):
@@ -83,6 +90,8 @@ class DepthAnythingConfig(PretrainedConfig):
         backbone_config=None,
         backbone=None,
         use_pretrained_backbone=False,
+        use_timm_backbone=False,
+        backbone_kwargs=None,
         patch_size=14,
         initializer_range=0.02,
         reassemble_hidden_size=384,
@@ -94,13 +103,6 @@ class DepthAnythingConfig(PretrainedConfig):
         **kwargs,
     ):
         super().__init__(**kwargs)
-
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Dinov2` backbone.")
             backbone_config = CONFIG_MAPPING["dinov2"](
@@ -116,6 +118,14 @@ class DepthAnythingConfig(PretrainedConfig):
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
+
         self.backbone_config = backbone_config
         self.backbone = backbone
         self.use_pretrained_backbone = use_pretrained_backbone

src/transformers/models/detr/configuration_detr.py

@@ -22,6 +22,7 @@ from packaging import version
 from ...configuration_utils import PretrainedConfig
 from ...onnx import OnnxConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -176,20 +177,6 @@ class DetrConfig(PretrainedConfig):
         eos_coefficient=0.1,
         **kwargs,
     ):
-        if not use_timm_backbone and use_pretrained_backbone:
-            raise ValueError(
-                "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`"
-            )
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
-        if backbone_config is not None and use_timm_backbone:
-            raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")
-
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
-
         # We default to values which were previously hard-coded in the model. This enables configurability of the config
         # while keeping the default behavior the same.
         if use_timm_backbone and backbone_kwargs is None:
@@ -211,6 +198,14 @@ class DetrConfig(PretrainedConfig):
             # set timm attributes to None
             dilation = None
 
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
+
         self.use_timm_backbone = use_timm_backbone
         self.backbone_config = backbone_config
         self.num_channels = num_channels

src/transformers/models/dpt/configuration_dpt.py

@@ -18,6 +18,7 @@ import copy
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto.configuration_auto import CONFIG_MAPPING
 from ..bit import BitConfig
 
@@ -179,9 +180,6 @@ class DPTConfig(PretrainedConfig):
         self.hidden_size = hidden_size
         self.is_hybrid = is_hybrid
 
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
         use_autobackbone = False
         if self.is_hybrid:
             if backbone_config is None and backbone is None:
@@ -226,11 +224,13 @@ class DPTConfig(PretrainedConfig):
             self.backbone_featmap_shape = None
             self.neck_ignore_stages = []
 
-        if use_autobackbone and backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
 
         self.backbone = backbone
         self.use_pretrained_backbone = use_pretrained_backbone

src/transformers/models/grounding_dino/configuration_grounding_dino.py

@@ -16,6 +16,7 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -198,14 +199,6 @@ class GroundingDinoConfig(PretrainedConfig):
         layer_norm_eps=1e-5,
         **kwargs,
     ):
-        if not use_timm_backbone and use_pretrained_backbone:
-            raise ValueError(
-                "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`"
-            )
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
             backbone_config = CONFIG_MAPPING["swin"](
@@ -221,8 +214,13 @@ class GroundingDinoConfig(PretrainedConfig):
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
 
         if text_config is None:
             text_config = {}

src/transformers/models/mask2former/configuration_mask2former.py

@@ -18,6 +18,7 @@ from typing import Dict, List, Optional
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -166,12 +167,6 @@ class Mask2FormerConfig(PretrainedConfig):
         backbone_kwargs: Optional[Dict] = None,
         **kwargs,
     ):
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
             backbone_config = CONFIG_MAPPING["swin"](
@@ -186,15 +181,18 @@ class Mask2FormerConfig(PretrainedConfig):
                 use_absolute_embeddings=False,
                 out_features=["stage1", "stage2", "stage3", "stage4"],
             )
-
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
-
-        if isinstance(backbone_config, dict):
+        elif isinstance(backbone_config, dict):
             backbone_model_type = backbone_config.pop("model_type")
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
         # verify that the backbone is supported
         if backbone_config is not None and backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(

src/transformers/models/maskformer/configuration_maskformer.py

@@ -18,6 +18,7 @@ from typing import Dict, Optional
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 from ..detr import DetrConfig
 from ..swin import SwinConfig
@@ -126,15 +127,6 @@ class MaskFormerConfig(PretrainedConfig):
         backbone_kwargs: Optional[Dict] = None,
         **kwargs,
     ):
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             backbone_config = SwinConfig(
@@ -148,12 +140,18 @@ class MaskFormerConfig(PretrainedConfig):
                 drop_path_rate=0.3,
                 out_features=["stage1", "stage2", "stage3", "stage4"],
             )
-
-        if isinstance(backbone_config, dict):
+        elif isinstance(backbone_config, dict):
             backbone_model_type = backbone_config.pop("model_type")
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
         # verify that the backbone is supported
         if backbone_config is not None and backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(

src/transformers/models/oneformer/configuration_oneformer.py

@@ -18,6 +18,7 @@ from typing import Dict, Optional
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -196,12 +197,6 @@ class OneFormerConfig(PretrainedConfig):
         common_stride: int = 4,
         **kwargs,
     ):
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone.")
             backbone_config = CONFIG_MAPPING["swin"](
@@ -221,8 +216,13 @@ class OneFormerConfig(PretrainedConfig):
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
 
         self.backbone_config = backbone_config
         self.backbone = backbone

src/transformers/models/table_transformer/configuration_table_transformer.py

@@ -22,6 +22,7 @@ from packaging import version
 from ...configuration_utils import PretrainedConfig
 from ...onnx import OnnxConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -177,20 +178,6 @@ class TableTransformerConfig(PretrainedConfig):
         eos_coefficient=0.1,
         **kwargs,
     ):
-        if not use_timm_backbone and use_pretrained_backbone:
-            raise ValueError(
-                "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`"
-            )
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
-        if backbone_config is not None and use_timm_backbone:
-            raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")
-
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
-
         # We default to values which were previously hard-coded in the model. This enables configurability of the config
         # while keeping the default behavior the same.
         if use_timm_backbone and backbone_kwargs is None:
@@ -212,6 +199,14 @@ class TableTransformerConfig(PretrainedConfig):
             # set timm attributes to None
             dilation = None
 
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
+
         self.use_timm_backbone = use_timm_backbone
         self.backbone_config = backbone_config
         self.num_channels = num_channels

src/transformers/models/tvp/configuration_tvp.py

@@ -18,6 +18,7 @@ import copy
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto import CONFIG_MAPPING
 
 
@@ -129,12 +130,6 @@ class TvpConfig(PretrainedConfig):
         **kwargs,
     ):
         super().__init__(**kwargs)
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
             backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
@@ -143,8 +138,13 @@ class TvpConfig(PretrainedConfig):
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
 
         self.backbone_config = backbone_config
         self.backbone = backbone

src/transformers/models/upernet/configuration_upernet.py

@@ -16,6 +16,7 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto.configuration_auto import CONFIG_MAPPING
 
 
@@ -103,12 +104,6 @@ class UperNetConfig(PretrainedConfig):
         **kwargs,
     ):
         super().__init__(**kwargs)
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
             backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
@@ -117,8 +112,13 @@ class UperNetConfig(PretrainedConfig):
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
 
         self.backbone_config = backbone_config
         self.backbone = backbone

src/transformers/models/vitmatte/configuration_vitmatte.py

@@ -19,6 +19,7 @@ from typing import List
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
 from ..auto.configuration_auto import CONFIG_MAPPING
 
 
@@ -94,12 +95,6 @@ class VitMatteConfig(PretrainedConfig):
     ):
         super().__init__(**kwargs)
 
-        if use_pretrained_backbone:
-            raise ValueError("Pretrained backbones are not supported yet.")
-
-        if backbone_config is not None and backbone is not None:
-            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
-
         if backbone_config is None and backbone is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `VitDet` backbone.")
             backbone_config = CONFIG_MAPPING["vitdet"](out_features=["stage4"])
@@ -108,8 +103,13 @@ class VitMatteConfig(PretrainedConfig):
             config_class = CONFIG_MAPPING[backbone_model_type]
             backbone_config = config_class.from_dict(backbone_config)
 
-        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
-            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
 
         self.backbone_config = backbone_config
         self.backbone = backbone

src/transformers/utils/backbone_utils.py

@@ -17,7 +17,11 @@
 
 import enum
 import inspect
-from typing import Iterable, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
+
+
+if TYPE_CHECKING:
+    from .configuration_utils import PretrainedConfig
 
 
 class BackboneType(enum.Enum):
@@ -352,3 +356,28 @@ def load_backbone(config):
             backbone_config = AutoConfig.from_pretrained(backbone_checkpoint, **backbone_kwargs)
         backbone = AutoBackbone.from_config(config=backbone_config)
     return backbone
+
+
+def verify_backbone_config_arguments(
+    use_timm_backbone: bool,
+    use_pretrained_backbone: bool,
+    backbone: Optional[str],
+    backbone_config: Optional[Union[dict, "PretrainedConfig"]],
+    backbone_kwargs: Optional[dict],
+):
+    """
+    Verify that the config arguments to be passed to load_backbone are valid
+    """
+    if not use_timm_backbone and use_pretrained_backbone:
+        raise ValueError(
+            "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`"
+        )
+
+    if backbone_config is not None and backbone is not None:
+        raise ValueError("You can't specify both `backbone` and `backbone_config`.")
+
+    if backbone_config is not None and use_timm_backbone:
+        raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")
+
+    if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
+        raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")