Merge branch 'main' into patch-5

src/transformers/models/clip/convert_clip_original_pytorch_to_hf.py

@@ -149,7 +149,7 @@ def convert_clip_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_pa
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
+    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to OpenAI checkpoint")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     args = parser.parse_args()
 

src/transformers/models/mask2former/configuration_mask2former.py

@@ -171,7 +171,7 @@ def __init__(
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
             backbone_config = CONFIG_MAPPING["swin"](
                 image_size=224,
-                in_channels=3,
+                num_channels=3,
                 patch_size=4,
                 embed_dim=96,
                 depths=[2, 2, 18, 2],

src/transformers/models/maskformer/configuration_maskformer.py

@@ -131,7 +131,7 @@ def __init__(
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             backbone_config = SwinConfig(
                 image_size=384,
-                in_channels=3,
+                num_channels=3,
                 patch_size=4,
                 embed_dim=128,
                 depths=[2, 2, 18, 2],

src/transformers/models/oneformer/configuration_oneformer.py

@@ -201,7 +201,7 @@ def __init__(
             logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone.")
             backbone_config = CONFIG_MAPPING["swin"](
                 image_size=224,
-                in_channels=3,
+                num_channels=3,
                 patch_size=4,
                 embed_dim=96,
                 depths=[2, 2, 6, 2],

src/transformers/models/timm_wrapper/modeling_timm_wrapper.py

@@ -82,6 +82,9 @@ class TimmWrapperPreTrainedModel(PreTrainedModel):
     config_class = TimmWrapperConfig
     _no_split_modules = []
 
+    # used in Trainer to avoid passing `loss_kwargs` to model forward
+    accepts_loss_kwargs = False
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision", "timm"])
         super().__init__(*args, **kwargs)

src/transformers/trainer.py

@@ -622,7 +622,15 @@ def __init__(
             else unwrapped_model.get_base_model().forward
         )
         forward_params = inspect.signature(model_forward).parameters
-        self.model_accepts_loss_kwargs = any(k.kind == inspect.Parameter.VAR_KEYWORD for k in forward_params.values())
+
+        # Check if the model has explicit setup for loss kwargs,
+        # if not, check if `**kwargs` are in model.forward
+        if hasattr(model, "accepts_loss_kwargs"):
+            self.model_accepts_loss_kwargs = model.accepts_loss_kwargs
+        else:
+            self.model_accepts_loss_kwargs = any(
+                k.kind == inspect.Parameter.VAR_KEYWORD for k in forward_params.values()
+            )
 
         self.neftune_noise_alpha = args.neftune_noise_alpha
 

src/transformers/training_args.py

@@ -2164,7 +2164,7 @@ def _setup_devices(self) -> "torch.device":
             if not is_accelerate_available():
                 raise ImportError(
                     f"Using the `Trainer` with `PyTorch` requires `accelerate>={ACCELERATE_MIN_VERSION}`: "
-                    "Please run `pip install transformers[torch]` or `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`"
+                    f"Please run `pip install transformers[torch]` or `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`"
                 )
         # We delay the init of `PartialState` to the end for clarity
         accelerator_state_kwargs = {"enabled": True, "use_configured_state": False}