Fix DPT /Dinov2 sdpa regression on main (huggingface#33660)

* fallback to eager if output attentions.

* fix copies

src/transformers/models/dinov2/modeling_dinov2.py

@@ -231,7 +231,6 @@ def forward(
         return outputs
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTSdpaSelfAttention with ViT->Dinov2
 class Dinov2SdpaSelfAttention(Dinov2SelfAttention):
     def __init__(self, config: Dinov2Config) -> None:
         super().__init__(config)
@@ -240,6 +239,16 @@ def __init__(self, config: Dinov2Config) -> None:
     def forward(
         self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
     ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Dinov2Model is using Dinov2SdpaSelfAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states, head_mask=head_mask, output_attentions=output_attentions
+            )
+
         mixed_query_layer = self.query(hidden_states)
 
         key_layer = self.transpose_for_scores(self.key(hidden_states))