[Vision] fix small nit on `BeitDropPath` layers (#20587)

* fix small nit

* add last file

src/transformers/models/beit/modeling_beit.py

@@ -118,8 +118,8 @@ class BeitDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/convnext/modeling_convnext.py

@@ -82,8 +82,8 @@ class ConvNextDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/cvt/modeling_cvt.py

@@ -107,8 +107,8 @@ class CvtDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/data2vec/modeling_data2vec_vision.py

@@ -120,8 +120,8 @@ class Data2VecVisionDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/dinat/modeling_dinat.py

@@ -295,8 +295,8 @@ class DinatDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/donut/modeling_donut_swin.py

@@ -325,8 +325,8 @@ class DonutSwinDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/glpn/modeling_glpn.py

@@ -82,8 +82,8 @@ class GLPNDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/maskformer/modeling_maskformer_swin.py

@@ -285,8 +285,8 @@ class MaskFormerSwinDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/nat/modeling_nat.py

@@ -289,8 +289,8 @@ class NatDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/poolformer/modeling_poolformer.py

@@ -79,8 +79,8 @@ class PoolFormerDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/segformer/modeling_segformer.py

@@ -114,8 +114,8 @@ class SegformerDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/swin/modeling_swin.py

@@ -397,8 +397,8 @@ class SwinDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/swinv2/modeling_swinv2.py

@@ -262,8 +262,8 @@ class Swinv2DropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/van/modeling_van.py

@@ -83,8 +83,8 @@ class VanDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

src/transformers/models/x_clip/modeling_x_clip.py

@@ -381,8 +381,8 @@ class XCLIPDropPath(nn.Module):
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)