complete the type annotations for config parameters (#16263)

src/transformers/models/deit/modeling_deit.py

@@ -147,7 +147,7 @@ class PatchEmbeddings(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->DeiT
 class DeiTSelfAttention(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: DeiTConfig) -> None:
         super().__init__()
         if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
             raise ValueError(
@@ -213,7 +213,7 @@ class DeiTSelfOutput(nn.Module):
     layernorm applied before each block.
     """
 
-    def __init__(self, config) -> None:
+    def __init__(self, config: DeiTConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -228,7 +228,7 @@ class DeiTSelfOutput(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->DeiT
 class DeiTAttention(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: DeiTConfig) -> None:
         super().__init__()
         self.attention = DeiTSelfAttention(config)
         self.output = DeiTSelfOutput(config)
@@ -268,7 +268,7 @@ class DeiTAttention(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->DeiT
 class DeiTIntermediate(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: DeiTConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
         if isinstance(config.hidden_act, str):
@@ -286,7 +286,7 @@ class DeiTIntermediate(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->DeiT
 class DeiTOutput(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: DeiTConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -304,7 +304,7 @@ class DeiTOutput(nn.Module):
 class DeiTLayer(nn.Module):
     """This corresponds to the Block class in the timm implementation."""
 
-    def __init__(self, config) -> None:
+    def __init__(self, config: DeiTConfig) -> None:
         super().__init__()
         self.chunk_size_feed_forward = config.chunk_size_feed_forward
         self.seq_len_dim = 1
@@ -345,7 +345,7 @@ class DeiTLayer(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->DeiT
 class DeiTEncoder(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: DeiTConfig) -> None:
         super().__init__()
         self.config = config
         self.layer = nn.ModuleList([DeiTLayer(config) for _ in range(config.num_hidden_layers)])
@@ -553,7 +553,7 @@ class DeiTModel(DeiTPreTrainedModel):
 
 # Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->DeiT
 class DeiTPooler(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config: DeiTConfig):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.activation = nn.Tanh()

src/transformers/models/vilt/modeling_vilt.py

@@ -388,7 +388,7 @@ class ViltSelfOutput(nn.Module):
     layernorm applied before each block.
     """
 
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViltConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -437,7 +437,7 @@ class ViltAttention(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->Vilt
 class ViltIntermediate(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViltConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
         if isinstance(config.hidden_act, str):
@@ -455,7 +455,7 @@ class ViltIntermediate(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->Vilt
 class ViltOutput(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViltConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)

src/transformers/models/vit/modeling_vit.py

@@ -77,7 +77,7 @@ class ViTEmbeddings(nn.Module):
 
     """
 
-    def __init__(self, config, use_mask_token: bool = False) -> None:
+    def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None:
         super().__init__()
 
         self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
@@ -192,7 +192,7 @@ class PatchEmbeddings(nn.Module):
 
 
 class ViTSelfAttention(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__()
         if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
             raise ValueError(
@@ -257,7 +257,7 @@ class ViTSelfOutput(nn.Module):
     layernorm applied before each block.
     """
 
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -271,7 +271,7 @@ class ViTSelfOutput(nn.Module):
 
 
 class ViTAttention(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__()
         self.attention = ViTSelfAttention(config)
         self.output = ViTSelfOutput(config)
@@ -310,7 +310,7 @@ class ViTAttention(nn.Module):
 
 
 class ViTIntermediate(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
         if isinstance(config.hidden_act, str):
@@ -327,7 +327,7 @@ class ViTIntermediate(nn.Module):
 
 
 class ViTOutput(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -344,7 +344,7 @@ class ViTOutput(nn.Module):
 class ViTLayer(nn.Module):
     """This corresponds to the Block class in the timm implementation."""
 
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__()
         self.chunk_size_feed_forward = config.chunk_size_feed_forward
         self.seq_len_dim = 1
@@ -384,7 +384,7 @@ class ViTLayer(nn.Module):
 
 
 class ViTEncoder(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__()
         self.config = config
         self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)])
@@ -595,7 +595,7 @@ class ViTModel(ViTPreTrainedModel):
 
 
 class ViTPooler(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config: ViTConfig):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.activation = nn.Tanh()
@@ -614,7 +614,7 @@ class ViTPooler(nn.Module):
     VIT_START_DOCSTRING,
 )
 class ViTForMaskedImageModeling(ViTPreTrainedModel):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__(config)
 
         self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True)
@@ -724,7 +724,7 @@ class ViTForMaskedImageModeling(ViTPreTrainedModel):
     VIT_START_DOCSTRING,
 )
 class ViTForImageClassification(ViTPreTrainedModel):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTConfig) -> None:
         super().__init__(config)
 
         self.num_labels = config.num_labels

src/transformers/models/vit_mae/modeling_vit_mae.py

@@ -134,7 +134,7 @@ class ViTMAEForPreTrainingOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
-# copied from transformers.models.vit.modeling_vit.to_2tuple
+# copied from transformers.models.vit.modeling_vit.to_2tuple ViT->ViTMAE
 def to_2tuple(x):
     if isinstance(x, collections.abc.Iterable):
         return x
@@ -316,9 +316,9 @@ class PatchEmbeddings(nn.Module):
         return x
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention
+# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention ViT->ViTMAE
 class ViTMAESelfAttention(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTMAEConfig) -> None:
         super().__init__()
         if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
             raise ValueError(
@@ -384,7 +384,7 @@ class ViTMAESelfOutput(nn.Module):
     layernorm applied before each block.
     """
 
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTMAEConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -399,7 +399,7 @@ class ViTMAESelfOutput(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTMAE
 class ViTMAEAttention(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTMAEConfig) -> None:
         super().__init__()
         self.attention = ViTMAESelfAttention(config)
         self.output = ViTMAESelfOutput(config)
@@ -437,9 +437,9 @@ class ViTMAEAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTIntermediate
+# Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->ViTMAE
 class ViTMAEIntermediate(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTMAEConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
         if isinstance(config.hidden_act, str):
@@ -455,9 +455,9 @@ class ViTMAEIntermediate(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTOutput
+# Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->ViTMAE
 class ViTMAEOutput(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTMAEConfig) -> None:
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -475,7 +475,7 @@ class ViTMAEOutput(nn.Module):
 class ViTMAELayer(nn.Module):
     """This corresponds to the Block class in the timm implementation."""
 
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTMAEConfig) -> None:
         super().__init__()
         self.chunk_size_feed_forward = config.chunk_size_feed_forward
         self.seq_len_dim = 1
@@ -516,7 +516,7 @@ class ViTMAELayer(nn.Module):
 
 # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTMAE
 class ViTMAEEncoder(nn.Module):
-    def __init__(self, config) -> None:
+    def __init__(self, config: ViTMAEConfig) -> None:
         super().__init__()
         self.config = config
         self.layer = nn.ModuleList([ViTMAELayer(config) for _ in range(config.num_hidden_layers)])