Create configuration_vivqa.py

configuration_vivqa.py

@@ -0,0 +1,28 @@
+from transformers import PretrainedConfig
+
+class ViVQAConfig(PretrainedConfig):
+    model_type = "vivqa"
+    
+    def __init__(
+        self,
+        drop_path_rate: float = 0.0,
+        mlp_ratio: float = 4.0,
+        encoder_layers: int = 4,
+        encoder_attention_heads: int = 4,
+        multiway: bool = True,
+        layernorm_embedding: bool = False,
+        normalize_output: bool = True,
+        no_output_layer: bool = True,
+        encoder_embed_dim: int = 768,
+         **kwargs
+    ):
+        args = EncoderConfig(
+            multiway=multiway, 
+            layernorm_embedding=layernorm_embedding, normalize_output=normalize_output, no_output_layer=no_output_layer, 
+            drop_path_rate=drop_path_rate, encoder_embed_dim=768, encoder_attention_heads=encoder_attention_heads, 
+            encoder_ffn_embed_dim=int(768 * mlp_ratio), encoder_layers=encoder_layers,
+        )
+        for key, value in args.__dict__.items():
+            setattr(self, key, value)
+            
+        super().__init__(**kwargs)