Fix: Guard against None num_query_tokens in Blip2Processor (to avoid TypeError)

src/transformers/models/blip_2/modeling_blip_2.py

@@ -2023,13 +2023,7 @@ def forward(
 
         if use_image_text_matching_head:
             query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
-            if self.config.image_token_index is not None:
-                input_ids = input_ids[:, self.config.num_query_tokens :]
-            else:
-                query_attention_mask = torch.ones(
-                    query_tokens.size()[:-1], dtype=torch.long, device=query_tokens.device
-                )
-                attention_mask = torch.cat([query_attention_mask, attention_mask], dim=1)
+            input_ids = input_ids[:, self.config.num_query_tokens :]
 
             query_embeds = self.embeddings(
                 input_ids=input_ids,
@@ -2061,9 +2055,8 @@ def forward(
             image_embeds = query_outputs[0] if not return_dict else query_outputs.last_hidden_state
             image_embeds = image_embeds.to(dtype=self.vision_projection.weight.dtype)
 
-            if self.config.image_token_index is not None:
-                input_ids = input_ids[:, self.config.num_query_tokens :]
-                attention_mask = attention_mask[:, self.config.num_query_tokens :]
+            input_ids = input_ids[:, self.config.num_query_tokens :]
+            attention_mask = attention_mask[:, self.config.num_query_tokens :]
 
             query_embeds = self.embeddings(
                 input_ids=input_ids,

src/transformers/models/blip_2/processing_blip_2.py

@@ -60,7 +60,7 @@ class Blip2Processor(ProcessorMixin):
             Number of tokens used by the Qformer as queries, should be same as in model's config.
     """
 
-    def __init__(self, image_processor, tokenizer, num_query_tokens=None, **kwargs):
+    def __init__(self, image_processor, tokenizer, num_query_tokens=32, **kwargs):
         tokenizer.return_token_type_ids = False
         if not hasattr(tokenizer, "image_token"):
             self.image_token = AddedToken("<image>", normalized=False, special=True)
@@ -107,8 +107,9 @@ def __call__(
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
         max_length = output_kwargs["text_kwargs"].pop("max_length", None)
         if max_length is not None:
-            output_kwargs["text_kwargs"]["max_length"] = max_length - self.num_query_tokens
-
+            adjusted_max_length = max_length - self.num_query_tokens
+            if adjusted_max_length > 0:
+                output_kwargs["text_kwargs"]["max_length"] = adjusted_max_length
         encoding = BatchFeature(tensor_type=return_tensors)
         if text is not None:
             if isinstance(text, str):