Fix padding for IDEFICS (#26396)

* fix

* fixup

* tests

* fixup

src/transformers/models/idefics/processing_idefics.py

@@ -280,7 +280,7 @@ class IdeficsProcessor(ProcessorMixin):
             else:
                 return fake_token + image_token + fake_token
 
-        all_texts = []
+        all_prompts = []
         all_images = []
         for sample in prompts:
             # the model was trained on samples starting with <s>
@@ -321,16 +321,17 @@ class IdeficsProcessor(ProcessorMixin):
 
             image_objects = self.image_processor(image_objects, transform=transform)
 
+            all_prompts.append(full_text)
+            all_images.append(image_objects)
+
         text_encoding = self.tokenizer(
-                text=full_text,
+            text=all_prompts,
             add_special_tokens=False,
             padding=padding,
             truncation=truncation,
             max_length=max_length,
         )
-
+        all_texts = text_encoding["input_ids"]
-            all_texts.append(text_encoding["input_ids"])
-            all_images.append(image_objects)
 
         max_seq_len = max(len(x) for x in all_texts)
 

tests/models/idefics/test_processor_idefics.py

@@ -141,6 +141,25 @@ class IdeficsProcessorTest(TestCasePlus):
 
         self.assertListEqual(decoded_tok, decoded_processor)
 
+    def test_tokenizer_padding(self):
+        image_processor = self.get_image_processor()
+        tokenizer = self.get_tokenizer(padding_side="right")
+
+        processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+        predicted_tokens = [
+            "<s>Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk>",
+            "<s>Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>",
+        ]
+
+        prompts = [[prompt] for prompt in self.prepare_prompts()[2]]
+        max_length = processor(prompts, padding="max_length", truncation=True, max_length=20)
+        longest = processor(prompts, padding="longest", truncation=True, max_length=30)
+        decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1])
+        decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1])
+        self.assertEqual(decoded_max_length, predicted_tokens[1])
+        self.assertEqual(decoded_longest, predicted_tokens[0])
+
     def test_model_input_names(self):
         image_processor = self.get_image_processor()
         tokenizer = self.get_tokenizer()