[fix] LlavaNextProcessor '_get_unpadded_features' method (#33263)

* [fix] LlavaNextProcessor '_get_unpadded_features' method

* [tests] add test_image_token_filling

* [chore] style + comment

* [minor] improve readability

* [chore] run make fix-copies

src/transformers/models/llava_next/processing_llava_next.py

@@ -199,8 +199,8 @@ class LlavaNextProcessor(ProcessorMixin):
         because it divided each image into patches depending on its resolution. Therefore we need to calculate how many
         patches an image is divided into and get the number of features from that.
         """
-        current_width = patches_height * scale_height
+        current_height = patches_height * scale_height
-        current_height = patches_width * scale_width
+        current_width = patches_width * scale_width
 
         original_aspect_ratio = width / height
         current_aspect_ratio = current_width / current_height

tests/models/llava_next/test_processor_llava_next.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 import unittest
 
+import torch
+
 from transformers.testing_utils import require_vision
 from transformers.utils import is_vision_available
 
@@ -39,3 +41,29 @@ class LlavaProcessorTest(unittest.TestCase):
 
         formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
         self.assertEqual(expected_prompt, formatted_prompt)
+
+    def test_image_token_filling(self):
+        processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-vicuna-7b-hf")
+        processor.patch_size = 14
+        processor.vision_feature_select_strategy = "default"
+        # Important to check with non square image
+        image = torch.randint(0, 2, (3, 500, 316))
+        expected_image_tokens = 1526
+        image_token_index = 32000
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        inputs = processor(
+            text=[processor.apply_chat_template(messages)],
+            images=[image],
+            return_tensors="pt",
+        )
+        image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
+        self.assertEqual(expected_image_tokens, image_tokens)