Gemma3 processor typo (#36710)

* fix typo when  is on

* tiny

* add test and remove 'text_crops'

* lint

src/transformers/models/gemma3/image_processing_gemma3.py

@@ -384,7 +384,7 @@ class Gemma3ImageProcessor(BaseImageProcessor):
             images_list = [images for images, _ in images_list_and_num_crops]
             num_crops = [num_crops for _, num_crops in images_list_and_num_crops]
         else:
-            num_crops = [[0] for images in images_list]
+            num_crops = [[0] for _ in images_list]
 
         processed_images = []
         for images in images_list:

src/transformers/models/gemma3/processing_gemma3.py

@@ -113,7 +113,6 @@ class Gemma3Processor(ProcessorMixin):
 
             # Replace image tokens by the full expanded sequence
             batch_num_crops = to_py_obj(image_inputs.pop("num_crops"))
-            text_with_crops = text
             for batch_idx, (prompt, images, num_crops) in enumerate(zip(text, batched_images, batch_num_crops)):
                 image_indexes = [m.start() for m in re.finditer(self.boi_token, prompt)]
 
@@ -130,7 +129,7 @@ class Gemma3Processor(ProcessorMixin):
                             + " ".join([self.boi_token] * num)
                         )
                         prompt = prompt[:idx] + formatted_image_text + prompt[idx + len(self.boi_token) :]
-                        text_with_crops[batch_idx] = prompt
+                        text[batch_idx] = prompt
 
             # Expand placeholder image tokens to the full image token sequence
             text = [prompt.replace(self.boi_token, self.full_image_sequence) for prompt in text]

tests/models/gemma3/test_modeling_gemma3.py

@@ -417,6 +417,39 @@ class Gemma3IntegrationTest(unittest.TestCase):
         ]  # fmt: skip
         self.assertEqual(output_text, EXPECTED_TEXTS)
 
+    def test_model_4b_crops(self):
+        model_id = "gg-hf-g/gemma-3-4b-it"
+
+        model = Gemma3ForConditionalGeneration.from_pretrained(
+            model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16
+        ).to(torch_device)
+
+        crop_config = {
+            "images_kwargs": {
+                "do_pan_and_scan": True,
+                "pan_and_scan_max_num_crops": 448,
+                "pan_and_scan_min_crop_size": 32,
+                "pan_and_scan_min_ratio_to_activate": 0.3,
+            }
+        }
+
+        inputs = self.processor.apply_chat_template(
+            self.messages,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            add_generation_prompt=True,
+            **crop_config,
+        ).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        output_text = self.processor.batch_decode(output, skip_special_tokens=True)
+
+        EXPECTED_NUM_IMAGES = 3  # one for the origin image and two crops of images
+        EXPECTED_TEXTS = ["user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nDescribe this image in detail.\nmodel\nHere's a detailed description of the image:\n\n**Overall Impression:**\n\nThe image is a close-up shot of a garden scene featuring several"]  # fmt: skip
+        self.assertEqual(len(inputs["pixel_values"]), EXPECTED_NUM_IMAGES)
+        self.assertEqual(output_text, EXPECTED_TEXTS)
+
     def test_model_4b_multiimage(self):
         model_id = "gg-hf-g/gemma-3-4b-it"