Fix Mistral3 tests

docs/source/en/model_doc/mistral3.md

@@ -54,7 +54,7 @@ Here is how you can use the `image-text-to-text` pipeline to perform inference w
 ...     },
 ... ]
 
->>> pipe = pipeline("image-text-to-text", model="../mistral3_weights", torch_dtype=torch.bfloat16)
+>>> pipe = pipeline("image-text-to-text", model="mistralai/Mistral-Small-3.1-24B-Instruct-2503", torch_dtype=torch.bfloat16)
 >>> outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
 >>> outputs[0]["generated_text"]
 'The image depicts a vibrant and lush garden scene featuring a variety of wildflowers and plants. The central focus is on a large, pinkish-purple flower, likely a Greater Celandine (Chelidonium majus), with a'

tests/models/mistral3/test_processor_mistral3.py

@@ -51,17 +51,75 @@ def setUpClass(cls):
 
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
-        processor = PixtralProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+        processor = self.processor_class.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
         processor.save_pretrained(self.tmpdirname)
 
+    def get_processor(self):
+        return self.processor_class.from_pretrained(self.tmpdirname)
+
     def tearDown(self):
         shutil.rmtree(self.tmpdirname)
 
+    def test_chat_template_accepts_processing_kwargs(self):
+        # override to use slow image processor to return numpy arrays
+        processor = self.processor_class.from_pretrained(self.tmpdirname, use_fast=False)
+        if processor.chat_template is None:
+            self.skipTest("Processor has no chat template")
+
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "What is shown in this image?"},
+                    ],
+                },
+            ]
+        ]
+
+        formatted_prompt_tokenized = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            padding="max_length",
+            truncation=True,
+            max_length=50,
+        )
+        self.assertEqual(len(formatted_prompt_tokenized[0]), 50)
+
+        formatted_prompt_tokenized = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            truncation=True,
+            max_length=5,
+        )
+        self.assertEqual(len(formatted_prompt_tokenized[0]), 5)
+
+        # Now test the ability to return dict
+        messages[0][0]["content"].append(
+            {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
+        )
+        out_dict = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            do_rescale=True,
+            rescale_factor=-1,
+            return_tensors="np",
+        )
+        self.assertLessEqual(out_dict[self.images_input_name][0][0].mean(), 0)
+
     def test_chat_template(self):
-        processor = self.processor_class.from_pretrained(self.tmpdirname)
-        expected_prompt = "<s>[INST][IMG]What is shown in this image?[/INST]"
+        processor = self.processor_class.from_pretrained(self.tmpdirname, use_fast=False)
+        expected_prompt = "<s>[SYSTEM_PROMPT][/SYSTEM_PROMPT][INST][IMG]What is shown in this image?[/INST]"
 
         messages = [
+            {
+                "role": "system",
+                "content": "",
+            },
             {
                 "role": "user",
                 "content": [
@@ -81,6 +139,10 @@ def test_image_token_filling(self):
         image_token_index = 10
 
         messages = [
+            {
+                "role": "system",
+                "content": "",
+            },
             {
                 "role": "user",
                 "content": [