fix mistral and mistral3 tests (#38978)

* fix

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/mistral/test_modeling_mistral.py

@@ -112,6 +112,7 @@ class MistralModelTest(CausalLMModelTest, unittest.TestCase):
 
 
 @require_torch_accelerator
+@require_read_token
 class MistralIntegrationTest(unittest.TestCase):
     # This variable is used to determine which accelerator are we using for our runners (e.g. A10 or T4)
     # Depending on the hardware we get different logits / generations
@@ -121,6 +122,9 @@ class MistralIntegrationTest(unittest.TestCase):
     def setUpClass(cls):
         cls.device_properties = get_device_properties()
 
+    def setUp(self):
+        cleanup(torch_device, gc_collect=True)
+
     def tearDown(self):
         cleanup(torch_device, gc_collect=True)
 
@@ -256,7 +260,7 @@ class MistralIntegrationTest(unittest.TestCase):
 
     @slow
     def test_speculative_generation(self):
-        EXPECTED_TEXT_COMPLETION = "My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"
+        EXPECTED_TEXT_COMPLETION = "My favourite condiment is 100% Sriracha. I love it on everything. I have it on my"
         prompt = "My favourite condiment is "
         tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
         model = MistralForCausalLM.from_pretrained(
@@ -273,7 +277,6 @@ class MistralIntegrationTest(unittest.TestCase):
         self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
 
     @slow
-    @require_read_token
     def test_compile_static_cache(self):
         # `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2
         # work as intended. See https://github.com/pytorch/pytorch/issues/121943
@@ -339,23 +342,32 @@ class MistralIntegrationTest(unittest.TestCase):
 @require_torch_accelerator
 class Mask4DTestHard(unittest.TestCase):
     model_name = "mistralai/Mistral-7B-v0.1"
-    _model = None
+    model = None
+    model_dtype = None
+
+    @classmethod
+    def setUpClass(cls):
+        cleanup(torch_device, gc_collect=True)
+        if cls.model_dtype is None:
+            cls.model_dtype = torch.float16
+        if cls.model is None:
+            cls.model = MistralForCausalLM.from_pretrained(cls.model_name, torch_dtype=cls.model_dtype).to(
+                torch_device
+            )
+
+    @classmethod
+    def tearDownClass(cls):
+        del cls.model_dtype
+        del cls.model
+        cleanup(torch_device, gc_collect=True)
+
+    def setUp(self):
+        cleanup(torch_device, gc_collect=True)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False)
 
     def tearDown(self):
         cleanup(torch_device, gc_collect=True)
 
-    @property
-    def model(self):
-        if self.__class__._model is None:
-            self.__class__._model = MistralForCausalLM.from_pretrained(
-                self.model_name, torch_dtype=self.model_dtype
-            ).to(torch_device)
-        return self.__class__._model
-
-    def setUp(self):
-        self.model_dtype = torch.float16
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False)
-
     def get_test_data(self):
         template = "my favorite {}"
         items = ("pet is a", "artist plays a", "name is L")  # same number of tokens in each item