Add missing None check for hf_quantizer (#28804)

* Add missing None check for hf_quantizer

* Add test, fix logic.

* make style

* Switch test model to Mistral

* Comment

* Update tests/test_modeling_utils.py

---------

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

tests/test_modeling_utils.py

@@ -34,6 +34,7 @@ from requests.exceptions import HTTPError
 from transformers import (
     AutoConfig,
     AutoModel,
+    AutoModelForSequenceClassification,
     OwlViTForObjectDetection,
     PretrainedConfig,
     is_torch_available,
@@ -201,6 +202,7 @@ if is_tf_available():
 
 TINY_T5 = "patrickvonplaten/t5-tiny-random"
 TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
+TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM"
 
 
 def check_models_equal(model1, model2):
@@ -300,6 +302,15 @@ class ModelUtilsTest(TestCasePlus):
                 BertModel.from_pretrained(TINY_T5)
         self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
 
+    @require_accelerate
+    def test_model_from_pretrained_with_none_quantization_config(self):
+        # Needs a device_map for to enter the low_cpu_mem branch. We also load AutoModelForSequenceClassification
+        # deliberately to enter the missing keys branch.
+        model = AutoModelForSequenceClassification.from_pretrained(
+            TINY_MISTRAL, device_map="auto", quantization_config=None
+        )
+        self.assertIsNotNone(model)
+
     def test_model_from_config_torch_dtype(self):
         # test that the model can be instantiated with dtype of user's choice - as long as it's a
         # float dtype. To make it happen config.torch_dtype needs to be set before instantiating the