Decorate `test_codegen_sample_max_time` as flaky (#22953)

* fix

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

tests/models/codegen/test_modeling_codegen.py

@@ -18,7 +18,8 @@ import datetime
 import unittest
 
 from transformers import CodeGenConfig, is_torch_available
-from transformers.testing_utils import require_torch, slow, torch_device
+from transformers.file_utils import cached_property
+from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -462,11 +463,19 @@ class CodeGenModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
 
 @require_torch
 class CodeGenModelLanguageGenerationTest(unittest.TestCase):
+    @cached_property
+    def cached_tokenizer(self):
+        return AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
+
+    @cached_property
+    def cached_model(self):
+        return CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
+
     @slow
     def test_lm_generate_codegen(self):
-        tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
+        tokenizer = self.cached_tokenizer
         for checkpointing in [True, False]:
-            model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
+            model = self.cached_model
 
             if checkpointing:
                 model.gradient_checkpointing_enable()
@@ -484,8 +493,8 @@ class CodeGenModelLanguageGenerationTest(unittest.TestCase):
 
     @slow
     def test_codegen_sample(self):
-        tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
-        model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
+        tokenizer = self.cached_tokenizer
+        model = self.cached_model
         model.to(torch_device)
 
         torch.manual_seed(0)
@@ -515,10 +524,11 @@ class CodeGenModelLanguageGenerationTest(unittest.TestCase):
             all([output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))])
         )  # token_type_ids should change output
 
+    @is_flaky(max_attempts=3, description="measure of timing is somehow flaky.")
     @slow
     def test_codegen_sample_max_time(self):
-        tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
-        model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
+        tokenizer = self.cached_tokenizer
+        model = self.cached_model
         model.to(torch_device)
 
         torch.manual_seed(0)