Fix testcase

tests/models/hunyuan_v1_moe/test_modeling_hunyuan_v1_moe.py

@@ -15,6 +15,8 @@
 
 import unittest
 
+import pytest
+
 from transformers import HunYuanMoEV1Config, is_torch_available
 from transformers.testing_utils import (
     cleanup,
@@ -26,6 +28,8 @@
 
 if is_torch_available():
     from transformers import (
+        AutoModelForCausalLM,
+        AutoTokenizer,
         HunYuanMoEV1ForCausalLM,
         HunYuanMoEV1ForSequenceClassification,
         HunYuanMoEV1Model,
@@ -78,6 +82,11 @@ def is_pipeline_test_to_skip(
     ):
         return True
 
+    @unittest.skip("Hunyuan model Unsupported")
+    @pytest.mark.torch_compile_test
+    def test_generate_compilation_all_outputs(self):
+        pass
+
 
 @require_torch
 class HunYuanMoEV1IntegrationTest(unittest.TestCase):
@@ -90,24 +99,22 @@ def tearDown(self):
     @slow
     def test_model_generation(self):
         # we will compele this when model file change over
-        pass
-        # EXPECTED_ANSWER = "\nRegular exercise offers numerous physical, mental, and emotional benefits. It improves cardiovascular health, strengthens muscles and bones, boosts metabolism, and helps"
-        # prompt = "Write a short summary of the benefits of regular exercise "
-        # tokenizer = AutoTokenizer.from_pretrained(
-        #     "tencent/Hunyuan-A13B-Instruct", use_fast=False, trust_remote_code=True
-        # )
-        # model = HunYuanMoEV1ForCausalLM.from_pretrained("tencent/Hunyuan-A13B-Instruct", device_map="auto")
-        # messages = [
-        #     {"role": "user", "content": prompt},
-        # ]
-        # tokenized_chat = tokenizer.apply_chat_template(
-        #     messages,
-        #     tokenize=True,
-        #     add_generation_prompt=True,
-        #     return_tensors="pt",
-        #     enable_thinking=False,  # Toggle thinking mode (default: True)
-        # )
-        # generated_ids = model.generate(tokenized_chat.to(model.device), max_new_tokens=30, top_k=1)
-        # text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-        # answer = text.split("<answer>")[1]
-        # self.assertEqual(EXPECTED_ANSWER, answer)
+        # pass
+        EXPECTED_ANSWER = "\nRegular exercise offers numerous physical, mental, and emotional benefits. It improves cardiovascular health, strengthens muscles and bones, boosts metabolism, and helps"
+        prompt = "Write a short summary of the benefits of regular exercise "
+        tokenizer = AutoTokenizer.from_pretrained("tencent/Hunyuan-A13B-Instruct")
+        model = AutoModelForCausalLM.from_pretrained("tencent/Hunyuan-A13B-Instruct", device_map="auto")
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+        tokenized_chat = tokenizer.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_tensors="pt",
+            enable_thinking=False,  # Toggle thinking mode (default: True)
+        )
+        generated_ids = model.generate(tokenized_chat.to(model.device), max_new_tokens=30, top_k=1)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        answer = text.split("<answer>")[1]
+        self.assertEqual(EXPECTED_ANSWER, answer)