step1 & 4: wrap code in main()

step1_sample_code.py

 from utils_vllm import vllm_chatcomplete
 from utils import read_config
 
-cfg = read_config()
-vllm_chatcomplete(
-    cfg["model"],
-    cfg["sample"]["sample_prompt_path"],
-    cfg["sample"]["sample_result_path"],
-    cfg["sample"]["sampling_params"]
-)
\ No newline at end of file
+
+if __name__ == "__main__":
+    cfg = read_config()
+    vllm_chatcomplete(
+        cfg["model"],
+        cfg["sample"]["sample_prompt_path"],
+        cfg["sample"]["sample_result_path"],
+        cfg["sample"]["sampling_params"],
+    )

step4_test_critic_model.py

@@ -2,22 +2,24 @@ from utils_vllm import vllm_chatcomplete, vllm_score
 from utils import read_config
 from transformers import AutoTokenizer
 
-cfg = read_config()
-vllm_chatcomplete(
-    cfg["critic"]["model_path"],
-    cfg["dataset"]["minimal_test_path"],
-    cfg["critic"]["test"]["reason_result_path"],
-    cfg["critic"]["test"]["sampling_params"]
-)
 
-tokenizer = AutoTokenizer.from_pretrained(cfg["model"])
-score_tokens = tokenizer.encode("Yes")
-assert len(score_tokens) == 1
-score_token = score_tokens[0]
+if __name__ == "__main__":
+    cfg = read_config()
+    vllm_chatcomplete(
+        cfg["critic"]["model_path"],
+        cfg["dataset"]["minimal_test_path"],
+        cfg["critic"]["test"]["reason_result_path"],
+        cfg["critic"]["test"]["sampling_params"]
+    )
 
-vllm_score(
-    cfg["critic"]["model_path"],
-    cfg["critic"]["test"]["reason_result_path"],
-    cfg["critic"]["test"]["score_result_path"],
-    score_token
-)
\ No newline at end of file
+    tokenizer = AutoTokenizer.from_pretrained(cfg["model"])
+    score_tokens = tokenizer.encode("Yes")
+    assert len(score_tokens) == 1
+    score_token = score_tokens[0]
+
+    vllm_score(
+        cfg["critic"]["model_path"],
+        cfg["critic"]["test"]["reason_result_path"],
+        cfg["critic"]["test"]["score_result_path"],
+        score_token
+    )
\ No newline at end of file