Make function name more clear and refactor the logic

src/transformers/integrations/integration_utils.py

@@ -131,17 +131,23 @@ def is_mlflow_available():
     return importlib.util.find_spec("mlflow") is not None
 
 
-def get_mlflow_version():
+def is_mlflow_async_log_available():
     # MLFlow can also be provided from mlflow-skinny package, which has the same versioning of mlflow package.
     for mlflow_package_name in ["mlflow", "mlflow-skinny"]:
         try:
-            return importlib.metadata.version(mlflow_package_name)
+            mlflow_version = importlib.metadata.version(mlflow_package_name)
+
+            # "synchronous" flag is only available with mlflow version >= 2.8.0
+            # https://github.com/mlflow/mlflow/pull/9705
+            # https://github.com/mlflow/mlflow/releases/tag/v2.8.0
+            if packaging.version.parse(mlflow_version) >= packaging.version.parse("2.8.0"):
+                return True
         except importlib.metadata.PackageNotFoundError:
             # We will try different mlflow package candidates
             pass
 
-    # Unable to determine the mlflow version
-    return None
+    # If MLFlow version cannot be determined, fallback to not doing async log to be safe
+    return False
 
 
 def is_dagshub_available():
@@ -1011,13 +1017,7 @@ def setup(self, args, state, model):
         self._experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None)
         self._flatten_params = os.getenv("MLFLOW_FLATTEN_PARAMS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
         self._run_id = os.getenv("MLFLOW_RUN_ID", None)
-        self._async_log = False
-        # "synchronous" flag is only available with mlflow version >= 2.8.0
-        # https://github.com/mlflow/mlflow/pull/9705
-        # https://github.com/mlflow/mlflow/releases/tag/v2.8.0
-        mlflow_version = get_mlflow_version()
-        if mlflow_version is not None and packaging.version.parse(mlflow_version) >= packaging.version.parse("2.8.0"):
-            self._async_log = True
+        self._async_log = is_mlflow_async_log_available() 
         logger.debug(
             f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run},"
             f" tags={self._nested_run}, tracking_uri={self._tracking_uri}"