Make self.inference_config default to empty dictionary (mlflow#8308)

Signed-off-by: harupy <hkawamura0130@gmail.com>
Signed-off-by: Larry O’Brien <larry.obrien@databricks.com>

mlflow/transformers.py

@@ -1323,7 +1323,7 @@ class _TransformersWrapper:
     def __init__(self, pipeline, flavor_config=None, inference_config=None):
         self.pipeline = pipeline
         self.flavor_config = flavor_config
-        self.inference_config = inference_config
+        self.inference_config = inference_config or {}
         self._conversation = None
         # NB: Current special-case custom pipeline types that have not been added to
         # the native-supported transformers package but require custom parsing:
@@ -1451,30 +1451,18 @@ def _predict(self, data):
         # formatting output), but if `include_prompt` is set to False in the `inference_config`
         # option during model saving, excess newline characters and the fed-in prompt will be
         # trimmed out from the start of the response.
-        include_prompt = (
-            self.inference_config.pop("include_prompt", True) if self.inference_config else True
-        )
+        include_prompt = self.inference_config.pop("include_prompt", True)
         # Optional stripping out of `\n` for specific generator pipelines.
-        collapse_whitespace = (
-            self.inference_config.pop("collapse_whitespace", False)
-            if self.inference_config
-            else False
-        )
+        collapse_whitespace = self.inference_config.pop("collapse_whitespace", False)
 
         # Generate inference data with the pipeline object
         if isinstance(self.pipeline, transformers.ConversationalPipeline):
             conversation_output = self.pipeline(self._conversation)
             return conversation_output.generated_responses[-1]
         elif isinstance(data, dict):
-            if self.inference_config:
-                raw_output = self.pipeline(**data, **self.inference_config)
-            else:
-                raw_output = self.pipeline(**data)
+            raw_output = self.pipeline(**data, **self.inference_config)
         else:
-            if self.inference_config:
-                raw_output = self.pipeline(data, **self.inference_config)
-            else:
-                raw_output = self.pipeline(data)
+            raw_output = self.pipeline(data, **self.inference_config)
 
         # Handle the pipeline outputs
         if type(self.pipeline).__name__ in self._supported_custom_generator_types or isinstance(