fix

optimum/exporters/tasks.py

@@ -1589,7 +1589,7 @@ def _infer_task_from_model_or_model_class(
             raise ValueError("Either a model or a model class must be provided, but none were given here.")
 
         target_name = model.__class__.__name__ if model is not None else model_class.__name__
-        task_name = None
+        inferred_task_name = None
         iterable = ()
         for _, model_loader in cls._LIBRARY_TO_MODEL_LOADERS_TO_TASKS_MAP.items():
             iterable += (model_loader.items(),)
@@ -1601,7 +1601,7 @@ def _infer_task_from_model_or_model_class(
         for auto_cls_name, task in itertools.chain.from_iterable(iterable):
             if any((target_name.startswith("Auto"), target_name.startswith("TFAuto"))):
                 if target_name == auto_cls_name:
-                    task_name = task
+                    inferred_task_name = task
                     break
                 continue
 
@@ -1614,20 +1614,20 @@ def _infer_task_from_model_or_model_class(
                 continue
             model_mapping = auto_cls._model_mapping._model_mapping
             if target_name in model_mapping.values():
-                task_name = task
+                inferred_task_name = task
                 break
 
         for task_name, model_type, pipeline_class in DIFFUSION_PIPELINES_MAPPING:
-            if target_name == pipeline_class:
-                task_name = task_name
+            if target_name == pipeline_class.__name__:
+                inferred_task_name = task_name
                 break
 
-        if task_name is None:
+        if inferred_task_name is None:
             raise ValueError(
                 "The task name could not be automatically inferred. If using the command-line, please provide the argument --task task-name. Example: `--task text-classification`."
             )
 
-        return task_name
+        return inferred_task_name
 
     @classmethod
     def _infer_task_from_model_name_or_path(
@@ -1755,8 +1755,10 @@ def infer_task_from_model(
                 token=token,
             )
         elif issubclass(model, object):
+            # checks if it's a model class
             task_name = cls._infer_task_from_model_or_model_class(model_class=model)
         elif isinstance(model, object):
+            # checks if it's a model instance
             task_name = cls._infer_task_from_model_or_model_class(model=model)
 
         if task_name is None:
@@ -1903,7 +1905,6 @@ def standardize_model_attributes(
 
         if library_name == "diffusers":
             for task_name, model_type, pipeline_class in DIFFUSION_PIPELINES_MAPPING:
-                print(task_name, model_type, pipeline_class)
                 if isinstance(model, pipeline_class):
                     # `model_type` is a class attribute in Transformers, let's avoid modifying it.
                     model.config.export_model_type = model_type