Merge remote-tracking branch 'upstream/main' into longjie/add_automat…

…ic_model_parallel_via_fx

optimum/exporters/tasks.py

@@ -162,7 +162,9 @@ def get_transformers_tasks_to_model_mapping(tasks_to_model_loader, framework="pt
         for model_loader in model_loaders:
             model_loader_class = getattr(auto_modeling_module, model_loader, None)
             if model_loader_class is not None:
-                # we can just update the model_type to model_class mapping since we only need one either way
+                # we can just update the model_type to model_class mapping since
+                # we can only have one task->model_type->model_class either way
+                # e.g. we merge automatic-speech-recognition's SpeechSeq2Seq and CTC models
                 tasks_to_model_mapping[task_name].update(model_loader_class._model_mapping._model_mapping)
 
     return tasks_to_model_mapping
@@ -1767,6 +1769,7 @@ def _infer_library_from_model_or_model_class(
         model: Optional[Union["PreTrainedModel", "TFPreTrainedModel", "DiffusionPipeline"]] = None,
         model_class: Optional[Type[Union["PreTrainedModel", "TFPreTrainedModel", "DiffusionPipeline"]]] = None,
     ):
+        inferred_library_name = None
         if model is not None and model_class is not None:
             raise ValueError("Either a model or a model class must be provided, but both were given here.")
         if model is None and model_class is None:
@@ -1775,20 +1778,20 @@ def _infer_library_from_model_or_model_class(
         target_class_module = model.__class__.__module__ if model is not None else model_class.__module__
 
         if target_class_module.startswith("sentence_transformers"):
-            library_name = "sentence_transformers"
+            inferred_library_name = "sentence_transformers"
         elif target_class_module.startswith("transformers"):
-            library_name = "transformers"
+            inferred_library_name = "transformers"
         elif target_class_module.startswith("diffusers"):
-            library_name = "diffusers"
+            inferred_library_name = "diffusers"
         elif target_class_module.startswith("timm"):
-            library_name = "timm"
+            inferred_library_name = "timm"
 
-        if library_name is None:
+        if inferred_library_name is None:
             raise ValueError(
                 "The library name could not be automatically inferred. If using the command-line, please provide the argument --library {transformers,diffusers,timm,sentence_transformers}. Example: `--library diffusers`."
             )
 
-        return library_name
+        return inferred_library_name
 
     @classmethod
     def _infer_library_from_model_name_or_path(