[from_pretrained] Simpler code for peft

src/transformers/modeling_utils.py

@@ -2368,28 +2368,18 @@ def from_pretrained(
                 " ignored."
             )
 
-        if is_peft_available() and _adapter_model_path is None:
-            maybe_adapter_model_path = find_adapter_config_file(
-                pretrained_model_name_or_path,
-                revision=revision,
-                subfolder=subfolder,
-                token=token,
-                commit_hash=commit_hash,
-            )
-        elif is_peft_available() and _adapter_model_path is not None:
-            maybe_adapter_model_path = _adapter_model_path
-        else:
-            maybe_adapter_model_path = None
-
-        has_adapter_config = maybe_adapter_model_path is not None
-
-        if has_adapter_config:
-            if _adapter_model_path is not None:
-                adapter_model_id = _adapter_model_path
-            else:
-                with open(maybe_adapter_model_path, "r", encoding="utf-8") as f:
-                    adapter_model_id = pretrained_model_name_or_path
-                    pretrained_model_name_or_path = json.load(f)["base_model_name_or_path"]
+        if is_peft_available():
+            if _adapter_model_path is None:
+                _adapter_model_path = find_adapter_config_file(
+                    pretrained_model_name_or_path,
+                    revision=revision,
+                    subfolder=subfolder,
+                    token=token,
+                    commit_hash=commit_hash,
+                )
+            if os.path.isfile(_adapter_model_path):
+                with open(_adapter_model_path, "r", encoding="utf-8") as f:
+                    _adapter_model_path = json.load(f)["base_model_name_or_path"]
 
         # change device_map into a map if we passed an int, a str or a torch.device
         if isinstance(device_map, torch.device):
@@ -3221,9 +3211,9 @@ def from_pretrained(
         if quantization_method_from_config == QuantizationMethod.GPTQ:
             model = quantizer.post_init_model(model)
 
-        if has_adapter_config:
+        if _adapter_model_path is not None:
             model.load_adapter(
-                adapter_model_id,
+                _adapter_model_path,
                 adapter_name=adapter_name,
                 revision=revision,
                 token=token,