[misc] upgrade format to py39 (#7256)

src/llamafactory/train/rm/trainer.py

@@ -18,7 +18,7 @@
 import json
 import os
 from types import MethodType
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Optional, Union
 
 import torch
 from transformers import Trainer
@@ -41,9 +41,7 @@ logger = logging.get_logger(__name__)
 
 
 class PairwiseTrainer(Trainer):
-    r"""
-    Inherits Trainer to compute pairwise loss.
-    """
+    r"""Inherits Trainer to compute pairwise loss."""
 
     def __init__(
         self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
@@ -88,10 +86,9 @@ class PairwiseTrainer(Trainer):
 
     @override
     def compute_loss(
-        self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
-    ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
-        r"""
-        Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
+        self, model: "PreTrainedModel", inputs: dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
+    ) -> Union["torch.Tensor", tuple["torch.Tensor", list["torch.Tensor"]]]:
+        r"""Compute pairwise loss. The first n examples are chosen and the last n examples are rejected.
 
         Subclass and override to inject custom behavior.
 
@@ -113,8 +110,7 @@ class PairwiseTrainer(Trainer):
             return loss
 
     def save_predictions(self, predict_results: "PredictionOutput") -> None:
-        r"""
-        Saves model predictions to `output_dir`.
+        r"""Save model predictions to `output_dir`.
 
         A custom behavior that not contained in Seq2SeqTrainer.
         """
@@ -126,7 +122,7 @@ class PairwiseTrainer(Trainer):
         chosen_scores, rejected_scores = predict_results.predictions
 
         with open(output_prediction_file, "w", encoding="utf-8") as writer:
-            res: List[str] = []
+            res: list[str] = []
             for c_score, r_score in zip(chosen_scores, rejected_scores):
                 res.append(json.dumps({"chosen": round(float(c_score), 2), "rejected": round(float(r_score), 2)}))