update llama4

src/llamafactory/eval/evaluator.py

@@ -39,7 +39,7 @@
 
 import json
 import os
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 import numpy as np
 import torch
@@ -59,7 +59,7 @@ if TYPE_CHECKING:
 
 
 class Evaluator:
-    def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
+    def __init__(self, args: Optional[dict[str, Any]] = None) -> None:
         self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
         self.tokenizer = load_tokenizer(self.model_args)["tokenizer"]
         self.tokenizer.padding_side = "right"  # avoid overflow issue in batched inference for llama2
@@ -69,7 +69,7 @@ class Evaluator:
         self.choice_inputs = [self.tokenizer.encode(ch, add_special_tokens=False)[-1] for ch in CHOICES]
 
     @torch.inference_mode()
-    def batch_inference(self, batch_input: Dict[str, "torch.Tensor"]) -> List[str]:
+    def batch_inference(self, batch_input: dict[str, "torch.Tensor"]) -> list[str]:
         logits = self.model(**batch_input).logits
         lengths = torch.sum(batch_input["attention_mask"], dim=-1)
         word_probs = torch.stack([logits[i, lengths[i] - 1] for i in range(len(lengths))], dim=0)
@@ -88,7 +88,7 @@ class Evaluator:
         )
 
         with open(mapping, encoding="utf-8") as f:
-            categorys: Dict[str, Dict[str, str]] = json.load(f)
+            categorys: dict[str, dict[str, str]] = json.load(f)
 
         category_corrects = {subj: np.array([], dtype="bool") for subj in SUBJECTS}
         pbar = tqdm(categorys.keys(), desc="Processing subjects", position=0)
@@ -136,7 +136,7 @@ class Evaluator:
         pbar.close()
         self._save_results(category_corrects, results)
 
-    def _save_results(self, category_corrects: Dict[str, "NDArray"], results: Dict[str, Dict[int, str]]) -> None:
+    def _save_results(self, category_corrects: dict[str, "NDArray"], results: dict[str, dict[int, str]]) -> None:
         score_info = "\n".join(
             [
                 f"{category_name:>15}: {100 * np.mean(category_correct):.2f}"