[misc] upgrade format to py39 (#7256)

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}"

src/llamafactory/eval/template.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections.abc import Sequence
 from dataclasses import dataclass
-from typing import Dict, List, Sequence, Tuple
 
 from ..data import Role
 from ..extras.constants import CHOICES
@@ -25,20 +25,19 @@ class EvalTemplate:
     choice: str
     answer: str
 
-    def _parse_example(self, example: Dict[str, str]) -> Tuple[str, str]:
-        r"""
+    def _parse_example(self, example: dict[str, str]) -> tuple[str, str]:
+        r"""Parse eval example.
+
         input: a dict with keys {"question", "A", "B", "C", "D", "answer"}
-        output: a tuple of (prompt, response)
+        output: a tuple of (prompt, response).
         """
         candidates = [self.choice.format(choice=ch, content=example[ch]) for ch in CHOICES if ch in example]
         return "".join([example["question"]] + candidates + [self.answer]), example["answer"]
 
     def format_example(
-        self, target_data: Dict[str, str], support_set: Sequence[Dict[str, str]], subject_name: str
-    ) -> List[Dict[str, str]]:
-        r"""
-        Converts dataset examples to messages.
-        """
+        self, target_data: dict[str, str], support_set: Sequence[dict[str, str]], subject_name: str
+    ) -> list[dict[str, str]]:
+        r"""Convert dataset examples to messages."""
         messages = []
         for k in range(len(support_set)):
             prompt, response = self._parse_example(support_set[k])
@@ -52,7 +51,7 @@ class EvalTemplate:
         return messages
 
 
-eval_templates: Dict[str, "EvalTemplate"] = {}
+eval_templates: dict[str, "EvalTemplate"] = {}
 
 
 def _register_eval_template(name: str, system: str, choice: str, answer: str) -> None: