Update drop.py

Change comments into English, fix the in/out params'  type.
fix too many values to unpack in line 140.
Unify the quotes.
Remove "if" at line 148

examples/aflow/benchmark/drop.py

@@ -19,7 +19,7 @@ def is_number(text: str) -> bool:
     except ValueError:
         return False
 
-def normalize_answer(s):
+def normalize_answer(s: str) -> List[str]:
     """
     Normalize answers for evaluation.
     """
@@ -39,7 +39,7 @@ def normalize_answer(s):
 
     return white_space_fix(remove_articles(remove_punc(lower(s))))
 
-def calculate_score(ground_truth: str, prediction: str):
+def calculate_score(ground_truth: str, prediction: str) -> Tuple[float, str]:
     """
     Compute the F1 score between prediction and ground truth answers.
     """
@@ -71,33 +71,33 @@ def log_mismatch(problem: str, expected_output, prediction: str, predicted_numbe
 
     log_file = os.path.join(path, 'log.json')
 
-    # 检查log文件是否已经存在
+    # Check if the log file already exists
     if os.path.exists(log_file):
-        # 如果存在，加载现有的日志数据
+        # If it exists, load the existing log data
         with open(log_file, 'r', encoding='utf-8') as f:
             try:
                 data = json.load(f)
             except json.JSONDecodeError:
                 data = []
     else:
-        # 如果不存在，创建一个新的日志列表
+        # If it doesn't exist, create an empty list
         data = []
 
-    # 添加新的日志记录
+    # Add the new log data to the existing list
     data.append(log_data)
 
-    # 将数据写回到log.json文件
+    # Write the updated list back to the log file
     with open(log_file, 'w', encoding='utf-8') as f:
         json.dump(data, f, indent=4, ensure_ascii=False)
 
 async def load_data(file_path: str, specific_indices: List[int] = None) -> List[dict]:
     data = []
-    # 异步读取文件内容
+    # Read the data from the file
     async with aiofiles.open(file_path, mode="r", encoding='utf-8') as file:
         async for line in file:
             data.append(json.loads(line))
 
-    # 然后在随机选择的样本中基于特定索引列表进行进一步筛选
+    # Then further filter based on a specific index list in randomly selected samples
     if specific_indices is not None:
         filtered_data = [data[i] for i in specific_indices if i < len(data)]
         return filtered_data
@@ -105,22 +105,22 @@ async def load_data(file_path: str, specific_indices: List[int] = None) -> List[
     return data
 
 def save_results_to_csv(results: List[Tuple[str, str, str, int]], path):
-    # 创建 DataFrame
+    # Create a DataFrame from the results
     df = pd.DataFrame(results, columns=["inputs", "prediction", "expected_output", "score", "cost"])
 
-    # 计算统计数据
+    # Calculate the average score and cost
     avg_score = df["score"].mean()
     t_cost = df["cost"].max()
     a_cost = t_cost / len(df) if len(df) > 0 else 0
 
-    # 获取当前时间，格式为 YYYYMMDD_HHMMSS
+    # Get the current time in the format YYYYMMDD_HHMMSS
     current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
 
-    # 生成文件名，包含平均分和当前时间，保留五位小数
+    # Generate a filename with the average score and the current time, rounded to five decimal places
     filename = f"{avg_score:.5f}_{current_time}.csv"
     output_file = os.path.join(path, filename)
 
-    # 保存到 CSV
+    # Save the DataFrame to a CSV file
     df.to_csv(output_file, index=False)
     print(f"Results saved to {output_file}")
 
@@ -137,26 +137,19 @@ async def evaluate_problem(annotation: dict, graph: Callable, log_path) -> Tuple
 
     while retries < max_retries:
         try:
-            output, cost = await graph(inputs) if graph else "None"
+            output, cost = await graph(inputs) if graph else (None, None)
 
             f1_scores = []
 
-            # if '|' in the output, split it and calculate the score for each part
-
+            # if "|" in the output, split it and calculate the score for each part
             for answer in answers:
                 if answer.strip() != "":
-                    if '|' in output:
-                        output_parts = output.split('|')
-                        for output_part in output_parts:
-                            f1_score, _ = calculate_score(answer, output_part)
-                            f1_scores.append(f1_score)
-                    else:
-                        f1_score, _ = calculate_score(answer, output)
+                    output_parts = output.split("|")
+                    for output_part in output_parts:
+                        f1_score, _ = calculate_score(answer, output_part)
                         f1_scores.append(f1_score)
 
-            max_score = max(f1_scores)
-
-            uni_score = max_score
+            uni_score = max(f1_scores)
 
             print("uni_score", uni_score)