Implemented reusable function

Signed-off-by: yerzhaisang <[email protected]>

opensearch_py_ml/ml_models/sentencetransformermodel.py

@@ -701,6 +701,39 @@ def zip_model(
             )
         print("zip file is saved to " + zip_file_path + "\n")
 
+    def fix_truncation(
+        self,
+        save_json_folder_path: str,
+        max_length: int,
+    ) -> None:
+        """
+        Description:
+        Fix truncation parameter in tokenizer.json file.
+        If this parameter value is null, it results in error
+
+        :param save_json_folder_path:
+             path to save model json file, e.g, "home/save_pre_trained_model_json/")
+        :type save_json_folder_path: string
+        :param max_length:
+             maximum sequence length for model
+        :type max_length: int
+        :return: no return value expected
+        :rtype: None
+        """
+        tokenizer_file_path = os.path.join(save_json_folder_path, "tokenizer.json")
+        with open(tokenizer_file_path) as user_file:
+            parsed_json = json.load(user_file)
+        if "truncation" not in parsed_json or parsed_json["truncation"] is None:
+            parsed_json["truncation"] = {
+                "direction": "Right",
+                "max_length": max_length,
+                "strategy": "LongestFirst",
+                "stride": 0,
+            }
+        with open(tokenizer_file_path, "w") as file:
+            json.dump(parsed_json, file, indent=2)
+
+
     def save_as_pt(
         self,
         sentences: [str],
@@ -760,18 +793,7 @@ def save_as_pt(
 
         # save tokenizer.json in save_json_folder_name
         model.save(save_json_folder_path)
-        tokenizer_file_path = os.path.join(save_json_folder_path, "tokenizer.json")
-        with open(tokenizer_file_path) as user_file:
-            parsed_json = json.load(user_file)
-        if "truncation" not in parsed_json or parsed_json["truncation"] is None:
-            parsed_json["truncation"] = {
-                "direction": "Right",
-                "max_length": model.tokenizer.model_max_length,
-                "strategy": "LongestFirst",
-                "stride": 0,
-            }
-        with open(tokenizer_file_path, "w") as file:
-            json.dump(parsed_json, file, indent=2)
+        self.fix_truncation(save_json_folder_path, model.tokenizer.model_max_length)
 
         # convert to pt format will need to be in cpu,
         # set the device to cpu, convert its input_ids and attention_mask in cpu and save as .pt format
@@ -863,18 +885,7 @@ def save_as_onnx(
 
         # save tokenizer.json in output_path
         model.save(save_json_folder_path)
-        tokenizer_file_path = os.path.join(save_json_folder_path, "tokenizer.json")
-        with open(tokenizer_file_path) as user_file:
-            parsed_json = json.load(user_file)
-        if "truncation" not in parsed_json or parsed_json["truncation"] is None:
-            parsed_json["truncation"] = {
-                "direction": "Right",
-                "max_length": model.tokenizer.model_max_length,
-                "strategy": "LongestFirst",
-                "stride": 0,
-            }
-        with open(tokenizer_file_path, "w") as file:
-            json.dump(parsed_json, file, indent=2)
+        self.fix_truncation(save_json_folder_path, model.tokenizer.model_max_length)
 
         convert(
             framework="pt",