Use main in conversion script (#25973)

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/falcon/convert_custom_code_checkpoint.py

@@ -9,65 +9,66 @@ library. After conversion, performance (especially for generation) should improv
 without needing trust_remote_code=True.
 """
 
-parser = ArgumentParser()
-parser.add_argument(
-    "--checkpoint_dir",
-    type=Path,
-    required=True,
-    help="Directory containing a custom code checkpoint to convert to a modern Falcon checkpoint.",
-)
-args = parser.parse_args()
-
-if not args.checkpoint_dir.is_dir():
-    raise ValueError("--checkpoint_dir argument should be a directory!")
-
-if (
-    not (args.checkpoint_dir / "configuration_RW.py").is_file()
-    or not (args.checkpoint_dir / "modelling_RW.py").is_file()
-):
-    raise ValueError(
-        "The model directory should contain configuration_RW.py and modelling_RW.py files! Are you sure this is a custom code checkpoint?"
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument(
+        "--checkpoint_dir",
+        type=Path,
+        required=True,
+        help="Directory containing a custom code checkpoint to convert to a modern Falcon checkpoint.",
     )
-(args.checkpoint_dir / "configuration_RW.py").unlink()
-(args.checkpoint_dir / "modelling_RW.py").unlink()
+    args = parser.parse_args()
 
-config = args.checkpoint_dir / "config.json"
-text = config.read_text()
-text = text.replace("RWForCausalLM", "FalconForCausalLM")
-text = text.replace("RefinedWebModel", "falcon")
-text = text.replace("RefinedWeb", "falcon")
-json_config = json.loads(text)
-del json_config["auto_map"]
+    if not args.checkpoint_dir.is_dir():
+        raise ValueError("--checkpoint_dir argument should be a directory!")
 
-if "n_head" in json_config:
-    json_config["num_attention_heads"] = json_config.pop("n_head")
-if "n_layer" in json_config:
-    json_config["num_hidden_layers"] = json_config.pop("n_layer")
-if "n_head_kv" in json_config:
-    json_config["num_kv_heads"] = json_config.pop("n_head_kv")
-    json_config["new_decoder_architecture"] = True
-else:
-    json_config["new_decoder_architecture"] = False
-bos_token_id = json_config.get("bos_token_id", 1)
-eos_token_id = json_config.get("eos_token_id", 2)
-config.unlink()
-config.write_text(json.dumps(json_config, indent=2, sort_keys=True))
+    if (
+        not (args.checkpoint_dir / "configuration_RW.py").is_file()
+        or not (args.checkpoint_dir / "modelling_RW.py").is_file()
+    ):
+        raise ValueError(
+            "The model directory should contain configuration_RW.py and modelling_RW.py files! Are you sure this is a custom code checkpoint?"
+        )
+    (args.checkpoint_dir / "configuration_RW.py").unlink()
+    (args.checkpoint_dir / "modelling_RW.py").unlink()
 
-tokenizer_config = args.checkpoint_dir / "tokenizer_config.json"
-if tokenizer_config.is_file():
-    text = tokenizer_config.read_text()
+    config = args.checkpoint_dir / "config.json"
+    text = config.read_text()
+    text = text.replace("RWForCausalLM", "FalconForCausalLM")
+    text = text.replace("RefinedWebModel", "falcon")
+    text = text.replace("RefinedWeb", "falcon")
     json_config = json.loads(text)
-    if json_config["tokenizer_class"] == "PreTrainedTokenizerFast":
-        json_config["model_input_names"] = ["input_ids", "attention_mask"]
-        tokenizer_config.unlink()
-        tokenizer_config.write_text(json.dumps(json_config, indent=2, sort_keys=True))
+    del json_config["auto_map"]
+
+    if "n_head" in json_config:
+        json_config["num_attention_heads"] = json_config.pop("n_head")
+    if "n_layer" in json_config:
+        json_config["num_hidden_layers"] = json_config.pop("n_layer")
+    if "n_head_kv" in json_config:
+        json_config["num_kv_heads"] = json_config.pop("n_head_kv")
+        json_config["new_decoder_architecture"] = True
+    else:
+        json_config["new_decoder_architecture"] = False
+    bos_token_id = json_config.get("bos_token_id", 1)
+    eos_token_id = json_config.get("eos_token_id", 2)
+    config.unlink()
+    config.write_text(json.dumps(json_config, indent=2, sort_keys=True))
+
+    tokenizer_config = args.checkpoint_dir / "tokenizer_config.json"
+    if tokenizer_config.is_file():
+        text = tokenizer_config.read_text()
+        json_config = json.loads(text)
+        if json_config["tokenizer_class"] == "PreTrainedTokenizerFast":
+            json_config["model_input_names"] = ["input_ids", "attention_mask"]
+            tokenizer_config.unlink()
+            tokenizer_config.write_text(json.dumps(json_config, indent=2, sort_keys=True))
 
-generation_config_path = args.checkpoint_dir / "generation_config.json"
-generation_dict = {
-    "_from_model_config": True,
-    "bos_token_id": bos_token_id,
-    "eos_token_id": eos_token_id,
-    "transformers_version": "4.33.0.dev0",
-}
-generation_config_path.write_text(json.dumps(generation_dict, indent=2, sort_keys=True))
-print("Done! Please double-check that the new checkpoint works as expected.")
+    generation_config_path = args.checkpoint_dir / "generation_config.json"
+    generation_dict = {
+        "_from_model_config": True,
+        "bos_token_id": bos_token_id,
+        "eos_token_id": eos_token_id,
+        "transformers_version": "4.33.0.dev0",
+    }
+    generation_config_path.write_text(json.dumps(generation_dict, indent=2, sort_keys=True))
+    print("Done! Please double-check that the new checkpoint works as expected.")