chore: import upstream snapshot with attribution

fastchat/data/optional_replace.py

@@ -0,0 +1,82 @@
+"""
+Do optional replace of bos/eos/pad/unk.
+
+Usage:
+python3 -m fastchat.data.optional_replace --in input.json --out output.json --model-name-or-path <your_token_path>
+
+Requirement:
+pip3 install transformers tqdm
+"""
+import argparse
+import json
+import traceback
+
+import transformers
+from tqdm import tqdm
+
+
+def replace_special_tokens(
+    tokenizer: transformers.PreTrainedTokenizer, text: str
+) -> str:
+    if not text:
+        return text
+
+    def _insert_vline(token: str) -> str:
+        if len(token) < 2:
+            return " "
+        elif len(token) == 2:
+            return f"{token[0]}|{token[1]}"
+        else:
+            return f"{token[:1]}|{token[1:-1]}|{token[-1:]}"
+
+    if tokenizer.bos_token:
+        text = text.replace(tokenizer.bos_token, _insert_vline(tokenizer.bos_token))
+    if tokenizer.eos_token:
+        text = text.replace(tokenizer.eos_token, _insert_vline(tokenizer.eos_token))
+    if tokenizer.pad_token:
+        text = text.replace(tokenizer.pad_token, _insert_vline(tokenizer.pad_token))
+    if tokenizer.unk_token:
+        text = text.replace(tokenizer.unk_token, _insert_vline(tokenizer.unk_token))
+    return text
+
+
+def replace(conv, tokenizer):
+    # Replace bos/eos/pad/unk tokens
+    if tokenizer:
+        try:
+            for sentence in conv["conversations"]:
+                sentence["value"] = replace_special_tokens(tokenizer, sentence["value"])
+        except Exception as e:
+            traceback.print_exc()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--in-file", type=str, required=True)
+    parser.add_argument("--out-file", type=str)
+    parser.add_argument(
+        "--model-name-or-path",
+        type=str,
+        help="The directory or address where the model token is stored.",
+    )
+    args = parser.parse_args()
+
+    in_file = args.in_file
+    out_file = args.out_file
+    tokenizer = None
+    if args.model_name_or_path:
+        tokenizer = transformers.AutoTokenizer.from_pretrained(
+            args.model_name_or_path,
+            trust_remote_code=True,
+            use_fast=False,
+        )
+
+    if out_file is None:
+        out_file = f"{in_file}_replace.json"
+
+    content = json.load(open(in_file, "r"))
+
+    for conv in tqdm(content):
+        replace(conv, tokenizer)
+
+    json.dump(content, open(out_file, "w"), indent=2, ensure_ascii=False)