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#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
https://docs.byteplus.com/en/docs/ModelArk/1099455
model list
https://docs.byteplus.com/en/docs/ModelArk/1330310
https://docs.byteplus.com/en/docs/ModelArk/Chat
"""
import argparse
from datetime import datetime
import json
import os
from pathlib import Path
import re
import sys
import time
from zoneinfo import ZoneInfo # Python 3.9+ 自带，无需安装
pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../"))
from openai import OpenAI
from project_settings import environment, project_path
def get_args():
"""
model list:
https://docs.byteplus.com/en/docs/ModelArk/1330310
bytedance-seed-1.6
seed-1-6-250615
bytedance-seed-1.6-flash
seed-1-6-flash-250615
deepseek-v3
deepseek-v3-250324
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
# default="seed-1-6-250615",
default="seed-1-6-flash-250615",
# default="deepseek-v3-250324",
type=str
)
parser.add_argument(
"--eval_dataset_name",
# default="agent-lingoace-zh-80-chat.jsonl",
# default="agent-bingoplus-ph-200-chat.jsonl",
default="agent-cod-zh-70-chat.jsonl",
type=str
)
parser.add_argument(
"--eval_dataset_dir",
default=(project_path / "data/dataset").as_posix(),
type=str
)
parser.add_argument(
"--eval_data_dir",
default=(project_path / "data/eval_data").as_posix(),
type=str
)
parser.add_argument(
"--client",
default="shenzhen_sase",
type=str
)
parser.add_argument(
"--service",
default="byteplus_api_key",
type=str
)
parser.add_argument(
"--create_time_str",
# default="null",
default="20250728_113641",
type=str
)
parser.add_argument(
"--interval",
default=1,
type=int
)
args = parser.parse_args()
return args
def main():
args = get_args()
eval_dataset_dir = Path(args.eval_dataset_dir)
eval_dataset_dir.mkdir(parents=True, exist_ok=True)
eval_data_dir = Path(args.eval_data_dir)
eval_data_dir.mkdir(parents=True, exist_ok=True)
if args.create_time_str == "null":
tz = ZoneInfo("Asia/Shanghai")
now = datetime.now(tz)
create_time_str = now.strftime("%Y%m%d_%H%M%S")
# create_time_str = "20250724_090615"
else:
create_time_str = args.create_time_str
eval_dataset = eval_dataset_dir / args.eval_dataset_name
output_file = eval_data_dir / f"byteplus/byteplus/{args.model_name}/{args.client}/{args.service}/{create_time_str}/{args.eval_dataset_name}.raw"
output_file.parent.mkdir(parents=True, exist_ok=True)
api_key = environment.get(args.service, dtype=str)
client = OpenAI(
base_url="https://ark.ap-southeast.bytepluses.com/api/v3/",
# Read your Ark API Key from the environment variable.
api_key=api_key
)
total = 0
# finished
finished_idx_set = set()
if os.path.exists(output_file.as_posix()):
with open(output_file.as_posix(), "r", encoding="utf-8") as f:
for row in f:
row = json.loads(row)
idx = row["idx"]
total = row["total"]
finished_idx_set.add(idx)
print(f"finished count: {len(finished_idx_set)}")
with open(eval_dataset.as_posix(), "r", encoding="utf-8") as fin, open(output_file.as_posix(), "a+", encoding="utf-8") as fout:
for row in fin:
row = json.loads(row)
idx = row["idx"]
prompt = row["prompt"]
response = row["response"]
if idx in finished_idx_set:
continue
finished_idx_set.add(idx)
# prompt
splits = prompt[::-1].split("\n\n", maxsplit=1)
conversation = splits[0]
system_prompt = splits[1]
conversation = conversation[::-1].strip()
system_prompt = system_prompt[::-1].strip()
pattern = "^(Client|Assistant): (.*?)(?=\n(?:Client|Assistant):)"
match = re.findall(pattern=pattern, string=conversation, flags=re.I|re.DOTALL|re.MULTILINE)
messages_ = list()
for m in match:
role = m[0].lower()
content = m[1]
if role in ("client", "Client"):
role = "user"
elif role in ("assistant", "Assistant"):
role = "assistant"
else:
raise AssertionError
messages_.append({
"role": role,
"content": content
})
messages = [
{"role": "system", "content": system_prompt},
*messages_
]
# print(json.dumps(messages, ensure_ascii=False, indent=4))
# exit(0)
try:
time.sleep(args.interval)
print(f"sleep: {args.interval}")
time_begin = time.time()
# https://docs.byteplus.com/en/docs/ModelArk/1449737
llm_response = client.chat.completions.create(
model=args.model_name,
messages=messages,
stream=False,
max_tokens=4096,
extra_body={
"thinking": {
"type": "disabled",
# "type": "enabled",
}
}
)
time_cost = time.time() - time_begin
print(f"time_cost: {time_cost}")
except Exception as e:
print(f"request failed, error type: {type(e)}, error text: {str(e)}")
continue
prediction = llm_response.choices[0].message.content
total += 1
row_ = {
"idx": idx,
"prompt": prompt,
"response": response,
"prediction": prediction,
"total": total,
"time_cost": time_cost,
}
row_ = json.dumps(row_, ensure_ascii=False)
fout.write(f"{row_}\n")
fout.flush()
return
if __name__ == "__main__":
main()
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