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import argparse
import json
import logging
import os
from tqdm import tqdm
from .utils import *
import re
import time
def fast_extract_answer(response) :
response = response.strip()
response = process_answer(response)
# Direct Strategy Multi-Choice
# A / A: / A.
for ch in 'ABCDEFGH':
if response.upper() == ch or response.startswith(f'{ch}:') or response.startswith(f'{ch}.'):
return ch
# Direct Strategy Open-ended
# 1
if is_number(response):
return response
# CoT strategy
if 'boxed{' in response:
try:
model_answers = extract_full_boxed_content(response)
if model_answers:
# for coding
# \\boxed{\\text{}}
try:
text_content = re.findall(r'\\text{(.*?)}', model_answers[-1])
if text_content:
return text_content[-1].strip()
except Exception:
pass
return model_answers[-1].strip()
except Exception:
pass
# for Coding
# the correct answer is\n D.
for flag in ['final answer is', 'correct answer is', 'answer should be', 'answer is', 'answer:']:
if flag in response.lower():
try:
model_answer = response.lower().split(flag)[-1].strip()
return model_answer.split('\n')[0].split('.')[0]
except Exception:
pass
return ""
def create_test_prompt(score_prompt, problem, label):
score_prompt = score_prompt.strip()
response = problem[label]
answer = problem['answer']
full_prompt = f'{score_prompt}\n' + f'Response: {response}\n' + f'Answer: {answer}\n' + 'Correct_or_not:'
return full_prompt
def call_gpt(client, model, user_prompt):
attempt = 0
while attempt < 5:
try:
response = client.chat.completions.create(
model=model,
messages=[
{"role": "user", "content": user_prompt}
]
)
return response.choices[0].message.content.strip()
except Exception as e:
logging.error(f"Attempt {attempt + 1} failed: {e}")
if 'error' in str(e) and 'message' in str(e):
error_message = str(e)
if 'The server had an error processing your request.' in error_message:
sleep_time = 30
logging.error(f"Server error, retrying in {sleep_time}s...")
time.sleep(sleep_time)
elif 'Please try again in ' in error_message:
sleep_time = float(error_message.split('Please try again in ')[1].split('s.')[0])
logging.error(f"Rate limit exceeded, retrying in {sleep_time * 2}s...")
time.sleep(sleep_time * 2)
else:
print("Unknown error, skipping this request.")
break
attempt += 1
def gen_true_false(answer_file, response_label='response', gpt_eval=False, model="", api_key="", rerun=True, save_every=20, logger=logging.getLogger(__name__)):
logger.info(f"Reading {answer_file}.....")
label = response_label
if gpt_eval:
from openai import OpenAI
client = OpenAI(api_key=api_key)
with open(answer_file, "r") as f:
results = json.load(f)
full_pids = list(results.keys())
skip_pids = []
# for pid, problem in results.items():
# flag = problem.get('true_false')
# if flag is not None:
# skip_pids.append(problem['pid'])
if rerun:
test_pids = full_pids
else:
if len(skip_pids) > 0:
logger.info(
f"Found existing results file with {len(skip_pids)} problems with valid responses. Skipping these problems..."
)
test_pids = [pid for pid in full_pids if pid not in skip_pids]
logger.info(f"Number of test problems to run: {len(test_pids)}")
for i, pid in enumerate(tqdm(test_pids)):
problem = results[pid]
flag = False
if label not in problem or not problem[label]:
results[pid]['extraction'] = None
results[pid]['true_false'] = False
continue
if gpt_eval:
user_prompt = create_test_prompt(score_demo_prompt, problem, label)
flag_cache = call_gpt(client, model, user_prompt)
results[pid]['gpt_eval'] = flag_cache
if flag_cache.lower() == 'correct':
flag = True
else:
flag = False
else:
model_answer = fast_extract_answer(problem[label])
results[pid]['extraction'] = model_answer
if is_equal(model_answer, results[pid]['answer']) or is_equal(model_answer, results[pid]['gt_content']):
flag = True
results[pid]['true_false'] = flag
if (i % save_every == 0 and i > 0) or i == len(test_pids) - 1:
with open(answer_file, "w") as f:
f.write(json.dumps(results, indent=2))
logger.info(f"Saved results to {answer_file}")
with open(answer_file, "w") as f:
f.write(json.dumps(results, indent=2))
logger.info(f"Saved results to {answer_file}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--results_dir', type=str, default='')
parser.add_argument('--response_label', type=str, default='response', help='response label for the input file')
parser.add_argument('--rerun', action='store_true', help='rerun the answer extraction')
parser.add_argument('--save_every', type=int, default=10, help='save every n problems')
parser.add_argument('--gpt_eval', action='store_true', help='use gpt to evaluate')
parser.add_argument('--api_key', type=str, default="")
parser.add_argument('--model', type=str, default="chatgpt-4o-latest")
args = parser.parse_args()
logging.info("Starting to extract answers.......")
for root, dirs, files in os.walk(args.results_dir):
for file in files:
if file.endswith(".json") and not file.endswith("_result.json"):
gen_true_false(os.path.join(root, file), args)
if __name__ == "__main__":
logging.basicConfig(
level=os.environ.get("LOGLEVEL", "INFO").upper(),
format="[%(name)s] %(message)s",
datefmt="[%X]"
)
logger_blocklist = [
"asyncio",
"azure",
"azureml",
"datasets",
"httpx",
"httpcore",
"filelock",
"fsspec",
"msal",
"msrest",
"openai",
"PIL",
"urllib3",
]
for module in logger_blocklist:
logging.getLogger(module).setLevel(logging.WARNING)
main() |