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import json
import os
import re
from copy import deepcopy
from typing import Any, Dict, List
from tqdm import tqdm
# TODO: Fill in REDIS OM URL in the form of `redis://:password@host:port`
os.environ["REDIS_OM_URL"] = "redis://:QzmCUD3C3RdsR@localhost:6381"
from reverse_engineering import run_reverse_by_pk_agent
with open("../../data/sotopia_pi_openai_log_attribution.jsonl", 'r') as f:
data: List[Dict[str, Any]] = [json.loads(d) for d in f.readlines()]
if not os.path.exists("../../data/episode_utterances"):
os.makedirs("../../data/episode_utterances")
for d in tqdm(data):
run_reverse_by_pk_agent(d['episode_id'], True, "../../data/episode_utterances")
run_reverse_by_pk_agent(d['episode_id'], False, "../../data/episode_utterances")
utterance_pattern = r'Utterance (\d+) by ([A-Za-z ]+)'
print("turning into attributed utterances")
attributed_data = []
print(len(data))
for d in tqdm(data):
for uttr_key, attributed_uttr in d['attributed_utterances'].items():
match = re.search(utterance_pattern, uttr_key)
if match:
turn_number = match.group(1)
agent_name = match.group(2)
else:
raise Exception(f"Utterance key not in correct format: {uttr_key}")
if agent_name != d['agent']:
continue
utterance_path = f"../../data/episode_utterances/{d['episode_id']}-{d['agent']}-{turn_number}.json"
if not os.path.exists(utterance_path):
raise Exception(f"Utterance not found: {utterance_path}")
with open(f"../../data/episode_utterances/{d['episode_id']}-{d['agent']}-{turn_number}.json", 'r') as f:
sotopia_utterance = json.load(f)
new_utterance = deepcopy(sotopia_utterance)
new_utterance['attribution'] = attributed_uttr[1]
new_utterance['turn_number'] = turn_number
new_utterance['goal_score'] = d['goal_score']
attributed_data.append(new_utterance)
def calc_reward(utter_attrib: float, goal_score: float) -> float:
if utter_attrib == -1:
reward = -1.0
else:
reward = utter_attrib / 3 * goal_score
return reward
sotopia_pi_utterance_reward = []
for d in tqdm(attributed_data):
sotopia_pi_utterance_reward.append(
{
"instruction": d['prompt'],
"input": "",
"output": "",
"value": calc_reward(d['attribution'], d['goal_score']),
"system": "",
"history": []
}
)
with open("../../data/sotopia_pi_utterance_reward_single_prompt.json", 'w') as f:
json.dump(sotopia_pi_utterance_reward, f, indent=4)
sotopia_pi_utterance_ppo = []
for d in tqdm(attributed_data):
sotopia_pi_utterance_ppo.append(
{
"instruction": d['prompt'],
"input": "",
"output": d["result"],
}
)
with open("../../data/sotopia_pi_utterance_ppo.json", 'w') as f:
json.dump(sotopia_pi_utterance_ppo, f, indent=4)
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