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import json
import os
import re
from copy import deepcopy
from typing import Any, Dict, List
import click
from db_free_reverse_engineering import run_reverse_by_pk_agent
from tqdm import tqdm
def get_attributed_data(data: List[Dict[str, Any]], utterance_pattern: str, do_normalize: bool) -> List[Dict[str, Any]]:
attributed_data = []
print(f"Processing {len(data)} episodes")
error_count = 0
for d in tqdm(data):
total_attributed_reward = 0.0
all_utterances = []
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)
# breakpoint()
try:
new_utterance['attributed_reward'] = attributed_uttr[1]["reward"]
# new_utterance['dimension'] = attributed_uttr[1]["dimension"]
# new_utterance['scale'] = attributed_uttr[1]["scale"]
# new_utterance['dim_score'] = attributed_uttr[1]["dim_score"]
new_utterance['dimension'] = "goal"
new_utterance['scale'] = "3"
new_utterance['dim_score'] = 10
except:
error_count += 1
continue
new_utterance['turn_number'] = turn_number
new_utterance['goal_score'] = d['goal_score']
total_attributed_reward += new_utterance['attributed_reward']
all_utterances.append(new_utterance)
if do_normalize:
for utterance in all_utterances:
utterance['attributed_reward'] = utterance['attributed_reward'] / total_attributed_reward * d['goal_score']
for utterance in all_utterances:
attributed_data.append(utterance)
print(f"Error Count: {error_count}")
return attributed_data
@click.command()
@click.option("--data_dir", type=str, required=True, help="Directory containing data files.")
@click.option("--input_file", type=str, required=True, help="Path to the raw JSONLINES file.")
@click.option("--reward_output_file", type=str, required=True, help="Path to the processed JSON file.")
@click.option("--do_normalize", type=bool, default=False, help="Whether to normalize the rewards.")
def main(data_dir: str, input_file: str, reward_output_file: str, do_normalize: bool = False) -> None:
with open(os.path.join(data_dir, input_file), 'r') as f:
data: List[Dict[str, Any]] = [json.loads(d) for d in f.readlines()]
cache_dir = os.path.join(data_dir, "episode_utterances")
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
for d in tqdm(data):
run_reverse_by_pk_agent(d['episode_id'], True, cache_dir)
run_reverse_by_pk_agent(d['episode_id'], False, cache_dir)
utterance_pattern = r'Utterance (\d+) by ([A-Za-z ]+)'
print("turning into attributed utterances")
attributed_data = get_attributed_data(data, utterance_pattern, do_normalize=do_normalize)
sotopia_pi_utterance_reward = []
# breakpoint()
for d in tqdm(attributed_data):
sotopia_pi_utterance_reward.append(
{
"input": d['prompt'],
"output": d['result'],
"value": d['attributed_reward'],
}
)
with open(os.path.join(data_dir, reward_output_file), 'w') as f:
json.dump(sotopia_pi_utterance_reward, f, indent=4)
if __name__ == "__main__":
main()
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