import os
from collections import Counter

import pandas as pd

dfs = []
base_path = "dataframes"
for file_path in os.listdir(base_path):
    dfs.append(pd.read_json(os.path.join(base_path, file_path), lines=True))
raw_df = pd.concat(dfs, ignore_index=True)
print(f"{raw_df=}")

clue_counts = Counter(clue for clues in raw_df['clues'] for clue in clues)

# take 20% of data with unique stories for a test set
unique_stories = set(c for c in raw_df['story'])
test_stories = set(x for i, x in enumerate(unique_stories) if i % 5 == 0)
test_df0 = raw_df[raw_df['story'].apply(lambda story: story in test_stories)]
test_df = test_df0[test_df0['clues'].apply(lambda clues: sum(clue_counts[clue] > 1 for clue in clues) <= 1)]
test_df.to_json("test.jsonl", orient='records', lines=True)
print(f"{test_df=}")

train_val_df = raw_df[raw_df['story'].apply(lambda story: story not in test_stories)]

val_df = train_val_df[train_val_df.index % 5 == 0]
val_df.to_json("validation.jsonl", orient='records', lines=True)
print(f"{val_df=}")

train_df = train_val_df[train_val_df.index % 5 != 0]
train_df.to_json("train.jsonl", orient='records', lines=True)
print(f"{train_df=}")