import time
import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from tqdm import tqdm

from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer

# Initialize transformer
calc = FPCalculator("ecfp")
mol_transf = MoleculeTransformer(calc, n_jobs=10)

def transform_and_save(df, output_path, split_name="", batch_size=100000):
    start = time.time()
    print(f"\nStarting transformation for {split_name}...")

    smiles = df['clean_smiles'].values
    values = df['value'].values
    all_features = []

    for i in tqdm(range(0, len(smiles), batch_size), desc=f"{split_name} batches"):
        batch_smiles = smiles[i:i + batch_size]
        batch_fps = mol_transf(batch_smiles)
        batch_fps = np.stack(batch_fps)
        all_features.append(batch_fps)

    features = np.vstack(all_features)
    df_fps = pd.DataFrame(features, columns=[f"feature_{i}" for i in range(features.shape[1])])
    df_fps["value"] = values  # Append the label

    pq.write_table(pa.Table.from_pandas(df_fps), output_path)

    end = time.time()
    print(f"Finished {split_name} in {end - start:.2f} seconds.")

# Process each split
data_train = pq.read_table("product/d2_split/train.parquet").to_pandas()
transform_and_save(data_train, "intermediate_data/d2/data_train_features.parquet", "train")

data_val = pq.read_table("product/d2_split/val.parquet").to_pandas()
transform_and_save(data_val, "intermediate_data/d2/data_val_features.parquet", "validation")

data_test = pq.read_table("product/d2_split/test.parquet").to_pandas()
transform_and_save(data_test, "intermediate_data/d2/data_test_features.parquet", "test")