import pandas as pd
import numpy as np
from pathlib import Path
import argparse
import logging
from sklearn.model_selection import train_test_split

def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s'
    )
    return logging.getLogger(__name__)

def split_dataset(input_path: str, output_dir: str, chunk_size: int = 100000):
    """
    Split a large CSV file into train, test, and validation sets.
    Processes the file in chunks to handle large datasets efficiently.
    
    Args:
        input_path: Path to input CSV file
        output_dir: Directory to save split datasets
        chunk_size: Number of rows to process at a time
    """
    logger = setup_logging()
    output_path = Path(output_dir)
    output_path.mkdir(parents=True, exist_ok=True)
    
    # Open output files
    train_file = open(output_path / 'train.csv', 'w')
    test_file = open(output_path / 'test.csv', 'w')
    val_file = open(output_path / 'val.csv', 'w')
    
    # Set random seed for reproducibility
    np.random.seed(42)
    
    # Process the CSV in chunks
    chunk_iterator = pd.read_csv(input_path, chunksize=chunk_size)
    
    is_first_chunk = True
    total_rows = 0
    
    logger.info("Starting dataset split...")
    
    for i, chunk in enumerate(chunk_iterator):
        # Split chunk into train (70%), test (20%), val (10%)
        train_chunk, test_val_chunk = train_test_split(chunk, train_size=0.7, random_state=42)
        test_chunk, val_chunk = train_test_split(test_val_chunk, train_size=0.67, random_state=42)
        
        # Write header for first chunk only
        if is_first_chunk:
            train_chunk.to_csv(train_file, index=False)
            test_chunk.to_csv(test_file, index=False)
            val_chunk.to_csv(val_file, index=False)
            is_first_chunk = False
        else:
            train_chunk.to_csv(train_file, index=False, header=False)
            test_chunk.to_csv(test_file, index=False, header=False)
            val_chunk.to_csv(val_file, index=False, header=False)
        
        total_rows += len(chunk)
        logger.info(f"Processed {total_rows} rows...")
    
    # Close files
    train_file.close()
    test_file.close()
    val_file.close()
    
    logger.info("Dataset splitting complete!")
    logger.info(f"Total rows processed: {total_rows}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Split large CSV dataset into train/test/val sets")
    parser.add_argument("--input_path", required=True, help="Path to input CSV file")
    parser.add_argument("--output_dir", required=True, help="Directory to save split datasets")
    parser.add_argument("--chunk_size", type=int, default=100000, help="Chunk size for processing")
    
    args = parser.parse_args()
    
    split_dataset(args.input_path, args.output_dir, args.chunk_size)