import json
import re
from pathlib import Path
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from tqdm import tqdm
import concurrent.futures
from typing import List, Dict
import argparse

class SelfiesTokenizer:
    def __init__(self, tokenizer_path: str):
        with open(tokenizer_path, 'r') as f:
            config = json.load(f)
        self.vocab = config['vocab']
        self.special_tokens = config['special_tokens']
        self.max_length = config['model_max_length']
        self.id2token = {v: k for k, v in self.vocab.items()}
    
    def tokenize(self, selfies_string: str) -> List[str]:
        """Split SELFIES string into tokens"""
        return re.findall(r'\[.*?\]', selfies_string)
    
    def convert_tokens_to_ids(self, tokens: List[str]) -> List[int]:
        """Convert tokens to their IDs"""
        return [self.vocab.get(token, self.vocab['[UNK]']) for token in tokens]
    
    def encode(self, selfies_string: str, add_special_tokens: bool = True) -> List[int]:
        """Full encoding process"""
        tokens = self.tokenize(selfies_string)
        ids = self.convert_tokens_to_ids(tokens)
        
        if add_special_tokens:
            ids = [self.vocab['[CLS]']] + ids + [self.vocab['[SEP]']]
        
        if len(ids) > self.max_length:
            ids = ids[:self.max_length]
        else:
            ids.extend([self.vocab['[PAD]']] * (self.max_length - len(ids)))
            
        return ids

def process_chunk(chunk: List[str], tokenizer: SelfiesTokenizer) -> Dict[str, List]:
    """Process a chunk of SELFIES strings"""
    sequences = [seq.strip() for seq in chunk]
    tokenized = [tokenizer.encode(seq) for seq in sequences]
    return {
        'sequence': sequences,
        'tokenized_sequence': tokenized
    }

def convert_to_parquet(input_files: List[str], output_file: str, tokenizer_path: str, 
                      chunk_size: int = 10000, num_workers: int = 4):
    """Convert SELFIES files to parquet with optimized settings"""
    
    tokenizer = SelfiesTokenizer(tokenizer_path)
    
    # Create schema for the parquet file
    schema = pa.schema([
        ('sequence', pa.string()),
        ('tokenized_sequence', pa.list_(pa.int16()))
    ])
    
    # Set up parquet writer with optimized settings
    writer = pq.ParquetWriter(
        output_file,
        schema,
        compression='zstd',
        compression_level=9,  # Maximum ZSTD compression
        use_dictionary=True,
        write_statistics=True
    )
    
    # Process each input file
    for input_file in input_files:
        print(f"Processing {input_file}...")
        
        # Count total lines for progress bar
        total_lines = sum(1 for _ in open(input_file, 'r'))
        
        with open(input_file, 'r') as f:
            # Process file in chunks
            for chunk_start in tqdm(range(0, total_lines, chunk_size)):
                chunk = []
                for _ in range(chunk_size):
                    line = f.readline()
                    if not line:
                        break
                    chunk.append(line)
                
                if not chunk:
                    break
                
                # Process chunks in parallel
                with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
                    chunk_size_per_worker = len(chunk) // num_workers
                    futures = []
                    
                    for i in range(0, len(chunk), chunk_size_per_worker):
                        chunk_part = chunk[i:i + chunk_size_per_worker]
                        futures.append(
                            executor.submit(process_chunk, chunk_part, tokenizer)
                        )
                    
                    # Combine results
                    results = {
                        'sequence': [],
                        'tokenized_sequence': []
                    }
                    
                    for future in concurrent.futures.as_completed(futures):
                        chunk_results = future.result()
                        results['sequence'].extend(chunk_results['sequence'])
                        results['tokenized_sequence'].extend(chunk_results['tokenized_sequence'])
                
                # Convert to PyArrow table and write
                table = pa.Table.from_pydict(results, schema=schema)
                writer.write_table(table)
    
    writer.close()
    
    # Print final statistics
    parquet_file = pq.ParquetFile(output_file)
    print(f"\nParquet file statistics:")
    print(f"Number of row groups: {parquet_file.num_row_groups}")
    print(f"Number of rows: {parquet_file.metadata.num_rows}")
    print(f"File size: {Path(output_file).stat().st_size / (1024*1024):.2f} MB")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Convert SELFIES files to parquet dataset')
    parser.add_argument('input_files', nargs='+', help='Input SELFIES files')
    parser.add_argument('output_file', help='Output parquet file')
    parser.add_argument('tokenizer_path', help='Path to tokenizer config JSON')
    parser.add_argument('--chunk-size', type=int, default=10000, help='Chunk size for processing')
    parser.add_argument('--num-workers', type=int, default=4, help='Number of worker threads')
    
    args = parser.parse_args()
    
    convert_to_parquet(
        args.input_files,
        args.output_file,
        args.tokenizer_path,
        args.chunk_size,
        args.num_workers
    )