import re
import json
from pathlib import Path


def process_banking_data(input_file, output_dir):
    """
    Process banking data into three subsets:
    1. Classification: text -> label (main aspect only)
    2. Sentiment: text -> sentiment (overall sentiment)
    3. Aspect-Sentiment: text -> aspect, sentiment pairs
    """

    classification_data = []
    sentiment_data = []
    aspect_sentiment_data = []

    with open(input_file, 'r', encoding='utf-8') as f:
        for line_num, line in enumerate(f, 1):
            line = line.strip()
            if not line:
                continue

            # Extract labels and sentiments
            label_pattern = r'__label__([A-Z_]+)#(positive|negative|neutral)'
            matches = re.findall(label_pattern, line)

            # Remove labels from text
            text = re.sub(r'__label__[A-Z_]+#(positive|negative|neutral)\s*', '', line).strip()

            if not text or not matches:
                print(f"Skipping line {line_num}: No valid data found")
                continue

            # Extract aspects and sentiments
            aspects = [m[0] for m in matches]
            sentiments = [m[1] for m in matches]

            # 1. Classification subset (first aspect as main label)
            classification_data.append({
                "text": text,
                "label": aspects[0]
            })

            # 2. Sentiment-only subset (overall sentiment)
            # If multiple sentiments, use the first one or most frequent
            overall_sentiment = sentiments[0]
            if len(set(sentiments)) == 1:
                overall_sentiment = sentiments[0]
            else:
                # Use most common sentiment
                sentiment_counts = {}
                for s in sentiments:
                    sentiment_counts[s] = sentiment_counts.get(s, 0) + 1
                overall_sentiment = max(sentiment_counts, key=sentiment_counts.get)

            sentiment_data.append({
                "text": text,
                "sentiment": overall_sentiment
            })

            # 3. Aspect-Sentiment subset
            aspect_sentiment_pairs = []
            for aspect, sentiment in zip(aspects, sentiments):
                aspect_sentiment_pairs.append({
                    "aspect": aspect,
                    "sentiment": sentiment
                })

            aspect_sentiment_data.append({
                "text": text,
                "aspects": aspect_sentiment_pairs
            })

    # Save the three subsets
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    # Determine if this is train or test based on input filename
    split = "train" if "train" in str(input_file) else "test"

    # Save classification subset
    classification_file = output_dir / "classification" / f"{split}.jsonl"
    classification_file.parent.mkdir(parents=True, exist_ok=True)
    with open(classification_file, 'w', encoding='utf-8') as f:
        for item in classification_data:
            f.write(json.dumps(item, ensure_ascii=False) + '\n')
    print(f"Saved {len(classification_data)} classification examples to {classification_file}")

    # Save sentiment subset
    sentiment_file = output_dir / "sentiment" / f"{split}.jsonl"
    sentiment_file.parent.mkdir(parents=True, exist_ok=True)
    with open(sentiment_file, 'w', encoding='utf-8') as f:
        for item in sentiment_data:
            f.write(json.dumps(item, ensure_ascii=False) + '\n')
    print(f"Saved {len(sentiment_data)} sentiment examples to {sentiment_file}")

    # Save aspect-sentiment subset
    aspect_sentiment_file = output_dir / "aspect_sentiment" / f"{split}.jsonl"
    aspect_sentiment_file.parent.mkdir(parents=True, exist_ok=True)
    with open(aspect_sentiment_file, 'w', encoding='utf-8') as f:
        for item in aspect_sentiment_data:
            f.write(json.dumps(item, ensure_ascii=False) + '\n')
    print(f"Saved {len(aspect_sentiment_data)} aspect-sentiment examples to {aspect_sentiment_file}")

    # Print statistics
    print("\n=== Statistics ===")
    print(f"Total examples processed: {len(classification_data)}")

    # Label distribution
    label_counts = {}
    for item in classification_data:
        label = item['label']
        label_counts[label] = label_counts.get(label, 0) + 1

    print("\nLabel distribution:")
    for label, count in sorted(label_counts.items(), key=lambda x: x[1], reverse=True):
        print(f"  {label}: {count}")

    # Sentiment distribution
    sentiment_counts = {}
    for item in sentiment_data:
        sentiment = item['sentiment']
        sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1

    print("\nSentiment distribution:")
    for sentiment, count in sorted(sentiment_counts.items(), key=lambda x: x[1], reverse=True):
        print(f"  {sentiment}: {count}")

    # Multi-aspect examples
    multi_aspect_count = sum(1 for item in aspect_sentiment_data if len(item['aspects']) > 1)
    print(f"\nExamples with multiple aspects: {multi_aspect_count}")


if __name__ == "__main__":
    # Process train data
    print("Processing train data...")
    process_banking_data("raw_data/train.txt", "data")

    # Process test data
    print("\n" + "="*50)
    print("Processing test data...")
    process_banking_data("raw_data/test.txt", "data")