#!/usr/bin/env python3
"""Generate statistics for the UTS2017_Bank dataset."""

import json
import statistics as stats
from collections import Counter
from pathlib import Path


def load_jsonl(file_path):
    """Load JSONL file and return list of items."""
    with open(file_path, encoding="utf-8") as f:
        return [json.loads(line.strip()) for line in f]


def text_stats(items):
    """Calculate text length statistics."""
    word_counts = [len(item["text"].split()) for item in items]
    return {
        "avg": stats.mean(word_counts),
        "min": min(word_counts),
        "max": max(word_counts),
        "median": stats.median(word_counts),
    }


def print_subset_stats(subset_name, emoji):
    """Print statistics for a dataset subset."""
    print(f"\n{emoji} {subset_name.upper()} SUBSET")
    print("-" * 40)

    for split in ["train", "test"]:
        file_path = Path(f"data/{subset_name}/{split}.jsonl")
        items = load_jsonl(file_path)

        print(f"\n{split.capitalize()}: {len(items)} examples")

        # Text statistics
        text_data = text_stats(items)
        print(f"  Words: avg={text_data['avg']:.1f}, range={text_data['min']}-{text_data['max']}")

        # Subset-specific stats
        if subset_name == "classification":
            labels = Counter(item["label"] for item in items)
            print(f"  Top labels: {', '.join(f'{k}({v})' for k, v in labels.most_common(3))}")

        elif subset_name == "sentiment":
            sentiments = Counter(item["sentiment"] for item in items)
            print(f"  Sentiments: {', '.join(f'{k}({v})' for k, v in sentiments.most_common())}")

        elif subset_name == "aspect_sentiment":
            multi_aspect = sum(1 for item in items if len(item["aspects"]) > 1)
            print(f"  Multi-aspect: {multi_aspect}/{len(items)} examples")


def main():
    """Generate and display dataset statistics."""
    print("📊 UTS2017_Bank Dataset Statistics")
    print("=" * 50)

    # Overall stats
    train_items = load_jsonl("data/classification/train.jsonl")
    test_items = load_jsonl("data/classification/test.jsonl")
    total = len(train_items) + len(test_items)

    print(f"\n📈 OVERALL: {total} examples ({len(train_items)} train, {len(test_items)} test)")

    # Subset statistics
    print_subset_stats("classification", "🏷️")
    print_subset_stats("sentiment", "😊")
    print_subset_stats("aspect_sentiment", "🎯")

    # Available configurations
    print("\n💡 USAGE:")
    print("  load_dataset('undertheseanlp/UTS2017_Bank', 'classification')")
    print("  load_dataset('undertheseanlp/UTS2017_Bank', 'sentiment')")
    print("  load_dataset('undertheseanlp/UTS2017_Bank', 'aspect_sentiment')")


if __name__ == "__main__":
    main()