"""
Fetch data from HuggingFace dataset undertheseanlp/UTS_VLC
- Get documents from law dataset
- Segment sentences using underthesea
- Get first 3000 sentences
"""

import re
from os.path import dirname, join

from datasets import load_dataset

from underthesea import sent_tokenize, text_normalize


def clean_text(text):
    """Remove markdown formatting and clean text."""
    # Normalize Unicode using underthesea
    text = text_normalize(text)
    # Remove markdown headers
    text = re.sub(r'^#+\s+', '', text, flags=re.MULTILINE)
    # Remove bold/italic markers
    text = re.sub(r'\*+', '', text)
    # Remove horizontal rules
    text = re.sub(r'^-+$', '', text, flags=re.MULTILINE)
    # Remove links
    text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)
    # Remove multiple newlines
    text = re.sub(r'\n{2,}', '\n', text)
    # Remove leading/trailing whitespace per line
    lines = [line.strip() for line in text.split('\n')]
    text = '\n'.join(lines)
    return text


def is_valid_sentence(sent):
    """Check if sentence is valid for UD annotation."""
    sent = sent.strip()
    # Remove trailing list markers like "1." or "a)"
    sent = re.sub(r'\n\d+\.$', '', sent)
    sent = re.sub(r'\n[a-z]\)$', '', sent)
    sent = sent.strip()

    if not sent:
        return False, sent
    # Too short
    if len(sent) < 20:
        return False, sent
    # Too long
    if len(sent) > 300:
        return False, sent
    # Skip headers (all caps, or starts with "Điều", "Chương", etc.)
    if re.match(r'^(QUỐC HỘI|CỘNG HÒA|Độc lập|Phần thứ|Chương [IVX]+|MỤC \d+)', sent):
        return False, sent
    # Skip article titles
    if re.match(r'^(Điều \d+|Khoản \d+|Mục \d+)', sent):
        return False, sent
    # Skip if mostly uppercase
    if sum(1 for c in sent if c.isupper()) > len(sent) * 0.5:
        return False, sent
    # Skip if starts with special markers
    if sent.startswith(('English:', 'Số hiệu:', 'Ngày hiệu lực:', '---', '|')):
        return False, sent
    # Must contain Vietnamese characters
    if not re.search(r'[àáảãạăắằẳẵặâấầẩẫậèéẻẽẹêếềểễệìíỉĩịòóỏõọôốồổỗộơớờởỡợùúủũụưứừửữựỳýỷỹỵđ]', sent, re.IGNORECASE):
        return False, sent
    # Skip if ends with just a number (incomplete sentence)
    if re.search(r'\n\d+$', sent):
        return False, sent
    return True, sent


def fetch_and_process():
    # Load dataset from HuggingFace
    print("Loading dataset from HuggingFace...")
    ds = load_dataset("undertheseanlp/UTS_VLC", split="2026")

    # Segment sentences from all documents until we have 3000
    print("Segmenting sentences...")
    all_sentences = []
    for idx, doc in enumerate(ds):
        content = doc["content"]
        content = clean_text(content)
        sentences = sent_tokenize(content)
        for sent in sentences:
            sent = sent.strip()
            is_valid, cleaned_sent = is_valid_sentence(sent)
            if is_valid:
                all_sentences.append(cleaned_sent)
        if len(all_sentences) >= 3000:
            print(f"Processed {idx + 1} documents")
            break

    # Get first 3000 sentences
    sentences_3000 = all_sentences[:3000]
    print(f"Total sentences collected: {len(sentences_3000)}")

    # Save to output file
    output_dir = dirname(dirname(__file__))
    output_file = join(output_dir, "sentences.txt")

    with open(output_file, "w", encoding="utf-8") as f:
        for i, sent in enumerate(sentences_3000, 1):
            f.write(f"{i}\t{sent}\n")

    print(f"Saved to: {output_file}")

    # Print sample
    print("\nSample sentences:")
    for i, sent in enumerate(sentences_3000[:5], 1):
        print(f"  {i}. {sent[:80]}...")


if __name__ == "__main__":
    fetch_and_process()