"""
Fetch data from HuggingFace dataset undertheseanlp/UVN-1
- Get documents from news dataset
- Segment sentences using underthesea
- Get first 8000 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 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()

    if not sent:
        return False, sent
    # Too short
    if len(sent) < 20:
        return False, sent
    # Too long
    if len(sent) > 300:
        return False, sent
    # Must contain Vietnamese characters
    if not re.search(r'[àáảãạăắằẳẵặâấầẩẫậèéẻẽẹêếềểễệìíỉĩịòóỏõọôốồổỗộơớờởỡợùúủũụưứừửữựỳýỷỹỵđ]', sent, re.IGNORECASE):
        return False, sent
    # Skip if mostly uppercase (headers, titles)
    if sum(1 for c in sent if c.isupper()) > len(sent) * 0.5:
        return False, sent
    # Skip bylines (e.g., "Theo VnExpress", "PV/Báo ...")
    if re.match(r'^(Theo |PV |Nguồn:|Ảnh:|Video:|Bài:|Tin ảnh:)', sent):
        return False, sent
    # Skip photo captions (short sentences ending with source attribution)
    if re.search(r'\(Ảnh:.*\)$', sent):
        return False, sent
    if re.search(r'\(Nguồn:.*\)$', sent):
        return False, sent
    # Skip date/time patterns at start
    if re.match(r'^\d{1,2}/\d{1,2}/\d{4}', sent):
        return False, sent
    if re.match(r'^\d{1,2}:\d{2}', sent):
        return False, sent
    # Skip sentences with URLs
    if re.search(r'(http|www\.|\.com|\.vn)', sent, re.IGNORECASE):
        return False, sent
    # Skip sentences that are just tags or categories
    if re.match(r'^(Tags?:|Chuyên mục:|Từ khóa:)', sent, re.IGNORECASE):
        return False, sent
    # Skip sentences with excessive numbers (data tables)
    num_digits = sum(1 for c in sent if c.isdigit())
    if num_digits > len(sent) * 0.3:
        return False, sent
    return True, sent


TARGET_COUNT = 8000


def fetch_and_process():
    # Load dataset from HuggingFace
    print("Loading UVN-1 dataset from HuggingFace...")
    ds = load_dataset("undertheseanlp/UVN-1", split="train")

    print(f"Total articles in dataset: {len(ds)}")

    # Segment sentences from all documents until we have enough
    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) >= TARGET_COUNT:
            print(f"Processed {idx + 1} documents")
            break

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

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

    with open(output_file, "w", encoding="utf-8") as f:
        for i, sent in enumerate(sentences_out, 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_out[:5], 1):
        print(f"  {i}. {sent[:80]}...")


if __name__ == "__main__":
    fetch_and_process()