import re
import unicodedata

from transformers import LlamaTokenizerFast


tokenizer = LlamaTokenizerFast.from_pretrained("local_tokenizer")

def fasttext_preprocess_func(content: str) -> str:
    """Fasttext preprocess function.

    Args:
        content (str): Content to process.

    Returns:
        str: Processed normalized content.
    """

    # 1. remove multiple newlines
    content = re.sub(r'\n{3,}', '\n\n', content)

    # 2. lower the content
    content = content.lower()

    # 3. remove diacritics
    content = ''.join(
        c for c in unicodedata.normalize('NFKD', content)
        if unicodedata.category(c) != 'Mn')

    # 4. word segmentation
    token_ids = tokenizer.encode(content, add_special_tokens=False)
    single_text_list = []
    for token_id in token_ids:
        curr_text = tokenizer.decode([token_id])
        single_text_list.append(curr_text)

    content = ' '.join(single_text_list)

    # 5. keep escape chars, \n, \t, \r -> \\n, \\t, \\r,
    # which will saved as \n, \t, \r in txt file.
    content = re.sub(r'\n', '\\\\n', content)
    content = re.sub(r'\r', '\\\\r', content)
    content = re.sub(r'\t', '\\\\t', content)
    content = re.sub(r' +', ' ', content)
    content = content.strip()

    return content