"""Nigerian text preprocessing utilities.

Provides functions to clean and normalize Nigerian text data, including:
- Unicode normalization (especially for Yorùbá diacritics)
- Social media text cleaning
- PII masking (phone numbers, BVN, NIN)
- Naira amount handling
"""
from __future__ import annotations

import re
import unicodedata
from typing import List, Optional

from naijaml.utils.constants import (
    PHONE_PATTERN_LOOSE,
    BVN_PATTERN,
    NIN_PATTERN,
    NAIRA_PATTERN,
    PIDGIN_PARTICLES,
)

# =============================================================================
# Unicode Normalization
# =============================================================================

def normalize_unicode(text: str, form: str = "NFC") -> str:
    """Normalize Unicode text to a standard form.

    Important for Yorùbá text where diacritics can be represented
    as combining characters or precomposed characters.

    Args:
        text: Input text to normalize.
        form: Unicode normalization form ('NFC', 'NFD', 'NFKC', 'NFKD').
            NFC (default) composes characters (recommended for Yorùbá).

    Returns:
        Normalized text.

    Example:
        >>> # These look the same but have different byte representations
        >>> text1 = "ọjọ́"  # precomposed
        >>> text2 = "ọjọ́"   # with combining characters
        >>> normalize_unicode(text1) == normalize_unicode(text2)
        True
    """
    return unicodedata.normalize(form, text)


def strip_diacritics(text: str) -> str:
    """Remove all diacritical marks from text.

    Useful for creating search-friendly versions of Yorùbá text.

    Args:
        text: Input text with diacritics.

    Returns:
        Text with diacritics removed.

    Example:
        >>> strip_diacritics("Ojó lo sí ọjà lánà")
        'Ojo lo si oja lana'
    """
    # NFD decomposes characters, then we filter out combining marks
    decomposed = unicodedata.normalize("NFD", text)
    stripped = "".join(
        char for char in decomposed
        if unicodedata.category(char) != "Mn"  # Mn = Mark, Nonspacing
    )
    return unicodedata.normalize("NFC", stripped)


# =============================================================================
# Social Media Text Cleaning
# =============================================================================

# Common patterns in social media text
_URL_PATTERN = re.compile(
    r"https?://\S+|www\.\S+"
)

_MENTION_PATTERN = re.compile(
    r"@[A-Za-z0-9_]+"
)

_HASHTAG_PATTERN = re.compile(
    r"#[A-Za-z0-9_]+"
)

_EMAIL_PATTERN = re.compile(
    r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"
)

_REPEATED_CHARS = re.compile(
    r"(.)\1{3,}"  # 4+ repeated characters
)

_MULTIPLE_SPACES = re.compile(
    r"\s+"
)


def clean_social_media(
    text: str,
    remove_urls: bool = True,
    remove_mentions: bool = True,
    remove_hashtags: bool = False,
    lowercase: bool = False,
    reduce_repeated: bool = True,
) -> str:
    """Clean social media text (tweets, comments, etc.).

    Args:
        text: Input text from social media.
        remove_urls: Remove URLs.
        remove_mentions: Remove @mentions.
        remove_hashtags: Remove #hashtags (default False, keeps them).
        lowercase: Convert to lowercase.
        reduce_repeated: Reduce repeated characters (e.g., "loooool" -> "lool").

    Returns:
        Cleaned text.

    Example:
        >>> clean_social_media("@user This film too sweet!!! https://t.co/abc #Nollywood")
        'This film too sweet!! #Nollywood'
    """
    result = text

    if remove_urls:
        result = _URL_PATTERN.sub("", result)

    if remove_mentions:
        result = _MENTION_PATTERN.sub("", result)

    if remove_hashtags:
        result = _HASHTAG_PATTERN.sub("", result)

    if reduce_repeated:
        # Reduce 4+ repeated chars to 2
        result = _REPEATED_CHARS.sub(r"\1\1", result)

    if lowercase:
        result = result.lower()

    # Clean up whitespace
    result = _MULTIPLE_SPACES.sub(" ", result).strip()

    return result


def extract_hashtags(text: str) -> List[str]:
    """Extract hashtags from text.

    Args:
        text: Input text.

    Returns:
        List of hashtags (without # symbol).

    Example:
        >>> extract_hashtags("Great movie! #Nollywood #NigerianFilm")
        ['Nollywood', 'NigerianFilm']
    """
    return [tag[1:] for tag in _HASHTAG_PATTERN.findall(text)]


def extract_mentions(text: str) -> List[str]:
    """Extract @mentions from text.

    Args:
        text: Input text.

    Returns:
        List of usernames (without @ symbol).

    Example:
        >>> extract_mentions("cc @funke_akindele @NigeriaFilms")
        ['funke_akindele', 'NigeriaFilms']
    """
    return [mention[1:] for mention in _MENTION_PATTERN.findall(text)]


# =============================================================================
# PII Masking
# =============================================================================

def mask_pii(
    text: str,
    mask_phones: bool = True,
    mask_emails: bool = True,
    mask_bvn: bool = True,
    mask_nin: bool = True,
    mask_naira: bool = False,
    phone_mask: str = "[PHONE]",
    email_mask: str = "[EMAIL]",
    bvn_mask: str = "[BVN]",
    nin_mask: str = "[NIN]",
    naira_mask: str = "[AMOUNT]",
) -> str:
    """Mask personally identifiable information (PII) in text.

    Args:
        text: Input text potentially containing PII.
        mask_phones: Mask Nigerian phone numbers.
        mask_emails: Mask email addresses.
        mask_bvn: Mask Bank Verification Numbers.
        mask_nin: Mask National Identification Numbers.
        mask_naira: Mask Naira amounts (default False).
        phone_mask: Replacement string for phone numbers.
        email_mask: Replacement string for emails.
        bvn_mask: Replacement string for BVN.
        nin_mask: Replacement string for NIN.
        naira_mask: Replacement string for Naira amounts.

    Returns:
        Text with PII masked.

    Example:
        >>> mask_pii("Call me on 08012345678 or email me@example.com")
        'Call me on [PHONE] or [EMAIL]'
    """
    result = text

    if mask_phones:
        result = PHONE_PATTERN_LOOSE.sub(phone_mask, result)

    if mask_emails:
        result = _EMAIL_PATTERN.sub(email_mask, result)

    if mask_bvn:
        # BVN is 11 digits starting with 22
        result = re.sub(r"\b22\d{9}\b", bvn_mask, result)

    if mask_nin:
        # NIN is any 11-digit number (more careful matching to avoid false positives)
        # Only match standalone 11-digit numbers that aren't phone numbers
        result = re.sub(r"(?<!\d)\d{11}(?!\d)", nin_mask, result)

    if mask_naira:
        result = NAIRA_PATTERN.sub(naira_mask, result)

    return result


def find_phones(text: str) -> List[str]:
    """Find all Nigerian phone numbers in text.

    Args:
        text: Input text.

    Returns:
        List of phone numbers found.

    Example:
        >>> find_phones("Call 0801-234-5678 or +234 902 123 4567")
        ['0801-234-5678', '+234 902 123 4567']
    """
    return PHONE_PATTERN_LOOSE.findall(text)


def find_naira_amounts(text: str) -> List[str]:
    """Find all Naira amounts in text.

    Args:
        text: Input text.

    Returns:
        List of Naira amount strings found.

    Example:
        >>> find_naira_amounts("Price is ₦5,000 or NGN 10,000")
        ['₦5,000', 'NGN 10,000']
    """
    return NAIRA_PATTERN.findall(text)


# =============================================================================
# Nigerian-Specific Normalization
# =============================================================================

def normalize_naira_symbol(text: str) -> str:
    """Normalize various Naira representations to ₦.

    Args:
        text: Input text with Naira amounts.

    Returns:
        Text with normalized Naira symbol.

    Example:
        >>> normalize_naira_symbol("NGN 5000 or N5,000")
        '₦5000 or ₦5,000'
    """
    # Replace NGN followed by optional space and number
    result = re.sub(r"NGN\s?(?=\d)", "₦", text)
    # Replace standalone N before numbers (be careful not to replace regular N)
    result = re.sub(r"(?<![A-Za-z])N(?=\d)", "₦", result)
    return result


def clean_nigerian_text(
    text: str,
    normalize: bool = True,
    clean_social: bool = True,
    mask_pii_data: bool = False,
    lowercase: bool = False,
) -> str:
    """All-in-one text cleaning for Nigerian text data.

    Applies sensible defaults for cleaning Nigerian text from social media
    and other sources.

    Args:
        text: Input text.
        normalize: Apply Unicode normalization (NFC).
        clean_social: Clean social media artifacts (URLs, mentions).
        mask_pii_data: Mask phone numbers, emails, BVN, NIN.
        lowercase: Convert to lowercase.

    Returns:
        Cleaned text.

    Example:
        >>> text = "@user Check https://t.co/abc Ọjọ́ is great! Call 08012345678"
        >>> clean_nigerian_text(text, mask_pii_data=True)
        'Check Ọjọ́ is great! Call [PHONE]'
    """
    result = text

    if normalize:
        result = normalize_unicode(result)

    if clean_social:
        result = clean_social_media(result)

    if mask_pii_data:
        result = mask_pii(result)

    if lowercase:
        result = result.lower()

    return result


# =============================================================================
# Nigerian Pidgin Handling
# =============================================================================

def is_pidgin_particle(word: str) -> bool:
    """Check if a word is a Nigerian Pidgin particle or discourse marker.

    These are words like 'sha', 'sef', 'abeg' that standard NLP tools often
    strip as noise or classify as errors, but are meaningful in Pidgin.

    Args:
        word: Word to check.

    Returns:
        True if the word is a known Pidgin particle.

    Example:
        >>> is_pidgin_particle("sha")
        True
        >>> is_pidgin_particle("hello")
        False
    """
    return word.lower() in PIDGIN_PARTICLES


def preserve_pidgin_particles(text: str, words_to_remove: List[str]) -> str:
    """Remove words from text while preserving Pidgin particles.

    Useful when cleaning text with a stopword list that might incorrectly
    include Pidgin discourse markers.

    Args:
        text: Input text.
        words_to_remove: List of words to remove (stopwords).

    Returns:
        Text with non-Pidgin stopwords removed but Pidgin particles kept.

    Example:
        >>> preserve_pidgin_particles("The film sha too sweet", ["the", "sha", "too"])
        'film sha sweet'
    """
    words = text.split()
    result = []
    for word in words:
        # Keep if not in removal list OR if it's a Pidgin particle
        word_lower = word.lower().strip(".,!?;:'\"")
        if word_lower not in words_to_remove or is_pidgin_particle(word_lower):
            result.append(word)
    return " ".join(result)


def get_pidgin_particles() -> set:
    """Get the set of known Nigerian Pidgin particles.

    Returns:
        Set of Pidgin particle strings.

    Example:
        >>> particles = get_pidgin_particles()
        >>> "abeg" in particles
        True
    """
    return PIDGIN_PARTICLES.copy()
