testbed/PyThaiNLP__pythainlp/pythainlp/util/normalize.py

# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
"""
Text normalization
"""

import re
from typing import List, Union

from pythainlp import thai_above_vowels as above_v
from pythainlp import thai_below_vowels as below_v
from pythainlp import thai_follow_vowels as follow_v
from pythainlp import thai_lead_vowels as lead_v
from pythainlp import thai_tonemarks as tonemarks
from pythainlp.tokenize import word_tokenize
from pythainlp.tools import warn_deprecation

_DANGLING_CHARS = f"{above_v}{below_v}{tonemarks}\u0e3a\u0e4c\u0e4d\u0e4e"
_RE_REMOVE_DANGLINGS = re.compile(f"^[{_DANGLING_CHARS}]+")

_ZERO_WIDTH_CHARS = "\u200b\u200c"  # ZWSP, ZWNJ

_REORDER_PAIRS = [
    ("\u0e40\u0e40", "\u0e41"),  # Sara E + Sara E -> Sara Ae
    (
        f"([{tonemarks}\u0e4c]+)([{above_v}{below_v}]+)",
        "\\2\\1",
    ),  # TONE/Thanthakhat + ABV/BLW VOWEL -> ABV/BLW VOWEL + TONE/Thanthakhat
    (
        f"\u0e4d([{tonemarks}]*)\u0e32",
        "\\1\u0e33",
    ),  # Nikhahit + TONEMARK + Sara Aa -> TONEMARK + Sara Am
    (
        f"([{follow_v}]+)([{tonemarks}]+)",
        "\\2\\1",
    ),  # FOLLOW VOWEL + TONEMARK+ -> TONEMARK + FOLLOW VOWEL
    ("([^\u0e24\u0e26])\u0e45", "\\1\u0e32"),  # Lakkhangyao -> Sara Aa
]

# VOWELS + Phinthu, Thanthakhat, Nikhahit, Yamakkan
_NOREPEAT_CHARS = (
    f"{follow_v}{lead_v}{above_v}{below_v}\u0e3a\u0e4c\u0e4d\u0e4e"
)
_NOREPEAT_PAIRS = list(
    zip([f"({ch}[ ]*)+{ch}" for ch in _NOREPEAT_CHARS], _NOREPEAT_CHARS)
)

_RE_TONEMARKS = re.compile(f"[{tonemarks}]+")

_RE_REMOVE_NEWLINES = re.compile("[ \n]*\n[ \n]*")


def _last_char(matchobj):  # to be used with _RE_NOREPEAT_TONEMARKS
    return matchobj.group(0)[-1]


def remove_dangling(text: str) -> str:
    """
    Remove Thai non-base characters at the beginning of text.

    This is a common "typo", especially for input field in a form,
    as these non-base characters can be visually hidden from user
    who may accidentally typed them in.

    A character to be removed should be both:

        * tone mark, above vowel, below vowel, or non-base sign AND
        * located at the beginning of the text

    :param str text: input text
    :return: text without dangling Thai characters at the beginning
    :rtype: str

    :Example:
    ::

        from pythainlp.util import remove_dangling

        remove_dangling("๊ก")
        # output: 'ก'
    """
    return _RE_REMOVE_DANGLINGS.sub("", text)


def remove_dup_spaces(text: str) -> str:
    """
    Remove duplicate spaces. Replace multiple spaces with one space.

    Multiple newline characters and empty lines will be replaced
    with one newline character.

    :param str text: input text
    :return: text without duplicated spaces and newlines
    :rtype: str

    :Example:
    ::

        from pythainlp.util import remove_dup_spaces

        remove_dup_spaces("ก    ข    ค")
        # output: 'ก ข ค'
    """
    while "  " in text:
        text = text.replace("  ", " ")
    text = _RE_REMOVE_NEWLINES.sub("\n", text)
    text = text.strip()
    return text


def remove_tonemark(text: str) -> str:
    """
    Remove all Thai tone marks from the text.

    Thai script has four tone marks indicating four tones as follows:

        * Down tone (Thai: ไม้เอก  _่ )
        * Falling tone  (Thai: ไม้โท  _้ )
        * High tone (Thai: ไม้ตรี  _๊ )
        * Rising tone (Thai: ไม้จัตวา _๋ )

    Putting wrong tone mark is a common mistake in Thai writing.
    By removing tone marks from the string, it could be used to
    for a approximate string matching.

    :param str text: input text
    :return: text without Thai tone marks
    :rtype: str

    :Example:
    ::

        from pythainlp.util import remove_tonemark

        remove_tonemark("สองพันหนึ่งร้อยสี่สิบเจ็ดล้านสี่แสนแปดหมื่นสามพันหกร้อยสี่สิบเจ็ด")
        # output: สองพันหนึงรอยสีสิบเจ็ดลานสีแสนแปดหมืนสามพันหกรอยสีสิบเจ็ด
    """
    for ch in tonemarks:
        while ch in text:
            text = text.replace(ch, "")
    return text


def remove_zw(text: str) -> str:
    """
    Remove zero-width characters.

    These non-visible characters may cause unexpected result from the
    user's point of view. Removing them can make string matching more robust.

    Characters to be removed:

        * Zero-width space (ZWSP)
        * Zero-width non-joiner (ZWJP)

    :param str text: input text
    :return: text without zero-width characters
    :rtype: str
    """
    for ch in _ZERO_WIDTH_CHARS:
        while ch in text:
            text = text.replace(ch, "")

    return text


def reorder_vowels(text: str) -> str:
    """
    Reorder vowels and tone marks to the standard logical order/spelling.

    Characters in input text will be reordered/transformed,
    according to these rules:

        * Sara E + Sara E -> Sara Ae
        * Nikhahit + Sara Aa -> Sara Am
        * tone mark + non-base vowel -> non-base vowel + tone mark
        * follow vowel + tone mark -> tone mark + follow vowel

    :param str text: input text
    :return: text with vowels and tone marks in the standard logical order
    :rtype: str
    """
    for pair in _REORDER_PAIRS:
        text = re.sub(pair[0], pair[1], text)

    return text


def remove_repeat_vowels(text: str) -> str:
    """
    Remove repeating vowels, tone marks, and signs.

    This function will call reorder_vowels() first, to make sure that
    double Sara E will be converted to Sara Ae and not be removed.

    :param str text: input text
    :return: text without repeating Thai vowels, tone marks, and signs
    :rtype: str
    """
    text = reorder_vowels(text)
    for pair in _NOREPEAT_PAIRS:
        text = re.sub(pair[0], pair[1], text)

    # remove repeating tone marks, use last tone mark
    text = _RE_TONEMARKS.sub(_last_char, text)

    return text


def normalize(text: str) -> str:
    """
    Normalize and clean Thai text with normalizing rules as follows:

        * Remove zero-width spaces
        * Remove duplicate spaces
        * Reorder tone marks and vowels to standard order/spelling
        * Remove duplicate vowels and signs
        * Remove duplicate tone marks
        * Remove dangling non-base characters at the beginning of text

    normalize() simply call remove_zw(), remove_dup_spaces(),
    remove_repeat_vowels(), and remove_dangling(), in that order.

    If a user wants to customize the selection or the order of rules
    to be applied, they can choose to call those functions by themselves.

    Note: for Unicode normalization, see unicodedata.normalize().

    :param str text: input text
    :return: normalized text according to the rules
    :rtype: str

    :Example:
    ::

        from pythainlp.util import normalize

        normalize("เเปลก")  # starts with two Sara E
        # output: แปลก

        normalize("นานาาา")
        # output: นานา
    """
    text = remove_zw(text)
    text = remove_dup_spaces(text)
    text = remove_repeat_vowels(text)
    text = remove_dangling(text)

    return text


def expand_maiyamok(sent: Union[str, List[str]]) -> List[str]:
    """
    Expand Maiyamok.

    Maiyamok (ๆ) (Unicode U+0E46) is a Thai character indicating word
    repetition. This function preprocesses Thai text by replacing
    Maiyamok with a word being repeated.

    :param Union[str, List[str]] sent: sentence (list or string)
    :return: list of words
    :rtype: List[str]

    :Example:
    ::
        from pythainlp.util import expand_maiyamok

        expand_maiyamok("คนๆนก")
        # output: ['คน', 'คน', 'นก']
    """
    if isinstance(sent, str):
        sent = word_tokenize(sent)

    yamok = "ๆ"

    # Breaks Maiyamok that attached to others, e.g. "นกๆๆ", "นกๆ ๆ", "นกๆคน"
    re_yamok = re.compile(rf"({yamok})")
    temp_toks: list[str] = []
    for token in sent:
        toks = re_yamok.split(token)
        toks = [tok for tok in toks if tok]  # remove empty string ("")
        temp_toks.extend(toks)
    sent = temp_toks
    del temp_toks

    output_toks: list[str] = []
    yamok_count = 0
    len_sent = len(sent)
    for i in range(len_sent - 1, -1, -1):  # do it backward
        if yamok_count == 0 or (i + 1 >= len_sent):
            if sent[i] == yamok:
                yamok_count = yamok_count + 1
            else:
                output_toks.append(sent[i])
            continue

        if sent[i] == yamok:
            yamok_count = yamok_count + 1
        else:
            if sent[i].isspace():
                if yamok_count > 0:  # remove space before yamok
                    continue
                else:  # with preprocessing above, this should not happen
                    output_toks.append(sent[i])
            else:
                output_toks.extend([sent[i]] * (yamok_count + 1))
                yamok_count = 0

    return output_toks[::-1]


def maiyamok(sent: Union[str, List[str]]) -> List[str]:
    """
    Expand Maiyamok.

    Deprecated. Use expand_maiyamok() instead.

    Maiyamok (ๆ) (Unicode U+0E46) is a Thai character indicating word
    repetition. This function preprocesses Thai text by replacing
    Maiyamok with a word being repeated.

    :param Union[str, List[str]] sent: sentence (list or string)
    :return: list of words
    :rtype: List[str]

    :Example:
    ::

        from pythainlp.util import expand_maiyamok

        expand_maiyamok("คนๆนก")
        # output: ['คน', 'คน', 'นก']
    """
    warn_deprecation(
        "pythainlp.util.maiyamok",
        "pythainlp.util.expand_maiyamok",
        "5.0.5",
        "5.2",
    )
    return expand_maiyamok(sent)