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﻿# -*- 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)

	# -- 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)