Python remove accents

def remove_accents(raw_text):
    """Removes common accent characters.

    Our goal is to brute force login mechanisms, and I work primary with
    companies deploying Engligh-language systems. From my experience, user
    accounts tend to be created without special accented characters. This
    function tries to swap those out for standard Engligh alphabet.
    """

    raw_text = re.sub(u"[àáâãäå]", 'a', raw_text)
    raw_text = re.sub(u"[èéêë]", 'e', raw_text)
    raw_text = re.sub(u"[ìíîï]", 'i', raw_text)
    raw_text = re.sub(u"[òóôõö]", 'o', raw_text)
    raw_text = re.sub(u"[ùúûü]", 'u', raw_text)
    raw_text = re.sub(u"[ýÿ]", 'y', raw_text)
    raw_text = re.sub(u"[ß]", 'ss', raw_text)
    raw_text = re.sub(u"[ñ]", 'n', raw_text)
    return raw_text 

def remove_accents(text: str) -> str:
    """
    Remove accents from any accented unicode characters in ``text`` str, either by
    transforming them into ascii equivalents or removing them entirely.

    Args:
        text (str): Urdu text
    Returns:
        str
    Examples:
        >>> from urduhack.preprocessing import remove_accents
        >>>text = "دالتِ عظمیٰ درخواست"
        >>> remove_accents(text)
    'دالت عظمی درخواست'
    """
    return ''.join(c for c in text if not unicodedata.combining(c)) 

def remove_accents(self, text):
		""" Normalise (normalize) unicode data in Python to remove umlauts, accents etc.

		Rule 10 - When special characters appear as part of the name, paternal surname and maternal surname,
		they must be excluded for the calculation of the homonym and the verification digit. 
		The characters will be interpreted, yes and only if, they are individually within the name,
		paternal surname and maternal surname. Examples:
			
		Roberto O’farril Carballo OACR-661121
		Rubén D’angelo Fargo DAFR-710108
		Luz Ma. Fernández Juárez FEJL-830120
		"""		 
		#s_no_accents = ''.join((c for c in unicodedata.normalize('NFD', text) if unicodedata.category(c) != 'Mn'))
		try:
			text = unicode(text, 'utf-8')
		except (TypeError, NameError): # unicode is a default on python 3 
			pass
		text = unicodedata.normalize('NFD', text)
		text = text.encode('ascii', 'ignore')
		text = text.decode("utf-8")
		return str(text) 

def remove_accents(text):
    """Replace accentuated characters by their non-accentuated counterparts

    A simple way to do this would be to decompose accentuated characters in the
    sequence using one of the unicode decomposition schemes and then filter the
    resulting sequence to remove combining characters (also known as
    diacritical marks).

    Comments: the following solution is a very naive implementation of that
    only uses basic operations on the sequence of unicode characters.

    A more efficient approach that works only for languages that use the
    latin alphabet would use batch conversion to ASCII characters as done in:

        sklearn.feature_extraction.text.strip_accents_ascii

    """
    text = unicodedata.normalize('NFKD', text)
    return "".join([c for c in text if not unicodedata.combining(c)]) 

def remove_accents(self, name):
        name = re.sub('á','a', name)
        name = re.sub('é','e', name)
        name = re.sub('í','i', name)
        name = re.sub('ó','o', name)
        name = re.sub('ú','u', name)
        name = re.sub('ý','y', name)
        name = re.sub('à','a', name)
        name = re.sub('è','e', name)
        name = re.sub('ì','i', name)
        name = re.sub('ò','o', name)
        name = re.sub('ù','u', name)
        name = re.sub('ä','a', name)
        name = re.sub('ë','e', name)
        name = re.sub('ï','i', name)
        name = re.sub('ö','o', name)
        name = re.sub('ü','u', name)
        name = re.sub('ÿ','y', name)
        name = re.sub('â','a', name)
        name = re.sub('ê','e', name)
        name = re.sub('î','i', name)
        name = re.sub('ô','o', name)
        name = re.sub('û','u', name)
        name = re.sub('ã','a', name)
        name = re.sub('õ','o', name)
        name = re.sub('@','a', name)
        return name
    # end remove_accents() 

def remove_accents(text):

    if isinstance(text, bytes):
        text = text.decode('ascii')

    category = unicodedata.category  # this gives a small (~10%) speedup
    return ''.join(
        c for c in unicodedata.normalize('NFKD', text) if category(c) != 'Mn'
    )

# Select the best transliteration method: 

def remove_accents(t):
    """based on https://stackoverflow.com/a/51230541"""
    nfkd_form = unicodedata.normalize('NFKD', t.casefold())
    return ''.join(
        NON_NFKD_MAP[c]
            if c in NON_NFKD_MAP
        else c
            for part in nfkd_form for c in part
            if unicodedata.category(part) != 'Mn'
        ) 

def removeAccents(self, text):
        text = str(text)
        normalized = unicodedata.normalize("NFKD", text)
        normalized = "".join([c for c in normalized if not unicodedata.combining(c)])
        return normalized 

def remove_accents(string):
    """
        Remove any accent in the string
    :param string: string to remove accents
    :type string: str or unicode
    :return: string without accents
    :rtype: unicode
    """
    if not isinstance(string, unicode):
        string = normalize_string(string)

    nfkd_form = unicodedata.normalize('NFKD', string)
    only_ascii = nfkd_form.encode('ASCII', 'ignore').strip()
    return string if only_ascii == u'' else only_ascii 

def remove_accents(string):
    """
    Removes unicode accents from a string, downgrading to the base character
    """

    nfkd = unicodedata.normalize('NFKD', string)
    return u"".join([c for c in nfkd if not unicodedata.combining(c)]) 

def RemoveAccents(text):
  if not isinstance(text, basestring):
    return text
  if isinstance(text, str):
    text = text.decode('utf-8')
  return u''.join([c for c in text if not unicodedata.combining(c)]) 

def RemoveAccentsNfkd(text):
  if not isinstance(text, basestring):
    return text
  if isinstance(text, str):
    text = text.decode('utf-8')
  return u''.join([c for c in unicodedata.normalize('NFKD', text)
                   if not unicodedata.combining(c)]) 

def remove_accents(text):

    if isinstance(text, bytes):
        text = text.decode('ascii')

    category = unicodedata.category  # this gives a small (~10%) speedup
    return ''.join(
        c for c in unicodedata.normalize('NFKD', text) if category(c) != 'Mn'
    )


# Select the best transliteration method: