""" from https://github.com/keithito/tacotron """

"""
Cleaners are transformations that run over the input text at both training and eval time.

Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
  1. "english_cleaners" for English text
  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
     the symbols in symbols.py to match your data).
"""

import re
from unidecode import unidecode
#from phonemizer import phonemize
#from phonemizer.backend import EspeakBackend
#backend = EspeakBackend("vi", preserve_punctuation=True, with_stress=True)


# Regular expression matching whitespace:
_whitespace_re = re.compile(r"\s+")

# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [
    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
    for x in [
        ("1", "một"),
        ("2", "hai"),
        ("3", "ba"),
        ("4", "bốn"),
        ("5", "năm"),
        ("6", "sáu"),
        ("7", "bảy"),
        ("8", "tám"),
        ("9", "chín"),
        ("10", "mười")
    ]
]


def expand_abbreviations(text):
    for regex, replacement in _abbreviations:
        text = re.sub(regex, replacement, text)
    return text


def expand_numbers(text):
    return normalize_numbers(text)


def lowercase(text):
    return text.lower()


def collapse_whitespace(text):
    return re.sub(_whitespace_re, " ", text)


def convert_to_ascii(text):
    return unidecode(text)


def basic_cleaners(text):
    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def transliteration_cleaners(text):
    """Pipeline for non-English text that transliterates to ASCII."""
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def english_cleaners(text):
    """Pipeline for English text, including abbreviation expansion."""
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = expand_abbreviations(text)
    phonemes = phonemize(text, language="vi", backend="espeak", strip=True)
    phonemes = collapse_whitespace(phonemes)
    return phonemes


def english_cleaners2(text):
    """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = expand_abbreviations(text)
    phonemes = phonemize(
        text,
        language="vi",
        backend="espeak",
        strip=True,
        preserve_punctuation=True,
        with_stress=True,
    )
    phonemes = collapse_whitespace(phonemes)
    return phonemes


def english_cleaners3(text):
    """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = expand_abbreviations(text)
    phonemes = backend.phonemize([text], strip=True)[0]
    phonemes = collapse_whitespace(phonemes)
    return phonemes