# """ 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 mynumbers import normalize_numbers
# from unidecode import unidecode

# # 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 [
#         ("mrs", "misess"),
#         ("mr", "mister"),
#         ("dr", "doctor"),
#         ("st", "saint"),
#         ("co", "company"),
#         ("jr", "junior"),
#         ("maj", "major"),
#         ("gen", "general"),
#         ("drs", "doctors"),
#         ("rev", "reverend"),
#         ("lt", "lieutenant"),
#         ("hon", "honorable"),
#         ("sgt", "sergeant"),
#         ("capt", "captain"),
#         ("esq", "esquire"),
#         ("ltd", "limited"),
#         ("col", "colonel"),
#         ("ft", "fort"),
#     ]
# ]


# 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 number and abbreviation expansion."""
#     text = convert_to_ascii(text)
#     text = lowercase(text)
#     text = expand_numbers(text)
#     text = expand_abbreviations(text)
#     text = collapse_whitespace(text)
#     return text