'''
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 .numbers import normalize_numbers

# 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 add_punctuation(text):
    if len(text) == 0:
        return text
    if text[-1] not in '!,.:;?':
        text = text + '.'  # without this decoder is confused when to output EOS
    return 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 = add_punctuation(text)
    text = lowercase(text)
    text = expand_numbers(text)
    text = expand_abbreviations(text)
    text = collapse_whitespace(text)
    return text
