"""
### english

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
import random
from . import cleaners
from .symbols import symbols

# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}

# Regular expression matching text enclosed in curly braces:
_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')


def get_arpabet(word, dictionary):
    word_arpabet = dictionary.lookup(word)
    if word_arpabet is not None:
        return "{" + word_arpabet[0] + "}"
    else:
        return word


def text_to_sequence(text, cleaner_names, dictionary=None, p_arpabet=1.0):
    '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.

      The text can optionally have ARPAbet sequences enclosed in curly braces embedded
      in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."

      Args:
        text: string to convert to a sequence
        cleaner_names: names of the cleaner functions to run the text through
        dictionary: arpabet class with arpabet dictionary

      Returns:
        List of integers corresponding to the symbols in the text
    '''
    sequence = []

    space = _symbols_to_sequence(' ')
    # Check for curly braces and treat their contents as ARPAbet:
    while len(text):
        m = _curly_re.match(text)
        if not m:
            clean_text = _clean_text(text, cleaner_names)
            if dictionary is not None:
                clean_text = [get_arpabet(w, dictionary)
                              if random.random() < p_arpabet else w
                              for w in clean_text.split(" ")]

                for i in range(len(clean_text)):
                    t = clean_text[i]
                    if t.startswith("{"):
                        sequence += _arpabet_to_sequence(t[1:-1])
                    else:
                        sequence += _symbols_to_sequence(t)
                    sequence += space
            else:
                sequence += _symbols_to_sequence(clean_text)
            break

        clean_text = _clean_text(text, cleaner_names)
        sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
        sequence += _arpabet_to_sequence(m.group(2))
        text = m.group(3)

    # remove trailing space
    sequence = sequence[:-1] if sequence[-1] == space[0] else sequence
    return sequence


def sequence_to_text(sequence):
    '''Converts a sequence of IDs back to a string'''
    result = []
    for symbol_id in sequence:
        if symbol_id in _id_to_symbol:
            s = _id_to_symbol[symbol_id]
            # Enclose ARPAbet back in curly braces:
            if len(s) > 1 and s[0] == '@':
                s = '{%s}' % s[1:]
            result.append(s)
    result = ''.join(result)
    return result.replace('}{', ' ')


def _clean_text(text, cleaner_names):
    for name in cleaner_names:
        cleaner = getattr(cleaners, name)
        if not cleaner:
            raise Exception('Unknown cleaner: %s' % name)
        text = cleaner(text)
    return text


def _symbols_to_sequence(symbols):
    return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]


def _arpabet_to_sequence(text):
    return _symbols_to_sequence(['@' + s for s in text.split()])


def _should_keep_symbol(s):
    return s in _symbol_to_id and s is not '_' and s is not '~'
