# tokenizer

import os
import re
from shutil import copyfile

import torch

import inflect
from transformers import PreTrainedTokenizer
from unidecode import unidecode


valid_symbols = [
    "AA",
    "AA0",
    "AA1",
    "AA2",
    "AE",
    "AE0",
    "AE1",
    "AE2",
    "AH",
    "AH0",
    "AH1",
    "AH2",
    "AO",
    "AO0",
    "AO1",
    "AO2",
    "AW",
    "AW0",
    "AW1",
    "AW2",
    "AY",
    "AY0",
    "AY1",
    "AY2",
    "B",
    "CH",
    "D",
    "DH",
    "EH",
    "EH0",
    "EH1",
    "EH2",
    "ER",
    "ER0",
    "ER1",
    "ER2",
    "EY",
    "EY0",
    "EY1",
    "EY2",
    "F",
    "G",
    "HH",
    "IH",
    "IH0",
    "IH1",
    "IH2",
    "IY",
    "IY0",
    "IY1",
    "IY2",
    "JH",
    "K",
    "L",
    "M",
    "N",
    "NG",
    "OW",
    "OW0",
    "OW1",
    "OW2",
    "OY",
    "OY0",
    "OY1",
    "OY2",
    "P",
    "R",
    "S",
    "SH",
    "T",
    "TH",
    "UH",
    "UH0",
    "UH1",
    "UH2",
    "UW",
    "UW0",
    "UW1",
    "UW2",
    "V",
    "W",
    "Y",
    "Z",
    "ZH",
]

_valid_symbol_set = set(valid_symbols)


def intersperse(lst, item):
    # Adds blank symbol
    result = [item] * (len(lst) * 2 + 1)
    result[1::2] = lst
    return result


class CMUDict:
    def __init__(self, file_or_path, keep_ambiguous=True):
        if isinstance(file_or_path, str):
            with open(file_or_path, encoding="latin-1") as f:
                entries = _parse_cmudict(f)
        else:
            entries = _parse_cmudict(file_or_path)
        if not keep_ambiguous:
            entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
        self._entries = entries

    def __len__(self):
        return len(self._entries)

    def lookup(self, word):
        return self._entries.get(word.upper())


_alt_re = re.compile(r"\([0-9]+\)")


def _parse_cmudict(file):
    cmudict = {}
    for line in file:
        if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"):
            parts = line.split("  ")
            word = re.sub(_alt_re, "", parts[0])
            pronunciation = _get_pronunciation(parts[1])
            if pronunciation:
                if word in cmudict:
                    cmudict[word].append(pronunciation)
                else:
                    cmudict[word] = [pronunciation]
    return cmudict


def _get_pronunciation(s):
    parts = s.strip().split(" ")
    for part in parts:
        if part not in _valid_symbol_set:
            return None
    return " ".join(parts)


_whitespace_re = re.compile(r"\s+")

_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):
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def transliteration_cleaners(text):
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def english_cleaners(text):
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = expand_numbers(text)
    text = expand_abbreviations(text)
    text = collapse_whitespace(text)
    return text


_inflect = inflect.engine()
_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
_number_re = re.compile(r"[0-9]+")


def _remove_commas(m):
    return m.group(1).replace(",", "")


def _expand_decimal_point(m):
    return m.group(1).replace(".", " point ")


def _expand_dollars(m):
    match = m.group(1)
    parts = match.split(".")
    if len(parts) > 2:
        return match + " dollars"
    dollars = int(parts[0]) if parts[0] else 0
    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
    if dollars and cents:
        dollar_unit = "dollar" if dollars == 1 else "dollars"
        cent_unit = "cent" if cents == 1 else "cents"
        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
    elif dollars:
        dollar_unit = "dollar" if dollars == 1 else "dollars"
        return "%s %s" % (dollars, dollar_unit)
    elif cents:
        cent_unit = "cent" if cents == 1 else "cents"
        return "%s %s" % (cents, cent_unit)
    else:
        return "zero dollars"


def _expand_ordinal(m):
    return _inflect.number_to_words(m.group(0))


def _expand_number(m):
    num = int(m.group(0))
    if num > 1000 and num < 3000:
        if num == 2000:
            return "two thousand"
        elif num > 2000 and num < 2010:
            return "two thousand " + _inflect.number_to_words(num % 100)
        elif num % 100 == 0:
            return _inflect.number_to_words(num // 100) + " hundred"
        else:
            return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ")
    else:
        return _inflect.number_to_words(num, andword="")


def normalize_numbers(text):
    text = re.sub(_comma_number_re, _remove_commas, text)
    text = re.sub(_pounds_re, r"\1 pounds", text)
    text = re.sub(_dollars_re, _expand_dollars, text)
    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
    text = re.sub(_ordinal_re, _expand_ordinal, text)
    text = re.sub(_number_re, _expand_number, text)
    return text


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


_pad = "_"
_punctuation = "!'(),.:;? "
_special = "-"
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"

# Prepend "@" to ARPAbet symbols to ensure uniqueness:
_arpabet = ["@" + s for s in valid_symbols]

# Export all symbols:
symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet


_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}

_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=[english_cleaners], dictionary=None):
    """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) 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
        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
    if dictionary is not None:
        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 += s
    return result.replace("}{", " ")


def _clean_text(text, cleaner_names):
    for cleaner in cleaner_names:
        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 != "_" and s != "~"


VOCAB_FILES_NAMES = {
    "dict_file": "dict_file.txt",
}


class GradTTSTokenizer(PreTrainedTokenizer):
    vocab_files_names = VOCAB_FILES_NAMES

    def __init__(self, dict_file, **kwargs):
        super().__init__(**kwargs)
        self.cmu = CMUDict(dict_file)
        self.dict_file = dict_file

    def __call__(self, text):
        x = torch.LongTensor(intersperse(text_to_sequence(text, dictionary=self.cmu), len(symbols)))[None]
        x_lengths = torch.LongTensor([x.shape[-1]])
        return x, x_lengths

    def save_vocabulary(self, save_directory: str, filename_prefix=None):
        dict_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["dict_file"]
        )

        copyfile(self.dict_file, dict_file)

        return (dict_file,)
