import os
import re
import locale
import tempfile
import torch
import commons
import subprocess

def utf_8(x = None):
    return "UTF-8"
locale.getpreferredencoding = utf_8


def preprocess_char(text, lang=None):
    """
    Special treatement of characters in certain languages
    """
    #print(lang)
    if lang == 'ron':
        text = text.replace("ț", "ţ")
    return text

class TextMapper(object):
    def __init__(self, vocab_file):
        self.symbols = [x.replace("\n", "") for x in open(vocab_file, encoding="utf-8").readlines()]
        self.SPACE_ID = self.symbols.index(" ")
        self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
        self._id_to_symbol = {i: s for i, s in enumerate(self.symbols)}

    def text_to_sequence(self, text, cleaner_names):
        '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
        Args:
        text: string to convert to a sequence
        cleaner_names: names of the cleaner functions to run the text through
        Returns:
        List of integers corresponding to the symbols in the text
        '''
        sequence = []
        clean_text = text.strip()
        for symbol in clean_text:
            symbol_id = self._symbol_to_id[symbol]
            sequence += [symbol_id]
        return sequence

    def uromanize(self, text, uroman_pl):
        iso = "xxx"
        with tempfile.NamedTemporaryFile() as tf, \
             tempfile.NamedTemporaryFile() as tf2:
            with open(tf.name, "w") as f:
                f.write("\n".join([text]))
            cmd = f"perl " + uroman_pl
            cmd += f" -l {iso} "
            cmd +=  f" < {tf.name} > {tf2.name}"
            os.system(cmd)
            outtexts = []
            with open(tf2.name) as f:
                for line in f:
                    line =  re.sub(r"\s+", " ", line).strip()
                    outtexts.append(line)
            outtext = outtexts[0]
        return outtext

    def get_text(self, text, hps):
        text_norm = self.text_to_sequence(text, hps.data.text_cleaners)
        if hps.data.add_blank:
            text_norm = commons.intersperse(text_norm, 0)
        text_norm = torch.LongTensor(text_norm)
        return text_norm

    def filter_oov(self, text):
        val_chars = self._symbol_to_id
        txt_filt = "".join(list(filter(lambda x: x in val_chars, text)))
        #print(f"text after filtering OOV: {txt_filt}")
        return txt_filt

def preprocess_text(txt, text_mapper, hps, uroman_dir=None, lang=None):
    txt = preprocess_char(txt, lang=lang)
    is_uroman = hps.data.training_files.split('.')[-1] == 'uroman'
    if is_uroman:
        with tempfile.TemporaryDirectory() as tmp_dir:
            if uroman_dir is None:
                cmd = f"git clone git@github.com:isi-nlp/uroman.git {tmp_dir}"
                print(cmd)
                subprocess.check_output(cmd, shell=True)
                uroman_dir = tmp_dir
            uroman_pl = os.path.join(uroman_dir, "bin", "uroman.pl")
            print(f"uromanize")
            txt = text_mapper.uromanize(txt, uroman_pl)
            print(f"uroman text: {txt}")
    txt = txt.lower()
    txt = text_mapper.filter_oov(txt)
    return txt

if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")