import os
import sys
import csv
import subprocess
import configparser

import numpy as np
import pandas as pd


## ======================= user define =======================
curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
config_ini = curr_dir + '\\config.ini'
forced_alignment_module  = r'C:\Users\Aki\source\repos\forced_alignment'
ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
csvfile = r"C:\OneDrive\Research\rug\stimmen\Frisian Variants Picture Task Stimmen.csv"

# procedure


## ======================= add paths =======================

sys.path.append(forced_alignment_module)
from forced_alignment import convert_phone_set

# for interactive window
sys.path.append(curr_dir)
import convert_xsampa2ipa
import acoustic_model_functions as am_func


## ======================= load variables =======================
config = configparser.ConfigParser()
config.sections()
config.read(config_ini)

FAME_dir = config['Settings']['FAME_dir']

lex_asr		= FAME_dir + '\\lexicon\\lex.asr'
lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'


## ======================= check phones included in FAME! =======================
# the phones used in the lexicon.
#phonelist = am_func.get_phonelist(lex_htk)

# the lines which include a specific phone.
#lines = am_func.find_phone(lex_asr, 'x')


## ======================= convert phones ======================
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)

with open(csvfile, encoding="utf-8") as fin:
	lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
	next(lines, None) # skip the headers

	filenames	   = []
	words		   = []
	pronunciations = []
	for line in lines:
		if line[1] is not '' and len(line) > 5:
			filenames.append(line[0])
			words.append(line[1])
			pron_xsampa = line[3]
			pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
			pron_ipa = pron_ipa.replace('ː', ':')
			pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
			
			# adjust to phones used in the acoustic model.
			pron_famehtk = pron_famehtk.replace('sp', 'sil')
			pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
			pron_famehtk = pron_famehtk.replace('w :', 'wh')
			pron_famehtk = pron_famehtk.replace('e :', 'eh')
			pron_famehtk = pron_famehtk.replace('eh :', 'eh')
			pron_famehtk = pron_famehtk.replace('ih :', 'ih')

			#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
			#pron = []
			#for phoneme in pron_famehtk.split(' '):
			#	pron.append(translation_key.get(phoneme, phoneme))
			#pronunciations.append(' '.join(pron_famehtk))
			pronunciations.append(pron_famehtk)

filenames	   = np.array(filenames)
words		   = np.array(words)
pronunciations = np.array(pronunciations)

del line, lines
del pron_xsampa, pron_ipa, pron_famehtk

# check if all phones are in the phonelist of the acoustic model.
#phonelist = ' '.join(pronunciations)
#np.unique(phonelist.split(' '))
#phonelist.find(':')

# make dict files.
word_list = np.unique(words)
word_id = 1
word = word_list[word_id]


## ======================= forced alignment =======================
#if forced_alignment:
#	try:
#		scripts.run_command([
#			'HVite','-T', '1', '-a', '-C', configHVite,
#			'-H', AcousticModel, '-m', '-I',
#			mlf_file, '-i', fa_file, '-S',
#			script_file, htk_dict_file, filePhoneList
#		])
#	except:
#		print("\033[91mHVite command failed with these input files:\033[0m")
#		print(_debug_show_file('HVite config', configHVite))
#		print(_debug_show_file('Accoustic model', AcousticModel))
#		print(_debug_show_file('Master Label file', mlf_file))
#		print(_debug_show_file('Output', fa_file))
#		print(_debug_show_file('Script file', script_file))
#		print(_debug_show_file('HTK dictionary', htk_dict_file))
#		print(_debug_show_file('Phoneme list', filePhoneList))
#		raise


##os.remove(hcopy_scp.name)