import sys
import os
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
from collections import Counter
import time
import re

import numpy as np
import pandas as pd

import fame_functions
import defaultfiles as default
sys.path.append(default.toolbox_dir)
from phoneset import fame_ipa, fame_asr
import convert_phoneset

lexicon_dir = os.path.join(default.fame_dir, 'lexicon') 
lexicon_ipa = os.path.join(lexicon_dir, 'lex.ipa')
lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
lexicon_htk = os.path.join(default.htk_dir, 'lexicon', 'lex.htk')

## check if all the phones in lexicon.ipa are in fame_ipa.py.
#timer_start = time.time()
#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_ipa, phoneset='ipa')	
#phoneset_py = fame_ipa.phoneset
#print("phones which is in lexicon.ipa but not in fame_ipa.py:\n{}".format(
#	set(phoneset_lex) - set(phoneset_py)))
#print("elapsed time: {}".format(time.time() - timer_start))

# check which word has the phone. 
#timer_start = time.time()
#extracted = find_phone(lexicon_ipa, 'ⁿ')
#print("elapsed time: {}".format(time.time() - timer_start))


## get the correspondence between lex_ipa and lex_asr.	
lex_asr  = fame_functions.load_lexicon(lexicon_asr)
lex_ipa  = fame_functions.load_lexicon(lexicon_ipa)		
if 0:
	timer_start = time.time()
	translation_key_ipa2asr, phone_unknown = fame_functions.get_translation_key(lexicon_ipa, lexicon_asr)
	print("elapsed time: {}".format(time.time() - timer_start))

	np.save(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy'), translation_key_ipa2asr)
	np.save(os.path.join('phoneset', 'output_get_translation_key_phone_unknown.npy'), phone_unknown)
else:
	translation_key_ipa2asr = np.load(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy')).item()
	phone_unknown   = np.load(os.path.join('phoneset', 'output_get_translation_key_phone_unknown.npy'))
	phone_unknown   = list(phone_unknown)

# manually check the correspondence for the phone in phone_unknown.
#p = phone_unknown[0]
#lex_ipa_ = find_phone(lexicon_ipa, p, phoneset='ipa')

#for word in lex_ipa_['word']:
#	ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
#	if np.sum(lex_asr['word'] == word) > 0:
#		asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
	
#		ipa_list = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
#		asr_list = asr.split(' ')
#		if p in ipa_list and (len(ipa_list) == len(asr_list)):
#			print("{0}: {1} --> {2}".format(word, ipa_list, asr_list))
#			for ipa_, asr_ in zip(ipa_list, asr_list):
#				if ipa_ in phone_unknown:
#					translation_key_ipa2asr[ipa_] = asr_
#					phone_unknown.remove(ipa_)

translation_key_ipa2asr['ə:'] = 'ə'
translation_key_ipa2asr['r.'] = 'r'
translation_key_ipa2asr['r:'] = 'r'
np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)


## check if all the phones in lexicon.asr are in translation_key_ipa2asr.
#timer_start = time.time()
#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_asr, phoneset='asr')
#phoneset_lex.remove("")
#phoneset_asr = list(set(translation_key_ipa2asr.values()))
#print("phones which is in lexicon.asr but not in the translation_key_ipa2asr:\n{}".format(
#	set(phoneset_lex) - set(phoneset_asr)))
#print("elapsed time: {}".format(time.time() - timer_start))


## check if all the phones in lexicon.htk are in fame_asr.py.
#timer_start = time.time()
#phoneset_htk = fame_asr.phoneset_htk
#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_htk)
#phoneset_lex.remove('')
#print("phones which is in lexicon.htk but not in the fame_asr.py are:\n{}".format(
#	set(phoneset_htk) - set(phoneset_lex)))
#print("elapsed time: {}".format(time.time() - timer_start))

## statistics over the lexicon
#lex_htk = fame_functions.load_lexicon(lexicon_htk)
#phones_all = (' '.join(lex_htk['pronunciation'])).split(' ')
#c = Counter(phones_all)

#lexicon_out = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\lexicon\lex.htk2'
#for i in lex_htk[lex_htk['word'].str.startswith('\'')].index.values:
#	lex_htk.iat[i, 0] = lex_htk.iat[i, 0].replace('\'', '\\\'')
## to_csv does not work with space seperator. therefore all tabs should manually be replaced.
##lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
#lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')


## check which letters are not coded in ascii. 
print('asr phones which cannot be coded in ascii:\n')
for i in fame_asr.phoneset_short:
	try:
		i_encoded = i.encode("ascii")
		#print("{0} --> {1}".format(i, i.encode("ascii")))
	except UnicodeEncodeError:
		print(">>> {}".format(i))

print("letters in the scripts which is not coded in ascii:\n")
for dataset in ['train', 'devel', 'test']:
	timer_start = time.time()

	script_list = os.path.join(default.fame_dir, 'data', dataset, 'text')
	with open(script_list, "rt", encoding="utf-8") as fin:
		scripts = fin.read().split('\n')

	for line in scripts:
		sentence  = ' '.join(line.split(' ')[1:])
		sentence_htk = fame_functions.word2htk(sentence)

		#if len(re.findall(r'[âêôûč\'àéèúćäëïöü]', sentence))==0:
		try:
			sentence_htk = bytes(sentence_htk, 'ascii')
		except UnicodeEncodeError:
			print(sentence)
			print(sentence_htk)