import os
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')

import sys
import csv

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
import novoapi 

from forced_alignment import pyhtk, convert_phone_set

import acoustic_model_functions as am_func
import convert_xsampa2ipa
import defaultfiles as default
import novoapi_functions
sys.path.append(default.accent_classification_dir)
import output_confusion_matrix

## procedure 
forced_alignment_novo70 = True


## ===== load novo phoneset =====
phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = novoapi_functions.load_phonset()


## ===== extract pronunciations written in novo70 only (not_in_novo70) =====

# As per Nederlandse phoneset_aki.xlsx recieved from David
# [ɔː] oh / ohr
# [ɪː] ih / ihr
# [iː] iy
# [œː] uh
# [ɛː] eh
# [w] wv in IPA written as ʋ. 
david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']

## read pronunciation variants.
stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
df = pd.read_excel(stimmen_transcription_, 'frequency')
#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
#    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
#    if not ipa_converted == ipa:
#        print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
transcription_ipa = list(df['IPA'])

# transcription mistake?
transcription_ipa = [ipa.replace(';', 'ː') for ipa in transcription_ipa if not ipa=='pypɪl' and not pd.isnull(ipa)]
transcription_ipa = [ipa.replace('ˑ', '') for ipa in transcription_ipa] # only one case.

not_in_novo70 = []
all_in_novo70 = []
for ipa in transcription_ipa:
	ipa = ipa.replace(':', 'ː')
	ipa = convert_phone_set.split_ipa(ipa)

	not_in_novo70_ = [phone for phone in ipa 
				   if not phone in phoneset_ipa and not phone in david_suggestion]
	not_in_novo70_ = [phone.replace('sp', '') for phone in not_in_novo70_]
	not_in_novo70_ = [phone.replace(':', '') for phone in not_in_novo70_]
	not_in_novo70_ = [phone.replace('ː', '') for phone in not_in_novo70_]

	if len(not_in_novo70_) == 0:
		all_in_novo70.append(''.join(ipa))

	#translation_key.get(phone, phone)
	not_in_novo70.extend(not_in_novo70_)
not_in_novo70_list = list(set(not_in_novo70))


## check which phones used in stimmen but not in novo70
# 'ʀ', 'ʁ',
# 'ɒ', 'ɐ', 
# 'o', 'a' (o:, a:?)
# [e] 'nyːver mɑntsjə' (1)
# [ɾ] 'ɪːɾ'(1)
# [ɹ] 'iːjəɹ' (1), 'ɪ:ɹ' (1)
# [ø] 'gʀøtəpi:r'(1), 'grøtəpi:r'(1)
# [æ] 'røːzəʀæt'(2), 'røːzəræt'(1)
# [ʊ] 'ʊ'(1) --> can be ʏ (uh)??
# [χ] --> can be x??

def search_phone_ipa(x, phone_list):
	x_in_item = []
	for ipa in phone_list:
		ipa_original = ipa
		ipa = ipa.replace(':', 'ː')
		ipa = convert_phone_set.split_ipa(ipa)
		if x in ipa and not x+':' in ipa:
			x_in_item.append(ipa_original)
	return x_in_item
#search_phone_ipa('ø', transcription_ipa)


## ===== load all transcriptions (df) =====
df = pd.read_excel(stimmen_transcription_, 'original')

# mapping from ipa to xsampa
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)

ipas     = []
famehtks = []
for xsampa in df['Self Xsampa']:
    if not isinstance(xsampa, float): # 'NaN'
        # typo?
        xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
        xsampa = xsampa.replace(';', ':')

        ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
        ipa = ipa.replace('ː', ':')
        ipa = ipa.replace(' ', '')
        ipas.append(ipa)
    else:
        ipas.append('')
        
# extract interesting cols.
df = pd.DataFrame({'filename': df['Filename'], 
                    'word': df['Word'], 
                    'xsampa': df['Self Xsampa'],
                    'ipa': pd.Series(ipas)})

word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
word_list = sorted(word_list)


## check frequency of each pronunciation variants
cols = ['word', 'ipa', 'frequency']
df_samples = pd.DataFrame(index=[], columns=cols)
for ipa in all_in_novo70:
	ipa = ipa.replace('ː', ':')
	samples = df[df['ipa'] == ipa]
	word = list(set(samples['word']))[0]
	samples_Series = pd.Series([word, ipa, len(samples)], index=df_samples.columns)
	df_samples = df_samples.append(samples_Series, ignore_index=True)

# each word
df_per_word = pd.DataFrame(index=[], columns=df_samples.keys())

for word in word_list:
	df_samples_ = df_samples[df_samples['word']==word]
	df_samples_ = df_samples_[df_samples_['frequency']>2]
	df_per_word = df_per_word.append(df_samples_, ignore_index=True)
#df_per_word.to_excel(os.path.join(default.stimmen_dir, 'pronunciation_variants_novo70.xlsx'), encoding="utf-8")


## ===== forced alignment =====
if forced_alignment_novo70:
	Results = pd.DataFrame(index=[],
		columns=['filename', 'word', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
	for word in word_list:
	#for word in ['Oor']:
		# pronunciation variants top 3
		df_per_word_ = df_per_word[df_per_word['word']==word]
		df_per_word_ = df_per_word_.sort_values('frequency', ascending=False)
		if len(df_per_word_) < 3: # pauw, rozen
			pronunciation_ipa = list(df_per_word_['ipa'])
		elif word=='Reuzenrad':
			pronunciation_ipa = [
				df_per_word_.iloc[0]['ipa'], 
				df_per_word_.iloc[1]['ipa'], 
				df_per_word_.iloc[2]['ipa'],
				df_per_word_.iloc[3]['ipa']]
		else:
			# oog, oor, reus, roeiboot
			pronunciation_ipa = [
				df_per_word_.iloc[0]['ipa'], 
				df_per_word_.iloc[1]['ipa'], 
				df_per_word_.iloc[2]['ipa']]
		#print("{0}: {1}".format(word, pronunciation_ipa))

		# samples for the word
		df_ = df[df['word']==word]

		# samples in which all pronunciations are written in novo70.
		samples = df_.query("ipa in @pronunciation_ipa")

		results = pd.DataFrame(index=[],
			columns=['filename', 'word', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
	
		#j = 0
		for i in range(0, len(samples)):
			sample = samples.iloc[i]
			wav_file = os.path.join(default.stimmen_wav_dir, sample['filename'])
			if os.path.exists(wav_file):
				#j += 1
				#print('{0} - {1}'.format(word, i))
				pronunciation_ipa_ = [ipa.replace(':', 'ː') for ipa in pronunciation_ipa]
				result = novoapi_functions.forced_alignment(wav_file, word, pronunciation_ipa_)
				result_ipa, result_novo70, llh = novoapi_functions.result2pronunciation(result, word)
				result_ = pd.Series([
					sample['filename'],
					sample['ipa'],
					sample['word'],
					' '.join(result_ipa),
					' '.join(result_novo70),
					llh
					], index=results.columns)
				results = results.append(result_, ignore_index = True)
				print('{0}/{1}: answer {2} - prediction {3}'.format( 
			 i+1, len(samples), result_['ipa'], result_['result_ipa']))

		if len(results) > 0:
			Results = Results.append(results, ignore_index = True)
		Results.to_excel(os.path.join(default.stimmen_dir, 'Results.xlsx'), encoding="utf-8")
else:
	Results_xlsx = pd.ExcelFile(os.path.join(default.stimmen_dir, 'Results.xlsx'), encoding="utf-8")
	Results = pd.read_excel(Results_xlsx, 'Sheet1')


## ===== analysis =====
result_novoapi_dir = os.path.join(default.stimmen_dir, 'result', 'novoapi')
for word in word_list:
	if not word == 'Oog':
#word = 'Reus'

		Results_ = Results[Results['word'] == word]
		y_true  = list(Results_['ipa'])
		y_pred_ = [ipa.replace(' ', '') for ipa in list(Results_['result_ipa'])]
		y_pred  = [ipa.replace('ː', ':') for ipa in y_pred_]
		pronunciation_variants = list(set(y_true))
		cm = confusion_matrix(y_true, y_pred, labels=pronunciation_variants)

		plt.figure()
		output_confusion_matrix.plot_confusion_matrix(cm, pronunciation_variants, normalize=False)
		#plt.show()
		plt.savefig(os.path.join(result_novoapi_dir, word + '.png'))