from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import random

import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F

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

SOS_token = 0
EOS_token = 1

class Lang:
    def __init__(self, name):
        self.name = name
        self.word2index = {}
        self.word2count = {}
        self.index2word = {0: "SOS", 1 : "EOS"}
        self.n_words = 2

    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.n_words
            self.word2count[word] = 1
            self.index2word[self.n_words] = word
            self.n_words += 1
        else:
            self.word2count[word] += 1

#Turn a unicode strng to a plain ASCII
def unicodeToAscii(s):
    return ''.join(
            c for c in unicodedata.normalize('NFD', s)
            if unicodedata.category(c) != 'Mn'
            )

def normalizeString(s):
    s = unicodeToAscii(s.lower().strip())
    s = re.sub(r"([.!?])", r" \1", s)
    s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
    return s

def readLangs(lang1, lang2, reverse=False):
    print("Reading lines...")

    # Read the file and split into lines
    lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\
           read().strip().split('\n')

    # Split every line into pairs and normalize
    pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]

    # Reverse pairs, make Lang instance
    if reverse:
        pairs = [list(reversed(p)) for p in pairs]
        input_lang = Lang(lang2)
        output_lang = Lang(lang1)
    else:
        input_lang = Lang(lang1)
        input_lang = Lang(lang2)

    return input_lang, output_lang, pairs

MAX_LENGTH = 10
eng_prefixes = (
    "i am ", "i m ",
    "he is", "he s ",
    "she is", "she s ",
    "you are", "you re ",
    "we are", "we re ",
    "they are", "they re "
)

def filterPair(p):
    return len(p[0].split(' ')) < MAX_LENGTH and \
        len(p[1].split(' ')) < MAX_LENGTH and \
        p[1].startswith(eng_prefixes)


def filterPairs(pairs):
    return [pair for pair in pairs if filterPair(pair)]


def prepareData(lang1, lang2, reverse=False):
    input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)
    print("Read %s sentence pairs" % len(pairs))
    pairs = filterPairs(pairs)

    print("Trimmed to %s sentence pairs" % len(pairs))
    print("Counting words...")

    for pair in pairs:
        input_lang.addSentence(pair[0])
        output_lang.addSentence(pair[1])

    #Write all processed sentences to a file, so
    #our c++ program does not need these pre-processing
    processed_file = open('data/%s-%s_procd.txt' % (lang1, lang2), 'w')
    for pair in pairs:
        processed_file.write("%s\t%s\n" %(pair[0], pair[1]))
    processed_file.close()
    print("Write to processed file done....")

    print("Counted words:")
    print(input_lang.name, input_lang.n_words)
    print(output_lang.name, output_lang.n_words)
    return input_lang, output_lang, pairs

input_lang, output_lang, pairs = prepareData('eng', 'fra', True)
print(random.choice(pairs))


class EncoderRNN(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(EncoderRNN, self).__init__()
        self.hidden_size = hidden_size
        
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size)

    def forward(self, input, hidden):
        embedded = self.embedding(input).view(1, 1, -1)
        output = embedded
        output, hidden = self.gru(output, hidden)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, 1, self.hidden_size, device=device)


class DecoderRN(nn.Module):
    def __init__(self, hidden_size, output_size):
        super(DecoderRNN, self).__init__()
        self.hidden_size = hidden_size

        self.embedding = nn.Embedding(output_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        output = self.embedding(input).view(1, 1, -1)
        output = F.relu(output)
        output, hidden = self.gru(output, hidden)
        output = self.softmax(self.out(output[0]))
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, 1, self.hidden_size, device=device)

#we need to concat the output and hidden here, so there
#is only one useful output(there attn_weights are not used by migraphx)
class AttnDecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size, dropout_p = 0.1, max_length = MAX_LENGTH):
        super(AttnDecoderRNN, self).__init__()
        super(AttnDecoderRNN, self).__init__()
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.dropout_p = dropout_p
        self.max_length = max_length

        self.embedding = nn.Embedding(self.output_size, self.hidden_size)
        self.attn = nn.Linear(self.hidden_size * 2, self.max_length)
        self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
        self.dropout = nn.Dropout(self.dropout_p)
        self.gru = nn.GRU(self.hidden_size, self.hidden_size)
        self.out = nn.Linear(self.hidden_size, self.output_size)

    def forward(self, input, hidden, encoder_outputs):
        embedded = self.embedding(input).view(1, 1, -1)
        embedded = self.dropout(embedded)

        attn_weights = F.softmax(
            self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)
        attn_applied = torch.bmm(attn_weights.unsqueeze(0),
                                 encoder_outputs.unsqueeze(0))

        output = torch.cat((embedded[0], attn_applied[0]), 1)
        output = self.attn_combine(output).unsqueeze(0)

        output = F.relu(output)
        output, hidden = self.gru(output, hidden)

        output = F.log_softmax(self.out(output[0]), dim=1)
        uns_output = torch.unsqueeze(output, 0)
        output = torch.cat((uns_output, hidden), 2)
        return output, attn_weights

    def initHidden(self):
        return torch.zeros(1, 1, self.hidden_size, device=device)

#Preparing training data
def indexesFromSentence(lang, sentence):
    return [lang.word2index[word] for word in sentence.split(' ')]

def tensorFromSentence(lang, sentence):
    indexes = indexesFromSentence(lang, sentence)
    indexes.append(EOS_token)
    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)

def tensorsFromPair(pair):
    input_tensor = tensorFromSentence(input_lang, pair[0])
    target_tensor = tensorFromSentence(output_lang, pair[1])

    return (input_tensor, target_tensor)

#training function
teacher_forcing_ratio = 0.5
use_teacher_forcing = True

def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length =
        MAX_LENGTH):
    encoder_hidden = encoder.initHidden()
    encoder_optimizer.zero_grad()
    decoder_optimizer.zero_grad()

    input_length = input_tensor.size(0)
    target_length = target_tensor.size(0)

    encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device = device)

    loss = 0

    for ei in range(input_length):
        encoder_output, encoder_hidden = encoder(
            input_tensor[ei], encoder_hidden)
        encoder_outputs[ei] = encoder_output[0, 0]

    decoder_input = torch.tensor([[SOS_token]], device=device)

    decoder_hidden = encoder_hidden

    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False

    if use_teacher_forcing:
        # Teacher forcing: Feed the target as the next input
        for di in range(target_length):
            output, decoder_attention = decoder(
                decoder_input, decoder_hidden, encoder_outputs)
            decoder_output = output.narrow(2, 0, output_lang.n_words)
            decoder_output = torch.squeeze(decoder_output, 0)
            decoder_hidden = output.narrow(2, output_lang.n_words, hidden_size)
            #decoder_output, decoder_hidden, decoder_attention = decoder(
            #    decoder_input, decoder_hidden, encoder_outputs)
            loss += criterion(decoder_output, target_tensor[di])
            decoder_input = target_tensor[di]  # Teacher forcing

    else:
        # Without teacher forcing: use its own predictions as the next input
        for di in range(target_length):
            output, decoder_attention = decoder(
                decoder_input, decoder_hidden, encoder_outputs)
            decoder_output = output.narrow(2, 0, output_lang.n_words)
            decoder_output = torch.squeeze(decoder_output, 0)
            decoder_hidden = output.narrow(2, output_lang.n_words, hidden_size)
            #decoder_output, decoder_hidden, decoder_attention = decoder(
            #    decoder_input, decoder_hidden, encoder_outputs)
            topv, topi = decoder_output.topk(1)
            decoder_input = topi.squeeze().detach()  # detach from history as input

            loss += criterion(decoder_output, target_tensor[di])
            if decoder_input.item() == EOS_token:
                break

    loss.backward()

    encoder_optimizer.step()
    decoder_optimizer.step()

    return loss.item() / target_length

import time
import math

def asMinutes(s):
    m = math.floor(s / 60)
    s -= m * 60

    return '%dm %ds' % (m, s)

def timeSince(since, percent):
    now = time.time()
    s = now - since
    es = s / (percent)
    rs = es - s

    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))


import matplotlib.pyplot as plt
plt.switch_backend('agg')
import matplotlib.ticker as ticker
import numpy as np

def showPlot(points):
    plt.figure()
    fig, ax = plt.subplots()

    loc = ticker.MultipleLocator(base = 0.2)
    ax.yaxis.set_major_locator(loc)
    plt.plot(points)

#train iterations
def trainIters(encoder, decoder, n_iters, print_every = 1000, plot_every = 100, learning_rate = 0.01):
    start = time.time()
    plot_losses = []
    print_loss_total = 0
    plot_loss_total = 0

    encoder_optimizer = optim.SGD(encoder.parameters(), lr = learning_rate)
    decoder_optimizer = optim.SGD(decoder.parameters(), lr = learning_rate)
    training_pairs = [tensorsFromPair(random.choice(pairs))
        for i in range(n_iters)]
    criterion = nn.NLLLoss()

    for iter in range(1, n_iters + 1):
        training_pair = training_pairs[iter - 1]
        input_tensor = training_pair[0]
        target_tensor = training_pair[1]

        loss = train(input_tensor, target_tensor, encoder,
                     decoder, encoder_optimizer, decoder_optimizer, criterion)
        print_loss_total += loss
        plot_loss_total += loss

        if iter % print_every == 0:
            print_loss_avg = print_loss_total / print_every
            print_loss_total = 0
            print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),
                                         iter, iter / n_iters * 100, print_loss_avg))

        if iter % plot_every == 0:
            plot_loss_avg = plot_loss_total / plot_every
            plot_losses.append(plot_loss_avg)
            plot_loss_total = 0

    showPlot(plot_losses)

def evaluate(encoder, decoder, sentence, max_length = MAX_LENGTH):
    with torch.no_grad():
        input_tensor = tensorFromSentence(input_lang, sentence)
        input_length = input_tensor.size()[0]
        encoder_hidden = encoder.initHidden()

        encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device = device)

        for ei in range(input_length):
            encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)
            encoder_outputs[ei] += encoder_output[0, 0]

        decoder_input = torch.tensor([[SOS_token]], device = device)
        decoder_hidden = encoder_hidden

        decoded_words = []
        decoder_attentions = torch.zeros(max_length, max_length)

        for di in range(max_length):
            #decode_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)
            output, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)
            decoder_output = output.narrow(2, 0, output_lang.n_words)
            decoder_output = torch.squeeze(decoder_output, 0)
            decoder_hidden = output.narrow(2, output_lang.n_words, hidden_size)
            decoder_attentions[di] = decoder_attention.data
            topv, topi = decoder_output.data.topk(1)

            if topi.item() == EOS_token:
                decoded_words.append('<EOS>')
                break;
            else:
                decoded_words.append(output_lang.index2word[topi.item()])

            decoder_input = topi.squeeze().detach()

        return decoded_words, decoder_attentions[:di + 1]

#evaluate randomly
def evaluateRandomly(encoder, decoder, n = 10):
    for i in range(n):
        pair = random.choice(pairs)
        print('>', pair[0])
        print('=', pair[1])
        output_words, attentions = evaluate(encoder, decoder, pair[0])
        output_sentence = ' '.join(output_words)
        print('<', output_sentence)
        print('')

#evaluate one pair
def evaluateOnePair(encoder, decoder, n = 50):
    for i in range(n):
        pair = pairs[i * 10]
        print('>', pair[0])
        print('=', pair[1])
        output_words, attentions = evaluate(encoder, decoder, pair[0])
        output_sentence = ' '.join(output_words)
        print('<', output_sentence)
        print('')

hidden_size = 256
encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)
attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p = 0.1).to(device)
trainIters(encoder1, attn_decoder1, 75000, print_every=5000)

evaluateOnePair(encoder1, attn_decoder1)

#export encoder onnx file
input_tensor, target_tensor = tensorsFromPair(random.choice(pairs))
encoder_hidden = encoder1.initHidden()
torch.onnx.export(encoder1, (input_tensor[0], encoder_hidden), "s2s_encoder.onnx", verbose=True)

#export decoder onnx file
decoder_input = torch.tensor([[SOS_token]], device = device)
decoder_hidden = encoder_hidden
encoder_outputs = torch.randn(MAX_LENGTH, encoder1.hidden_size)
torch.onnx.export(attn_decoder1, (decoder_input, decoder_hidden, encoder_outputs), "s2s_decoder.onnx", verbose=True)