<jupyter_start><jupyter_text><jupyter_code>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")
device
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  # Count SOS and EOS

    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 string to plain ASCII, thanks to
# # https://stackoverflow.com/a/518232/2809427
# def unicodeToAscii(s):
#     return ''.join(
#         c for c in unicodedata.normalize('NFD', s)
#         if unicodedata.category(c) != 'Mn'
#     )

# # Lowercase, trim, and remove non-letter characters
def readLangs(lang1, lang2, lang3, reverse=False):
    print("Reading lines...")

    # Read the file and split into lines
    lines = open('/content/drive/MyDrive/NLP/Trance/CSV/dataset.txt', encoding='utf-8').read().strip().split('\n')

    # Split every line into pairs and normalize
    pairs = [[s for s in l.split(',')] for l in lines]

    # Reverse pairs, make Lang instances
    if reverse:
        pairs = [list(reversed(p)) for p in pairs]
        input_lang_1 = Lang(lang2)
        input_lang_2 = Lang(lang1)
        output_lang = Lang(lang3)
    else:
        input_lang_1 = Lang(lang1)
        input_lang_2 = Lang(lang2)
        output_lang = Lang(lang3)
    return input_lang_1, input_lang_2, output_lang, pairs
def prepareData(lang1, lang2, lang3, reverse=False):
    input_lang_1, input_lang_2, output_lang, pairs = readLangs(lang1, lang2, lang3, reverse)
    print(lang1)
    print("Read %s sentence pairs" % len(pairs))
    # pairs = filterPairs(pairs)
    print("Trimmed to %s sentence pairs" % len(pairs))
    print("Counting words...")
    # ctr = 0
    for pair in pairs:
        # print('pair - ', pair)
        # ctr += 1
        # if ctr > 10:
        #     break
        input_lang_1.addSentence(pair[0])
        input_lang_1.addSentence(pair[1])
        # input_lang_1.addSentence(pair[2])
        
        input_lang_2.addSentence(pair[1])
        # input_lang_2.addSentence(pair[2])
        input_lang_2.addSentence(pair[0])

        output_lang.addSentence(pair[2])
        # output_lang.addSentence(pair[1])
        # output_lang.addSentence(pair[0])
        
        # for word in pair[0].split(' '):
        #     input_lang_1.addWord(word)
        # for word in pair[1].split(' '):
        #     input_lang_2.addWord(word)
        # for word in pair[2].split(' '):
        #     output_lang.addWord(word)

    print("Counted words:")
    print(input_lang_1.name, input_lang_1.n_words)
    print(input_lang_2.name, input_lang_2.n_words)
    print(output_lang.name, output_lang.n_words)
    return input_lang_1, input_lang_2, output_lang, pairs


input_lang_1, input_lang_2, output_lang, pairs = prepareData('init', 'final', 'transformation', True)
print(random.choice(pairs))
class EncoderRNNL1(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(EncoderRNNL1, 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 EncoderRNNL2(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(EncoderRNNL2, 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 DecoderRNN(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)
MAX_LENGTH = 128
class AttnDecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):
        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)
        return output, hidden, attn_weights

    def initHidden(self):
        return torch.zeros(1, 1, self.hidden_size, device=device)
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_1 = tensorFromSentence(input_lang_1, pair[0])
    input_tensor_2 = tensorFromSentence(input_lang_2, pair[1])
    target_tensor = tensorFromSentence(output_lang, pair[2])
    return (input_tensor_1, input_tensor_2, target_tensor)
teacher_forcing_ratio = 0.5


def train(input_tensor_1, input_tensor_2, target_tensor, encoder_1, encoder_2, decoder, encoder_optimizer_1, encoder_optimizer_2, decoder_optimizer, criterion, max_length=MAX_LENGTH):
    encoder_hidden_1 = encoder_1.initHidden()
    encoder_hidden_2 = encoder_2.initHidden()

    encoder_optimizer_1.zero_grad()
    encoder_optimizer_2 .zero_grad()
    decoder_optimizer.zero_grad()

    input_length_1 = input_tensor_1.size(0)
    input_length_2 = input_tensor_2.size(0)
    target_length = target_tensor.size(0)

    encoder_outputs_1 = torch.zeros(max_length, encoder_1.hidden_size, device=device)
    encoder_outputs_2 = torch.zeros(max_length, encoder_2.hidden_size, device=device)

    loss = 0
    for ei in range(input_length_1):
        encoder_output_1, encoder_hidden_1 = encoder_1(input_tensor_1[ei], encoder_hidden_1)
        encoder_outputs_1[ei] = encoder_output_1[0, 0]
    for ei in range(input_length_2):
        encoder_output_2, encoder_hidden_2 = encoder_2(input_tensor_2[ei], encoder_hidden_2)
        encoder_outputs_2[ei] = encoder_output_2[0, 0]
    encoder_outputs = encoder_outputs_1 - encoder_outputs_2

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

# marker
    decoder_hidden = encoder_hidden_1 - encoder_hidden_2

    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):
            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):
            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_1.step()
    encoder_optimizer_2.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 time
from tqdm.notebook import tqdm
def trainIters(encoder_1, encoder_2, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01):
    start = time.time()
    plot_losses = []
    print_loss_total = 0  # Reset every print_every
    plot_loss_total = 0  # Reset every plot_every

    encoder_optimizer_1 = optim.SGD(encoder_1.parameters(), lr=learning_rate)
    encoder_optimizer_2 = optim.SGD(encoder_2.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 tqdm(range(1, n_iters + 1)):
        training_pair = training_pairs[iter - 1]
        input_tensor_1 = training_pair[0]
        input_tensor_2 = training_pair[1]
        target_tensor = training_pair[2]

        loss = train(input_tensor_1, input_tensor_2, target_tensor, encoder_1, encoder_2,
                     decoder, encoder_optimizer_1, encoder_optimizer_2, 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))
            # torch.save(encoder_1.state_dict(), '/content/drive/MyDrive/NLP/Trance/models/encoder_weights_1')
            # torch.save(encoder_2.state_dict(), '/content/drive/MyDrive/NLP/Trance/models/encoder_weights_2')
            # torch.save(decoder.state_dict(), '/content/drive/MyDrive/NLP/Trance/models/decoder_weights')
            # torch.save(encoder_optimizer_1.state_dict(), '/content/drive/MyDrive/NLP/Trance/models/encoder_optimizer_1')
            # torch.save(encoder_optimizer_2.state_dict(), '/content/drive/MyDrive/NLP/Trance/models/encoder_optimizer_2')
            # torch.save(decoder_optimizer.state_dict(), '/content/drive/MyDrive/NLP/Trance/models/decoder_optimizer')
        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)
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()
    # this locator puts ticks at regular intervals
    loc = ticker.MultipleLocator(base=0.2)
    ax.yaxis.set_major_locator(loc)
    plt.plot(points)
hidden_size = 256
encoder_1 = EncoderRNNL1(input_lang_1.n_words, hidden_size).to(device)
encoder_2 = EncoderRNNL2(input_lang_2.n_words, hidden_size).to(device)
attn_decoder = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=0.1).to(device)

trainIters(encoder_1, encoder_2, attn_decoder, 75000, print_every=100)
# modl arch, performance, analyze few examples, common patterns that are failing, overcoming existing baselines
hidden_size=256
encoder_1 = EncoderRNNL1(input_lang_1.n_words, hidden_size).to(device)
encoder_2 = EncoderRNNL2(input_lang_2.n_words, hidden_size).to(device)
attn_decoder = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=0.1).to(device)
encoder_1.load_state_dict(torch.load('/content/drive/MyDrive/NLP/Trance/models/encoder_weights_1', map_location=torch.device('cpu')))
encoder_2.load_state_dict(torch.load('/content/drive/MyDrive/NLP/Trance/models/encoder_weights_2', map_location=torch.device('cpu')))
attn_decoder.load_state_dict(torch.load('/content/drive/MyDrive/NLP/Trance/models/decoder_weights', map_location=torch.device('cpu')))
def evaluate(encoder_1, encoder_2, decoder, sentence_1, sentence_2, max_length=MAX_LENGTH):
    with torch.no_grad():
        input_tensor_1 = tensorFromSentence(input_lang_1, sentence_1)
        input_tensor_2 = tensorFromSentence(input_lang_2, sentence_2)
        input_length_1 = input_tensor_1.size()[0]
        input_length_2 = input_tensor_2.size()[0]
        encoder_hidden_1 = encoder_1.initHidden()
        encoder_hidden_2 = encoder_2.initHidden()

        encoder_outputs_1 = torch.zeros(max_length, encoder_1.hidden_size, device=device)
        encoder_outputs_2 = torch.zeros(max_length, encoder_2.hidden_size, device=device)
        

        for ei in range(input_length_1):
            encoder_output_1, encoder_hidden_1 = encoder_1(input_tensor_1[ei],
                                                     encoder_hidden_1)
            encoder_outputs_1[ei] += encoder_output_1[0, 0]
            
        for ei in range(input_length_2):
            encoder_output_2, encoder_hidden_2 = encoder_2(input_tensor_2[ei],
                                                     encoder_hidden_2)
            encoder_outputs_2[ei] += encoder_output_2[0, 0]

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

        decoder_hidden = encoder_hidden_1 - encoder_hidden_2

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

        encoder_outputs = encoder_outputs_1 - encoder_outputs_2
        
        for di in range(max_length):
            decoder_output, decoder_hidden, decoder_attention = decoder(
                decoder_input, decoder_hidden, encoder_outputs)
            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]
def evaluateRandomly(encoder_1, encoder_2, decoder, n=10):
    for i in range(n):
        pair = random.choice(pairs)
        print('>', pair[0])
        print('=', pair[1])
        output_words, attentions = evaluate(encoder_1, encoder_2, decoder, pair[0], pair[1])
        output_sentence = ' '.join(output_words)
        print(len(output_words))
        print('<', output_sentence)
        print('')
evaluateRandomly(encoder_1, encoder_2, attn_decoder, 1)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10724427527037642, 'fraction_numerical': 0.021146758504017885, 'mean_word_length': 3.132833957553059, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 7, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '85650', 'license': 'MIT', 'n_tokens_mistral': 5696, 'n_tokens_neox': 5360, 'n_words': 1034, 'path': 'GraphDiffToTransformation.ipynb', 'repo_name': 'saurabh1mohite/TDVR', 'size': '16551'}