def init_model():
characters = list("0123456789abcd ")
characters.append("<EOS>")
global int2char
int2char = list(characters)
global char2int
char2int = {c: i for i, c in enumerate(characters)}
global VOCAB_SIZE
VOCAB_SIZE = len(characters)
dyparams = dy.DynetParams()
dyparams.set_random_seed(666)
dyparams.init()
pc = dy.ParameterCollection()
lstm = dy.LSTMBuilder(LAYERS, INPUT_DIM, HIDDEN_DIM, pc)
params = {}
params["lookup"] = pc.add_lookup_parameters((VOCAB_SIZE, INPUT_DIM))
params["R"] = pc.add_parameters((LSTM_OUTPUT_SIZE, HIDDEN_DIM))
params["bias"] = pc.add_parameters((LSTM_OUTPUT_SIZE))
params["w1"] = pc.add_parameters((N1, LSTM_OUTPUT_SIZE))
params["w2"] = pc.add_parameters((MLP_OUTPUT_SIZE, N1))
params["b1"] = pc.add_parameters((N1))
params["b2"] = pc.add_parameters((MLP_OUTPUT_SIZE))
trainer = dy.RMSPropTrainer(pc)
return (lstm, params, pc), trainer
import argparse
from random import shuffle
import sys
import string
from nltk.corpus import stopwords as SW
#import hunspell_checker
#from hunspell_checker import HunspellChecker
import attacks
from tqdm import tqdm
import pickle
import pandas as pd
import dynet_config
dynet_config.set(random_seed=42)
import dynet as dy
dyparams = dy.DynetParams()
dyparams.set_mem(8000)
dynet_config.set_gpu()
import numpy as np
np.random.seed(42)
import random
random.seed(42)
sys.path.insert(0, 'defenses/scRNN/')
sys.path.append('spell_checkers/')
nlp = English()
spell = SpellChecker()
from corrector import ScRNNChecker
# personal logging lib
import log