import sys
_module = sys.modules[__name__]
del sys
bilstm = _module
data_helper = _module
lstm = _module
train_batch = _module

from _paritybench_helpers import _mock_config, patch_functional
from unittest.mock import mock_open, MagicMock
from torch.autograd import Function
from torch.nn import Module
import abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings
import numpy as np
from torch import Tensor
patch_functional()
open = mock_open()
yaml = logging = sys = argparse = MagicMock()
ArgumentParser = argparse.ArgumentParser
_global_config = args = argv = cfg = config = params = _mock_config()
argparse.ArgumentParser.return_value.parse_args.return_value = _global_config
yaml.load.return_value = _global_config
sys.argv = _global_config
__version__ = '1.0.0'
xrange = range
wraps = functools.wraps


import torch


from torch.autograd import Variable


import torch.nn as nn


import torch.nn.functional as F


import re


import numpy as np


import torchtext.datasets as datasets


from torch import optim


import time


import random


from torchtext import data


class BiLSTMSentiment(nn.Module):

    def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, use_gpu, batch_size, dropout=0.5):
        super(BiLSTMSentiment, self).__init__()
        self.hidden_dim = hidden_dim
        self.use_gpu = use_gpu
        self.batch_size = batch_size
        self.dropout = dropout
        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_dim, bidirectional=True)
        self.hidden2label = nn.Linear(hidden_dim * 2, label_size)
        self.hidden = self.init_hidden()

    def init_hidden(self):
        if self.use_gpu:
            return Variable(torch.zeros(2, self.batch_size, self.hidden_dim)), Variable(torch.zeros(2, self.batch_size, self.hidden_dim))
        else:
            return Variable(torch.zeros(2, self.batch_size, self.hidden_dim)), Variable(torch.zeros(2, self.batch_size, self.hidden_dim))

    def forward(self, sentence):
        x = self.embeddings(sentence).view(len(sentence), self.batch_size, -1)
        lstm_out, self.hidden = self.lstm(x, self.hidden)
        y = self.hidden2label(lstm_out[-1])
        log_probs = F.log_softmax(y)
        return log_probs


class LSTMSentiment(nn.Module):

    def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, use_gpu, batch_size, dropout=0.5):
        super(LSTMSentiment, self).__init__()
        self.hidden_dim = hidden_dim
        self.use_gpu = use_gpu
        self.batch_size = batch_size
        self.dropout = dropout
        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_dim)
        self.hidden2label = nn.Linear(hidden_dim, label_size)
        self.hidden = self.init_hidden()

    def init_hidden(self):
        if self.use_gpu:
            return Variable(torch.zeros(1, self.batch_size, self.hidden_dim)), Variable(torch.zeros(1, self.batch_size, self.hidden_dim))
        else:
            return Variable(torch.zeros(1, self.batch_size, self.hidden_dim)), Variable(torch.zeros(1, self.batch_size, self.hidden_dim))

    def forward(self, sentence):
        x = self.embeddings(sentence).view(len(sentence), self.batch_size, -1)
        lstm_out, self.hidden = self.lstm(x, self.hidden)
        y = self.hidden2label(lstm_out[-1])
        log_probs = F.log_softmax(y)
        return log_probs

