import torch.nn as nn


class SentimentLSTM(nn.Module):
    """
    The RNN model that will be used to perform Sentiment analysis.
    """

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.7):
        """
        Initialize the model by setting up the layers.
        """
        super(SentimentLSTM, self).__init__()

        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim


        # define all layers
        self.embed = nn.Embedding(vocab_size,embedding_dim)
        self.lstm = nn.LSTM(embedding_dim,hidden_dim,n_layers,dropout=drop_prob,batch_first=True)
        self.fc = nn.Linear(hidden_dim,output_size)
        self.sigmoid = nn.Sigmoid()
        self.drp = nn.Dropout(p=0.7)

    def forward(self, x, hidden):
        """
        Perform a forward pass of our model on some input and hidden state.
        """
        batch_size=x.shape[0]

        x = self.embed(x)

        x,hidden = self.lstm(x,hidden)

        x = x.reshape(-1,self.hidden_dim)

        x = self.drp(x)

        x = self.fc(x)

        sig_out = self.sigmoid(x)

        # return last sigmoid output and hidden state
        sig_out = sig_out.reshape(batch_size,-1)
        sig_out = sig_out[:,-1]

        return sig_out, hidden


    def init_hidden(self, batch_size):
        ''' Initializes hidden state '''
        # Create two new tensors with sizes n_layers x batch_size x hidden_dim,
        # initialized to zero, for hidden state and cell state of LSTM
        weight = next(self.parameters()).data
        train_on_gpu = torch.cuda.is_available()
        if (train_on_gpu):
            hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda(),
                  weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda())
        else:
            hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),
                      weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())


        return hidden

a4d4ec893c77f94f5666bff0352606e1e16a23bf
<mask token>


class SentimentLSTM(nn.Module):
    <mask token>

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim,
        n_layers, drop_prob=0.7):
        """
        Initialize the model by setting up the layers.
        """
        super(SentimentLSTM, self).__init__()
        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim
        self.embed = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=
            drop_prob, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_size)
        self.sigmoid = nn.Sigmoid()
        self.drp = nn.Dropout(p=0.7)
    <mask token>

    def init_hidden(self, batch_size):
        """ Initializes hidden state """
        weight = next(self.parameters()).data
        train_on_gpu = torch.cuda.is_available()
        if train_on_gpu:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_().cuda(), weight.new(self.n_layers, batch_size,
                self.hidden_dim).zero_().cuda()
        else:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_(), weight.new(self.n_layers, batch_size, self.
                hidden_dim).zero_()
        return hidden

<mask token>


class SentimentLSTM(nn.Module):
    <mask token>

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim,
        n_layers, drop_prob=0.7):
        """
        Initialize the model by setting up the layers.
        """
        super(SentimentLSTM, self).__init__()
        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim
        self.embed = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=
            drop_prob, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_size)
        self.sigmoid = nn.Sigmoid()
        self.drp = nn.Dropout(p=0.7)

    def forward(self, x, hidden):
        """
        Perform a forward pass of our model on some input and hidden state.
        """
        batch_size = x.shape[0]
        x = self.embed(x)
        x, hidden = self.lstm(x, hidden)
        x = x.reshape(-1, self.hidden_dim)
        x = self.drp(x)
        x = self.fc(x)
        sig_out = self.sigmoid(x)
        sig_out = sig_out.reshape(batch_size, -1)
        sig_out = sig_out[:, -1]
        return sig_out, hidden

    def init_hidden(self, batch_size):
        """ Initializes hidden state """
        weight = next(self.parameters()).data
        train_on_gpu = torch.cuda.is_available()
        if train_on_gpu:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_().cuda(), weight.new(self.n_layers, batch_size,
                self.hidden_dim).zero_().cuda()
        else:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_(), weight.new(self.n_layers, batch_size, self.
                hidden_dim).zero_()
        return hidden

<mask token>


class SentimentLSTM(nn.Module):
    """
    The RNN model that will be used to perform Sentiment analysis.
    """

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim,
        n_layers, drop_prob=0.7):
        """
        Initialize the model by setting up the layers.
        """
        super(SentimentLSTM, self).__init__()
        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim
        self.embed = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=
            drop_prob, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_size)
        self.sigmoid = nn.Sigmoid()
        self.drp = nn.Dropout(p=0.7)

    def forward(self, x, hidden):
        """
        Perform a forward pass of our model on some input and hidden state.
        """
        batch_size = x.shape[0]
        x = self.embed(x)
        x, hidden = self.lstm(x, hidden)
        x = x.reshape(-1, self.hidden_dim)
        x = self.drp(x)
        x = self.fc(x)
        sig_out = self.sigmoid(x)
        sig_out = sig_out.reshape(batch_size, -1)
        sig_out = sig_out[:, -1]
        return sig_out, hidden

    def init_hidden(self, batch_size):
        """ Initializes hidden state """
        weight = next(self.parameters()).data
        train_on_gpu = torch.cuda.is_available()
        if train_on_gpu:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_().cuda(), weight.new(self.n_layers, batch_size,
                self.hidden_dim).zero_().cuda()
        else:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_(), weight.new(self.n_layers, batch_size, self.
                hidden_dim).zero_()
        return hidden

import torch.nn as nn


class SentimentLSTM(nn.Module):
    """
    The RNN model that will be used to perform Sentiment analysis.
    """

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim,
        n_layers, drop_prob=0.7):
        """
        Initialize the model by setting up the layers.
        """
        super(SentimentLSTM, self).__init__()
        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim
        self.embed = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=
            drop_prob, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_size)
        self.sigmoid = nn.Sigmoid()
        self.drp = nn.Dropout(p=0.7)

    def forward(self, x, hidden):
        """
        Perform a forward pass of our model on some input and hidden state.
        """
        batch_size = x.shape[0]
        x = self.embed(x)
        x, hidden = self.lstm(x, hidden)
        x = x.reshape(-1, self.hidden_dim)
        x = self.drp(x)
        x = self.fc(x)
        sig_out = self.sigmoid(x)
        sig_out = sig_out.reshape(batch_size, -1)
        sig_out = sig_out[:, -1]
        return sig_out, hidden

    def init_hidden(self, batch_size):
        """ Initializes hidden state """
        weight = next(self.parameters()).data
        train_on_gpu = torch.cuda.is_available()
        if train_on_gpu:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_().cuda(), weight.new(self.n_layers, batch_size,
                self.hidden_dim).zero_().cuda()
        else:
            hidden = weight.new(self.n_layers, batch_size, self.hidden_dim
                ).zero_(), weight.new(self.n_layers, batch_size, self.
                hidden_dim).zero_()
        return hidden

import torch.nn as nn


class SentimentLSTM(nn.Module):
    """
    The RNN model that will be used to perform Sentiment analysis.
    """

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.7):
        """
        Initialize the model by setting up the layers.
        """
        super(SentimentLSTM, self).__init__()

        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim


        # define all layers
        self.embed = nn.Embedding(vocab_size,embedding_dim)
        self.lstm = nn.LSTM(embedding_dim,hidden_dim,n_layers,dropout=drop_prob,batch_first=True)
        self.fc = nn.Linear(hidden_dim,output_size)
        self.sigmoid = nn.Sigmoid()
        self.drp = nn.Dropout(p=0.7)

    def forward(self, x, hidden):
        """
        Perform a forward pass of our model on some input and hidden state.
        """
        batch_size=x.shape[0]

        x = self.embed(x)

        x,hidden = self.lstm(x,hidden)

        x = x.reshape(-1,self.hidden_dim)

        x = self.drp(x)

        x = self.fc(x)

        sig_out = self.sigmoid(x)

        # return last sigmoid output and hidden state
        sig_out = sig_out.reshape(batch_size,-1)
        sig_out = sig_out[:,-1]

        return sig_out, hidden


    def init_hidden(self, batch_size):
        ''' Initializes hidden state '''
        # Create two new tensors with sizes n_layers x batch_size x hidden_dim,
        # initialized to zero, for hidden state and cell state of LSTM
        weight = next(self.parameters()).data
        train_on_gpu = torch.cuda.is_available()
        if (train_on_gpu):
            hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda(),
                  weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda())
        else:
            hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),
                      weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())


        return hidden
