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import theano
from theano import tensor
import numpy

from blocks.bricks import Tanh, Softmax, Linear, MLP, Identity, Rectifier
from blocks.bricks.lookup import LookupTable
from blocks.bricks.recurrent import LSTM

from blocks.graph import ComputationGraph, apply_dropout


class Model():
    def __init__(self, config, vocab_size):
        question = tensor.imatrix('question')
        question_mask = tensor.imatrix('question_mask')
        answer = tensor.ivector('answer')
        candidates = tensor.imatrix('candidates')
        candidates_mask = tensor.imatrix('candidates_mask')

        bricks = []


        # set time as first dimension
        question = question.dimshuffle(1, 0)
        question_mask = question_mask.dimshuffle(1, 0)

        # Embed questions
        embed = LookupTable(vocab_size, config.embed_size, name='question_embed')
        bricks.append(embed)
        qembed = embed.apply(question)

        # Create and apply LSTM stack
        curr_dim = config.embed_size
        curr_hidden = qembed

        hidden_list = []
        for k, dim in enumerate(config.lstm_size):
            lstm_in = Linear(input_dim=curr_dim, output_dim=4*dim, name='lstm_in_%d'%k)
            lstm = LSTM(dim=dim, activation=Tanh(), name='lstm_%d'%k)
            bricks = bricks + [lstm_in, lstm]

            tmp = lstm_in.apply(curr_hidden)
            hidden, _ = lstm.apply(tmp, mask=question_mask.astype(theano.config.floatX))
            hidden_list.append(hidden)
            if config.skip_connections:
                curr_hidden = tensor.concatenate([hidden, qembed], axis=2)
                curr_dim =  dim + config.embed_size
            else:
                curr_hidden = hidden
                curr_dim = dim

        # Create and apply output MLP
        if config.skip_connections:
            out_mlp = MLP(dims=[sum(config.lstm_size)] + config.out_mlp_hidden + [config.n_entities],
                          activations=config.out_mlp_activations + [Identity()],
                          name='out_mlp')
            bricks.append(out_mlp)

            probs = out_mlp.apply(tensor.concatenate([h[-1,:,:] for h in hidden_list], axis=1))
        else:
            out_mlp = MLP(dims=[config.lstm_size[-1]] + config.out_mlp_hidden + [config.n_entities],
                          activations=config.out_mlp_activations + [Identity()],
                          name='out_mlp')
            bricks.append(out_mlp)

            probs = out_mlp.apply(hidden_list[-1][-1,:,:])

        is_candidate = tensor.eq(tensor.arange(config.n_entities, dtype='int32')[None, None, :],
                                 tensor.switch(candidates_mask, candidates, -tensor.ones_like(candidates))[:, :, None]).sum(axis=1)
        probs = tensor.switch(is_candidate, probs, -1000 * tensor.ones_like(probs))

        # Calculate prediction, cost and error rate
        pred = probs.argmax(axis=1)
        cost = Softmax().categorical_cross_entropy(answer, probs).mean()
        error_rate = tensor.neq(answer, pred).mean()

        # Apply dropout
        cg = ComputationGraph([cost, error_rate])
        if config.dropout > 0:
            cg = apply_dropout(cg, hidden_list, config.dropout)
        [cost_reg, error_rate_reg] = cg.outputs

        # Other stuff
        cost_reg.name = cost.name = 'cost'
        error_rate_reg.name = error_rate.name = 'error_rate'

        self.sgd_cost = cost_reg
        self.monitor_vars = [[cost_reg], [error_rate_reg]]
        self.monitor_vars_valid = [[cost], [error_rate]]

        # Initialize bricks
        for brick in bricks:
            brick.weights_init = config.weights_init
            brick.biases_init = config.biases_init
            brick.initialize()

        

#  vim: set sts=4 ts=4 sw=4 tw=0 et :