import copy
from keras import backend as K
from keras import objectives
from keras.models import Model
from keras.layers import Input, Dense, Lambda
from keras.layers.core import Dense, Activation, Flatten, RepeatVector
from keras.layers.wrappers import TimeDistributed
from keras.layers.recurrent import GRU
from keras.layers.convolutional import Convolution1D
import pdb

class MoleculeVAE():

    autoencoder = None
    
    def create(self,
               charset,
               max_length = 120,
               latent_rep_size = 292,
               weights_file = None):
        charset_length = len(charset)
        
        x = Input(shape=(max_length, charset_length))
        _, z = self._buildEncoder(x, latent_rep_size, max_length)
        self.encoder = Model(x, z)

        encoded_input = Input(shape=(latent_rep_size,))
        self.decoder = Model(
            encoded_input,
            self._buildDecoder(
                encoded_input,
                latent_rep_size,
                max_length,
                charset_length
            )
        )

        x1 = Input(shape=(max_length, charset_length))
        vae_loss, z1 = self._buildEncoder(x1, latent_rep_size, max_length)
        self.autoencoder = Model(
            x1,
            self._buildDecoder(
                z1,
                latent_rep_size,
                max_length,
                charset_length
            )
        )


        x2 = Input(shape=(max_length, charset_length))
        (z_m, z_l_v) = self._encoderMeanVar(x2, latent_rep_size, max_length)
        self.encoderMV = Model(input=x2, output=[z_m, z_l_v])
        if weights_file:
            self.autoencoder.load_weights(weights_file)
            self.encoder.load_weights(weights_file, by_name = True)
            self.decoder.load_weights(weights_file, by_name = True)
            self.encoderMV.load_weights(weights_file, by_name = True)

        self.autoencoder.compile(optimizer = 'Adam',
                                 loss = vae_loss,
                                 metrics = ['accuracy'])

    def _encoderMeanVar(self, x, latent_rep_size, max_length, epsilon_std = 0.01):
        h = Convolution1D(9, 9, activation = 'relu', name='conv_1')(x) # used to be 2, 1
        h = Convolution1D(9, 9, activation = 'relu', name='conv_2')(h) # used to be 2, 1
        h = Convolution1D(10, 11, activation = 'relu', name='conv_3')(h) # used to be 3
        h = Flatten(name='flatten_1')(h)
        h = Dense(435, activation = 'relu', name='dense_1')(h) # used to be 100, 30, 50, 200

        z_mean = Dense(latent_rep_size, name='z_mean', activation = 'linear')(h)
        z_log_var = Dense(latent_rep_size, name='z_log_var', activation = 'linear')(h)

        return (z_mean, z_log_var) 


    def _buildEncoder(self, x, latent_rep_size, max_length, epsilon_std = 0.01):
        h = Convolution1D(9, 9, activation = 'relu', name='conv_1')(x)
        h = Convolution1D(9, 9, activation = 'relu', name='conv_2')(h)
        h = Convolution1D(10, 11, activation = 'relu', name='conv_3')(h)
        h = Flatten(name='flatten_1')(h)
        h = Dense(435, activation = 'relu', name='dense_1')(h)

        def sampling(args):
            z_mean_, z_log_var_ = args
            batch_size = K.shape(z_mean_)[0]
            epsilon = K.random_normal(shape=(batch_size, latent_rep_size), mean=0., std = epsilon_std)
            return z_mean_ + K.exp(z_log_var_ / 2) * epsilon

        z_mean = Dense(latent_rep_size, name='z_mean', activation = 'linear')(h)
        z_log_var = Dense(latent_rep_size, name='z_log_var', activation = 'linear')(h)

        def vae_loss(x, x_decoded_mean):
            x = K.flatten(x)
            x_decoded_mean = K.flatten(x_decoded_mean)
            xent_loss = max_length * objectives.binary_crossentropy(x, x_decoded_mean)
            kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis = -1)
            return xent_loss + kl_loss

        return (vae_loss, Lambda(sampling, output_shape=(latent_rep_size,), name='lambda')([z_mean, z_log_var]))

    def _buildDecoder(self, z, latent_rep_size, max_length, charset_length):
        h = Dense(latent_rep_size, name='latent_input', activation = 'relu')(z)
        h = RepeatVector(max_length, name='repeat_vector')(h)
        h = GRU(501, return_sequences = True, name='gru_1')(h)
        h = GRU(501, return_sequences = True, name='gru_2')(h)
        h = GRU(501, return_sequences = True, name='gru_3')(h)
        return TimeDistributed(Dense(charset_length, activation='softmax'), name='decoded_mean')(h)

    def save(self, filename):
        self.autoencoder.save_weights(filename)
    
    def load(self, charset, weights_file, latent_rep_size = 292, max_length = 120):
        self.create(charset, weights_file = weights_file, latent_rep_size = latent_rep_size)
