import tensorflow as tf
from models.base import AEEncoder, Decoder, AEEncoderNew, DecoderNew


class AutoEncoder(tf.keras.Model):
    
    def __init__(self, config):
        super(AutoEncoder, self).__init__()

        self.encoder : AEEncoder = AEEncoder(config['latent_dim'], config['encoder_depths'], config['encoder_dims'], config['bottleneck_size'], config['kernel_size'])
        self.decoder : Decoder = Decoder(config['decoder_input'], config['decoder_depths'], config['decoder_dims'], kernel_size=config['kernel_size'], is_label=config['is_label'], upscale=config['upscale_output'])


    def call(self, inputs, training):
        x = inputs

        x = self.encoder(x, training=training)
        x = self.decoder(x, training=training)

        return x


class AutoEncoderNew(tf.keras.Model):
    
    def __init__(self, config):
        super(AutoEncoderNew, self).__init__()

        self.encoder : AEEncoderNew = AEEncoderNew(config['layer_params'], config['bottleneck_size'], config['activation'])
        self.decoder : DecoderNew = DecoderNew(config['layer_params'], config['bottleneck_size'], is_label=config['is_label'])


    def call(self, inputs, training):
        x = inputs

        x = self.encoder(x, training=training)
        x = self.decoder(x, training=training)

        return x