from gpu_task_scheduler.gpu_task import GPUTask


class FactorVAETask(GPUTask):
    def main(self):
        import os
        import tensorflow as tf
        from gan.load_data import load_3Dpots
        from gan.latent import GaussianLatent, JointLatent
        from gan.network import VAEDecoder, VAEEncoder, TCDiscriminator
        from gan.factorVAE import FactorVAE
        from gan.metric import FactorVAEMetric

        data, metric_data, latent_values = load_3Dpots("data/3Dpots")
        _, height, width, depth = data.shape

        latent_list = []

        for i in range(self._config["gaussian_dim"]):
            latent_list.append(GaussianLatent(
                in_dim=1, out_dim=1, loc=0.0, scale=1.0, q_std=1.0,
                apply_reg=True))
        latent = JointLatent(latent_list=latent_list)

        decoder = VAEDecoder(
            output_width=width, output_height=height, output_depth=depth)
        encoder = VAEEncoder(output_length=latent.reg_in_dim)
        tcDiscriminator = TCDiscriminator()

        checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
        if not os.path.exists(checkpoint_dir):
            os.makedirs(checkpoint_dir)
        sample_dir = os.path.join(self._work_dir, "sample")
        if not os.path.exists(sample_dir):
            os.makedirs(sample_dir)
        time_path = os.path.join(self._work_dir, "time.txt")
        metric_path = os.path.join(self._work_dir, "metric.csv")

        run_config = tf.ConfigProto()
        with tf.Session(config=run_config) as sess:

            factorVAEMetric = FactorVAEMetric(metric_data, sess=sess)
            metric_callbacks = [factorVAEMetric]
            vae = FactorVAE(
                sess=sess,
                checkpoint_dir=checkpoint_dir,
                sample_dir=sample_dir,
                time_path=time_path,
                epoch=self._config["epoch"],
                batch_size=self._config["batch_size"],
                data=data,
                vis_freq=self._config["vis_freq"],
                vis_num_sample=self._config["vis_num_sample"],
                vis_num_rep=self._config["vis_num_rep"],
                latent=latent,
                decoder=decoder,
                encoder=encoder,
                tcDiscriminator=tcDiscriminator,
                tc_coe=self._config["tc_coe"],
                metric_callbacks=metric_callbacks,
                metric_freq=self._config["metric_freq"],
                metric_path=metric_path,
                output_reverse=self._config["output_reverse"])
            vae.build()
            vae.train()
