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# Get the dependency .py files, if any.
! git clone https://github.com/GoogleCloudPlatform/cloudml-samples.git
! cp cloudml-samples/tpu/templates/tpu_gan_estimator/* .


    

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import argparse
import numpy as np
import os
import tensorflow as tf


    

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INPUT_DIM = 5
OUTPUT_DIM = 3


    

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def generator_fn(generator_inputs):
    outputs = tf.layers.dense(generator_inputs, OUTPUT_DIM)
    return outputs


    

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def discriminator_fn(data, generator_inputs):
    outputs = tf.layers.dense(data, 1)
    return outputs


    

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def gen_model_fn(features, labels, mode, params):
    # build model
    global_step = tf.train.get_global_step()

    generator_inputs = features
    real_data = labels

    with tf.variable_scope('shared', reuse=tf.AUTO_REUSE):
        gan_model = tf.contrib.gan.gan_model(generator_fn, discriminator_fn, real_data, generator_inputs)

    predictions = gan_model.generated_data
    loss = None
    train_op = None

    if mode == tf.estimator.ModeKeys.TRAIN:
        # define loss
        gan_loss = tf.contrib.gan.gan_loss(gan_model, add_summaries=False)
        loss = gan_loss.generator_loss

        # define train_op
        optimizer = tf.train.RMSPropOptimizer(learning_rate=0.05)
        dummy_optimizer = tf.train.RMSPropOptimizer(learning_rate=0.05)

        # wrapper to make the optimizer work with TPUs
        if params['use_tpu']:
            optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)

        gan_train_ops = tf.contrib.gan.gan_train_ops(gan_model, gan_loss, optimizer, dummy_optimizer)

        # tf.contrib.gan's train op does not manage global steps in it
        train_op = tf.group(gan_train_ops.generator_train_op, global_step.assign_add(1))

    if params['use_tpu']:
        # TPU version of EstimatorSpec
        return tf.contrib.tpu.TPUEstimatorSpec(
            mode=mode,
            predictions=predictions,
            loss=loss,
            train_op=train_op)
    else:
        return tf.estimator.EstimatorSpec(
            mode=mode,
            predictions=predictions,
            loss=loss,
            train_op=train_op)


    

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def dis_model_fn(features, labels, mode, params):
    # build model
    global_step = tf.train.get_global_step()

    generator_inputs = features
    real_data = labels

    with tf.variable_scope('shared', reuse=tf.AUTO_REUSE):
        gan_model = tf.contrib.gan.gan_model(generator_fn, discriminator_fn, real_data, generator_inputs)

    predictions = {
        'discriminator_gen_outputs': gan_model.discriminator_gen_outputs,
        'discriminator_real_outputs': gan_model.discriminator_real_outputs}
    loss = None
    train_op = None

    if mode == tf.estimator.ModeKeys.TRAIN:
        # define loss
        gan_loss = tf.contrib.gan.gan_loss(gan_model, add_summaries=False)
        loss = gan_loss.discriminator_loss

        # define train_op
        optimizer = tf.train.RMSPropOptimizer(learning_rate=0.05)
        dummy_optimizer = tf.train.RMSPropOptimizer(learning_rate=0.05)

        # wrapper to make the optimizer work with TPUs
        if params['use_tpu']:
            optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)

        gan_train_ops = tf.contrib.gan.gan_train_ops(gan_model, gan_loss, dummy_optimizer, optimizer)

        # tf.contrib.gan's train op does not manage global steps in it
        train_op = tf.group(gan_train_ops.discriminator_train_op, global_step.assign_add(1))

    if params['use_tpu']:
        # TPU version of EstimatorSpec
        return tf.contrib.tpu.TPUEstimatorSpec(
            mode=mode,
            predictions=predictions,
            loss=loss,
            train_op=train_op)
    else:
        return tf.estimator.EstimatorSpec(
            mode=mode,
            predictions=predictions,
            loss=loss,
            train_op=train_op)


    

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def train_input_fn(params={}):
    # make some fake noise
    data_size = 100
    noise_tensor = tf.random_normal((data_size, INPUT_DIM))
    real_data_tensor = tf.random_uniform((data_size, OUTPUT_DIM))

    dataset = tf.data.Dataset.from_tensor_slices((noise_tensor, real_data_tensor))
    dataset = dataset.repeat().shuffle(10)

    # TPUEstimator passes params when calling input_fn
    batch_size = params.get('train_batch_size', 16)
    dataset = dataset.batch(batch_size, drop_remainder=True)

    # TPUs need to know all dimensions when the graph is built
    # Datasets know the batch size only when the graph is run
    def set_shapes(features, labels):
        features_shape = features.get_shape().merge_with([batch_size, None])
        labels_shape = labels.get_shape().merge_with([batch_size, None])

        features.set_shape(features_shape)
        labels.set_shape(labels_shape)

        return features, labels

    dataset = dataset.map(set_shapes)
    dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE)

    return dataset


    

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def main(args):
    # pass the args as params so the model_fn can use
    # the TPU specific args
    params = vars(args)

    if args.use_tpu:
        # additional configs required for using TPUs
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(args.tpu)
        tpu_config = tf.contrib.tpu.TPUConfig(
            num_shards=8, # using Cloud TPU v2-8
            iterations_per_loop=args.save_checkpoints_steps)

        # use the TPU version of RunConfig
        gen_config = tf.contrib.tpu.RunConfig(
            cluster=tpu_cluster_resolver,
            model_dir=os.path.join(args.model_dir, 'generator'),
            tpu_config=tpu_config,
            save_checkpoints_steps=args.save_checkpoints_steps,
            save_summary_steps=100)

        dis_config = tf.contrib.tpu.RunConfig(
            cluster=tpu_cluster_resolver,
            model_dir=os.path.join(args.model_dir, 'discriminator'),
            tpu_config=tpu_config,
            save_checkpoints_steps=args.save_checkpoints_steps,
            save_summary_steps=100)

        # TPUEstimator
        gen_estimator = tf.contrib.tpu.TPUEstimator(
            model_fn=gen_model_fn,
            config=gen_config,
            params=params,
            train_batch_size=args.train_batch_size,
            eval_batch_size=32,
            export_to_tpu=False)

        dis_estimator = tf.contrib.tpu.TPUEstimator(
            model_fn=dis_model_fn,
            config=dis_config,
            params=params,
            train_batch_size=args.train_batch_size,
            eval_batch_size=32,
            export_to_tpu=False)
    else:
        gen_config = tf.estimator.RunConfig(model_dir=os.path.join(args.model_dir, 'generator'))
        dis_config = tf.estimator.RunConfig(model_dir=os.path.join(args.model_dir, 'discriminator'))

        gen_estimator = tf.estimator.Estimator(
            gen_model_fn,
            config=gen_config,
            params=params)

        dis_estimator = tf.estimator.Estimator(
            dis_model_fn,
            config=dis_config,
            params=params)

    # manage the training loop
    for _ in range(3):
        tf.logging.info('Training Discriminator')
        dis_estimator.train(train_input_fn, steps=100)

        tf.logging.info('Training Generator')
        gen_estimator.train(train_input_fn, steps=10)


    

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parser = argparse.ArgumentParser()

parser.add_argument(
    '--model-dir',
    type=str,
    default='/tmp/tpu-template',
    help='Location to write checkpoints and summaries to.  Must be a GCS URI when using Cloud TPU.')
parser.add_argument(
    '--train-batch-size',
    type=int,
    default=16,
    help='The training batch size.  The training batch is divided evenly across the TPU cores.')
parser.add_argument(
    '--save-checkpoints-steps',
    type=int,
    default=100,
    help='The number of training steps before saving each checkpoint.')
parser.add_argument(
    '--use-tpu',
    action='store_true',
    help='Whether to use TPU.')
parser.add_argument(
    '--tpu',
    default=None,
    help='The name or GRPC URL of the TPU node.  Leave it as `None` when training on CMLE.')

args, _ = parser.parse_known_args()


    

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# TODO(user): change this
args.model_dir = 'gs://your-gcs-bucket'

# Get hostname from environment using ipython magic.
# This returns a list.
hostname = !hostname

args.tpu = hostname[0]
args.use_tpu = True


    

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# Use gcloud command line tool to create a TPU in the same zone as the VM instance.
! gcloud compute tpus create `hostname` \
  --zone `gcloud compute instances list --filter="name=$(hostname)" --format 'csv[no-heading](zone)'`\
  --network default \
  --range 10.101.1.0 \
  --version 1.13


    

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main(args)


    

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# Use gcloud command line tool to delete the TPU.
! gcloud compute tpus delete `hostname` \
  --zone `gcloud compute instances list --filter="name=$(hostname)" --format 'csv[no-heading](zone)'`\
  --quiet