# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Train and Eval the MNIST network.

This version is like fully_connected_feed.py but uses data converted
to a TFRecords file containing tf.train.Example protocol buffers.
See:
https://www.tensorflow.org/guide/reading_data#reading_from_files
for context.

YOU MUST run convert_to_records before running this (but you only need to
run it once).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os.path
import sys
import time

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import mnist

# Basic model parameters as external flags.
FLAGS = None

# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = "train.tfrecords"
VALIDATION_FILE = "validation.tfrecords"


def decode(serialized_example):
    """Parses an image and label from the given `serialized_example`."""
    features = tf.parse_single_example(
        serialized_example,
        # Defaults are not specified since both keys are required.
        features={
            "image_raw": tf.FixedLenFeature([], tf.string),
            "label": tf.FixedLenFeature([], tf.int64),
        },
    )

    # Convert from a scalar string tensor (whose single string has
    # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
    # [mnist.IMAGE_PIXELS].
    image = tf.decode_raw(features["image_raw"], tf.uint8)
    image.set_shape((mnist.IMAGE_PIXELS))

    # Convert label from a scalar uint8 tensor to an int32 scalar.
    label = tf.cast(features["label"], tf.int32)

    return image, label


def augment(image, label):
    """Placeholder for data augmentation."""
    # OPTIONAL: Could reshape into a 28x28 image and apply distortions
    # here.  Since we are not applying any distortions in this
    # example, and the next step expects the image to be flattened
    # into a vector, we don't bother.
    return image, label


def normalize(image, label):
    """Convert `image` from [0, 255] -> [-0.5, 0.5] floats."""
    image = tf.cast(image, tf.float32) * (1.0 / 255) - 0.5
    return image, label


def inputs(train, batch_size, num_epochs):
    """Reads input data num_epochs times.

    Args:
      train: Selects between the training (True) and validation (False) data.
      batch_size: Number of examples per returned batch.
      num_epochs: Number of times to read the input data, or 0/None to
         train forever.

    Returns:
      A tuple (images, labels), where:
      * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
        in the range [-0.5, 0.5].
      * labels is an int32 tensor with shape [batch_size] with the true label,
        a number in the range [0, mnist.NUM_CLASSES).

      This function creates a one_shot_iterator, meaning that it will only iterate
      over the dataset once. On the other hand there is no special initialization
      required.
    """
    if not num_epochs:
        num_epochs = None
    filename = os.path.join(FLAGS.train_dir, TRAIN_FILE if train else VALIDATION_FILE)

    with tf.name_scope("input"):
        # TFRecordDataset opens a binary file and reads one record at a time.
        # `filename` could also be a list of filenames, which will be read in order.
        dataset = tf.data.TFRecordDataset(filename)

        # The map transformation takes a function and applies it to every element
        # of the dataset.
        dataset = dataset.map(decode)
        dataset = dataset.map(augment)
        dataset = dataset.map(normalize)

        # The shuffle transformation uses a finite-sized buffer to shuffle elements
        # in memory. The parameter is the number of elements in the buffer. For
        # completely uniform shuffling, set the parameter to be the same as the
        # number of elements in the dataset.
        dataset = dataset.shuffle(1000 + 3 * batch_size)

        dataset = dataset.repeat(num_epochs)
        dataset = dataset.batch(batch_size)

        iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
    return iterator.get_next()


def run_training():
    """Train MNIST for a number of steps."""

    # Tell TensorFlow that the model will be built into the default Graph.
    with tf.Graph().as_default():
        # Input images and labels.
        image_batch, label_batch = inputs(
            train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs
        )

        # Build a Graph that computes predictions from the inference model.
        logits = mnist.inference(image_batch, FLAGS.hidden1, FLAGS.hidden2)

        # Add to the Graph the loss calculation.
        loss = mnist.loss(logits, label_batch)

        # Add to the Graph operations that train the model.
        train_op = mnist.training(loss, FLAGS.learning_rate)

        # The op for initializing the variables.
        init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

        # Create a session for running operations in the Graph.
        with tf.Session() as sess:
            # Initialize the variables (the trained variables and the
            # epoch counter).
            sess.run(init_op)
            try:
                step = 0
                while True:  # Train until OutOfRangeError
                    start_time = time.time()

                    # Run one step of the model.  The return values are
                    # the activations from the `train_op` (which is
                    # discarded) and the `loss` op.  To inspect the values
                    # of your ops or variables, you may include them in
                    # the list passed to sess.run() and the value tensors
                    # will be returned in the tuple from the call.
                    _, loss_value = sess.run([train_op, loss])

                    duration = time.time() - start_time

                    # Print an overview fairly often.
                    if step % 100 == 0:
                        print("Step %d: loss = %.2f (%.3f sec)" % (step, loss_value, duration))
                    step += 1
            except tf.errors.OutOfRangeError:
                print("Done training for %d epochs, %d steps." % (FLAGS.num_epochs, step))


def main(_):
    run_training()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--learning_rate", type=float, default=0.01, help="Initial learning rate.")
    parser.add_argument(
        "--num_epochs", type=int, default=2, help="Number of epochs to run trainer."
    )
    parser.add_argument(
        "--hidden1", type=int, default=128, help="Number of units in hidden layer 1."
    )
    parser.add_argument(
        "--hidden2", type=int, default=32, help="Number of units in hidden layer 2."
    )
    parser.add_argument("--batch_size", type=int, default=100, help="Batch size.")
    parser.add_argument(
        "--train_dir",
        type=str,
        default="s3://dlami-dataset-test/Tensorflow",
        help="Directory with the training data.",
    )
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)