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"""
Utility functions related to data and models that are common to all the model conditioning examples.
"""
import tensorflow as tf
import numpy as np


def get_data():
    """Downloads and returns the pre-processed data and labels for training and testing.

    Returns:
        Tuple of: (train data, train labels, test data, test labels)
    """

    # To save time we use the MNIST dataset for this example.
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

    # Convolution operations require data to have 4 dimensions.
    # We divide by 255 to help training and cast to float32 for TensorFlow.
    x_train = (x_train[..., np.newaxis] / 255.0).astype(np.float32)
    x_test = (x_test[..., np.newaxis] / 255.0).astype(np.float32)

    return x_train, y_train, x_test, y_test


def create_model():
    """Create and returns a simple Keras model for training MNIST.

    We will use a simple convolutional neural network for this example,
    but the model optimization methods employed should be compatible with a
    wide variety of CNN architectures such as Mobilenet and Inception etc.

    Returns:
        Uncompiled Keras model.
    """

    keras_model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, 3, padding='same', input_shape=(28, 28, 1), activation=tf.nn.relu),
        tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu),
        tf.keras.layers.MaxPool2D(),
        tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu),
        tf.keras.layers.MaxPool2D(),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(units=10, activation=tf.nn.softmax)
    ])

    return keras_model