import pandas as pd
import tensorflow as tf

TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"

CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
                    'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']

def maybe_download():
    train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
    test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)

    return train_path, test_path

def load_data(y_name='Species'):
    """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
    train_path, test_path = maybe_download()

    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
    train_x, train_y = train, train.pop(y_name)

    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
    test_x, test_y = test, test.pop(y_name)

    return (train_x, train_y), (test_x, test_y)

def train_input_fn(features, labels, batch_size, hook):
    #dataset = tf.data.Dataset.from_tensor_slices((features, labels))
    #dataset = dataset.shuffle(1000).repeat().batch(batch_size)
    #return dataset
    assert features.shape[0] == labels.shape[0]
    features_placeholder = tf.placeholder(features.dtype, features.shape)
    labels_placeholder = tf.placeholder(labels.dtype, labels.shape)
    dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
    dataset = dataset.shuffle(features.shape[0]).repeat().batch(batch_size)
    iterator = dataset.make_initializable_iterator()
    next_element = iterator.get_next()
    hook.iterator_initializer_func = \
        lambda sess: sess.run(
            iterator.initializer,
            feed_dict={features_placeholder: features, labels_placeholder: labels})
    #sess = tf.Session()
    #sess.run(iterator.initializer, feed_dict={features_placeholder: features, labels_placeholder: labels})
    return next_element


def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the dataset.
    return dataset


# The remainder of this file contains a simple example of a csv parser,
#     implemented using the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0]]

def _parse_line(line):
    # Decode the line into its fields
    fields = tf.decode_csv(line, record_defaults=CSV_TYPES)

    # Pack the result into a dictionary
    features = dict(zip(CSV_COLUMN_NAMES, fields))

    # Separate the label from the features
    label = features.pop('Species')

    return features, label


def csv_input_fn(csv_path, batch_size):
    # Create a dataset containing the text lines.
    dataset = tf.data.TextLineDataset(csv_path).skip(1)

    # Parse each line.
    dataset = dataset.map(_parse_line)

    # Shuffle, repeat, and batch the examples.
    dataset = dataset.shuffle(1000).repeat().batch(batch_size)

    # Return the dataset.
    return dataset
