# From https://github.com/tensorflow/models/blob/master/samples/core/get_started/iris_data.py
# This file is the example used by TensorFlow to get users started. This code is used for testing.
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_y = train.pop(y_name)
    train_x = train

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

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


def train_input_fn(features, labels, batch_size):
    """An input function for training"""
    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

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


def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features = dict(features)

    # Use only features when labels are null.
    inputs = features if labels is None else (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"
    return dataset.batch(batch_size)


# 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.
    return dataset.shuffle(1000).repeat().batch(batch_size)