"""This test for the LFW require medium-size data dowloading and processing

If the data has not been already downloaded by running the examples,
the tests won't run (skipped).

If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
joblib, successive runs will be fast (less than 200ms).
"""

import random
import os
import shutil
import tempfile
import numpy as np
from sklearn.externals import six
try:
    try:
        from scipy.misc import imsave
    except ImportError:
        from scipy.misc.pilutil import imsave
except ImportError:
    imsave = None

from sklearn.datasets import load_lfw_pairs
from sklearn.datasets import load_lfw_people

from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import raises


SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
SCIKIT_LEARN_EMPTY_DATA = tempfile.mkdtemp(prefix="scikit_learn_empty_test_")

LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, 'lfw_home')
FAKE_NAMES = [
    'Abdelatif_Smith',
    'Abhati_Kepler',
    'Camara_Alvaro',
    'Chen_Dupont',
    'John_Lee',
    'Lin_Bauman',
    'Onur_Lopez',
]


def setup_module():
    """Test fixture run once and common to all tests of this module"""
    if imsave is None:
        raise SkipTest("PIL not installed.")

    if not os.path.exists(LFW_HOME):
        os.makedirs(LFW_HOME)

    random_state = random.Random(42)
    np_rng = np.random.RandomState(42)

    # generate some random jpeg files for each person
    counts = {}
    for name in FAKE_NAMES:
        folder_name = os.path.join(LFW_HOME, 'lfw_funneled', name)
        if not os.path.exists(folder_name):
            os.makedirs(folder_name)

        n_faces = np_rng.randint(1, 5)
        counts[name] = n_faces
        for i in range(n_faces):
            file_path = os.path.join(folder_name, name + '_%04d.jpg' % i)
            uniface = np_rng.randint(0, 255, size=(250, 250, 3))
            try:
                imsave(file_path, uniface)
            except ImportError:
                raise SkipTest("PIL not installed")

    # add some random file pollution to test robustness
    with open(os.path.join(LFW_HOME, 'lfw_funneled', '.test.swp'), 'wb') as f:
        f.write(six.b('Text file to be ignored by the dataset loader.'))

    # generate some pairing metadata files using the same format as LFW
    with open(os.path.join(LFW_HOME, 'pairsDevTrain.txt'), 'wb') as f:
        f.write(six.b("10\n"))
        more_than_two = [name for name, count in six.iteritems(counts)
                         if count >= 2]
        for i in range(5):
            name = random_state.choice(more_than_two)
            first, second = random_state.sample(range(counts[name]), 2)
            f.write(six.b('%s\t%d\t%d\n' % (name, first, second)))

        for i in range(5):
            first_name, second_name = random_state.sample(FAKE_NAMES, 2)
            first_index = random_state.choice(np.arange(counts[first_name]))
            second_index = random_state.choice(np.arange(counts[second_name]))
            f.write(six.b('%s\t%d\t%s\t%d\n' % (first_name, first_index,
                                                second_name, second_index)))

    with open(os.path.join(LFW_HOME, 'pairsDevTest.txt'), 'wb') as f:
        f.write(six.b("Fake place holder that won't be tested"))

    with open(os.path.join(LFW_HOME, 'pairs.txt'), 'wb') as f:
        f.write(six.b("Fake place holder that won't be tested"))


def teardown_module():
    """Test fixture (clean up) run once after all tests of this module"""
    if os.path.isdir(SCIKIT_LEARN_DATA):
        shutil.rmtree(SCIKIT_LEARN_DATA)
    if os.path.isdir(SCIKIT_LEARN_EMPTY_DATA):
        shutil.rmtree(SCIKIT_LEARN_EMPTY_DATA)


@raises(IOError)
def test_load_empty_lfw_people():
    load_lfw_people(data_home=SCIKIT_LEARN_EMPTY_DATA)


def test_load_fake_lfw_people():
    lfw_people = load_lfw_people(data_home=SCIKIT_LEARN_DATA,
                                 min_faces_per_person=3)

    # The data is croped around the center as a rectangular bounding box
    # arounthe the face. Colors are converted to gray levels:
    assert_equal(lfw_people.images.shape, (10, 62, 47))
    assert_equal(lfw_people.data.shape, (10, 2914))

    # the target is array of person integer ids
    assert_array_equal(lfw_people.target, [2, 0, 1, 0, 2, 0, 2, 1, 1, 2])

    # names of the persons can be found using the target_names array
    expected_classes = ['Abdelatif Smith', 'Abhati Kepler', 'Onur Lopez']
    assert_array_equal(lfw_people.target_names, expected_classes)

    # It is possible to ask for the original data without any croping or color
    # conversion and not limit on the number of picture per person
    lfw_people = load_lfw_people(data_home=SCIKIT_LEARN_DATA,
                                 resize=None, slice_=None, color=True)
    assert_equal(lfw_people.images.shape, (17, 250, 250, 3))

    # the ids and class names are the same as previously
    assert_array_equal(lfw_people.target,
                       [0, 0, 1, 6, 5, 6, 3, 6, 0, 3, 6, 1, 2, 4, 5, 1, 2])
    assert_array_equal(lfw_people.target_names,
                       ['Abdelatif Smith', 'Abhati Kepler', 'Camara Alvaro',
                        'Chen Dupont', 'John Lee', 'Lin Bauman', 'Onur Lopez'])


@raises(ValueError)
def test_load_fake_lfw_people_too_restrictive():
    load_lfw_people(data_home=SCIKIT_LEARN_DATA, min_faces_per_person=100)


@raises(IOError)
def test_load_empty_lfw_pairs():
    load_lfw_pairs(data_home=SCIKIT_LEARN_EMPTY_DATA)


def test_load_fake_lfw_pairs():
    lfw_pairs_train = load_lfw_pairs(data_home=SCIKIT_LEARN_DATA)

    # The data is croped around the center as a rectangular bounding box
    # arounthe the face. Colors are converted to gray levels:
    assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 62, 47))

    # the target is whether the person is the same or not
    assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])

    # names of the persons can be found using the target_names array
    expected_classes = ['Different persons', 'Same person']
    assert_array_equal(lfw_pairs_train.target_names, expected_classes)

    # It is possible to ask for the original data without any croping or color
    # conversion
    lfw_pairs_train = load_lfw_pairs(data_home=SCIKIT_LEARN_DATA,
                                     resize=None, slice_=None, color=True)
    assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 250, 250, 3))

    # the ids and class names are the same as previously
    assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
    assert_array_equal(lfw_pairs_train.target_names, expected_classes)
