"""
Test the hashing module.
"""

# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Copyright (c) 2009 Gael Varoquaux
# License: BSD Style, 3 clauses.

import time
import hashlib
import sys
import gc
import io
import collections
import itertools
import pickle
import random
from decimal import Decimal

from joblib.hashing import hash
from joblib.func_inspect import filter_args
from joblib.memory import Memory
from joblib.testing import raises, skipif, fixture, parametrize
from joblib.test.common import np, with_numpy
from joblib.my_exceptions import TransportableException
from joblib._compat import PY3_OR_LATER


try:
    # Python 2/Python 3 compat
    unicode('str')
except NameError:
    unicode = lambda s: s


###############################################################################
# Helper functions for the tests
def time_func(func, *args):
    """ Time function func on *args.
    """
    times = list()
    for _ in range(3):
        t1 = time.time()
        func(*args)
        times.append(time.time() - t1)
    return min(times)


def relative_time(func1, func2, *args):
    """ Return the relative time between func1 and func2 applied on
        *args.
    """
    time_func1 = time_func(func1, *args)
    time_func2 = time_func(func2, *args)
    relative_diff = 0.5 * (abs(time_func1 - time_func2)
                           / (time_func1 + time_func2))
    return relative_diff


class Klass(object):

    def f(self, x):
        return x


class KlassWithCachedMethod(object):

    def __init__(self, cachedir):
        mem = Memory(cachedir=cachedir)
        self.f = mem.cache(self.f)

    def f(self, x):
        return x


###############################################################################
# Tests

input_list = [1, 2, 1., 2., 1 + 1j, 2. + 1j,
              'a', 'b',
              (1,), (1, 1,), [1, ], [1, 1, ],
              {1: 1}, {1: 2}, {2: 1},
              None,
              gc.collect,
              [1, ].append,
              # Next 2 sets have unorderable elements in python 3.
              set(('a', 1)),
              set(('a', 1, ('a', 1))),
              # Next 2 dicts have unorderable type of keys in python 3.
              {'a': 1, 1: 2},
              {'a': 1, 1: 2, 'd': {'a': 1}}]


@parametrize('obj1', input_list)
@parametrize('obj2', input_list)
def test_trivial_hash(obj1, obj2):
    """Smoke test hash on various types."""
    # Check that 2 objects have the same hash only if they are the same.
    are_hashes_equal = hash(obj1) == hash(obj2)
    are_objs_identical = obj1 is obj2
    assert are_hashes_equal == are_objs_identical


def test_hash_methods():
    # Check that hashing instance methods works
    a = io.StringIO(unicode('a'))
    assert hash(a.flush) == hash(a.flush)
    a1 = collections.deque(range(10))
    a2 = collections.deque(range(9))
    assert hash(a1.extend) != hash(a2.extend)


@fixture(scope='function')
@with_numpy
def three_np_arrays():
    rnd = np.random.RandomState(0)
    arr1 = rnd.random_sample((10, 10))
    arr2 = arr1.copy()
    arr3 = arr2.copy()
    arr3[0] += 1
    return arr1, arr2, arr3


def test_hash_numpy_arrays(three_np_arrays):
    arr1, arr2, arr3 = three_np_arrays

    for obj1, obj2 in itertools.product(three_np_arrays, repeat=2):
        are_hashes_equal = hash(obj1) == hash(obj2)
        are_arrays_equal = np.all(obj1 == obj2)
        assert are_hashes_equal == are_arrays_equal

    assert hash(arr1) != hash(arr1.T)


def test_hash_numpy_dict_of_arrays(three_np_arrays):
    arr1, arr2, arr3 = three_np_arrays

    d1 = {1: arr1, 2: arr2}
    d2 = {1: arr2, 2: arr1}
    d3 = {1: arr2, 2: arr3}

    assert hash(d1) == hash(d2)
    assert hash(d1) != hash(d3)


@with_numpy
@parametrize('dtype', ['datetime64[s]', 'timedelta64[D]'])
def test_numpy_datetime_array(dtype):
    # memoryview is not supported for some dtypes e.g. datetime64
    # see https://github.com/joblib/joblib/issues/188 for more details
    a_hash = hash(np.arange(10))
    array = np.arange(0, 10, dtype=dtype)
    assert hash(array) != a_hash


@with_numpy
def test_hash_numpy_noncontiguous():
    a = np.asarray(np.arange(6000).reshape((1000, 2, 3)),
                   order='F')[:, :1, :]
    b = np.ascontiguousarray(a)
    assert hash(a) != hash(b)

    c = np.asfortranarray(a)
    assert hash(a) != hash(c)


@with_numpy
@parametrize('coerce_mmap', [True, False])
def test_hash_memmap(tmpdir, coerce_mmap):
    """Check that memmap and arrays hash identically if coerce_mmap is True."""
    filename = tmpdir.join('memmap_temp').strpath
    try:
        m = np.memmap(filename, shape=(10, 10), mode='w+')
        a = np.asarray(m)
        are_hashes_equal = (hash(a, coerce_mmap=coerce_mmap) ==
                            hash(m, coerce_mmap=coerce_mmap))
        assert are_hashes_equal == coerce_mmap
    finally:
        if 'm' in locals():
            del m
            # Force a garbage-collection cycle, to be certain that the
            # object is delete, and we don't run in a problem under
            # Windows with a file handle still open.
            gc.collect()


@with_numpy
@skipif(sys.platform == 'win32', reason='This test is not stable under windows'
                                        ' for some reason')
def test_hash_numpy_performance():
    """ Check the performance of hashing numpy arrays:

        In [22]: a = np.random.random(1000000)

        In [23]: %timeit hashlib.md5(a).hexdigest()
        100 loops, best of 3: 20.7 ms per loop

        In [24]: %timeit hashlib.md5(pickle.dumps(a, protocol=2)).hexdigest()
        1 loops, best of 3: 73.1 ms per loop

        In [25]: %timeit hashlib.md5(cPickle.dumps(a, protocol=2)).hexdigest()
        10 loops, best of 3: 53.9 ms per loop

        In [26]: %timeit hash(a)
        100 loops, best of 3: 20.8 ms per loop
    """
    rnd = np.random.RandomState(0)
    a = rnd.random_sample(1000000)
    if hasattr(np, 'getbuffer'):
        # Under python 3, there is no getbuffer
        getbuffer = np.getbuffer
    else:
        getbuffer = memoryview
    md5_hash = lambda x: hashlib.md5(getbuffer(x)).hexdigest()

    relative_diff = relative_time(md5_hash, hash, a)
    assert relative_diff < 0.3

    # Check that hashing an tuple of 3 arrays takes approximately
    # 3 times as much as hashing one array
    time_hashlib = 3 * time_func(md5_hash, a)
    time_hash = time_func(hash, (a, a, a))
    relative_diff = 0.5 * (abs(time_hash - time_hashlib)
                           / (time_hash + time_hashlib))
    assert relative_diff < 0.3


def test_bound_methods_hash():
    """ Make sure that calling the same method on two different instances
    of the same class does resolve to the same hashes.
    """
    a = Klass()
    b = Klass()
    assert (hash(filter_args(a.f, [], (1, ))) ==
            hash(filter_args(b.f, [], (1, ))))


def test_bound_cached_methods_hash(tmpdir):
    """ Make sure that calling the same _cached_ method on two different
    instances of the same class does resolve to the same hashes.
    """
    a = KlassWithCachedMethod(tmpdir.strpath)
    b = KlassWithCachedMethod(tmpdir.strpath)
    assert (hash(filter_args(a.f.func, [], (1, ))) ==
            hash(filter_args(b.f.func, [], (1, ))))


@with_numpy
def test_hash_object_dtype():
    """ Make sure that ndarrays with dtype `object' hash correctly."""

    a = np.array([np.arange(i) for i in range(6)], dtype=object)
    b = np.array([np.arange(i) for i in range(6)], dtype=object)

    assert hash(a) == hash(b)


@with_numpy
def test_numpy_scalar():
    # Numpy scalars are built from compiled functions, and lead to
    # strange pickling paths explored, that can give hash collisions
    a = np.float64(2.0)
    b = np.float64(3.0)
    assert hash(a) != hash(b)


def test_dict_hash(tmpdir):
    # Check that dictionaries hash consistently, eventhough the ordering
    # of the keys is not garanteed
    k = KlassWithCachedMethod(tmpdir.strpath)

    d = {'#s12069__c_maps.nii.gz': [33],
         '#s12158__c_maps.nii.gz': [33],
         '#s12258__c_maps.nii.gz': [33],
         '#s12277__c_maps.nii.gz': [33],
         '#s12300__c_maps.nii.gz': [33],
         '#s12401__c_maps.nii.gz': [33],
         '#s12430__c_maps.nii.gz': [33],
         '#s13817__c_maps.nii.gz': [33],
         '#s13903__c_maps.nii.gz': [33],
         '#s13916__c_maps.nii.gz': [33],
         '#s13981__c_maps.nii.gz': [33],
         '#s13982__c_maps.nii.gz': [33],
         '#s13983__c_maps.nii.gz': [33]}

    a = k.f(d)
    b = k.f(a)

    assert hash(a) == hash(b)


def test_set_hash(tmpdir):
    # Check that sets hash consistently, even though their ordering
    # is not guaranteed
    k = KlassWithCachedMethod(tmpdir.strpath)

    s = set(['#s12069__c_maps.nii.gz',
             '#s12158__c_maps.nii.gz',
             '#s12258__c_maps.nii.gz',
             '#s12277__c_maps.nii.gz',
             '#s12300__c_maps.nii.gz',
             '#s12401__c_maps.nii.gz',
             '#s12430__c_maps.nii.gz',
             '#s13817__c_maps.nii.gz',
             '#s13903__c_maps.nii.gz',
             '#s13916__c_maps.nii.gz',
             '#s13981__c_maps.nii.gz',
             '#s13982__c_maps.nii.gz',
             '#s13983__c_maps.nii.gz'])

    a = k.f(s)
    b = k.f(a)

    assert hash(a) == hash(b)


def test_set_decimal_hash():
    # Check that sets containing decimals hash consistently, even though
    # ordering is not guaranteed
    assert (hash(set([Decimal(0), Decimal('NaN')])) ==
            hash(set([Decimal('NaN'), Decimal(0)])))


def test_string():
    # Test that we obtain the same hash for object owning several strings,
    # whatever the past of these strings (which are immutable in Python)
    string = 'foo'
    a = {string: 'bar'}
    b = {string: 'bar'}
    c = pickle.loads(pickle.dumps(b))
    assert hash([a, b]) == hash([a, c])


@with_numpy
def test_dtype():
    # Test that we obtain the same hash for object owning several dtype,
    # whatever the past of these dtypes. Catter for cache invalidation with
    # complex dtype
    a = np.dtype([('f1', np.uint), ('f2', np.int32)])
    b = a
    c = pickle.loads(pickle.dumps(a))
    assert hash([a, c]) == hash([a, b])


@parametrize('to_hash,expected',
             [('This is a string to hash',
                 {'py2': '80436ada343b0d79a99bfd8883a96e45',
                  'py3': '71b3f47df22cb19431d85d92d0b230b2'}),
              (u"C'est l\xe9t\xe9",
                 {'py2': '2ff3a25200eb6219f468de2640913c2d',
                  'py3': '2d8d189e9b2b0b2e384d93c868c0e576'}),
              ((123456, 54321, -98765),
                 {'py2': '50d81c80af05061ac4dcdc2d5edee6d6',
                  'py3': 'e205227dd82250871fa25aa0ec690aa3'}),
              ([random.Random(42).random() for _ in range(5)],
                 {'py2': '1a36a691b2e2ba3a9df72de3dccf17ea',
                  'py3': 'a11ffad81f9682a7d901e6edc3d16c84'}),
              ([3, 'abc', None, TransportableException('foo', ValueError)],
                 {'py2': 'adb6ba84990ee5e462dc138383f11802',
                  'py3': '994f663c64ba5e64b2a85ebe75287829'}),
              ({'abcde': 123, 'sadfas': [-9999, 2, 3]},
                 {'py2': 'fc9314a39ff75b829498380850447047',
                  'py3': 'aeda150553d4bb5c69f0e69d51b0e2ef'})])
def test_hashes_stay_the_same(to_hash, expected):
    # We want to make sure that hashes don't change with joblib
    # version. For end users, that would mean that they have to
    # regenerate their cache from scratch, which potentially means
    # lengthy recomputations.
    # Expected results have been generated with joblib 0.9.2

    py_version_str = 'py3' if PY3_OR_LATER else 'py2'
    assert hash(to_hash) == expected[py_version_str]


@with_numpy
def test_hashes_are_different_between_c_and_fortran_contiguous_arrays():
    # We want to be sure that the c-contiguous and f-contiguous versions of the
    # same array produce 2 different hashes.
    rng = np.random.RandomState(0)
    arr_c = rng.random_sample((10, 10))
    arr_f = np.asfortranarray(arr_c)
    assert hash(arr_c) != hash(arr_f)


@with_numpy
def test_0d_array():
    hash(np.array(0))


@with_numpy
def test_0d_and_1d_array_hashing_is_different():
    assert hash(np.array(0)) != hash(np.array([0]))


@with_numpy
def test_hashes_stay_the_same_with_numpy_objects():
    # We want to make sure that hashes don't change with joblib
    # version. For end users, that would mean that they have to
    # regenerate their cache from scratch, which potentially means
    # lengthy recomputations.
    rng = np.random.RandomState(42)
    # Being explicit about dtypes in order to avoid
    # architecture-related differences. Also using 'f4' rather than
    # 'f8' for float arrays because 'f8' arrays generated by
    # rng.random.randn don't seem to be bit-identical on 32bit and
    # 64bit machines.
    to_hash_list = [
        rng.randint(-1000, high=1000, size=50).astype('<i8'),
        tuple(rng.randn(3).astype('<f4') for _ in range(5)),
        [rng.randn(3).astype('<f4') for _ in range(5)],
        {
            -3333: rng.randn(3, 5).astype('<f4'),
            0: [
                rng.randint(10, size=20).astype('<i8'),
                rng.randn(10).astype('<f4')
            ]
        },
        # Non regression cases for https://github.com/joblib/joblib/issues/308.
        # Generated with joblib 0.9.4.
        np.arange(100, dtype='<i8').reshape((10, 10)),
        # Fortran contiguous array
        np.asfortranarray(np.arange(100, dtype='<i8').reshape((10, 10))),
        # Non contiguous array
        np.arange(100, dtype='<i8').reshape((10, 10))[:, :2],
    ]

    # These expected results have been generated with joblib 0.9.0
    expected_dict = {'py2': ['80f2387e7752abbda2658aafed49e086',
                             '0d700f7f25ea670fd305e4cd93b0e8cd',
                             '83a2bdf843e79e4b3e26521db73088b9',
                             '63e0efd43c0a9ad92a07e8ce04338dd3',
                             '03fef702946b602c852b8b4e60929914',
                             '07074691e90d7098a85956367045c81e',
                             'd264cf79f353aa7bbfa8349e3df72d8f'],
                     'py3': ['10a6afc379ca2708acfbaef0ab676eab',
                             '988a7114f337f381393025911ebc823b',
                             'c6809f4b97e35f2fa0ee8d653cbd025c',
                             'b3ad17348e32728a7eb9cda1e7ede438',
                             '927b3e6b0b6a037e8e035bda134e0b05',
                             '108f6ee98e7db19ea2006ffd208f4bf1',
                             'bd48ccaaff28e16e6badee81041b7180']}

    py_version_str = 'py3' if PY3_OR_LATER else 'py2'
    expected_list = expected_dict[py_version_str]

    for to_hash, expected in zip(to_hash_list, expected_list):
        assert hash(to_hash) == expected


def test_hashing_pickling_error():
    def non_picklable():
        return 42

    with raises(pickle.PicklingError) as excinfo:
        hash(non_picklable)
    excinfo.match('PicklingError while hashing')


def test_wrong_hash_name():
    msg = "Valid options for 'hash_name' are"
    with raises(ValueError, match=msg):
        data = {'foo': 'bar'}
        hash(data, hash_name='invalid')