import warnings
import unittest
import sys

from nose.tools import assert_raises

from sklearn.utils.testing import (
    _assert_less,
    _assert_greater,
    assert_less_equal,
    assert_greater_equal,
    assert_warns,
    assert_no_warnings,
    assert_equal,
    set_random_state,
    assert_raise_message)

from sklearn.tree import DecisionTreeClassifier
from sklearn.lda import LDA

try:
    from nose.tools import assert_less

    def test_assert_less():
        # Check that the nose implementation of assert_less gives the
        # same thing as the scikit's
        assert_less(0, 1)
        _assert_less(0, 1)
        assert_raises(AssertionError, assert_less, 1, 0)
        assert_raises(AssertionError, _assert_less, 1, 0)

except ImportError:
    pass

try:
    from nose.tools import assert_greater

    def test_assert_greater():
        # Check that the nose implementation of assert_less gives the
        # same thing as the scikit's
        assert_greater(1, 0)
        _assert_greater(1, 0)
        assert_raises(AssertionError, assert_greater, 0, 1)
        assert_raises(AssertionError, _assert_greater, 0, 1)

except ImportError:
    pass


def test_assert_less_equal():
    assert_less_equal(0, 1)
    assert_less_equal(1, 1)
    assert_raises(AssertionError, assert_less_equal, 1, 0)


def test_assert_greater_equal():
    assert_greater_equal(1, 0)
    assert_greater_equal(1, 1)
    assert_raises(AssertionError, assert_greater_equal, 0, 1)


def test_set_random_state():
    lda = LDA()
    tree = DecisionTreeClassifier()
    # LDA doesn't have random state: smoke test
    set_random_state(lda, 3)
    set_random_state(tree, 3)
    assert_equal(tree.random_state, 3)


def test_assert_raise_message():
    def _raise_ValueError(message):
        raise ValueError(message)

    assert_raise_message(ValueError, "test",
                         _raise_ValueError, "test")

    assert_raises(AssertionError,
                  assert_raise_message, ValueError, "something else",
                  _raise_ValueError, "test")

    assert_raises(ValueError,
                  assert_raise_message, TypeError, "something else",
                  _raise_ValueError, "test")


# This class is inspired from numpy 1.7 with an alteration to check
# the reset warning filters after calls to assert_warns.
# This assert_warns behavior is specific to scikit-learn because
#`clean_warning_registry()` is called internally by assert_warns
# and clears all previous filters.
class TestWarns(unittest.TestCase):
    def test_warn(self):
        def f():
            warnings.warn("yo")
            return 3

        # Test that assert_warns is not impacted by externally set
        # filters and is reset internally.
        # This is because `clean_warning_registry()` is called internally by
        # assert_warns and clears all previous filters.
        warnings.simplefilter("ignore", UserWarning)
        assert_equal(assert_warns(UserWarning, f), 3)

        # Test that the warning registry is empty after assert_warns
        assert_equal(sys.modules['warnings'].filters, [])

        assert_raises(AssertionError, assert_no_warnings, f)
        assert_equal(assert_no_warnings(lambda x: x, 1), 1)

    def test_warn_wrong_warning(self):
        def f():
            warnings.warn("yo", DeprecationWarning)

        failed = False
        filters = sys.modules['warnings'].filters[:]
        try:
            try:
                # Should raise an AssertionError
                assert_warns(UserWarning, f)
                failed = True
            except AssertionError:
                pass
        finally:
            sys.modules['warnings'].filters = filters

        if failed:
            raise AssertionError("wrong warning caught by assert_warn")
