from __future__ import division, absolute_import, print_function

import numpy as np
from numpy.testing import (
    run_module_suite, assert_equal, assert_array_equal,
    assert_raises, assert_
    )
from numpy.lib.stride_tricks import (
    as_strided, broadcast_arrays, _broadcast_shape, broadcast_to
)

def assert_shapes_correct(input_shapes, expected_shape):
    # Broadcast a list of arrays with the given input shapes and check the
    # common output shape.

    inarrays = [np.zeros(s) for s in input_shapes]
    outarrays = broadcast_arrays(*inarrays)
    outshapes = [a.shape for a in outarrays]
    expected = [expected_shape] * len(inarrays)
    assert_equal(outshapes, expected)


def assert_incompatible_shapes_raise(input_shapes):
    # Broadcast a list of arrays with the given (incompatible) input shapes
    # and check that they raise a ValueError.

    inarrays = [np.zeros(s) for s in input_shapes]
    assert_raises(ValueError, broadcast_arrays, *inarrays)


def assert_same_as_ufunc(shape0, shape1, transposed=False, flipped=False):
    # Broadcast two shapes against each other and check that the data layout
    # is the same as if a ufunc did the broadcasting.

    x0 = np.zeros(shape0, dtype=int)
    # Note that multiply.reduce's identity element is 1.0, so when shape1==(),
    # this gives the desired n==1.
    n = int(np.multiply.reduce(shape1))
    x1 = np.arange(n).reshape(shape1)
    if transposed:
        x0 = x0.T
        x1 = x1.T
    if flipped:
        x0 = x0[::-1]
        x1 = x1[::-1]
    # Use the add ufunc to do the broadcasting. Since we're adding 0s to x1, the
    # result should be exactly the same as the broadcasted view of x1.
    y = x0 + x1
    b0, b1 = broadcast_arrays(x0, x1)
    assert_array_equal(y, b1)


def test_same():
    x = np.arange(10)
    y = np.arange(10)
    bx, by = broadcast_arrays(x, y)
    assert_array_equal(x, bx)
    assert_array_equal(y, by)


def test_one_off():
    x = np.array([[1, 2, 3]])
    y = np.array([[1], [2], [3]])
    bx, by = broadcast_arrays(x, y)
    bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
    by0 = bx0.T
    assert_array_equal(bx0, bx)
    assert_array_equal(by0, by)


def test_same_input_shapes():
    # Check that the final shape is just the input shape.

    data = [
        (),
        (1,),
        (3,),
        (0, 1),
        (0, 3),
        (1, 0),
        (3, 0),
        (1, 3),
        (3, 1),
        (3, 3),
    ]
    for shape in data:
        input_shapes = [shape]
        # Single input.
        assert_shapes_correct(input_shapes, shape)
        # Double input.
        input_shapes2 = [shape, shape]
        assert_shapes_correct(input_shapes2, shape)
        # Triple input.
        input_shapes3 = [shape, shape, shape]
        assert_shapes_correct(input_shapes3, shape)


def test_two_compatible_by_ones_input_shapes():
    # Check that two different input shapes of the same length, but some have
    # ones, broadcast to the correct shape.

    data = [
        [[(1,), (3,)], (3,)],
        [[(1, 3), (3, 3)], (3, 3)],
        [[(3, 1), (3, 3)], (3, 3)],
        [[(1, 3), (3, 1)], (3, 3)],
        [[(1, 1), (3, 3)], (3, 3)],
        [[(1, 1), (1, 3)], (1, 3)],
        [[(1, 1), (3, 1)], (3, 1)],
        [[(1, 0), (0, 0)], (0, 0)],
        [[(0, 1), (0, 0)], (0, 0)],
        [[(1, 0), (0, 1)], (0, 0)],
        [[(1, 1), (0, 0)], (0, 0)],
        [[(1, 1), (1, 0)], (1, 0)],
        [[(1, 1), (0, 1)], (0, 1)],
    ]
    for input_shapes, expected_shape in data:
        assert_shapes_correct(input_shapes, expected_shape)
        # Reverse the input shapes since broadcasting should be symmetric.
        assert_shapes_correct(input_shapes[::-1], expected_shape)


def test_two_compatible_by_prepending_ones_input_shapes():
    # Check that two different input shapes (of different lengths) broadcast
    # to the correct shape.

    data = [
        [[(), (3,)], (3,)],
        [[(3,), (3, 3)], (3, 3)],
        [[(3,), (3, 1)], (3, 3)],
        [[(1,), (3, 3)], (3, 3)],
        [[(), (3, 3)], (3, 3)],
        [[(1, 1), (3,)], (1, 3)],
        [[(1,), (3, 1)], (3, 1)],
        [[(1,), (1, 3)], (1, 3)],
        [[(), (1, 3)], (1, 3)],
        [[(), (3, 1)], (3, 1)],
        [[(), (0,)], (0,)],
        [[(0,), (0, 0)], (0, 0)],
        [[(0,), (0, 1)], (0, 0)],
        [[(1,), (0, 0)], (0, 0)],
        [[(), (0, 0)], (0, 0)],
        [[(1, 1), (0,)], (1, 0)],
        [[(1,), (0, 1)], (0, 1)],
        [[(1,), (1, 0)], (1, 0)],
        [[(), (1, 0)], (1, 0)],
        [[(), (0, 1)], (0, 1)],
    ]
    for input_shapes, expected_shape in data:
        assert_shapes_correct(input_shapes, expected_shape)
        # Reverse the input shapes since broadcasting should be symmetric.
        assert_shapes_correct(input_shapes[::-1], expected_shape)


def test_incompatible_shapes_raise_valueerror():
    # Check that a ValueError is raised for incompatible shapes.

    data = [
        [(3,), (4,)],
        [(2, 3), (2,)],
        [(3,), (3,), (4,)],
        [(1, 3, 4), (2, 3, 3)],
    ]
    for input_shapes in data:
        assert_incompatible_shapes_raise(input_shapes)
        # Reverse the input shapes since broadcasting should be symmetric.
        assert_incompatible_shapes_raise(input_shapes[::-1])


def test_same_as_ufunc():
    # Check that the data layout is the same as if a ufunc did the operation.

    data = [
        [[(1,), (3,)], (3,)],
        [[(1, 3), (3, 3)], (3, 3)],
        [[(3, 1), (3, 3)], (3, 3)],
        [[(1, 3), (3, 1)], (3, 3)],
        [[(1, 1), (3, 3)], (3, 3)],
        [[(1, 1), (1, 3)], (1, 3)],
        [[(1, 1), (3, 1)], (3, 1)],
        [[(1, 0), (0, 0)], (0, 0)],
        [[(0, 1), (0, 0)], (0, 0)],
        [[(1, 0), (0, 1)], (0, 0)],
        [[(1, 1), (0, 0)], (0, 0)],
        [[(1, 1), (1, 0)], (1, 0)],
        [[(1, 1), (0, 1)], (0, 1)],
        [[(), (3,)], (3,)],
        [[(3,), (3, 3)], (3, 3)],
        [[(3,), (3, 1)], (3, 3)],
        [[(1,), (3, 3)], (3, 3)],
        [[(), (3, 3)], (3, 3)],
        [[(1, 1), (3,)], (1, 3)],
        [[(1,), (3, 1)], (3, 1)],
        [[(1,), (1, 3)], (1, 3)],
        [[(), (1, 3)], (1, 3)],
        [[(), (3, 1)], (3, 1)],
        [[(), (0,)], (0,)],
        [[(0,), (0, 0)], (0, 0)],
        [[(0,), (0, 1)], (0, 0)],
        [[(1,), (0, 0)], (0, 0)],
        [[(), (0, 0)], (0, 0)],
        [[(1, 1), (0,)], (1, 0)],
        [[(1,), (0, 1)], (0, 1)],
        [[(1,), (1, 0)], (1, 0)],
        [[(), (1, 0)], (1, 0)],
        [[(), (0, 1)], (0, 1)],
    ]
    for input_shapes, expected_shape in data:
        assert_same_as_ufunc(input_shapes[0], input_shapes[1],
                             "Shapes: %s %s" % (input_shapes[0], input_shapes[1]))
        # Reverse the input shapes since broadcasting should be symmetric.
        assert_same_as_ufunc(input_shapes[1], input_shapes[0])
        # Try them transposed, too.
        assert_same_as_ufunc(input_shapes[0], input_shapes[1], True)
        # ... and flipped for non-rank-0 inputs in order to test negative
        # strides.
        if () not in input_shapes:
            assert_same_as_ufunc(input_shapes[0], input_shapes[1], False, True)
            assert_same_as_ufunc(input_shapes[0], input_shapes[1], True, True)


def test_broadcast_to_succeeds():
    data = [
        [np.array(0), (0,), np.array(0)],
        [np.array(0), (1,), np.zeros(1)],
        [np.array(0), (3,), np.zeros(3)],
        [np.ones(1), (1,), np.ones(1)],
        [np.ones(1), (2,), np.ones(2)],
        [np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
        [np.arange(3), (3,), np.arange(3)],
        [np.arange(3), (1, 3), np.arange(3).reshape(1, -1)],
        [np.arange(3), (2, 3), np.array([[0, 1, 2], [0, 1, 2]])],
        # test if shape is not a tuple
        [np.ones(0), 0, np.ones(0)],
        [np.ones(1), 1, np.ones(1)],
        [np.ones(1), 2, np.ones(2)],
        # these cases with size 0 are strange, but they reproduce the behavior
        # of broadcasting with ufuncs (see test_same_as_ufunc above)
        [np.ones(1), (0,), np.ones(0)],
        [np.ones((1, 2)), (0, 2), np.ones((0, 2))],
        [np.ones((2, 1)), (2, 0), np.ones((2, 0))],
    ]
    for input_array, shape, expected in data:
        actual = broadcast_to(input_array, shape)
        assert_array_equal(expected, actual)


def test_broadcast_to_raises():
    data = [
        [(0,), ()],
        [(1,), ()],
        [(3,), ()],
        [(3,), (1,)],
        [(3,), (2,)],
        [(3,), (4,)],
        [(1, 2), (2, 1)],
        [(1, 1), (1,)],
        [(1,), -1],
        [(1,), (-1,)],
        [(1, 2), (-1, 2)],
    ]
    for orig_shape, target_shape in data:
        arr = np.zeros(orig_shape)
        assert_raises(ValueError, lambda: broadcast_to(arr, target_shape))


def test_broadcast_shape():
    # broadcast_shape is already exercized indirectly by broadcast_arrays
    assert_raises(ValueError, _broadcast_shape)
    assert_equal(_broadcast_shape([1, 2]), (2,))
    assert_equal(_broadcast_shape(np.ones((1, 1))), (1, 1))
    assert_equal(_broadcast_shape(np.ones((1, 1)), np.ones((3, 4))), (3, 4))
    assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 32)), (1, 2))
    assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 100)), (1, 2))

    # regression tests for gh-5862
    assert_equal(_broadcast_shape(*([np.ones(2)] * 32 + [1])), (2,))
    bad_args = [np.ones(2)] * 32 + [np.ones(3)] * 32
    assert_raises(ValueError, lambda: _broadcast_shape(*bad_args))


def test_as_strided():
    a = np.array([None])
    a_view = as_strided(a)
    expected = np.array([None])
    assert_array_equal(a_view, np.array([None]))

    a = np.array([1, 2, 3, 4])
    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
    expected = np.array([1, 3])
    assert_array_equal(a_view, expected)

    a = np.array([1, 2, 3, 4])
    a_view = as_strided(a, shape=(3, 4), strides=(0, 1 * a.itemsize))
    expected = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
    assert_array_equal(a_view, expected)

    # Regression test for gh-5081
    dt = np.dtype([('num', 'i4'), ('obj', 'O')])
    a = np.empty((4,), dtype=dt)
    a['num'] = np.arange(1, 5)
    a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
    expected_num = [[1, 2, 3, 4]] * 3
    expected_obj = [[None]*4]*3
    assert_equal(a_view.dtype, dt)
    assert_array_equal(expected_num, a_view['num'])
    assert_array_equal(expected_obj, a_view['obj'])

    # Make sure that void types without fields are kept unchanged
    a = np.empty((4,), dtype='V4')
    a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
    assert_equal(a.dtype, a_view.dtype)

    # Make sure that the only type that could fail is properly handled
    dt = np.dtype({'names': [''], 'formats': ['V4']})
    a = np.empty((4,), dtype=dt)
    a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
    assert_equal(a.dtype, a_view.dtype)


class VerySimpleSubClass(np.ndarray):
    def __new__(cls, *args, **kwargs):
        kwargs['subok'] = True
        return np.array(*args, **kwargs).view(cls)


class SimpleSubClass(VerySimpleSubClass):
    def __new__(cls, *args, **kwargs):
        kwargs['subok'] = True
        self = np.array(*args, **kwargs).view(cls)
        self.info = 'simple'
        return self

    def __array_finalize__(self, obj):
        self.info = getattr(obj, 'info', '') + ' finalized'


def test_subclasses():
    # test that subclass is preserved only if subok=True
    a = VerySimpleSubClass([1, 2, 3, 4])
    assert_(type(a) is VerySimpleSubClass)
    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
    assert_(type(a_view) is np.ndarray)
    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
    assert_(type(a_view) is VerySimpleSubClass)
    # test that if a subclass has __array_finalize__, it is used
    a = SimpleSubClass([1, 2, 3, 4])
    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
    assert_(type(a_view) is SimpleSubClass)
    assert_(a_view.info == 'simple finalized')

    # similar tests for broadcast_arrays
    b = np.arange(len(a)).reshape(-1, 1)
    a_view, b_view = broadcast_arrays(a, b)
    assert_(type(a_view) is np.ndarray)
    assert_(type(b_view) is np.ndarray)
    assert_(a_view.shape == b_view.shape)
    a_view, b_view = broadcast_arrays(a, b, subok=True)
    assert_(type(a_view) is SimpleSubClass)
    assert_(a_view.info == 'simple finalized')
    assert_(type(b_view) is np.ndarray)
    assert_(a_view.shape == b_view.shape)

    # and for broadcast_to
    shape = (2, 4)
    a_view = broadcast_to(a, shape)
    assert_(type(a_view) is np.ndarray)
    assert_(a_view.shape == shape)
    a_view = broadcast_to(a, shape, subok=True)
    assert_(type(a_view) is SimpleSubClass)
    assert_(a_view.info == 'simple finalized')
    assert_(a_view.shape == shape)


def test_writeable():
    # broadcast_to should return a readonly array
    original = np.array([1, 2, 3])
    result = broadcast_to(original, (2, 3))
    assert_equal(result.flags.writeable, False)
    assert_raises(ValueError, result.__setitem__, slice(None), 0)

    # but the result of broadcast_arrays needs to be writeable (for now), to
    # preserve backwards compatibility
    for results in [broadcast_arrays(original),
                    broadcast_arrays(0, original)]:
        for result in results:
            assert_equal(result.flags.writeable, True)
    # keep readonly input readonly
    original.flags.writeable = False
    _, result = broadcast_arrays(0, original)
    assert_equal(result.flags.writeable, False)


def test_reference_types():
    input_array = np.array('a', dtype=object)
    expected = np.array(['a'] * 3, dtype=object)
    actual = broadcast_to(input_array, (3,))
    assert_array_equal(expected, actual)

    actual, _ = broadcast_arrays(input_array, np.ones(3))
    assert_array_equal(expected, actual)


if __name__ == "__main__":
    run_module_suite()
