from nose.plugins.skip import SkipTest
import numpy

import theano
from theano import shared, function
from theano.gof.python25 import any
import theano.tensor as T
from neighbours import images2neibs, neibs2images, Images2Neibs

from theano.tests import unittest_tools

if theano.config.mode == 'FAST_COMPILE':
    mode_without_gpu = theano.compile.mode.get_mode(
        'FAST_RUN').excluding('gpu')
else:
    mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')

if not theano.config.cxx:
    raise SkipTest("G++ not available, so we need to skip this test.")


class T_Images2Neibs(unittest_tools.InferShapeTester):
    mode = mode_without_gpu
    op = Images2Neibs
    dtypes = ['int64', 'float32', 'float64']

    def test_neibs(self):
        for shape, pshape in [((100, 40, 18, 18), (2, 2)),
                              ((100, 40, 6, 18), (3, 2)),
                              ((10, 40, 66, 66), (33, 33)),
                              ((10, 40, 68, 66), (34, 33))
                                  ]:
            for border in ['valid', 'ignore_borders']:
                for dtype in self.dtypes:
                    images = shared(
                            numpy.arange(numpy.prod(shape), dtype=dtype
                            ).reshape(shape))
                    neib_shape = T.as_tensor_variable(pshape)

                    f = function([],
                                 images2neibs(images, neib_shape, mode=border),
                                 mode=self.mode)

                    #print images.get_value(borrow=True)
                    neibs = f()
                    #print neibs
                    g = function([],
                                 neibs2images(neibs, neib_shape, images.shape),
                                 mode=self.mode)
                    if border in ['valid']:
                        assert any([isinstance(node.op, self.op)
                                    for node in f.maker.fgraph.toposort()])

                    #print g()
                    assert numpy.allclose(images.get_value(borrow=True), g())

    def test_neibs_manual(self):
        shape = (2, 3, 4, 4)
        for dtype in self.dtypes:
            images = shared(
                    numpy.arange(numpy.prod(shape), dtype=dtype
                    ).reshape(shape))
            neib_shape = T.as_tensor_variable((2, 2))

            for border in ['valid', 'ignore_borders']:
                f = function([], images2neibs(images, neib_shape, mode=border),
                             mode=self.mode)

                #print images.get_value(borrow=True)
                neibs = f()
                #print neibs
                assert numpy.allclose(neibs,[[ 0,  1,  4,  5],
                   [ 2,  3,  6,  7],
                   [ 8,  9, 12, 13],
                   [10, 11, 14, 15],
                   [16, 17, 20, 21],
                   [18, 19, 22, 23],
                   [24, 25, 28, 29],
                   [26, 27, 30, 31],
                   [32, 33, 36, 37],
                   [34, 35, 38, 39],
                   [40, 41, 44, 45],
                   [42, 43, 46, 47],
                   [48, 49, 52, 53],
                   [50, 51, 54, 55],
                   [56, 57, 60, 61],
                   [58, 59, 62, 63],
                   [64, 65, 68, 69],
                   [66, 67, 70, 71],
                   [72, 73, 76, 77],
                   [74, 75, 78, 79],
                   [80, 81, 84, 85],
                   [82, 83, 86, 87],
                   [88, 89, 92, 93],
                   [90, 91, 94, 95]])
                g = function([], neibs2images(neibs, neib_shape, images.shape),
                             mode=self.mode)

                assert numpy.allclose(images.get_value(borrow=True), g())

    def test_neibs_manual_step(self):
        shape = (2, 3, 5, 5)
        for dtype in self.dtypes:
            images = shared(numpy.asarray(numpy.arange(numpy.prod(
                            shape)).reshape(shape), dtype=dtype))
            neib_shape = T.as_tensor_variable((3, 3))
            neib_step = T.as_tensor_variable((2, 2))
            for border in ['valid', 'ignore_borders']:
                f = function([],
                             images2neibs(images, neib_shape, neib_step,
                                          mode=border),
                             mode=self.mode)

                neibs = f()
                if border in ['valid']:
                    assert self.op in [type(node.op)
                                       for node in f.maker.fgraph.toposort()]

                assert numpy.allclose(neibs,
              [[  0,   1,   2,   5,   6,   7,  10,  11,  12],
               [  2,   3,   4,   7,   8,   9,  12,  13,  14],
               [ 10,  11,  12,  15,  16,  17,  20,  21,  22],
               [ 12,  13,  14,  17,  18,  19,  22,  23,  24],
               [ 25,  26,  27,  30,  31,  32,  35,  36,  37],
               [ 27,  28,  29,  32,  33,  34,  37,  38,  39],
               [ 35,  36,  37,  40,  41,  42,  45,  46,  47],
               [ 37,  38,  39,  42,  43,  44,  47,  48,  49],
               [ 50,  51,  52,  55,  56,  57,  60,  61,  62],
               [ 52,  53,  54,  57,  58,  59,  62,  63,  64],
               [ 60,  61,  62,  65,  66,  67,  70,  71,  72],
               [ 62,  63,  64,  67,  68,  69,  72,  73,  74],
               [ 75,  76,  77,  80,  81,  82,  85,  86,  87],
               [ 77,  78,  79,  82,  83,  84,  87,  88,  89],
               [ 85,  86,  87,  90,  91,  92,  95,  96,  97],
               [ 87,  88,  89,  92,  93,  94,  97,  98,  99],
               [100, 101, 102, 105, 106, 107, 110, 111, 112],
               [102, 103, 104, 107, 108, 109, 112, 113, 114],
               [110, 111, 112, 115, 116, 117, 120, 121, 122],
               [112, 113, 114, 117, 118, 119, 122, 123, 124],
               [125, 126, 127, 130, 131, 132, 135, 136, 137],
               [127, 128, 129, 132, 133, 134, 137, 138, 139],
               [135, 136, 137, 140, 141, 142, 145, 146, 147],
               [137, 138, 139, 142, 143, 144, 147, 148, 149]])

                #neibs2images do not seam to support step != neib_shape
                #g = function([], neibs2images(neibs, neib_shape, images.shape),
                #             mode=self.mode)

                #print g()
                #assert numpy.allclose(images.get_value(borrow=True), g())

    def test_neibs_bad_shape(self):
        shape = (2, 3, 10, 10)
        for dtype in self.dtypes:
            images = shared(numpy.arange(
                numpy.prod(shape), dtype=dtype
                ).reshape(shape))

            for neib_shape in [(3, 2), (2, 3)]:
                neib_shape = T.as_tensor_variable(neib_shape)
                f = function([], images2neibs(images, neib_shape),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

                #Test that ignore border work in that case.
                f = function([],
                             images2neibs(images, neib_shape,
                                          mode='ignore_borders'),
                             mode=self.mode)
                f()

    def test_neibs_wrap_centered_step_manual(self):

        expected1 = [[24, 20, 21,  4,  0,  1,  9,  5,  6],
                     [21, 22, 23,  1,  2,  3,  6,  7,  8],
                     [23, 24, 20,  3,  4,  0,  8,  9,  5],
                     [ 9,  5,  6, 14, 10, 11, 19, 15, 16],
                     [ 6,  7,  8, 11, 12, 13, 16, 17, 18],
                     [ 8,  9,  5, 13, 14, 10, 18, 19, 15],
                     [19, 15, 16, 24, 20, 21,  4,  0,  1],
                     [16, 17, 18, 21, 22, 23,  1,  2,  3],
                     [18, 19, 15, 23, 24, 20,  3,  4,  0]]
        expected2 = [[ 24,  20,  21,   4,   0,   1,   9,   5,   6],
                     [ 22,  23,  24,   2,   3,   4,   7,   8,   9],
                     [ 14,  10,  11,  19,  15,  16,  24,  20,  21],
                     [ 12,  13,  14,  17,  18,  19,  22,  23,  24]]
        expected3 = [[19, 15, 16, 24, 20, 21,  4,  0,  1,  9,  5,  6, 14, 10, 11],
                     [17, 18, 19, 22, 23, 24,  2,  3,  4,  7,  8,  9, 12, 13, 14],
                     [ 9,  5,  6, 14, 10, 11, 19, 15, 16, 24, 20, 21,  4,  0,  1],
                     [ 7,  8,  9, 12, 13, 14, 17, 18, 19, 22, 23, 24,  2,  3,  4]]
        expected4 = [[23, 24, 20, 21, 22,  3,  4,  0,  1,  2,  8,  9,  5,  6,  7],
                     [21, 22, 23, 24, 20,  1,  2,  3,  4,  0,  6,  7,  8,  9,  5],
                     [13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
                     [11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20]]
        expected5 = [[24, 20, 21,  4,  0,  1,  9,  5,  6],
                     [22, 23, 24,  2,  3,  4,  7,  8,  9],
                     [ 9,  5,  6, 14, 10, 11, 19, 15, 16],
                     [ 7,  8,  9, 12, 13, 14, 17, 18, 19],
                     [19, 15, 16, 24, 20, 21,  4,  0,  1],
                     [17, 18, 19, 22, 23, 24,  2,  3,  4]]
        expected6 = [[24, 20, 21,  4,  0,  1,  9,  5,  6],
                     [21, 22, 23,  1,  2,  3,  6,  7,  8],
                     [23, 24, 20,  3,  4,  0,  8,  9,  5],
                     [14, 10, 11, 19, 15, 16, 24, 20, 21],
                     [11, 12, 13, 16, 17, 18, 21, 22, 23],
                     [13, 14, 10, 18, 19, 15, 23, 24, 20]]

        #TODO test discontinous image

        for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate([
            [(7, 8, 5, 5), (3, 3), (2, 2), expected1],
            [(7, 8, 5, 5), (3, 3), (3, 3), expected2],
            [(7, 8, 5, 5), (5, 3), (3, 3), expected3],
            [(7, 8, 5, 5), (3, 5), (3, 3), expected4],
            [(80, 90, 5, 5), (3, 3), (2, 3), expected5],
            [(1025, 9, 5, 5), (3, 3), (3, 2), expected6],
            [(1, 1, 5, 1035), (3, 3), (3, 3), None],
            [(1, 1, 1045, 5), (3, 3), (3, 3), None],
            ]):

            for dtype in self.dtypes:

                images = shared(numpy.asarray(numpy.arange(numpy.prod(
                                shape)).reshape(shape), dtype=dtype))
                neib_shape = T.as_tensor_variable(neib_shape)
                neib_step = T.as_tensor_variable(neib_step)
                expected = numpy.asarray(expected)

                f = function([], images2neibs(images, neib_shape, neib_step,
                                              mode="wrap_centered"),
                             mode=self.mode)
                neibs = f()

                if expected.size > 1:
                    for i in range(shape[0] * shape[1]):
                        assert numpy.allclose(
                                neibs[i * expected.shape[0]:
                                      (i + 1) * expected.shape[0], :],
                                expected + 25 * i), "wrap_centered"

                assert self.op in [type(node.op)
                                   for node in f.maker.fgraph.toposort()]

                #g = function([], neibs2images(neibs, neib_shape, images.shape), mode=self.mode)
                #TODO: why this is commented?
                #assert numpy.allclose(images.get_value(borrow=True), g())

    def test_neibs_bad_shape_wrap_centered(self):
        shape = (2, 3, 10, 10)

        for dtype in self.dtypes:
            images = shared(numpy.arange(
                numpy.prod(shape), dtype=dtype
                ).reshape(shape))

            for neib_shape in [(3, 2), (2, 3)]:
                neib_shape = T.as_tensor_variable(neib_shape)

                f = function([], images2neibs(images, neib_shape,
                                              mode="wrap_centered"),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

            for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
                images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
                neib_shape = T.as_tensor_variable((3, 3))
                f = function([], images2neibs(images, neib_shape,
                                              mode="wrap_centered"),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

            # Test a valid shapes
            shape = (2, 3, 3, 3)
            images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
            neib_shape = T.as_tensor_variable((3, 3))

            f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
                         mode=self.mode)
            f()

    def test_grad_wrap_centered(self):
        # It is not implemented for now. So test that we raise an error.
        shape = (2, 3, 6, 6)
        images_val = numpy.random.rand(*shape).astype('float32')

        def fn(images):
            return images2neibs(images, (3, 3), mode='wrap_centered')

        self.assertRaises(TypeError, unittest_tools.verify_grad,
                          fn, [images_val], mode=self.mode)


    def test_grad_valid(self):
        shape = (2, 3, 4, 4)
        images_val = numpy.random.rand(*shape).astype('float32')

        def fn(images):
            return images2neibs(images, (2, 2))

        unittest_tools.verify_grad(fn, [images_val], mode=self.mode,
                                   eps=0.1)

        # The grad is not defined when the neib_shape and neib_step
        # are not the same.
        def fn(images):
            return images2neibs(images, (2, 2), (1, 1))
        self.assertRaises(TypeError,
                          unittest_tools.verify_grad, fn, [images_val],
                          mode=self.mode)

    def test_grad_ignore_border(self):
        shape = (2, 3, 5, 5)
        images = T.dtensor4()
        images_val = numpy.random.rand(*shape).astype('float32')

        def fn(images):
            return images2neibs(images, (2, 2),
                                mode='ignore_borders')

        unittest_tools.verify_grad(fn, [images_val], mode=self.mode,
                                   eps=0.1)

    def test_neibs2images_grad(self):
        # say we had images of size (2, 3, 20, 20)
        # then we extracted 2x2 neighbors on this, we get (2 * 3 * 10 * 10, 4)
        neibs = T.dmatrix()
        neibs_val = numpy.random.rand(600, 4)

        def fn(neibs):
            return neibs2images(neibs, (2, 2), (2, 3, 20, 20))
        unittest_tools.verify_grad(fn, [neibs_val], mode=self.mode,
                                   eps=0.1)

    def test_neibs_valid_with_inconsistent_borders(self):
        shape = (2, 3, 5, 5)
        images = T.dtensor4()
        images_val = numpy.arange(numpy.prod(shape),
                                  dtype='float32').reshape(shape)

        def fn(images):
            return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
                         axis=[0, 1])

        f = theano.function([images],
                            T.sqr(images2neibs(images, (2, 2), mode='valid')),
                            mode=self.mode)
        self.assertRaises(TypeError, f, images_val)

    def speed_neibs(self):
        shape = (100, 40, 18, 18)
        images = shared(numpy.arange(numpy.prod(shape),
                                     dtype='float32').reshape(shape))
        neib_shape = T.as_tensor_variable((3, 3))

        f = function([], images2neibs(images, neib_shape),
                     mode=self.mode)

        for i in range(1000):
            f()

    def speed_neibs_wrap_centered(self):
        shape = (100, 40, 18, 18)
        images = shared(numpy.arange(numpy.prod(shape),
                                     dtype='float32').reshape(shape))
        neib_shape = T.as_tensor_variable((3, 3))

        f = function([],
                     images2neibs(images, neib_shape, mode="wrap_centered"),
                     mode=self.mode)

        for i in range(1000):
            f()


if __name__ == '__main__':
    unittest.main()
