"""Tests for the basic transform definitions."""
import unittest

import numpy as np
import torch

from nde.transforms import base
from nde.transforms import standard
from nde.transforms.transform_test import TransformTest

class CompositeTransformTest(TransformTest):

    def test_forward(self):
        batch_size = 10
        shape = [2, 3, 4]
        inputs = torch.randn(batch_size, *shape)
        transforms = [
            standard.AffineScalarTransform(scale=2.0),
            standard.IdentityTransform(),
            standard.AffineScalarTransform(scale=-0.25),
        ]
        composite = base.CompositeTransform(transforms)
        reference = standard.AffineScalarTransform(scale=-0.5)
        outputs, logabsdet = composite(inputs)
        outputs_ref, logabsdet_ref = reference(inputs)
        self.assert_tensor_is_good(outputs, [batch_size] + shape)
        self.assert_tensor_is_good(logabsdet, [batch_size])
        self.assertEqual(outputs, outputs_ref)
        self.assertEqual(logabsdet, logabsdet_ref)

    def test_inverse(self):
        batch_size = 10
        shape = [2, 3, 4]
        inputs = torch.randn(batch_size, *shape)
        transforms = [
            standard.AffineScalarTransform(scale=2.0),
            standard.IdentityTransform(),
            standard.AffineScalarTransform(scale=-0.25),
        ]
        composite = base.CompositeTransform(transforms)
        reference = standard.AffineScalarTransform(scale=-0.5)
        outputs, logabsdet = composite.inverse(inputs)
        outputs_ref, logabsdet_ref = reference.inverse(inputs)
        self.assert_tensor_is_good(outputs, [batch_size] + shape)
        self.assert_tensor_is_good(logabsdet, [batch_size])
        self.assertEqual(outputs, outputs_ref)
        self.assertEqual(logabsdet, logabsdet_ref)

class MultiscaleCompositeTransformTest(TransformTest):
    def create_transform(self, shape, split_dim=1):
        mct = base.MultiscaleCompositeTransform(num_transforms=4,
                                                split_dim=split_dim)
        for transform in [ standard.AffineScalarTransform(scale=2.),
                           standard.AffineScalarTransform(scale=4.),
                           standard.AffineScalarTransform(scale=0.5),
                           standard.AffineScalarTransform(scale=0.25)]:
            shape = mct.add_transform(transform, shape)

        return mct


    def test_forward(self):
        batch_size = 5
        for shape in [(32,4,4),
                      (64,),
                      (65,)]:
            with self.subTest(shape=shape):
                inputs = torch.ones(batch_size, *shape)
                transform = self.create_transform(shape)
                outputs, logabsdet = transform(inputs)
                self.assert_tensor_is_good(outputs, [batch_size] + [np.prod(shape)])
                self.assert_tensor_is_good(logabsdet, [batch_size])

    def test_forward_bad_shape(self):
        shape = (8,)
        with self.assertRaises(ValueError):
            transform = self.create_transform(shape)

    def test_forward_bad_split_dim(self):
        batch_size = 5
        shape = [32]
        inputs = torch.randn(batch_size, *shape)
        with self.assertRaises(ValueError):
            transform = self.create_transform(shape, split_dim=2)

    def test_inverse_not_flat(self):
        batch_size = 5
        shape = [32, 4, 4]
        inputs = torch.randn(batch_size, *shape)
        transform = self.create_transform(shape)
        with self.assertRaises(ValueError):
            transform.inverse(inputs)

    def test_forward_inverse_are_consistent(self):
        batch_size = 5
        for shape in [(32,4,4),
                      (64,),
                      (65,),
                      (21,)]:
            with self.subTest(shape=shape):
                transform = self.create_transform(shape)
                inputs = torch.randn(batch_size, *shape).view(batch_size, -1)
                self.assert_forward_inverse_are_consistent(transform, inputs)

class InverseTransformTest(TransformTest):

    def test_forward(self):
        batch_size = 10
        shape = [2, 3, 4]
        inputs = torch.randn(batch_size, *shape)
        transform = base.InverseTransform(standard.AffineScalarTransform(scale=-2.0))
        reference = standard.AffineScalarTransform(scale=-0.5)
        outputs, logabsdet = transform(inputs)
        outputs_ref, logabsdet_ref = reference(inputs)
        self.assert_tensor_is_good(outputs, [batch_size] + shape)
        self.assert_tensor_is_good(logabsdet, [batch_size])
        self.assertEqual(outputs, outputs_ref)
        self.assertEqual(logabsdet, logabsdet_ref)

    def test_inverse(self):
        batch_size = 10
        shape = [2, 3, 4]
        inputs = torch.randn(batch_size, *shape)
        transform = base.InverseTransform(standard.AffineScalarTransform(scale=-2.0))
        reference = standard.AffineScalarTransform(scale=-0.5)
        outputs, logabsdet = transform.inverse(inputs)
        outputs_ref, logabsdet_ref = reference.inverse(inputs)
        self.assert_tensor_is_good(outputs, [batch_size] + shape)
        self.assert_tensor_is_good(logabsdet, [batch_size])
        self.assertEqual(outputs, outputs_ref)
        self.assertEqual(logabsdet, logabsdet_ref)


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