from pina.model.block import FourierBlock1D, FourierBlock2D, FourierBlock3D
import torch

input_numb_fields = 3
output_numb_fields = 4
batch = 5


def test_constructor_1d():
    FourierBlock1D(input_numb_fields=input_numb_fields,
                   output_numb_fields=output_numb_fields,
                   n_modes=5)


def test_forward_1d():
    sconv = FourierBlock1D(input_numb_fields=input_numb_fields,
                           output_numb_fields=output_numb_fields,
                           n_modes=4)
    x = torch.rand(batch, input_numb_fields, 10)
    sconv(x)


def test_backward_1d():
    sconv = FourierBlock1D(input_numb_fields=input_numb_fields,
                           output_numb_fields=output_numb_fields,
                           n_modes=4)
    x = torch.rand(batch, input_numb_fields, 10)
    x.requires_grad = True
    sconv(x)
    l = torch.mean(sconv(x))
    l.backward()
    assert x._grad.shape == torch.Size([5, 3, 10])


def test_constructor_2d():
    FourierBlock2D(input_numb_fields=input_numb_fields,
                   output_numb_fields=output_numb_fields,
                   n_modes=[5, 4])


def test_forward_2d():
    sconv = FourierBlock2D(input_numb_fields=input_numb_fields,
                           output_numb_fields=output_numb_fields,
                           n_modes=[5, 4])
    x = torch.rand(batch, input_numb_fields, 10, 10)
    sconv(x)


def test_backward_2d():
    sconv = FourierBlock2D(input_numb_fields=input_numb_fields,
                           output_numb_fields=output_numb_fields,
                           n_modes=[5, 4])
    x = torch.rand(batch, input_numb_fields, 10, 10)
    x.requires_grad = True
    sconv(x)
    l = torch.mean(sconv(x))
    l.backward()
    assert x._grad.shape == torch.Size([5, 3, 10, 10])


def test_constructor_3d():
    FourierBlock3D(input_numb_fields=input_numb_fields,
                   output_numb_fields=output_numb_fields,
                   n_modes=[5, 4, 4])


def test_forward_3d():
    sconv = FourierBlock3D(input_numb_fields=input_numb_fields,
                           output_numb_fields=output_numb_fields,
                           n_modes=[5, 4, 4])
    x = torch.rand(batch, input_numb_fields, 10, 10, 10)
    sconv(x)


def test_backward_3d():
    sconv = FourierBlock3D(input_numb_fields=input_numb_fields,
                           output_numb_fields=output_numb_fields,
                           n_modes=[5, 4, 4])
    x = torch.rand(batch, input_numb_fields, 10, 10, 10)
    x.requires_grad = True
    sconv(x)
    l = torch.mean(sconv(x))
    l.backward()
    assert x._grad.shape == torch.Size([5, 3, 10, 10, 10])