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import torch
from sudoku.models import SudokuNet
def test_same_output_under_rotation():
model = SudokuNet()
arr1 = torch.zeros((1, 2, 9, 9, 9))
arr1[0, 0, 1, 2, 3] = 1
output_1 = model.forward(arr1.view(1, 2, 9 * 9 * 9))
assert output_1.shape == (1, 2, 9 * 9 * 9), output_1
arr2 = torch.zeros((1, 2, 9, 9, 9))
arr2[0, 0, 2, 3, 4] = 1
output_2 = model.forward(arr2.view(1, 2, 9 * 9 * 9))
assert (
output_1.view(1, 2, 9, 9, 9)[0, 0, 1, 2, 3]
== output_2.view(1, 2, 9, 9, 9)[0, 0, 2, 3, 4]
)
assert (
output_1.view(1, 2, 9, 9, 9)[0, 0, 1, 2, 4]
== output_2.view(1, 2, 9, 9, 9)[0, 0, 2, 3, 6]
)
assert (
output_1.view(1, 2, 9, 9, 9)[0, 1, 1, 2, 4]
== output_2.view(1, 2, 9, 9, 9)[0, 1, 2, 3, 6]
)
assert (
output_1.view(1, 2, 9, 9, 9)[0, 1, 2, 2, 4]
== output_2.view(1, 2, 9, 9, 9)[0, 1, 1, 3, 6]
)
assert (
output_1.view(1, 2, 9, 9, 9)[0, 1, 2, 3, 4]
== output_2.view(1, 2, 9, 9, 9)[0, 1, 1, 2, 6]
)
# 0, 1, 2 | 3, 4, 5 | 6, 7, 8
# 0, 1, a | 3, 4, 5 | 6, 7, 8
# 0, 1, 2 | b, 4, 5 | 6, 7, 8
# ----------------------------
# 0, 1, 2 | 3, 4, 5 | 6, 7, 8
# 0, 1, 2 | 3, 4, 5 | 6, 7, 8
# 0, 1, 2 | 3, 4, 5 | 6, 7, 8
# ----------------------------
# 0, 1, 2 | 3, 4, 5 | 6, 7, 8
# 0, 1, 2 | 3, 4, 5 | 6, 7, 8
# 0, 1, 2 | 3, 4, 5 | 6, 7, 8
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