from sympy.core.symbol import symbols
from sympy.matrices.dense import Matrix
from sympy.matrices.expressions.matexpr import MatrixSymbol
from sympy.tensor.array.ndim_array import NDimArray
from sympy.matrices.common import MatrixCommon
from sympy.tensor.array.array_derivatives import ArrayDerivative

x, y, z, t = symbols("x y z t")

m = Matrix([[x, y], [z, t]])

M = MatrixSymbol("M", 3, 2)
N = MatrixSymbol("N", 4, 3)


def test_array_derivative_construction():

    d = ArrayDerivative(x, m, evaluate=False)
    assert d.shape == (2, 2)
    expr = d.doit()
    assert isinstance(expr, MatrixCommon)
    assert expr.shape == (2, 2)

    d = ArrayDerivative(m, m, evaluate=False)
    assert d.shape == (2, 2, 2, 2)
    expr = d.doit()
    assert isinstance(expr, NDimArray)
    assert expr.shape == (2, 2, 2, 2)

    d = ArrayDerivative(m, x, evaluate=False)
    assert d.shape == (2, 2)
    expr = d.doit()
    assert isinstance(expr, MatrixCommon)
    assert expr.shape == (2, 2)

    d = ArrayDerivative(M, N, evaluate=False)
    assert d.shape == (4, 3, 3, 2)
    expr = d.doit()
    assert isinstance(expr, ArrayDerivative)
    assert expr.shape == (4, 3, 3, 2)

    d = ArrayDerivative(M, (N, 2), evaluate=False)
    assert d.shape == (4, 3, 4, 3, 3, 2)
    expr = d.doit()
    assert isinstance(expr, ArrayDerivative)
    assert expr.shape == (4, 3, 4, 3, 3, 2)

    d = ArrayDerivative(M.as_explicit(), (N.as_explicit(), 2), evaluate=False)
    assert d.doit().shape == (4, 3, 4, 3, 3, 2)
    expr = d.doit()
    assert isinstance(expr, NDimArray)
    assert expr.shape == (4, 3, 4, 3, 3, 2)
