MLPY/Lib/site-packages/sympy/matrices/expressions/matexpr.py

from __future__ import annotations
from functools import wraps

from sympy.core import S, Integer, Basic, Mul, Add
from sympy.core.assumptions import check_assumptions
from sympy.core.decorators import call_highest_priority
from sympy.core.expr import Expr, ExprBuilder
from sympy.core.logic import FuzzyBool
from sympy.core.symbol import Str, Dummy, symbols, Symbol
from sympy.core.sympify import SympifyError, _sympify
from sympy.external.gmpy import SYMPY_INTS
from sympy.functions import conjugate, adjoint
from sympy.functions.special.tensor_functions import KroneckerDelta
from sympy.matrices.exceptions import NonSquareMatrixError
from sympy.matrices.kind import MatrixKind
from sympy.matrices.matrixbase import MatrixBase
from sympy.multipledispatch import dispatch
from sympy.utilities.misc import filldedent


def _sympifyit(arg, retval=None):
    # This version of _sympifyit sympifies MutableMatrix objects
    def deco(func):
        @wraps(func)
        def __sympifyit_wrapper(a, b):
            try:
                b = _sympify(b)
                return func(a, b)
            except SympifyError:
                return retval

        return __sympifyit_wrapper

    return deco


class MatrixExpr(Expr):
    """Superclass for Matrix Expressions

    MatrixExprs represent abstract matrices, linear transformations represented
    within a particular basis.

    Examples
    ========

    >>> from sympy import MatrixSymbol
    >>> A = MatrixSymbol('A', 3, 3)
    >>> y = MatrixSymbol('y', 3, 1)
    >>> x = (A.T*A).I * A * y

    See Also
    ========

    MatrixSymbol, MatAdd, MatMul, Transpose, Inverse
    """
    __slots__: tuple[str, ...] = ()

    # Should not be considered iterable by the
    # sympy.utilities.iterables.iterable function. Subclass that actually are
    # iterable (i.e., explicit matrices) should set this to True.
    _iterable = False

    _op_priority = 11.0

    is_Matrix: bool = True
    is_MatrixExpr: bool = True
    is_Identity: FuzzyBool = None
    is_Inverse = False
    is_Transpose = False
    is_ZeroMatrix = False
    is_MatAdd = False
    is_MatMul = False

    is_commutative = False
    is_number = False
    is_symbol = False
    is_scalar = False

    kind: MatrixKind = MatrixKind()

    def __new__(cls, *args, **kwargs):
        args = map(_sympify, args)
        return Basic.__new__(cls, *args, **kwargs)

    # The following is adapted from the core Expr object

    @property
    def shape(self) -> tuple[Expr, Expr]:
        raise NotImplementedError

    @property
    def _add_handler(self):
        return MatAdd

    @property
    def _mul_handler(self):
        return MatMul

    def __neg__(self):
        return MatMul(S.NegativeOne, self).doit()

    def __abs__(self):
        raise NotImplementedError

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__radd__')
    def __add__(self, other):
        return MatAdd(self, other).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__add__')
    def __radd__(self, other):
        return MatAdd(other, self).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__rsub__')
    def __sub__(self, other):
        return MatAdd(self, -other).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__sub__')
    def __rsub__(self, other):
        return MatAdd(other, -self).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__rmul__')
    def __mul__(self, other):
        return MatMul(self, other).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__rmul__')
    def __matmul__(self, other):
        return MatMul(self, other).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__mul__')
    def __rmul__(self, other):
        return MatMul(other, self).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__mul__')
    def __rmatmul__(self, other):
        return MatMul(other, self).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__rpow__')
    def __pow__(self, other):
        return MatPow(self, other).doit()

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__pow__')
    def __rpow__(self, other):
        raise NotImplementedError("Matrix Power not defined")

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__rtruediv__')
    def __truediv__(self, other):
        return self * other**S.NegativeOne

    @_sympifyit('other', NotImplemented)
    @call_highest_priority('__truediv__')
    def __rtruediv__(self, other):
        raise NotImplementedError()
        #return MatMul(other, Pow(self, S.NegativeOne))

    @property
    def rows(self):
        return self.shape[0]

    @property
    def cols(self):
        return self.shape[1]

    @property
    def is_square(self) -> bool | None:
        rows, cols = self.shape
        if isinstance(rows, Integer) and isinstance(cols, Integer):
            return rows == cols
        if rows == cols:
            return True
        return None

    def _eval_conjugate(self):
        from sympy.matrices.expressions.adjoint import Adjoint
        return Adjoint(Transpose(self))

    def as_real_imag(self, deep=True, **hints):
        return self._eval_as_real_imag()

    def _eval_as_real_imag(self):
        real = S.Half * (self + self._eval_conjugate())
        im = (self - self._eval_conjugate())/(2*S.ImaginaryUnit)
        return (real, im)

    def _eval_inverse(self):
        return Inverse(self)

    def _eval_determinant(self):
        return Determinant(self)

    def _eval_transpose(self):
        return Transpose(self)

    def _eval_trace(self):
        return None

    def _eval_power(self, exp):
        """
        Override this in sub-classes to implement simplification of powers.  The cases where the exponent
        is -1, 0, 1 are already covered in MatPow.doit(), so implementations can exclude these cases.
        """
        return MatPow(self, exp)

    def _eval_simplify(self, **kwargs):
        if self.is_Atom:
            return self
        else:
            from sympy.simplify import simplify
            return self.func(*[simplify(x, **kwargs) for x in self.args])

    def _eval_adjoint(self):
        from sympy.matrices.expressions.adjoint import Adjoint
        return Adjoint(self)

    def _eval_derivative_n_times(self, x, n):
        return Basic._eval_derivative_n_times(self, x, n)

    def _eval_derivative(self, x):
        # `x` is a scalar:
        if self.has(x):
            # See if there are other methods using it:
            return super()._eval_derivative(x)
        else:
            return ZeroMatrix(*self.shape)

    @classmethod
    def _check_dim(cls, dim):
        """Helper function to check invalid matrix dimensions"""
        ok = not dim.is_Float and check_assumptions(
            dim, integer=True, nonnegative=True)
        if ok is False:
            raise ValueError(
                "The dimension specification {} should be "
                "a nonnegative integer.".format(dim))


    def _entry(self, i, j, **kwargs):
        raise NotImplementedError(
            "Indexing not implemented for %s" % self.__class__.__name__)

    def adjoint(self):
        return adjoint(self)

    def as_coeff_Mul(self, rational=False):
        """Efficiently extract the coefficient of a product."""
        return S.One, self

    def conjugate(self):
        return conjugate(self)

    def transpose(self):
        from sympy.matrices.expressions.transpose import transpose
        return transpose(self)

    @property
    def T(self):
        '''Matrix transposition'''
        return self.transpose()

    def inverse(self):
        if self.is_square is False:
            raise NonSquareMatrixError('Inverse of non-square matrix')
        return self._eval_inverse()

    def inv(self):
        return self.inverse()

    def det(self):
        from sympy.matrices.expressions.determinant import det
        return det(self)

    @property
    def I(self):
        return self.inverse()

    def valid_index(self, i, j):
        def is_valid(idx):
            return isinstance(idx, (int, Integer, Symbol, Expr))
        return (is_valid(i) and is_valid(j) and
                (self.rows is None or
                (i >= -self.rows) != False and (i < self.rows) != False) and
                (j >= -self.cols) != False and (j < self.cols) != False)

    def __getitem__(self, key):
        if not isinstance(key, tuple) and isinstance(key, slice):
            from sympy.matrices.expressions.slice import MatrixSlice
            return MatrixSlice(self, key, (0, None, 1))
        if isinstance(key, tuple) and len(key) == 2:
            i, j = key
            if isinstance(i, slice) or isinstance(j, slice):
                from sympy.matrices.expressions.slice import MatrixSlice
                return MatrixSlice(self, i, j)
            i, j = _sympify(i), _sympify(j)
            if self.valid_index(i, j) != False:
                return self._entry(i, j)
            else:
                raise IndexError("Invalid indices (%s, %s)" % (i, j))
        elif isinstance(key, (SYMPY_INTS, Integer)):
            # row-wise decomposition of matrix
            rows, cols = self.shape
            # allow single indexing if number of columns is known
            if not isinstance(cols, Integer):
                raise IndexError(filldedent('''
                    Single indexing is only supported when the number
                    of columns is known.'''))
            key = _sympify(key)
            i = key // cols
            j = key % cols
            if self.valid_index(i, j) != False:
                return self._entry(i, j)
            else:
                raise IndexError("Invalid index %s" % key)
        elif isinstance(key, (Symbol, Expr)):
            raise IndexError(filldedent('''
                Only integers may be used when addressing the matrix
                with a single index.'''))
        raise IndexError("Invalid index, wanted %s[i,j]" % self)

    def _is_shape_symbolic(self) -> bool:
        return (not isinstance(self.rows, (SYMPY_INTS, Integer))
            or not isinstance(self.cols, (SYMPY_INTS, Integer)))

    def as_explicit(self):
        """
        Returns a dense Matrix with elements represented explicitly

        Returns an object of type ImmutableDenseMatrix.

        Examples
        ========

        >>> from sympy import Identity
        >>> I = Identity(3)
        >>> I
        I
        >>> I.as_explicit()
        Matrix([
        [1, 0, 0],
        [0, 1, 0],
        [0, 0, 1]])

        See Also
        ========
        as_mutable: returns mutable Matrix type

        """
        if self._is_shape_symbolic():
            raise ValueError(
                'Matrix with symbolic shape '
                'cannot be represented explicitly.')
        from sympy.matrices.immutable import ImmutableDenseMatrix
        return ImmutableDenseMatrix([[self[i, j]
                            for j in range(self.cols)]
                            for i in range(self.rows)])

    def as_mutable(self):
        """
        Returns a dense, mutable matrix with elements represented explicitly

        Examples
        ========

        >>> from sympy import Identity
        >>> I = Identity(3)
        >>> I
        I
        >>> I.shape
        (3, 3)
        >>> I.as_mutable()
        Matrix([
        [1, 0, 0],
        [0, 1, 0],
        [0, 0, 1]])

        See Also
        ========
        as_explicit: returns ImmutableDenseMatrix
        """
        return self.as_explicit().as_mutable()

    def __array__(self, dtype=object, copy=None):
        if copy is not None and not copy:
            raise TypeError("Cannot implement copy=False when converting Matrix to ndarray")
        from numpy import empty
        a = empty(self.shape, dtype=object)
        for i in range(self.rows):
            for j in range(self.cols):
                a[i, j] = self[i, j]
        return a

    def equals(self, other):
        """
        Test elementwise equality between matrices, potentially of different
        types

        >>> from sympy import Identity, eye
        >>> Identity(3).equals(eye(3))
        True
        """
        return self.as_explicit().equals(other)

    def canonicalize(self):
        return self

    def as_coeff_mmul(self):
        return S.One, MatMul(self)

    @staticmethod
    def from_index_summation(expr, first_index=None, last_index=None, dimensions=None):
        r"""
        Parse expression of matrices with explicitly summed indices into a
        matrix expression without indices, if possible.

        This transformation expressed in mathematical notation:

        `\sum_{j=0}^{N-1} A_{i,j} B_{j,k} \Longrightarrow \mathbf{A}\cdot \mathbf{B}`

        Optional parameter ``first_index``: specify which free index to use as
        the index starting the expression.

        Examples
        ========

        >>> from sympy import MatrixSymbol, MatrixExpr, Sum
        >>> from sympy.abc import i, j, k, l, N
        >>> A = MatrixSymbol("A", N, N)
        >>> B = MatrixSymbol("B", N, N)
        >>> expr = Sum(A[i, j]*B[j, k], (j, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A*B

        Transposition is detected:

        >>> expr = Sum(A[j, i]*B[j, k], (j, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A.T*B

        Detect the trace:

        >>> expr = Sum(A[i, i], (i, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        Trace(A)

        More complicated expressions:

        >>> expr = Sum(A[i, j]*B[k, j]*A[l, k], (j, 0, N-1), (k, 0, N-1))
        >>> MatrixExpr.from_index_summation(expr)
        A*B.T*A.T
        """
        from sympy.tensor.array.expressions.from_indexed_to_array import convert_indexed_to_array
        from sympy.tensor.array.expressions.from_array_to_matrix import convert_array_to_matrix
        first_indices = []
        if first_index is not None:
            first_indices.append(first_index)
        if last_index is not None:
            first_indices.append(last_index)
        arr = convert_indexed_to_array(expr, first_indices=first_indices)
        return convert_array_to_matrix(arr)

    def applyfunc(self, func):
        from .applyfunc import ElementwiseApplyFunction
        return ElementwiseApplyFunction(func, self)


@dispatch(MatrixExpr, Expr)
def _eval_is_eq(lhs, rhs): # noqa:F811
    return False

@dispatch(MatrixExpr, MatrixExpr)  # type: ignore
def _eval_is_eq(lhs, rhs): # noqa:F811
    if lhs.shape != rhs.shape:
        return False
    if (lhs - rhs).is_ZeroMatrix:
        return True

def get_postprocessor(cls):
    def _postprocessor(expr):
        # To avoid circular imports, we can't have MatMul/MatAdd on the top level
        mat_class = {Mul: MatMul, Add: MatAdd}[cls]
        nonmatrices = []
        matrices = []
        for term in expr.args:
            if isinstance(term, MatrixExpr):
                matrices.append(term)
            else:
                nonmatrices.append(term)

        if not matrices:
            return cls._from_args(nonmatrices)

        if nonmatrices:
            if cls == Mul:
                for i in range(len(matrices)):
                    if not matrices[i].is_MatrixExpr:
                        # If one of the matrices explicit, absorb the scalar into it
                        # (doit will combine all explicit matrices into one, so it
                        # doesn't matter which)
                        matrices[i] = matrices[i].__mul__(cls._from_args(nonmatrices))
                        nonmatrices = []
                        break

            else:
                # Maintain the ability to create Add(scalar, matrix) without
                # raising an exception. That way different algorithms can
                # replace matrix expressions with non-commutative symbols to
                # manipulate them like non-commutative scalars.
                return cls._from_args(nonmatrices + [mat_class(*matrices).doit(deep=False)])

        if mat_class == MatAdd:
            return mat_class(*matrices).doit(deep=False)
        return mat_class(cls._from_args(nonmatrices), *matrices).doit(deep=False)
    return _postprocessor


Basic._constructor_postprocessor_mapping[MatrixExpr] = {
    "Mul": [get_postprocessor(Mul)],
    "Add": [get_postprocessor(Add)],
}


def _matrix_derivative(expr, x, old_algorithm=False):

    if isinstance(expr, MatrixBase) or isinstance(x, MatrixBase):
        # Do not use array expressions for explicit matrices:
        old_algorithm = True

    if old_algorithm:
        return _matrix_derivative_old_algorithm(expr, x)

    from sympy.tensor.array.expressions.from_matrix_to_array import convert_matrix_to_array
    from sympy.tensor.array.expressions.arrayexpr_derivatives import array_derive
    from sympy.tensor.array.expressions.from_array_to_matrix import convert_array_to_matrix

    array_expr = convert_matrix_to_array(expr)
    diff_array_expr = array_derive(array_expr, x)
    diff_matrix_expr = convert_array_to_matrix(diff_array_expr)
    return diff_matrix_expr


def _matrix_derivative_old_algorithm(expr, x):
    from sympy.tensor.array.array_derivatives import ArrayDerivative
    lines = expr._eval_derivative_matrix_lines(x)

    parts = [i.build() for i in lines]

    from sympy.tensor.array.expressions.from_array_to_matrix import convert_array_to_matrix

    parts = [[convert_array_to_matrix(j) for j in i] for i in parts]

    def _get_shape(elem):
        if isinstance(elem, MatrixExpr):
            return elem.shape
        return 1, 1

    def get_rank(parts):
        return sum(j not in (1, None) for i in parts for j in _get_shape(i))

    ranks = [get_rank(i) for i in parts]
    rank = ranks[0]

    def contract_one_dims(parts):
        if len(parts) == 1:
            return parts[0]
        else:
            p1, p2 = parts[:2]
            if p2.is_Matrix:
                p2 = p2.T
            if p1 == Identity(1):
                pbase = p2
            elif p2 == Identity(1):
                pbase = p1
            else:
                pbase = p1*p2
            if len(parts) == 2:
                return pbase
            else:  # len(parts) > 2
                if pbase.is_Matrix:
                    raise ValueError("")
                return pbase*Mul.fromiter(parts[2:])

    if rank <= 2:
        return Add.fromiter([contract_one_dims(i) for i in parts])

    return ArrayDerivative(expr, x)


class MatrixElement(Expr):
    parent = property(lambda self: self.args[0])
    i = property(lambda self: self.args[1])
    j = property(lambda self: self.args[2])
    _diff_wrt = True
    is_symbol = True
    is_commutative = True

    def __new__(cls, name, n, m):
        n, m = map(_sympify, (n, m))
        if isinstance(name, str):
            name = Symbol(name)
        else:
            if isinstance(name, MatrixBase):
                if n.is_Integer and m.is_Integer:
                    return name[n, m]
                name = _sympify(name)  # change mutable into immutable
            else:
                name = _sympify(name)
                if not isinstance(name.kind, MatrixKind):
                    raise TypeError("First argument of MatrixElement should be a matrix")
            if not getattr(name, 'valid_index', lambda n, m: True)(n, m):
                raise IndexError('indices out of range')
        obj = Expr.__new__(cls, name, n, m)
        return obj

    @property
    def symbol(self):
        return self.args[0]

    def doit(self, **hints):
        deep = hints.get('deep', True)
        if deep:
            args = [arg.doit(**hints) for arg in self.args]
        else:
            args = self.args
        return args[0][args[1], args[2]]

    @property
    def indices(self):
        return self.args[1:]

    def _eval_derivative(self, v):

        if not isinstance(v, MatrixElement):
            return self.parent.diff(v)[self.i, self.j]

        M = self.args[0]

        m, n = self.parent.shape

        if M == v.args[0]:
            return KroneckerDelta(self.args[1], v.args[1], (0, m-1)) * \
                   KroneckerDelta(self.args[2], v.args[2], (0, n-1))

        if isinstance(M, Inverse):
            from sympy.concrete.summations import Sum
            i, j = self.args[1:]
            i1, i2 = symbols("z1, z2", cls=Dummy)
            Y = M.args[0]
            r1, r2 = Y.shape
            return -Sum(M[i, i1]*Y[i1, i2].diff(v)*M[i2, j], (i1, 0, r1-1), (i2, 0, r2-1))

        if self.has(v.args[0]):
            return None

        return S.Zero


class MatrixSymbol(MatrixExpr):
    """Symbolic representation of a Matrix object

    Creates a SymPy Symbol to represent a Matrix. This matrix has a shape and
    can be included in Matrix Expressions

    Examples
    ========

    >>> from sympy import MatrixSymbol, Identity
    >>> A = MatrixSymbol('A', 3, 4) # A 3 by 4 Matrix
    >>> B = MatrixSymbol('B', 4, 3) # A 4 by 3 Matrix
    >>> A.shape
    (3, 4)
    >>> 2*A*B + Identity(3)
    I + 2*A*B
    """
    is_commutative = False
    is_symbol = True
    _diff_wrt = True

    def __new__(cls, name, n, m):
        n, m = _sympify(n), _sympify(m)

        cls._check_dim(m)
        cls._check_dim(n)

        if isinstance(name, str):
            name = Str(name)
        obj = Basic.__new__(cls, name, n, m)
        return obj

    @property
    def shape(self):
        return self.args[1], self.args[2]

    @property
    def name(self):
        return self.args[0].name

    def _entry(self, i, j, **kwargs):
        return MatrixElement(self, i, j)

    @property
    def free_symbols(self):
        return {self}

    def _eval_simplify(self, **kwargs):
        return self

    def _eval_derivative(self, x):
        # x is a scalar:
        return ZeroMatrix(self.shape[0], self.shape[1])

    def _eval_derivative_matrix_lines(self, x):
        if self != x:
            first = ZeroMatrix(x.shape[0], self.shape[0]) if self.shape[0] != 1 else S.Zero
            second = ZeroMatrix(x.shape[1], self.shape[1]) if self.shape[1] != 1 else S.Zero
            return [_LeftRightArgs(
                [first, second],
            )]
        else:
            first = Identity(self.shape[0]) if self.shape[0] != 1 else S.One
            second = Identity(self.shape[1]) if self.shape[1] != 1 else S.One
            return [_LeftRightArgs(
                [first, second],
            )]


def matrix_symbols(expr):
    return [sym for sym in expr.free_symbols if sym.is_Matrix]


class _LeftRightArgs:
    r"""
    Helper class to compute matrix derivatives.

    The logic: when an expression is derived by a matrix `X_{mn}`, two lines of
    matrix multiplications are created: the one contracted to `m` (first line),
    and the one contracted to `n` (second line).

    Transposition flips the side by which new matrices are connected to the
    lines.

    The trace connects the end of the two lines.
    """

    def __init__(self, lines, higher=S.One):
        self._lines = list(lines)
        self._first_pointer_parent = self._lines
        self._first_pointer_index = 0
        self._first_line_index = 0
        self._second_pointer_parent = self._lines
        self._second_pointer_index = 1
        self._second_line_index = 1
        self.higher = higher

    @property
    def first_pointer(self):
       return self._first_pointer_parent[self._first_pointer_index]

    @first_pointer.setter
    def first_pointer(self, value):
        self._first_pointer_parent[self._first_pointer_index] = value

    @property
    def second_pointer(self):
        return self._second_pointer_parent[self._second_pointer_index]

    @second_pointer.setter
    def second_pointer(self, value):
        self._second_pointer_parent[self._second_pointer_index] = value

    def __repr__(self):
        built = [self._build(i) for i in self._lines]
        return "_LeftRightArgs(lines=%s, higher=%s)" % (
            built,
            self.higher,
        )

    def transpose(self):
        self._first_pointer_parent, self._second_pointer_parent = self._second_pointer_parent, self._first_pointer_parent
        self._first_pointer_index, self._second_pointer_index = self._second_pointer_index, self._first_pointer_index
        self._first_line_index, self._second_line_index = self._second_line_index, self._first_line_index
        return self

    @staticmethod
    def _build(expr):
        if isinstance(expr, ExprBuilder):
            return expr.build()
        if isinstance(expr, list):
            if len(expr) == 1:
                return expr[0]
            else:
                return expr[0](*[_LeftRightArgs._build(i) for i in expr[1]])
        else:
            return expr

    def build(self):
        data = [self._build(i) for i in self._lines]
        if self.higher != 1:
            data += [self._build(self.higher)]
        data = list(data)
        return data

    def matrix_form(self):
        if self.first != 1 and self.higher != 1:
            raise ValueError("higher dimensional array cannot be represented")

        def _get_shape(elem):
            if isinstance(elem, MatrixExpr):
                return elem.shape
            return (None, None)

        if _get_shape(self.first)[1] != _get_shape(self.second)[1]:
            # Remove one-dimensional identity matrices:
            # (this is needed by `a.diff(a)` where `a` is a vector)
            if _get_shape(self.second) == (1, 1):
                return self.first*self.second[0, 0]
            if _get_shape(self.first) == (1, 1):
                return self.first[1, 1]*self.second.T
            raise ValueError("incompatible shapes")
        if self.first != 1:
            return self.first*self.second.T
        else:
            return self.higher

    def rank(self):
        """
        Number of dimensions different from trivial (warning: not related to
        matrix rank).
        """
        rank = 0
        if self.first != 1:
            rank += sum(i != 1 for i in self.first.shape)
        if self.second != 1:
            rank += sum(i != 1 for i in self.second.shape)
        if self.higher != 1:
            rank += 2
        return rank

    def _multiply_pointer(self, pointer, other):
        from ...tensor.array.expressions.array_expressions import ArrayTensorProduct
        from ...tensor.array.expressions.array_expressions import ArrayContraction

        subexpr = ExprBuilder(
            ArrayContraction,
            [
                ExprBuilder(
                    ArrayTensorProduct,
                    [
                        pointer,
                        other
                    ]
                ),
                (1, 2)
            ],
            validator=ArrayContraction._validate
        )

        return subexpr

    def append_first(self, other):
        self.first_pointer *= other

    def append_second(self, other):
        self.second_pointer *= other


def _make_matrix(x):
    from sympy.matrices.immutable import ImmutableDenseMatrix
    if isinstance(x, MatrixExpr):
        return x
    return ImmutableDenseMatrix([[x]])


from .matmul import MatMul
from .matadd import MatAdd
from .matpow import MatPow
from .transpose import Transpose
from .inverse import Inverse
from .special import ZeroMatrix, Identity
from .determinant import Determinant