import itertools
from collections.abc import Iterable

from sympy import S, Tuple, diff, Basic
from sympy.core.sympify import _sympify

from sympy.tensor.array.ndim_array import NDimArray
from sympy.tensor.array.dense_ndim_array import DenseNDimArray, ImmutableDenseNDimArray
from sympy.tensor.array.sparse_ndim_array import SparseNDimArray


def _arrayfy(a):
    from sympy.matrices import MatrixBase

    if isinstance(a, NDimArray):
        return a
    if isinstance(a, (MatrixBase, list, tuple, Tuple)):
        return ImmutableDenseNDimArray(a)
    return a


def tensorproduct(*args):
    """
    Tensor product among scalars or array-like objects.

    Examples
    ========

    >>> from sympy.tensor.array import tensorproduct, Array
    >>> from sympy.abc import x, y, z, t
    >>> A = Array([[1, 2], [3, 4]])
    >>> B = Array([x, y])
    >>> tensorproduct(A, B)
    [[[x, y], [2*x, 2*y]], [[3*x, 3*y], [4*x, 4*y]]]
    >>> tensorproduct(A, x)
    [[x, 2*x], [3*x, 4*x]]
    >>> tensorproduct(A, B, B)
    [[[[x**2, x*y], [x*y, y**2]], [[2*x**2, 2*x*y], [2*x*y, 2*y**2]]], [[[3*x**2, 3*x*y], [3*x*y, 3*y**2]], [[4*x**2, 4*x*y], [4*x*y, 4*y**2]]]]

    Applying this function on two matrices will result in a rank 4 array.

    >>> from sympy import Matrix, eye
    >>> m = Matrix([[x, y], [z, t]])
    >>> p = tensorproduct(eye(3), m)
    >>> p
    [[[[x, y], [z, t]], [[0, 0], [0, 0]], [[0, 0], [0, 0]]], [[[0, 0], [0, 0]], [[x, y], [z, t]], [[0, 0], [0, 0]]], [[[0, 0], [0, 0]], [[0, 0], [0, 0]], [[x, y], [z, t]]]]
    """
    from sympy.tensor.array import SparseNDimArray, ImmutableSparseNDimArray

    if len(args) == 0:
        return S.One
    if len(args) == 1:
        return _arrayfy(args[0])
    from sympy.tensor.array.expressions.array_expressions import _CodegenArrayAbstract
    from sympy.tensor.array.expressions.array_expressions import ArrayTensorProduct
    from sympy.tensor.array.expressions.array_expressions import _ArrayExpr
    from sympy import MatrixSymbol
    if any(isinstance(arg, (_ArrayExpr, _CodegenArrayAbstract, MatrixSymbol)) for arg in args):
        return ArrayTensorProduct(*args)
    if len(args) > 2:
        return tensorproduct(tensorproduct(args[0], args[1]), *args[2:])

    # length of args is 2:
    a, b = map(_arrayfy, args)

    if not isinstance(a, NDimArray) or not isinstance(b, NDimArray):
        return a*b

    if isinstance(a, SparseNDimArray) and isinstance(b, SparseNDimArray):
        lp = len(b)
        new_array = {k1*lp + k2: v1*v2 for k1, v1 in a._sparse_array.items() for k2, v2 in b._sparse_array.items()}
        return ImmutableSparseNDimArray(new_array, a.shape + b.shape)

    product_list = [i*j for i in Flatten(a) for j in Flatten(b)]
    return ImmutableDenseNDimArray(product_list, a.shape + b.shape)


def _util_contraction_diagonal(array, *contraction_or_diagonal_axes):
    array = _arrayfy(array)

    # Verify contraction_axes:
    taken_dims = set()
    for axes_group in contraction_or_diagonal_axes:
        if not isinstance(axes_group, Iterable):
            raise ValueError("collections of contraction/diagonal axes expected")

        dim = array.shape[axes_group[0]]

        for d in axes_group:
            if d in taken_dims:
                raise ValueError("dimension specified more than once")
            if dim != array.shape[d]:
                raise ValueError("cannot contract or diagonalize between axes of different dimension")
            taken_dims.add(d)

    rank = array.rank()

    remaining_shape = [dim for i, dim in enumerate(array.shape) if i not in taken_dims]
    cum_shape = [0]*rank
    _cumul = 1
    for i in range(rank):
        cum_shape[rank - i - 1] = _cumul
        _cumul *= int(array.shape[rank - i - 1])

    # DEFINITION: by absolute position it is meant the position along the one
    # dimensional array containing all the tensor components.

    # Possible future work on this module: move computation of absolute
    # positions to a class method.

    # Determine absolute positions of the uncontracted indices:
    remaining_indices = [[cum_shape[i]*j for j in range(array.shape[i])]
                         for i in range(rank) if i not in taken_dims]

    # Determine absolute positions of the contracted indices:
    summed_deltas = []
    for axes_group in contraction_or_diagonal_axes:
        lidx = []
        for js in range(array.shape[axes_group[0]]):
            lidx.append(sum([cum_shape[ig] * js for ig in axes_group]))
        summed_deltas.append(lidx)

    return array, remaining_indices, remaining_shape, summed_deltas


def tensorcontraction(array, *contraction_axes):
    """
    Contraction of an array-like object on the specified axes.

    Examples
    ========

    >>> from sympy import Array, tensorcontraction
    >>> from sympy import Matrix, eye
    >>> tensorcontraction(eye(3), (0, 1))
    3
    >>> A = Array(range(18), (3, 2, 3))
    >>> A
    [[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]], [[12, 13, 14], [15, 16, 17]]]
    >>> tensorcontraction(A, (0, 2))
    [21, 30]

    Matrix multiplication may be emulated with a proper combination of
    ``tensorcontraction`` and ``tensorproduct``

    >>> from sympy import tensorproduct
    >>> from sympy.abc import a,b,c,d,e,f,g,h
    >>> m1 = Matrix([[a, b], [c, d]])
    >>> m2 = Matrix([[e, f], [g, h]])
    >>> p = tensorproduct(m1, m2)
    >>> p
    [[[[a*e, a*f], [a*g, a*h]], [[b*e, b*f], [b*g, b*h]]], [[[c*e, c*f], [c*g, c*h]], [[d*e, d*f], [d*g, d*h]]]]
    >>> tensorcontraction(p, (1, 2))
    [[a*e + b*g, a*f + b*h], [c*e + d*g, c*f + d*h]]
    >>> m1*m2
    Matrix([
    [a*e + b*g, a*f + b*h],
    [c*e + d*g, c*f + d*h]])
    """
    from sympy.tensor.array.expressions.array_expressions import ArrayContraction
    from sympy.tensor.array.expressions.array_expressions import _CodegenArrayAbstract
    from sympy.tensor.array.expressions.array_expressions import _ArrayExpr
    from sympy import MatrixSymbol
    if isinstance(array, (_ArrayExpr, _CodegenArrayAbstract, MatrixSymbol)):
        return ArrayContraction(array, *contraction_axes)

    array, remaining_indices, remaining_shape, summed_deltas = _util_contraction_diagonal(array, *contraction_axes)

    # Compute the contracted array:
    #
    # 1. external for loops on all uncontracted indices.
    #    Uncontracted indices are determined by the combinatorial product of
    #    the absolute positions of the remaining indices.
    # 2. internal loop on all contracted indices.
    #    It sums the values of the absolute contracted index and the absolute
    #    uncontracted index for the external loop.
    contracted_array = []
    for icontrib in itertools.product(*remaining_indices):
        index_base_position = sum(icontrib)
        isum = S.Zero
        for sum_to_index in itertools.product(*summed_deltas):
            idx = array._get_tuple_index(index_base_position + sum(sum_to_index))
            isum += array[idx]

        contracted_array.append(isum)

    if len(remaining_indices) == 0:
        assert len(contracted_array) == 1
        return contracted_array[0]

    return type(array)(contracted_array, remaining_shape)


def tensordiagonal(array, *diagonal_axes):
    """
    Diagonalization of an array-like object on the specified axes.

    This is equivalent to multiplying the expression by Kronecker deltas
    uniting the axes.

    The diagonal indices are put at the end of the axes.

    Examples
    ========

    ``tensordiagonal`` acting on a 2-dimensional array by axes 0 and 1 is
    equivalent to the diagonal of the matrix:

    >>> from sympy import Array, tensordiagonal
    >>> from sympy import Matrix, eye
    >>> tensordiagonal(eye(3), (0, 1))
    [1, 1, 1]

    >>> from sympy.abc import a,b,c,d
    >>> m1 = Matrix([[a, b], [c, d]])
    >>> tensordiagonal(m1, [0, 1])
    [a, d]

    In case of higher dimensional arrays, the diagonalized out dimensions
    are appended removed and appended as a single dimension at the end:

    >>> A = Array(range(18), (3, 2, 3))
    >>> A
    [[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]], [[12, 13, 14], [15, 16, 17]]]
    >>> tensordiagonal(A, (0, 2))
    [[0, 7, 14], [3, 10, 17]]
    >>> from sympy import permutedims
    >>> tensordiagonal(A, (0, 2)) == permutedims(Array([A[0, :, 0], A[1, :, 1], A[2, :, 2]]), [1, 0])
    True

    """
    if any([len(i) <= 1 for i in diagonal_axes]):
        raise ValueError("need at least two axes to diagonalize")

    from sympy.tensor.array.expressions.array_expressions import _ArrayExpr
    from sympy.tensor.array.expressions.array_expressions import _CodegenArrayAbstract
    from sympy.tensor.array.expressions.array_expressions import ArrayDiagonal
    from sympy import MatrixSymbol
    if isinstance(array, (_ArrayExpr, _CodegenArrayAbstract, MatrixSymbol)):
        return ArrayDiagonal(array, *diagonal_axes)

    array, remaining_indices, remaining_shape, diagonal_deltas = _util_contraction_diagonal(array, *diagonal_axes)

    # Compute the diagonalized array:
    #
    # 1. external for loops on all undiagonalized indices.
    #    Undiagonalized indices are determined by the combinatorial product of
    #    the absolute positions of the remaining indices.
    # 2. internal loop on all diagonal indices.
    #    It appends the values of the absolute diagonalized index and the absolute
    #    undiagonalized index for the external loop.
    diagonalized_array = []
    diagonal_shape = [len(i) for i in diagonal_deltas]
    for icontrib in itertools.product(*remaining_indices):
        index_base_position = sum(icontrib)
        isum = []
        for sum_to_index in itertools.product(*diagonal_deltas):
            idx = array._get_tuple_index(index_base_position + sum(sum_to_index))
            isum.append(array[idx])

        isum = type(array)(isum).reshape(*diagonal_shape)
        diagonalized_array.append(isum)

    return type(array)(diagonalized_array, remaining_shape + diagonal_shape)


def derive_by_array(expr, dx):
    r"""
    Derivative by arrays. Supports both arrays and scalars.

    Explanation
    ===========

    Given the array `A_{i_1, \ldots, i_N}` and the array `X_{j_1, \ldots, j_M}`
    this function will return a new array `B` defined by

    `B_{j_1,\ldots,j_M,i_1,\ldots,i_N} := \frac{\partial A_{i_1,\ldots,i_N}}{\partial X_{j_1,\ldots,j_M}}`

    Examples
    ========

    >>> from sympy import derive_by_array
    >>> from sympy.abc import x, y, z, t
    >>> from sympy import cos
    >>> derive_by_array(cos(x*t), x)
    -t*sin(t*x)
    >>> derive_by_array(cos(x*t), [x, y, z, t])
    [-t*sin(t*x), 0, 0, -x*sin(t*x)]
    >>> derive_by_array([x, y**2*z], [[x, y], [z, t]])
    [[[1, 0], [0, 2*y*z]], [[0, y**2], [0, 0]]]

    """
    from sympy.matrices import MatrixBase
    from sympy.tensor.array import SparseNDimArray
    array_types = (Iterable, MatrixBase, NDimArray)

    if isinstance(dx, array_types):
        dx = ImmutableDenseNDimArray(dx)
        for i in dx:
            if not i._diff_wrt:
                raise ValueError("cannot derive by this array")

    if isinstance(expr, array_types):
        if isinstance(expr, NDimArray):
            expr = expr.as_immutable()
        else:
            expr = ImmutableDenseNDimArray(expr)

        if isinstance(dx, array_types):
            if isinstance(expr, SparseNDimArray):
                lp = len(expr)
                new_array = {k + i*lp: v
                             for i, x in enumerate(Flatten(dx))
                             for k, v in expr.diff(x)._sparse_array.items()}
            else:
                new_array = [[y.diff(x) for y in Flatten(expr)] for x in Flatten(dx)]
            return type(expr)(new_array, dx.shape + expr.shape)
        else:
            return expr.diff(dx)
    else:
        expr = _sympify(expr)
        if isinstance(dx, array_types):
            return ImmutableDenseNDimArray([expr.diff(i) for i in Flatten(dx)], dx.shape)
        else:
            dx = _sympify(dx)
            return diff(expr, dx)


def permutedims(expr, perm):
    """
    Permutes the indices of an array.

    Parameter specifies the permutation of the indices.

    Examples
    ========

    >>> from sympy.abc import x, y, z, t
    >>> from sympy import sin
    >>> from sympy import Array, permutedims
    >>> a = Array([[x, y, z], [t, sin(x), 0]])
    >>> a
    [[x, y, z], [t, sin(x), 0]]
    >>> permutedims(a, (1, 0))
    [[x, t], [y, sin(x)], [z, 0]]

    If the array is of second order, ``transpose`` can be used:

    >>> from sympy import transpose
    >>> transpose(a)
    [[x, t], [y, sin(x)], [z, 0]]

    Examples on higher dimensions:

    >>> b = Array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
    >>> permutedims(b, (2, 1, 0))
    [[[1, 5], [3, 7]], [[2, 6], [4, 8]]]
    >>> permutedims(b, (1, 2, 0))
    [[[1, 5], [2, 6]], [[3, 7], [4, 8]]]

    ``Permutation`` objects are also allowed:

    >>> from sympy.combinatorics import Permutation
    >>> permutedims(b, Permutation([1, 2, 0]))
    [[[1, 5], [2, 6]], [[3, 7], [4, 8]]]

    """
    from sympy.tensor.array import SparseNDimArray

    from sympy.tensor.array.expressions.array_expressions import _ArrayExpr
    from sympy.tensor.array.expressions.array_expressions import _CodegenArrayAbstract
    from sympy.tensor.array.expressions.array_expressions import PermuteDims
    from sympy import MatrixSymbol
    if isinstance(expr, (_ArrayExpr, _CodegenArrayAbstract, MatrixSymbol)):
        return PermuteDims(expr, perm)

    if not isinstance(expr, NDimArray):
        expr = ImmutableDenseNDimArray(expr)

    from sympy.combinatorics import Permutation
    if not isinstance(perm, Permutation):
        perm = Permutation(list(perm))

    if perm.size != expr.rank():
        raise ValueError("wrong permutation size")

    # Get the inverse permutation:
    iperm = ~perm
    new_shape = perm(expr.shape)

    if isinstance(expr, SparseNDimArray):
        return type(expr)({tuple(perm(expr._get_tuple_index(k))): v
                           for k, v in expr._sparse_array.items()}, new_shape)

    indices_span = perm([range(i) for i in expr.shape])

    new_array = [None]*len(expr)
    for i, idx in enumerate(itertools.product(*indices_span)):
        t = iperm(idx)
        new_array[i] = expr[t]

    return type(expr)(new_array, new_shape)


class Flatten(Basic):
    '''
    Flatten an iterable object to a list in a lazy-evaluation way.

    Notes
    =====

    This class is an iterator with which the memory cost can be economised.
    Optimisation has been considered to ameliorate the performance for some
    specific data types like DenseNDimArray and SparseNDimArray.

    Examples
    ========

    >>> from sympy.tensor.array.arrayop import Flatten
    >>> from sympy.tensor.array import Array
    >>> A = Array(range(6)).reshape(2, 3)
    >>> Flatten(A)
    Flatten([[0, 1, 2], [3, 4, 5]])
    >>> [i for i in Flatten(A)]
    [0, 1, 2, 3, 4, 5]
    '''
    def __init__(self, iterable):
        from sympy.matrices.matrices import MatrixBase
        from sympy.tensor.array import NDimArray

        if not isinstance(iterable, (Iterable, MatrixBase)):
            raise NotImplementedError("Data type not yet supported")

        if isinstance(iterable, list):
            iterable = NDimArray(iterable)

        self._iter = iterable
        self._idx = 0

    def __iter__(self):
        return self

    def __next__(self):
        from sympy.matrices.matrices import MatrixBase

        if len(self._iter) > self._idx:
            if isinstance(self._iter, DenseNDimArray):
                result = self._iter._array[self._idx]

            elif isinstance(self._iter, SparseNDimArray):
                if self._idx in self._iter._sparse_array:
                    result = self._iter._sparse_array[self._idx]
                else:
                    result = 0

            elif isinstance(self._iter, MatrixBase):
                result = self._iter[self._idx]

            elif hasattr(self._iter, '__next__'):
                result = next(self._iter)

            else:
                result = self._iter[self._idx]

        else:
            raise StopIteration

        self._idx += 1
        return result

    def next(self):
        return self.__next__()
