import numpy

import cupy
from cupy import _core
from cupyx.scipy.sparse import _util
from cupyx.scipy.sparse import _sputils


try:
    import scipy.sparse as _sparse
    SparseWarning = _sparse.SparseWarning
    SparseEfficiencyWarning = _sparse.SparseEfficiencyWarning
except ImportError:
    class SparseWarning(Warning):  # type: ignore
        pass

    class SparseEfficiencyWarning(SparseWarning):  # type: ignore
        pass


# TODO(asi1024): Implement _spbase


class spmatrix(object):

    """Base class of all sparse matrixes.

    See :class:`scipy.sparse.spmatrix`
    """

    __array_priority__ = 101

    def __init__(self, maxprint=50):
        if self.__class__ == spmatrix:
            raise ValueError(
                'This class is not intended to be instantiated directly.')
        self.maxprint = maxprint

    @property
    def device(self):
        """CUDA device on which this array resides."""
        raise NotImplementedError

    def get(self, stream=None):
        """Return a copy of the array on host memory.

        Args:
            stream (cupy.cuda.Stream): CUDA stream object. If it is given, the
                copy runs asynchronously. Otherwise, the copy is synchronous.

        Returns:
            scipy.sparse.spmatrix: An array on host memory.

        """
        raise NotImplementedError

    def __len__(self):
        raise TypeError('sparse matrix length is ambiguous; '
                        'use getnnz() or shape[0]')

    def __str__(self):
        # TODO(unno): Do not use get method which is only available when scipy
        # is installed.
        return str(self.get())

    def __iter__(self):
        for r in range(self.shape[0]):
            yield self[r, :]

    def __bool__(self):
        if self.shape == (1, 1):
            return self.nnz != 0
        else:
            raise ValueError('The truth value of an array with more than one '
                             'element is ambiguous. Use a.any() or a.all().')

    __nonzero__ = __bool__

    def __eq__(self, other):
        return self.tocsr().__eq__(other)

    def __ne__(self, other):
        return self.tocsr().__ne__(other)

    def __lt__(self, other):
        return self.tocsr().__lt__(other)

    def __gt__(self, other):
        return self.tocsr().__gt__(other)

    def __le__(self, other):
        return self.tocsr().__le__(other)

    def __ge__(self, other):
        return self.tocsr().__ge__(other)

    def __abs__(self):
        return self.tocsr().__abs__()

    def __add__(self, other):
        return self.tocsr().__add__(other)

    def __radd__(self, other):
        return self.tocsr().__radd__(other)

    def __sub__(self, other):
        return self.tocsr().__sub__(other)

    def __rsub__(self, other):
        return self.tocsr().__rsub__(other)

    def __mul__(self, other):
        return self.tocsr().__mul__(other)

    def __rmul__(self, other):
        if cupy.isscalar(other) or isdense(other) and other.ndim == 0:
            return self * other
        else:
            try:
                tr = other.T
            except AttributeError:
                return NotImplemented
            return (self.T * tr).T

    # matmul (@) operator
    def __matmul__(self, other):
        if _util.isscalarlike(other):
            raise ValueError('Scalar operands are not allowed, '
                             'use \'*\' instead')
        return self.__mul__(other)

    def __rmatmul__(self, other):
        if _util.isscalarlike(other):
            raise ValueError('Scalar operands are not allowed, '
                             'use \'*\' instead')
        return self.__rmul__(other)

    def __div__(self, other):
        return self.tocsr().__div__(other)

    def __rdiv__(self, other):
        return self.tocsr().__rdiv__(other)

    def __truediv__(self, other):
        return self.tocsr().__truediv__(other)

    def __rtruediv__(self, other):
        return self.tocsr().__rtruediv__(other)

    def __neg__(self):
        return -self.tocsr()

    def __iadd__(self, other):
        return NotImplemented

    def __isub__(self, other):
        return NotImplemented

    def __imul__(self, other):
        return NotImplemented

    def __idiv__(self, other):
        return self.__itruediv__(other)

    def __itruediv__(self, other):
        return NotImplemented

    def __pow__(self, other):
        """Calculates n-th power of the matrix.

        This method calculates n-th power of a given matrix. The matrix must
        be a squared matrix, and a given exponent must be an integer.

        Args:
            other (int): Exponent.

        Returns:
            cupyx.scipy.sparse.spmatrix: A sparse matrix representing n-th
            power of this matrix.

        """
        m, n = self.shape
        if m != n:
            raise TypeError('matrix is not square')

        if _util.isintlike(other):
            other = int(other)
            if other < 0:
                raise ValueError('exponent must be >= 0')

            if other == 0:
                import cupyx.scipy.sparse
                return cupyx.scipy.sparse.identity(
                    m, dtype=self.dtype, format='csr')
            elif other == 1:
                return self.copy()
            else:
                tmp = self.__pow__(other // 2)
                if other % 2:
                    return self * tmp * tmp
                else:
                    return tmp * tmp
        elif _util.isscalarlike(other):
            raise ValueError('exponent must be an integer')
        else:
            return NotImplemented

    @property
    def A(self):
        """Dense ndarray representation of this matrix.

        This property is equivalent to
        :meth:`~cupyx.scipy.sparse.spmatrix.toarray` method.

        """
        return self.toarray()

    @property
    def T(self):
        return self.transpose()

    @property
    def H(self):
        return self.getH()

    @property
    def ndim(self):
        return 2

    @property
    def size(self):
        return self.getnnz()

    @property
    def nnz(self):
        return self.getnnz()

    @property
    def shape(self):
        return self.get_shape()

    @shape.setter
    def shape(self, value):
        self.set_shape(value)

    def asformat(self, format):
        """Return this matrix in a given sparse format.

        Args:
            format (str or None): Format you need.
        """
        if format is None or format == self.format:
            return self
        else:
            return getattr(self, 'to' + format)()

    def asfptype(self):
        """Upcasts matrix to a floating point format.

        When the matrix has floating point type, the method returns itself.
        Otherwise it makes a copy with floating point type and the same format.

        Returns:
            cupyx.scipy.sparse.spmatrix: A matrix with float type.

        """
        if self.dtype.kind == 'f':
            return self
        else:
            typ = numpy.promote_types(self.dtype, 'f')
            return self.astype(typ)

    def astype(self, t):
        """Casts the array to given data type.

        Args:
            t: Type specifier.

        Returns:
            cupyx.scipy.sparse.spmatrix:
                A copy of the array with the given type and the same format.

        """
        return self.tocsr().astype(t).asformat(self.format)

    def conj(self, copy=True):
        """Element-wise complex conjugation.

        If the matrix is of non-complex data type and `copy` is False,
        this method does nothing and the data is not copied.

        Args:
            copy (bool):
                If True, the result is guaranteed to not share data with self.

        Returns:
            cupyx.scipy.sparse.spmatrix : The element-wise complex conjugate.

        """
        if self.dtype.kind == 'c':
            return self.tocsr(copy=copy).conj(copy=False)
        elif copy:
            return self.copy()
        else:
            return self

    def conjugate(self, copy=True):
        return self.conj(copy=copy)

    conjugate.__doc__ = conj.__doc__

    def copy(self):
        """Returns a copy of this matrix.

        No data/indices will be shared between the returned value and current
        matrix.
        """
        return self.__class__(self, copy=True)

    def count_nonzero(self):
        """Number of non-zero entries, equivalent to"""
        raise NotImplementedError

    def diagonal(self, k=0):
        """Returns the k-th diagonal of the matrix.

        Args:
            k (int, optional): Which diagonal to get, corresponding to elements
            a[i, i+k]. Default: 0 (the main diagonal).

        Returns:
            cupy.ndarray : The k-th diagonal.
        """
        return self.tocsr().diagonal(k=k)

    def dot(self, other):
        """Ordinary dot product"""
        if numpy.isscalar(other):
            return self * other
        else:
            return self @ other

    def getH(self):
        return self.transpose().conj()

    def get_shape(self):
        raise NotImplementedError

    # TODO(unno): Implement getcol

    def getformat(self):
        return self.format

    def getmaxprint(self):
        return self.maxprint

    def getnnz(self, axis=None):
        """Number of stored values, including explicit zeros."""
        raise NotImplementedError

    # TODO(unno): Implement getrow

    def maximum(self, other):
        return self.tocsr().maximum(other)

    def mean(self, axis=None, dtype=None, out=None):
        """
        Compute the arithmetic mean along the specified axis.

        Returns the average of the matrix elements. The average is taken
        over all elements in the matrix by default, otherwise over the
        specified axis. `float64` intermediate and return values are used
        for integer inputs.

        Args:
            axis {-2, -1, 0, 1, None}: optional
                Axis along which the mean is computed. The default is to
                compute the mean of all elements in the matrix
                (i.e., `axis` = `None`).
            dtype (dtype): optional
                Type to use in computing the mean. For integer inputs, the
                default is `float64`; for floating point inputs, it is the same
                as the input dtype.
            out (cupy.ndarray): optional
                Alternative output matrix in which to place the result. It must
                have the same shape as the expected output, but the type of the
                output values will be cast if necessary.

        Returns:
            m (cupy.ndarray) : Output array of means

        .. seealso::
            :meth:`scipy.sparse.spmatrix.mean`

        """
        def _is_integral(dtype):
            return (cupy.issubdtype(dtype, cupy.integer) or
                    cupy.issubdtype(dtype, cupy.bool_))

        _sputils.validateaxis(axis)

        res_dtype = self.dtype.type
        integral = _is_integral(self.dtype)

        # output dtype
        if dtype is None:
            if integral:
                res_dtype = cupy.float64
        else:
            res_dtype = cupy.dtype(dtype).type

        # intermediate dtype for summation
        inter_dtype = cupy.float64 if integral else res_dtype
        inter_self = self.astype(inter_dtype)

        if axis is None:
            return (inter_self / cupy.array(
                self.shape[0] * self.shape[1]))\
                .sum(dtype=res_dtype, out=out)

        if axis < 0:
            axis += 2

        # axis = 0 or 1 now
        if axis == 0:
            return (inter_self * (1.0 / self.shape[0])).sum(
                axis=0, dtype=res_dtype, out=out)
        else:
            return (inter_self * (1.0 / self.shape[1])).sum(
                axis=1, dtype=res_dtype, out=out)

    def minimum(self, other):
        return self.tocsr().minimum(other)

    def multiply(self, other):
        """Point-wise multiplication by another matrix"""
        return self.tocsr().multiply(other)

    # TODO(unno): Implement nonzero

    def power(self, n, dtype=None):
        return self.tocsr().power(n, dtype=dtype)

    def reshape(self, *shape, order='C'):
        """Gives a new shape to a sparse matrix without changing its data.

        Args:
            shape (tuple):
                The new shape should be compatible with the original shape.
            order: {'C', 'F'} (optional)
                Read the elements using this index order. 'C' means to read and
                write the elements using C-like index order. 'F' means to read
                and write the elements using Fortran-like index order. Default:
                C.

        Returns:
            cupyx.scipy.sparse.coo_matrix: sparse matrix

        """
        shape = _sputils.check_shape(shape, self.shape)

        if shape == self.shape:
            return self

        return self.tocoo().reshape(shape, order=order)

    def set_shape(self, shape):
        self.reshape(shape)

    def setdiag(self, values, k=0):
        """Set diagonal or off-diagonal elements of the array.

        Args:
            values (cupy.ndarray): New values of the diagonal elements.
                Values may have any length. If the diagonal is longer than
                values, then the remaining diagonal entries will not be set.
                If values is longer than the diagonal, then the remaining
                values are ignored. If a scalar value is given, all of the
                diagonal is set to it.
            k (int, optional): Which diagonal to set, corresponding to elements
                a[i, i+k]. Default: 0 (the main diagonal).
        """
        raise NotImplementedError

    def sum(self, axis=None, dtype=None, out=None):
        """Sums the matrix elements over a given axis.

        Args:
            axis (int or ``None``): Axis along which the sum is computed.
                If it is ``None``, it computes the sum of all the elements.
                Select from ``{None, 0, 1, -2, -1}``.
            dtype: The type of returned matrix. If it is not specified, type
                of the array is used.
            out (cupy.ndarray): Output matrix.

        Returns:
            cupy.ndarray: Summed array.

        .. seealso::
           :meth:`scipy.sparse.spmatrix.sum`

        """
        _sputils.validateaxis(axis)

        # This implementation uses multiplication, though it is not efficient
        # for some matrix types. These should override this function.

        m, n = self.shape

        if axis is None:
            return self.dot(cupy.ones(n, dtype=self.dtype)).sum(
                dtype=dtype, out=out)

        if axis < 0:
            axis += 2

        if axis == 0:
            ret = self.T.dot(cupy.ones(m, dtype=self.dtype)).reshape(1, n)
        else:  # axis == 1
            ret = self.dot(cupy.ones(n, dtype=self.dtype)).reshape(m, 1)

        if out is not None:
            if out.shape != ret.shape:
                raise ValueError('dimensions do not match')
            _core.elementwise_copy(ret, out)
            return out
        elif dtype is not None:
            return ret.astype(dtype, copy=False)
        else:
            return ret

    def toarray(self, order=None, out=None):
        """Return a dense ndarray representation of this matrix."""
        return self.tocsr().toarray(order=order, out=out)

    def tobsr(self, blocksize=None, copy=False):
        """Convert this matrix to Block Sparse Row format."""
        return self.tocsr(copy=copy).tobsr(copy=False)

    def tocoo(self, copy=False):
        """Convert this matrix to COOrdinate format."""
        return self.tocsr(copy=copy).tocoo(copy=False)

    def tocsc(self, copy=False):
        """Convert this matrix to Compressed Sparse Column format."""
        return self.tocsr(copy=copy).tocsc(copy=False)

    def tocsr(self, copy=False):
        """Convert this matrix to Compressed Sparse Row format."""
        raise NotImplementedError

    def todense(self, order=None, out=None):
        """Return a dense matrix representation of this matrix."""
        return self.toarray(order=order, out=out)

    def todia(self, copy=False):
        """Convert this matrix to sparse DIAgonal format."""
        return self.tocsr(copy=copy).todia(copy=False)

    def todok(self, copy=False):
        """Convert this matrix to Dictionary Of Keys format."""
        return self.tocsr(copy=copy).todok(copy=False)

    def tolil(self, copy=False):
        """Convert this matrix to LInked List format."""
        return self.tocsr(copy=copy).tolil(copy=False)

    def transpose(self, axes=None, copy=False):
        """Reverses the dimensions of the sparse matrix."""
        return self.tocsr(copy=copy).transpose(axes=axes, copy=False)


def issparse(x):
    """Checks if a given matrix is a sparse matrix.

    Returns:
        bool: Returns if ``x`` is :class:`cupyx.scipy.sparse.spmatrix` that is
        a base class of all sparse matrix classes.

    """
    return isinstance(x, spmatrix)


isdense = _util.isdense
isspmatrix = issparse