"""
This module contains the block vector definition
"""

from typing import TypeVar, List
import functools as ftls

import numpy as np

from . import subops as gops
from .blockarray import BlockArray
from .blockmat import BlockMatrix

from . import require_petsc, _HAS_PETSC

if _HAS_PETSC:
    from petsc4py import PETSc

## pylint: disable=no-member


# Type variable for a 'sub'vector
T = TypeVar('T')


class BlockVector(BlockArray[T]):
    """
    Represents a block vector with blocks indexed by labels
    """

    def __init__(
        self, subarrays, shape=None, labels=None, wrap=gops.wrap, check_bshape=True
    ):
        super().__init__(subarrays, shape, labels, wrap=wrap, check_bshape=check_bshape)

        if len(self.shape) > 1:
            raise ValueError(
                f"BlockVector must have dimension == 1, not {len(self.shape)}"
            )

    ## Conversion to monolithic formats
    def to_mono_ndarray(self):
        ndarray_vecs = [np.array(vec) for vec in self.sub_blocks]
        return np.concatenate(ndarray_vecs, axis=0)

    @require_petsc
    def to_mono_petsc_seq(self, comm=None):
        total_size = np.sum(self.mshape)
        vec = PETSc.Vec().createSeq(total_size, comm=comm)
        vec.setUp()
        vec.setArray(self.to_mono_ndarray())
        vec.assemble()
        return vec

    @require_petsc
    def to_mono_petsc(self, comm=None):
        total_size = np.sum(self.mshape)
        vec = PETSc.Vec().create(comm=comm)
        vec.setSizes(total_size)
        vec.setUp()
        vec.setArray(self.to_mono_ndarray())
        vec.assemble()
        return vec

    def set_mono(self, vec: T):
        """
        Set values from an equivalent monolithic vector
        """
        # Check sizes are compatible
        assert vec.size == np.sum(self.bshape[0])

        # indices of the boundaries of each block
        n_blocks = np.concatenate(([0], np.cumsum(self.bshape[0])))
        for i, (n_start, n_stop) in enumerate(zip(n_blocks[:-1], n_blocks[1:])):
            self[i][:] = vec[n_start:n_stop]

    ## Special vector operations
    def norm(self):
        return dot(self, self) ** 0.5

    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
        # Import this here to avoid ciruclar import errors
        # TODO: Fix bad module layout?
        from . import ufunc as _ufunc

        return _ufunc.apply_ufunc_mat_vec(ufunc, method, *inputs, **kwargs)


# TODO: Remove this unused code?
def validate_blockvec_size(*args):
    """
    Check if a collection of BlockVecs have compatible block sizes
    """
    ref_bsize = args[0].bshape[0]
    valid_bsizes = [arg.bshape[0] == ref_bsize for arg in args]

    return all(valid_bsizes)


# Utilities


def chunk(bvec, block_sizes):
    """
    Splits a block vector into multiple block vectors
    """
    block_cumul_sizes = [0] + np.cumsum(block_sizes).tolist()
    split_bvecs = [
        bvec[ii:jj] for ii, jj in zip(block_cumul_sizes[:-1], block_cumul_sizes[1:])
    ]
    return tuple(split_bvecs)


def concatenate(args, labels=None):
    """
    Concatenate a series of BlockVecs into a single BlockVector

    Parameters
    ----------
    args : List of BlockVector
    """
    if labels is None:
        labels = [ftls.reduce(lambda a, b: a + b, [bvec.labels[0] for bvec in args])]

    vecs = np.concatenate([bvec.blocks for bvec in args])

    return BlockVector(vecs, labels=labels)


def concatenate_with_prefix(bvecs: List[BlockVector], prefix=''):
    """
    Concatenate a series of block vectors with a numbered prefix
    """
    labels = tuple(
        f'{prefix}{n}.{label}'
        for n, bvec in enumerate(bvecs)
        for label in bvec.labels[0]
    )
    return concatenate(bvecs, (labels,))


# Converting subtypes
@require_petsc
def convert_subtype_to_petsc(bvec):
    """
    Converts a block matrix from one submatrix type to the PETSc submatrix type

    Parameters
    ----------
    bmat: BlockMatrix
    """
    vecs = [gops.convert_vec_to_petsc(subvec) for subvec in bvec.blocks]
    return BlockVector(vecs, labels=bvec.labels)


def convert_subtype_to_numpy(bvec):
    """
    Converts a block matrix from one submatrix type to the PETSc submatrix type

    Parameters
    ----------
    bmat: BlockMatrix
    """
    vecs = [gops.convert_vec_to_numpy(subvec) for subvec in bvec.blocks]
    return BlockVector(vecs, labels=bvec.labels)


# Converting to monolithic vectors
@require_petsc
def to_mono_petsc(bvec, comm=None, finalize=True):
    raise NotImplementedError()


# Converting to block matrix formats
@require_petsc
def to_block_rowmat(bvec):
    mats = tuple(gops.convert_vec_to_rowmat(subvec) for subvec in bvec.sub_blocks)
    return BlockMatrix(mats, shape=(1, len(mats)))


@require_petsc
def to_block_colmat(bvec):
    mats = tuple(gops.convert_vec_to_colmat(subvec) for subvec in bvec.sub_blocks)
    return BlockMatrix(mats, shape=(len(mats), 1))


# Basic operations
def dot(a, b):
    """
    Return the dot product of a and b
    """
    c = a * b
    ret = 0
    for subvec in c.sub_blocks:
        # using the [:] indexing notation makes sum interpret the different data types as np arrays
        # which can improve performance a lot
        ret += sum(subvec[:])
    return ret


def norm(a):
    """Return the 2-norm of a vector"""
    return dot(a, a) ** 0.5


class MonotoBlock:
    def __init__(self, bvec):
        self.bvec = bvec

    def __setitem__(self, key, value):
        assert isinstance(key, slice)
        total_size = np.sum(self.bvec.size)

        # Let n refer to a monolithic index
        # and m refer to a block index (from 0 to # of blocks-1)
        nstart, nstop, nstep = self.slice_to_numeric(key, total_size)

        # Get the monolithic ending indices of each block
        # nblock = np.concatenate(np.cumsum(self.bvec.size))

        # istart = np.where()
        # istop = 0, 0
        raise NotImplementedError("do this later I guess")

    def slice_to_numeric(self, slice_obj, array_len):
        nstart, nstop, nstep = 0, 0, 1
        if slice_obj.start is not None:
            nstart = slice_obj.start

        if slice_obj.stop is None:
            nstop = array_len + 1
        elif slice_obj.stop < 0:
            nstop = array_len + slice_obj.stop
        else:
            nstop = slice_obj.stop

        if slice_obj.step is not None:
            nstep = slice_obj.step
        return (nstart, nstop, nstep)