# This file is a part of Julia. License is MIT: https://julialang.org/license

# Compressed sparse columns data structure
# No assumptions about stored zeros in the data structure
# Assumes that row values in rowval for each column are sorted
#      issorted(rowval[colptr[i]:(colptr[i+1]-1)]) == true
# Assumes that 1 <= colptr[i] <= colptr[i+1] for i in 1..n
# Assumes that nnz <= length(rowval) < typemax(Ti)
# Assumes that 0   <= length(nzval) < typemax(Ti)

"""
    SparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrixCSC{Tv,Ti}

Matrix type for storing sparse matrices in the
[Compressed Sparse Column](@ref man-csc) format. The standard way
of constructing SparseMatrixCSC is through the [`sparse`](@ref) function.
See also [`spzeros`](@ref), [`spdiagm`](@ref) and [`sprand`](@ref).
"""
struct SparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrixCSC{Tv,Ti}
    m::Int                  # Number of rows
    n::Int                  # Number of columns
    colptr::Vector{Ti}      # Column i is in colptr[i]:(colptr[i+1]-1)
    rowval::Vector{Ti}      # Row indices of stored values
    nzval::Vector{Tv}       # Stored values, typically nonzeros

    function SparseMatrixCSC{Tv,Ti}(m::Integer, n::Integer, colptr::Vector{Ti},
                            rowval::Vector{Ti}, nzval::Vector{Tv}) where {Tv,Ti<:Integer}
        sparse_check_Ti(m, n, Ti)
        _goodbuffers(Int(m), Int(n), colptr, rowval, nzval) ||
            throw(ArgumentError("Illegal buffers for SparseMatrixCSC construction $n $colptr $rowval $nzval"))
        new(Int(m), Int(n), colptr, rowval, nzval)
    end
end
function SparseMatrixCSC(m::Integer, n::Integer, colptr::Vector, rowval::Vector, nzval::Vector)
    Tv = eltype(nzval)
    Ti = promote_type(eltype(colptr), eltype(rowval))
    sparse_check_Ti(m, n, Ti)
    sparse_check(n, colptr, rowval, nzval)
    # silently shorten rowval and nzval to usable index positions.
    maxlen = abs(widemul(m, n))
    isbitstype(Ti) && (maxlen = min(maxlen, typemax(Ti) - 1))
    length(rowval) > maxlen && resize!(rowval, maxlen)
    length(nzval) > maxlen && resize!(nzval, maxlen)
    SparseMatrixCSC{Tv,Ti}(m, n, colptr, rowval, nzval)
end

function sparse_check_Ti(m::Integer, n::Integer, Ti::Type)
        @noinline throwTi(str, lbl, k) =
            throw(ArgumentError("$str ($lbl = $k) does not fit in Ti = $(Ti)"))
        0 ≤ m && (!isbitstype(Ti) || m ≤ typemax(Ti)) || throwTi("number of rows", "m", m)
        0 ≤ n && (!isbitstype(Ti) || n ≤ typemax(Ti)) || throwTi("number of columns", "n", n)
end

function sparse_check(n::Integer, colptr::Vector{Ti}, rowval, nzval) where Ti
    # String interpolation is a performance bottleneck when it's part of the same function,
    # ensure we only do it once committed to the error.
    throwstart(ckp) = throw(ArgumentError("$ckp == colptr[1] != 1"))
    throwmonotonic(ckp, ck, k) = throw(ArgumentError("$ckp == colptr[$(k-1)] > colptr[$k] == $ck"))

    sparse_check_length("colptr", colptr, n+1, String) # don't check upper bound
    ckp = Ti(1)
    ckp == colptr[1] || throwstart(ckp)
    @inbounds for k = 2:n+1
        ck = colptr[k]
        ckp <= ck || throwmonotonic(ckp, ck, k)
        ckp = ck
    end
    sparse_check_length("rowval", rowval, ckp-1, Ti)
    sparse_check_length("nzval", nzval, 0, Ti) # we allow empty nzval !!!
end
function sparse_check_length(rowstr, rowval, minlen, Ti)
    throwmin(len, minlen, rowstr) = throw(ArgumentError("$len == length($rowstr) < $minlen"))
    throwmax(len, max, rowstr) = throw(ArgumentError("$len == length($rowstr) >= $max"))

    len = length(rowval)
    len >= minlen || throwmin(len, minlen, rowstr)
    !isbitstype(Ti) || len < typemax(Ti) || throwmax(len, typemax(Ti), rowstr)
end

size(S::SparseMatrixCSC) = (getfield(S, :m), getfield(S, :n))

_goodbuffers(S::SparseMatrixCSC) = _goodbuffers(size(S)..., getcolptr(S), getrowval(S), nonzeros(S))
_checkbuffers(S::SparseMatrixCSC) = (@assert _goodbuffers(S); S)

function _goodbuffers(m, n, colptr, rowval, nzval)
    (length(colptr) == n + 1 && colptr[end] - 1 == length(rowval) == length(nzval))
    # stronger check for debugging purposes
    # && all(issorted(@view rowval[colptr[i]:colptr[i+1]-1]) for i=1:n)
end

# Define an alias for views of a SparseMatrixCSC which include all rows and a unit range of the columns.
# Also define a union of SparseMatrixCSC and this view since many methods can be defined efficiently for
# this union by extracting the fields via the get function: getcolptr, getrowval, and getnzval. The key
# insight is that getcolptr on a SparseMatrixCSCView returns an offset view of the colptr of the
# underlying SparseMatrixCSC
const SparseMatrixCSCView{Tv,Ti} =
    SubArray{Tv,2,<:AbstractSparseMatrixCSC{Tv,Ti},
        Tuple{Base.Slice{Base.OneTo{Int}},I}} where {I<:AbstractUnitRange}
const SparseMatrixCSCUnion{Tv,Ti} = Union{AbstractSparseMatrixCSC{Tv,Ti}, SparseMatrixCSCView{Tv,Ti}}

getcolptr(S::SparseMatrixCSC)     = getfield(S, :colptr)
getcolptr(S::SparseMatrixCSCView) = view(getcolptr(parent(S)), first(axes(S, 2)):(last(axes(S, 2)) + 1))
getrowval(S::AbstractSparseMatrixCSC) = rowvals(S)
getrowval(S::SparseMatrixCSCView) = rowvals(parent(S))
getnzval( S::AbstractSparseMatrixCSC) = nonzeros(S)
getnzval( S::SparseMatrixCSCView) = nonzeros(parent(S))
nzvalview(S::AbstractSparseMatrixCSC) = view(nonzeros(S), 1:nnz(S))

"""
    nnz(A)

Returns the number of stored (filled) elements in a sparse array.

# Examples
```jldoctest
julia> A = sparse(2I, 3, 3)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 2  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  2

julia> nnz(A)
3
```
"""
nnz(S::AbstractSparseMatrixCSC) = Int(getcolptr(S)[size(S, 2) + 1]) - 1
nnz(S::ReshapedArray{<:Any,1,<:AbstractSparseMatrixCSC}) = nnz(parent(S))
nnz(S::UpperTriangular{<:Any,<:AbstractSparseMatrixCSC}) = nnz1(S)
nnz(S::LowerTriangular{<:Any,<:AbstractSparseMatrixCSC}) = nnz1(S)
nnz(S::SparseMatrixCSCView) = nnz1(S)
nnz1(S) = sum(length.(nzrange.(Ref(S), axes(S, 2))))

function count(pred, S::AbstractSparseMatrixCSC)
    count(pred, nzvalview(S)) + pred(zero(eltype(S)))*(prod(size(S)) - nnz(S))
end

"""
    nonzeros(A)

Return a vector of the structural nonzero values in sparse array `A`. This
includes zeros that are explicitly stored in the sparse array. The returned
vector points directly to the internal nonzero storage of `A`, and any
modifications to the returned vector will mutate `A` as well. See
[`rowvals`](@ref) and [`nzrange`](@ref).

# Examples
```jldoctest
julia> A = sparse(2I, 3, 3)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 2  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  2

julia> nonzeros(A)
3-element Vector{Int64}:
 2
 2
 2
```
"""
nonzeros(S::SparseMatrixCSC) = getfield(S, :nzval)
nonzeros(S::SparseMatrixCSCView)  = nonzeros(S.parent)
nonzeros(S::UpperTriangular{<:Any,<:SparseMatrixCSCUnion}) = nonzeros(S.data)
nonzeros(S::LowerTriangular{<:Any,<:SparseMatrixCSCUnion}) = nonzeros(S.data)

"""
    rowvals(A::AbstractSparseMatrixCSC)

Return a vector of the row indices of `A`. Any modifications to the returned
vector will mutate `A` as well. Providing access to how the row indices are
stored internally can be useful in conjunction with iterating over structural
nonzero values. See also [`nonzeros`](@ref) and [`nzrange`](@ref).

# Examples
```jldoctest
julia> A = sparse(2I, 3, 3)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 2  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  2

julia> rowvals(A)
3-element Vector{Int64}:
 1
 2
 3
```
"""
rowvals(S::SparseMatrixCSC) = getfield(S, :rowval)
rowvals(S::SparseMatrixCSCView) = rowvals(S.parent)
rowvals(S::UpperTriangular{<:Any,<:SparseMatrixCSCUnion}) = rowvals(S.data)
rowvals(S::LowerTriangular{<:Any,<:SparseMatrixCSCUnion}) = rowvals(S.data)

"""
    nzrange(A::AbstractSparseMatrixCSC, col::Integer)

Return the range of indices to the structural nonzero values of a sparse matrix
column. In conjunction with [`nonzeros`](@ref) and
[`rowvals`](@ref), this allows for convenient iterating over a sparse matrix :

    A = sparse(I,J,V)
    rows = rowvals(A)
    vals = nonzeros(A)
    m, n = size(A)
    for j = 1:n
       for i in nzrange(A, j)
          row = rows[i]
          val = vals[i]
          # perform sparse wizardry...
       end
    end
"""
nzrange(S::AbstractSparseMatrixCSC, col::Integer) = getcolptr(S)[col]:(getcolptr(S)[col+1]-1)
nzrange(S::SparseMatrixCSCView, col::Integer) = nzrange(S.parent, S.indices[2][col])
nzrange(S::UpperTriangular{<:Any,<:SparseMatrixCSCUnion}, i::Integer) = nzrangeup(S.data, i)
nzrange(S::LowerTriangular{<:Any,<:SparseMatrixCSCUnion}, i::Integer) = nzrangelo(S.data, i)

function Base.isstored(A::AbstractSparseMatrixCSC, i::Integer, j::Integer)
    @boundscheck checkbounds(A, i, j)
    rows = rowvals(A)
    for istored in nzrange(A, j) # could do binary search if the row indices are sorted?
        i == rows[istored] && return true
    end
    return false
end

Base.replace_in_print_matrix(A::AbstractSparseMatrix, i::Integer, j::Integer, s::AbstractString) =
    Base.isstored(A, i, j) ? s : Base.replace_with_centered_mark(s)

function Base.array_summary(io::IO, S::AbstractSparseMatrixCSC, dims::Tuple{Vararg{Base.OneTo}})
    _checkbuffers(S)
    xnnz = nnz(S)
    m, n = size(S)
    print(io, m, "×", n, " ", typeof(S), " with ", xnnz, " stored ",
              xnnz == 1 ? "entry" : "entries")
    nothing
end

# called by `show(io, MIME("text/plain"), ::AbstractSparseMatrixCSC)`
function Base.print_array(io::IO, S::AbstractSparseMatrixCSC)
    if max(size(S)...) < 16
        Base.print_matrix(io, S)
    else
        _show_with_braille_patterns(io, S)
    end
end

# always show matrices as `sparse(I, J, K)`
function Base.show(io::IO, S::AbstractSparseMatrixCSC)
    _checkbuffers(S)
    # can't use `findnz`, because that expects all values not to be #undef
    I = rowvals(S)
    J = [col for col = 1 : size(S, 2) for k = getcolptr(S)[col] : (getcolptr(S)[col+1]-1)]
    K = nonzeros(S)
    m, n = size(S)
    print(io, "sparse(", I, ", ", J, ", ", K, ", ", m, ", ", n, ")")
end

const brailleBlocks = UInt16['⠁', '⠂', '⠄', '⡀', '⠈', '⠐', '⠠', '⢀']
function _show_with_braille_patterns(io::IO, S::AbstractSparseMatrixCSC)
    m, n = size(S)
    (m == 0 || n == 0) && return show(io, MIME("text/plain"), S)

    # The maximal number of characters we allow to display the matrix
    local maxHeight::Int, maxWidth::Int
    maxHeight = displaysize(io)[1] - 4 # -4 from [Prompt, header, newline after elements, new prompt]
    maxWidth = displaysize(io)[2] ÷ 2

    # In the process of generating the braille pattern to display the nonzero
    # structure of `S`, we need to be able to scale the matrix `S` to a
    # smaller matrix with the same aspect ratio as `S`, but fits on the
    # available screen space. The size of that smaller matrix is stored
    # in the variables `scaleHeight` and `scaleWidth`. If no scaling is needed,
    # we can use the size `m × n` of `S` directly.
    # We determine if scaling is needed and set the scaling factors
    # `scaleHeight` and `scaleWidth` accordingly. Note that each available
    # character can contain up to 4 braille dots in its height (⡇) and up to
    # 2 braille dots in its width (⠉).
    if get(io, :limit, true) && (m > 4maxHeight || n > 2maxWidth)
        s = min(2maxWidth / n, 4maxHeight / m)
        scaleHeight = floor(Int, s * m)
        scaleWidth = floor(Int, s * n)
    else
        scaleHeight = m
        scaleWidth = n
    end

    # `brailleGrid` is used to store the needed braille characters for
    # the matrix `S`. Each row of the braille pattern to print is stored
    # in a column of `brailleGrid`.
    brailleGrid = fill(UInt16(10240), (scaleWidth - 1) ÷ 2 + 2, (scaleHeight - 1) ÷ 4 + 1)
    brailleGrid[end, :] .= '\n'

    rvals = rowvals(S)
    rowscale = max(1, scaleHeight - 1) / max(1, m - 1)
    colscale = max(1, scaleWidth - 1) / max(1, n - 1)
    @inbounds for j = 1:n
        # Scale the column index `j` to the best matching column index
        # of a matrix of size `scaleHeight × scaleWidth`
        sj = round(Int, (j - 1) * colscale + 1)
        for x in nzrange(S, j)
            # Scale the row index `i` to the best matching row index
            # of a matrix of size `scaleHeight × scaleWidth`
            si = round(Int, (rvals[x] - 1) * rowscale + 1)

            # Given the index pair `(si, sj)` of the scaled matrix,
            # calculate the corresponding triple `(k, l, p)` such that the
            # element at `(si, sj)` can be found at position `(k, l)` in the
            # braille grid `brailleGrid` and corresponds to the 1-dot braille
            # character `brailleBlocks[p]`
            k = (sj - 1) ÷ 2 + 1
            l = (si - 1) ÷ 4 + 1
            p = ((sj - 1) % 2) * 4 + ((si - 1) % 4 + 1)

            brailleGrid[k, l] |= brailleBlocks[p]
        end
    end
    foreach(c -> print(io, Char(c)), @view brailleGrid[1:end-1])
end

## Reshape

function sparse_compute_reshaped_colptr_and_rowval!(colptrS::Vector{Ti}, rowvalS::Vector{Ti},
                                                   mS::Int, nS::Int, colptrA::Vector{Ti},
                                                   rowvalA::Vector{Ti}, mA::Int, nA::Int) where Ti
    lrowvalA = length(rowvalA)
    maxrowvalA = (lrowvalA > 0) ? maximum(rowvalA) : zero(Ti)
    ((length(colptrA) == (nA+1)) && (maximum(colptrA) <= (lrowvalA+1)) && (maxrowvalA <= mA)) || throw(BoundsError())

    colptrS[1] = 1
    colA = 1
    colS = 1
    ptr = 1

    @inbounds while colA <= nA
        offsetA = (colA - 1) * mA
        while ptr <= colptrA[colA+1]-1
            rowA = rowvalA[ptr]
            i = offsetA + rowA - 1
            colSn = div(i, mS) + 1
            rowS = mod(i, mS) + 1
            while colS < colSn
                colptrS[colS+1] = ptr
                colS += 1
            end
            rowvalS[ptr] = rowS
            ptr += 1
        end
        colA += 1
    end
    @inbounds while colS <= nS
        colptrS[colS+1] = ptr
        colS += 1
    end
end

function copy(ra::ReshapedArray{<:Any,2,<:AbstractSparseMatrixCSC})
    mS,nS = size(ra)
    a = parent(ra)
    mA,nA = size(a)
    numnz = nnz(a)
    colptr = similar(getcolptr(a), nS+1)
    rowval = similar(rowvals(a))
    nzval = copy(nonzeros(a))

    sparse_compute_reshaped_colptr_and_rowval!(colptr, rowval, mS, nS, getcolptr(a), rowvals(a), mA, nA)

    return SparseMatrixCSC(mS, nS, colptr, rowval, nzval)
end

## Alias detection and prevention
using Base: dataids, unaliascopy
Base.dataids(S::AbstractSparseMatrixCSC) = (dataids(getcolptr(S))..., dataids(rowvals(S))..., dataids(nonzeros(S))...)
Base.unaliascopy(S::AbstractSparseMatrixCSC) = typeof(S)(size(S, 1), size(S, 2), unaliascopy(getcolptr(S)), unaliascopy(rowvals(S)), unaliascopy(nonzeros(S)))

## Constructors

copy(S::AbstractSparseMatrixCSC) =
    SparseMatrixCSC(size(S, 1), size(S, 2), copy(getcolptr(S)), copy(rowvals(S)), copy(nonzeros(S)))

function copyto!(A::AbstractSparseMatrixCSC, B::AbstractSparseMatrixCSC)
    # If the two matrices have the same length then all the
    # elements in A will be overwritten.
    if widelength(A) == widelength(B)
        resize!(nonzeros(A), length(nonzeros(B)))
        resize!(rowvals(A), length(rowvals(B)))
        if size(A) == size(B)
            # Simple case: we can simply copy the internal fields of B to A.
            copyto!(getcolptr(A), getcolptr(B))
            copyto!(rowvals(A), rowvals(B))
        else
            # This is like a "reshape B into A".
            sparse_compute_reshaped_colptr_and_rowval!(getcolptr(A), rowvals(A), size(A, 1), size(A, 2), getcolptr(B), rowvals(B), size(B, 1), size(B, 2))
        end
    else
        widelength(A) >= widelength(B) || throw(BoundsError())
        lB = widelength(B)
        nnzA = nnz(A)
        nnzB = nnz(B)
        # Up to which col, row, and ptr in rowval/nzval will A be overwritten?
        lastmodcolA = Int(div(lB - 1, size(A, 1))) + 1
        lastmodrowA = Int(mod(lB - 1, size(A, 1))) + 1
        lastmodptrA = getcolptr(A)[lastmodcolA]
        while lastmodptrA < getcolptr(A)[lastmodcolA+1] && rowvals(A)[lastmodptrA] <= lastmodrowA
            lastmodptrA += 1
        end
        lastmodptrA -= 1
        if lastmodptrA >= nnzB
            # A will have fewer non-zero elements; unmodified elements are kept at the end.
            deleteat!(rowvals(A), nnzB+1:lastmodptrA)
            deleteat!(nonzeros(A), nnzB+1:lastmodptrA)
        else
            # A will have more non-zero elements; unmodified elements are kept at the end.
            resize!(rowvals(A), nnzB + nnzA - lastmodptrA)
            resize!(nonzeros(A), nnzB + nnzA - lastmodptrA)
            copyto!(rowvals(A), nnzB+1, rowvals(A), lastmodptrA+1, nnzA-lastmodptrA)
            copyto!(nonzeros(A), nnzB+1, nonzeros(A), lastmodptrA+1, nnzA-lastmodptrA)
        end
        # Adjust colptr accordingly.
        @inbounds for i in 2:length(getcolptr(A))
            getcolptr(A)[i] += nnzB - lastmodptrA
        end
        sparse_compute_reshaped_colptr_and_rowval!(getcolptr(A), rowvals(A), size(A, 1), lastmodcolA-1, getcolptr(B), rowvals(B), size(B, 1), size(B, 2))
    end
    copyto!(nonzeros(A), nonzeros(B))
    return _checkbuffers(A)
end

copyto!(A::AbstractMatrix, B::AbstractSparseMatrixCSC) = _sparse_copyto!(A, B)
# Ambiguity resolution
copyto!(A::PermutedDimsArray, B::AbstractSparseMatrixCSC) = _sparse_copyto!(A, B)

function _sparse_copyto!(dest::AbstractMatrix, src::AbstractSparseMatrixCSC)
    (dest === src || isempty(src)) && return dest
    z = convert(eltype(dest), zero(eltype(src))) # should throw if not possible
    isrc = LinearIndices(src)
    checkbounds(dest, isrc)
    # If src is not dense, zero out the portion of dest spanned by isrc
    if widelength(src) > nnz(src)
        for i in isrc
            @inbounds dest[i] = z
        end
    end
    @inbounds for col in axes(src, 2), ptr in nzrange(src, col)
        row = rowvals(src)[ptr]
        val = nonzeros(src)[ptr]
        dest[isrc[row, col]] = val
    end
    return dest
end

function copyto!(dest::AbstractMatrix, Rdest::CartesianIndices{2},
                 src::AbstractSparseMatrixCSC{T}, Rsrc::CartesianIndices{2}) where {T}
    isempty(Rdest) && return dest
    if size(Rdest) != size(Rsrc)
        throw(ArgumentError("source and destination must have same size (got $(size(Rsrc)) and $(size(Rdest)))"))
    end
    checkbounds(dest, Rdest)
    checkbounds(src, Rsrc)
    src′ = Base.unalias(dest, src)
    for I in Rdest
        @inbounds dest[I] = zero(T) # implicitly convert to eltype(dest), throw if not possible
    end
    rows, cols = Rsrc.indices
    lin = LinearIndices(Base.IdentityUnitRange.(Rsrc.indices))
    @inbounds for col in cols, ptr in nzrange(src′, col)
        row = rowvals(src′)[ptr]
        if row in rows
            val = nonzeros(src′)[ptr]
            I = Rdest[lin[row, col]]
            dest[I] = val
        end
    end
    return dest
end

## similar
#
# parent method for similar that preserves stored-entry structure (for when new and old dims match)
function _sparsesimilar(S::AbstractSparseMatrixCSC, ::Type{TvNew}, ::Type{TiNew}) where {TvNew,TiNew}
    newcolptr = copyto!(similar(getcolptr(S), TiNew), getcolptr(S))
    newrowval = copyto!(similar(rowvals(S), TiNew), rowvals(S))
    return SparseMatrixCSC(size(S, 1), size(S, 2), newcolptr, newrowval, similar(nonzeros(S), TvNew))
end
# parent methods for similar that preserves only storage space (for when new dims are 2-d)
_sparsesimilar(S::AbstractSparseMatrixCSC, ::Type{TvNew}, ::Type{TiNew}, dims::Dims{2}) where {TvNew,TiNew} =
    sizehint!(spzeros(TvNew, TiNew, dims...), length(nonzeros(S)))
# parent method for similar that allocates an empty sparse vector (for when new dims are 1-d)
_sparsesimilar(S::AbstractSparseMatrixCSC, ::Type{TvNew}, ::Type{TiNew}, dims::Dims{1}) where {TvNew,TiNew} =
    SparseVector(dims..., similar(rowvals(S), TiNew, 0), similar(nonzeros(S), TvNew, 0))

# The following methods hook into the AbstractArray similar hierarchy. The first method
# covers similar(A[, Tv]) calls, which preserve stored-entry structure, and the latter
# methods cover similar(A[, Tv], shape...) calls, which partially preserve
# storage space when the shape calls for a two-dimensional result.
similar(S::AbstractSparseMatrixCSC{<:Any,Ti}, ::Type{TvNew}) where {Ti,TvNew} = _sparsesimilar(S, TvNew, Ti)
similar(S::AbstractSparseMatrixCSC{<:Any,Ti}, ::Type{TvNew}, dims::Union{Dims{1},Dims{2}}) where {Ti,TvNew} =
    _sparsesimilar(S, TvNew, Ti, dims)
# The following methods cover similar(A, Tv, Ti[, shape...]) calls, which specify the
# result's index type in addition to its entry type, and aren't covered by the hooks above.
# The calls without shape again preserve stored-entry structure, whereas those with shape
# preserve storage space when the shape calls for a two-dimensional result.
similar(S::AbstractSparseMatrixCSC, ::Type{TvNew}, ::Type{TiNew}) where{TvNew,TiNew} =
    _sparsesimilar(S, TvNew, TiNew)
similar(S::AbstractSparseMatrixCSC, ::Type{TvNew}, ::Type{TiNew}, dims::Union{Dims{1},Dims{2}}) where {TvNew,TiNew} =
    _sparsesimilar(S, TvNew, TiNew, dims)
similar(S::AbstractSparseMatrixCSC, ::Type{TvNew}, ::Type{TiNew}, m::Integer) where {TvNew,TiNew} =
    _sparsesimilar(S, TvNew, TiNew, (m,))
similar(S::AbstractSparseMatrixCSC, ::Type{TvNew}, ::Type{TiNew}, m::Integer, n::Integer) where {TvNew,TiNew} =
    _sparsesimilar(S, TvNew, TiNew, (m, n))

function Base.sizehint!(S::SparseMatrixCSC, n::Integer)
    nhint = min(n, widelength(S))
    sizehint!(getrowval(S), nhint)
    sizehint!(nonzeros(S),  nhint)
    return S
end

# converting between SparseMatrixCSC types
SparseMatrixCSC(S::AbstractSparseMatrixCSC) = copy(S)
AbstractMatrix{Tv}(A::AbstractSparseMatrixCSC) where {Tv} = SparseMatrixCSC{Tv}(A)
SparseMatrixCSC{Tv}(S::AbstractSparseMatrixCSC{Tv}) where {Tv} = copy(S)
SparseMatrixCSC{Tv}(S::AbstractSparseMatrixCSC) where {Tv} = SparseMatrixCSC{Tv,eltype(getcolptr(S))}(S)
SparseMatrixCSC{Tv,Ti}(S::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti} = copy(S)
function SparseMatrixCSC{Tv,Ti}(S::AbstractSparseMatrixCSC) where {Tv,Ti}
    eltypeTicolptr = Vector{Ti}(getcolptr(S))
    eltypeTirowval = Vector{Ti}(rowvals(S))
    eltypeTvnzval = Vector{Tv}(nonzeros(S))
    return SparseMatrixCSC(size(S, 1), size(S, 2), eltypeTicolptr, eltypeTirowval, eltypeTvnzval)
end

# converting from other matrix types to SparseMatrixCSC (also see sparse())
SparseMatrixCSC(M::Matrix) = sparse(M)
SparseMatrixCSC(T::Tridiagonal{Tv}) where Tv = SparseMatrixCSC{Tv,Int}(T)
function SparseMatrixCSC{Tv,Ti}(T::Tridiagonal) where {Tv,Ti}
    m = length(T.d)

    colptr = Vector{Ti}(undef, m+1)
    colptr[1] = 1
    @inbounds for i=1:m-1
        colptr[i+1] = 3i
    end
    colptr[end] = 3m-1

    rowval = Vector{Ti}(undef, 3m-2)
    rowval[1] = 1
    rowval[2] = 2
    @inbounds for i=2:m-1, j=-1:1
        rowval[3i+j-2] = i+j
    end
    rowval[end-1] = m - 1
    rowval[end] = m

    nzval = Vector{Tv}(undef, 3m-2)
    @inbounds for i=1:(m-1)
        nzval[3i-2] = T.d[i]
        nzval[3i-1] = T.dl[i]
        nzval[3i]   = T.du[i]
    end
    nzval[end] = T.d[end]

    return SparseMatrixCSC(m, m, colptr, rowval, nzval)
end
SparseMatrixCSC(T::SymTridiagonal{Tv}) where Tv = SparseMatrixCSC{Tv,Int}(T)
function SparseMatrixCSC{Tv,Ti}(T::SymTridiagonal) where {Tv,Ti}
    m = length(T.dv)

    colptr = Vector{Ti}(undef, m+1)
    colptr[1] = 1
    @inbounds for i=1:m-1
        colptr[i+1] = 3i
    end
    colptr[end] = 3m-1

    rowval = Vector{Ti}(undef, 3m-2)
    rowval[1] = 1
    rowval[2] = 2
    @inbounds for i=2:m-1, j=-1:1
        rowval[3i+j-2] = i+j
    end
    rowval[end-1] = m - 1
    rowval[end] = m

    nzval = Vector{Tv}(undef, 3m-2)
    @inbounds for i=1:(m-1)
        nzval[3i-2] = T.dv[i]
        nzval[3i-1] = T.ev[i]
        nzval[3i]   = T.ev[i]
    end
    nzval[end] = T.dv[end]

    return SparseMatrixCSC(m, m, colptr, rowval, nzval)
end
SparseMatrixCSC(B::Bidiagonal{Tv}) where Tv = SparseMatrixCSC{Tv,Int}(B)
function SparseMatrixCSC{Tv,Ti}(B::Bidiagonal) where {Tv,Ti}
    m = length(B.dv)
    m == 0 && return SparseMatrixCSC{Tv,Ti}(zeros(Tv, 0, 0))

    colptr = Vector{Ti}(undef, m+1)
    colptr[1] = 1
    @inbounds for i=1:m-1
        colptr[i+1] = B.uplo == 'U' ? 2i : 2i+1
    end
    colptr[end] = 2m

    rowval = Vector{Ti}(undef, 2m-1)
    @inbounds for i=1:m-1
        rowval[2i-1] = i
        rowval[2i]   = B.uplo == 'U' ? i : i+1
    end
    rowval[end] = m

    nzval = Vector{Tv}(undef, 2m-1)
    nzval[1] = B.dv[1]
    @inbounds for i=1:m-1
        nzval[2i-1] = B.dv[i]
        nzval[2i]   = B.ev[i]
    end
    nzval[end] = B.dv[end]

    return SparseMatrixCSC(m, m, colptr, rowval, nzval)
end
SparseMatrixCSC(D::Diagonal{Tv}) where Tv = SparseMatrixCSC{Tv,Int}(D)
function SparseMatrixCSC{Tv,Ti}(D::Diagonal) where {Tv,Ti}
    m = length(D.diag)
    return SparseMatrixCSC(m, m, Vector(1:(m+1)), Vector(1:m), Vector{Tv}(D.diag))
end
SparseMatrixCSC(M::AbstractMatrix{Tv}) where {Tv} = SparseMatrixCSC{Tv,Int}(M)
SparseMatrixCSC{Tv}(M::AbstractMatrix{Tv}) where {Tv} = SparseMatrixCSC{Tv,Int}(M)
function SparseMatrixCSC{Tv,Ti}(M::AbstractMatrix) where {Tv,Ti}
    require_one_based_indexing(M)
    I = Ti[]
    V = Tv[]
    i = 0
    for v in M
        i += 1
        if !iszero(v)
            push!(I, i)
            push!(V, v)
        end
    end
    return sparse_sortedlinearindices!(I, V, size(M)...)
end

function SparseMatrixCSC{Tv,Ti}(M::StridedMatrix) where {Tv,Ti}
    nz = count(!iszero, M)
    colptr = zeros(Ti, size(M, 2) + 1)
    nzval = Vector{Tv}(undef, nz)
    rowval = Vector{Ti}(undef, nz)
    colptr[1] = 1
    cnt = 1
    @inbounds for j in 1:size(M, 2)
        for i in 1:size(M, 1)
            v = M[i, j]
            if !iszero(v)
                rowval[cnt] = i
                nzval[cnt] = v
                cnt += 1
            end
        end
        colptr[j+1] = cnt
    end
    return SparseMatrixCSC(size(M, 1), size(M, 2), colptr, rowval, nzval)
end
SparseMatrixCSC(M::Adjoint{<:Any,<:AbstractSparseMatrixCSC}) = copy(M)
SparseMatrixCSC(M::Transpose{<:Any,<:AbstractSparseMatrixCSC}) = copy(M)
SparseMatrixCSC{Tv}(M::Adjoint{Tv,<:AbstractSparseMatrixCSC{Tv}}) where {Tv} = copy(M)
SparseMatrixCSC{Tv}(M::Transpose{Tv,<:AbstractSparseMatrixCSC{Tv}}) where {Tv} = copy(M)
SparseMatrixCSC{Tv,Ti}(M::Adjoint{Tv,<:AbstractSparseMatrixCSC{Tv,Ti}}) where {Tv,Ti} = copy(M)
SparseMatrixCSC{Tv,Ti}(M::Transpose{Tv,<:AbstractSparseMatrixCSC{Tv,Ti}}) where {Tv,Ti} = copy(M)

# converting from adjoint or transpose sparse matrices to sparse matrices with different eltype
SparseMatrixCSC{Tv}(M::Adjoint{<:Any,<:AbstractSparseMatrixCSC}) where {Tv} = SparseMatrixCSC{Tv}(copy(M))
SparseMatrixCSC{Tv}(M::Transpose{<:Any,<:AbstractSparseMatrixCSC}) where {Tv} = SparseMatrixCSC{Tv}(copy(M))
SparseMatrixCSC{Tv,Ti}(M::Adjoint{<:Any,<:AbstractSparseMatrixCSC}) where {Tv,Ti} = SparseMatrixCSC{Tv,Ti}(copy(M))
SparseMatrixCSC{Tv,Ti}(M::Transpose{<:Any,<:AbstractSparseMatrixCSC}) where {Tv,Ti} = SparseMatrixCSC{Tv,Ti}(copy(M))

# converting from SparseMatrixCSC to other matrix types
function Matrix(S::AbstractSparseMatrixCSC{Tv}) where Tv
    _checkbuffers(S)
    A = Matrix{Tv}(undef, size(S, 1), size(S, 2))
    copyto!(A, S)
    return A
end
Array(S::AbstractSparseMatrixCSC) = Matrix(S)

convert(T::Type{<:AbstractSparseMatrixCSC}, m::AbstractMatrix) = m isa T ? m : T(m)

convert(T::Type{<:Diagonal},       m::AbstractSparseMatrixCSC) = m isa T ? m :
    isdiag(m) ? T(m) : throw(ArgumentError("matrix cannot be represented as Diagonal"))
convert(T::Type{<:SymTridiagonal}, m::AbstractSparseMatrixCSC) = m isa T ? m :
    issymmetric(m) && isbanded(m, -1, 1) ? T(m) : throw(ArgumentError("matrix cannot be represented as SymTridiagonal"))
convert(T::Type{<:Tridiagonal},    m::AbstractSparseMatrixCSC) = m isa T ? m :
    isbanded(m, -1, 1) ? T(m) : throw(ArgumentError("matrix cannot be represented as Tridiagonal"))
convert(T::Type{<:LowerTriangular}, m::AbstractSparseMatrixCSC) = m isa T ? m :
    istril(m) ? T(m) : throw(ArgumentError("matrix cannot be represented as LowerTriangular"))
convert(T::Type{<:UpperTriangular}, m::AbstractSparseMatrixCSC) = m isa T ? m :
    istriu(m) ? T(m) : throw(ArgumentError("matrix cannot be represented as UpperTriangular"))

float(S::SparseMatrixCSC) = SparseMatrixCSC(size(S, 1), size(S, 2), copy(getcolptr(S)), copy(rowvals(S)), float.(nonzeros(S)))
complex(S::SparseMatrixCSC) = SparseMatrixCSC(size(S, 1), size(S, 2), copy(getcolptr(S)), copy(rowvals(S)), complex(copy(nonzeros(S))))

"""
    sparse(A)

Convert an AbstractMatrix `A` into a sparse matrix.

# Examples
```jldoctest
julia> A = Matrix(1.0I, 3, 3)
3×3 Matrix{Float64}:
 1.0  0.0  0.0
 0.0  1.0  0.0
 0.0  0.0  1.0

julia> sparse(A)
3×3 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
 1.0   ⋅    ⋅
  ⋅   1.0   ⋅
  ⋅    ⋅   1.0
```
"""
sparse(A::AbstractMatrix{Tv}) where {Tv} = convert(SparseMatrixCSC{Tv}, A)

sparse(S::AbstractSparseMatrixCSC) = copy(S)

sparse(T::SymTridiagonal) = SparseMatrixCSC(T)

sparse(T::Tridiagonal) = SparseMatrixCSC(T)

sparse(B::Bidiagonal) = SparseMatrixCSC(B)

sparse(D::Diagonal) = SparseMatrixCSC(D)

"""
    sparse(I, J, V,[ m, n, combine])

Create a sparse matrix `S` of dimensions `m x n` such that `S[I[k], J[k]] = V[k]`. The
`combine` function is used to combine duplicates. If `m` and `n` are not specified, they
are set to `maximum(I)` and `maximum(J)` respectively. If the `combine` function is not
supplied, `combine` defaults to `+` unless the elements of `V` are Booleans in which case
`combine` defaults to `|`. All elements of `I` must satisfy `1 <= I[k] <= m`, and all
elements of `J` must satisfy `1 <= J[k] <= n`. Numerical zeros in (`I`, `J`, `V`) are
retained as structural nonzeros; to drop numerical zeros, use [`dropzeros!`](@ref).

For additional documentation and an expert driver, see `SparseArrays.sparse!`.

# Examples
```jldoctest
julia> Is = [1; 2; 3];

julia> Js = [1; 2; 3];

julia> Vs = [1; 2; 3];

julia> sparse(Is, Js, Vs)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 1  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  3
```
"""
function sparse(I::AbstractVector{Ti}, J::AbstractVector{Ti}, V::AbstractVector{Tv}, m::Integer, n::Integer, combine) where {Tv,Ti<:Integer}
    require_one_based_indexing(I, J, V)
    coolen = length(I)
    if length(J) != coolen || length(V) != coolen
        throw(ArgumentError(string("the first three arguments' lengths must match, ",
              "length(I) (=$(length(I))) == length(J) (= $(length(J))) == length(V) (= ",
              "$(length(V)))")))
    end
    if Base.hastypemax(Ti) && coolen >= typemax(Ti)
        throw(ArgumentError("the index type $Ti cannot hold $coolen elements; use a larger index type"))
    end
    if m == 0 || n == 0 || coolen == 0
        if coolen != 0
            if n == 0
                throw(ArgumentError("column indices J[k] must satisfy 1 <= J[k] <= n"))
            elseif m == 0
                throw(ArgumentError("row indices I[k] must satisfy 1 <= I[k] <= m"))
            end
        end
        SparseMatrixCSC(m, n, fill(one(Ti), n+1), Vector{Ti}(), Vector{Tv}())
    else
        # Allocate storage for CSR form
        csrrowptr = Vector{Ti}(undef, m+1)
        csrcolval = Vector{Ti}(undef, coolen)
        csrnzval = Vector{Tv}(undef, coolen)

        # Allocate storage for the CSC form's column pointers and a necessary workspace
        csccolptr = Vector{Ti}(undef, n+1)
        klasttouch = Vector{Ti}(undef, n)

        # Allocate empty arrays for the CSC form's row and nonzero value arrays
        # The parent method called below automagically resizes these arrays
        cscrowval = Vector{Ti}()
        cscnzval = Vector{Tv}()

        sparse!(I, J, V, m, n, combine, klasttouch,
                csrrowptr, csrcolval, csrnzval,
                csccolptr, cscrowval, cscnzval)
    end
end

sparse(I::AbstractVector, J::AbstractVector, V::AbstractVector, m::Integer, n::Integer, combine) =
    sparse(AbstractVector{Int}(I), AbstractVector{Int}(J), V, m, n, combine)

"""
    sparse!(I::AbstractVector{Ti}, J::AbstractVector{Ti}, V::AbstractVector{Tv},
            m::Integer, n::Integer, combine, klasttouch::Vector{Ti},
            csrrowptr::Vector{Ti}, csrcolval::Vector{Ti}, csrnzval::Vector{Tv},
            [csccolptr::Vector{Ti}], [cscrowval::Vector{Ti}, cscnzval::Vector{Tv}] ) where {Tv,Ti<:Integer}

Parent of and expert driver for [`sparse`](@ref);
see [`sparse`](@ref) for basic usage. This method
allows the user to provide preallocated storage for `sparse`'s intermediate objects and
result as described below. This capability enables more efficient successive construction
of [`SparseMatrixCSC`](@ref)s from coordinate representations, and also enables extraction
of an unsorted-column representation of the result's transpose at no additional cost.

This method consists of three major steps: (1) Counting-sort the provided coordinate
representation into an unsorted-row CSR form including repeated entries. (2) Sweep through
the CSR form, simultaneously calculating the desired CSC form's column-pointer array,
detecting repeated entries, and repacking the CSR form with repeated entries combined;
this stage yields an unsorted-row CSR form with no repeated entries. (3) Counting-sort the
preceding CSR form into a fully-sorted CSC form with no repeated entries.

Input arrays `csrrowptr`, `csrcolval`, and `csrnzval` constitute storage for the
intermediate CSR forms and require `length(csrrowptr) >= m + 1`,
`length(csrcolval) >= length(I)`, and `length(csrnzval >= length(I))`. Input
array `klasttouch`, workspace for the second stage, requires `length(klasttouch) >= n`.
Optional input arrays `csccolptr`, `cscrowval`, and `cscnzval` constitute storage for the
returned CSC form `S`. `csccolptr` requires `length(csccolptr) >= n + 1`. If necessary,
`cscrowval` and `cscnzval` are automatically resized to satisfy
`length(cscrowval) >= nnz(S)` and `length(cscnzval) >= nnz(S)`; hence, if `nnz(S)` is
unknown at the outset, passing in empty vectors of the appropriate type (`Vector{Ti}()`
and `Vector{Tv}()` respectively) suffices, or calling the `sparse!` method
neglecting `cscrowval` and `cscnzval`.

On return, `csrrowptr`, `csrcolval`, and `csrnzval` contain an unsorted-column
representation of the result's transpose.

You may reuse the input arrays' storage (`I`, `J`, `V`) for the output arrays
(`csccolptr`, `cscrowval`, `cscnzval`). For example, you may call
`sparse!(I, J, V, csrrowptr, csrcolval, csrnzval, I, J, V)`.

For the sake of efficiency, this method performs no argument checking beyond
`1 <= I[k] <= m` and `1 <= J[k] <= n`. Use with care. Testing with `--check-bounds=yes`
is wise.

This method runs in `O(m, n, length(I))` time. The HALFPERM algorithm described in
F. Gustavson, "Two fast algorithms for sparse matrices: multiplication and permuted
transposition," ACM TOMS 4(3), 250-269 (1978) inspired this method's use of a pair of
counting sorts.
"""
function sparse!(I::AbstractVector{Ti}, J::AbstractVector{Ti},
        V::AbstractVector{Tv}, m::Integer, n::Integer, combine, klasttouch::Vector{Tj},
        csrrowptr::Vector{Tj}, csrcolval::Vector{Ti}, csrnzval::Vector{Tv},
        csccolptr::Vector{Ti}, cscrowval::Vector{Ti}, cscnzval::Vector{Tv}) where {Tv,Ti<:Integer,Tj<:Integer}

    require_one_based_indexing(I, J, V)
    sparse_check_Ti(m, n, Ti)
    sparse_check_length("I", I, 0, Tj)
    # Compute the CSR form's row counts and store them shifted forward by one in csrrowptr
    fill!(csrrowptr, Tj(0))
    coolen = length(I)
    min(length(J), length(V)) >= coolen || throw(ArgumentError("J and V need length >= length(I) = $coolen"))
    @inbounds for k in 1:coolen
        Ik = I[k]
        if 1 > Ik || m < Ik
            throw(ArgumentError("row indices I[k] must satisfy 1 <= I[k] <= m"))
        end
        csrrowptr[Ik+1] += Tj(1)
    end

    # Compute the CSR form's rowptrs and store them shifted forward by one in csrrowptr
    countsum = Tj(1)
    csrrowptr[1] = Tj(1)
    @inbounds for i in 2:(m+1)
        overwritten = csrrowptr[i]
        csrrowptr[i] = countsum
        countsum += overwritten
    end

    # Counting-sort the column and nonzero values from J and V into csrcolval and csrnzval
    # Tracking write positions in csrrowptr corrects the row pointers
    @inbounds for k in 1:coolen
        Ik, Jk = I[k], J[k]
        if Ti(1) > Jk || Ti(n) < Jk
            throw(ArgumentError("column indices J[k] must satisfy 1 <= J[k] <= n"))
        end
        csrk = csrrowptr[Ik+1]
        @assert csrk >= Tj(1) "index into csrcolval exceeds typemax(Ti)"
        csrrowptr[Ik+1] = csrk + Tj(1)
        csrcolval[csrk] = Jk
        csrnzval[csrk] = V[k]
    end
    # This completes the unsorted-row, has-repeats CSR form's construction

    # Sweep through the CSR form, simultaneously (1) calculating the CSC form's column
    # counts and storing them shifted forward by one in csccolptr; (2) detecting repeated
    # entries; and (3) repacking the CSR form with the repeated entries combined.
    #
    # Minimizing extraneous communication and nonlocality of reference, primarily by using
    # only a single auxiliary array in this step, is the key to this method's performance.
    fill!(csccolptr, Ti(0))
    fill!(klasttouch, Tj(0))
    writek = Tj(1)
    newcsrrowptri = Ti(1)
    origcsrrowptri = Tj(1)
    origcsrrowptrip1 = csrrowptr[2]
    @inbounds for i in 1:m
        for readk in origcsrrowptri:(origcsrrowptrip1-Tj(1))
            j = csrcolval[readk]
            if klasttouch[j] < newcsrrowptri
                klasttouch[j] = writek
                if writek != readk
                    csrcolval[writek] = j
                    csrnzval[writek] = csrnzval[readk]
                end
                writek += Tj(1)
                csccolptr[j+1] += Ti(1)
            else
                klt = klasttouch[j]
                csrnzval[klt] = combine(csrnzval[klt], csrnzval[readk])
            end
        end
        newcsrrowptri = writek
        origcsrrowptri = origcsrrowptrip1
        origcsrrowptrip1 != writek && (csrrowptr[i+1] = writek)
        i < m && (origcsrrowptrip1 = csrrowptr[i+2])
    end

    # Compute the CSC form's colptrs and store them shifted forward by one in csccolptr
    countsum = Tj(1)
    csccolptr[1] = Ti(1)
    @inbounds for j in 2:(n+1)
        overwritten = csccolptr[j]
        csccolptr[j] = countsum
        countsum += overwritten
        Base.hastypemax(Ti) && (countsum <= typemax(Ti) || throw(ArgumentError("more than typemax(Ti)-1 == $(typemax(Ti)-1) entries")))
    end

    # Now knowing the CSC form's entry count, resize cscrowval and cscnzval if necessary
    cscnnz = countsum - Tj(1)
    length(cscrowval) < cscnnz && resize!(cscrowval, cscnnz)
    length(cscnzval) < cscnnz && resize!(cscnzval, cscnnz)

    # Finally counting-sort the row and nonzero values from the CSR form into cscrowval and
    # cscnzval. Tracking write positions in csccolptr corrects the column pointers.
    @inbounds for i in 1:m
        for csrk in csrrowptr[i]:(csrrowptr[i+1]-Tj(1))
            j = csrcolval[csrk]
            x = csrnzval[csrk]
            csck = csccolptr[j+1]
            csccolptr[j+1] = csck + Ti(1)
            cscrowval[csck] = i
            cscnzval[csck] = x
        end
    end

    SparseMatrixCSC(m, n, csccolptr, cscrowval, cscnzval)
end
function sparse!(I::AbstractVector{Ti}, J::AbstractVector{Ti},
        V::AbstractVector{Tv}, m::Integer, n::Integer, combine, klasttouch::Vector{Tj},
        csrrowptr::Vector{Tj}, csrcolval::Vector{Ti}, csrnzval::Vector{Tv},
        csccolptr::Vector{Ti}) where {Tv,Ti<:Integer,Tj<:Integer}
    sparse!(I, J, V, m, n, combine, klasttouch,
            csrrowptr, csrcolval, csrnzval,
            csccolptr, Vector{Ti}(), Vector{Tv}())
end
function sparse!(I::AbstractVector{Ti}, J::AbstractVector{Ti},
        V::AbstractVector{Tv}, m::Integer, n::Integer, combine, klasttouch::Vector{Tj},
        csrrowptr::Vector{Tj}, csrcolval::Vector{Ti}, csrnzval::Vector{Tv}) where {Tv,Ti<:Integer,Tj<:Integer}
    sparse!(I, J, V, m, n, combine, klasttouch,
            csrrowptr, csrcolval, csrnzval,
            Vector{Ti}(undef, n+1), Vector{Ti}(), Vector{Tv}())
end

dimlub(I) = isempty(I) ? 0 : Int(maximum(I)) #least upper bound on required sparse matrix dimension

sparse(I,J,v::Number) = sparse(I, J, fill(v,length(I)))

sparse(I,J,V::AbstractVector) = sparse(I, J, V, dimlub(I), dimlub(J))

sparse(I,J,v::Number,m,n) = sparse(I, J, fill(v,length(I)), Int(m), Int(n))

sparse(I,J,V::AbstractVector,m,n) = sparse(I, J, V, Int(m), Int(n), +)

sparse(I,J,V::AbstractVector{Bool},m,n) = sparse(I, J, V, Int(m), Int(n), |)

sparse(I,J,v::Number,m,n,combine::Function) = sparse(I, J, fill(v,length(I)), Int(m), Int(n), combine)

## Transposition and permutation methods

"""
    halfperm!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{TvA,Ti},
              q::AbstractVector{<:Integer}, f::Function = identity) where {Tv,TvA,Ti}

Column-permute and transpose `A`, simultaneously applying `f` to each entry of `A`, storing
the result `(f(A)Q)^T` (`map(f, transpose(A[:,q]))`) in `X`.

Element type `Tv` of `X` must match `f(::TvA)`, where `TvA` is the element type of `A`.
`X`'s dimensions must match those of `transpose(A)` (`size(X, 1) == size(A, 2)` and
`size(X, 2) == size(A, 1)`), and `X` must have enough storage to accommodate all allocated
entries in `A` (`length(rowvals(X)) >= nnz(A)` and `length(nonzeros(X)) >= nnz(A)`).
Column-permutation `q`'s length must match `A`'s column count (`length(q) == size(A, 2)`).

This method is the parent of several methods performing transposition and permutation
operations on [`SparseMatrixCSC`](@ref)s. As this method performs no argument checking,
prefer the safer child methods (`[c]transpose[!]`, `permute[!]`) to direct use.

This method implements the `HALFPERM` algorithm described in F. Gustavson, "Two fast
algorithms for sparse matrices: multiplication and permuted transposition," ACM TOMS 4(3),
250-269 (1978). The algorithm runs in `O(size(A, 1), size(A, 2), nnz(A))` time and requires no space
beyond that passed in.
"""
function halfperm!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{TvA,Ti},
        q::AbstractVector{<:Integer}, f::Function = identity) where {Tv,TvA,Ti}
    _computecolptrs_halfperm!(X, A)
    _distributevals_halfperm!(X, A, q, f)
    return X
end
"""
Helper method for `halfperm!`. Computes `transpose(A[:,q])`'s column pointers, storing them
shifted one position forward in `getcolptr(X)`; `_distributevals_halfperm!` fixes this shift.
"""
function _computecolptrs_halfperm!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{TvA,Ti}) where {Tv,TvA,Ti}
    # Compute `transpose(A[:,q])`'s column counts. Store shifted forward one position in getcolptr(X).
    fill!(getcolptr(X), 0)
    @inbounds for k in 1:nnz(A)
        getcolptr(X)[rowvals(A)[k] + 1] += 1
    end
    # Compute `transpose(A[:,q])`'s column pointers. Store shifted forward one position in getcolptr(X).
    getcolptr(X)[1] = 1
    countsum = 1
    @inbounds for k in 2:(size(A, 1) + 1)
        overwritten = getcolptr(X)[k]
        getcolptr(X)[k] = countsum
        countsum += overwritten
    end
end
"""
Helper method for `halfperm!`. With `transpose(A[:,q])`'s column pointers shifted one
position forward in `getcolptr(X)`, computes `map(f, transpose(A[:,q]))` by appropriately
distributing `rowvals(A)` and `f`-transformed `nonzeros(A)` into `rowvals(X)` and `nonzeros(X)`
respectively. Simultaneously fixes the one-position-forward shift in `getcolptr(X)`.
"""
@noinline function _distributevals_halfperm!(X::AbstractSparseMatrixCSC{Tv,Ti},
        A::AbstractSparseMatrixCSC{TvA,Ti}, q::AbstractVector{<:Integer}, f::Function) where {Tv,TvA,Ti}
    resize!(nonzeros(X), nnz(A))
    resize!(rowvals(X), nnz(A))
    @inbounds for Xi in 1:size(A, 2)
        Aj = q[Xi]
        for Ak in nzrange(A, Aj)
            Ai = rowvals(A)[Ak]
            Xk = getcolptr(X)[Ai + 1]
            rowvals(X)[Xk] = Xi
            nonzeros(X)[Xk] = f(nonzeros(A)[Ak])
            getcolptr(X)[Ai + 1] += 1
        end
    end
    return # kill potential type instability
end

function ftranspose!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti}, f::Function) where {Tv,Ti}
    # Check compatibility of source argument A and destination argument X
    if size(X, 2) != size(A, 1)
        throw(DimensionMismatch(string("destination argument `X`'s column count, ",
            "`size(X, 2) (= $(size(X, 2)))`, must match source argument `A`'s row count, `size(A, 1) (= $(size(A, 1)))`")))
    elseif size(X, 1) != size(A, 2)
        throw(DimensionMismatch(string("destination argument `X`'s row count, ",
            "`size(X, 1) (= $(size(X, 1)))`, must match source argument `A`'s column count, `size(A, 2) (= $(size(A, 2)))`")))
    end
    halfperm!(X, A, 1:size(A, 2), f)
end
transpose!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti} = ftranspose!(X, A, identity)
adjoint!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti} = ftranspose!(X, A, conj)

# manually specifying eltype allows to avoid calling return_type of f on TvA
function ftranspose(A::AbstractSparseMatrixCSC{TvA,Ti}, f::Function, eltype::Type{Tv} = TvA) where {Tv,TvA,Ti}
    X = SparseMatrixCSC(size(A, 2), size(A, 1),
                        ones(Ti, size(A, 1)+1),
                        Vector{Ti}(undef, 0),
                        Vector{Tv}(undef, 0))
    sizehint!(X, nnz(A))
    halfperm!(X, A, 1:size(A, 2), f)
end
adjoint(A::AbstractSparseMatrixCSC) = Adjoint(A)
transpose(A::AbstractSparseMatrixCSC) = Transpose(A)
Base.copy(A::Adjoint{<:Any,<:AbstractSparseMatrixCSC}) =
    ftranspose(A.parent, x -> adjoint(copy(x)), eltype(A))
Base.copy(A::Transpose{<:Any,<:AbstractSparseMatrixCSC}) =
    ftranspose(A.parent, x -> transpose(copy(x)), eltype(A))
function Base.permutedims(A::AbstractSparseMatrixCSC, (a,b))
    (a, b) == (2, 1) && return ftranspose(A, identity)
    (a, b) == (1, 2) && return copy(A)
    throw(ArgumentError("no valid permutation of dimensions"))
end

"""
    unchecked_noalias_permute!(X::AbstractSparseMatrixCSC{Tv,Ti},
        A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
        q::AbstractVector{<:Integer}, C::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}

See [`permute!`](@ref) for basic usage. Parent of `permute[!]`
methods operating on `SparseMatrixCSC`s that assume none of `X`, `A`, and `C` alias each
other. As this method performs no argument checking, prefer the safer child methods
(`permute[!]`) to direct use.

This method consists of two major steps: (1) Column-permute (`Q`,`I[:,q]`) and transpose `A`
to generate intermediate result `(AQ)^T` (`transpose(A[:,q])`) in `C`. (2) Column-permute
(`P^T`, I[:,p]) and transpose intermediate result `(AQ)^T` to generate result
`((AQ)^T P^T)^T = PAQ` (`A[p,q]`) in `X`.

The first step is a call to `halfperm!`, and the second is a variant on `halfperm!` that
avoids an unnecessary length-`nnz(A)` array-sweep and associated recomputation of column
pointers. See [`halfperm!`](:func:SparseArrays.halfperm!) for additional algorithmic
information.

See also `unchecked_aliasing_permute!`.
"""
function unchecked_noalias_permute!(X::AbstractSparseMatrixCSC{Tv,Ti},
        A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
        q::AbstractVector{<:Integer}, C::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    halfperm!(C, A, q)
    _computecolptrs_permute!(X, A, q, getcolptr(X))
    _distributevals_halfperm!(X, C, p, identity)
    return X
end
"""
    unchecked_aliasing_permute!(A::AbstractSparseMatrixCSC{Tv,Ti},
        p::AbstractVector{<:Integer}, q::AbstractVector{<:Integer},
        C::AbstractSparseMatrixCSC{Tv,Ti}, workcolptr::Vector{Ti}) where {Tv,Ti}

See [`permute!`](@ref) for basic usage. Parent of `permute!`
methods operating on [`SparseMatrixCSC`](@ref)s where the source and destination matrices
are the same. See `unchecked_noalias_permute!`
for additional information; these methods are identical but for this method's requirement of
the additional `workcolptr`, `length(workcolptr) >= size(A, 2) + 1`, which enables efficient
handling of the source-destination aliasing.
"""
function unchecked_aliasing_permute!(A::AbstractSparseMatrixCSC{Tv,Ti},
        p::AbstractVector{<:Integer}, q::AbstractVector{<:Integer},
        C::AbstractSparseMatrixCSC{Tv,Ti}, workcolptr::Vector{Ti}) where {Tv,Ti}
    halfperm!(C, A, q)
    _computecolptrs_permute!(A, A, q, workcolptr)
    _distributevals_halfperm!(A, C, p, identity)
    return A
end
"""
Helper method for `unchecked_noalias_permute!` and `unchecked_aliasing_permute!`.
Computes `PAQ`'s column pointers, storing them shifted one position forward in `getcolptr(X)`;
`_distributevals_halfperm!` fixes this shift. Saves some work relative to
`_computecolptrs_halfperm!` as described in `uncheckednoalias_permute!`'s documentation.
"""
function _computecolptrs_permute!(X::AbstractSparseMatrixCSC{Tv,Ti},
        A::AbstractSparseMatrixCSC{Tv,Ti}, q::AbstractVector{<:Integer}, workcolptr::Vector{Ti}) where {Tv,Ti}
    # Compute `A[p,q]`'s column counts. Store shifted forward one position in workcolptr.
    @inbounds for k in 1:size(A, 2)
        workcolptr[k+1] = getcolptr(A)[q[k] + 1] - getcolptr(A)[q[k]]
    end
    # Compute `A[p,q]`'s column pointers. Store shifted forward one position in getcolptr(X).
    getcolptr(X)[1] = 1
    countsum = 1
    @inbounds for k in 2:(size(X, 2) + 1)
        overwritten = workcolptr[k]
        getcolptr(X)[k] = countsum
        countsum += overwritten
    end
end

"""
Helper method for `permute` and `permute!` methods operating on `SparseMatrixCSC`s.
Checks compatibility of source argument `A`, row-permutation argument `p`, and
column-permutation argument `q`.
"""
function _checkargs_sourcecompatperms_permute!(A::AbstractSparseMatrixCSC,
        p::AbstractVector{<:Integer}, q::AbstractVector{<:Integer})
    require_one_based_indexing(p, q)
    if length(q) != size(A, 2)
         throw(DimensionMismatch(string("the length of column-permutation argument `q`, ",
             "`length(q) (= $(length(q)))`, must match source argument `A`'s column ",
             "count, `size(A, 2) (= $(size(A, 2)))`")))
     elseif length(p) != size(A, 1)
         throw(DimensionMismatch(string("the length of row-permutation argument `p`, ",
             "`length(p) (= $(length(p)))`, must match source argument `A`'s row count, ",
             "`size(A, 1) (= $(size(A, 1)))`")))
     end
end
"""
Helper method for `permute` and `permute!` methods operating on `SparseMatrixCSC`s.
Checks whether row- and column- permutation arguments `p` and `q` are valid permutations.
"""
function _checkargs_permutationsvalid_permute!(
        p::AbstractVector{<:Integer}, pcheckspace::Vector{Ti},
        q::AbstractVector{<:Integer}, qcheckspace::Vector{Ti}) where Ti<:Integer
    if !_ispermutationvalid_permute!(p, pcheckspace)
        throw(ArgumentError("row-permutation argument `p` must be a valid permutation"))
    elseif !_ispermutationvalid_permute!(q, qcheckspace)
        throw(ArgumentError("column-permutation argument `q` must be a valid permutation"))
    end
end
function _ispermutationvalid_permute!(perm::AbstractVector{<:Integer},
        checkspace::Vector{<:Integer})
    require_one_based_indexing(perm)
    n = length(perm)
    checkspace[1:n] .= 0
    for k in perm
        (0 < k ≤ n) && ((checkspace[k] ⊻= 1) == 1) || return false
    end
    return true
end
"""
Helper method for `permute` and `permute!` methods operating on `SparseMatrixCSC`s.
Checks compatibility of source argument `A` and destination argument `X`.
"""
function _checkargs_sourcecompatdest_permute!(A::AbstractSparseMatrixCSC{Tv,Ti},
        X::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    if size(X, 1) != size(A, 1)
        throw(DimensionMismatch(string("destination argument `X`'s row count, ",
            "`size(X, 1) (= $(size(X, 1)))`, must match source argument `A`'s row count, `size(A, 1) (= $(size(A, 1)))`")))
    elseif size(X, 2) != size(A, 2)
        throw(DimensionMismatch(string("destination argument `X`'s column count, ",
            "`size(X, 2) (= $(size(X, 2)))`, must match source argument `A`'s column count, `size(A, 2) (= $(size(A, 2)))`")))
    elseif length(rowvals(X)) < nnz(A)
        throw(ArgumentError(string("the length of destination argument `X`'s `rowval` ",
            "array, `length(rowvals(X)) (= $(length(rowvals(X))))`, must be greater than or ",
            "equal to source argument `A`'s allocated entry count, `nnz(A) (= $(nnz(A)))`")))
    elseif length(nonzeros(X)) < nnz(A)
        throw(ArgumentError(string("the length of destination argument `X`'s `nzval` ",
            "array, `length(nonzeros(X)) (= $(length(nonzeros(X))))`, must be greater than or ",
            "equal to source argument `A`'s allocated entry count, `nnz(A) (= $(nnz(A)))`")))
    end
end
"""
Helper method for `permute` and `permute!` methods operating on `SparseMatrixCSC`s.
Checks compatibility of source argument `A` and intermediate result argument `C`.
"""
function _checkargs_sourcecompatworkmat_permute!(A::AbstractSparseMatrixCSC{Tv,Ti},
        C::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    if size(C, 2) != size(A, 1)
        throw(DimensionMismatch(string("intermediate result argument `C`'s column count, ",
            "`size(C, 2) (= $(size(C, 2)))`, must match source argument `A`'s row count, `size(A, 1) (= $(size(A, 1)))`")))
    elseif size(C, 1) != size(A, 2)
        throw(DimensionMismatch(string("intermediate result argument `C`'s row count, ",
            "`size(C, 1) (= $(size(C, 1)))`, must match source argument `A`'s column count, `size(A, 2) (= $(size(A, 2)))`")))
    elseif length(rowvals(C)) < nnz(A)
        throw(ArgumentError(string("the length of intermediate result argument `C`'s ",
            "`rowval` array, `length(rowvals(C)) (= $(length(rowvals(C))))`, must be greater than ",
            "or equal to source argument `A`'s allocated entry count, `nnz(A) (= $(nnz(A)))`")))
    elseif length(nonzeros(C)) < nnz(A)
        throw(ArgumentError(string("the length of intermediate result argument `C`'s ",
            "`rowval` array, `length(nonzeros(C)) (= $(length(nonzeros(C))))`, must be greater than ",
            "or equal to source argument `A`'s allocated entry count, `nnz(A)` (= $(nnz(A)))")))
    end
end
"""
Helper method for `permute` and `permute!` methods operating on `SparseMatrixCSC`s.
Checks compatibility of source argument `A` and workspace argument `workcolptr`.
"""
function _checkargs_sourcecompatworkcolptr_permute!(A::AbstractSparseMatrixCSC{Tv,Ti},
        workcolptr::Vector{Ti}) where {Tv,Ti}
    if length(workcolptr) <= size(A, 2)
        throw(DimensionMismatch(string("argument `workcolptr`'s length, ",
            "`length(workcolptr) (= $(length(workcolptr)))`, must exceed source argument ",
            "`A`'s column count, `size(A, 2) (= $(size(A, 2)))`")))
    end
end
"""
    permute!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti},
             p::AbstractVector{<:Integer}, q::AbstractVector{<:Integer},
             [C::AbstractSparseMatrixCSC{Tv,Ti}]) where {Tv,Ti}

Bilaterally permute `A`, storing result `PAQ` (`A[p,q]`) in `X`. Stores intermediate result
`(AQ)^T` (`transpose(A[:,q])`) in optional argument `C` if present. Requires that none of
`X`, `A`, and, if present, `C` alias each other; to store result `PAQ` back into `A`, use
the following method lacking `X`:

    permute!(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
             q::AbstractVector{<:Integer}[, C::AbstractSparseMatrixCSC{Tv,Ti},
             [workcolptr::Vector{Ti}]]) where {Tv,Ti}

`X`'s dimensions must match those of `A` (`size(X, 1) == size(A, 1)` and `size(X, 2) == size(A, 2)`), and `X` must
have enough storage to accommodate all allocated entries in `A` (`length(rowvals(X)) >= nnz(A)`
and `length(nonzeros(X)) >= nnz(A)`). Column-permutation `q`'s length must match `A`'s column
count (`length(q) == size(A, 2)`). Row-permutation `p`'s length must match `A`'s row count
(`length(p) == size(A, 1)`).

`C`'s dimensions must match those of `transpose(A)` (`size(C, 1) == size(A, 2)` and `size(C, 2) == size(A, 1)`), and `C`
must have enough storage to accommodate all allocated entries in `A` (`length(rowvals(C)) >= nnz(A)`
and `length(nonzeros(C)) >= nnz(A)`).

For additional (algorithmic) information, and for versions of these methods that forgo
argument checking, see (unexported) parent methods `unchecked_noalias_permute!`
and `unchecked_aliasing_permute!`.

See also [`permute`](@ref).
"""
function permute!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti},
        p::AbstractVector{<:Integer}, q::AbstractVector{<:Integer}) where {Tv,Ti}
    _checkargs_sourcecompatdest_permute!(A, X)
    _checkargs_sourcecompatperms_permute!(A, p, q)
    # bypass strict buffer checking
    C = spzeros(Tv, Ti, size(A,2), size(A,1))
    resize!(getrowval(C), nnz(A))
    resize!(getnzval(C), nnz(A))

    _checkargs_permutationsvalid_permute!(p, getcolptr(C), q, getcolptr(X))
    unchecked_noalias_permute!(X, A, p, q, C)
end
function permute!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti},
        p::AbstractVector{<:Integer}, q::AbstractVector{<:Integer},
        C::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    _checkargs_sourcecompatdest_permute!(A, X)
    _checkargs_sourcecompatperms_permute!(A, p, q)
    _checkargs_sourcecompatworkmat_permute!(A, C)
    _checkargs_permutationsvalid_permute!(p, getcolptr(C), q, getcolptr(X))
    unchecked_noalias_permute!(X, A, p, q, C)
end
function permute!(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
        q::AbstractVector{<:Integer}) where {Tv,Ti}
    _checkargs_sourcecompatperms_permute!(A, p, q)
    C = spzeros(Tv, Ti, size(A,2), size(A,1))
    resize!(getrowval(C), nnz(A))
    resize!(getnzval(C), nnz(A))
    workcolptr = Vector{Ti}(undef, size(A, 2) + 1)
    _checkargs_permutationsvalid_permute!(p, getcolptr(C), q, workcolptr)
    unchecked_aliasing_permute!(A, p, q, C, workcolptr)
end
function permute!(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
        q::AbstractVector{<:Integer}, C::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    _checkargs_sourcecompatperms_permute!(A, p, q)
    _checkargs_sourcecompatworkmat_permute!(A, C)
    workcolptr = Vector{Ti}(undef, size(A, 2) + 1)
    _checkargs_permutationsvalid_permute!(p, getcolptr(C), q, workcolptr)
    unchecked_aliasing_permute!(A, p, q, C, workcolptr)
end
function permute!(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
        q::AbstractVector{<:Integer}, C::AbstractSparseMatrixCSC{Tv,Ti},
        workcolptr::Vector{Ti}) where {Tv,Ti}
    _checkargs_sourcecompatperms_permute!(A, p, q)
    _checkargs_sourcecompatworkmat_permute!(A, C)
    _checkargs_sourcecompatworkcolptr_permute!(A, workcolptr)
    _checkargs_permutationsvalid_permute!(p, getcolptr(C), q, workcolptr)
    unchecked_aliasing_permute!(A, p, q, C, workcolptr)
end
"""
    permute(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
            q::AbstractVector{<:Integer}) where {Tv,Ti}

Bilaterally permute `A`, returning `PAQ` (`A[p,q]`). Column-permutation `q`'s length must
match `A`'s column count (`length(q) == size(A, 2)`). Row-permutation `p`'s length must match `A`'s
row count (`length(p) == size(A, 1)`).

For expert drivers and additional information, see [`permute!`](@ref).

# Examples
```jldoctest
julia> A = spdiagm(0 => [1, 2, 3, 4], 1 => [5, 6, 7])
4×4 SparseMatrixCSC{Int64, Int64} with 7 stored entries:
 1  5  ⋅  ⋅
 ⋅  2  6  ⋅
 ⋅  ⋅  3  7
 ⋅  ⋅  ⋅  4

julia> permute(A, [4, 3, 2, 1], [1, 2, 3, 4])
4×4 SparseMatrixCSC{Int64, Int64} with 7 stored entries:
 ⋅  ⋅  ⋅  4
 ⋅  ⋅  3  7
 ⋅  2  6  ⋅
 1  5  ⋅  ⋅

julia> permute(A, [1, 2, 3, 4], [4, 3, 2, 1])
4×4 SparseMatrixCSC{Int64, Int64} with 7 stored entries:
 ⋅  ⋅  5  1
 ⋅  6  2  ⋅
 7  3  ⋅  ⋅
 4  ⋅  ⋅  ⋅
```
"""
function permute(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
        q::AbstractVector{<:Integer}) where {Tv,Ti}
    _checkargs_sourcecompatperms_permute!(A, p, q)
    # bypass strict buffer checking
    X = spzeros(Tv, Ti, size(A,1), size(A,2))
    resize!(getrowval(X), nnz(A))
    resize!(getnzval(X), nnz(A))
    # bypass strict buffer checking
    C = spzeros(Tv, Ti, size(A,2), size(A,1))
    resize!(getrowval(C), nnz(A))
    resize!(getnzval(C), nnz(A))
    _checkargs_permutationsvalid_permute!(p, getcolptr(C), q, getcolptr(X))
    unchecked_noalias_permute!(X, A, p, q, C)
end

## fkeep! and children tril!, triu!, droptol!, dropzeros[!]

"""
    fkeep!(A::AbstractSparseArray, f)

Keep elements of `A` for which test `f` returns `true`. `f`'s signature should be

    f(i::Integer, [j::Integer,] x) -> Bool

where `i` and `j` are an element's row and column indices and `x` is the element's
value. This method makes a single sweep
through `A`, requiring `O(size(A, 2), nnz(A))`-time for matrices and `O(nnz(A))`-time for vectors
and no space beyond that passed in.

# Examples
```jldoctest
julia> A = sparse(Diagonal([1, 2, 3, 4]))
4×4 SparseMatrixCSC{Int64, Int64} with 4 stored entries:
 1  ⋅  ⋅  ⋅
 ⋅  2  ⋅  ⋅
 ⋅  ⋅  3  ⋅
 ⋅  ⋅  ⋅  4

julia> SparseArrays.fkeep!(A, (i, j, v) -> isodd(v))
4×4 SparseMatrixCSC{Int64, Int64} with 2 stored entries:
 1  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  3  ⋅
 ⋅  ⋅  ⋅  ⋅
```
"""
function fkeep!(A::AbstractSparseMatrixCSC, f, trim::Bool = true)
    An = size(A, 2)
    Acolptr = getcolptr(A)
    Arowval = rowvals(A)
    Anzval = nonzeros(A)

    # Sweep through columns, rewriting kept elements in their new positions
    # and updating the column pointers accordingly as we go.
    Awritepos = 1
    oldAcolptrAj = 1
    @inbounds for Aj in 1:An
        for Ak in oldAcolptrAj:(Acolptr[Aj+1]-1)
            Ai = Arowval[Ak]
            Ax = Anzval[Ak]
            # If this element should be kept, rewrite in new position
            if f(Ai, Aj, Ax)
                if Awritepos != Ak
                    Arowval[Awritepos] = Ai
                    Anzval[Awritepos] = Ax
                end
                Awritepos += 1
            end
        end
        oldAcolptrAj = Acolptr[Aj+1]
        Acolptr[Aj+1] = Awritepos
    end

    # Trim A's storage if necessary
    Annz = Acolptr[end] - 1
    resize!(Arowval, Annz)
    resize!(Anzval, Annz)

    return A
end

tril!(A::AbstractSparseMatrixCSC, k::Integer = 0) =
    fkeep!(A, (i, j, x) -> i + k >= j)
triu!(A::AbstractSparseMatrixCSC, k::Integer = 0) =
    fkeep!(A, (i, j, x) -> j >= i + k)

"""
    droptol!(A::AbstractSparseMatrixCSC, tol)

Removes stored values from `A` whose absolute value is less than or equal to `tol`.
"""
droptol!(A::AbstractSparseMatrixCSC, tol) =
    fkeep!(A, (i, j, x) -> abs(x) > tol)

"""
    dropzeros!(A::AbstractSparseMatrixCSC;)

Removes stored numerical zeros from `A`.

For an out-of-place version, see [`dropzeros`](@ref). For
algorithmic information, see `fkeep!`.
"""
dropzeros!(A::AbstractSparseMatrixCSC) = fkeep!(A, (i, j, x) -> !iszero(x))

"""
    dropzeros(A::AbstractSparseMatrixCSC;)

Generates a copy of `A` and removes stored numerical zeros from that copy.

For an in-place version and algorithmic information, see [`dropzeros!`](@ref).

# Examples
```jldoctest
julia> A = sparse([1, 2, 3], [1, 2, 3], [1.0, 0.0, 1.0])
3×3 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
 1.0   ⋅    ⋅
  ⋅   0.0   ⋅
  ⋅    ⋅   1.0

julia> dropzeros(A)
3×3 SparseMatrixCSC{Float64, Int64} with 2 stored entries:
 1.0   ⋅    ⋅
  ⋅    ⋅    ⋅
  ⋅    ⋅   1.0
```
"""
dropzeros(A::AbstractSparseMatrixCSC) = dropzeros!(copy(A))

## Find methods

function findall(S::AbstractSparseMatrixCSC)
    return findall(identity, S)
end

function findall(p::Function, S::AbstractSparseMatrixCSC)
    if p(zero(eltype(S)))
        return invoke(findall, Tuple{Function, Any}, p, S)
    end

    numnz = nnz(S)
    inds = Vector{CartesianIndex{2}}(undef, numnz)

    count = 0
    @inbounds for col = 1 : size(S, 2), k = getcolptr(S)[col] : (getcolptr(S)[col+1]-1)
        if p(nonzeros(S)[k])
            count += 1
            inds[count] = CartesianIndex(rowvals(S)[k], col)
        end
    end

    resize!(inds, count)

    return inds
end
findall(p::Base.Fix2{typeof(in)}, x::AbstractSparseMatrixCSC) =
    invoke(findall, Tuple{Base.Fix2{typeof(in)}, AbstractArray}, p, x)

function findnz(S::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    numnz = nnz(S)
    I = Vector{Ti}(undef, numnz)
    J = Vector{Ti}(undef, numnz)
    V = Vector{Tv}(undef, numnz)

    count = 1
    @inbounds for col = 1 : size(S, 2), k = getcolptr(S)[col] : (getcolptr(S)[col+1]-1)
        I[count] = rowvals(S)[k]
        J[count] = col
        V[count] = nonzeros(S)[k]
        count += 1
    end

    return (I, J, V)
end

function _sparse_findnextnz(m::AbstractSparseMatrixCSC, ij::CartesianIndex{2})
    row, col = Tuple(ij)
    col > size(m, 2) && return nothing

    lo, hi = getcolptr(m)[col], getcolptr(m)[col+1]
    n = searchsortedfirst(rowvals(m), row, lo, hi-1, Base.Order.Forward)
    if lo <= n <= hi-1
        return CartesianIndex(rowvals(m)[n], col)
    end
    nextcol = searchsortedfirst(getcolptr(m), hi + 1, col + 1, length(getcolptr(m)), Base.Order.Forward)
    nextcol > length(getcolptr(m)) && return nothing
    nextlo = getcolptr(m)[nextcol-1]
    return CartesianIndex(rowvals(m)[nextlo], nextcol - 1)
end

function _sparse_findprevnz(m::AbstractSparseMatrixCSC, ij::CartesianIndex{2})
    row, col = Tuple(ij)
    iszero(col) && return nothing

    lo, hi = getcolptr(m)[col], getcolptr(m)[col+1]
    n = searchsortedlast(rowvals(m), row, lo, hi-1, Base.Order.Forward)
    if lo <= n <= hi-1
        return CartesianIndex(rowvals(m)[n], col)
    end
    prevcol = searchsortedlast(getcolptr(m), lo - 1, 1, col - 1, Base.Order.Forward)
    prevcol < 1 && return nothing
    prevhi = getcolptr(m)[prevcol+1]
    return CartesianIndex(rowvals(m)[prevhi-1], prevcol)
end


function sparse_sortedlinearindices!(I::Vector{Ti}, V::Vector, m::Int, n::Int) where Ti
    length(I) == length(V) || throw(ArgumentError("I and V should have the same length"))
    nnz = length(V)
    colptr = Vector{Ti}(undef, n + 1)
    j, colm = 1, 0
    @inbounds for col = 1:n+1
        colptr[col] = j
        while j <= nnz && (I[j] -= colm) <= m
            j += 1
        end
        j <= nnz && (I[j] += colm)
        colm += m
    end
    return SparseMatrixCSC(m, n, colptr, I, V)
end

"""
    sprand([rng],[type],m,[n],p::AbstractFloat,[rfn])

Create a random length `m` sparse vector or `m` by `n` sparse matrix, in
which the probability of any element being nonzero is independently given by
`p` (and hence the mean density of nonzeros is also exactly `p`). Nonzero
values are sampled from the distribution specified by `rfn` and have the type `type`. The uniform
distribution is used in case `rfn` is not specified. The optional `rng`
argument specifies a random number generator, see [Random Numbers](@ref).

# Examples
```jldoctest; setup = :(using Random; Random.seed!(1234))
julia> sprand(Bool, 2, 2, 0.5)
2×2 SparseMatrixCSC{Bool, Int64} with 2 stored entries:
 1  ⋅
 ⋅  1

julia> sprand(Float64, 3, 0.75)
3-element SparseVector{Float64, Int64} with 1 stored entry:
  [3]  =  0.787459
```
"""
function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T}=eltype(rfn(r, 1))) where T
    m, n = Int(m), Int(n)
    (m < 0 || n < 0) && throw(ArgumentError("invalid Array dimensions"))
    0 <= density <= 1 || throw(ArgumentError("$density not in [0,1]"))
    I = randsubseq(r, 1:(m*n), density)
    return sparse_sortedlinearindices!(I, convert(Vector{T}, rfn(r,length(I))), m, n)
end

sprand(m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T} = eltype(rfn(1))) where {T} =
    sprand(default_rng(), m, n, density, (r, i) -> rfn(i))

truebools(r::AbstractRNG, n::Integer) = fill(true, n)

sprand(m::Integer, n::Integer, density::AbstractFloat) = sprand(default_rng(), m, n, density)

sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) =
    sprand(r, m, n, density, rand, Float64)
sprand(r::AbstractRNG, ::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} =
    sprand(r, m, n, density, (r, i) -> rand(r, T, i), T)
sprand(r::AbstractRNG, ::Type{Bool}, m::Integer, n::Integer, density::AbstractFloat) =
    sprand(r, m, n, density, truebools, Bool)
sprand(::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} =
    sprand(default_rng(), T, m, n, density)

"""
    sprandn([rng][,Type],m[,n],p::AbstractFloat)

Create a random sparse vector of length `m` or sparse matrix of size `m` by `n`
with the specified (independent) probability `p` of any entry being nonzero,
where nonzero values are sampled from the normal distribution. The optional `rng`
argument specifies a random number generator, see [Random Numbers](@ref).

!!! compat "Julia 1.1"
    Specifying the output element type `Type` requires at least Julia 1.1.

# Examples
```jldoctest; setup = :(using Random; Random.seed!(0))
julia> sprandn(2, 2, 0.75)
2×2 SparseMatrixCSC{Float64, Int64} with 1 stored entry:
  ⋅    ⋅
  ⋅   1.32078
```
"""
sprandn(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) =
    sprand(r, m, n, density, randn, Float64)
sprandn(m::Integer, n::Integer, density::AbstractFloat) =
    sprandn(default_rng(), m, n, density)
sprandn(r::AbstractRNG, ::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} =
    sprand(r, m, n, density, (r, i) -> randn(r, T, i), T)
sprandn(::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} =
    sprandn(default_rng(), T, m, n, density)

LinearAlgebra.fillstored!(S::AbstractSparseMatrixCSC, x) = (fill!(nzvalview(S), x); S)

"""
    spzeros([type,]m[,n])

Create a sparse vector of length `m` or sparse matrix of size `m x n`. This
sparse array will not contain any nonzero values. No storage will be allocated
for nonzero values during construction. The type defaults to [`Float64`](@ref) if not
specified.

# Examples
```jldoctest
julia> spzeros(3, 3)
3×3 SparseMatrixCSC{Float64, Int64} with 0 stored entries:
  ⋅    ⋅    ⋅
  ⋅    ⋅    ⋅
  ⋅    ⋅    ⋅

julia> spzeros(Float32, 4)
4-element SparseVector{Float32, Int64} with 0 stored entries
```
"""
spzeros(m::Integer, n::Integer) = spzeros(Float64, m, n)
spzeros(::Type{Tv}, m::Integer, n::Integer) where {Tv} = spzeros(Tv, Int, m, n)
function spzeros(::Type{Tv}, ::Type{Ti}, m::Integer, n::Integer) where {Tv, Ti}
    ((m < 0) || (n < 0)) && throw(ArgumentError("invalid Array dimensions"))
    SparseMatrixCSC(m, n, fill(one(Ti), n+1), Vector{Ti}(), Vector{Tv}())
end
# de-splatting variants
function spzeros(::Type{Tv}, ::Type{Ti}, sz::Tuple{Integer,Integer}) where {Tv, Ti}
    spzeros(Tv, Ti, sz[1], sz[2])
end
spzeros(::Type{Tv}, sz::Tuple{Integer,Integer}) where {Tv} = spzeros(Tv, Int, sz[1], sz[2])
spzeros(sz::Tuple{Integer,Integer}) = spzeros(Float64, Int, sz[1], sz[2])

import Base._one
function Base._one(unit::T, S::AbstractSparseMatrixCSC) where T
    size(S, 1) == size(S, 2) || throw(DimensionMismatch("multiplicative identity only defined for square matrices"))
    return SparseMatrixCSC{T}(I, size(S, 1), size(S, 2))
end

## SparseMatrixCSC construction from UniformScaling
SparseMatrixCSC{Tv,Ti}(s::UniformScaling, m::Integer, n::Integer) where {Tv,Ti} = SparseMatrixCSC{Tv,Ti}(s, Dims((m, n)))
SparseMatrixCSC{Tv}(s::UniformScaling, m::Integer, n::Integer) where {Tv} = SparseMatrixCSC{Tv}(s, Dims((m, n)))
SparseMatrixCSC(s::UniformScaling, m::Integer, n::Integer) = SparseMatrixCSC(s, Dims((m, n)))
SparseMatrixCSC{Tv}(s::UniformScaling, dims::Dims{2}) where {Tv} = SparseMatrixCSC{Tv,Int}(s, dims)
SparseMatrixCSC(s::UniformScaling, dims::Dims{2}) = SparseMatrixCSC{eltype(s)}(s, dims)
function SparseMatrixCSC{Tv,Ti}(s::UniformScaling, dims::Dims{2}) where {Tv,Ti}
    @boundscheck first(dims) < 0 && throw(ArgumentError("first dimension invalid ($(first(dims)) < 0)"))
    @boundscheck last(dims) < 0 && throw(ArgumentError("second dimension invalid ($(last(dims)) < 0)"))
    iszero(s.λ) && return spzeros(Tv, Ti, dims...)
    m, n, k = dims..., min(dims...)
    nzval = fill!(Vector{Tv}(undef, k), Tv(s.λ))
    rowval = copyto!(Vector{Ti}(undef, k), 1:k)
    colptr = copyto!(Vector{Ti}(undef, n + 1), 1:(k + 1))
    for i in (k + 2):(n + 1) colptr[i] = (k + 1) end
    SparseMatrixCSC{Tv,Ti}(dims..., colptr, rowval, nzval)
end

Base.iszero(A::AbstractSparseMatrixCSC) = iszero(nzvalview(A))

function Base.isone(A::AbstractSparseMatrixCSC)
    m, n = size(A)
    m == n && getcolptr(A)[n+1] >= n+1 || return false
    for j in 1:n, k in getcolptr(A)[j]:(getcolptr(A)[j+1] - 1)
        i, x = rowvals(A)[k], nonzeros(A)[k]
        ifelse(i == j, isone(x), iszero(x)) || return false
    end
    return true
end

sparse(s::UniformScaling, dims::Dims{2}) = SparseMatrixCSC(s, dims)
sparse(s::UniformScaling, m::Integer, n::Integer) = sparse(s, Dims((m, n)))

# TODO: More appropriate location?
function conj!(A::AbstractSparseMatrixCSC)
    map!(conj, nzvalview(A), nzvalview(A))
    return A
end
function (-)(A::AbstractSparseMatrixCSC)
    nzval = similar(nonzeros(A), typeof(-zero(eltype(A))))
    map!(-, view(nzval, 1:nnz(A)), nzvalview(A))
    return SparseMatrixCSC(size(A, 1), size(A, 2), copy(getcolptr(A)), copy(rowvals(A)), nzval)
end

# the rest of real, conj, imag are handled correctly via AbstractArray methods
function conj(A::AbstractSparseMatrixCSC{<:Complex})
    nzval = similar(nonzeros(A))
    map!(conj, view(nzval, 1:nnz(A)), nzvalview(A))
    return SparseMatrixCSC(size(A, 1), size(A, 2), copy(getcolptr(A)), copy(rowvals(A)), nzval)
end
imag(A::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv<:Real,Ti} = spzeros(Tv, Ti, size(A, 1), size(A, 2))

## Binary arithmetic and boolean operators
(+)(A::AbstractSparseMatrixCSC, B::AbstractSparseMatrixCSC) = map(+, A, B)
(-)(A::AbstractSparseMatrixCSC, B::AbstractSparseMatrixCSC) = map(-, A, B)

(+)(A::AbstractSparseMatrixCSC, B::Array) = Array(A) + B
(+)(A::Array, B::AbstractSparseMatrixCSC) = A + Array(B)
(-)(A::AbstractSparseMatrixCSC, B::Array) = Array(A) - B
(-)(A::Array, B::AbstractSparseMatrixCSC) = A - Array(B)

## full equality
function ==(A1::AbstractSparseMatrixCSC, A2::AbstractSparseMatrixCSC)
    size(A1) != size(A2) && return false
    vals1, vals2 = nonzeros(A1), nonzeros(A2)
    rows1, rows2 = rowvals(A1), rowvals(A2)
    m, n = size(A1)
    @inbounds for i = 1:n
        nz1,nz2 = nzrange(A1,i), nzrange(A2,i)
        j1,j2 = first(nz1), first(nz2)
        # step through the rows of both matrices at once:
        while j1 <= last(nz1) && j2 <= last(nz2)
            r1,r2 = rows1[j1], rows2[j2]
            if r1==r2
                vals1[j1]!=vals2[j2] && return false
                j1+=1
                j2+=1
            else
                if r1<r2
                    vals1[j1]!=0 && return false
                    j1+=1
                else
                    vals2[j2]!=0 && return false
                    j2+=1
                end
            end
        end
        # finish off any left-overs:
        for j = j1:last(nz1)
            vals1[j]!=0 && return false
        end
        for j = j2:last(nz2)
            vals2[j]!=0 && return false
        end
    end
    return true
end

## Reductions

# In general, output of sparse matrix reductions will not be sparse,
# and computing reductions along columns into SparseMatrixCSC is
# non-trivial, so use Arrays for output. Array element type is given by `R`.
function Base.reducedim_initarray(A::AbstractSparseMatrixCSC, region, v0, ::Type{R}) where {R}
    fill!(Array{R}(undef, Base.to_shape(Base.reduced_indices(A, region))), v0)
end

# General mapreduce
function _mapreducezeros(f, op, ::Type{T}, nzeros::Integer, v0) where T
    nzeros == 0 && return v0

    # Reduce over first zero
    zeroval = f(zero(T))
    v = op(v0, zeroval)
    isequal(v, v0) && return v

    # Reduce over remaining zeros
    for i = 2:nzeros
        lastv = v
        v = op(v, zeroval)
        # Bail out early if we reach a fixed point
        isequal(v, lastv) && break
    end

    v
end

function Base._mapreduce(f, op, ::Base.IndexCartesian, A::AbstractSparseMatrixCSC{T}) where T
    z = nnz(A)
    n = widelength(A)
    if z == 0
        if n == 0
            Base.mapreduce_empty(f, op, T)
        else
            _mapreducezeros(f, op, T, n-z-1, f(zero(T)))
        end
    else
        _mapreducezeros(f, op, T, n-z, Base._mapreduce(f, op, nzvalview(A)))
    end
end

# Specialized mapreduce for +/*
_mapreducezeros(f, ::typeof(+), ::Type{T}, nzeros::Integer, v0) where {T} =
    nzeros == 0 ? v0 : f(zero(T))*nzeros + v0
_mapreducezeros(f, ::typeof(*), ::Type{T}, nzeros::Integer, v0) where {T} =
    nzeros == 0 ? v0 : f(zero(T))^nzeros * v0

function Base._mapreduce(f, op::typeof(*), A::AbstractSparseMatrixCSC{T}) where T
    nzeros = widelength(A)-nnz(A)
    if nzeros == 0
        # No zeros, so don't compute f(0) since it might throw
        Base._mapreduce(f, op, nzvalview(A))
    else
        v = f(zero(T))^(nzeros)
        # Bail out early if initial reduction value is zero
        v == zero(T) ? v : v*Base._mapreduce(f, op, nzvalview(A))
    end
end

# General mapreducedim
function _mapreducerows!(f, op, R::AbstractArray, A::AbstractSparseMatrixCSC{T}) where T
    require_one_based_indexing(A, R)
    colptr = getcolptr(A)
    rowval = rowvals(A)
    nzval = nonzeros(A)
    m, n = size(A)
    @inbounds for col = 1:n
        r = R[1, col]
        @simd for j = colptr[col]:colptr[col+1]-1
            r = op(r, f(nzval[j]))
        end
        R[1, col] = _mapreducezeros(f, op, T, m-(colptr[col+1]-colptr[col]), r)
    end
    R
end

function _mapreducecols!(f, op, R::AbstractArray, A::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    require_one_based_indexing(A, R)
    colptr = getcolptr(A)
    rowval = rowvals(A)
    nzval = nonzeros(A)
    m, n = size(A)
    rownz = fill(convert(Ti, n), m)
    @inbounds for col = 1:n
        @simd for j = colptr[col]:colptr[col+1]-1
            row = rowval[j]
            R[row, 1] = op(R[row, 1], f(nzval[j]))
            rownz[row] -= 1
        end
    end
    @inbounds for i = 1:m
        R[i, 1] = _mapreducezeros(f, op, Tv, Int(rownz[i]), R[i, 1])
    end
    R
end

function Base._mapreducedim!(f, op, R::AbstractArray, A::AbstractSparseMatrixCSC{T}) where T
    require_one_based_indexing(A, R)
    lsiz = Base.check_reducedims(R,A)
    isempty(A) && return R

    if size(R, 1) == size(R, 2) == 1
        # Reduction along both columns and rows
        R[1, 1] = mapreduce(f, op, A)
    elseif size(R, 1) == 1
        # Reduction along rows
        _mapreducerows!(f, op, R, A)
    elseif size(R, 2) == 1
        # Reduction along columns
        _mapreducecols!(f, op, R, A)
    else
        # Reduction along a dimension > 2
        # Compute op(R, f(A))
        m, n = size(A)
        nzval = nonzeros(A)
        if length(nzval) == m*n
            # No zeros, so don't compute f(0) since it might throw
            for col = 1:n
                @simd for row = 1:size(A, 1)
                    @inbounds R[row, col] = op(R[row, col], f(nzval[(col-1)*m+row]))
                end
            end
        else
            colptr = getcolptr(A)
            rowval = rowvals(A)
            zeroval = f(zero(T))
            @inbounds for col = 1:n
                lastrow = 0
                for j = colptr[col]:colptr[col+1]-1
                    row = rowval[j]
                    @simd for i = lastrow+1:row-1 # Zeros before this nonzero
                        R[i, col] = op(R[i, col], zeroval)
                    end
                    R[row, col] = op(R[row, col], f(nzval[j]))
                    lastrow = row
                end
                @simd for i = lastrow+1:m         # Zeros at end
                    R[i, col] = op(R[i, col], zeroval)
                end
            end
        end
    end
    R
end

# Specialized mapreducedim for + cols to avoid allocating a
# temporary array when f(0) == 0
function _mapreducecols!(f, op::typeof(+), R::AbstractArray, A::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}
    require_one_based_indexing(A, R)
    nzval = nonzeros(A)
    m, n = size(A)
    if length(nzval) == m*n
        # No zeros, so don't compute f(0) since it might throw
        for col = 1:n
            @simd for row = 1:size(A, 1)
                @inbounds R[row, 1] = op(R[row, 1], f(nzval[(col-1)*m+row]))
            end
        end
    else
        colptr = getcolptr(A)
        rowval = rowvals(A)
        zeroval = f(zero(Tv))
        if isequal(zeroval, zero(Tv))
            # Case where f(0) == 0
            @inbounds for col = 1:size(A, 2)
                @simd for j = colptr[col]:colptr[col+1]-1
                    R[rowval[j], 1] += f(nzval[j])
                end
            end
        else
            # Case where f(0) != 0
            rownz = fill(convert(Ti, n), m)
            @inbounds for col = 1:size(A, 2)
                @simd for j = colptr[col]:colptr[col+1]-1
                    row = rowval[j]
                    R[row, 1] += f(nzval[j])
                    rownz[row] -= 1
                end
            end
            for i = 1:m
                R[i, 1] += rownz[i]*zeroval
            end
        end
    end
    R
end

# any(pred, A, dims = 1) => mapreduce(pred, |, A, dims = 1)
# final argument `post` is to allow post-mapping each columnar mapreduce
function _mapreducerows!(pred::P, ::typeof(|), R::AbstractMatrix{Bool}, A::AbstractSparseMatrixCSC{Tv},
                         post::F = identity) where {P, F, Tv}
    nzval = nonzeros(A)
    colptr = getcolptr(A)
    m, n = size(A)
    @inbounds for ii in 1:n
        bi, ei = colptr[ii], colptr[ii+1]
        len = ei - bi
        # An empty column is trivial
        if len == 0
            R[1, ii] = post(pred(zero(Tv)))
            continue
        end
        # If predicate on zero is true, then sparse column can be short-circuited
        if pred(zero(Tv)) && len < m
            R[1, ii] = post(true)
            continue
        end
        # Otherwise reduce over the stored values
        r = false
        for jj in bi:(ei - 1)
            r = pred(nzval[jj])
            r && break
        end
        R[1, ii] = post(r)
    end
    return R
end
# all(pred, A, dims = 1) => mapreduce(pred, &, A, dims = 1) == .!mapreduce(!pred, |, A, dims = 1)
_mapreducerows!(pred::P, ::typeof(&), R::AbstractMatrix{Bool},
                A::AbstractSparseMatrixCSC) where {P} = _mapreducerows!(!pred, |, R, A, !)

# findmax/min and argmax/min methods
# find first zero value in sparse matrix - return linear index in full matrix
# non-structural zeros are identified by x == 0 in line with the sparse constructors.
function _findz(A::AbstractSparseMatrixCSC{Tv,Ti}, rows=1:size(A, 1), cols=1:size(A, 2)) where {Tv,Ti}
    colptr = getcolptr(A); rowval = rowvals(A); nzval = nonzeros(A)
    zval = 0
    row = 0
    rowmin = rows[1]; rowmax = rows[end]
    allrows = (rows == 1:size(A, 1))
    @inbounds for col in cols
        r1::Int = colptr[col]
        r2::Int = colptr[col+1] - 1
        if !allrows && (r1 <= r2)
            r1 = searchsortedfirst(rowval, rowmin, r1, r2, Forward)
            (r1 <= r2 ) && (r2 = searchsortedlast(rowval, rowmax, r1, r2, Forward))
        end
        row = rowmin
        while (r1 <= r2) && (row == rowval[r1]) && (nzval[r1] != zval)
            r1 += 1
            row += 1
        end
        (row <= rowmax) && (return CartesianIndex(row, col))
    end
    return CartesianIndex(0, 0)
end

function _findr(op, A, region, Tv)
    require_one_based_indexing(A)
    Ti = eltype(keys(A))
    i1 = first(keys(A))
    N = nnz(A)
    L = widelength(A)
    if L == 0
        if prod(map(length, Base.reduced_indices(A, region))) != 0
            throw(ArgumentError("array slices must be non-empty"))
        else
            ri = Base.reduced_indices0(A, region)
            return (similar(A, ri), zeros(Ti, ri))
        end
    end

    colptr = getcolptr(A); rowval = rowvals(A); nzval = nonzeros(A); m = size(A, 1); n = size(A, 2)
    zval = zero(Tv)
    szA = size(A)

    if region == 1 || region == (1,)
        (N == 0) && (return (fill(zval,1,n), fill(i1,1,n)))
        S = Vector{Tv}(undef, n); I = Vector{Ti}(undef, n)
        @inbounds for i = 1 : n
            Sc = zval; Ic = _findz(A, 1:m, i:i)
            if Ic == CartesianIndex(0, 0)
                j = colptr[i]
                Ic = CartesianIndex(rowval[j], i)
                Sc = nzval[j]
            end
            for j = colptr[i] : colptr[i+1]-1
                if op(nzval[j], Sc)
                    Sc = nzval[j]
                    Ic = CartesianIndex(rowval[j], i)
                end
            end
            S[i] = Sc; I[i] = Ic
        end
        return(reshape(S,1,n), reshape(I,1,n))
    elseif region == 2 || region == (2,)
        (N == 0) && (return (fill(zval,m,1), fill(i1,m,1)))
        S = Vector{Tv}(undef, m)
        I = Vector{Ti}(undef, m)
        @inbounds for row in 1:m
            S[row] = zval; I[row] = _findz(A, row:row, 1:n)
            if I[row] == CartesianIndex(0, 0)
                I[row] = CartesianIndex(row, 1)
                S[row] = A[row,1]
            end
        end
        @inbounds for i = 1 : n, j = colptr[i] : colptr[i+1]-1
            row = rowval[j]
            if op(nzval[j], S[row])
                S[row] = nzval[j]
                I[row] = CartesianIndex(row, i)
            end
        end
        return (reshape(S,m,1), reshape(I,m,1))
    elseif region == (1,2)
        (N == 0) && (return (fill(zval,1,1), fill(i1,1,1)))
        hasz = nnz(A) != widelength(A)
        Sv = hasz ? zval : nzval[1]
        Iv::(Ti) = hasz ? _findz(A) : i1
        @inbounds for i = 1 : size(A, 2), j = colptr[i] : (colptr[i+1]-1)
            if op(nzval[j], Sv)
                Sv = nzval[j]
                Iv = CartesianIndex(rowval[j], i)
            end
        end
        return (fill(Sv,1,1), fill(Iv,1,1))
    else
        throw(ArgumentError("invalid value for region; must be 1, 2, or (1,2)"))
    end
end

_isless_fm(a, b)    =  b == b && ( a != a || isless(a, b) )
_isgreater_fm(a, b) =  b == b && ( a != a || isless(b, a) )

findmin(A::AbstractSparseMatrixCSC{Tv,Ti}, region) where {Tv,Ti} = _findr(_isless_fm, A, region, Tv)
findmax(A::AbstractSparseMatrixCSC{Tv,Ti}, region) where {Tv,Ti} = _findr(_isgreater_fm, A, region, Tv)
findmin(A::AbstractSparseMatrixCSC) = (r=findmin(A,(1,2)); (r[1][1], r[2][1]))
findmax(A::AbstractSparseMatrixCSC) = (r=findmax(A,(1,2)); (r[1][1], r[2][1]))

argmin(A::AbstractSparseMatrixCSC) = findmin(A)[2]
argmax(A::AbstractSparseMatrixCSC) = findmax(A)[2]

## getindex
function rangesearch(haystack::AbstractRange, needle)
    (i,rem) = divrem(needle - first(haystack), step(haystack))
    (rem==0 && 1<=i+1<=length(haystack)) ? i+1 : 0
end

getindex(A::AbstractSparseMatrixCSC, I::Tuple{Integer,Integer}) = getindex(A, I[1], I[2])

function getindex(A::AbstractSparseMatrixCSC{T}, i0::Integer, i1::Integer) where T
    if !(1 <= i0 <= size(A, 1) && 1 <= i1 <= size(A, 2)); throw(BoundsError()); end
    r1 = Int(getcolptr(A)[i1])
    r2 = Int(getcolptr(A)[i1+1]-1)
    (r1 > r2) && return zero(T)
    r1 = searchsortedfirst(rowvals(A), i0, r1, r2, Forward)
    ((r1 > r2) || (rowvals(A)[r1] != i0)) ? zero(T) : nonzeros(A)[r1]
end

# Colon translation
getindex(A::AbstractSparseMatrixCSC, ::Colon, ::Colon) = copy(A)
getindex(A::AbstractSparseMatrixCSC, i, ::Colon)       = getindex(A, i, 1:size(A, 2))
getindex(A::AbstractSparseMatrixCSC, ::Colon, i)       = getindex(A, 1:size(A, 1), i)

function getindex_cols(A::AbstractSparseMatrixCSC{Tv,Ti}, J::AbstractVector) where {Tv,Ti}
    require_one_based_indexing(A, J)
    # for indexing whole columns
    (m, n) = size(A)
    nJ = length(J)

    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A)

    colptrS = Vector{Ti}(undef, nJ+1)
    colptrS[1] = 1
    nnzS = 0

    @inbounds for j = 1:nJ
        col = J[j]
        1 <= col <= n || throw(BoundsError())
        nnzS += colptrA[col+1] - colptrA[col]
        colptrS[j+1] = nnzS + 1
    end

    rowvalS = Vector{Ti}(undef, nnzS)
    nzvalS  = Vector{Tv}(undef, nnzS)
    ptrS = 0

    @inbounds for j = 1:nJ
        col = J[j]
        for k = colptrA[col]:colptrA[col+1]-1
            ptrS += 1
            rowvalS[ptrS] = rowvalA[k]
            nzvalS[ptrS] = nzvalA[k]
        end
    end
    return SparseMatrixCSC(m, nJ, colptrS, rowvalS, nzvalS)
end

getindex_traverse_col(::AbstractUnitRange, lo::Integer, hi::Integer) = lo:hi
getindex_traverse_col(I::StepRange, lo::Integer, hi::Integer) = step(I) > 0 ? (lo:1:hi) : (hi:-1:lo)

function getindex(A::AbstractSparseMatrixCSC{Tv,Ti}, I::AbstractRange, J::AbstractVector) where {Tv,Ti<:Integer}
    require_one_based_indexing(A, I, J)
    # Ranges for indexing rows
    (m, n) = size(A)
    # whole columns:
    if I == 1:m
        return getindex_cols(A, J)
    end

    nI = length(I)
    nI == 0 || (minimum(I) >= 1 && maximum(I) <= m) || throw(BoundsError())
    nJ = length(J)
    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A)
    colptrS = Vector{Ti}(undef, nJ+1)
    colptrS[1] = 1
    nnzS = 0

    # Form the structure of the result and compute space
    @inbounds for j = 1:nJ
        col = J[j]
        1 <= col <= n || throw(BoundsError())
        @simd for k in colptrA[col]:colptrA[col+1]-1
            nnzS += rowvalA[k] in I # `in` is fast for ranges
        end
        colptrS[j+1] = nnzS+1
    end

    # Populate the values in the result
    rowvalS = Vector{Ti}(undef, nnzS)
    nzvalS  = Vector{Tv}(undef, nnzS)
    ptrS    = 1

    @inbounds for j = 1:nJ
        col = J[j]
        for k = getindex_traverse_col(I, colptrA[col], colptrA[col+1]-1)
            rowA = rowvalA[k]
            i = rangesearch(I, rowA)
            if i > 0
                rowvalS[ptrS] = i
                nzvalS[ptrS] = nzvalA[k]
                ptrS += 1
            end
        end
    end

    return SparseMatrixCSC(nI, nJ, colptrS, rowvalS, nzvalS)
end

function getindex_I_sorted(A::AbstractSparseMatrixCSC{Tv,Ti}, I::AbstractVector, J::AbstractVector) where {Tv,Ti}
    require_one_based_indexing(A, I, J)
    # Sorted vectors for indexing rows.
    # Similar to getindex_general but without the transpose trick.
    (m, n) = size(A)

    nI   = length(I)
    nzA  = nnz(A)
    avgM = div(nzA,n)
    # Heuristics based on experiments discussed in:
    # https://github.com/JuliaLang/julia/issues/12860
    # https://github.com/JuliaLang/julia/pull/12934
    alg = ((m > nzA) && (m > nI)) ? 0 :
          ((nI - avgM) > 2^8) ? 1 :
          ((avgM - nI) > 2^10) ? 0 : 2

    (alg == 0) ? getindex_I_sorted_bsearch_A(A, I, J) :
    (alg == 1) ? getindex_I_sorted_bsearch_I(A, I, J) :
    getindex_I_sorted_linear(A, I, J)
end

function getindex_I_sorted_bsearch_A(A::AbstractSparseMatrixCSC{Tv,Ti}, I::AbstractVector, J::AbstractVector) where {Tv,Ti}
    require_one_based_indexing(A, I, J)
    nI = length(I)
    nJ = length(J)

    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A)
    colptrS = Vector{Ti}(undef, nJ+1)
    colptrS[1] = 1

    ptrS = 1
    # determine result size
    @inbounds for j = 1:nJ
        col = J[j]
        ptrI::Int = 1 # runs through I
        ptrA::Int = colptrA[col]
        stopA::Int = colptrA[col+1]-1
        if ptrA <= stopA
            while ptrI <= nI
                rowI = I[ptrI]
                ptrI += 1
                (rowvalA[ptrA] > rowI) && continue
                ptrA = searchsortedfirst(rowvalA, rowI, ptrA, stopA, Base.Order.Forward)
                (ptrA <= stopA) || break
                if rowvalA[ptrA] == rowI
                    ptrS += 1
                end
            end
        end
        colptrS[j+1] = ptrS
    end

    rowvalS = Vector{Ti}(undef, ptrS-1)
    nzvalS  = Vector{Tv}(undef, ptrS-1)

    # fill the values
    ptrS = 1
    @inbounds for j = 1:nJ
        col = J[j]
        ptrI::Int = 1 # runs through I
        ptrA::Int = colptrA[col]
        stopA::Int = colptrA[col+1]-1
        if ptrA <= stopA
            while ptrI <= nI
                rowI = I[ptrI]
                if rowvalA[ptrA] <= rowI
                    ptrA = searchsortedfirst(rowvalA, rowI, ptrA, stopA, Base.Order.Forward)
                    (ptrA <= stopA) || break
                    if rowvalA[ptrA] == rowI
                        rowvalS[ptrS] = ptrI
                        nzvalS[ptrS] = nzvalA[ptrA]
                        ptrS += 1
                    end
                end
                ptrI += 1
            end
        end
    end
    return SparseMatrixCSC(nI, nJ, colptrS, rowvalS, nzvalS)
end

function getindex_I_sorted_linear(A::AbstractSparseMatrixCSC{Tv,Ti}, I::AbstractVector, J::AbstractVector) where {Tv,Ti}
    require_one_based_indexing(A, I, J)
    nI = length(I)
    nJ = length(J)

    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A)
    colptrS = Vector{Ti}(undef, nJ+1)
    colptrS[1] = 1
    cacheI = zeros(Int, size(A, 1))

    ptrS   = 1
    # build the cache and determine result size
    @inbounds for j = 1:nJ
        col = J[j]
        ptrI::Int = 1 # runs through I
        ptrA::Int = colptrA[col]
        stopA::Int = colptrA[col+1]
        while ptrI <= nI && ptrA < stopA
            rowA = rowvalA[ptrA]
            rowI = I[ptrI]

            if rowI > rowA
                ptrA += 1
            elseif rowI < rowA
                ptrI += 1
            else
                (cacheI[rowA] == 0) && (cacheI[rowA] = ptrI)
                ptrS += 1
                ptrI += 1
            end
        end
        colptrS[j+1] = ptrS
    end

    rowvalS = Vector{Ti}(undef, ptrS-1)
    nzvalS  = Vector{Tv}(undef, ptrS-1)

    # fill the values
    ptrS = 1
    @inbounds for j = 1:nJ
        col = J[j]
        ptrA::Int = colptrA[col]
        stopA::Int = colptrA[col+1]
        while ptrA < stopA
            rowA = rowvalA[ptrA]
            ptrI = cacheI[rowA]
            if ptrI > 0
                while ptrI <= nI && I[ptrI] == rowA
                    rowvalS[ptrS] = ptrI
                    nzvalS[ptrS] = nzvalA[ptrA]
                    ptrS += 1
                    ptrI += 1
                end
            end
            ptrA += 1
        end
    end
    return SparseMatrixCSC(nI, nJ, colptrS, rowvalS, nzvalS)
end

function getindex_I_sorted_bsearch_I(A::AbstractSparseMatrixCSC{Tv,Ti}, I::AbstractVector, J::AbstractVector) where {Tv,Ti}
    require_one_based_indexing(A, I, J)
    nI = length(I)
    nJ = length(J)

    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A)
    colptrS = Vector{Ti}(undef, nJ+1)
    colptrS[1] = 1

    m = size(A, 1)

    # cacheI is used first to store num occurrences of each row in columns of interest
    # and later to store position of first occurrence of each row in I
    cacheI = zeros(Int, m)

    # count rows
    @inbounds for j = 1:nJ
        col = J[j]
        for ptrA in colptrA[col]:(colptrA[col+1]-1)
            cacheI[rowvalA[ptrA]] += 1
        end
    end

    # fill cache and count nnz
    ptrS::Int = 0
    ptrI::Int = 1
    @inbounds for j = 1:m
        cval = cacheI[j]
        (cval == 0) && continue
        ptrI = searchsortedfirst(I, j, ptrI, nI, Base.Order.Forward)
        cacheI[j] = ptrI
        while ptrI <= nI && I[ptrI] == j
            ptrS += cval
            ptrI += 1
        end
        if ptrI > nI
            @simd for i=(j+1):m; @inbounds cacheI[i]=ptrI; end
            break
        end
    end
    rowvalS = Vector{Ti}(undef, ptrS)
    nzvalS  = Vector{Tv}(undef, ptrS)
    colptrS[nJ+1] = ptrS+1

    # fill the values
    ptrS = 1
    @inbounds for j = 1:nJ
        col = J[j]
        ptrA::Int = colptrA[col]
        stopA::Int = colptrA[col+1]
        while ptrA < stopA
            rowA = rowvalA[ptrA]
            ptrI = cacheI[rowA]
            (ptrI > nI) && break
            if ptrI > 0
                while I[ptrI] == rowA
                    rowvalS[ptrS] = ptrI
                    nzvalS[ptrS] = nzvalA[ptrA]
                    ptrS += 1
                    ptrI += 1
                    (ptrI > nI) && break
                end
            end
            ptrA += 1
        end
        colptrS[j+1] = ptrS
    end
    return SparseMatrixCSC(nI, nJ, colptrS, rowvalS, nzvalS)
end

function permute_rows!(S::AbstractSparseMatrixCSC{Tv,Ti}, pI::Vector{Int}) where {Tv,Ti}
    (m, n) = size(S)
    colptrS = getcolptr(S); rowvalS = rowvals(S); nzvalS = nonzeros(S)
    # preallocate temporary sort space
    nr = min(nnz(S), m)

    rowperm = Vector{Int}(undef, nr)
    rowval_temp = Vector{Ti}(undef, nr)
    rnzval_temp = Vector{Tv}(undef, nr)
    perm = Base.Perm(Base.ord(isless, identity, false, Base.Order.Forward), rowval_temp)

    @inbounds for j in 1:n
        rowrange = nzrange(S, j)
        nr = length(rowrange)
        resize!(rowperm, nr)
        resize!(rowval_temp, nr)
        (nr > 0) || continue
        k = 1
        for i in rowrange
            rowA = rowvalS[i]
            rowval_temp[k] = pI[rowA]
            rnzval_temp[k] = nzvalS[i]
            k += 1
        end

        if nr <= 16
            alg = Base.Sort.InsertionSort
        else
            alg = Base.Sort.QuickSort
        end

        # Reset permutation
        rowperm .= 1:nr
        sort!(rowperm, alg, perm)

        k = 1
        for i in rowrange
            kperm = rowperm[k]
            rowvalS[i] = rowval_temp[kperm]
            nzvalS[i] = rnzval_temp[kperm]
            k += 1
        end
    end
    return _checkbuffers(S)
end

function getindex_general(A::AbstractSparseMatrixCSC, I::AbstractVector, J::AbstractVector)
    require_one_based_indexing(A, I, J)
    pI = sortperm(I)
    @inbounds Is = I[pI]
    permute_rows!(getindex_I_sorted(A, Is, J), pI)
end

# the general case:
function getindex(A::AbstractSparseMatrixCSC{Tv,Ti}, I::AbstractVector, J::AbstractVector) where {Tv,Ti}
    require_one_based_indexing(A, I, J)
    (m, n) = size(A)

    if !isempty(J)
        minj, maxj = extrema(J)
        ((minj < 1) || (maxj > n)) && throw(BoundsError())
    end

    if !isempty(I)
        mini, maxi = extrema(I)
        ((mini < 1) || (maxi > m)) && throw(BoundsError())
    end

    if isempty(I) || isempty(J) || (0 == nnz(A))
        return spzeros(Tv, Ti, length(I), length(J))
    end

    if issorted(I)
        return getindex_I_sorted(A, I, J)
    else
        return getindex_general(A, I, J)
    end
end

function getindex(A::AbstractSparseMatrixCSC{Tv,Ti}, I::AbstractArray) where {Tv,Ti}
    require_one_based_indexing(A, I)
    szA = size(A)
    nA = szA[1]*szA[2]
    colptrA = getcolptr(A)
    rowvalA = rowvals(A)
    nzvalA = nonzeros(A)

    n = length(I)
    outm = size(I,1)
    outn = size(I,2)
    szB = (outm, outn)
    colptrB = zeros(Ti, outn+1)
    rowvalB = Vector{Ti}(undef, n)
    nzvalB = Vector{Tv}(undef, n)

    colB = 1
    rowB = 1
    colptrB[colB] = 1
    idxB = 1

    for i in 1:n
        ((I[i] < 1) | (I[i] > nA)) && throw(BoundsError())
        row,col = Base._ind2sub(szA, I[i])
        for r in colptrA[col]:(colptrA[col+1]-1)
            @inbounds if rowvalA[r] == row
                rowB,colB = Base._ind2sub(szB, i)
                colptrB[colB+1] += 1
                rowvalB[idxB] = rowB
                nzvalB[idxB] = nzvalA[r]
                idxB += 1
                break
            end
        end
    end
    cumsum!(colptrB,colptrB)
    if n > (idxB-1)
        deleteat!(nzvalB, idxB:n)
        deleteat!(rowvalB, idxB:n)
    end
    SparseMatrixCSC(outm, outn, colptrB, rowvalB, nzvalB)
end

# logical getindex
getindex(A::AbstractSparseMatrixCSC{<:Any,<:Integer}, I::AbstractRange{Bool}, J::AbstractVector{Bool}) = error("Cannot index with AbstractRange{Bool}")
getindex(A::AbstractSparseMatrixCSC{<:Any,<:Integer}, I::AbstractRange{Bool}, J::AbstractVector{<:Integer}) = error("Cannot index with AbstractRange{Bool}")

getindex(A::AbstractSparseMatrixCSC, I::AbstractRange{<:Integer}, J::AbstractVector{Bool}) = A[I,findall(J)]
getindex(A::AbstractSparseMatrixCSC, I::Integer, J::AbstractVector{Bool}) = A[I,findall(J)]
getindex(A::AbstractSparseMatrixCSC, I::AbstractVector{Bool}, J::Integer) = A[findall(I),J]
getindex(A::AbstractSparseMatrixCSC, I::AbstractVector{Bool}, J::AbstractVector{Bool}) = A[findall(I),findall(J)]
getindex(A::AbstractSparseMatrixCSC, I::AbstractVector{<:Integer}, J::AbstractVector{Bool}) = A[I,findall(J)]
getindex(A::AbstractSparseMatrixCSC, I::AbstractVector{Bool}, J::AbstractVector{<:Integer}) = A[findall(I),J]

## setindex!

# dispatch helper for #29034
setindex!(A::AbstractSparseMatrixCSC, _v, _i::Integer, _j::Integer) = _setindex_scalar!(A, _v, _i, _j)

function _setindex_scalar!(A::AbstractSparseMatrixCSC{Tv,Ti}, _v, _i::Integer, _j::Integer) where {Tv,Ti<:Integer}
    v = convert(Tv, _v)
    i = convert(Ti, _i)
    j = convert(Ti, _j)
    if !((1 <= i <= size(A, 1)) & (1 <= j <= size(A, 2)))
        throw(BoundsError(A, (i,j)))
    end
    coljfirstk = Int(getcolptr(A)[j])
    coljlastk = Int(getcolptr(A)[j+1] - 1)
    searchk = searchsortedfirst(rowvals(A), i, coljfirstk, coljlastk, Base.Order.Forward)
    if searchk <= coljlastk && rowvals(A)[searchk] == i
        # Column j contains entry A[i,j]. Update and return
        nonzeros(A)[searchk] = v
        return A
    end
    # Column j does not contain entry A[i,j]. If v is nonzero, insert entry A[i,j] = v
    # and return. If to the contrary v is zero, then simply return.
    if !iszero(v)
        nz = getcolptr(A)[size(A, 2)+1]
        # throw exception before state is partially modified
        !isbitstype(Ti) || nz < typemax(Ti) ||
            throw(ArgumentError("nnz(A) going to exceed typemax(Ti) = $(typemax(Ti))"))

        # if nnz(A) < length(rowval/nzval): no need to grow rowval and preserve values
        _insert!(rowvals(A), searchk, i, nz)
        _insert!(nonzeros(A), searchk, v, nz)
        @simd for m in (j + 1):(size(A, 2) + 1)
            @inbounds getcolptr(A)[m] += Ti(1)
        end
    end
    return A
end

# insert item at position pos, shifting only from pos+1 to nz
function _insert!(v::Vector, pos::Integer, item, nz::Integer)
    if nz > length(v)
        insert!(v, pos, item)
    else # nz < length(v)
        Base.unsafe_copyto!(v, pos+1, v, pos, nz - pos)
        v[pos] = item
        v
    end
end

function Base.fill!(V::SubArray{Tv, <:Any, <:AbstractSparseMatrixCSC{Tv}, <:Tuple{Vararg{Union{Integer, AbstractVector{<:Integer}},2}}}, x) where Tv
    A = V.parent
    I, J = V.indices
    if isempty(I) || isempty(J); return A; end
    # lt=≤ to check for strict sorting
    if !issorted(I, lt=≤); I = sort!(unique(I)); end
    if !issorted(J, lt=≤); J = sort!(unique(J)); end
    if (I[1] < 1 || I[end] > size(A, 1)) || (J[1] < 1 || J[end] > size(A, 2))
        throw(BoundsError(A, (I, J)))
    end
    if x == 0
        _spsetz_setindex!(A, I, J)
    else
        _spsetnz_setindex!(A, convert(Tv, x), I, J)
    end
    return _checkbuffers(A)
end
"""
Helper method for immediately preceding fill! method. For all (i,j) such that i in I and
j in J, assigns zero to A[i,j] if A[i,j] is a presently-stored entry, and otherwise does nothing.
"""
function _spsetz_setindex!(A::AbstractSparseMatrixCSC,
        I::Union{Integer, AbstractVector{<:Integer}}, J::Union{Integer, AbstractVector{<:Integer}})
    require_one_based_indexing(A, I, J)
    lengthI = length(I)
    for j in J
        coljAfirstk = getcolptr(A)[j]
        coljAlastk = getcolptr(A)[j+1] - 1
        coljAfirstk > coljAlastk && continue
        kA = coljAfirstk
        kI = 1
        entrykArow = rowvals(A)[kA]
        entrykIrow = I[kI]
        while true
            if entrykArow < entrykIrow
                kA += 1
                kA > coljAlastk && break
                entrykArow = rowvals(A)[kA]
            elseif entrykArow > entrykIrow
                kI += 1
                kI > lengthI && break
                entrykIrow = I[kI]
            else # entrykArow == entrykIrow
                nonzeros(A)[kA] = 0
                kA += 1
                kI += 1
                (kA > coljAlastk || kI > lengthI) && break
                entrykArow = rowvals(A)[kA]
                entrykIrow = I[kI]
            end
        end
    end
end
"""
Helper method for immediately preceding fill! method. For all (i,j) such that i in I
and j in J, assigns x to A[i,j] if A[i,j] is a presently-stored entry, and allocates and
assigns x to A[i,j] if A[i,j] is not presently stored.
"""
function _spsetnz_setindex!(A::AbstractSparseMatrixCSC{Tv}, x::Tv,
        I::Union{Integer, AbstractVector{<:Integer}}, J::Union{Integer, AbstractVector{<:Integer}}) where Tv
    require_one_based_indexing(A, I, J)
    m, n = size(A)
    lenI = length(I)

    nnzA = nnz(A) + lenI * length(J)

    rowvalA = rowval = rowvals(A)
    nzvalA = nzval = nonzeros(A)

    rowidx = 1
    nadd = 0
    @inbounds for col in 1:n
        rrange = nzrange(A, col)
        if nadd > 0
            getcolptr(A)[col] = getcolptr(A)[col] + nadd
        end

        if col in J
            if isempty(rrange) # set new vals only
                nincl = lenI
                if nadd == 0
                    rowval = copy(rowvalA)
                    nzval = copy(nzvalA)
                    resize!(rowvalA, nnzA)
                    resize!(nzvalA, nnzA)
                end
                r = rowidx:(rowidx+nincl-1)
                rowvalA[r] .= I
                for rr in r
                    nzvalA[rr] = x
                end
                rowidx += nincl
                nadd += nincl
            else # set old + new vals
                old_ptr = rrange[1]
                old_stop = rrange[end]
                new_ptr = 1
                new_stop = lenI

                while true
                    old_row = rowval[old_ptr]
                    new_row = I[new_ptr]
                    if old_row < new_row
                        rowvalA[rowidx] = old_row
                        nzvalA[rowidx] = nzval[old_ptr]
                        rowidx += 1
                        old_ptr += 1
                    else
                        if old_row == new_row
                            old_ptr += 1
                        else
                            if nadd == 0
                                rowval = copy(rowvalA)
                                nzval = copy(nzvalA)
                                resize!(rowvalA, nnzA)
                                resize!(nzvalA, nnzA)
                            end
                            nadd += 1
                        end
                        rowvalA[rowidx] = new_row
                        nzvalA[rowidx] = x
                        rowidx += 1
                        new_ptr += 1
                    end

                    if old_ptr > old_stop
                        if new_ptr <= new_stop
                            if nadd == 0
                                rowval = copy(rowvalA)
                                nzval = copy(nzvalA)
                                resize!(rowvalA, nnzA)
                                resize!(nzvalA, nnzA)
                            end
                            r = rowidx:(rowidx+(new_stop-new_ptr))
                            rowvalA[r] .= I isa Number ? I : I[new_ptr:new_stop]
                            for rr in r
                                nzvalA[rr] = x
                            end
                            rowidx += length(r)
                            nadd += length(r)
                        end
                        break
                    end

                    if new_ptr > new_stop
                        nincl = old_stop-old_ptr+1
                        copyto!(rowvalA, rowidx, rowval, old_ptr, nincl)
                        copyto!(nzvalA, rowidx, nzval, old_ptr, nincl)
                        rowidx += nincl
                        break
                    end
                end
            end
        elseif !isempty(rrange) # set old vals only
            nincl = length(rrange)
            copyto!(rowvalA, rowidx, rowval, rrange[1], nincl)
            copyto!(nzvalA, rowidx, nzval, rrange[1], nincl)
            rowidx += nincl
        end
    end

    if nadd > 0
        getcolptr(A)[n+1] = rowidx
        deleteat!(rowvalA, rowidx:nnzA)
        deleteat!(nzvalA, rowidx:nnzA)
    end
    return A
end

# Nonscalar A[I,J] = B: Convert B to a SparseMatrixCSC of the appropriate shape first
_to_same_csc(::AbstractSparseMatrixCSC{Tv, Ti}, V::AbstractMatrix, I...) where {Tv,Ti} = convert(SparseMatrixCSC{Tv,Ti}, V)
_to_same_csc(::AbstractSparseMatrixCSC{Tv, Ti}, V::AbstractVector, I...) where {Tv,Ti} = convert(SparseMatrixCSC{Tv,Ti}, reshape(V, map(length, I)))

setindex!(A::AbstractSparseMatrixCSC{Tv}, B::AbstractVecOrMat, I::Integer, J::Integer) where {Tv} = _setindex_scalar!(A, B, I, J)

function setindex!(A::AbstractSparseMatrixCSC{Tv,Ti}, V::AbstractVecOrMat, Ix::Union{Integer, AbstractVector{<:Integer}, Colon}, Jx::Union{Integer, AbstractVector{<:Integer}, Colon}) where {Tv,Ti<:Integer}
    require_one_based_indexing(A, V, Ix, Jx)
    (I, J) = Base.ensure_indexable(to_indices(A, (Ix, Jx)))
    checkbounds(A, I, J)
    Base.setindex_shape_check(V, length(I), length(J))
    B = _to_same_csc(A, V, I, J)

    issortedI = issorted(I)
    issortedJ = issorted(J)

    if !issortedI && !issortedJ
        pI = sortperm(I); @inbounds I = I[pI]
        pJ = sortperm(J); @inbounds J = J[pJ]
        B = B[pI, pJ]
    elseif !issortedI
        pI = sortperm(I); @inbounds I = I[pI]
        B = B[pI,:]
    elseif !issortedJ
        pJ = sortperm(J); @inbounds J = J[pJ]
        B = B[:, pJ]
    end

    m, n = size(A)
    mB, nB = size(B)

    if (!isempty(I) && (I[1] < 1 || I[end] > m)) || (!isempty(J) && (J[1] < 1 || J[end] > n))
        throw(BoundsError(A, (I, J)))
    end

    if isempty(I) || isempty(J)
        return A
    end

    nI = length(I)
    nJ = length(J)

    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A)
    colptrB = getcolptr(B); rowvalB = rowvals(B); nzvalB = nonzeros(B)

    nnzS = nnz(A) + nnz(B)

    colptrS = copy(getcolptr(A))
    rowvalS = copy(rowvals(A))
    nzvalS = copy(nonzeros(A))

    resize!(rowvalA, nnzS)
    resize!(nzvalA, nnzS)

    colB = 1
    asgn_col = J[colB]

    I_asgn = falses(m)
    fill!(view(I_asgn, I), true)

    ptrS = 1

    @inbounds for col = 1:n

        # Copy column of A if it is not being assigned into
        if colB > nJ || col != J[colB]
            colptrA[col+1] = colptrA[col] + (colptrS[col+1]-colptrS[col])

            for k = colptrS[col]:colptrS[col+1]-1
                rowvalA[ptrS] = rowvalS[k]
                nzvalA[ptrS] = nzvalS[k]
                ptrS += 1
            end
            continue
        end

        ptrA::Int  = colptrS[col]
        stopA::Int = colptrS[col+1]
        ptrB::Int  = colptrB[colB]
        stopB::Int = colptrB[colB+1]

        while ptrA < stopA && ptrB < stopB
            rowA = rowvalS[ptrA]
            rowB = I[rowvalB[ptrB]]
            if rowA < rowB
                rowvalA[ptrS] = rowA
                nzvalA[ptrS] = I_asgn[rowA] ? zero(Tv) : nzvalS[ptrA]
                ptrS += 1
                ptrA += 1
            elseif rowB < rowA
                if nzvalB[ptrB] != zero(Tv)
                    rowvalA[ptrS] = rowB
                    nzvalA[ptrS] = nzvalB[ptrB]
                    ptrS += 1
                end
                ptrB += 1
            else
                rowvalA[ptrS] = rowB
                nzvalA[ptrS] = nzvalB[ptrB]
                ptrS += 1
                ptrB += 1
                ptrA += 1
            end
        end

        while ptrA < stopA
            rowA = rowvalS[ptrA]
            rowvalA[ptrS] = rowA
            nzvalA[ptrS] = I_asgn[rowA] ? zero(Tv) : nzvalS[ptrA]
            ptrS += 1
            ptrA += 1
        end

        while ptrB < stopB
            rowB = I[rowvalB[ptrB]]
            if nzvalB[ptrB] != zero(Tv)
                rowvalA[ptrS] = rowB
                nzvalA[ptrS] = nzvalB[ptrB]
                ptrS += 1
            end
            ptrB += 1
        end

        colptrA[col+1] = ptrS
        colB += 1
    end

    deleteat!(rowvalA, colptrA[end]:length(rowvalA))
    deleteat!(nzvalA, colptrA[end]:length(nzvalA))

    return _checkbuffers(A)
end

# Logical setindex!

setindex!(A::Matrix, x::AbstractSparseMatrixCSC, I::Integer, J::AbstractVector{Bool}) = setindex!(A, Array(x), I, findall(J))
setindex!(A::Matrix, x::AbstractSparseMatrixCSC, I::AbstractVector{Bool}, J::Integer) = setindex!(A, Array(x), findall(I), J)
setindex!(A::Matrix, x::AbstractSparseMatrixCSC, I::AbstractVector{Bool}, J::AbstractVector{Bool}) = setindex!(A, Array(x), findall(I), findall(J))
setindex!(A::Matrix, x::AbstractSparseMatrixCSC, I::AbstractVector{<:Integer}, J::AbstractVector{Bool}) = setindex!(A, Array(x), I, findall(J))
setindex!(A::Matrix, x::AbstractSparseMatrixCSC, I::AbstractVector{Bool}, J::AbstractVector{<:Integer}) = setindex!(A, Array(x), findall(I), J)

function setindex!(A::AbstractSparseMatrixCSC, x::AbstractArray, I::AbstractMatrix{Bool})
    require_one_based_indexing(A, x, I)
    checkbounds(A, I)
    n = sum(I)
    (n == 0) && (return A)

    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A)
    colptrB = colptrA; rowvalB = rowvalA; nzvalB = nzvalA
    nadd = 0
    bidx = xidx = 1
    r1 = r2 = 0

    @inbounds for col in 1:size(A, 2)
        r1 = Int(colptrA[col])
        r2 = Int(colptrA[col+1]-1)

        for row in 1:size(A, 1)
            if I[row, col]
                v = x[xidx]
                xidx += 1

                if r1 <= r2
                    copylen = searchsortedfirst(rowvalA, row, r1, r2, Forward) - r1
                    if (copylen > 0)
                        if (nadd > 0)
                            copyto!(rowvalB, bidx, rowvalA, r1, copylen)
                            copyto!(nzvalB, bidx, nzvalA, r1, copylen)
                        end
                        bidx += copylen
                        r1 += copylen
                    end
                end

                # 0: no change, 1: update, 2: add new
                mode = ((r1 <= r2) && (rowvalA[r1] == row)) ? 1 : ((v == 0) ? 0 : 2)

                if (mode > 1) && (nadd == 0)
                    # copy storage to take changes
                    colptrA = copy(colptrB)
                    memreq = (x == 0) ? 0 : n
                    # this x == 0 check and approach doesn't jive with use of v above
                    # and may not make sense generally, as scalar x == 0 probably
                    # means this section should never be called. also may not be generic.
                    # TODO: clean this up, maybe separate scalar and array X cases
                    rowvalA = copy(rowvalB)
                    nzvalA = copy(nzvalB)
                    resize!(rowvalB, length(rowvalA)+memreq)
                    resize!(nzvalB, length(rowvalA)+memreq)
                end
                if mode == 1
                    rowvalB[bidx] = row
                    nzvalB[bidx] = v
                    bidx += 1
                    r1 += 1
                elseif mode == 2
                    rowvalB[bidx] = row
                    nzvalB[bidx] = v
                    bidx += 1
                    nadd += 1
                end
                (xidx > n) && break
            end # if I[row, col]
        end # for row in 1:size(A, 1)

        if (nadd != 0)
            l = r2-r1+1
            if l > 0
                copyto!(rowvalB, bidx, rowvalA, r1, l)
                copyto!(nzvalB, bidx, nzvalA, r1, l)
                bidx += l
            end
            colptrB[col+1] = bidx

            if (xidx > n) && (length(colptrB) > (col+1))
                diff = nadd
                colptrB[(col+2):end] = colptrA[(col+2):end] .+ diff
                r1 = colptrA[col+1]
                r2 = colptrA[end]-1
                l = r2-r1+1
                if l > 0
                    copyto!(rowvalB, bidx, rowvalA, r1, l)
                    copyto!(nzvalB, bidx, nzvalA, r1, l)
                    bidx += l
                end
            end
        else
            bidx = colptrA[col+1]
        end
        (xidx > n) && break
    end # for col in 1:size(A, 2)

    if (nadd != 0)
        n = length(nzvalB)
        if n > (bidx-1)
            deleteat!(nzvalB, bidx:n)
            deleteat!(rowvalB, bidx:n)
        end
    end
    return _checkbuffers(A)
end

function setindex!(A::AbstractSparseMatrixCSC, x::AbstractArray, Ix::AbstractVector{<:Integer})
    require_one_based_indexing(A, x, Ix)
    (I,) = Base.ensure_indexable(to_indices(A, (Ix,)))
    # We check bounds after sorting I
    n = length(I)
    (n == 0) && (return A)

    colptrA = getcolptr(A); rowvalA = rowvals(A); nzvalA = nonzeros(A); szA = size(A)
    colptrB = colptrA; rowvalB = rowvalA; nzvalB = nzvalA
    nadd = 0
    bidx = aidx = 1

    S = issorted(I) ? (1:n) : sortperm(I)
    sxidx = r1 = r2 = 0

    if (!isempty(I) && (I[S[1]] < 1 || I[S[end]] > widelength(A)))
        throw(BoundsError(A, I))
    end

    isa(x, AbstractArray) && setindex_shape_check(x, length(I))

    lastcol = 0
    (nrowA, ncolA) = szA
    @inbounds for xidx in 1:n
        sxidx = S[xidx]
        (sxidx < n) && (I[sxidx] == I[sxidx+1]) && continue

        row,col = Base._ind2sub(szA, I[sxidx])
        v = x[sxidx]

        if col > lastcol
            r1 = Int(colptrA[col])
            r2 = Int(colptrA[col+1] - 1)

            # copy from last position till current column
            if (nadd > 0)
                colptrB[(lastcol+1):col] = colptrA[(lastcol+1):col] .+ nadd
                copylen = r1 - aidx
                if copylen > 0
                    copyto!(rowvalB, bidx, rowvalA, aidx, copylen)
                    copyto!(nzvalB, bidx, nzvalA, aidx, copylen)
                    aidx += copylen
                    bidx += copylen
                end
            else
                aidx = bidx = r1
            end
            lastcol = col
        end

        if r1 <= r2
            copylen = searchsortedfirst(rowvalA, row, r1, r2, Forward) - r1
            if (copylen > 0)
                if (nadd > 0)
                    copyto!(rowvalB, bidx, rowvalA, r1, copylen)
                    copyto!(nzvalB, bidx, nzvalA, r1, copylen)
                end
                bidx += copylen
                r1 += copylen
                aidx += copylen
            end
        end

        # 0: no change, 1: update, 2: add new
        mode = ((r1 <= r2) && (rowvalA[r1] == row)) ? 1 : ((v == 0) ? 0 : 2)

        if (mode > 1) && (nadd == 0)
            # copy storage to take changes
            colptrA = copy(colptrB)
            memreq = (x == 0) ? 0 : n
            # see comment/TODO for same statement in preceding logical setindex! method
            rowvalA = copy(rowvalB)
            nzvalA = copy(nzvalB)
            resize!(rowvalB, length(rowvalA)+memreq)
            resize!(nzvalB, length(rowvalA)+memreq)
        end
        if mode == 1
            rowvalB[bidx] = row
            nzvalB[bidx] = v
            bidx += 1
            aidx += 1
            r1 += 1
        elseif mode == 2
            rowvalB[bidx] = row
            nzvalB[bidx] = v
            bidx += 1
            nadd += 1
        end
    end

    # copy the rest
    @inbounds if (nadd > 0)
        colptrB[(lastcol+1):end] = colptrA[(lastcol+1):end] .+ nadd
        r1 = colptrA[end]-1
        copylen = r1 - aidx + 1
        if copylen > 0
            copyto!(rowvalB, bidx, rowvalA, aidx, copylen)
            copyto!(nzvalB, bidx, nzvalA, aidx, copylen)
            aidx += copylen
            bidx += copylen
        end

        n = length(nzvalB)
        if n > (bidx-1)
            deleteat!(nzvalB, bidx:n)
            deleteat!(rowvalB, bidx:n)
        end
    end
    return _checkbuffers(A)
end

## dropstored! methods
"""
    dropstored!(A::AbstractSparseMatrixCSC, i::Integer, j::Integer)

Drop entry `A[i,j]` from `A` if `A[i,j]` is stored, and otherwise do nothing.

```jldoctest
julia> A = sparse([1 2; 0 0])
2×2 SparseMatrixCSC{Int64, Int64} with 2 stored entries:
 1  2
 ⋅  ⋅

julia> SparseArrays.dropstored!(A, 1, 2); A
2×2 SparseMatrixCSC{Int64, Int64} with 1 stored entry:
 1  ⋅
 ⋅  ⋅
```
"""
function dropstored!(A::AbstractSparseMatrixCSC, i::Integer, j::Integer)
    if !((1 <= i <= size(A, 1)) & (1 <= j <= size(A, 2)))
        throw(BoundsError(A, (i,j)))
    end
    coljfirstk = Int(getcolptr(A)[j])
    coljlastk = Int(getcolptr(A)[j+1] - 1)
    searchk = searchsortedfirst(rowvals(A), i, coljfirstk, coljlastk, Base.Order.Forward)
    if searchk <= coljlastk && rowvals(A)[searchk] == i
        # Entry A[i,j] is stored. Drop and return.
        deleteat!(rowvals(A), searchk)
        deleteat!(nonzeros(A), searchk)
        @simd for m in (j+1):(size(A, 2) + 1)
            @inbounds getcolptr(A)[m] -= 1
        end
    end
    return _checkbuffers(A)
end
"""
    dropstored!(A::AbstractSparseMatrixCSC, I::AbstractVector{<:Integer}, J::AbstractVector{<:Integer})

For each `(i,j)` where `i in I` and `j in J`, drop entry `A[i,j]` from `A` if `A[i,j]` is
stored and otherwise do nothing. Derivative forms:

    dropstored!(A::AbstractSparseMatrixCSC, i::Integer, J::AbstractVector{<:Integer})
    dropstored!(A::AbstractSparseMatrixCSC, I::AbstractVector{<:Integer}, j::Integer)

# Examples
```jldoctest
julia> A = sparse(Diagonal([1, 2, 3, 4]))
4×4 SparseMatrixCSC{Int64, Int64} with 4 stored entries:
 1  ⋅  ⋅  ⋅
 ⋅  2  ⋅  ⋅
 ⋅  ⋅  3  ⋅
 ⋅  ⋅  ⋅  4

julia> SparseArrays.dropstored!(A, [1, 2], [1, 1])
4×4 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 ⋅  ⋅  ⋅  ⋅
 ⋅  2  ⋅  ⋅
 ⋅  ⋅  3  ⋅
 ⋅  ⋅  ⋅  4
```
"""
function dropstored!(A::AbstractSparseMatrixCSC,
        I::AbstractVector{<:Integer}, J::AbstractVector{<:Integer})
    require_one_based_indexing(A, I, J)
    m, n = size(A)
    nnzA = nnz(A)
    (nnzA == 0) && (return A)

    !issorted(I) && (I = sort(I))
    !issorted(J) && (J = sort(J))

    if (!isempty(I) && (I[1] < 1 || I[end] > m)) || (!isempty(J) && (J[1] < 1 || J[end] > n))
        throw(BoundsError(A, (I, J)))
    end

    if isempty(I) || isempty(J)
        return A
    end

    rowval = rowvalA = rowvals(A)
    nzval = nzvalA = nonzeros(A)
    rowidx = 1
    ndel = 0
    @inbounds for col in 1:n
        rrange = nzrange(A, col)
        if ndel > 0
            getcolptr(A)[col] = getcolptr(A)[col] - ndel
        end

        if isempty(rrange) || !(col in J)
            nincl = length(rrange)
            if(ndel > 0) && !isempty(rrange)
                copyto!(rowvalA, rowidx, rowval, rrange[1], nincl)
                copyto!(nzvalA, rowidx, nzval, rrange[1], nincl)
            end
            rowidx += nincl
        else
            for ridx in rrange
                if rowval[ridx] in I
                    if ndel == 0
                        rowval = copy(rowvalA)
                        nzval = copy(nzvalA)
                    end
                    ndel += 1
                else
                    if ndel > 0
                        rowvalA[rowidx] = rowval[ridx]
                        nzvalA[rowidx] = nzval[ridx]
                    end
                    rowidx += 1
                end
            end
        end
    end

    if ndel > 0
        getcolptr(A)[n+1] = rowidx
        deleteat!(rowvalA, rowidx:nnzA)
        deleteat!(nzvalA, rowidx:nnzA)
    end
    return _checkbuffers(A)
end
dropstored!(A::AbstractSparseMatrixCSC, i::Integer, J::AbstractVector{<:Integer}) = dropstored!(A, [i], J)
dropstored!(A::AbstractSparseMatrixCSC, I::AbstractVector{<:Integer}, j::Integer) = dropstored!(A, I, [j])
dropstored!(A::AbstractSparseMatrixCSC, ::Colon, j::Union{Integer,AbstractVector}) = dropstored!(A, 1:size(A,1), j)
dropstored!(A::AbstractSparseMatrixCSC, i::Union{Integer,AbstractVector}, ::Colon) = dropstored!(A, i, 1:size(A,2))
dropstored!(A::AbstractSparseMatrixCSC, ::Colon, ::Colon) = dropstored!(A, 1:size(A,1), 1:size(A,2))
dropstored!(A::AbstractSparseMatrixCSC, ::Colon) = dropstored!(A, :, :)
# TODO: Several of the preceding methods are optimization candidates.
# TODO: Implement linear indexing methods for dropstored! ?
# TODO: Implement logical indexing methods for dropstored! ?

# Sparse concatenation

function vcat(X::AbstractSparseMatrixCSC...)
    num = length(X)
    mX = Int[ size(x, 1) for x in X ]
    nX = Int[ size(x, 2) for x in X ]
    m = sum(mX)
    n = nX[1]

    for i = 2 : num
        if nX[i] != n
            throw(DimensionMismatch("All inputs to vcat should have the same number of columns"))
        end
    end

    Tv = promote_eltype(X...)
    Ti = promote_eltype(map(x->rowvals(x), X)...)

    nnzX = Int[ nnz(x) for x in X ]
    nnz_res = sum(nnzX)
    colptr = Vector{Ti}(undef, n+1)
    rowval = Vector{Ti}(undef, nnz_res)
    nzval  = Vector{Tv}(undef, nnz_res)

    colptr[1] = 1
    for c = 1:n
        mX_sofar = 0
        ptr_res = colptr[c]
        for i = 1 : num
            colptrXi = getcolptr(X[i])
            col_length = (colptrXi[c + 1] - 1) - colptrXi[c]
            ptr_Xi = colptrXi[c]

            stuffcol!(X[i], colptr, rowval, nzval,
                      ptr_res, ptr_Xi, col_length, mX_sofar)

            ptr_res += col_length + 1
            mX_sofar += mX[i]
        end
        colptr[c + 1] = ptr_res
    end
    SparseMatrixCSC(m, n, colptr, rowval, nzval)
end

@inline function stuffcol!(Xi::AbstractSparseMatrixCSC, colptr, rowval, nzval,
                           ptr_res, ptr_Xi, col_length, mX_sofar)
    colptrXi = getcolptr(Xi)
    rowvalXi = rowvals(Xi)
    nzvalXi  = nonzeros(Xi)

    for k=ptr_res:(ptr_res + col_length)
        @inbounds rowval[k] = rowvalXi[ptr_Xi] + mX_sofar
        @inbounds nzval[k]  = nzvalXi[ptr_Xi]
        ptr_Xi += 1
    end
end

function hcat(X::AbstractSparseMatrixCSC...)
    num = length(X)
    mX = Int[ size(x, 1) for x in X ]
    nX = Int[ size(x, 2) for x in X ]
    m = mX[1]
    for i = 2 : num
        if mX[i] != m; throw(DimensionMismatch("")); end
    end
    n = sum(nX)

    Tv = promote_eltype(X...)
    Ti = promote_eltype(map(x->rowvals(x), X)...)

    colptr = Vector{Ti}(undef, n+1)
    nnzX = Int[ nnz(x) for x in X ]
    nnz_res = sum(nnzX)
    rowval = Vector{Ti}(undef, nnz_res)
    nzval = Vector{Tv}(undef, nnz_res)

    nnz_sofar = 0
    nX_sofar = 0
    @inbounds for i = 1 : num
        XI = X[i]
        colptr[(1 : nX[i] + 1) .+ nX_sofar] = getcolptr(XI) .+ nnz_sofar
        if nnzX[i] == length(rowvals(XI))
            rowval[(1 : nnzX[i]) .+ nnz_sofar] = rowvals(XI)
            nzval[(1 : nnzX[i]) .+ nnz_sofar] = nonzeros(XI)
        else
            rowval[(1 : nnzX[i]) .+ nnz_sofar] = rowvals(XI)[1:nnzX[i]]
            nzval[(1 : nnzX[i]) .+ nnz_sofar] = nonzeros(XI)[1:nnzX[i]]
        end
        nnz_sofar += nnzX[i]
        nX_sofar += nX[i]
    end

    SparseMatrixCSC(m, n, colptr, rowval, nzval)
end

"""
    blockdiag(A...)

Concatenate matrices block-diagonally. Currently only implemented for sparse matrices.

# Examples
```jldoctest
julia> blockdiag(sparse(2I, 3, 3), sparse(4I, 2, 2))
5×5 SparseMatrixCSC{Int64, Int64} with 5 stored entries:
 2  ⋅  ⋅  ⋅  ⋅
 ⋅  2  ⋅  ⋅  ⋅
 ⋅  ⋅  2  ⋅  ⋅
 ⋅  ⋅  ⋅  4  ⋅
 ⋅  ⋅  ⋅  ⋅  4
```
"""
blockdiag() = spzeros(promote_type(), Int, 0, 0)

function blockdiag(X::AbstractSparseMatrixCSC{Tv, Ti}...) where {Tv, Ti <: Integer}
    _blockdiag(Tv, Ti, X...)
end

function blockdiag(X::AbstractSparseMatrixCSC...)
    Tv = promote_type(map(x->eltype(nonzeros(x)), X)...)
    Ti = promote_type(map(x->eltype(rowvals(x)), X)...)
    _blockdiag(Tv, Ti, X...)
end

function _blockdiag(::Type{Tv}, ::Type{Ti}, X::AbstractSparseMatrixCSC...) where {Tv, Ti <: Integer}
    num = length(X)
    mX = Int[ size(x, 1) for x in X ]
    nX = Int[ size(x, 2) for x in X ]
    m = sum(mX)
    n = sum(nX)

    colptr = Vector{Ti}(undef, n+1)
    nnzX = Int[ nnz(x) for x in X ]
    nnz_res = sum(nnzX)
    rowval = Vector{Ti}(undef, nnz_res)
    nzval = Vector{Tv}(undef, nnz_res)

    nnz_sofar = 0
    nX_sofar = 0
    mX_sofar = 0
    for i = 1 : num
        colptr[(1 : nX[i] + 1) .+ nX_sofar] = getcolptr(X[i]) .+ nnz_sofar
        rowval[(1 : nnzX[i]) .+ nnz_sofar] = rowvals(X[i]) .+ mX_sofar
        nzval[(1 : nnzX[i]) .+ nnz_sofar] = nonzeros(X[i])
        nnz_sofar += nnzX[i]
        nX_sofar += nX[i]
        mX_sofar += mX[i]
    end
    colptr[n+1] = nnz_sofar + 1

    SparseMatrixCSC(m, n, colptr, rowval, nzval)
end

## Structure query functions
issymmetric(A::AbstractSparseMatrixCSC) = is_hermsym(A, identity)

ishermitian(A::AbstractSparseMatrixCSC) = is_hermsym(A, conj)

function is_hermsym(A::AbstractSparseMatrixCSC, check::Function)
    m, n = size(A)
    if m != n; return false; end

    colptr = getcolptr(A)
    rowval = rowvals(A)
    nzval = nonzeros(A)
    tracker = copy(getcolptr(A))
    for col = 1:size(A, 2)
        # `tracker` is updated such that, for symmetric matrices,
        # the loop below starts from an element at or below the
        # diagonal element of column `col`"
        for p = tracker[col]:colptr[col+1]-1
            val = nzval[p]
            row = rowval[p]

            # Ignore stored zeros
            if val == 0
                continue
            end

            # If the matrix was symmetric we should have updated
            # the tracker to start at the diagonal or below. Here
            # we are above the diagonal so the matrix can't be symmetric.
            if row < col
                return false
            end

            # Diagonal element
            if row == col
                if val != check(val)
                    return false
                end
            else
                offset = tracker[row]

                # If the matrix is unsymmetric, there might not exist
                # a rowval[offset]
                if offset > length(rowval)
                    return false
                end

                row2 = rowval[offset]

                # row2 can be less than col if the tracker didn't
                # get updated due to stored zeros in previous elements.
                # We therefore "catch up" here while making sure that
                # the elements are actually zero.
                while row2 < col
                    if !iszero(nzval[offset])
                        return false
                    end
                    offset += 1
                    row2 = rowval[offset]
                    tracker[row] += 1
                end

                # Non zero A[i,j] exists but A[j,i] does not exist
                if row2 > col
                    return false
                end

                # A[i,j] and A[j,i] exists
                if row2 == col
                    if val != check(nzval[offset])
                        return false
                    end
                    tracker[row] += 1
                end
            end
        end
    end
    return true
end

function istriu(A::AbstractSparseMatrixCSC)
    m, n = size(A)
    colptr = getcolptr(A)
    rowval = rowvals(A)
    nzval  = nonzeros(A)

    for col = 1:min(n, m-1)
        l1 = colptr[col+1]-1
        for i = 0 : (l1 - colptr[col])
            if rowval[l1-i] <= col
                break
            end
            if !iszero(nzval[l1-i])
                return false
            end
        end
    end
    return true
end

function istril(A::AbstractSparseMatrixCSC)
    m, n = size(A)
    colptr = getcolptr(A)
    rowval = rowvals(A)
    nzval  = nonzeros(A)

    for col = 2:n
        for i = colptr[col] : (colptr[col+1]-1)
            if rowval[i] >= col
                break
            end
            if !iszero(nzval[i])
                return false
            end
        end
    end
    return true
end

_nnz(v::AbstractSparseVector) = nnz(v)
_nnz(v::AbstractVector) = length(v)

function _indices(v::AbstractSparseVector, row, col)
    ix = nonzeroinds(v)
    return (row .+ ix, col .+ ix)
end
function _indices(v::AbstractVector, row, col)
    veclen = length(v)
    return (row+1:row+veclen, col+1:col+veclen)
end

_nzvals(v::AbstractSparseVector) = nonzeros(v)
_nzvals(v::AbstractVector) = v

function spdiagm_internal(kv::Pair{<:Integer,<:AbstractVector}...)
    ncoeffs = 0
    for p in kv
        ncoeffs += _nnz(p.second)
    end
    I = Vector{Int}(undef, ncoeffs)
    J = Vector{Int}(undef, ncoeffs)
    V = Vector{promote_type(map(x -> eltype(x.second), kv)...)}(undef, ncoeffs)
    i = 0
    m = 0
    n = 0
    for p in kv
        k = p.first
        v = p.second
        if k < 0
            row = -k
            col = 0
        elseif k > 0
            row = 0
            col = k
        else
            row = 0
            col = 0
        end
        numel = _nnz(v)
        r = 1+i:numel+i
        I[r], J[r] = _indices(v, row, col)
        copyto!(view(V, r), _nzvals(v))
        veclen = length(v)
        m = max(m, row + veclen)
        n = max(n, col + veclen)
        i += numel
    end
    return I, J, V, m, n
end

"""
    spdiagm(kv::Pair{<:Integer,<:AbstractVector}...)
    spdiagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)

Construct a sparse diagonal matrix from `Pair`s of vectors and diagonals.
Each vector `kv.second` will be placed on the `kv.first` diagonal.  By
default, the matrix is square and its size is inferred
from `kv`, but a non-square size `m`×`n` (padded with zeros as needed)
can be specified by passing `m,n` as the first arguments.

# Examples
```jldoctest
julia> spdiagm(-1 => [1,2,3,4], 1 => [4,3,2,1])
5×5 SparseMatrixCSC{Int64, Int64} with 8 stored entries:
 ⋅  4  ⋅  ⋅  ⋅
 1  ⋅  3  ⋅  ⋅
 ⋅  2  ⋅  2  ⋅
 ⋅  ⋅  3  ⋅  1
 ⋅  ⋅  ⋅  4  ⋅
```
"""
spdiagm(kv::Pair{<:Integer,<:AbstractVector}...) = _spdiagm(nothing, kv...)
spdiagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...) = _spdiagm((Int(m),Int(n)), kv...)

"""
    spdiagm(v::AbstractVector)
    spdiagm(m::Integer, n::Integer, v::AbstractVector)

Construct a sparse matrix with elements of the vector as diagonal elements.
By default (no given `m` and `n`), the matrix is square and its size is given
by `length(v)`, but a non-square size `m`×`n` can be specified by passing `m`
and `n` as the first arguments.

!!! compat "Julia 1.6"
    These functions require at least Julia 1.6.

# Examples
```jldoctest
julia> spdiagm([1,2,3])
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 1  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  3

julia> spdiagm(sparse([1,0,3]))
3×3 SparseMatrixCSC{Int64, Int64} with 2 stored entries:
 1  ⋅  ⋅
 ⋅  ⋅  ⋅
 ⋅  ⋅  3
```
"""
spdiagm(v::AbstractVector) = _spdiagm(nothing, 0 => v)
spdiagm(m::Integer, n::Integer, v::AbstractVector) = _spdiagm((Int(m), Int(n)), 0 => v)

function _spdiagm(size, kv::Pair{<:Integer,<:AbstractVector}...)
    I, J, V, mmax, nmax = spdiagm_internal(kv...)
    mnmax = max(mmax, nmax)
    m, n = something(size, (mnmax,mnmax))
    (m ≥ mmax && n ≥ nmax) || throw(DimensionMismatch("invalid size=$size"))
    return sparse(I, J, V, m, n)
end

## expand a colptr or rowptr into a dense index vector
function expandptr(V::Vector{<:Integer})
    if V[1] != 1 throw(ArgumentError("first index must be one")) end
    res = similar(V, (Int64(V[end]-1),))
    for i in 1:(length(V)-1), j in V[i]:(V[i+1] - 1); res[j] = i end
    res
end


function diag(A::AbstractSparseMatrixCSC{Tv,Ti}, d::Integer=0) where {Tv,Ti}
    m, n = size(A)
    k = Int(d)
    l = k < 0 ? min(m+k,n) : min(n-k,m)
    r, c = k <= 0 ? (-k, 0) : (0, k) # start row/col -1
    ind = Vector{Ti}()
    val = Vector{Tv}()
    for i in 1:l
        r += 1; c += 1
        r1 = Int(getcolptr(A)[c])
        r2 = Int(getcolptr(A)[c+1]-1)
        r1 > r2 && continue
        r1 = searchsortedfirst(rowvals(A), r, r1, r2, Forward)
        ((r1 > r2) || (rowvals(A)[r1] != r)) && continue
        push!(ind, i)
        push!(val, nonzeros(A)[r1])
    end
    return SparseVector{Tv,Ti}(l, ind, val)
end

function tr(A::AbstractSparseMatrixCSC{Tv}) where Tv
    n = checksquare(A)
    s = zero(Tv)
    for i in 1:n
        s += A[i,i]
    end
    return s
end

## rotations

function rot180(A::AbstractSparseMatrixCSC)
    I,J,V = findnz(A)
    m,n = size(A)
    for i=1:length(I)
        I[i] = m - I[i] + 1
        J[i] = n - J[i] + 1
    end
    return sparse(I,J,V,m,n)
end

function rotr90(A::AbstractSparseMatrixCSC)
    I,J,V = findnz(A)
    m,n = size(A)
    #old col inds are new row inds
    for i=1:length(I)
        I[i] = m - I[i] + 1
    end
    return sparse(J, I, V, n, m)
end

function rotl90(A::AbstractSparseMatrixCSC)
    I,J,V = findnz(A)
    m,n = size(A)
    #old row inds are new col inds
    for i=1:length(J)
        J[i] = n - J[i] + 1
    end
    return sparse(J, I, V, n, m)
end

## Uniform matrix arithmetic

(+)(A::AbstractSparseMatrixCSC, J::UniformScaling) = A + sparse(J, size(A)...)
(-)(A::AbstractSparseMatrixCSC, J::UniformScaling) = A - sparse(J, size(A)...)
(-)(J::UniformScaling, A::AbstractSparseMatrixCSC) = sparse(J, size(A)...) - A

## circular shift

function circshift!(O::AbstractSparseMatrixCSC, X::AbstractSparseMatrixCSC, (r,c)::Base.DimsInteger{2})
    nnz = length(nonzeros(X))

    iszero(nnz) && return copy!(O, X)

    ##### column shift
    c = mod(c, size(X, 2))
    if iszero(c)
        copy!(O, X)
    else
        ##### readjust output
        resize!(getcolptr(O), size(X, 2) + 1)
        resize!(rowvals(O), nnz)
        resize!(nonzeros(O), nnz)
        getcolptr(O)[size(X, 2) + 1] = nnz + 1

        # exchange left and right blocks
        nleft = getcolptr(X)[size(X, 2) - c + 1] - 1
        nright = nnz - nleft
        @inbounds for i=c+1:size(X, 2)
            getcolptr(O)[i] = getcolptr(X)[i-c] + nright
        end
        @inbounds for i=1:c
            getcolptr(O)[i] = getcolptr(X)[size(X, 2) - c + i] - nleft
        end
        # rotate rowval and nzval by the right number of elements
        circshift!(rowvals(O), rowvals(X), (nright,))
        circshift!(nonzeros(O), nonzeros(X), (nright,))
    end
    ##### row shift
    r = mod(r, size(X, 1))
    iszero(r) && return O
    @inbounds for i=1:size(O, 2)
        subvector_shifter!(rowvals(O), nonzeros(O), getcolptr(O)[i], getcolptr(O)[i+1]-1, size(O, 1), r)
    end
    return _checkbuffers(O)
end

circshift!(O::AbstractSparseMatrixCSC, X::AbstractSparseMatrixCSC, (r,)::Base.DimsInteger{1}) = circshift!(O, X, (r,0))
circshift!(O::AbstractSparseMatrixCSC, X::AbstractSparseMatrixCSC, r::Real) = circshift!(O, X, (Integer(r),0))
