#include "array_view.hh" #include "vector.hh" #include #include namespace Kakoune { template struct MirroredArray : public ArrayView { MirroredArray(ArrayView data, int size) : ArrayView(data), size(size) { kak_assert(2 * size + 1 <= data.size()); (*this)[1] = 0; } [[gnu::always_inline]] T& operator[](int n) { return ArrayView::operator[](n + size); } [[gnu::always_inline]] const T& operator[](int n) const { return ArrayView::operator[](n + size); } private: int size; }; struct Snake{ int x, y, u, v; bool add; }; template Snake find_end_snake_of_further_reaching_dpath(Iterator a, int N, Iterator b, int M, const MirroredArray& V, const int D, const int k, Equal eq) { int x; // our position along a const bool add = k == -D or (k != D and V[k-1] < V[k+1]); // if diagonal on the right goes further along x than diagonal on the left, // then we take a vertical edge from it to this diagonal, hence x = V[k+1] if (add) x = V[k+1]; // else, we take an horizontal edge from our left diagonal,x = V[k-1]+1 else x = V[k-1]+1; int y = x - k; // we are by construction on diagonal k, so our position along // b (y) is x - k. int u = x, v = y; // follow end snake along diagonal k while (u < N and v < M and eq(a[u], b[v])) ++u, ++v; return { x, y, u, v, add }; } struct SnakeLen : Snake { SnakeLen(Snake s, int d) : Snake(s), d(d) {} int d; }; template SnakeLen find_middle_snake(Iterator a, int N, Iterator b, int M, ArrayView data1, ArrayView data2, Equal eq) { const int delta = N - M; MirroredArray V1{data1, N + M}; MirroredArray V2{data2, N + M}; std::reverse_iterator ra{a + N}, rb{b + M}; for (int D = 0; D <= (M + N + 1) / 2; ++D) { for (int k1 = -D; k1 <= D; k1 += 2) { auto p = find_end_snake_of_further_reaching_dpath(a, N, b, M, V1, D, k1, eq); V1[k1] = p.u; const int k2 = -(k1 - delta); if ((delta % 2 != 0) and -(D-1) <= k2 and k2 <= (D-1)) { if (V1[k1] + V2[k2] >= N) return { p, 2 * D - 1 };// return last snake on forward path } } for (int k2 = -D; k2 <= D; k2 += 2) { auto p = find_end_snake_of_further_reaching_dpath(ra, N, rb, M, V2, D, k2, eq); V2[k2] = p.u; const int k1 = -(k2 - delta); if ((delta % 2 == 0) and -D <= k1 and k1 <= D) { if (V1[k1] + V2[k2] >= N) return { { N - p.u, M - p.v, N - p.x , M - p.y, p.add } , 2 * D };// return last snake on reverse path } } } kak_assert(false); return { {}, 0 }; } struct Diff { enum { Keep, Add, Remove } mode; int len; int posB; }; template void find_diff_rec(Iterator a, int offA, int lenA, Iterator b, int offB, int lenB, ArrayView data1, ArrayView data2, Equal eq, Vector& diffs) { if (lenA > 0 and lenB > 0) { auto middle_snake = find_middle_snake(a + offA, lenA, b + offB, lenB, data1, data2, eq); kak_assert(middle_snake.u <= lenA and middle_snake.v <= lenB); if (middle_snake.d > 1) { find_diff_rec(a, offA, middle_snake.x, b, offB, middle_snake.y, data1, data2, eq, diffs); if (int len = middle_snake.u - middle_snake.x) diffs.push_back({Diff::Keep, len, 0}); find_diff_rec(a, offA + middle_snake.u, lenA - middle_snake.u, b, offB + middle_snake.v, lenB - middle_snake.v, data1, data2, eq, diffs); } else if (middle_snake.d == 1) { if (int diag = middle_snake.x - (middle_snake.add ? 0 : 1)) diffs.push_back({Diff::Keep, diag, 0}); if (middle_snake.add) diffs.push_back({Diff::Add, 1, offB + middle_snake.y-1}); else diffs.push_back({Diff::Remove, 1, 0}); } else if (int len = middle_snake.u - middle_snake.x) diffs.push_back({Diff::Keep, len, 0}); } else if (lenB > 0) diffs.push_back({Diff::Add, lenB, offB}); else if (lenA > 0) diffs.push_back({Diff::Remove, lenA, 0}); } inline void compact_diffs(Vector& diffs) { if (diffs.size() < 2) return; auto out_it = diffs.begin(); for (auto it = out_it + 1; it != diffs.end(); ++it) { if (it->mode == out_it->mode and (it->mode != Diff::Add or it->posB == out_it->posB + out_it->len)) out_it->len += it->len; else if (++out_it != it) *out_it = *it; } diffs.erase(out_it+1, diffs.end()); } template::value_type>> Vector find_diff(Iterator a, int N, Iterator b, int M, Equal eq = Equal{}) { const int max = 2 * (N + M) + 1; Vector data(2*max); Vector diffs; find_diff_rec(a, 0, N, b, 0, M, {data.data(), (size_t)max}, {data.data() + max, (size_t)max}, eq, diffs); compact_diffs(diffs); return diffs; } }