/*
 * Copyright 2022 Rive
 */

// An SSE / NEON / WASM_SIMD library based on clang vector types.
//
// This header makes use of the clang vector builtins specified in
// https://reviews.llvm.org/D111529. This effort in clang is still a work in
// progress, so getting maximum performance from this header requires an
// extremely recent version of clang.
//
// To explore the codegen from this header, paste it into https://godbolt.org/,
// select a recent clang compiler, and add an -O3 flag.

#ifndef _RIVE_SIMD_HPP_
#define _RIVE_SIMD_HPP_

// #define RIVE_SIMD_PERF_WARNINGS

#include <algorithm>
#include <cassert>
#include <limits>
#include <math.h>
#include <stdint.h>
#include <tuple>
#include <type_traits>

#ifdef __SSE__
#include <immintrin.h>
#endif

#if defined(__ARM_NEON__) || defined(__aarch64__)
#include <arm_neon.h>
#endif

#if defined(__GNUC__) || defined(__clang__)
#ifndef __has_builtin
#define __has_builtin(x) 0
#endif
#define SIMD_ALWAYS_INLINE inline __attribute__((always_inline))
#else
#define __has_builtin(x) 0
#define SIMD_ALWAYS_INLINE inline
#endif

#if __has_builtin(__builtin_memcpy)
#define SIMD_INLINE_MEMCPY __builtin_memcpy
#else
#define SIMD_INLINE_MEMCPY memcpy
#endif

// SIMD math can expect conformant IEEE 754 behavior for NaN and Inf.
static_assert(std::numeric_limits<float>::is_iec559,
              "Conformant IEEE 754 behavior for NaN and Inf is required.");

#if defined(__clang__)

namespace rive
{
namespace simd
{
// gvec can be native vectors inside the compiler.
// (The GLSL spec uses "gvec" to denote a vector of unspecified type.)
template <typename T, int N>
using gvec = T __attribute__((ext_vector_type(N)))
__attribute__((aligned(sizeof(T) * N)));

// Vector booleans are masks of integer type, where true is -1 and false is 0.
// Vector booleans masks are generated using the builtin boolean operators: ==,
// !=, <=, >=, <, >
template <typename T> struct extract_element_type;
template <typename T, int N> struct extract_element_type<gvec<T, N>>
{
    using type = T;
};
template <typename T> struct boolean_mask_type
{
    using type = typename extract_element_type<decltype(gvec<T, 4>() ==
                                                        gvec<T, 4>())>::type;
};
} // namespace simd
} // namespace rive

#define SIMD_NATIVE_GVEC 1

#else

// gvec needs to be polyfilled with templates.
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: ext_vector_type not supported. Consider using clang.")
#endif
#include "simd_gvec_polyfill.hpp"

#define SIMD_NATIVE_GVEC 0

#endif

namespace rive
{
namespace simd
{
////// Boolean logic //////
//
// Vector booleans are masks of integer type, where true is -1 and false is 0.
// Vector booleans masks can be generated using the builtin boolean operators:
// ==, !=, <=, >=, <, >
//

// Returns true if all elements in x are equal to 0.
template <typename T, int N> SIMD_ALWAYS_INLINE bool any(gvec<T, N> x)
{
#if __has_builtin(__builtin_reduce_or) && SIMD_NATIVE_GVEC
    return __builtin_reduce_or(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_or() not supported. Consider updating clang.")
#endif
    // This particular logic structure gets decent codegen in clang.
    for (int i = 0; i < N; ++i)
    {
        if (x[i])
            return true;
    }
    return false;
#endif
}

// Returns true if all elements in x are equal to ~0.
template <typename T, int N> SIMD_ALWAYS_INLINE bool all(gvec<T, N> x)
{
#if __has_builtin(__builtin_reduce_and) && SIMD_NATIVE_GVEC
    return __builtin_reduce_and(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_and() not supported. Consider updating clang.")
#endif
    // In vector, true is represented by -1 exactly, so we use ~x for "not".
    return !any(~x);
#endif
}

template <
    typename T,
    int N,
    typename std::enable_if<std::is_floating_point<T>::value>::type* = nullptr>
SIMD_ALWAYS_INLINE gvec<typename boolean_mask_type<T>::type, N> isnan(
    gvec<T, N> x)
{
    return ~(x == x);
}

template <typename T,
          int N,
          typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
constexpr gvec<typename boolean_mask_type<T>::type, N> isnan(gvec<T, N>)
{
    return {}; // Integer types are never NaN.
}

////// Math //////

// Elementwise ternary expression: "_if ? _then : _else" for each component.
template <typename T, int N>
SIMD_ALWAYS_INLINE gvec<T, N> if_then_else(
    gvec<typename boolean_mask_type<T>::type, N> _if,
    gvec<T, N> _then,
    gvec<T, N> _else)
{
#if defined(__clang_major__) && __clang_major__ >= 13
    // The '?:' operator supports a vector condition beginning in clang 13.
    return _if ? _then : _else;
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: vectorized '?:' operator not supported. Consider updating clang.")
#endif
    gvec<T, N> ret{};
    for (int i = 0; i < N; ++i)
        ret[i] = _if[i] ? _then[i] : _else[i];
    return ret;
#endif
}

// Similar to std::min(), with a noteworthy difference:
// If a[i] or b[i] is NaN and the other is not, returns whichever is _not_ NaN.
template <typename T, int N>
SIMD_ALWAYS_INLINE gvec<T, N> min(gvec<T, N> a, gvec<T, N> b)
{
#if __has_builtin(__builtin_elementwise_min) && SIMD_NATIVE_GVEC
    return __builtin_elementwise_min(a, b);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_elementwise_min() not supported. Consider updating clang.")
#endif
    // Generate the same behavior for NaN as the SIMD builtins. (isnan() is a
    // no-op for int types.)
    return if_then_else(b < a || isnan(a), b, a);
#endif
}

// Similar to std::max(), with a noteworthy difference:
// If a[i] or b[i] is NaN and the other is not, returns whichever is _not_ NaN.
template <typename T, int N>
SIMD_ALWAYS_INLINE gvec<T, N> max(gvec<T, N> a, gvec<T, N> b)
{
#if __has_builtin(__builtin_elementwise_max) && SIMD_NATIVE_GVEC
    return __builtin_elementwise_max(a, b);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_elementwise_max() not supported. Consider updating clang.")
#endif
    // Generate the same behavior for NaN as the SIMD builtins. (isnan() is a
    // no-op for int types.)
    return if_then_else(a < b || isnan(a), b, a);
#endif
}

// Unlike std::clamp(), simd::clamp() always returns a value between lo and hi.
//
//   Returns lo if x == NaN, but std::clamp() returns NaN.
//   Returns hi if hi <= lo.
//   Ignores hi and/or lo if they are NaN.
//
template <typename T, int N>
SIMD_ALWAYS_INLINE gvec<T, N> clamp(gvec<T, N> x, gvec<T, N> lo, gvec<T, N> hi)
{
    return min(max(lo, x), hi);
}

// Returns the absolute value of x per element, with one exception:
// If x[i] is an integer type and equal to the minimum representable value,
// returns x[i].
template <typename T, int N> SIMD_ALWAYS_INLINE gvec<T, N> abs(gvec<T, N> x)
{
#if __has_builtin(__builtin_elementwise_abs) && SIMD_NATIVE_GVEC
    return __builtin_elementwise_abs(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_elementwise_abs() not supported. Consider updating clang.")
#endif
    return if_then_else(x < (T)0,
                        -x,
                        x); // Negate on the "true" side so we never negate NaN.
#endif
}

template <typename T, int N>
SIMD_ALWAYS_INLINE typename std::enable_if<std::is_integral<T>::value, T>::type
reduce_add(gvec<T, N> x)
{
#if __has_builtin(__builtin_reduce_add) && SIMD_NATIVE_GVEC
    return __builtin_reduce_add(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_and() not supported. Consider updating clang.")
#endif
    T s = x[0];
    for (int i = 1; i < N; ++i)
        s += x[i];
    return s;
#endif
}

template <typename T, int N>
SIMD_ALWAYS_INLINE typename std::enable_if<!std::is_integral<T>::value, T>::type
reduce_add(gvec<T, N> x)
{
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_and() not supported. Consider updating clang.")
#endif
    T s = x[0];
    for (int i = 1; i < N; ++i)
        s += x[i];
    return s;
}

template <typename T, int N> SIMD_ALWAYS_INLINE T reduce_min(gvec<T, N> x)
{
#if __has_builtin(__builtin_reduce_and) && SIMD_NATIVE_GVEC
    return __builtin_reduce_min(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_and() not supported. Consider updating clang.")
#endif
    T reduced = x[0];
    for (int i = 1; i < N; ++i)
        reduced = std::min(reduced, x[i]);
    return reduced;
#endif
}

template <typename T, int N> SIMD_ALWAYS_INLINE T reduce_max(gvec<T, N> x)
{
#if __has_builtin(__builtin_reduce_and) && SIMD_NATIVE_GVEC
    return __builtin_reduce_max(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_and() not supported. Consider updating clang.")
#endif
    T reduced = x[0];
    for (int i = 1; i < N; ++i)
        reduced = std::max(reduced, x[i]);
    return reduced;
#endif
}

template <typename T, int N> SIMD_ALWAYS_INLINE T reduce_and(gvec<T, N> x)
{
#if __has_builtin(__builtin_reduce_and) && SIMD_NATIVE_GVEC
    return __builtin_reduce_and(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_and() not supported. Consider updating clang.")
#endif
    T reduced = x[0];
    for (int i = 1; i < N; ++i)
        reduced &= x[i];
    return reduced;
#endif
}

template <typename T, int N> SIMD_ALWAYS_INLINE T reduce_or(gvec<T, N> x)
{
#if __has_builtin(__builtin_reduce_and) && SIMD_NATIVE_GVEC
    return __builtin_reduce_or(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_reduce_and() not supported. Consider updating clang.")
#endif
    T reduced = x[0];
    for (int i = 1; i < N; ++i)
        reduced |= x[i];
    return reduced;
#endif
}

////// Floating Point Functions //////

template <int N> SIMD_ALWAYS_INLINE gvec<float, N> floor(gvec<float, N> x)
{
#if __has_builtin(__builtin_elementwise_floor) && SIMD_NATIVE_GVEC
    return __builtin_elementwise_floor(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_elementwise_floor() not supported. Consider updating clang.")
#endif
    for (int i = 0; i < N; ++i)
        x[i] = floorf(x[i]);
    return x;
#endif
}

template <int N> SIMD_ALWAYS_INLINE gvec<float, N> ceil(gvec<float, N> x)
{
#if __has_builtin(__builtin_elementwise_ceil) && SIMD_NATIVE_GVEC
    return __builtin_elementwise_ceil(x);
#else
#ifdef RIVE_SIMD_PERF_WARNINGS
#pragma message(                                                               \
    "performance: __builtin_elementwise_ceil() not supported. Consider updating clang.")
#endif
    for (int i = 0; i < N; ++i)
        x[i] = ceilf(x[i]);
    return x;
#endif
}

template <int N>
SIMD_ALWAYS_INLINE gvec<float, N> copysign(gvec<float, N> x, gvec<float, N> y)
{
    constexpr static uint32_t SIGN_BIT = 0x80000000u;
    // Type punning is free in vector registers.
    gvec<uint32_t, N> bitsX, bitsY;
    SIMD_INLINE_MEMCPY(&bitsX, &x, sizeof(x));
    SIMD_INLINE_MEMCPY(&bitsY, &y, sizeof(y));
    gvec<uint32_t, N> bitsRet = (bitsY & SIGN_BIT) | (bitsX & ~SIGN_BIT);
    gvec<float, N> ret;
    SIMD_INLINE_MEMCPY(&ret, &bitsRet, sizeof(ret));
    return ret;
}

// IEEE compliant sqrt.
template <int N> SIMD_ALWAYS_INLINE gvec<float, N> sqrt(gvec<float, N> x)
{
    // There isn't an elementwise builtin for sqrt. We define
    // architecture-specific specializations of this function later.
    for (int i = 0; i < N; ++i)
        x[i] = sqrtf(x[i]);
    return x;
}

#ifdef __SSE__
template <> SIMD_ALWAYS_INLINE gvec<float, 4> sqrt(gvec<float, 4> x)
{
    __m128 _x;
    SIMD_INLINE_MEMCPY(&_x, &x, sizeof(float) * 4);
    _x = _mm_sqrt_ps(_x);
    SIMD_INLINE_MEMCPY(&x, &_x, sizeof(float) * 4);
    return x;
}

template <> SIMD_ALWAYS_INLINE gvec<float, 2> sqrt(gvec<float, 2> x)
{
    __m128 _x;
    SIMD_INLINE_MEMCPY(&_x, &x, sizeof(float) * 2);
    _x = _mm_sqrt_ps(_x);
    SIMD_INLINE_MEMCPY(&x, &_x, sizeof(float) * 2);
    return x;
}
#endif

#ifdef __aarch64__
template <> SIMD_ALWAYS_INLINE gvec<float, 4> sqrt(gvec<float, 4> x)
{
    float32x4_t _x;
    SIMD_INLINE_MEMCPY(&_x, &x, sizeof(float) * 4);
    _x = vsqrtq_f32(_x);
    SIMD_INLINE_MEMCPY(&x, &_x, sizeof(float) * 4);
    return x;
}

template <> SIMD_ALWAYS_INLINE gvec<float, 2> sqrt(gvec<float, 2> x)
{
    float32x2_t _x;
    SIMD_INLINE_MEMCPY(&_x, &x, sizeof(float) * 2);
    _x = vsqrt_f32(_x);
    SIMD_INLINE_MEMCPY(&x, &_x, sizeof(float) * 2);
    return x;
}
#endif

// This will only be present when building with Emscripten and "-msimd128".
#if __has_builtin(__builtin_wasm_sqrt_f32x4) && SIMD_NATIVE_GVEC
template <> SIMD_ALWAYS_INLINE gvec<float, 4> sqrt(gvec<float, 4> x)
{
    return __builtin_wasm_sqrt_f32x4(x);
}

template <> SIMD_ALWAYS_INLINE gvec<float, 2> sqrt(gvec<float, 2> x)
{
    gvec<float, 4> _x{x.x, x.y};
    _x = __builtin_wasm_sqrt_f32x4(_x);
    return _x.xy;
}
#endif

// Returns "(x + 127) / 255", for x in the range 0..255*255.
template <int N>
SIMD_ALWAYS_INLINE gvec<uint16_t, N> div255(gvec<uint16_t, N> x)
{
    assert(all(x <= 255u * 255u));
    x += 128;
    return (x + (x >> 8)) >> 8;
}

// Approximates acos(x) within 0.96 degrees, using the rational polynomial:
//
//     acos(x) ~= (bx^3 + ax) / (dx^4 + cx^2 + 1) + pi/2
//
// See: https://stackoverflow.com/a/36387954
#define SIMD_FAST_ACOS_MAX_ERROR 0.0167552f // .96 degrees
template <int N> SIMD_ALWAYS_INLINE gvec<float, N> fast_acos(gvec<float, N> x)
{
    constexpr static float a = -0.939115566365855f;
    constexpr static float b = 0.9217841528914573f;
    constexpr static float c = -1.2845906244690837f;
    constexpr static float d = 0.295624144969963174f;
    constexpr static float pi_over_2 = 1.5707963267948966f;
    auto xx = x * x;
    auto numer = b * xx + a;
    auto denom = xx * (d * xx + c) + 1.f;
    return x * (numer / denom) + pi_over_2;
}

////// Type conversion //////

template <typename U, typename T, int N>
SIMD_ALWAYS_INLINE gvec<U, N> cast(gvec<T, N> x)
{
#if __has_builtin(__builtin_convertvector) && SIMD_NATIVE_GVEC
    return __builtin_convertvector(x, gvec<U, N>);
#else
    gvec<U, N> y{};
    for (int i = 0; i < N; ++i)
        y[i] = static_cast<U>(x[i]);
    return y;
#endif
}

////// Loading and storing //////

template <typename T, int N> SIMD_ALWAYS_INLINE gvec<T, N> load(const void* ptr)
{
    gvec<T, N> ret;
    SIMD_INLINE_MEMCPY(&ret, ptr, sizeof(T) * N);
    return ret;
}
SIMD_ALWAYS_INLINE gvec<float, 2> load2f(const void* ptr)
{
    return load<float, 2>(ptr);
}
SIMD_ALWAYS_INLINE gvec<float, 4> load4f(const void* ptr)
{
    return load<float, 4>(ptr);
}
SIMD_ALWAYS_INLINE gvec<int32_t, 2> load2i(const void* ptr)
{
    return load<int32_t, 2>(ptr);
}
SIMD_ALWAYS_INLINE gvec<int32_t, 4> load4i(const void* ptr)
{
    return load<int32_t, 4>(ptr);
}
SIMD_ALWAYS_INLINE gvec<uint32_t, 2> load2ui(const void* ptr)
{
    return load<uint32_t, 2>(ptr);
}
SIMD_ALWAYS_INLINE gvec<uint32_t, 4> load4ui(const void* ptr)
{
    return load<uint32_t, 4>(ptr);
}

template <typename T, int N>
SIMD_ALWAYS_INLINE void store(void* dst, gvec<T, N> vec)
{
    SIMD_INLINE_MEMCPY(dst, &vec, sizeof(T) * N);
}

////// Column-major (transposed) loads //////

#if defined(__ARM_NEON__) || defined(__aarch64__)
SIMD_ALWAYS_INLINE std::
    tuple<gvec<float, 4>, gvec<float, 4>, gvec<float, 4>, gvec<float, 4>>
    load4x4f(const float* matrix)
{
    float32x4x4_t m = vld4q_f32(matrix);
    gvec<float, 4> c0, c1, c2, c3;
    SIMD_INLINE_MEMCPY(&c0, &m.val[0], sizeof(c0));
    SIMD_INLINE_MEMCPY(&c1, &m.val[1], sizeof(c1));
    SIMD_INLINE_MEMCPY(&c2, &m.val[2], sizeof(c2));
    SIMD_INLINE_MEMCPY(&c3, &m.val[3], sizeof(c3));
    return {c0, c1, c2, c3};
}
#elif defined(__SSE__)
SIMD_ALWAYS_INLINE std::
    tuple<gvec<float, 4>, gvec<float, 4>, gvec<float, 4>, gvec<float, 4>>
    load4x4f(const float* m)
{
    __m128 r0, r1, r2, r3;
    SIMD_INLINE_MEMCPY(&r0, m + 4 * 0, sizeof(r0));
    SIMD_INLINE_MEMCPY(&r1, m + 4 * 1, sizeof(r1));
    SIMD_INLINE_MEMCPY(&r2, m + 4 * 2, sizeof(r2));
    SIMD_INLINE_MEMCPY(&r3, m + 4 * 3, sizeof(r3));
    _MM_TRANSPOSE4_PS(r0, r1, r2, r3);
    gvec<float, 4> c0, c1, c2, c3;
    SIMD_INLINE_MEMCPY(&c0, &r0, sizeof(c0));
    SIMD_INLINE_MEMCPY(&c1, &r1, sizeof(c1));
    SIMD_INLINE_MEMCPY(&c2, &r2, sizeof(c2));
    SIMD_INLINE_MEMCPY(&c3, &r3, sizeof(c3));
    return {c0, c1, c2, c3};
}
#else
SIMD_ALWAYS_INLINE std::
    tuple<gvec<float, 4>, gvec<float, 4>, gvec<float, 4>, gvec<float, 4>>
    load4x4f(const float* m)
{
    gvec<float, 4> c0 = {m[0], m[4], m[8], m[12]};
    gvec<float, 4> c1 = {m[1], m[5], m[9], m[13]};
    gvec<float, 4> c2 = {m[2], m[6], m[10], m[14]};
    gvec<float, 4> c3 = {m[3], m[7], m[11], m[15]};
    return {c0, c1, c2, c3};
}
#endif

template <typename T, int M, int N>
SIMD_ALWAYS_INLINE gvec<T, M + N> join(gvec<T, M> a, gvec<T, N> b)
{
    T data[M + N];
    SIMD_INLINE_MEMCPY(data, &a, sizeof(T) * M);
    SIMD_INLINE_MEMCPY(data + M, &b, sizeof(T) * N);
    return load<T, M + N>(data);
}

template <typename T, int M, int N, int O>
SIMD_ALWAYS_INLINE gvec<T, M + N + O> join(gvec<T, M> a,
                                           gvec<T, N> b,
                                           gvec<T, O> c)
{
    T data[M + N + O];
    SIMD_INLINE_MEMCPY(data, &a, sizeof(T) * M);
    SIMD_INLINE_MEMCPY(data + M, &b, sizeof(T) * N);
    SIMD_INLINE_MEMCPY(data + M + N, &c, sizeof(T) * O);
    return load<T, M + N + O>(data);
}

template <typename T, int M, int N, int O, int P>
SIMD_ALWAYS_INLINE gvec<T, M + N + O + P> join(gvec<T, M> a,
                                               gvec<T, N> b,
                                               gvec<T, O> c,
                                               gvec<T, P> d)
{
    T data[M + N + O + P];
    SIMD_INLINE_MEMCPY(data, &a, sizeof(T) * M);
    SIMD_INLINE_MEMCPY(data + M, &b, sizeof(T) * N);
    SIMD_INLINE_MEMCPY(data + M + N, &c, sizeof(T) * O);
    SIMD_INLINE_MEMCPY(data + M + N + O, &d, sizeof(T) * P);
    return load<T, M + N + O + P>(data);
}

template <typename T>
SIMD_ALWAYS_INLINE gvec<T, 4> zip(gvec<T, 2> a, gvec<T, 2> b)
{
#if __has_builtin(__builtin_shufflevector) && SIMD_NATIVE_GVEC
    return __builtin_shufflevector(a, b, 0, 2, 1, 3);
#else
    return gvec<T, 4>{a.x, b.x, a.y, b.y};
#endif
}

template <typename T>
SIMD_ALWAYS_INLINE gvec<T, 8> zip(gvec<T, 4> a, gvec<T, 4> b)
{
#if __has_builtin(__builtin_shufflevector) && SIMD_NATIVE_GVEC
    return __builtin_shufflevector(a, b, 0, 4, 1, 5, 2, 6, 3, 7);
#else
    return gvec<T, 8>{a.x, b.x, a.y, b.y, a.z, b.z, a.w, b.w};
#endif
}

template <typename T>
SIMD_ALWAYS_INLINE gvec<T, 16> zip(gvec<T, 8> a, gvec<T, 8> b)
{
#if __has_builtin(__builtin_shufflevector) && SIMD_NATIVE_GVEC
    return __builtin_shufflevector(a,
                                   b,
                                   0,
                                   8,
                                   1,
                                   9,
                                   2,
                                   10,
                                   3,
                                   11,
                                   4,
                                   12,
                                   5,
                                   13,
                                   6,
                                   14,
                                   7,
                                   15);
#else
    return gvec<T, 16>{a[0],
                       b[0],
                       a[1],
                       b[1],
                       a[2],
                       b[2],
                       a[3],
                       b[3],
                       a[4],
                       b[4],
                       a[5],
                       b[5],
                       a[6],
                       b[6],
                       a[7],
                       b[7]};
#endif
}

template <typename T, int N>
SIMD_ALWAYS_INLINE
    typename std::enable_if<N != 2 && N != 4 && N != 8, gvec<T, N * 2>>::type
    zip(gvec<T, N> a, gvec<T, N> b)
{
    gvec<T, N * 2> ret{};
    for (int i = 0; i < N; ++i)
    {
        ret[i * 2] = a[i];
        ret[i * 2 + 1] = b[i];
    }
    return ret;
}

////// Basic linear algebra //////

template <typename T, int N>
SIMD_ALWAYS_INLINE T dot(gvec<T, N> a, gvec<T, N> b)
{
    return reduce_add(a * b);
}

SIMD_ALWAYS_INLINE float cross(gvec<float, 2> a, gvec<float, 2> b)
{
    auto c = a * b.yx;
    return c.x - c.y;
}

// Linearly interpolates between a and b.
//
// NOTE: mix(a, b, 1) !== b (!!)
//
// The floating point numerics are not precise in the case where t === 1. But
// overall, this structure seems to get better precision for things like
// chopping cubics on exact cusp points than "a*(1 - t) + b*t" (which would
// return exactly b when t == 1).
template <int N>
SIMD_ALWAYS_INLINE gvec<float, N> mix(gvec<float, N> a,
                                      gvec<float, N> b,
                                      gvec<float, N> t)
{
    assert(simd::all(0.f <= t && t < 1.f));
    return (b - a) * t + a;
}
// Called when it doesn't matter if mix(a, b, 1) is only ~= b (it may not be
// precisely b).
template <int N>
SIMD_ALWAYS_INLINE gvec<float, N> unchecked_mix(gvec<float, N> a,
                                                gvec<float, N> b,
                                                gvec<float, N> t)
{
    return (b - a) * t + a;
}
// Returns precisely 'a' if t==0 and precisely 'b' if t==1.
template <int N>
SIMD_ALWAYS_INLINE gvec<float, N> precise_mix(gvec<float, N> a,
                                              gvec<float, N> b,
                                              gvec<float, N> t)
{
    return a * (1.f - t) + b * t;
}
} // namespace simd
} // namespace rive

namespace rive
{
template <int N> using vec = simd::gvec<float, N>;
using float2 = vec<2>;
using float4 = vec<4>;

template <int N> using ivec = simd::gvec<int32_t, N>;
using int2 = ivec<2>;
using int4 = ivec<4>;

template <int N> using uvec = simd::gvec<uint32_t, N>;
using uint2 = uvec<2>;
using uint4 = uvec<4>;

using int8x8 = simd::gvec<int8_t, 8>;
using int8x16 = simd::gvec<int8_t, 16>;
using int8x32 = simd::gvec<int8_t, 32>;

using uint8x8 = simd::gvec<uint8_t, 8>;
using uint8x16 = simd::gvec<uint8_t, 16>;
using uint8x32 = simd::gvec<uint8_t, 32>;

using int16x4 = simd::gvec<int16_t, 4>;
using int16x8 = simd::gvec<int16_t, 8>;
using int16x16 = simd::gvec<int16_t, 16>;

using uint16x4 = simd::gvec<uint16_t, 4>;
using uint16x8 = simd::gvec<uint16_t, 8>;
using uint16x16 = simd::gvec<uint16_t, 16>;

using int64x2 = simd::gvec<int64_t, 2>;
using int64x4 = simd::gvec<int64_t, 4>;

using uint64x2 = simd::gvec<uint64_t, 2>;
using uint64x4 = simd::gvec<uint64_t, 4>;

} // namespace rive

#undef SIMD_INLINE_MEMCPY
#undef SIMD_ALWAYS_INLINE

#endif