/*
 * 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 "rive/rive_types.hpp"
#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

// 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)));
} // namespace simd
} // namespace rive

#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"

#endif

namespace rive
{
namespace simd
{
////// Boolean logic //////
//
// Vector booleans are of type int32_t, where true is ~0 and false is 0. Vector booleans can be
// generated using the builtin boolean operators: ==, !=, <=, >=, <, >
//

// Returns true if all elements in x are equal to 0.
template <int N> RIVE_ALWAYS_INLINE bool any(gvec<int32_t, N> x)
{
#if __has_builtin(__builtin_reduce_or)
    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 <int N> RIVE_ALWAYS_INLINE bool all(gvec<int32_t, N> x)
{
#if __has_builtin(__builtin_reduce_and)
    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>
RIVE_ALWAYS_INLINE gvec<int32_t, 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<int32_t, 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>
RIVE_ALWAYS_INLINE gvec<T, N> if_then_else(gvec<int32_t, 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> RIVE_ALWAYS_INLINE gvec<T, N> min(gvec<T, N> a, gvec<T, N> b)
{
#if __has_builtin(__builtin_elementwise_min)
    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> RIVE_ALWAYS_INLINE gvec<T, N> max(gvec<T, N> a, gvec<T, N> b)
{
#if __has_builtin(__builtin_elementwise_max)
    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>
RIVE_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> RIVE_ALWAYS_INLINE gvec<T, N> abs(gvec<T, N> x)
{
#if __has_builtin(__builtin_elementwise_abs)
    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> RIVE_ALWAYS_INLINE T sum(gvec<T, N> x)
{
    T s = x[0];
    for (int i = 1; i < N; ++i)
        s += x[i];
    return s;
}

// We can use __builtin_reduce_add for integer types.
#if __has_builtin(__builtin_reduce_add)
template <int N> RIVE_ALWAYS_INLINE int32_t sum(gvec<int32_t, N> x)
{
    return __builtin_reduce_add(x);
}

template <int N> RIVE_ALWAYS_INLINE uint32_t sum(gvec<uint32_t, N> x)
{
    return __builtin_reduce_add(x);
}
#endif

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

template <int N> RIVE_ALWAYS_INLINE gvec<float, N> floor(gvec<float, N> x)
{
#if __has_builtin(__builtin_elementwise_floor)
    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> RIVE_ALWAYS_INLINE gvec<float, N> ceil(gvec<float, N> x)
{
#if __has_builtin(__builtin_elementwise_ceil)
    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
}

// IEEE compliant sqrt.
template <int N> RIVE_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 <> RIVE_ALWAYS_INLINE gvec<float, 4> sqrt(gvec<float, 4> x)
{
    __m128 _x;
    RIVE_INLINE_MEMCPY(&_x, &x, sizeof(float) * 4);
    _x = _mm_sqrt_ps(_x);
    RIVE_INLINE_MEMCPY(&x, &_x, sizeof(float) * 4);
    return x;
}

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

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

template <> RIVE_ALWAYS_INLINE gvec<float, 2> sqrt(gvec<float, 2> x)
{
    float32x2_t _x;
    RIVE_INLINE_MEMCPY(&_x, &x, sizeof(float) * 2);
    _x = vsqrt_f32(_x);
    RIVE_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)
template <> RIVE_ALWAYS_INLINE gvec<float, 4> sqrt(gvec<float, 4> x)
{
    return __builtin_wasm_sqrt_f32x4(x);
}

template <> RIVE_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

// 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> RIVE_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> RIVE_ALWAYS_INLINE gvec<U, N> cast(gvec<T, N> x)
{
    gvec<U, N> y{};
    for (int i = 0; i < N; ++i)
        y[i] = static_cast<U>(x[i]);
    return y;
}

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

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

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

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

#if defined(__ARM_NEON__) || defined(__aarch64__)
RIVE_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;
    RIVE_INLINE_MEMCPY(&c0, &m.val[0], sizeof(c0));
    RIVE_INLINE_MEMCPY(&c1, &m.val[1], sizeof(c1));
    RIVE_INLINE_MEMCPY(&c2, &m.val[2], sizeof(c2));
    RIVE_INLINE_MEMCPY(&c3, &m.val[3], sizeof(c3));
    return {c0, c1, c2, c3};
}
#elif defined(__SSE__)
RIVE_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;
    RIVE_INLINE_MEMCPY(&r0, m + 4 * 0, sizeof(r0));
    RIVE_INLINE_MEMCPY(&r1, m + 4 * 1, sizeof(r1));
    RIVE_INLINE_MEMCPY(&r2, m + 4 * 2, sizeof(r2));
    RIVE_INLINE_MEMCPY(&r3, m + 4 * 3, sizeof(r3));
    _MM_TRANSPOSE4_PS(r0, r1, r2, r3);
    gvec<float, 4> c0, c1, c2, c3;
    RIVE_INLINE_MEMCPY(&c0, &r0, sizeof(c0));
    RIVE_INLINE_MEMCPY(&c1, &r1, sizeof(c1));
    RIVE_INLINE_MEMCPY(&c2, &r2, sizeof(c2));
    RIVE_INLINE_MEMCPY(&c3, &r3, sizeof(c3));
    return {c0, c1, c2, c3};
}
#else
RIVE_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>
RIVE_ALWAYS_INLINE gvec<T, M + N> join(gvec<T, M> a, gvec<T, N> b)
{
    T data[M + N];
    RIVE_INLINE_MEMCPY(data, &a, sizeof(T) * M);
    RIVE_INLINE_MEMCPY(data + M, &b, sizeof(T) * N);
    return load<T, M + N>(data);
}

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

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

RIVE_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>
RIVE_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;
}

// Linearly interpolates between a and b, returning precisely 'a' if t==0 and precisely 'b' if t==1.
template <int N>
RIVE_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>;
} // namespace rive

#endif