// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
// SPDX-License-Identifier: MIT

package core

import "testing"

// Einsum benchmarks guard both the fast paths and the general engine.
// The batched shapes are the training-loop workloads; the general
// engine shapes are the ones the dispatch table declines.

func benchVals(b *testing.B, seed, n int) []float64 {
	b.Helper()
	v := make([]float64, n)
	for i := range v {
		v[i] = float64(i%17)*float64(seed%5) + float64(i%7) - 6
	}
	return v
}

func benchMat(b *testing.B, seed int, shape ...int) *Array {
	b.Helper()
	n := 1
	for _, d := range shape {
		n *= d
	}
	a, err := FromFloats(benchVals(b, seed, n), shape...)
	if err != nil {
		b.Fatal(err)
	}
	return a
}

// BenchmarkEinsumMatMulPath exercises the dispatch table's MatMul fast
// path as the control.
func BenchmarkEinsumMatMulPath(b *testing.B) {
	a := benchMat(b, 1, 64, 64)
	c := benchMat(b, 2, 64, 64)
	b.ReportAllocs()
	for b.Loop() {
		if _, err := Einsum("ij,jk->ik", a, c); err != nil {
			b.Fatal(err)
		}
	}
}

// BenchmarkEinsumBatchedMatMul is the batched product the table has no
// fast path for: it falls to the general engine.
func BenchmarkEinsumBatchedMatMul(b *testing.B) {
	a := benchMat(b, 3, 16, 64, 64)
	c := benchMat(b, 4, 16, 64, 64)
	b.ReportAllocs()
	for b.Loop() {
		if _, err := Einsum("bij,bjk->bik", a, c); err != nil {
			b.Fatal(err)
		}
	}
}

// BenchmarkEinsumBatchedSmall keeps the general engine's fixed costs
// visible at a size where they dominate.
func BenchmarkEinsumBatchedSmall(b *testing.B) {
	a := benchMat(b, 5, 4, 32, 32)
	c := benchMat(b, 6, 4, 32, 32)
	b.ReportAllocs()
	for b.Loop() {
		if _, err := Einsum("bij,bjk->bik", a, c); err != nil {
			b.Fatal(err)
		}
	}
}

// BenchmarkEinsumEllipsisMatMul is the ellipsis spelling of the batched
// product.
func BenchmarkEinsumEllipsisMatMul(b *testing.B) {
	a := benchMat(b, 7, 8, 48, 48)
	c := benchMat(b, 8, 48, 48)
	b.ReportAllocs()
	for b.Loop() {
		if _, err := Einsum("...ij,jk->...ik", a, c); err != nil {
			b.Fatal(err)
		}
	}
}

// BenchmarkEinsumBilinear sums a shared label across three operands,
// the pattern attention and tensordot shapes reduce to.
func BenchmarkEinsumBilinear(b *testing.B) {
	a := benchMat(b, 9, 16, 32)
	c := benchMat(b, 10, 32, 32)
	d := benchMat(b, 11, 32, 16)
	b.ReportAllocs()
	for b.Loop() {
		if _, err := Einsum("ik,kj,jl->il", a, c, d); err != nil {
			b.Fatal(err)
		}
	}
}