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

package io

// The A/B benchmark for the CSV read path: the legacy encoding/csv
// reader and the hand-rolled tokenizer side by side, in one process.
// The two sub-benchmarks alternate within every -count round, so both
// see the same machine, and the decision reads the medians with 1 to 2
// percent treated as noise.

import (
	"bytes"
	"io"
	"testing"

	"sourcedock.dev/petrbalvin/tensor/internal/core"
)

// The A/B table: a hundred thousand rows of sixteen float64 values, in
// the text shape SaveCSV emits, sized so the per-value work dominates
// everything else.
const (
	benchCSVABRows = 100000
	benchCSVABCols = 16
)

// benchCSVABData renders the A/B table as CSV bytes, once, before the
// measured loops.
func benchCSVABData(b *testing.B) []byte {
	b.Helper()
	var buf bytes.Buffer
	if err := SaveCSVWriter(&buf, benchFloats(benchCSVABRows, benchCSVABCols)); err != nil {
		b.Fatal(err)
	}
	return buf.Bytes()
}

// BenchmarkLoadCSVReader races the two readers over the same table.
func BenchmarkLoadCSVReader(b *testing.B) {
	data := benchCSVABData(b)
	impls := []struct {
		name string
		load func(io.Reader, bool) (*core.Array, error)
	}{
		{"old", loadCSVReaderLegacy},
		{"new", LoadCSVReader},
	}
	for _, impl := range impls {
		b.Run(impl.name, func(b *testing.B) {
			r := bytes.NewReader(data)
			b.ReportAllocs()
			for b.Loop() {
				r.Reset(data)
				a, err := impl.load(r, false)
				if err != nil {
					b.Fatal(err)
				}
				if a.Shape()[0] != benchCSVABRows || a.Shape()[1] != benchCSVABCols {
					b.Fatalf("array came back with shape %v", a.Shape())
				}
			}
		})
	}
}