You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: symbol.go
    path of file: ./repos/goreportcard/vendor/golang.org/x/tools/internal/lsp/fuzzy
    
    the code of the file until where you have to start completion: // Copyright 2021 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package fuzzy

import (
	"unicode"
)

// SymbolMatcher implements a fuzzy matching algorithm optimized for Go symbols
// of the form:
//  example.com/path/to/package.object.field
//
// Knowing that we are matching symbols like this allows us to make the
// following optimizations:
//  - We can incorporate right-to-left relevance directly into the score
//    calculation.
//  - We can match from right to left, discarding leading bytes if the input is
//    too long.
//  - We just take the right-most match without losing too much precision. This
//    allows us to use an O(n) algorithm.
//  - We can operate directly on chunked strings; in many cas
    
func (m *SymbolMatcher) Match(chunks []string) (int, float64) {
	// Explicit behavior for an empty pattern.
	//
	// As a minor optimization, this also avoids nilness checks later on, since
	// the compiler can prove that m != nil.
	if m.patternLen == 0 {
		return -1, 0
	}

	// First phase: populate the input buffer with lower-cased runes.
	//
	// We could also check for a forward match here, but since we'd have to write
	// the entire input anyway this has negligible impact on performance.

	var (
		inputLen  = uint8(0)
		modifiers = wordStart | segmentStart
	)

input:
	for _, chunk := range chunks {
		for _, r := range chunk {
			if r == '.' || r == '/' {
				modifiers |= separator
			}
			// optimization: avoid calls to unicode.ToLower, which can't be inlined.
			l := r
			if r <= unicode.MaxASCII {
				if 'A' <= r && r <= 'Z' {
					l = r + 'a' - 'A'
				}
			} else {
				l = unicode.ToLower(r)
			}
			if l != r {
				modifiers |= wordStart
			}
			m.inputBuffer[inputLen] = l
			m.roles[inputLen] = modifiers
			inputLen++
			if m.roles[inputLen-1]&separator != 0 {
				modifiers = wordStart | segmentStart
			} else {
				modifiers = 0
			}
			// TODO: we should prefer the right-most input if it overflows, rather
			//       than the left-most as we're doing here.
			if inputLen == 255 {
				break input
			}
		}
	}

	// Second phase: find the right-most match, and count segments from the
	// right.

	var (
		pi    = uint8(m.patternLen - 1) // pattern index
		p     = m.pattern[pi]           // pattern rune
		start = -1                      // start offset of match
		rseg  = uint8(0)
	)
	const maxSeg = 3 // maximum number of segments from the right to count, for scoring purposes.

	for ii := inputLen - 1; ; ii-- {
		r := m.inputBuffer[ii]
		if rseg < maxSeg && m.roles[ii]&separator != 0 {
			rseg++
		}
		m.segments[ii] = rseg
		if p == r {
			if pi == 0 {
				start = int(ii)
				break
			}
			pi--
			p = m.pattern[pi]
		}
		// Don't check ii >= 0 in the loop condition: ii is a uint8.
		if ii == 0 {
			break
		}
	}

	if start < 0 {
		// no match: skip scoring
		return -1, 0
	}

	// Third phase: find the shortest match, and compute the score.

	// Score is the average score for each character.
	//
	// A character score is the multiple of:
	//   1. 1.0 if the character starts a segment, .8 if the character start a
	//      mid-segment word, otherwise 0.6. This carries over to immediately
	//      following characters.
	//   2. For the final character match, the multiplier from (1) is reduced to
	//     .8 if the next character in the input is a mid-segment word, or 0.6 if
	//      the next character in the input is not a word or segment start. This
	//      ensures that we favor whole-word or whole-segment matches over prefix
	//      matches.
	//   3. 1.0 if the character is part of the last segment, otherwise
	//      1.0-.2*<segments from the right>, with a max segment count of 3.
	//
	// This is a very naive algorithm, but it is fast. There's lots of prior art
	// here, and we should leverage it. For example, we could explicitly consider
	// character distance, and exact matches of words or segments.
	//
	// Also note that this might not actually find the highest scoring match, as
	// doing so could require a non-linear algorithm, depending on how the score
	// is calculated.

	pi = 0
	p = m.pattern[pi]

	const (
		segStreak  = 1.0
		wordStreak = 0.8
		noStreak   = 0.6
		perSegment = 0.2 // we count at most 3 segments above
	)

	streakBonus := noStreak
	totScore := 0.0
	for ii := uint8(start); ii < inputLen; ii++ {
		r := m.inputBuffer[ii]
		if r == p {
			pi++
			p = m.pattern[pi]
			// Note: this could be optimized with some bit operations.
			switch {
			case m.roles[ii]&segmentStart != 0 && segStreak > streakBonus:
				streakBonus = segStreak
			case m.roles[ii]&wordStart != 0 && wordStreak > streakBonus:
				streakBonus = wordStreak
			}
			finalChar := pi >= m.patternLen
			// finalCost := 1.0
			if finalChar && streakBonus > noStreak {
				switch {
				case ii == inputLen-1 || m.roles[ii+1]&segmentStart != 0:
					// Full segment: no reduction
				case m.roles[ii+1]&wordStart != 0:
					streakBonus = wordStreak
				default:
					streakBonus = noStreak
				}
			}
			totScore += streakBonus * (1.0 - float64(m.segments[ii])*perSegment)
			if finalChar {
				break
			}
		} else {
			streakBonus = noStreak
		}
	}

	return start, totScore / float64(m.patternLen)
}