package jstream

import (
	"unicode/utf8"
)

type scratch struct {
	data []byte
	fill int
}

// reset scratch buffer
func (s *scratch) reset() { s.fill = 0 }

// bytes returns the written contents of scratch buffer
func (s *scratch) bytes() []byte { return s.data[0:s.fill] }

// grow scratch buffer
func (s *scratch) grow() {
	ndata := make([]byte, cap(s.data)*2)
	copy(ndata, s.data)
	s.data = ndata
}

    ByteBuffer scratch = scratch();
    scratch.putLong(l);
    return update(scratch, Longs.BYTES);
  }

  @Override
  @CanIgnoreReturnValue
  public Hasher putChar(char c) {
    ByteBuffer scratch = scratch();
    scratch.putChar(c);
    return update(scratch, Chars.BYTES);
  }

  private ByteBuffer scratch() {
    if (scratch == null) {

  @BeforeEach
  fun setUp() {
    testResourceDir = "./build/resources/test/okhttp3/osgi".toPath()
    workspaceDir = testResourceDir / "workspace"

    // Ensure we start from scratch.
    fileSystem.deleteRecursively(workspaceDir)
    fileSystem.createDirectories(workspaceDir)
  }

  /**
   * Resolve the OSGi metadata of the all okhttp3 modules. If required modules do not have OSGi

### Target language

Translate to Turkish (Türkçe).

Language code: tr.

### Core principle

Don't translate word-by-word. Rewrite naturally in Turkish as if writing the doc from scratch. Preserve meaning, but prioritize fluency over literal accuracy.

### Grammar and tone

- Use instructional Turkish, consistent with existing Turkish docs.
- Use imperative/guide language (e.g. "açalım", "gidin", "kopyalayalım", "bir bakalım").

* loop (as in async loop): цикл событий
* Machine Learning: Машинное обучение
* Deep Learning: Глубокое обучение
* callback hell: callback hell (clarify as `ад обратных вызовов`)
* on the fly: на лету
* scratch the surface: поверхностно ознакомиться
* tip: совет (or `подсказка` depending on context)
* Pydantic model: Pydantic-модель (`модель Pydantic` and `Pydantic модель` are also fine)
* declare: объявить

	return p.ctxt.PosTable.XPos(src.MakePos(p.lex.Base(), uint(p.lineNum), 0))
}

func (p *Parser) Parse() (*obj.Prog, bool) {
	scratch := make([][]lex.Token, 0, 3)
	for {
		word, cond, operands, ok := p.line(scratch)
		if !ok {
			break
		}
		scratch = operands

		if p.pseudo(word, operands) {
			continue
		}
		i, present := p.arch.Instructions[word]
		if present {

## Build a Docker Image for FastAPI { #build-a-docker-image-for-fastapi }

Okay, let's build something now! 🚀

I'll show you how to build a **Docker image** for FastAPI **from scratch**, based on the **official Python** image.

This is what you would want to do in **most cases**, for example:

* Using **Kubernetes** or similar tools
* When running on a **Raspberry Pi**

In the next chapter about [FastAPI in Containers - Docker](docker.md) I'll explain some strategies you could use to handle the other **deployment concepts**.

I'll show you how to **build your own image from scratch** to run a single Uvicorn process. It is a simple process and is probably what you would want to do when using a distributed container management system like **Kubernetes**.

## Recap { #recap }

 * fragile because they assume that only certain methods will be called and because they often
 * implement subtleties of the API improperly.
 *
 * <p><b>Custom implementation</b>: Avoid implementing {@code ListenableFuture} from scratch. If you
 * can't get by with the standard implementations, prefer to derive a new {@code Future} instance
 * with the methods in {@link Futures} or, if necessary, to extend {@link AbstractFuture}.
 *

        } catch (IllegalStateException e) {
            return new Builder(getUserProperties(), getSystemProperties(), getStartTime(), getTopDirectory(), null);
        }
    }

    /**
     * Returns new builder from scratch.
     */
    static Builder newBuilder() {
        return new Builder().withStartTime(MonotonicClock.now());