callback.assertTextMessage("Hello")
  }

  @Test fun clientTwoFrameHelloWithPongs() {
    data.write("010348656c".decodeHex()) // Hel
    data.write("8a00".decodeHex()) // Pong
    data.write("8a00".decodeHex()) // Pong
    data.write("8a00".decodeHex()) // Pong
    data.write("8a00".decodeHex()) // Pong
    data.write("80026c6f".decodeHex()) // lo
    clientReader.processNextFrame()
    callback.assertPong(EMPTY)

      """
      |--AaB03x
      |Content-Disposition: form-data; name="field with spaces"; filename="filename with spaces.txt"
      |Content-Type: text/plain; charset=utf-8
      |
      |okay
      |--AaB03x
      |Content-Disposition: form-data; name="field with %22"
      |
      |"
      |--AaB03x
      |Content-Disposition: form-data; name="field with %22"
      |
      |%22
      |--AaB03x

      sink.writeInt(errorCode.httpCode)
      sink.flush()
    }
  }

  /** The maximum size of bytes that may be sent in a single call to [data]. */
  fun maxDataLength(): Int = maxFrameSize

  /**
   * `source.length` may be longer than the max length of the variant's data frame. Implementations
   * must send multiple frames as necessary.
   *
   * @param source the buffer to draw bytes from. May be null if byteCount is 0.

  ) {
    if (streamId == 0) throw IOException("PROTOCOL_ERROR: TYPE_DATA streamId == 0")

    // TODO: checkState open or half-closed (local) or raise STREAM_CLOSED
    val inFinished = flags and FLAG_END_STREAM != 0
    val gzipped = flags and FLAG_COMPRESSED != 0
    if (gzipped) {
      throw IOException("PROTOCOL_ERROR: FLAG_COMPRESSED without SETTINGS_COMPRESS_DATA")
    }

    ```Python hl_lines="3  77-79"
    {!> ../../../docs_src/security/tutorial003.py!}
    ```

### Check the password

At this point we have the user data from our database, but we haven't checked the password.

Let's put that data in the Pydantic `UserInDB` model first.

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

wss:example.com/  s:wss h:example.com p:/
data:example.com/  s:data p:example.com/
javascript:example.com/  s:javascript p:example.com/
mailto:example.com/  s:mailto p:example.com/
/a/b/c  s:http h:example.org p:/a/b/c
/a/\s/c  s:http h:example.org p:/a/%20/c
/a%2fc  s:http h:example.org p:/a%2fc
/a/%2f/c  s:http h:example.org p:/a/%2f/c
\#\u03B2  s:http h:example.org p:/foo/bar f:#\u03B2
data:text/html,test#test  s:data p:text/html,test f:#test

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` یعنی:

    کلید ها و مقادیر دیکشنری `second_user_data` را مستقیما به عنوان ارگومان های key-value بفرست، که معادل است با : `User(id=4, name="Mary", joined="2018-11-30")`

* **Kodlaması Hızlı**: Geliştirme hızını yaklaşık %200 ile %300 aralığında arttırır. *
* **Daha az hata**: İnsan (geliştirici) kaynaklı hataları yaklaşık %40 azaltır. *
* **Sezgisel**: Muhteşem bir editör desteği. Her yerde <abbr title="Otomatik Tamamlama: auto-complete, autocompletion, IntelliSense">otomatik tamamlama</abbr>. Hata ayıklama ile daha az zaman harcayacaksınız.

## Create the database models

Let's now see the file `sql_app/models.py`.

### Create Peewee models for our data

Now create the Peewee models (classes) for `User` and `Item`.

This is the same you would do if you followed the Peewee tutorial and updated the models to have the same data as in the SQLAlchemy tutorial.

!!! tip

* Editor support, including:
    * Completion.
    * Type checks.
* Validation of data:
    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.
* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from:
    * JSON.
    * Path parameters.
    * Query parameters.