DefaultToolchain toolchain = newDefaultToolchain(model);
        toolchain.addProvideToken("name", RequirementMatcherFactory.createExactMatcher("Jane Doe"));

        assertFalse(toolchain.matchesRequirements(Collections.singletonMap("name", "John Doe")));
        verify(logger).debug("Toolchain {} doesn't match required property: {}", toolchain, "name");
    }

    @Test
    void testEquals() {

Pydantic models have a `.dict()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="john.doe@example.com")
```

and then we call:

```Python
user_dict = user_in.dict()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

        String jwtClaim =
                "{\"email\":\"******@****.***\",\"sub\":\"1234567890\",\"name\":\"John Doe\",\"groups\":[\"group1\",\"group2\"]}";

        // Execute
        authenticator.parseJwtClaim(jwtClaim, attributes);

        // Verify
        assertEquals("1234567890", attributes.get("sub"));
        assertEquals("John Doe", attributes.get("name"));

        // Check groups array

Los modelos Pydantic tienen un método `.dict()` que devuelve un `dict` con los datos del modelo.

Así que, si creamos un objeto Pydantic `user_in` como:

```Python
user_in = UserIn(username="john", password="secret", email="john.doe@example.com")
```

y luego llamamos a:

```Python
user_dict = user_in.dict()
```

ahora tenemos un `dict` con los datos en la variable `user_dict` (es un `dict` en lugar de un objeto modelo Pydantic).

        value = "\"John Doe\",\"Software Engineer\",\"john.doe@example.com\",\"Loves \"\"coding\"\" and coffee\"";
        result = KuromojiCSVUtil.parse(value);
        assertEquals(4, result.length);
        assertEquals("John Doe", result[0]);
        assertEquals("Software Engineer", result[1]);
        assertEquals("john.doe@example.com", result[2]);

`user_in`은 Pydantic 모델 클래스인 `UserIn`입니다.

Pydantic 모델은 모델 데이터를 포함한 `dict`를 반환하는 `.dict()` 메서드를 제공합니다.

따라서, 다음과 같이 Pydantic 객체 `user_in`을 생성할 수 있습니다:

```Python
user_in = UserIn(username="john", password="secret", email="john.doe@example.com")
```

그 다음, 다음과 같이 호출합니다:

```Python
user_dict = user_in.dict()
```

이제 변수 `user_dict`에 데이터가 포함된 `dict`를 가지게 됩니다(이는 Pydantic 모델 객체가 아닌 `dict`입니다).

그리고 다음과 같이 호출하면:

```Python

        Map<String, Object> context = new HashMap<>();
        context.put("password", "secretpassword123");
        context.put("sessionId", "abc123def456");
        context.put("username", "john.doe@example.com");

        logger.logEvent(EventType.AUTHENTICATION_SUCCESS, Severity.INFO, "Login attempt", context);

        // The test verifies that the logger runs without errors when masking is enabled

Ahora usa la operación `GET` con la path `/users/me`.

Obtendrás los datos de tu usuario, como:

```JSON
{
  "username": "johndoe",
  "email": "******@****.***",
  "full_name": "John Doe",
  "disabled": false,
  "hashed_password": "fakehashedsecret"
}
```

<img src="/img/tutorial/security/image06.png">

///

## Motivación

Comencemos con un ejemplo simple:

{* ../../docs_src/python_types/tutorial001.py *}

Llamar a este programa genera:

```
John Doe
```

La función hace lo siguiente:

* Toma un `first_name` y `last_name`.
* Convierte la primera letra de cada uno a mayúsculas con `title()`.

///

## انگیزه

بیاید با یه مثال ساده شروع کنیم:

{* ../../docs_src/python_types/tutorial001.py *}

وقتی این برنامه رو اجرا کنی، خروجی اینه:

```
John Doe
```

این تابع این کارا رو می‌کنه:

* یه `first_name` و `last_name` می‌گیره.
* حرف اول هر کدوم رو با `title()` بزرگ می‌کنه.