* under the License.
 */
package org.apache.maven.model.profile.activation;

import javax.inject.Named;
import javax.inject.Singleton;

import java.util.Locale;

import org.apache.maven.model.Activation;
import org.apache.maven.model.ActivationOS;
import org.apache.maven.model.Profile;
import org.apache.maven.model.building.ModelProblemCollector;
import org.apache.maven.model.profile.ProfileActivationContext;

    }

    public MaxCodePoint(String userFriendly) {
      value = decode(userFriendly);
    }
  }

  /**
   * The default values of maxCodePoint below provide pretty good performance models of different
   * kinds of common human text.
   *
   * @see MaxCodePoint#decode
   */
  @Param({"0x80", "0x90", "0x100", "0x800", "0x10000", "0x10ffff"})
  MaxCodePoint maxCodePoint;

  @Param({"16384"})

/**
 * Provides access to the contents of a source independently of the
 * backing store (e.g. file system, database, memory).
 * <p>
 * This is mainly used to parse files into objects such as Maven projects,
 * models, settings, or toolchains. The source implementation handles
 * all the details of accessing the underlying content while providing
 * a uniform API to consumers.
 * <p>
 * Sources can represent:
 * <ul>

{* ../../docs_src/stream_json_lines/tutorial001_py310.py ln[1:24] hl[24] *}

Göndermek istediğiniz her JSON öğesi `Item` tipindeyse (bir Pydantic modeli) ve fonksiyon async ise, dönüş tipini `AsyncIterable[Item]` olarak belirtebilirsiniz:

{* ../../docs_src/stream_json_lines/tutorial001_py310.py ln[1:24] hl[9:11,22] *}

app = FastAPI()


class Model(BaseModel):
    param: str = Field(alias="param_alias")


@app.get("/query")
async def query_model(data: Model = Query()):
    return {"param": data.param}


@app.get("/header")
async def header_model(data: Model = Header()):
    return {"param": data.param}


@app.get("/cookie")
async def cookie_model(data: Model = Cookie()):
    return {"param": data.param}

Use base64 only if you definitely need to include binary data in JSON, and you can't use files for that.

## Pydantic `bytes` { #pydantic-bytes }

You can declare a Pydantic model with `bytes` fields, and then use `val_json_bytes` in the model config to tell it to use base64 to *validate* input JSON data, as part of that validation it will decode the base64 string into bytes.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3

 * under the License.
 */
package org.apache.maven.model.building;

import org.apache.maven.building.StringSource;

/**
 * Wraps an ordinary {@link CharSequence} as a model source.
 *
 *
 * @deprecated instead use {@link StringSource}
 */
@Deprecated
public class StringModelSource extends StringSource implements ModelSource {

    /**
     * Creates a new model source backed by the specified string.
     *

app = FastAPI()


class Model(BaseModel):
    param: str

    model_config = {"extra": "allow"}


@app.get("/query")
async def query_model_with_extra(data: Model = Query()):
    return data


@app.get("/header")
async def header_model_with_extra(data: Model = Header()):
    return data


@app.get("/cookie")
async def cookies_model_with_extra(data: Model = Cookie()):
    return data

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

In this example, it would convert the Pydantic model to a `dict`, and the `datetime` to a `str`.