KafkaRouter

def add_middleware(self, middleware: "BrokerMiddleware[Any, Any]") -> None:
    """Append BrokerMiddleware to the end of middlewares list.

    Current middleware will be used as a most inner of the stack.
    """
    self.config.add_middleware(middleware)

def insert_middleware(self, middleware: "BrokerMiddleware[Any, Any]") -> None:
    """Insert BrokerMiddleware to the start of middlewares list.

    Current middleware will be used as a most outer of the stack.
    """
    self.config.insert_middleware(middleware)

@override
def subscriber(
    self,
    *topics: str,
    partitions: Sequence["TopicPartition"] = (),
    polling_interval: float = 0.1,
    group_id: str | None = None,
    group_instance_id: str | None = None,
    fetch_max_wait_ms: int = 500,
    fetch_max_bytes: int = 50 * 1024 * 1024,
    fetch_min_bytes: int = 1,
    max_partition_fetch_bytes: int = 1 * 1024 * 1024,
    auto_offset_reset: Literal["latest", "earliest", "none"] = "latest",
    auto_commit_interval_ms: int = 5 * 1000,
    check_crcs: bool = True,
    partition_assignment_strategy: Sequence[str] = ("roundrobin",),
    max_poll_interval_ms: int = 5 * 60 * 1000,
    session_timeout_ms: int = 10 * 1000,
    heartbeat_interval_ms: int = 3 * 1000,
    isolation_level: Literal[
        "read_uncommitted",
        "read_committed",
    ] = "read_uncommitted",
    # rebalance callbacks
    on_assign: Callable[..., None] | None = None,
    on_revoke: Callable[..., None] | None = None,
    on_lost: Callable[..., None] | None = None,
    batch: bool = False,
    max_records: int | None = None,
    # broker args
    persistent: bool = True,
    dependencies: Iterable["Dependant"] = (),
    parser: Optional["CustomCallable"] = None,
    decoder: Optional["CustomCallable"] = None,
    ack_policy: AckPolicy = EMPTY,
    no_reply: bool = False,
    # Specification args
    title: str | None = None,
    description: str | None = None,
    include_in_schema: bool = True,
    max_workers: int | None = None,
) -> Union[
    "DefaultSubscriber",
    "BatchSubscriber",
    "ConcurrentDefaultSubscriber",
]:
    """Create a subscriber for Kafka topics.

    Args:
        *topics: Kafka topics to consume messages from.
        partitions: Sequence of topic partitions.
        polling_interval: Polling interval in seconds.
        group_id: Name of the consumer group to join for dynamic
            partition assignment (if enabled), and to use for fetching and
            committing offsets. If `None`, auto-partition assignment (via
            group coordinator) and offset commits are disabled.
        group_instance_id: A unique string that identifies the consumer instance.
            If set, the consumer is treated as a static member of the group
            and does not participate in consumer group management (e.g.
            partition assignment, rebalances). This can be used to assign
            partitions to specific consumers, rather than letting the group
            assign partitions based on consumer metadata.
        fetch_max_wait_ms: The maximum amount of time in milliseconds
            the server will block before answering the fetch request if
            there isn't sufficient data to immediately satisfy the
            requirement given by `fetch_min_bytes`.
        fetch_max_bytes: The maximum amount of data the server should
            return for a fetch request. This is not an absolute maximum, if
            the first message in the first non-empty partition of the fetch
            is larger than this value, the message will still be returned
            to ensure that the consumer can make progress. NOTE: consumer
            performs fetches to multiple brokers in parallel so memory
            usage will depend on the number of brokers containing
            partitions for the topic.
        fetch_min_bytes: Minimum amount of data the server should
            return for a fetch request, otherwise wait up to
            `fetch_max_wait_ms` for more data to accumulate.
        max_partition_fetch_bytes: The maximum amount of data
            per-partition the server will return. The maximum total memory
            used for a request ``= #partitions * max_partition_fetch_bytes``.
            This size must be at least as large as the maximum message size
            the server allows or else it is possible for the producer to
            send messages larger than the consumer can fetch. If that
            happens, the consumer can get stuck trying to fetch a large
            message on a certain partition.
        auto_offset_reset: A policy for resetting offsets on `OffsetOutOfRangeError` errors:

            * `earliest` will move to the oldest available message
            * `latest` will move to the most recent
            * `none` will raise an exception so you can handle this case
        auto_commit: If `True` the consumer's offset will be
            periodically committed in the background.
        auto_commit_interval_ms: Milliseconds between automatic
            offset commits, if `auto_commit` is `True`.
        check_crcs: Automatically check the CRC32 of the records
            consumed. This ensures no on-the-wire or on-disk corruption to
            the messages occurred. This check adds some overhead, so it may
            be disabled in cases seeking extreme performance.
        partition_assignment_strategy: List of objects to use to
            distribute partition ownership amongst consumer instances when
            group management is used. This preference is implicit in the order
            of the strategies in the list. When assignment strategy changes:
            to support a change to the assignment strategy, new versions must
            enable support both for the old assignment strategy and the new
            one. The coordinator will choose the old assignment strategy until
            all members have been updated. Then it will choose the new
            strategy.
        max_poll_interval_ms: Maximum allowed time between calls to
            consume messages in batches. If this interval
            is exceeded the consumer is considered failed and the group will
            rebalance in order to reassign the partitions to another consumer
            group member. If API methods block waiting for messages, that time
            does not count against this timeout.
        session_timeout_ms: Client group session and failure detection
            timeout. The consumer sends periodic heartbeats
            (`heartbeat.interval.ms`) to indicate its liveness to the broker.
            If no hearts are received by the broker for a group member within
            the session timeout, the broker will remove the consumer from the
            group and trigger a rebalance. The allowed range is configured with
            the **broker** configuration properties
            `group.min.session.timeout.ms` and `group.max.session.timeout.ms`.
        heartbeat_interval_ms: The expected time in milliseconds
            between heartbeats to the consumer coordinator when using
            Kafka's group management feature. Heartbeats are used to ensure
            that the consumer's session stays active and to facilitate
            rebalancing when new consumers join or leave the group. The
            value must be set lower than `session_timeout_ms`, but typically
            should be set no higher than 1/3 of that value. It can be
            adjusted even lower to control the expected time for normal
            rebalances.
        isolation_level: Controls how to read messages written
            transactionally.

            * `read_committed`, batch consumer will only return
            transactional messages which have been committed.

            * `read_uncommitted` (the default), batch consumer will
            return all messages, even transactional messages which have been
            aborted.

            Non-transactional messages will be returned unconditionally in
            either mode.

            Messages will always be returned in offset order. Hence, in
            `read_committed` mode, batch consumer will only return
            messages up to the last stable offset (ALSO), which is the one less
            than the offset of the first open transaction. In particular any
            messages appearing after messages belonging to ongoing transactions
            will be withheld until the relevant transaction has been completed.
            As a result, `read_committed` consumers will not be able to read up
            to the high watermark when there are in flight transactions.
            Further, when in `read_committed` the seek_to_end method will
            return the ALSO. See method docs below.
        batch: Whether to consume messages in batches or not.
        max_records: Number of messages to consume as one batch.
        on_assign: Callback called when partitions are assigned to the consumer
            during a rebalance. Receives ``(consumer, partitions)`` arguments.
        on_revoke: Callback called when partitions are revoked from the consumer
            during a rebalance. Receives ``(consumer, partitions)`` arguments.
        on_lost: Callback called when partitions are lost (e.g., due to session
            timeout). Receives ``(consumer, partitions)`` arguments.
        dependencies: Dependencies list (`[Dependant(),]`) to apply to the subscriber.
        parser: Parser to map original **Message** object to FastStream one.
        decoder: Function to decode FastStream msg bytes body to python objects.
        middlewares: Subscriber middlewares to wrap incoming message processing.
        no_ack: Whether to disable **FastStream** auto acknowledgement logic or not.
        ack_policy: Acknowledgement policy for the subscriber.
        no_reply: Whether to disable **FastStream** RPC and Reply To auto responses or not.
        title: Specification subscriber object title.
        description: Specification subscriber object description.
            Uses decorated docstring as default.
        include_in_schema: Whether to include operation in Specification schema or not.
        max_workers: Number of workers to process messages concurrently.
        persistent: Whether to make the subscriber persistent or not.

    Returns:
        Union of DefaultSubscriber, BatchSubscriber, or ConcurrentDefaultSubscriber
            depending on the configuration.
    """
    workers = max_workers or 1

    subscriber = create_subscriber(
        *topics,
        max_workers=workers,
        polling_interval=polling_interval,
        partitions=partitions,
        batch=batch,
        max_records=max_records,
        group_id=group_id,
        connection_data={
            "group_instance_id": group_instance_id,
            "fetch_max_wait_ms": fetch_max_wait_ms,
            "fetch_max_bytes": fetch_max_bytes,
            "fetch_min_bytes": fetch_min_bytes,
            "max_partition_fetch_bytes": max_partition_fetch_bytes,
            "auto_offset_reset": auto_offset_reset,
            "auto_commit_interval_ms": auto_commit_interval_ms,
            "check_crcs": check_crcs,
            "partition_assignment_strategy": partition_assignment_strategy,
            "max_poll_interval_ms": max_poll_interval_ms,
            "session_timeout_ms": session_timeout_ms,
            "heartbeat_interval_ms": heartbeat_interval_ms,
            "isolation_level": isolation_level,
            "on_assign": on_assign,
            "on_revoke": on_revoke,
            "on_lost": on_lost,
        },
        ack_policy=ack_policy,
        no_reply=no_reply,
        config=cast("KafkaBrokerConfig", self.config),
        # Specification
        title_=title,
        description_=description,
        include_in_schema=include_in_schema,
    )

    if batch:
        subscriber = cast("BatchSubscriber", subscriber)
    elif workers > 1:
        subscriber = cast("ConcurrentDefaultSubscriber", subscriber)
    else:
        subscriber = cast("DefaultSubscriber", subscriber)

    subscriber = super().subscriber(subscriber, persistent=persistent)  # type: ignore[assignment]

    return subscriber.add_call(
        parser_=parser or self._parser,
        decoder_=decoder or self._decoder,
        dependencies_=dependencies,
    )

@override
def publisher(
    self,
    topic: str,
    *,
    key: bytes | str | None = None,
    partition: int | None = None,
    headers: dict[str, str] | None = None,
    reply_to: str = "",
    batch: bool = False,
    # basic args
    persistent: bool = True,
    # Specification args
    title: str | None = None,
    description: str | None = None,
    schema: Any | None = None,
    include_in_schema: bool = True,
    autoflush: bool = False,
) -> Union[
    "BatchPublisher",
    "DefaultPublisher",
]:
    """Creates long-living and Specification-documented publisher object.

    You can use it as a handler decorator (handler should be decorated by `@broker.subscriber(...)` too) - `@broker.publisher(...)`.
    In such case publisher will publish your handler return value.

    Or you can create a publisher object to call it lately - `broker.publisher(...).publish(...)`.

    Args:
        topic: Topic where the message will be published.
        key: A key to associate with the message. Can be used to
            determine which partition to send the message to. If partition
            is `None` (and producer's partitioner config is left as default),
            then messages with the same key will be delivered to the same
            partition (but if key is `None`, partition is chosen randomly).
            Must be type `bytes`, or be serializable to bytes via configured
            `key_serializer`.
        partition: Specify a partition. If not set, the partition will be
            selected using the configured `partitioner`.
        headers: Message headers to store metainformation.
            **content-type** and **correlation_id** will be set automatically by framework anyway.
            Can be overridden by `publish.headers` if specified.
        reply_to: Topic name to send response.
        batch: Whether to send messages in batches or not.
        title: Specification publisher object title.
        description: Specification publisher object description.
        schema: Specification publishing message type.
            Should be any python-native object annotation or `pydantic.BaseModel`.
        include_in_schema: Whetever to include operation in Specification schema or not.
        autoflush: Whether to flush the producer or not on every publish call.
        persistent: Whether to make the publisher persistent or not.
    """
    publisher = create_publisher(
        # batch flag
        batch=batch,
        # default args
        key=key,
        # both args
        topic=topic,
        partition=partition,
        headers=headers,
        reply_to=reply_to,
        # publisher-specific
        config=cast("KafkaBrokerConfig", self.config),
        # Specification
        title_=title,
        description_=description,
        schema_=schema,
        include_in_schema=include_in_schema,
        autoflush=autoflush,
    )

    super().publisher(publisher, persistent=persistent)

    if batch:
        return cast("BatchPublisher", publisher)
    return cast("DefaultPublisher", publisher)

@override
def include_router(
    self,
    router: "KafkaRegistrator",  # type: ignore[override]
    *,
    prefix: str = "",
    dependencies: Iterable["Dependant"] = (),
    middlewares: Sequence["BrokerMiddleware[Any, Any]"] = (),
    include_in_schema: bool | None = None,
) -> None:
    if not isinstance(router, KafkaRegistrator):
        msg = (
            f"Router must be an instance of KafkaRegistrator, "
            f"got {type(router).__name__} instead"
        )
        raise SetupError(msg)

    super().include_router(
        router,
        prefix=prefix,
        dependencies=dependencies,
        middlewares=middlewares,
        include_in_schema=include_in_schema,
    )

def include_routers(
    self,
    *routers: "Registrator[MsgType, Any]",
) -> None:
    """Includes routers in the object."""
    for r in routers:
        self.include_router(r)