Node

def add_task(self, task: dict):
    """Adds a task to the pending tasks queue.

    Args:
        task: A `Message` object describing a training task
    """
    self._tasks_queue.add(task)

def is_connected(self) -> bool:
    """Checks if node is ready for communication with researcher

    Returns:
        True if node is ready, False if node is not ready
    """
    return self._grpc_client.is_connected()

def on_message(self, msg: dict):
    """Handler to be used with `Messaging` class (ie the messager).

    Called when a  message arrives through the `Messaging`.
    It reads and triggers instructions received by node from Researcher,
    mainly:
    - ping requests,
    - train requests (then a new task will be added on node's task queue),
    - search requests (for searching data in node's database).

    Args:
        msg: Incoming message from Researcher.
    """
    message: Message
    try:
        message = Message.from_dict(msg)
    except FedbiomedError as e:
        logger.error(e)  # Message was not properly formatted
        resid = msg.get("researcher_id", "unknown_researcher_id")
        self.send_error(
            ErrorNumbers.FB301,
            extra_msg="Message was not properly formatted",
            researcher_id=resid,
        )
    else:
        no_print = [
            "aggregator_args",
            "optim_aux_var",
            "params",
            "training_plan",
            "overlay",
        ]
        msg_print = {
            key: value
            for key, value in message.get_dict().items()
            if key not in no_print
        }
        logger.debug("Message received: " + str(msg_print))

        match message.__name__:

            case (
                TrainRequest.__name__
                | SecaggRequest.__name__
                | AdditiveSSSetupRequest.__name__
            ):
                self.add_task(message)
            case SecaggDeleteRequest.__name__:
                self._task_secagg_delete(message)
            case OverlayMessage.__name__:
                self._n2n_router.submit(message)
            case SearchRequest.__name__:
                databases = self.dataset_manager.search_by_tags(message.tags)
                if len(databases) != 0:
                    databases = self.dataset_manager.obfuscate_private_information(
                        databases
                    )
                self._grpc_client.send(
                    SearchReply(
                        request_id=message.request_id,
                        node_id=self._node_id,
                        researcher_id=message.researcher_id,
                        databases=databases,
                        count=len(databases),
                    )
                )
            case ListRequest.__name__:
                # Get list of all datasets
                databases = self.dataset_manager.list_my_data(verbose=False)
                databases = self.dataset_manager.obfuscate_private_information(databases)
                self._grpc_client.send(
                    ListReply(
                        success=True,
                        request_id=message.request_id,
                        node_id=self._node_id,
                        researcher_id=message.researcher_id,
                        databases=databases,
                        count=len(databases),
                    )
                )
            case ListRequest.__name__:
                # Get list of all datasets
                databases = self.dataset_manager.list_my_data(verbose=False)
                databases = self.dataset_manager.obfuscate_private_information(databases)
                self._grpc_client.send(
                    ListReply(
                        success=True,
                        request_id=message.request_id,
                        node_id=environ['NODE_ID'],
                        researcher_id=message.researcher_id,
                        databases=databases,
                        count=len(databases),
                    )
                )
            case ListRequest.__name__:
                # Get list of all datasets
                databases = self.dataset_manager.list_my_data(verbose=False)
                databases = self.dataset_manager.obfuscate_private_information(databases)
                self._grpc_client.send(
                    ListReply(
                        success=True,
                        request_id=message.request_id,
                        node_id=environ['NODE_ID'],
                        researcher_id=message.researcher_id,
                        databases=databases,
                        count=len(databases),
                    )
                )

            case PingRequest.__name__:
                self._grpc_client.send(
                    PingReply(
                        request_id=message.request_id,
                        researcher_id=message.researcher_id,
                        node_id=self._node_id,
                    )
                )
            case ApprovalRequest.__name__:
                reply = (
                    self.tp_security_manager.reply_training_plan_approval_request(
                        message
                    )
                )
                self._grpc_client.send(reply)
            case TrainingPlanStatusRequest.__name__:
                reply = self.tp_security_manager.reply_training_plan_status_request(
                    message
                )
                self._grpc_client.send(reply)
            case _:
                resid = msg.get("researcher_id", "unknown_researcher_id")
                self.send_error(
                    ErrorNumbers.FB301,
                    extra_msg="This request handler is not implemented "
                    f"{message.__class__.__name__} is not implemented",
                    researcher_id=resid,
                )

def parser_task_train(self, msg: TrainRequest) -> Union[Round, None]:
    """Parses a given training task message to create a round instance

    Args:
        msg: `TrainRequest` message object to parse

    Returns:
        a `Round` object for the training to perform, or None if no training
    """
    round_ = None
    hist_monitor = HistoryMonitor(
        node_id=self._node_id,
        experiment_id=msg.experiment_id,
        researcher_id=msg.researcher_id,
        send=self._grpc_client.send,
    )

    dataset_id = msg.get_param("dataset_id")
    data = self.dataset_manager.get_by_id(dataset_id)

    if data is None:
        return self.send_error(
            extra_msg="Did not found proper data in local datasets "
            f'on node={self._node_id}',
            request_id=msg.request_id,
            researcher_id=msg.researcher_id,
            errnum=ErrorNumbers.FB313,
        )

    dlp_and_loading_block_metadata = None
    if "dlp_id" in data:
        dlp_and_loading_block_metadata = self.dataset_manager.get_dlp_by_id(
            data["dlp_id"]
        )

    round_ = Round(
        root_dir=self._config.root,
        db=self._db_path,
        node_id=self._node_id,
        training_plan=msg.get_param("training_plan"),
        training_plan_class=msg.get_param("training_plan_class"),
        model_kwargs=msg.get_param("model_args") or {},
        training_kwargs=msg.get_param("training_args") or {},
        training=msg.get_param("training") or False,
        dataset=data,
        params=msg.get_param("params"),
        experiment_id=msg.get_param("experiment_id"),
        researcher_id=msg.get_param("researcher_id"),
        history_monitor=hist_monitor,
        aggregator_args=msg.get_param("aggregator_args") or None,
        node_args=self.node_args,
        tp_security_manager=self.tp_security_manager,
        round_number=msg.get_param("round"),
        dlp_and_loading_block_metadata=dlp_and_loading_block_metadata,
        aux_vars=msg.get_param('optim_aux_var'),
    )

    # the round raises an error if it cannot initialize
    err_msg = round_.initialize_arguments(msg.get_param("state_id"))
    if err_msg is not None:
        self._grpc_client.send(
            ErrorMessage(
                node_id=self._node_id,
                errnum=ErrorNumbers.FB300,
                researcher_id=msg.researcher_id,
                extra_msg="Could not initialize arguments",
            )
        )

    return round_

def send_error(
    self,
    errnum: ErrorNumbers = ErrorNumbers.FB300,
    extra_msg: str = "",
    researcher_id: str = "<unknown>",
    broadcast: bool = False,
    request_id: str = None,
):
    """Sends an error message.

    Args:
        errnum: Code of the error.
        extra_msg: Additional human readable error message.
        researcher_id: Destination researcher.
        broadcast: Broadcast the message all available researchers
            regardless of specific researcher.
        request_id: Optional request i to reply as error to a request.
    """
    try:
        logger.error(extra_msg)
        self._grpc_client.send(
            ErrorMessage(
                request_id=request_id,
                errnum=errnum.name,
                node_id=self._node_id,
                extra_msg=extra_msg,
                researcher_id=researcher_id,
            ),
            broadcast=broadcast,
        )
    except Exception as e:
        logger.error(f"{ErrorNumbers.FB601.value}: Cannot send error message: {e}")

def start_messaging(self, on_finish: Optional[Callable] = None):
    """Calls the start method of messaging class.

    Args:
        on_finish: Called when the tasks for handling all known researchers have finished.
            Callable has no argument.
    """
    self._grpc_client.start(on_finish)

def start_protocol(self) -> None:
    """Start the node to node router thread, for handling node to node message"""
    self._n2n_router.start()

def task_manager(self):
    """Manages training tasks in the queue."""

    while True:

        item: TrainRequest = self._tasks_queue.get()
        # don't want to treat again in case of failure
        self._tasks_queue.task_done()

        logger.info(
            f"[TASKS QUEUE] Task received by task manager: "
            f"Researcher: {item.researcher_id} "
            f"Experiment: {item.experiment_id}"
        )
        try:

            match item.__name__:
                case TrainRequest.__name__:
                    round_ = self.parser_task_train(item)
                    # once task is out of queue, initiate training rounds
                    if round_ is not None:
                        msg = round_.run_model_training(
                            tp_approval=self._config.getbool('security', 'training_plan_approval'),
                            secagg_insecure_validation=self._config.getbool('security',
                                "secagg_insecure_validation"),
                            secagg_active=self._config.getbool("security", "secure_aggregation"),
                            force_secagg=self._config.getbool(
                                "security", "force_secure_aggregation"),
                            secagg_arguments=item.get_param("secagg_arguments"),
                        )
                        msg.request_id = item.request_id
                        self._grpc_client.send(msg)
                        del round_

                case SecaggRequest.__name__ | AdditiveSSSetupRequest.__name__:
                    self._task_secagg(item)
                case _:
                    errmess = f"{ErrorNumbers.FB319.value}: Undefined request message"
                    self.send_error(errnum=ErrorNumbers.FB319, extra_msg=errmess)

        # TODO: Test exception
        except Exception as e:
            self.send_error(
                request_id=item.request_id,
                researcher_id=item.researcher_id,
                errnum=ErrorNumbers.FB300,
                extra_msg="Round error: " + str(e),
        )