# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations

import logging
from collections.abc import Sequence
from functools import partial
from typing import Any, Callable

from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.apache.kafka.hooks.produce import KafkaProducerHook
from airflow.utils.module_loading import import_string

local_logger = logging.getLogger("airflow")


def acked(err, msg):
    if err is not None:
        local_logger.error("Failed to deliver message: %s", err)
    else:
        local_logger.info(
            "Produced record to topic %s, partition [%s] @ offset %s",
            msg.topic(),
            msg.partition(),
            msg.offset(),
        )


class ProduceToTopicOperator(BaseOperator):
    """
    An operator that produces messages to a Kafka topic.

    Registers a producer to a kafka topic and publishes messages to the log.

    :param kafka_config_id: The connection object to use, defaults to "kafka_default"
    :param topic: The topic the producer should produce to, defaults to None
    :param producer_function: The function that generates key/value pairs as messages for production,
        defaults to None
    :param producer_function_args: Additional arguments to be applied to the producer callable,
        defaults to None
    :param producer_function_kwargs: Additional keyword arguments to be applied to the producer callable,
        defaults to None
    :param delivery_callback: The callback to apply after delivery(or failure) of a message, defaults to None
    :param synchronous: If writes to kafka should be fully synchronous, defaults to True
    :param poll_timeout: How long of a delay should be applied when calling poll after production to kafka,
        defaults to 0
    :raises AirflowException: _description_

    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:ProduceToTopicOperator`
    """

    template_fields = (
        "topic",
        "producer_function_args",
        "producer_function_kwargs",
        "kafka_config_id",
    )

    def __init__(
        self,
        topic: str,
        producer_function: str | Callable[..., Any],
        kafka_config_id: str = "kafka_default",
        producer_function_args: Sequence[Any] | None = None,
        producer_function_kwargs: dict[Any, Any] | None = None,
        delivery_callback: str | None = None,
        synchronous: bool = True,
        poll_timeout: float = 0,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)

        if delivery_callback:
            dc = import_string(delivery_callback)
        else:
            dc = acked

        self.kafka_config_id = kafka_config_id
        self.topic = topic
        self.producer_function = producer_function
        self.producer_function_args = producer_function_args or ()
        self.producer_function_kwargs = producer_function_kwargs or {}
        self.delivery_callback = dc
        self.synchronous = synchronous
        self.poll_timeout = poll_timeout

        if not (self.topic and self.producer_function):
            raise AirflowException(
                "topic and producer_function must be provided. Got topic="
                f"{self.topic} and producer_function={self.producer_function}"
            )

        return

    def execute(self, context) -> None:
        # Get producer and callable
        producer = KafkaProducerHook(kafka_config_id=self.kafka_config_id).get_producer()

        if isinstance(self.producer_function, str):
            self.producer_function = import_string(self.producer_function)

        producer_callable = partial(
            self.producer_function,  # type: ignore
            *self.producer_function_args,
            **self.producer_function_kwargs,
        )

        # For each returned k/v in the callable : publish and flush if needed.
        for k, v in producer_callable():
            producer.produce(self.topic, key=k, value=v, on_delivery=self.delivery_callback)
            producer.poll(self.poll_timeout)
            if self.synchronous:
                producer.flush()

        producer.flush()
