# 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

from collections.abc import Callable, Sequence
from datetime import timedelta
from typing import Any

from airflow.providers.apache.kafka.triggers.await_message import AwaitMessageTrigger
from airflow.providers.common.compat.sdk import BaseSensorOperator

VALID_COMMIT_CADENCE = {"never", "end_of_batch", "end_of_operator"}


class AwaitMessageSensor(BaseSensorOperator):
    """
    An Airflow sensor that defers until a specific message is published to Kafka.

    The sensor creates a consumer that reads the Kafka log until it encounters a positive event.

    The behavior of the consumer for this trigger is as follows:
    - poll the Kafka topics for a message
    - if no message returned, sleep
    - process the message with provided callable
    - if commit_offset is True (default), commit the message offset after processing
    - if callable returns any data, raise a TriggerEvent with the return data
    - else continue to next message
    - return event (as default xcom or specific xcom key)


    :param kafka_config_id: The connection object to use, defaults to "kafka_default"
    :param topics: Topics (or topic regex) to use for reading from
    :param apply_function: The function to apply to messages to determine if an event occurred. As a dot
        notation string.
    :param apply_function_args: Arguments to be applied to the processing function,
        defaults to None
    :param apply_function_kwargs: Key word arguments to be applied to the processing function,
        defaults to None
    :param poll_timeout: How long the kafka consumer should wait for a message to arrive from the kafka
        cluster,defaults to 1
    :param poll_interval: How long the kafka consumer should sleep after reaching the end of the Kafka log,
        defaults to 5
    :param xcom_push_key: the name of a key to push the returned message to, defaults to None
    :param commit_offset: Whether to commit the message offset after processing.
        If False, the offset is not committed by the sensor, allowing downstream
        tasks to commit it manually (e.g., after successful processing). Defaults to True.
    :param soft_fail: Set to true to mark the task as SKIPPED on failure
    :param timeout: Time elapsed before the task times out and fails (in seconds)
    :param poke_interval: This parameter is inherited but not used in this deferrable implementation
    :param mode: This parameter is inherited but not used in this deferrable implementation


    """

    BLUE = "#ffefeb"
    ui_color = BLUE

    template_fields = (
        "topics",
        "apply_function",
        "apply_function_args",
        "apply_function_kwargs",
        "kafka_config_id",
        "commit_offset",
    )

    def __init__(
        self,
        topics: Sequence[str],
        apply_function: str | None,
        kafka_config_id: str = "kafka_default",
        apply_function_args: Sequence[Any] | None = None,
        apply_function_kwargs: dict[Any, Any] | None = None,
        poll_timeout: float = 1,
        poll_interval: float = 5,
        xcom_push_key: str | None = None,
        commit_offset: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)

        self.topics = topics
        self.apply_function = apply_function
        self.apply_function_args = apply_function_args
        self.apply_function_kwargs = apply_function_kwargs
        self.kafka_config_id = kafka_config_id
        self.poll_timeout = poll_timeout
        self.poll_interval = poll_interval
        self.xcom_push_key = xcom_push_key
        self.commit_offset = commit_offset

    def execute(self, context) -> Any:
        if isinstance(self.timeout, (int, float)):
            timeout = timedelta(seconds=self.timeout)
        else:
            timeout = self.timeout
        self.defer(
            trigger=AwaitMessageTrigger(
                topics=self.topics,
                apply_function=self.apply_function,
                apply_function_args=self.apply_function_args,
                apply_function_kwargs=self.apply_function_kwargs,
                kafka_config_id=self.kafka_config_id,
                poll_timeout=self.poll_timeout,
                poll_interval=self.poll_interval,
                commit_offset=self.commit_offset,
            ),
            method_name="execute_complete",
            timeout=timeout,
        )

    def execute_complete(self, context, event=None):
        if self.xcom_push_key:
            context["task_instance"].xcom_push(key=self.xcom_push_key, value=event)
        return event


class AwaitMessageTriggerFunctionSensor(BaseSensorOperator):
    """
    Defer until a specific message is published to Kafka, trigger a registered function, then resume waiting.

    The behavior of the consumer for this trigger is as follows:
    - poll the Kafka topics for a message
    - if no message returned, sleep
    - process the message with provided callable and commit the message offset
    - if callable returns any data, raise a TriggerEvent with the return data
    - else continue to next message
    - return event (as default xcom or specific xcom key)


    :param kafka_config_id: The connection object to use, defaults to "kafka_default"
    :param topics: Topics (or topic regex) to use for reading from
    :param apply_function: The function to apply to messages to determine if an event occurred. As a dot
        notation string.
    :param event_triggered_function: The callable to trigger once the apply_function encounters a
        positive event.
    :param apply_function_args: Arguments to be applied to the processing function, defaults to None
    :param apply_function_kwargs: Key word arguments to be applied to the processing function,
        defaults to None
    :param poll_timeout: How long the kafka consumer should wait for a message to arrive from the kafka
        cluster, defaults to 1
    :param poll_interval: How long the kafka consumer should sleep after reaching the end of the Kafka log,
        defaults to 5
    :param soft_fail: Set to true to mark the task as SKIPPED on failure
    :param timeout: Time elapsed before the task times out and fails (in seconds)
    :param poke_interval: This parameter is inherited but not used in this deferrable implementation
    :param mode: This parameter is inherited but not used in this deferrable implementation


    """

    BLUE = "#ffefeb"
    ui_color = BLUE

    template_fields = (
        "topics",
        "apply_function",
        "apply_function_args",
        "apply_function_kwargs",
        "kafka_config_id",
    )

    def __init__(
        self,
        topics: Sequence[str],
        apply_function: str | None,
        event_triggered_function: Callable,
        kafka_config_id: str = "kafka_default",
        apply_function_args: Sequence[Any] | None = None,
        apply_function_kwargs: dict[Any, Any] | None = None,
        poll_timeout: float = 1,
        poll_interval: float = 5,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)

        self.topics = topics
        self.apply_function = apply_function
        self.apply_function_args = apply_function_args
        self.apply_function_kwargs = apply_function_kwargs
        self.kafka_config_id = kafka_config_id
        self.poll_timeout = poll_timeout
        self.poll_interval = poll_interval
        self.event_triggered_function = event_triggered_function

        if not callable(self.event_triggered_function):
            raise TypeError(
                "parameter event_triggered_function is expected to be of type callable,"
                f"got {type(event_triggered_function)}"
            )

    def execute(self, context, event=None) -> Any:
        self.defer(
            trigger=AwaitMessageTrigger(
                topics=self.topics,
                apply_function=self.apply_function,
                apply_function_args=self.apply_function_args,
                apply_function_kwargs=self.apply_function_kwargs,
                kafka_config_id=self.kafka_config_id,
                poll_timeout=self.poll_timeout,
                poll_interval=self.poll_interval,
            ),
            method_name="execute_complete",
        )

        return event

    def execute_complete(self, context, event=None):
        self.event_triggered_function(event, **context)

        self.defer(
            trigger=AwaitMessageTrigger(
                topics=self.topics,
                apply_function=self.apply_function,
                apply_function_args=self.apply_function_args,
                apply_function_kwargs=self.apply_function_kwargs,
                kafka_config_id=self.kafka_config_id,
                poll_timeout=self.poll_timeout,
                poll_interval=self.poll_interval,
            ),
            method_name="execute_complete",
        )
