# 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.

"""
AWS ECS Executor.

Each Airflow task gets delegated out to an Amazon ECS Task.
"""

from __future__ import annotations

import time
import warnings
from collections import defaultdict, deque
from collections.abc import Sequence
from copy import deepcopy
from typing import TYPE_CHECKING, TypeAlias

from botocore.exceptions import ClientError, NoCredentialsError

from airflow.exceptions import AirflowProviderDeprecationWarning
from airflow.executors.base_executor import BaseExecutor
from airflow.providers.amazon.aws.executors.ecs.boto_schema import BotoDescribeTasksSchema, BotoRunTaskSchema
from airflow.providers.amazon.aws.executors.ecs.utils import (
    CONFIG_DEFAULTS,
    CONFIG_GROUP_NAME,
    AllEcsConfigKeys,
    EcsExecutorException,
    EcsQueuedTask,
    EcsTaskCollection,
)
from airflow.providers.amazon.aws.executors.utils.exponential_backoff_retry import (
    calculate_next_attempt_delay,
    exponential_backoff_retry,
)
from airflow.providers.amazon.aws.hooks.ecs import EcsHook
from airflow.providers.amazon.version_compat import AIRFLOW_V_3_0_PLUS, AIRFLOW_V_3_3_PLUS
from airflow.providers.common.compat.sdk import AirflowException, Stats, timezone
from airflow.utils.helpers import merge_dicts
from airflow.utils.state import State

if TYPE_CHECKING:
    from sqlalchemy.orm import Session

    from airflow.executors import workloads
    from airflow.models.taskinstance import TaskInstance, TaskInstanceKey
    from airflow.providers.amazon.aws.executors.ecs.utils import (
        CommandType,
        ExecutorConfigType,
    )

    if AIRFLOW_V_3_3_PLUS:
        from airflow.executors.workloads.types import WorkloadKey as _EcsWorkloadKey

        WorkloadKey: TypeAlias = _EcsWorkloadKey
    else:
        WorkloadKey: TypeAlias = TaskInstanceKey  # type: ignore[no-redef, misc]

INVALID_CREDENTIALS_EXCEPTIONS = [
    "ExpiredTokenException",
    "InvalidClientTokenId",
    "UnrecognizedClientException",
]


class AwsEcsExecutor(BaseExecutor):
    """
    Executes the provided Airflow command on an ECS instance.

    The Airflow Scheduler creates a shell command, and passes it to the executor. This ECS Executor
    runs said Airflow command on a remote Amazon ECS Cluster with a task-definition configured to
    launch the same containers as the Scheduler. It then periodically checks in with the launched
    tasks (via task ARNs) to determine the status.

    This allows individual tasks to specify CPU, memory, GPU, env variables, etc. When initializing a task,
    there's an option for "executor config" which should be a dictionary with keys that match the
    ``ContainerOverride`` definition per AWS documentation (see link below).

    Prerequisite: proper configuration of Boto3 library
    .. seealso:: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html for
    authentication and access-key management. You can store an environmental variable, setup aws config from
    console, or use IAM roles.

    .. seealso:: https://docs.aws.amazon.com/AmazonECS/latest/APIReference/API_ContainerOverride.html for an
     Airflow TaskInstance's executor_config.
    """

    supports_multi_team: bool = True

    if AIRFLOW_V_3_3_PLUS:
        supports_callbacks: bool = True

    # AWS limits the maximum number of ARNs in the describe_tasks function.
    DESCRIBE_TASKS_BATCH_SIZE = 99

    if TYPE_CHECKING and AIRFLOW_V_3_0_PLUS:
        # In the v3 path, we store workloads, not commands as strings.
        # TODO: TaskSDK: move this type change into BaseExecutor
        queued_tasks: dict[TaskInstanceKey, workloads.All]  # type: ignore[assignment]

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.active_workers: EcsTaskCollection = EcsTaskCollection()
        self.pending_workloads: deque = deque()

        # Check if self has the ExecutorConf set on the self.conf attribute, and if not, set it to the global
        # configuration object. This allows the changes to be backwards compatible with older versions of
        # Airflow.
        # Can be removed when minimum supported provider version is equal to the version of core airflow
        # which introduces multi-team configuration.
        if not hasattr(self, "conf"):
            from airflow.providers.common.compat.sdk import conf

            self.conf = conf

        self.cluster = self.conf.get(CONFIG_GROUP_NAME, AllEcsConfigKeys.CLUSTER)
        self.container_name = self.conf.get(CONFIG_GROUP_NAME, AllEcsConfigKeys.CONTAINER_NAME)
        self.attempts_since_last_successful_connection = 0

        self.load_ecs_connection(check_connection=False)
        self.IS_BOTO_CONNECTION_HEALTHY = False

        self.run_task_kwargs = self._load_run_kwargs()

        # Maximum number of retries to run an ECS task.
        self.max_run_task_attempts = self.conf.get(
            CONFIG_GROUP_NAME,
            AllEcsConfigKeys.MAX_RUN_TASK_ATTEMPTS,
            fallback=CONFIG_DEFAULTS[AllEcsConfigKeys.MAX_RUN_TASK_ATTEMPTS],
        )

    # TODO: Remove this once the minimum supported version is 3.3+, and defer to BaseExecutor.queue_workload.
    def queue_workload(self, workload: workloads.All, session: Session | None) -> None:
        from airflow.executors import workloads

        if isinstance(workload, workloads.ExecuteTask):
            self.queued_tasks[workload.ti.key] = workload
            return
        if AIRFLOW_V_3_3_PLUS and isinstance(workload, workloads.ExecuteCallback):
            self.queued_callbacks[workload.callback.key] = workload
            return
        raise RuntimeError(f"{type(self)} cannot handle workloads of type {type(workload)}")

    def _process_workloads(self, workload_items: Sequence[workloads.All]) -> None:
        """:sphinx-autoapi-skip:."""
        from airflow.executors import workloads

        for workload in workload_items:
            queue: str | None
            key: WorkloadKey
            command: CommandType
            if isinstance(workload, workloads.ExecuteTask):
                command = [workload]
                key = workload.ti.key
                queue = workload.ti.queue
                executor_config = workload.ti.executor_config or {}

                del self.queued_tasks[key]
                self.execute_async(key=key, command=command, queue=queue, executor_config=executor_config)
                self.running.add(key)

            elif AIRFLOW_V_3_3_PLUS and isinstance(workload, workloads.ExecuteCallback):
                command = [workload]
                key = workload.callback.key

                del self.queued_callbacks[key]
                self.execute_async(key=key, command=command, queue=None)
                self.running.add(key)

            else:
                raise RuntimeError(f"{type(self)} cannot handle workloads of type {type(workload)}")

    def start(self):
        """Call this when the Executor is run for the first time by the scheduler."""
        check_health = self.conf.getboolean(
            CONFIG_GROUP_NAME, AllEcsConfigKeys.CHECK_HEALTH_ON_STARTUP, fallback=False
        )

        if not check_health:
            return

        self.log.info("Starting ECS Executor and determining health...")
        try:
            self.check_health()
        except AirflowException:
            self.log.error("Stopping the Airflow Scheduler from starting until the issue is resolved.")
            raise

    def check_health(self):
        """
        Make a test API call to check the health of the ECS Executor.

        Deliberately use an invalid task ID, some potential outcomes in order:
          1. `AccessDeniedException` is raised if there are insufficient permissions.
          2. `ClusterNotFoundException` is raised if permissions exist but the cluster does not.
          3. The API responds with a failure message if the cluster is found and there
             are permissions, but the cluster itself has issues.
          4. `InvalidParameterException` is raised if the permissions and cluster exist but the task does not.

        The last one is considered a success state for the purposes of this check.
        """
        success_status = "succeeded."
        status = success_status

        try:
            invalid_task_id = "a" * 32
            self.ecs.stop_task(cluster=self.cluster, task=invalid_task_id)

            # If it got this far, something is wrong.  stop_task() called with an
            # invalid taskID should have thrown a ClientError.  All known reasons are
            # covered in the ``except`` block below, and this should never be reached.
            status = "failed for an unknown reason. "
        except ClientError as ex:
            error_code = ex.response["Error"]["Code"]
            error_message = ex.response["Error"]["Message"]

            if ("InvalidParameterException" in error_code) and ("task was not found" in error_message):
                # This failure is expected, and means we're healthy
                pass
            else:
                # Catch all for unexpected failures
                status = f"failed because: {error_message}. "
        except Exception as e:
            # Any non-ClientError exceptions. This can include Botocore exceptions for example
            status = f"failed because: {e}. "
        finally:
            msg_prefix = "ECS Executor health check has %s"
            if status == success_status:
                self.IS_BOTO_CONNECTION_HEALTHY = True
                self.log.info(msg_prefix, status)
            else:
                msg_error_suffix = (
                    "The ECS executor will not be able to run Airflow tasks until the issue is addressed."
                )
                raise AirflowException(msg_prefix % status + msg_error_suffix)

    def load_ecs_connection(self, check_connection: bool = True):
        self.log.info("Loading Connection information")
        aws_conn_id = self.conf.get(
            CONFIG_GROUP_NAME,
            AllEcsConfigKeys.AWS_CONN_ID,
            fallback=CONFIG_DEFAULTS[AllEcsConfigKeys.AWS_CONN_ID],
        )
        region_name = self.conf.get(CONFIG_GROUP_NAME, AllEcsConfigKeys.REGION_NAME, fallback=None)
        self.ecs = EcsHook(aws_conn_id=aws_conn_id, region_name=region_name).conn
        self.attempts_since_last_successful_connection += 1
        self.last_connection_reload = timezone.utcnow()

        if check_connection:
            self.check_health()
            self.attempts_since_last_successful_connection = 0

    def sync(self):
        if not self.IS_BOTO_CONNECTION_HEALTHY:
            exponential_backoff_retry(
                self.last_connection_reload,
                self.attempts_since_last_successful_connection,
                self.load_ecs_connection,
            )
            if not self.IS_BOTO_CONNECTION_HEALTHY:
                return
        try:
            self.sync_running_workloads()
            self.attempt_workload_runs()
        except (ClientError, NoCredentialsError) as error:
            error_code = error.response["Error"]["Code"]
            if error_code in INVALID_CREDENTIALS_EXCEPTIONS:
                self.IS_BOTO_CONNECTION_HEALTHY = False
                self.log.warning(
                    "AWS credentials are either missing or expired: %s.\nRetrying connection", error
                )

        except Exception:
            # We catch any and all exceptions because otherwise they would bubble
            # up and kill the scheduler process
            self.log.exception("Failed to sync %s", self.__class__.__name__)

    def sync_running_workloads(self):
        """Check and update state on all running workloads (tasks and callbacks)."""
        all_task_arns = self.active_workers.get_all_arns()
        if not all_task_arns:
            self.log.debug("No active Airflow workloads, skipping sync.")
            return

        describe_tasks_response = self.__describe_tasks(all_task_arns)

        self.log.debug("Active Workers: %s", describe_tasks_response)

        if describe_tasks_response["failures"]:
            for failure in describe_tasks_response["failures"]:
                self.__handle_failed_workload(failure["arn"], failure["reason"])

        updated_tasks = describe_tasks_response["tasks"]
        for task in updated_tasks:
            self.__update_running_workload(task)

    def __update_running_workload(self, task):
        self.active_workers.update_task(task)
        # Get state of current task.
        task_state = task.get_task_state()
        task_key = self.active_workers.arn_to_key[task.task_arn]

        # Mark finished tasks as either a success/failure.
        if task_state == State.FAILED or task_state == State.REMOVED:
            self.__log_container_failures(task_arn=task.task_arn)
            self.__handle_failed_workload(task.task_arn, task.stopped_reason)
        elif task_state == State.SUCCESS:
            self.log.debug(
                "Airflow workload %s marked as %s after running on ECS Task (arn) %s",
                task_key,
                task_state,
                task.task_arn,
            )
            self.success(task_key)
            self.active_workers.pop_by_key(task_key)

    def __describe_tasks(self, task_arns):
        all_task_descriptions = {"tasks": [], "failures": []}
        for i in range(0, len(task_arns), self.DESCRIBE_TASKS_BATCH_SIZE):
            batched_task_arns = task_arns[i : i + self.DESCRIBE_TASKS_BATCH_SIZE]
            if not batched_task_arns:
                continue
            boto_describe_tasks = self.ecs.describe_tasks(tasks=batched_task_arns, cluster=self.cluster)
            describe_tasks_response = BotoDescribeTasksSchema().load(boto_describe_tasks)

            all_task_descriptions["tasks"].extend(describe_tasks_response["tasks"])
            all_task_descriptions["failures"].extend(describe_tasks_response["failures"])
        return all_task_descriptions

    def __log_container_failures(self, task_arn: str):
        """Check if the task failed due to issues with the containers."""
        containers = self.active_workers.task_by_arn(task_arn).containers
        has_exit_codes = all(["exit_code" in x for x in containers])
        if not has_exit_codes:
            return ""
        reasons = [
            f"{container['container_arn']} - {container['reason']}"
            for container in containers
            if "reason" in container
        ]
        if reasons:
            self.log.warning(
                "The ECS task failed due to the following containers failing:\n%s", "\n".join(reasons)
            )

    def __handle_failed_workload(self, task_arn: str, reason: str):
        """
        If an API failure occurs, the workload is rescheduled.

        This function will determine whether the task has been attempted the appropriate number
        of times, and determine whether the task should be marked failed or not. The task will
        be removed active_workers, and marked as FAILED, or set into pending_workloads depending on
        how many times it has been retried.
        """
        task_key = self.active_workers.arn_to_key[task_arn]
        task_info = self.active_workers.info_by_key(task_key)
        task_cmd = task_info.cmd
        queue = task_info.queue
        exec_info = task_info.config
        failure_count = self.active_workers.failure_count_by_key(task_key)
        if int(failure_count) < int(self.max_run_task_attempts):
            self.log.warning(
                "Airflow workload %s failed due to %s. Failure %s out of %s occurred on %s. Rescheduling.",
                task_key,
                reason,
                failure_count,
                self.max_run_task_attempts,
                task_arn,
            )
            self.pending_workloads.append(
                EcsQueuedTask(
                    task_key,
                    task_cmd,
                    queue,
                    exec_info,
                    failure_count + 1,
                    timezone.utcnow() + calculate_next_attempt_delay(failure_count),
                )
            )
        else:
            self.log.error(
                "Airflow workload %s has failed a maximum of %s times. Marking as failed",
                task_key,
                failure_count,
            )
            self.fail(task_key)
        self.active_workers.pop_by_key(task_key)

    def attempt_workload_runs(self):
        """
        Take tasks from the pending_workloads queue, and attempts to find an instance to run it on.

        If the launch type is EC2, this will attempt to place tasks on empty EC2 instances.  If
            there are no EC2 instances available, no task is placed and this function will be
            called again in the next heart-beat.

        If the launch type is FARGATE, this will run the tasks on new AWS Fargate instances.
        """
        queue_len = len(self.pending_workloads)
        failure_reasons = defaultdict(int)
        for _ in range(queue_len):
            ecs_task = self.pending_workloads.popleft()
            task_key = ecs_task.key
            cmd = ecs_task.command
            queue = ecs_task.queue
            exec_config = ecs_task.executor_config
            attempt_number = ecs_task.attempt_number
            failure_reasons = []
            if timezone.utcnow() < ecs_task.next_attempt_time:
                self.pending_workloads.append(ecs_task)
                continue
            try:
                run_task_response = self._run_task(task_key, cmd, queue, exec_config)
            except NoCredentialsError:
                self.pending_workloads.append(ecs_task)
                raise
            except ClientError as e:
                error_code = e.response["Error"]["Code"]
                if error_code in INVALID_CREDENTIALS_EXCEPTIONS:
                    self.pending_workloads.append(ecs_task)
                    raise
                failure_reasons.append(str(e))
            except Exception as e:
                # Failed to even get a response back from the Boto3 API or something else went
                # wrong.  For any possible failure we want to add the exception reasons to the
                # failure list so that it is logged to the user and most importantly the task is
                # added back to the pending list to be retried later.
                failure_reasons.append(str(e))
            else:
                # We got a response back, check if there were failures. If so, add them to the
                # failures list so that it is logged to the user and most importantly the task
                # is added back to the pending list to be retried later.
                if run_task_response["failures"]:
                    failure_reasons.extend([f["reason"] for f in run_task_response["failures"]])

            if failure_reasons:
                # Make sure the number of attempts does not exceed max_run_task_attempts
                if int(attempt_number) < int(self.max_run_task_attempts):
                    ecs_task.attempt_number += 1
                    ecs_task.next_attempt_time = timezone.utcnow() + calculate_next_attempt_delay(
                        attempt_number
                    )
                    self.pending_workloads.append(ecs_task)
                else:
                    reasons_str = ", ".join(failure_reasons)
                    self.log.error(
                        "ECS workload %s has failed a maximum of %s times. Marking as failed. Reasons: %s",
                        task_key,
                        attempt_number,
                        reasons_str,
                    )
                    self.log_task_event(
                        event="ecs task submit failure",
                        ti_key=task_key,
                        extra=(
                            f"Task could not be queued after {attempt_number} attempts. "
                            f"Marking as failed. Reasons: {reasons_str}"
                        ),
                    )
                    self.fail(task_key)
            elif not run_task_response["tasks"]:
                self.log.error("ECS RunTask Response: %s", run_task_response)
                self.log_task_event(
                    event="ecs task submit failure",
                    extra=f"ECS RunTask Response: {run_task_response}",
                    ti_key=task_key,
                )
                raise EcsExecutorException(
                    "No failures and no ECS tasks provided in response. This should never happen."
                )
            else:
                task = run_task_response["tasks"][0]
                self.active_workers.add_task(task, task_key, queue, cmd, exec_config, attempt_number)
                self.running_state(task_key, task.task_arn)

    def _run_task(
        self,
        task_id: WorkloadKey,
        cmd: CommandType,
        queue: str | None,
        exec_config: ExecutorConfigType,
    ):
        """
        Run a queued-up Airflow task.

        Not to be confused with execute_async() which inserts tasks into the queue.
        The command and executor config will be placed in the container-override
        section of the JSON request before calling Boto3's "run_task" function.
        """
        run_task_kwargs = self._run_task_kwargs(task_id, cmd, queue, exec_config)
        boto_run_task = self.ecs.run_task(**run_task_kwargs)
        run_task_response = BotoRunTaskSchema().load(boto_run_task)
        return run_task_response

    def _run_task_kwargs(
        self,
        task_id: WorkloadKey,
        cmd: CommandType,
        queue: str | None,
        exec_config: ExecutorConfigType,
    ) -> dict:
        """
        Update the Airflow command by modifying container overrides for task-specific kwargs.

        One last chance to modify Boto3's "run_task" kwarg params before it gets passed into the Boto3 client.
        """
        run_task_kwargs = deepcopy(self.run_task_kwargs)
        run_task_kwargs = merge_dicts(run_task_kwargs, exec_config)
        container_override = self.get_container(run_task_kwargs["overrides"]["containerOverrides"])
        container_override["command"] = cmd

        # Inject the env variable to configure logging for containerized execution environment
        if "environment" not in container_override:
            container_override["environment"] = []
        container_override["environment"].append({"name": "AIRFLOW_IS_EXECUTOR_CONTAINER", "value": "true"})

        return run_task_kwargs

    def execute_async(self, key: WorkloadKey, command: CommandType, queue=None, executor_config=None):
        """Save the workload to be executed in the next sync by inserting the commands into a queue."""
        if executor_config and ("name" in executor_config or "command" in executor_config):
            raise ValueError('Executor Config should never override "name" or "command"')
        if len(command) == 1:
            from airflow.executors import workloads

            if isinstance(command[0], workloads.ExecuteTask) or (
                AIRFLOW_V_3_3_PLUS and isinstance(command[0], workloads.ExecuteCallback)
            ):
                command = self._serialize_workload_to_command(command[0])
            else:
                raise ValueError(
                    f"EcsExecutor doesn't know how to handle workload of type: {type(command[0])}"
                )

        self.pending_workloads.append(
            EcsQueuedTask(key, command, queue, executor_config or {}, 1, timezone.utcnow())
        )

    def end(self, heartbeat_interval=10):
        """Wait for all currently running tasks to end, and don't launch any tasks."""
        try:
            while True:
                self.sync()
                if not self.active_workers:
                    break
                time.sleep(heartbeat_interval)
        except Exception:
            # We catch any and all exceptions because otherwise they would bubble
            # up and kill the scheduler process.
            self.log.exception("Failed to end %s", self.__class__.__name__)

    def terminate(self):
        """Kill all ECS processes by calling Boto3's StopTask API."""
        try:
            for arn in self.active_workers.get_all_arns():
                self.ecs.stop_task(
                    cluster=self.cluster, task=arn, reason="Airflow Executor received a SIGTERM"
                )
            self.end()
        except Exception:
            # We catch any and all exceptions because otherwise they would bubble
            # up and kill the scheduler process.
            self.log.exception("Failed to terminate %s", self.__class__.__name__)

    def _load_run_kwargs(self) -> dict:
        from airflow.providers.amazon.aws.executors.ecs.ecs_executor_config import build_task_kwargs

        ecs_executor_run_task_kwargs = build_task_kwargs(self.conf)

        try:
            self.get_container(ecs_executor_run_task_kwargs["overrides"]["containerOverrides"])["command"]
        except KeyError:
            raise KeyError(
                "Rendered JSON template does not contain key "
                '"overrides[containerOverrides][containers][x][command]"'
            )
        return ecs_executor_run_task_kwargs

    def get_container(self, container_list):
        """Search task list for core Airflow container."""
        for container in container_list:
            try:
                if container["name"] == self.container_name:
                    return container
            except KeyError:
                raise EcsExecutorException(
                    'container "name" must be provided in "containerOverrides" configuration'
                )
        raise KeyError(f"No such container found by container name: {self.container_name}")

    @staticmethod
    def _serialize_workload_to_command(workload) -> CommandType:
        """
        Serialize a workload into a command for the Task SDK.

        :param workload: ExecuteTask or ExecuteCallback workload to serialize
        :return: Command as list of strings for Task SDK execution
        """
        return [
            "python",
            "-m",
            "airflow.sdk.execution_time.execute_workload",
            "--json-string",
            workload.model_dump_json(),
        ]

    def _build_task_command(self, ti: TaskInstance) -> CommandType:
        """
        Build task command for execution based on Airflow version.

        For Airflow 3.x+, generates an ExecuteTask workload with JSON serialization.
        For Airflow 2.x, uses the legacy command_as_list() method.

        :param ti: TaskInstance to build command for
        :return: Command as list of strings
        """
        if AIRFLOW_V_3_0_PLUS:
            from airflow.executors.workloads import ExecuteTask

            workload = ExecuteTask.make(ti)
            return self._serialize_workload_to_command(workload)
        return ti.command_as_list()

    def try_adopt_task_instances(self, tis: Sequence[TaskInstance]) -> Sequence[TaskInstance]:
        """
        Adopt task instances which have an external_executor_id (the ECS task ARN).

        Anything that is not adopted will be cleared by the scheduler and becomes eligible for re-scheduling.
        """
        with Stats.timer("ecs_executor.adopt_task_instances.duration"):
            adopted_tis: list[TaskInstance] = []

            if task_arns := [ti.external_executor_id for ti in tis if ti.external_executor_id]:
                task_descriptions = self.__describe_tasks(task_arns).get("tasks", [])

                for task in task_descriptions:
                    ti = next(ti for ti in tis if ti.external_executor_id == task.task_arn)
                    command = self._build_task_command(ti)

                    self.active_workers.add_task(
                        task,
                        ti.key,
                        ti.queue or "",
                        command,
                        ti.executor_config or {},
                        ti.try_number,
                    )
                    adopted_tis.append(ti)

            if adopted_tis:
                tasks = [f"{task} in state {task.state}" for task in adopted_tis]
                task_instance_str = "\n\t".join(tasks)
                self.log.info(
                    "Adopted the following %d tasks from a dead executor:\n\t%s",
                    len(adopted_tis),
                    task_instance_str,
                )

            not_adopted_tis = [ti for ti in tis if ti not in adopted_tis]
            return not_adopted_tis

    # ── Back-compat shims for renamed methods/attrs ────────────────────────

    @property
    def pending_tasks(self) -> deque:
        """Use pending_workloads as pending_tasks is deprecated."""
        warnings.warn(
            "pending_tasks is deprecated, use pending_workloads instead.",
            AirflowProviderDeprecationWarning,
            stacklevel=2,
        )
        return self.pending_workloads

    def sync_running_tasks(self):
        """Use sync_running_workloads as sync_running_tasks is deprecated."""
        warnings.warn(
            "sync_running_tasks is deprecated, use sync_running_workloads instead.",
            AirflowProviderDeprecationWarning,
            stacklevel=2,
        )
        return self.sync_running_workloads()

    def attempt_task_runs(self):
        """Use attempt_workload_runs as attempt_task_runs is deprecated."""
        warnings.warn(
            "attempt_task_runs is deprecated, use attempt_workload_runs instead.",
            AirflowProviderDeprecationWarning,
            stacklevel=2,
        )
        return self.attempt_workload_runs()
