text: #
  # 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.
  
  import copy
  import functools
  import logging
  import os
  import pickle
  import re
  import sys
  import traceback
  import warnings
  from collections import OrderedDict
  from datetime import datetime, timedelta
  from inspect import signature
  from typing import (
      TYPE_CHECKING,
      Callable,
      Collection,
      Dict,
      FrozenSet,
      Iterable,
      List,
      Optional,
      Set,
      Tuple,
      Type,
      Union,
      cast,
      overload,
  )
  
  import jinja2
  import pendulum
  from dateutil.relativedelta import relativedelta
  from jinja2.nativetypes import NativeEnvironment
  from sqlalchemy import Boolean, Column, ForeignKey, Index, Integer, String, Text, func, or_
  from sqlalchemy.orm import backref, joinedload, relationship
  from sqlalchemy.orm.session import Session
  from sqlalchemy.sql import expression
  
  import airflow.templates
  from airflow import settings, utils
  from airflow.compat.functools import cached_property
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException, DuplicateTaskIdFound, TaskNotFound
  from airflow.models.base import ID_LEN, Base
  from airflow.models.baseoperator import BaseOperator
  from airflow.models.dagbag import DagBag
  from airflow.models.dagcode import DagCode
  from airflow.models.dagparam import DagParam
  from airflow.models.dagpickle import DagPickle
  from airflow.models.dagrun import DagRun
  from airflow.models.taskinstance import Context, TaskInstance, TaskInstanceKey, clear_task_instances
  from airflow.security import permissions
  from airflow.stats import Stats
  from airflow.timetables.base import TimeRestriction, Timetable
  from airflow.timetables.interval import CronDataIntervalTimetable, DeltaDataIntervalTimetable
  from airflow.timetables.schedules import Schedule
  from airflow.timetables.simple import NullTimetable, OnceTimetable
  from airflow.typing_compat import Literal, RePatternType
  from airflow.utils import timezone
  from airflow.utils.dates import cron_presets, date_range as utils_date_range
  from airflow.utils.file import correct_maybe_zipped
  from airflow.utils.helpers import validate_key
  from airflow.utils.log.logging_mixin import LoggingMixin
  from airflow.utils.session import provide_session
  from airflow.utils.sqlalchemy import Interval, UtcDateTime, skip_locked, with_row_locks
  from airflow.utils.state import DagRunState, State
  from airflow.utils.types import DagRunType, EdgeInfoType
  
  if TYPE_CHECKING:
      from airflow.utils.task_group import TaskGroup
  
  
  log = logging.getLogger(__name__)
  
  ScheduleInterval = Union[str, timedelta, relativedelta]
  DEFAULT_VIEW_PRESETS = ['tree', 'graph', 'duration', 'gantt', 'landing_times']
  ORIENTATION_PRESETS = ['LR', 'TB', 'RL', 'BT']
  
  DagStateChangeCallback = Callable[[Context], None]
  
  
  def get_last_dagrun(dag_id, session, include_externally_triggered=False):
      """
      Returns the last dag run for a dag, None if there was none.
      Last dag run can be any type of run eg. scheduled or backfilled.
      Overridden DagRuns are ignored.
      """
      DR = DagRun
      query = session.query(DR).filter(DR.dag_id == dag_id)
      if not include_externally_triggered:
          query = query.filter(DR.external_trigger == expression.false())
      query = query.order_by(DR.execution_date.desc())
      return query.first()
  
  
  @functools.total_ordering
  class DAG(LoggingMixin):
      """
      A dag (directed acyclic graph) is a collection of tasks with directional
      dependencies. A dag also has a schedule, a start date and an end date
      (optional). For each schedule, (say daily or hourly), the DAG needs to run
      each individual tasks as their dependencies are met. Certain tasks have
      the property of depending on their own past, meaning that they can't run
      until their previous schedule (and upstream tasks) are completed.
  
      DAGs essentially act as namespaces for tasks. A task_id can only be
      added once to a DAG.
  
      :param dag_id: The id of the DAG; must consist exclusively of alphanumeric
          characters, dashes, dots and underscores (all ASCII)
      :type dag_id: str
      :param description: The description for the DAG to e.g. be shown on the webserver
      :type description: str
      :param schedule_interval: Defines how often that DAG runs, this
          timedelta object gets added to your latest task instance's
          execution_date to figure out the next schedule
      :type schedule_interval: datetime.timedelta or
          dateutil.relativedelta.relativedelta or str that acts as a cron
          expression
      :param start_date: The timestamp from which the scheduler will
          attempt to backfill
      :type start_date: datetime.datetime
      :param end_date: A date beyond which your DAG won't run, leave to None
          for open ended scheduling
      :type end_date: datetime.datetime
      :param template_searchpath: This list of folders (non relative)
          defines where jinja will look for your templates. Order matters.
          Note that jinja/airflow includes the path of your DAG file by
          default
      :type template_searchpath: str or list[str]
      :param template_undefined: Template undefined type.
      :type template_undefined: jinja2.StrictUndefined
      :param user_defined_macros: a dictionary of macros that will be exposed
          in your jinja templates. For example, passing ``dict(foo='bar')``
          to this argument allows you to ``{{ foo }}`` in all jinja
          templates related to this DAG. Note that you can pass any
          type of object here.
      :type user_defined_macros: dict
      :param user_defined_filters: a dictionary of filters that will be exposed
          in your jinja templates. For example, passing
          ``dict(hello=lambda name: 'Hello %s' % name)`` to this argument allows
          you to ``{{ 'world' | hello }}`` in all jinja templates related to
          this DAG.
      :type user_defined_filters: dict
      :param default_args: A dictionary of default parameters to be used
          as constructor keyword parameters when initialising operators.
          Note that operators have the same hook, and precede those defined
          here, meaning that if your dict contains `'depends_on_past': True`
          here and `'depends_on_past': False` in the operator's call
          `default_args`, the actual value will be `False`.
      :type default_args: dict
      :param params: a dictionary of DAG level parameters that are made
          accessible in templates, namespaced under `params`. These
          params can be overridden at the task level.
      :type params: dict
      :param concurrency: the number of task instances allowed to run
          concurrently
      :type concurrency: int
      :param max_active_runs: maximum number of active DAG runs, beyond this
          number of DAG runs in a running state, the scheduler won't create
          new active DAG runs
      :type max_active_runs: int
      :param dagrun_timeout: specify how long a DagRun should be up before
          timing out / failing, so that new DagRuns can be created. The timeout
          is only enforced for scheduled DagRuns.
      :type dagrun_timeout: datetime.timedelta
      :param sla_miss_callback: specify a function to call when reporting SLA
          timeouts.
      :type sla_miss_callback: types.FunctionType
      :param default_view: Specify DAG default view (tree, graph, duration,
                                                     gantt, landing_times), default tree
      :type default_view: str
      :param orientation: Specify DAG orientation in graph view (LR, TB, RL, BT), default LR
      :type orientation: str
      :param catchup: Perform scheduler catchup (or only run latest)? Defaults to True
      :type catchup: bool
      :param on_failure_callback: A function to be called when a DagRun of this dag fails.
          A context dictionary is passed as a single parameter to this function.
      :type on_failure_callback: callable
      :param on_success_callback: Much like the ``on_failure_callback`` except
          that it is executed when the dag succeeds.
      :type on_success_callback: callable
      :param access_control: Specify optional DAG-level actions, e.g.,
          "{'role1': {'can_read'}, 'role2': {'can_read', 'can_edit'}}"
      :type access_control: dict
      :param is_paused_upon_creation: Specifies if the dag is paused when created for the first time.
          If the dag exists already, this flag will be ignored. If this optional parameter
          is not specified, the global config setting will be used.
      :type is_paused_upon_creation: bool or None
      :param jinja_environment_kwargs: additional configuration options to be passed to Jinja
          ``Environment`` for template rendering
  
          **Example**: to avoid Jinja from removing a trailing newline from template strings ::
  
              DAG(dag_id='my-dag',
                  jinja_environment_kwargs={
                      'keep_trailing_newline': True,
                      # some other jinja2 Environment options here
                  }
              )
  
          **See**: `Jinja Environment documentation
          <https://jinja.palletsprojects.com/en/2.11.x/api/#jinja2.Environment>`_
  
      :type jinja_environment_kwargs: dict
      :param render_template_as_native_obj: If True, uses a Jinja ```NativeEnvironment``
          to render templates as native Python types. If False, a Jinja
          ``Environment`` is used to render templates as string values.
      :type render_template_as_native_obj: bool
      :param tags: List of tags to help filtering DAGS in the UI.
      :type tags: List[str]
      """
  
      _comps = {
          'dag_id',
          'task_ids',
          'parent_dag',
          'start_date',
          'schedule_interval',
          'full_filepath',
          'template_searchpath',
          'last_loaded',
      }
  
      __serialized_fields: Optional[FrozenSet[str]] = None
  
      def __init__(
          self,
          dag_id: str,
          description: Optional[str] = None,
          schedule_interval: Optional[ScheduleInterval] = timedelta(days=1),
          start_date: Optional[datetime] = None,
          end_date: Optional[datetime] = None,
          full_filepath: Optional[str] = None,
          template_searchpath: Optional[Union[str, Iterable[str]]] = None,
          template_undefined: Type[jinja2.StrictUndefined] = jinja2.StrictUndefined,
          user_defined_macros: Optional[Dict] = None,
          user_defined_filters: Optional[Dict] = None,
          default_args: Optional[Dict] = None,
          concurrency: Optional[int] = None,
          max_active_tasks: int = conf.getint('core', 'max_active_tasks_per_dag'),
          max_active_runs: int = conf.getint('core', 'max_active_runs_per_dag'),
          dagrun_timeout: Optional[timedelta] = None,
          sla_miss_callback: Optional[Callable] = None,
          default_view: str = conf.get('webserver', 'dag_default_view').lower(),
          orientation: str = conf.get('webserver', 'dag_orientation'),
          catchup: bool = conf.getboolean('scheduler', 'catchup_by_default'),
          on_success_callback: Optional[DagStateChangeCallback] = None,
          on_failure_callback: Optional[DagStateChangeCallback] = None,
          doc_md: Optional[str] = None,
          params: Optional[Dict] = None,
          access_control: Optional[Dict] = None,
          is_paused_upon_creation: Optional[bool] = None,
          jinja_environment_kwargs: Optional[Dict] = None,
          render_template_as_native_obj: bool = False,
          tags: Optional[List[str]] = None,
      ):
          from airflow.utils.task_group import TaskGroup
  
          self.user_defined_macros = user_defined_macros
          self.user_defined_filters = user_defined_filters
          self.default_args = copy.deepcopy(default_args or {})
          self.params = params or {}
  
          # merging potentially conflicting default_args['params'] into params
          if 'params' in self.default_args:
              self.params.update(self.default_args['params'])
              del self.default_args['params']
  
          validate_key(dag_id)
  
          self._dag_id = dag_id
          self._full_filepath = full_filepath if full_filepath else ''
          if concurrency and not max_active_tasks:
              # TODO: Remove in Airflow 3.0
              warnings.warn(
                  "The 'concurrency' parameter is deprecated. Please use 'max_active_tasks'.",
                  DeprecationWarning,
                  stacklevel=2,
              )
              max_active_tasks = concurrency
          self._max_active_tasks = max_active_tasks
          self._pickle_id: Optional[int] = None
  
          self._description = description
          # set file location to caller source path
          back = sys._getframe().f_back
          self.fileloc = back.f_code.co_filename if back else ""
          self.task_dict: Dict[str, BaseOperator] = {}
  
          # set timezone from start_date
          if start_date and start_date.tzinfo:
              self.timezone = start_date.tzinfo
          elif 'start_date' in self.default_args and self.default_args['start_date']:
              if isinstance(self.default_args['start_date'], str):
                  self.default_args['start_date'] = timezone.parse(self.default_args['start_date'])
              self.timezone = self.default_args['start_date'].tzinfo
  
          if not hasattr(self, 'timezone') or not self.timezone:
              self.timezone = settings.TIMEZONE
  
          # Apply the timezone we settled on to end_date if it wasn't supplied
          if 'end_date' in self.default_args and self.default_args['end_date']:
              if isinstance(self.default_args['end_date'], str):
                  self.default_args['end_date'] = timezone.parse(
                      self.default_args['end_date'], timezone=self.timezone
                  )
  
          self.start_date = timezone.convert_to_utc(start_date)
          self.end_date = timezone.convert_to_utc(end_date)
  
          # also convert tasks
          if 'start_date' in self.default_args:
              self.default_args['start_date'] = timezone.convert_to_utc(self.default_args['start_date'])
          if 'end_date' in self.default_args:
              self.default_args['end_date'] = timezone.convert_to_utc(self.default_args['end_date'])
  
          self.schedule_interval = schedule_interval
          if isinstance(template_searchpath, str):
              template_searchpath = [template_searchpath]
          self.template_searchpath = template_searchpath
          self.template_undefined = template_undefined
          self.parent_dag: Optional[DAG] = None  # Gets set when DAGs are loaded
          self.last_loaded = timezone.utcnow()
          self.safe_dag_id = dag_id.replace('.', '__dot__')
          self.max_active_runs = max_active_runs
          self.dagrun_timeout = dagrun_timeout
          self.sla_miss_callback = sla_miss_callback
          if default_view in DEFAULT_VIEW_PRESETS:
              self._default_view: str = default_view
          else:
              raise AirflowException(
                  f'Invalid values of dag.default_view: only support '
                  f'{DEFAULT_VIEW_PRESETS}, but get {default_view}'
              )
          if orientation in ORIENTATION_PRESETS:
              self.orientation = orientation
          else:
              raise AirflowException(
                  f'Invalid values of dag.orientation: only support '
                  f'{ORIENTATION_PRESETS}, but get {orientation}'
              )
          self.catchup = catchup
          self.is_subdag = False  # DagBag.bag_dag() will set this to True if appropriate
  
          self.partial = False
          self.on_success_callback = on_success_callback
          self.on_failure_callback = on_failure_callback
  
          # Keeps track of any extra edge metadata (sparse; will not contain all
          # edges, so do not iterate over it for that). Outer key is upstream
          # task ID, inner key is downstream task ID.
          self.edge_info: Dict[str, Dict[str, EdgeInfoType]] = {}
  
          # To keep it in parity with Serialized DAGs
          # and identify if DAG has on_*_callback without actually storing them in Serialized JSON
          self.has_on_success_callback = self.on_success_callback is not None
          self.has_on_failure_callback = self.on_failure_callback is not None
  
          self.doc_md = doc_md
  
          self._access_control = DAG._upgrade_outdated_dag_access_control(access_control)
          self.is_paused_upon_creation = is_paused_upon_creation
  
          self.jinja_environment_kwargs = jinja_environment_kwargs
          self.render_template_as_native_obj = render_template_as_native_obj
          self.tags = tags
          self._task_group = TaskGroup.create_root(self)
  
      def __repr__(self):
          return f"<DAG: {self.dag_id}>"
  
      def __eq__(self, other):
          if type(self) == type(other):
              # Use getattr() instead of __dict__ as __dict__ doesn't return
              # correct values for properties.
              return all(getattr(self, c, None) == getattr(other, c, None) for c in self._comps)
          return False
  
      def __ne__(self, other):
          return not self == other
  
      def __lt__(self, other):
          return self.dag_id < other.dag_id
  
      def __hash__(self):
          hash_components = [type(self)]
          for c in self._comps:
              # task_ids returns a list and lists can't be hashed
              if c == 'task_ids':
                  val = tuple(self.task_dict.keys())
              else:
                  val = getattr(self, c, None)
              try:
                  hash(val)
                  hash_components.append(val)
              except TypeError:
                  hash_components.append(repr(val))
          return hash(tuple(hash_components))
  
      # Context Manager -----------------------------------------------
      def __enter__(self):
          DagContext.push_context_managed_dag(self)
          return self
  
      def __exit__(self, _type, _value, _tb):
          DagContext.pop_context_managed_dag()
  
      # /Context Manager ----------------------------------------------
  
      @staticmethod
      def _upgrade_outdated_dag_access_control(access_control=None):
          """
          Looks for outdated dag level actions (can_dag_read and can_dag_edit) in DAG
          access_controls (for example, {'role1': {'can_dag_read'}, 'role2': {'can_dag_read', 'can_dag_edit'}})
          and replaces them with updated actions (can_read and can_edit).
          """
          if not access_control:
              return None
          new_perm_mapping = {
              permissions.DEPRECATED_ACTION_CAN_DAG_READ: permissions.ACTION_CAN_READ,
              permissions.DEPRECATED_ACTION_CAN_DAG_EDIT: permissions.ACTION_CAN_EDIT,
          }
          updated_access_control = {}
          for role, perms in access_control.items():
              updated_access_control[role] = {new_perm_mapping.get(perm, perm) for perm in perms}
  
          if access_control != updated_access_control:
              warnings.warn(
                  "The 'can_dag_read' and 'can_dag_edit' permissions are deprecated. "
                  "Please use 'can_read' and 'can_edit', respectively.",
                  DeprecationWarning,
                  stacklevel=3,
              )
  
          return updated_access_control
  
      def date_range(
          self,
          start_date: datetime,
          num: Optional[int] = None,
          end_date: Optional[datetime] = timezone.utcnow(),
      ) -> List[datetime]:
          message = "`DAG.date_range()` is deprecated."
          if num is not None:
              result = utils_date_range(start_date=start_date, num=num)
          else:
              message += " Please use `DAG.get_run_dates(..., align=False)` instead."
              result = self.get_run_dates(start_date, end_date, align=False)
          warnings.warn(message, category=DeprecationWarning, stacklevel=2)
          return result
  
      def is_fixed_time_schedule(self):
          warnings.warn(
              "`DAG.is_fixed_time_schedule()` is deprecated.",
              category=DeprecationWarning,
              stacklevel=2,
          )
          try:
              return not self.timetable._schedule._should_fix_dst
          except AttributeError:
              return True
  
      def following_schedule(self, dttm):
          """
          Calculates the following schedule for this dag in UTC.
  
          :param dttm: utc datetime
          :return: utc datetime
          """
          current = pendulum.instance(dttm)
          between = TimeRestriction(earliest=None, latest=None, catchup=True)
          next_info = self.timetable.next_dagrun_info(current, between)
          if next_info is None:
              return None
          return next_info.data_interval.start
  
      def previous_schedule(self, dttm):
          warnings.warn(
              "`DAG.previous_schedule()` is deprecated.",
              category=DeprecationWarning,
              stacklevel=2,
          )
          try:
              schedule: Schedule = self.timetable._schedule
          except AttributeError:
              return None
          return schedule.get_prev(pendulum.instance(dttm))
  
      def next_dagrun_info(
          self,
          date_last_automated_dagrun: Optional[pendulum.DateTime],
      ) -> Tuple[Optional[pendulum.DateTime], Optional[pendulum.DateTime]]:
          """Get information about the next DagRun of this dag after ``date_last_automated_dagrun``.
  
          This calculates what time interval the next DagRun should operate on
          (its execution date), and when it can be scheduled, , according to the
          dag's timetable, start_date, end_date, etc. This doesn't check max
          active run or any other "max_active_tasks" type limits, but only
          performs calculations based on the various date and interval fields of
          this dag and its tasks.
  
          :param date_last_automated_dagrun: The ``max(execution_date)`` of
              existing "automated" DagRuns for this dag (scheduled or backfill,
              but not manual).
          :return: A 2-tuple containing the DagRun's execution date, and the
              earliest it could be scheduled.
          """
          # XXX: The timezone.coerce_datetime calls in this function should not
          # be necessary since the function annotation suggests it only accepts
          # pendulum.DateTime, and someone is passing datetime.datetime into this
          # function. We should fix whatever is doing that.
          if self.is_subdag:
              return (None, None)
          next_info = self.timetable.next_dagrun_info(
              timezone.coerce_datetime(date_last_automated_dagrun),
              self._time_restriction,
          )
          if next_info is None:
              return (None, None)
          return (next_info.data_interval.start, next_info.run_after)
  
      def next_dagrun_after_date(self, date_last_automated_dagrun: Optional[pendulum.DateTime]):
          warnings.warn(
              "`DAG.next_dagrun_after_date()` is deprecated. Please use `DAG.next_dagrun_info()` instead.",
              category=DeprecationWarning,
              stacklevel=2,
          )
          return self.next_dagrun_info(date_last_automated_dagrun)[0]
  
      @cached_property
      def _time_restriction(self) -> TimeRestriction:
          start_dates = [t.start_date for t in self.tasks if t.start_date]
          if self.start_date is not None:
              start_dates.append(self.start_date)
          if start_dates:
              earliest = timezone.coerce_datetime(min(start_dates))
          else:
              earliest = None
          end_dates = [t.end_date for t in self.tasks if t.end_date]
          if self.end_date is not None:
              end_dates.append(self.end_date)
          if end_dates:
              latest = timezone.coerce_datetime(max(end_dates))
          else:
              latest = None
          return TimeRestriction(earliest, latest, self.catchup)
  
      @cached_property
      def timetable(self) -> Timetable:
          interval = self.schedule_interval
          if interval is None:
              return NullTimetable()
          if interval == "@once":
              return OnceTimetable()
          if isinstance(interval, (timedelta, relativedelta)):
              return DeltaDataIntervalTimetable(interval)
          if isinstance(interval, str):
              return CronDataIntervalTimetable(interval, self.timezone)
          type_name = type(interval).__name__
          raise TypeError(f"{type_name} is not a valid DAG.schedule_interval.")
  
      def get_run_dates(self, start_date, end_date=None, *, align: bool = True):
          """
          Returns a list of dates between the interval received as parameter using this
          dag's schedule interval. Returned dates can be used for execution dates.
  
          :param start_date: The start date of the interval.
          :type start_date: datetime
          :param end_date: The end date of the interval. Defaults to ``timezone.utcnow()``.
          :type end_date: datetime
          :param align: Whether the first run should be delayed to "align" with
              the schedule, or can happen immediately at start_date. The default is
              ``True``, but subdags will ignore this value and always behave as if
              this is set to ``False`` for backward compatibility.
          :type align: bool
          :return: A list of dates within the interval following the dag's schedule.
          :rtype: list
          """
          if start_date is None:
              start = self._time_restriction.earliest
          else:
              start = pendulum.instance(start_date)
          if end_date is None:
              end = pendulum.now(timezone.utc)
          else:
              end = pendulum.instance(end_date)
          # HACK: Sub-DAGs are currently scheduled differently. For example, say
          # the schedule is @daily and start is 2021-06-03 22:16:00, a top-level
          # DAG should be first scheduled to run on midnight 2021-06-04, but a
          # sub-DAG should be first scheduled to run RIGHT NOW. We can change
          # this, but since the sub-DAG is going away in 3.0 anyway, let's keep
          # compatibility for now and remove this entirely later.
          if self.is_subdag:
              align = False
          return sorted(self.timetable.iter_between(start, end, align=align))
  
      def normalize_schedule(self, dttm):
          warnings.warn(
              "`DAG.normalize_schedule()` is deprecated.",
              category=DeprecationWarning,
              stacklevel=2,
          )
          following = self.following_schedule(dttm)
  
          # in case of @once
          if not following:
              return dttm
          if self.previous_schedule(following) != dttm:
              return following
  
          return dttm
  
      @provide_session
      def get_last_dagrun(self, session=None, include_externally_triggered=False):
          return get_last_dagrun(
              self.dag_id, session=session, include_externally_triggered=include_externally_triggered
          )
  
      @provide_session
      def has_dag_runs(self, session=None, include_externally_triggered=True) -> bool:
          return (
              get_last_dagrun(
                  self.dag_id, session=session, include_externally_triggered=include_externally_triggered
              )
              is not None
          )
  
      @property
      def dag_id(self) -> str:
          return self._dag_id
  
      @dag_id.setter
      def dag_id(self, value: str) -> None:
          self._dag_id = value
  
      @property
      def full_filepath(self) -> str:
          return self._full_filepath
  
      @full_filepath.setter
      def full_filepath(self, value) -> None:
          self._full_filepath = value
  
      @property
      def concurrency(self) -> int:
          # TODO: Remove in Airflow 3.0
          warnings.warn(
              "The 'DAG.concurrency' attribute is deprecated. Please use 'DAG.max_active_tasks'.",
              DeprecationWarning,
              stacklevel=2,
          )
          return self._max_active_tasks
  
      @concurrency.setter
      def concurrency(self, value: int):
          self._max_active_tasks = value
  
      @property
      def max_active_tasks(self) -> int:
          return self._max_active_tasks
  
      @max_active_tasks.setter
      def max_active_tasks(self, value: int):
          self._max_active_tasks = value
  
      @property
      def access_control(self):
          return self._access_control
  
      @access_control.setter
      def access_control(self, value):
          self._access_control = DAG._upgrade_outdated_dag_access_control(value)
  
      @property
      def description(self) -> Optional[str]:
          return self._description
  
      @property
      def default_view(self) -> str:
          return self._default_view
  
      @property
      def pickle_id(self) -> Optional[int]:
          return self._pickle_id
  
      @pickle_id.setter
      def pickle_id(self, value: int) -> None:
          self._pickle_id = value
  
      def param(self, name: str, default=None) -> DagParam:
          """
          Return a DagParam object for current dag.
  
          :param name: dag parameter name.
          :param default: fallback value for dag parameter.
          :return: DagParam instance for specified name and current dag.
          """
          return DagParam(current_dag=self, name=name, default=default)
  
      @property
      def tasks(self) -> List[BaseOperator]:
          return list(self.task_dict.values())
  
      @tasks.setter
      def tasks(self, val):
          raise AttributeError('DAG.tasks can not be modified. Use dag.add_task() instead.')
  
      @property
      def task_ids(self) -> List[str]:
          return list(self.task_dict.keys())
  
      @property
      def task_group(self) -> "TaskGroup":
          return self._task_group
  
      @property
      def filepath(self) -> str:
          """File location of where the dag object is instantiated"""
          fn = self.full_filepath.replace(settings.DAGS_FOLDER + '/', '')
          fn = fn.replace(os.path.dirname(__file__) + '/', '')
          return fn
  
      @property
      def folder(self) -> str:
          """Folder location of where the DAG object is instantiated."""
          return os.path.dirname(self.full_filepath)
  
      @property
      def owner(self) -> str:
          """
          Return list of all owners found in DAG tasks.
  
          :return: Comma separated list of owners in DAG tasks
          :rtype: str
          """
          return ", ".join({t.owner for t in self.tasks})
  
      @property
      def allow_future_exec_dates(self) -> bool:
          return settings.ALLOW_FUTURE_EXEC_DATES and self.schedule_interval is None
  
      @provide_session
      def get_concurrency_reached(self, session=None) -> bool:
          """
          Returns a boolean indicating whether the max_active_tasks limit for this DAG
          has been reached
          """
          TI = TaskInstance
          qry = session.query(func.count(TI.task_id)).filter(
              TI.dag_id == self.dag_id,
              TI.state == State.RUNNING,
          )
          return qry.scalar() >= self.max_active_tasks
  
      @property
      def concurrency_reached(self):
          """This attribute is deprecated. Please use `airflow.models.DAG.get_concurrency_reached` method."""
          warnings.warn(
              "This attribute is deprecated. Please use `airflow.models.DAG.get_concurrency_reached` method.",
              DeprecationWarning,
              stacklevel=2,
          )
          return self.get_concurrency_reached()
  
      @provide_session
      def get_is_active(self, session=None) -> Optional[None]:
          """Returns a boolean indicating whether this DAG is active"""
          qry = session.query(DagModel).filter(DagModel.dag_id == self.dag_id)
          return qry.value(DagModel.is_active)
  
      @provide_session
      def get_is_paused(self, session=None) -> Optional[None]:
          """Returns a boolean indicating whether this DAG is paused"""
          qry = session.query(DagModel).filter(DagModel.dag_id == self.dag_id)
          return qry.value(DagModel.is_paused)
  
      @property
      def is_paused(self):
          """This attribute is deprecated. Please use `airflow.models.DAG.get_is_paused` method."""
          warnings.warn(
              "This attribute is deprecated. Please use `airflow.models.DAG.get_is_paused` method.",
              DeprecationWarning,
              stacklevel=2,
          )
          return self.get_is_paused()
  
      @property
      def normalized_schedule_interval(self) -> Optional[ScheduleInterval]:
          warnings.warn(
              "DAG.normalized_schedule_interval() is deprecated.",
              category=DeprecationWarning,
              stacklevel=2,
          )
          if isinstance(self.schedule_interval, str) and self.schedule_interval in cron_presets:
              _schedule_interval = cron_presets.get(self.schedule_interval)  # type: Optional[ScheduleInterval]
          elif self.schedule_interval == '@once':
              _schedule_interval = None
          else:
              _schedule_interval = self.schedule_interval
          return _schedule_interval
  
      @provide_session
      def handle_callback(self, dagrun, success=True, reason=None, session=None):
          """
          Triggers the appropriate callback depending on the value of success, namely the
          on_failure_callback or on_success_callback. This method gets the context of a
          single TaskInstance part of this DagRun and passes that to the callable along
          with a 'reason', primarily to differentiate DagRun failures.
  
          .. note: The logs end up in
              ``$AIRFLOW_HOME/logs/scheduler/latest/PROJECT/DAG_FILE.py.log``
  
          :param dagrun: DagRun object
          :param success: Flag to specify if failure or success callback should be called
          :param reason: Completion reason
          :param session: Database session
          """
          callback = self.on_success_callback if success else self.on_failure_callback
          if callback:
              self.log.info('Executing dag callback function: %s', callback)
              tis = dagrun.get_task_instances()
              ti = tis[-1]  # get first TaskInstance of DagRun
              ti.task = self.get_task(ti.task_id)
              context = ti.get_template_context(session=session)
              context.update({'reason': reason})
              try:
                  callback(context)
              except Exception:
                  self.log.exception("failed to invoke dag state update callback")
                  Stats.incr("dag.callback_exceptions")
  
      def get_active_runs(self):
          """
          Returns a list of dag run execution dates currently running
  
          :return: List of execution dates
          """
          runs = DagRun.find(dag_id=self.dag_id, state=State.RUNNING)
  
          active_dates = []
          for run in runs:
              active_dates.append(run.execution_date)
  
          return active_dates
  
      @provide_session
      def get_num_active_runs(self, external_trigger=None, session=None):
          """
          Returns the number of active "running" dag runs
  
          :param external_trigger: True for externally triggered active dag runs
          :type external_trigger: bool
          :param session:
          :return: number greater than 0 for active dag runs
          """
          # .count() is inefficient
          query = (
              session.query(func.count())
              .filter(DagRun.dag_id == self.dag_id)
              .filter(DagRun.state == State.RUNNING)
          )
  
          if external_trigger is not None:
              query = query.filter(
                  DagRun.external_trigger == (expression.true() if external_trigger else expression.false())
              )
  
          return query.scalar()
  
      @provide_session
      def get_dagrun(
          self,
          execution_date: Optional[str] = None,
          run_id: Optional[str] = None,
          session: Optional[Session] = None,
      ):
          """
          Returns the dag run for a given execution date or run_id if it exists, otherwise
          none.
  
          :param execution_date: The execution date of the DagRun to find.
          :param run_id: The run_id of the DagRun to find.
          :param session:
          :return: The DagRun if found, otherwise None.
          """
          if not (execution_date or run_id):
              raise TypeError("You must provide either the execution_date or the run_id")
          query = session.query(DagRun)
          if execution_date:
              query = query.filter(DagRun.dag_id == self.dag_id, DagRun.execution_date == execution_date)
          if run_id:
              query = query.filter(DagRun.dag_id == self.dag_id, DagRun.run_id == run_id)
          return query.first()
  
      @provide_session
      def get_dagruns_between(self, start_date, end_date, session=None):
          """
          Returns the list of dag runs between start_date (inclusive) and end_date (inclusive).
  
          :param start_date: The starting execution date of the DagRun to find.
          :param end_date: The ending execution date of the DagRun to find.
          :param session:
          :return: The list of DagRuns found.
          """
          dagruns = (
              session.query(DagRun)
              .filter(
                  DagRun.dag_id == self.dag_id,
                  DagRun.execution_date >= start_date,
                  DagRun.execution_date <= end_date,
              )
              .all()
          )
  
          return dagruns
  
      @provide_session
      def get_latest_execution_date(self, session=None):
          """Returns the latest date for which at least one dag run exists"""
          return session.query(func.max(DagRun.execution_date)).filter(DagRun.dag_id == self.dag_id).scalar()
  
      @property
      def latest_execution_date(self):
          """This attribute is deprecated. Please use `airflow.models.DAG.get_latest_execution_date` method."""
          warnings.warn(
              "This attribute is deprecated. Please use `airflow.models.DAG.get_latest_execution_date` method.",
              DeprecationWarning,
              stacklevel=2,
          )
          return self.get_latest_execution_date()
  
      @property
      def subdags(self):
          """Returns a list of the subdag objects associated to this DAG"""
          # Check SubDag for class but don't check class directly
          from airflow.operators.subdag import SubDagOperator
  
          subdag_lst = []
          for task in self.tasks:
              if (
                  isinstance(task, SubDagOperator)
                  or
                  # TODO remove in Airflow 2.0
                  type(task).__name__ == 'SubDagOperator'
                  or task.task_type == 'SubDagOperator'
              ):
                  subdag_lst.append(task.subdag)
                  subdag_lst += task.subdag.subdags
          return subdag_lst
  
      def resolve_template_files(self):
          for t in self.tasks:
              t.resolve_template_files()
  
      def get_template_env(self) -> jinja2.Environment:
          """Build a Jinja2 environment."""
          # Collect directories to search for template files
          searchpath = [self.folder]
          if self.template_searchpath:
              searchpath += self.template_searchpath
  
          # Default values (for backward compatibility)
          jinja_env_options = {
              'loader': jinja2.FileSystemLoader(searchpath),
              'undefined': self.template_undefined,
              'extensions': ["jinja2.ext.do"],
              'cache_size': 0,
          }
          if self.jinja_environment_kwargs:
              jinja_env_options.update(self.jinja_environment_kwargs)
          if self.render_template_as_native_obj:
              env = NativeEnvironment(**jinja_env_options)
          else:
              env = airflow.templates.SandboxedEnvironment(**jinja_env_options)  # type: ignore
  
          # Add any user defined items. Safe to edit globals as long as no templates are rendered yet.
          # http://jinja.pocoo.org/docs/2.10/api/#jinja2.Environment.globals
          if self.user_defined_macros:
              env.globals.update(self.user_defined_macros)
          if self.user_defined_filters:
              env.filters.update(self.user_defined_filters)
  
          return env
  
      def set_dependency(self, upstream_task_id, downstream_task_id):
          """
          Simple utility method to set dependency between two tasks that
          already have been added to the DAG using add_task()
          """
          self.get_task(upstream_task_id).set_downstream(self.get_task(downstream_task_id))
  
      @provide_session
      def get_task_instances_before(
          self,
          base_date: datetime,
          num: int,
          *,
          session: Session,
      ) -> List[TaskInstance]:
          """Get ``num`` task instances before (including) ``base_date``.
  
          The returned list may contain exactly ``num`` task instances. It can
          have less if there are less than ``num`` scheduled DAG runs before
          ``base_date``, or more if there are manual task runs between the
          requested period, which does not count toward ``num``.
          """
          min_date = (
              session.query(DagRun)
              .filter(
                  DagRun.dag_id == self.dag_id,
                  DagRun.execution_date <= base_date,
                  DagRun.run_type != DagRunType.MANUAL,
              )
              .order_by(DagRun.execution_date.desc())
              .offset(num)
              .first()
          )
          if min_date is None:
              min_date = timezone.utc_epoch()
          return self.get_task_instances(start_date=min_date, end_date=base_date, session=session)
  
      @provide_session
      def get_task_instances(
          self, start_date=None, end_date=None, state=None, session=None
      ) -> List[TaskInstance]:
          if not start_date:
              start_date = (timezone.utcnow() - timedelta(30)).date()
              start_date = timezone.make_aware(datetime.combine(start_date, datetime.min.time()))
  
          return (
              self._get_task_instances(
                  task_ids=None,
                  start_date=start_date,
                  end_date=end_date,
                  state=state,
                  include_subdags=False,
                  include_parentdag=False,
                  include_dependent_dags=False,
                  exclude_task_ids=[],
                  as_pk_tuple=False,
                  session=session,
              )
              .order_by(TaskInstance.execution_date)
              .all()
          )
  
      @overload
      def _get_task_instances(
          self,
          *,
          task_ids,
          start_date: Optional[datetime],
          end_date: Optional[datetime],
          state: Union[str, List[str]],
          include_subdags: bool,
          include_parentdag: bool,
          include_dependent_dags: bool,
          exclude_task_ids: Collection[str],
          as_pk_tuple: Literal[True],
          session: Session,
          dag_bag: "DagBag" = None,
          recursion_depth: int = 0,
          max_recursion_depth: int = None,
          visited_external_tis: Set[Tuple[str, str, datetime]] = None,
      ) -> Set["TaskInstanceKey"]:
          ...  # pragma: no cover
  
      @overload
      def _get_task_instances(
          self,
          *,
          task_ids,
          start_date: Optional[datetime],
          end_date: Optional[datetime],
          state: Union[str, List[str]],
          include_subdags: bool,
          include_parentdag: bool,
          include_dependent_dags: bool,
          as_pk_tuple: Literal[False],
          exclude_task_ids: Collection[str],
          session: Session,
          dag_bag: "DagBag" = None,
          recursion_depth: int = 0,
          max_recursion_depth: int = None,
          visited_external_tis: Set[Tuple[str, str, datetime]] = None,
      ) -> Iterable[TaskInstance]:
          ...  # pragma: no cover
  
      def _get_task_instances(
          self,
          *,
          task_ids,
          start_date: Optional[datetime],
          end_date: Optional[datetime],
          state: Union[str, List[str]],
          include_subdags: bool,
          include_parentdag: bool,
          include_dependent_dags: bool,
          as_pk_tuple: bool,
          exclude_task_ids: Collection[str],
          session: Session,
          dag_bag: "DagBag" = None,
          recursion_depth: int = 0,
          max_recursion_depth: int = None,
          visited_external_tis: Set[Tuple[str, str, datetime]] = None,
      ) -> Union[Iterable[TaskInstance], Set[TaskInstanceKey]]:
          TI = TaskInstance
  
          # If we are looking at subdags/dependent dags we want to avoid UNION calls
          # in SQL (it doesn't play nice with fields that have no equality operator,
          # like JSON types), we instead build our result set separately.
          #
          # This will be empty if we are only looking at one dag, in which case
          # we can return the filtered TI query object directly.
          result: Set[TaskInstanceKey] = set()
  
          # Do we want full objects, or just the primary columns?
          if as_pk_tuple:
              tis = session.query(TI.dag_id, TI.task_id, TI.execution_date)
          else:
              tis = session.query(TaskInstance)
  
          if include_subdags:
              # Crafting the right filter for dag_id and task_ids combo
              conditions = []
              for dag in self.subdags + [self]:
                  conditions.append(
                      (TaskInstance.dag_id == dag.dag_id) & TaskInstance.task_id.in_(dag.task_ids)
                  )
              tis = tis.filter(or_(*conditions))
          else:
              tis = tis.filter(TaskInstance.dag_id == self.dag_id, TaskInstance.task_id.in_(self.task_ids))
          if start_date:
              tis = tis.filter(TaskInstance.execution_date >= start_date)
          if task_ids:
              tis = tis.filter(TaskInstance.task_id.in_(task_ids))
  
          # This allows allow_trigger_in_future config to take affect, rather than mandating exec_date <= UTC
          if end_date or not self.allow_future_exec_dates:
              end_date = end_date or timezone.utcnow()
              tis = tis.filter(TaskInstance.execution_date <= end_date)
  
          if state:
              if isinstance(state, str):
                  tis = tis.filter(TaskInstance.state == state)
              elif len(state) == 1:
                  tis = tis.filter(TaskInstance.state == state[0])
              else:
                  # this is required to deal with NULL values
                  if None in state:
                      if all(x is None for x in state):
                          tis = tis.filter(TaskInstance.state.is_(None))
                      else:
                          not_none_state = [s for s in state if s]
                          tis = tis.filter(
                              or_(TaskInstance.state.in_(not_none_state), TaskInstance.state.is_(None))
                          )
                  else:
                      tis = tis.filter(TaskInstance.state.in_(state))
  
          # Next, get any of them from our parent DAG (if there is one)
          if include_parentdag and self.is_subdag and self.parent_dag is not None:
              p_dag = self.parent_dag.partial_subset(
                  task_ids_or_regex=r"^{}$".format(self.dag_id.split('.')[1]),
                  include_upstream=False,
                  include_downstream=True,
              )
              result.update(
                  p_dag._get_task_instances(
                      task_ids=task_ids,
                      start_date=start_date,
                      end_date=end_date,
                      state=state,
                      include_subdags=include_subdags,
                      include_parentdag=False,
                      include_dependent_dags=include_dependent_dags,
                      as_pk_tuple=True,
                      exclude_task_ids=exclude_task_ids,
                      session=session,
                      dag_bag=dag_bag,
                      recursion_depth=recursion_depth,
                      max_recursion_depth=max_recursion_depth,
                      visited_external_tis=visited_external_tis,
                  )
              )
  
          if include_dependent_dags:
              # Recursively find external tasks indicated by ExternalTaskMarker
              from airflow.sensors.external_task import ExternalTaskMarker
  
              query = tis
              if as_pk_tuple:
                  condition = TI.filter_for_tis(TaskInstanceKey(*cols) for cols in tis.all())
                  if condition is not None:
                      query = session.query(TI).filter(condition)
  
              if visited_external_tis is None:
                  visited_external_tis = set()
  
              for ti in query.filter(TI.operator == ExternalTaskMarker.__name__):
                  ti_key = ti.key.primary
                  if ti_key in visited_external_tis:
                      continue
  
                  visited_external_tis.add(ti_key)
  
                  task: ExternalTaskMarker = cast(ExternalTaskMarker, copy.copy(self.get_task(ti.task_id)))
                  ti.task = task
  
                  if max_recursion_depth is None:
                      # Maximum recursion depth allowed is the recursion_depth of the first
                      # ExternalTaskMarker in the tasks to be visited.
                      max_recursion_depth = task.recursion_depth
  
                  if recursion_depth + 1 > max_recursion_depth:
                      # Prevent cycles or accidents.
                      raise AirflowException(
                          "Maximum recursion depth {} reached for {} {}. "
                          "Attempted to clear too many tasks "
                          "or there may be a cyclic dependency.".format(
                              max_recursion_depth, ExternalTaskMarker.__name__, ti.task_id
                          )
                      )
                  ti.render_templates()
                  external_tis = session.query(TI).filter(
                      TI.dag_id == task.external_dag_id,
                      TI.task_id == task.external_task_id,
                      TI.execution_date == pendulum.parse(task.execution_date),
                  )
  
                  for tii in external_tis:
                      if not dag_bag:
                          dag_bag = DagBag(read_dags_from_db=True)
                      external_dag = dag_bag.get_dag(tii.dag_id, session=session)
                      if not external_dag:
                          raise AirflowException(f"Could not find dag {tii.dag_id}")
                      downstream = external_dag.partial_subset(
                          task_ids_or_regex=[tii.task_id],
                          include_upstream=False,
                          include_downstream=True,
                      )
                      result.update(
                          downstream._get_task_instances(
                              task_ids=None,
                              start_date=tii.execution_date,
                              end_date=tii.execution_date,
                              state=state,
                              include_subdags=include_subdags,
                              include_dependent_dags=include_dependent_dags,
                              include_parentdag=False,
                              as_pk_tuple=True,
                              exclude_task_ids=exclude_task_ids,
                              dag_bag=dag_bag,
                              session=session,
                              recursion_depth=recursion_depth + 1,
                              max_recursion_depth=max_recursion_depth,
                              visited_external_tis=visited_external_tis,
                          )
                      )
  
          if result or as_pk_tuple:
              # Only execute the `ti` query if we have also collected some other results (i.e. subdags etc.)
              if as_pk_tuple:
                  result.update(TaskInstanceKey(*cols) for cols in tis.all())
              else:
                  result.update(ti.key for ti in tis.all())
  
              if exclude_task_ids:
                  result = set(
                      filter(
                          lambda key: key.task_id not in exclude_task_ids,
                          result,
                      )
                  )
  
          if as_pk_tuple:
              return result
          elif result:
              # We've been asked for objects, lets combine it all back in to a result set
              tis = tis.with_entities(TI.dag_id, TI.task_id, TI.execution_date)
  
              tis = session.query(TI).filter(TI.filter_for_tis(result))
          elif exclude_task_ids:
              tis = tis.filter(TI.task_id.notin_(list(exclude_task_ids)))
  
          return tis
  
      @provide_session
      def set_task_instance_state(
          self,
          task_id: str,
          execution_date: datetime,
          state: State,
          upstream: Optional[bool] = False,
          downstream: Optional[bool] = False,
          future: Optional[bool] = False,
          past: Optional[bool] = False,
          commit: Optional[bool] = True,
          session=None,
      ) -> List[TaskInstance]:
          """
          Set the state of a TaskInstance to the given state, and clear its downstream tasks that are
          in failed or upstream_failed state.
  
          :param task_id: Task ID of the TaskInstance
          :type task_id: str
          :param execution_date: execution_date of the TaskInstance
          :type execution_date: datetime
          :param state: State to set the TaskInstance to
          :type state: State
          :param upstream: Include all upstream tasks of the given task_id
          :type upstream: bool
          :param downstream: Include all downstream tasks of the given task_id
          :type downstream: bool
          :param future: Include all future TaskInstances of the given task_id
          :type future: bool
          :param commit: Commit changes
          :type commit: bool
          :param past: Include all past TaskInstances of the given task_id
          :type past: bool
          """
          from airflow.api.common.experimental.mark_tasks import set_state
  
          task = self.get_task(task_id)
          task.dag = self
  
          altered = set_state(
              tasks=[task],
              execution_date=execution_date,
              upstream=upstream,
              downstream=downstream,
              future=future,
              past=past,
              state=state,
              commit=commit,
              session=session,
          )
  
          if not commit:
              return altered
  
          # Clear downstream tasks that are in failed/upstream_failed state to resume them.
          # Flush the session so that the tasks marked success are reflected in the db.
          session.flush()
          subdag = self.partial_subset(
              task_ids_or_regex={task_id},
              include_downstream=True,
              include_upstream=False,
          )
  
          end_date = execution_date if not future else None
          start_date = execution_date if not past else None
  
          subdag.clear(
              start_date=start_date,
              end_date=end_date,
              include_subdags=True,
              include_parentdag=True,
              only_failed=True,
              session=session,
              # Exclude the task itself from being cleared
              exclude_task_ids={task_id},
          )
  
          return altered
  
      @property
      def roots(self) -> List[BaseOperator]:
          """Return nodes with no parents. These are first to execute and are called roots or root nodes."""
          return [task for task in self.tasks if not task.upstream_list]
  
      @property
      def leaves(self) -> List[BaseOperator]:
          """Return nodes with no children. These are last to execute and are called leaves or leaf nodes."""
          return [task for task in self.tasks if not task.downstream_list]
  
      def topological_sort(self, include_subdag_tasks: bool = False):
          """
          Sorts tasks in topographical order, such that a task comes after any of its
          upstream dependencies.
  
          Heavily inspired by:
          http://blog.jupo.org/2012/04/06/topological-sorting-acyclic-directed-graphs/
  
          :param include_subdag_tasks: whether to include tasks in subdags, default to False
          :return: list of tasks in topological order
          """
          from airflow.operators.subdag import SubDagOperator  # Avoid circular import
  
          # convert into an OrderedDict to speedup lookup while keeping order the same
          graph_unsorted = OrderedDict((task.task_id, task) for task in self.tasks)
  
          graph_sorted = []  # type: List[BaseOperator]
  
          # special case
          if len(self.tasks) == 0:
              return tuple(graph_sorted)
  
          # Run until the unsorted graph is empty.
          while graph_unsorted:
              # Go through each of the node/edges pairs in the unsorted
              # graph. If a set of edges doesn't contain any nodes that
              # haven't been resolved, that is, that are still in the
              # unsorted graph, remove the pair from the unsorted graph,
              # and append it to the sorted graph. Note here that by using
              # using the items() method for iterating, a copy of the
              # unsorted graph is used, allowing us to modify the unsorted
              # graph as we move through it. We also keep a flag for
              # checking that graph is acyclic, which is true if any
              # nodes are resolved during each pass through the graph. If
              # not, we need to exit as the graph therefore can't be
              # sorted.
              acyclic = False
              for node in list(graph_unsorted.values()):
                  for edge in node.upstream_list:
                      if edge.task_id in graph_unsorted:
                          break
                  # no edges in upstream tasks
                  else:
                      acyclic = True
                      del graph_unsorted[node.task_id]
                      graph_sorted.append(node)
                      if include_subdag_tasks and isinstance(node, SubDagOperator):
                          graph_sorted.extend(node.subdag.topological_sort(include_subdag_tasks=True))
  
              if not acyclic:
                  raise AirflowException(f"A cyclic dependency occurred in dag: {self.dag_id}")
  
          return tuple(graph_sorted)
  
      @provide_session
      def set_dag_runs_state(
          self,
          state: str = State.RUNNING,
          session: Session = None,
          start_date: Optional[datetime] = None,
          end_date: Optional[datetime] = None,
          dag_ids: List[str] = None,
      ) -> None:
          warnings.warn(
              "This method is deprecated and will be removed in a future version.",
              DeprecationWarning,
              stacklevel=3,
          )
          dag_ids = dag_ids or [self.dag_id]
          query = session.query(DagRun).filter(DagRun.dag_id.in_(dag_ids))
          if start_date:
              query = query.filter(DagRun.execution_date >= start_date)
          if end_date:
              query = query.filter(DagRun.execution_date <= end_date)
          query.update({DagRun.state: state}, synchronize_session='fetch')
  
      @provide_session
      def clear(
          self,
          task_ids=None,
          start_date=None,
          end_date=None,
          only_failed=False,
          only_running=False,
          confirm_prompt=False,
          include_subdags=True,
          include_parentdag=True,
          dag_run_state: DagRunState = DagRunState.QUEUED,
          dry_run=False,
          session=None,
          get_tis=False,
          recursion_depth=0,
          max_recursion_depth=None,
          dag_bag=None,
          exclude_task_ids: FrozenSet[str] = frozenset({}),
      ):
          """
          Clears a set of task instances associated with the current dag for
          a specified date range.
  
          :param task_ids: List of task ids to clear
          :type task_ids: List[str]
          :param start_date: The minimum execution_date to clear
          :type start_date: datetime.datetime or None
          :param end_date: The maximum execution_date to clear
          :type end_date: datetime.datetime or None
          :param only_failed: Only clear failed tasks
          :type only_failed: bool
          :param only_running: Only clear running tasks.
          :type only_running: bool
          :param confirm_prompt: Ask for confirmation
          :type confirm_prompt: bool
          :param include_subdags: Clear tasks in subdags and clear external tasks
              indicated by ExternalTaskMarker
          :type include_subdags: bool
          :param include_parentdag: Clear tasks in the parent dag of the subdag.
          :type include_parentdag: bool
          :param dag_run_state: state to set DagRun to. If set to False, dagrun state will not
              be changed.
          :param dry_run: Find the tasks to clear but don't clear them.
          :type dry_run: bool
          :param session: The sqlalchemy session to use
          :type session: sqlalchemy.orm.session.Session
          :param dag_bag: The DagBag used to find the dags subdags (Optional)
          :type dag_bag: airflow.models.dagbag.DagBag
          :param exclude_task_ids: A set of ``task_id`` that should not be cleared
          :type exclude_task_ids: frozenset
          """
          if get_tis:
              warnings.warn(
                  "Passing `get_tis` to dag.clear() is deprecated. Use `dry_run` parameter instead.",
                  DeprecationWarning,
                  stacklevel=2,
              )
              dry_run = True
  
          if recursion_depth:
              warnings.warn(
                  "Passing `recursion_depth` to dag.clear() is deprecated.",
                  DeprecationWarning,
                  stacklevel=2,
              )
          if max_recursion_depth:
              warnings.warn(
                  "Passing `max_recursion_depth` to dag.clear() is deprecated.",
                  DeprecationWarning,
                  stacklevel=2,
              )
  
          state = []
          if only_failed:
              state += [State.FAILED, State.UPSTREAM_FAILED]
              only_failed = None
          if only_running:
              # Yes, having `+=` doesn't make sense, but this was the existing behaviour
              state += [State.RUNNING]
              only_running = None
  
          tis = self._get_task_instances(
              task_ids=task_ids,
              start_date=start_date,
              end_date=end_date,
              state=state,
              include_subdags=include_subdags,
              include_parentdag=include_parentdag,
              include_dependent_dags=include_subdags,  # compat, yes this is not a typo
              as_pk_tuple=False,
              session=session,
              dag_bag=dag_bag,
              exclude_task_ids=exclude_task_ids,
          )
  
          if dry_run:
              return tis
  
          tis = tis.all()
  
          count = len(tis)
          do_it = True
          if count == 0:
              return 0
          if confirm_prompt:
              ti_list = "\n".join(str(t) for t in tis)
              question = (
                  "You are about to delete these {count} tasks:\n{ti_list}\n\nAre you sure? (yes/no): "
              ).format(count=count, ti_list=ti_list)
              do_it = utils.helpers.ask_yesno(question)
  
          if do_it:
              clear_task_instances(
                  tis,
                  session,
                  dag=self,
                  dag_run_state=dag_run_state,
              )
          else:
              count = 0
              print("Cancelled, nothing was cleared.")
  
          session.flush()
          return count
  
      @classmethod
      def clear_dags(
          cls,
          dags,
          start_date=None,
          end_date=None,
          only_failed=False,
          only_running=False,
          confirm_prompt=False,
          include_subdags=True,
          include_parentdag=False,
          dag_run_state=DagRunState.QUEUED,
          dry_run=False,
      ):
          all_tis = []
          for dag in dags:
              tis = dag.clear(
                  start_date=start_date,
                  end_date=end_date,
                  only_failed=only_failed,
                  only_running=only_running,
                  confirm_prompt=False,
                  include_subdags=include_subdags,
                  include_parentdag=include_parentdag,
                  dag_run_state=dag_run_state,
                  dry_run=True,
              )
              all_tis.extend(tis)
  
          if dry_run:
              return all_tis
  
          count = len(all_tis)
          do_it = True
          if count == 0:
              print("Nothing to clear.")
              return 0
          if confirm_prompt:
              ti_list = "\n".join(str(t) for t in all_tis)
              question = f"You are about to delete these {count} tasks:\n{ti_list}\n\nAre you sure? (yes/no): "
              do_it = utils.helpers.ask_yesno(question)
  
          if do_it:
              for dag in dags:
                  dag.clear(
                      start_date=start_date,
                      end_date=end_date,
                      only_failed=only_failed,
                      only_running=only_running,
                      confirm_prompt=False,
                      include_subdags=include_subdags,
                      dag_run_state=dag_run_state,
                      dry_run=False,
                  )
          else:
              count = 0
              print("Cancelled, nothing was cleared.")
          return count
  
      def __deepcopy__(self, memo):
          # Switcharoo to go around deepcopying objects coming through the
          # backdoor
          cls = self.__class__
          result = cls.__new__(cls)
          memo[id(self)] = result
          for k, v in self.__dict__.items():
              if k not in ('user_defined_macros', 'user_defined_filters', 'params', '_log'):
                  setattr(result, k, copy.deepcopy(v, memo))
  
          result.user_defined_macros = self.user_defined_macros
          result.user_defined_filters = self.user_defined_filters
          result.params = self.params
          if hasattr(self, '_log'):
              result._log = self._log
          return result
  
      def sub_dag(self, *args, **kwargs):
          """This method is deprecated in favor of partial_subset"""
          warnings.warn(
              "This method is deprecated and will be removed in a future version. Please use partial_subset",
              DeprecationWarning,
              stacklevel=2,
          )
          return self.partial_subset(*args, **kwargs)
  
      def partial_subset(
          self,
          task_ids_or_regex: Union[str, RePatternType, Iterable[str]],
          include_downstream=False,
          include_upstream=True,
          include_direct_upstream=False,
      ):
          """
          Returns a subset of the current dag as a deep copy of the current dag
          based on a regex that should match one or many tasks, and includes
          upstream and downstream neighbours based on the flag passed.
  
          :param task_ids_or_regex: Either a list of task_ids, or a regex to
              match against task ids (as a string, or compiled regex pattern).
          :type task_ids_or_regex: [str] or str or re.Pattern
          :param include_downstream: Include all downstream tasks of matched
              tasks, in addition to matched tasks.
          :param include_upstream: Include all upstream tasks of matched tasks,
              in addition to matched tasks.
          """
          # deep-copying self.task_dict and self._task_group takes a long time, and we don't want all
          # the tasks anyway, so we copy the tasks manually later
          memo = {id(self.task_dict): None, id(self._task_group): None}
          dag = copy.deepcopy(self, memo)  # type: ignore
  
          if isinstance(task_ids_or_regex, (str, RePatternType)):
              matched_tasks = [t for t in self.tasks if re.findall(task_ids_or_regex, t.task_id)]
          else:
              matched_tasks = [t for t in self.tasks if t.task_id in task_ids_or_regex]
  
          also_include = []
          for t in matched_tasks:
              if include_downstream:
                  also_include += t.get_flat_relatives(upstream=False)
              if include_upstream:
                  also_include += t.get_flat_relatives(upstream=True)
              elif include_direct_upstream:
                  also_include += t.upstream_list
  
          # Compiling the unique list of tasks that made the cut
          # Make sure to not recursively deepcopy the dag while copying the task
          dag.task_dict = {
              t.task_id: copy.deepcopy(t, {id(t.dag): dag})  # type: ignore
              for t in matched_tasks + also_include
          }
  
          def filter_task_group(group, parent_group):
              """Exclude tasks not included in the subdag from the given TaskGroup."""
              copied = copy.copy(group)
              copied.used_group_ids = set(copied.used_group_ids)
              copied._parent_group = parent_group
  
              copied.children = {}
  
              for child in group.children.values():
                  if isinstance(child, BaseOperator):
                      if child.task_id in dag.task_dict:
                          copied.children[child.task_id] = dag.task_dict[child.task_id]
                      else:
                          copied.used_group_ids.discard(child.task_id)
                  else:
                      filtered_child = filter_task_group(child, copied)
  
                      # Only include this child TaskGroup if it is non-empty.
                      if filtered_child.children:
                          copied.children[child.group_id] = filtered_child
  
              return copied
  
          dag._task_group = filter_task_group(self._task_group, None)
  
          # Removing upstream/downstream references to tasks and TaskGroups that did not make
          # the cut.
          subdag_task_groups = dag.task_group.get_task_group_dict()
          for group in subdag_task_groups.values():
              group.upstream_group_ids = group.upstream_group_ids.intersection(subdag_task_groups.keys())
              group.downstream_group_ids = group.downstream_group_ids.intersection(subdag_task_groups.keys())
              group.upstream_task_ids = group.upstream_task_ids.intersection(dag.task_dict.keys())
              group.downstream_task_ids = group.downstream_task_ids.intersection(dag.task_dict.keys())
  
          for t in dag.tasks:
              # Removing upstream/downstream references to tasks that did not
              # make the cut
              t._upstream_task_ids = t.upstream_task_ids.intersection(dag.task_dict.keys())
              t._downstream_task_ids = t.downstream_task_ids.intersection(dag.task_dict.keys())
  
          if len(dag.tasks) < len(self.tasks):
              dag.partial = True
  
          return dag
  
      def has_task(self, task_id: str):
          return task_id in (t.task_id for t in self.tasks)
  
      def get_task(self, task_id: str, include_subdags: bool = False) -> BaseOperator:
          if task_id in self.task_dict:
              return self.task_dict[task_id]
          if include_subdags:
              for dag in self.subdags:
                  if task_id in dag.task_dict:
                      return dag.task_dict[task_id]
          raise TaskNotFound(f"Task {task_id} not found")
  
      def pickle_info(self):
          d = {}
          d['is_picklable'] = True
          try:
              dttm = timezone.utcnow()
              pickled = pickle.dumps(self)
              d['pickle_len'] = len(pickled)
              d['pickling_duration'] = str(timezone.utcnow() - dttm)
          except Exception as e:
              self.log.debug(e)
              d['is_picklable'] = False
              d['stacktrace'] = traceback.format_exc()
          return d
  
      @provide_session
      def pickle(self, session=None) -> DagPickle:
          dag = session.query(DagModel).filter(DagModel.dag_id == self.dag_id).first()
          dp = None
          if dag and dag.pickle_id:
              dp = session.query(DagPickle).filter(DagPickle.id == dag.pickle_id).first()
          if not dp or dp.pickle != self:
              dp = DagPickle(dag=self)
              session.add(dp)
              self.last_pickled = timezone.utcnow()
              session.commit()
              self.pickle_id = dp.id
  
          return dp
  
      def tree_view(self) -> None:
          """Print an ASCII tree representation of the DAG."""
  
          def get_downstream(task, level=0):
              print((" " * level * 4) + str(task))
              level += 1
              for t in task.downstream_list:
                  get_downstream(t, level)
  
          for t in self.roots:
              get_downstream(t)
  
      @property
      def task(self):
          from airflow.decorators import task
  
          return functools.partial(task, dag=self)
  
      def add_task(self, task):
          """
          Add a task to the DAG
  
          :param task: the task you want to add
          :type task: task
          """
          if not self.start_date and not task.start_date:
              raise AirflowException("Task is missing the start_date parameter")
          # if the task has no start date, assign it the same as the DAG
          elif not task.start_date:
              task.start_date = self.start_date
          # otherwise, the task will start on the later of its own start date and
          # the DAG's start date
          elif self.start_date:
              task.start_date = max(task.start_date, self.start_date)
  
          # if the task has no end date, assign it the same as the dag
          if not task.end_date:
              task.end_date = self.end_date
          # otherwise, the task will end on the earlier of its own end date and
          # the DAG's end date
          elif task.end_date and self.end_date:
              task.end_date = min(task.end_date, self.end_date)
  
          if (
              task.task_id in self.task_dict and self.task_dict[task.task_id] is not task
          ) or task.task_id in self._task_group.used_group_ids:
              raise DuplicateTaskIdFound(f"Task id '{task.task_id}' has already been added to the DAG")
          else:
              self.task_dict[task.task_id] = task
              task.dag = self
              # Add task_id to used_group_ids to prevent group_id and task_id collisions.
              self._task_group.used_group_ids.add(task.task_id)
  
          self.task_count = len(self.task_dict)
  
      def add_tasks(self, tasks):
          """
          Add a list of tasks to the DAG
  
          :param tasks: a lit of tasks you want to add
          :type tasks: list of tasks
          """
          for task in tasks:
              self.add_task(task)
  
      def run(
          self,
          start_date=None,
          end_date=None,
          mark_success=False,
          local=False,
          executor=None,
          donot_pickle=conf.getboolean('core', 'donot_pickle'),
          ignore_task_deps=False,
          ignore_first_depends_on_past=True,
          pool=None,
          delay_on_limit_secs=1.0,
          verbose=False,
          conf=None,
          rerun_failed_tasks=False,
          run_backwards=False,
      ):
          """
          Runs the DAG.
  
          :param start_date: the start date of the range to run
          :type start_date: datetime.datetime
          :param end_date: the end date of the range to run
          :type end_date: datetime.datetime
          :param mark_success: True to mark jobs as succeeded without running them
          :type mark_success: bool
          :param local: True to run the tasks using the LocalExecutor
          :type local: bool
          :param executor: The executor instance to run the tasks
          :type executor: airflow.executor.base_executor.BaseExecutor
          :param donot_pickle: True to avoid pickling DAG object and send to workers
          :type donot_pickle: bool
          :param ignore_task_deps: True to skip upstream tasks
          :type ignore_task_deps: bool
          :param ignore_first_depends_on_past: True to ignore depends_on_past
              dependencies for the first set of tasks only
          :type ignore_first_depends_on_past: bool
          :param pool: Resource pool to use
          :type pool: str
          :param delay_on_limit_secs: Time in seconds to wait before next attempt to run
              dag run when max_active_runs limit has been reached
          :type delay_on_limit_secs: float
          :param verbose: Make logging output more verbose
          :type verbose: bool
          :param conf: user defined dictionary passed from CLI
          :type conf: dict
          :param rerun_failed_tasks:
          :type: bool
          :param run_backwards:
          :type: bool
  
          """
          from airflow.jobs.backfill_job import BackfillJob
  
          if not executor and local:
              from airflow.executors.local_executor import LocalExecutor
  
              executor = LocalExecutor()
          elif not executor:
              from airflow.executors.executor_loader import ExecutorLoader
  
              executor = ExecutorLoader.get_default_executor()
          job = BackfillJob(
              self,
              start_date=start_date,
              end_date=end_date,
              mark_success=mark_success,
              executor=executor,
              donot_pickle=donot_pickle,
              ignore_task_deps=ignore_task_deps,
              ignore_first_depends_on_past=ignore_first_depends_on_past,
              pool=pool,
              delay_on_limit_secs=delay_on_limit_secs,
              verbose=verbose,
              conf=conf,
              rerun_failed_tasks=rerun_failed_tasks,
              run_backwards=run_backwards,
          )
          job.run()
  
      def cli(self):
          """Exposes a CLI specific to this DAG"""
          from airflow.cli import cli_parser
  
          parser = cli_parser.get_parser(dag_parser=True)
          args = parser.parse_args()
          args.func(args, self)
  
      @provide_session
      def create_dagrun(
          self,
          state: DagRunState,
          execution_date: Optional[datetime] = None,
          run_id: Optional[str] = None,
          start_date: Optional[datetime] = None,
          external_trigger: Optional[bool] = False,
          conf: Optional[dict] = None,
          run_type: Optional[DagRunType] = None,
          session=None,
          dag_hash: Optional[str] = None,
          creating_job_id: Optional[int] = None,
      ):
          """
          Creates a dag run from this dag including the tasks associated with this dag.
          Returns the dag run.
  
          :param run_id: defines the run id for this dag run
          :type run_id: str
          :param run_type: type of DagRun
          :type run_type: airflow.utils.types.DagRunType
          :param execution_date: the execution date of this dag run
          :type execution_date: datetime.datetime
          :param state: the state of the dag run
          :type state: airflow.utils.state.DagRunState
          :param start_date: the date this dag run should be evaluated
          :type start_date: datetime
          :param external_trigger: whether this dag run is externally triggered
          :type external_trigger: bool
          :param conf: Dict containing configuration/parameters to pass to the DAG
          :type conf: dict
          :param creating_job_id: id of the job creating this DagRun
          :type creating_job_id: int
          :param session: database session
          :type session: sqlalchemy.orm.session.Session
          :param dag_hash: Hash of Serialized DAG
          :type dag_hash: str
          """
          if run_id and not run_type:
              if not isinstance(run_id, str):
                  raise ValueError(f"`run_id` expected to be a str is {type(run_id)}")
              run_type: DagRunType = DagRunType.from_run_id(run_id)
          elif run_type and execution_date:
              if not isinstance(run_type, DagRunType):
                  raise ValueError(f"`run_type` expected to be a DagRunType is {type(run_type)}")
              run_id = DagRun.generate_run_id(run_type, execution_date)
          elif not run_id:
              raise AirflowException(
                  "Creating DagRun needs either `run_id` or both `run_type` and `execution_date`"
              )
  
          run = DagRun(
              dag_id=self.dag_id,
              run_id=run_id,
              execution_date=execution_date,
              start_date=start_date,
              external_trigger=external_trigger,
              conf=conf,
              state=state,
              run_type=run_type,
              dag_hash=dag_hash,
              creating_job_id=creating_job_id,
          )
          session.add(run)
          session.flush()
  
          run.dag = self
  
          # create the associated task instances
          # state is None at the moment of creation
          run.verify_integrity(session=session)
  
          return run
  
      @classmethod
      @provide_session
      def bulk_sync_to_db(cls, dags: Collection["DAG"], session=None):
          """This method is deprecated in favor of bulk_write_to_db"""
          warnings.warn(
              "This method is deprecated and will be removed in a future version. Please use bulk_write_to_db",
              DeprecationWarning,
              stacklevel=2,
          )
          return cls.bulk_write_to_db(dags, session)
  
      @classmethod
      @provide_session
      def bulk_write_to_db(cls, dags: Collection["DAG"], session=None):
          """
          Ensure the DagModel rows for the given dags are up-to-date in the dag table in the DB, including
          calculated fields.
  
          Note that this method can be called for both DAGs and SubDAGs. A SubDag is actually a SubDagOperator.
  
          :param dags: the DAG objects to save to the DB
          :type dags: List[airflow.models.dag.DAG]
          :return: None
          """
          if not dags:
              return
  
          log.info("Sync %s DAGs", len(dags))
          dag_by_ids = {dag.dag_id: dag for dag in dags}
          dag_ids = set(dag_by_ids.keys())
          query = (
              session.query(DagModel)
              .options(joinedload(DagModel.tags, innerjoin=False))
              .filter(DagModel.dag_id.in_(dag_ids))
          )
          orm_dags = with_row_locks(query, of=DagModel, session=session).all()
  
          existing_dag_ids = {orm_dag.dag_id for orm_dag in orm_dags}
          missing_dag_ids = dag_ids.difference(existing_dag_ids)
  
          for missing_dag_id in missing_dag_ids:
              orm_dag = DagModel(dag_id=missing_dag_id)
              dag = dag_by_ids[missing_dag_id]
              if dag.is_paused_upon_creation is not None:
                  orm_dag.is_paused = dag.is_paused_upon_creation
              orm_dag.tags = []
              log.info("Creating ORM DAG for %s", dag.dag_id)
              session.add(orm_dag)
              orm_dags.append(orm_dag)
  
          # Get the latest dag run for each existing dag as a single query (avoid n+1 query)
          most_recent_dag_runs = dict(
              session.query(DagRun.dag_id, func.max_(DagRun.execution_date))
              .filter(
                  DagRun.dag_id.in_(existing_dag_ids),
                  or_(
                      DagRun.run_type == DagRunType.BACKFILL_JOB,
                      DagRun.run_type == DagRunType.SCHEDULED,
                  ),
              )
              .group_by(DagRun.dag_id)
              .all()
          )
  
          # Get number of active dagruns for all dags we are processing as a single query.
          num_active_runs = dict(
              session.query(DagRun.dag_id, func.count('*'))
              .filter(
                  DagRun.dag_id.in_(existing_dag_ids),
                  DagRun.state == State.RUNNING,
                  DagRun.external_trigger == expression.false(),
              )
              .group_by(DagRun.dag_id)
              .all()
          )
  
          for orm_dag in sorted(orm_dags, key=lambda d: d.dag_id):
              dag = dag_by_ids[orm_dag.dag_id]
              if dag.is_subdag:
                  orm_dag.is_subdag = True
                  orm_dag.fileloc = dag.parent_dag.fileloc  # type: ignore
                  orm_dag.root_dag_id = dag.parent_dag.dag_id  # type: ignore
                  orm_dag.owners = dag.parent_dag.owner  # type: ignore
              else:
                  orm_dag.is_subdag = False
                  orm_dag.fileloc = dag.fileloc
                  orm_dag.owners = dag.owner
              orm_dag.is_active = True
              orm_dag.last_parsed_time = timezone.utcnow()
              orm_dag.default_view = dag.default_view
              orm_dag.description = dag.description
              orm_dag.schedule_interval = dag.schedule_interval
              orm_dag.max_active_tasks = dag.max_active_tasks
              orm_dag.has_task_concurrency_limits = any(t.task_concurrency is not None for t in dag.tasks)
  
              orm_dag.calculate_dagrun_date_fields(
                  dag,
                  most_recent_dag_runs.get(dag.dag_id),
                  num_active_runs.get(dag.dag_id, 0),
              )
  
              for orm_tag in list(orm_dag.tags):
                  if orm_tag.name not in orm_dag.tags:
                      session.delete(orm_tag)
                  orm_dag.tags.remove(orm_tag)
              if dag.tags:
                  orm_tag_names = [t.name for t in orm_dag.tags]
                  for dag_tag in list(dag.tags):
                      if dag_tag not in orm_tag_names:
                          dag_tag_orm = DagTag(name=dag_tag, dag_id=dag.dag_id)
                          orm_dag.tags.append(dag_tag_orm)
                          session.add(dag_tag_orm)
  
          if settings.STORE_DAG_CODE:
              DagCode.bulk_sync_to_db([dag.fileloc for dag in orm_dags])
  
          # Issue SQL/finish "Unit of Work", but let @provide_session commit (or if passed a session, let caller
          # decide when to commit
          session.flush()
  
          for dag in dags:
              cls.bulk_write_to_db(dag.subdags, session=session)
  
      @provide_session
      def sync_to_db(self, session=None):
          """
          Save attributes about this DAG to the DB. Note that this method
          can be called for both DAGs and SubDAGs. A SubDag is actually a
          SubDagOperator.
  
          :return: None
          """
          self.bulk_write_to_db([self], session)
  
      def get_default_view(self):
          """This is only there for backward compatible jinja2 templates"""
          if self.default_view is None:
              return conf.get('webserver', 'dag_default_view').lower()
          else:
              return self.default_view
  
      @staticmethod
      @provide_session
      def deactivate_unknown_dags(active_dag_ids, session=None):
          """
          Given a list of known DAGs, deactivate any other DAGs that are
          marked as active in the ORM
  
          :param active_dag_ids: list of DAG IDs that are active
          :type active_dag_ids: list[unicode]
          :return: None
          """
          if len(active_dag_ids) == 0:
              return
          for dag in session.query(DagModel).filter(~DagModel.dag_id.in_(active_dag_ids)).all():
              dag.is_active = False
              session.merge(dag)
          session.commit()
  
      @staticmethod
      @provide_session
      def deactivate_stale_dags(expiration_date, session=None):
          """
          Deactivate any DAGs that were last touched by the scheduler before
          the expiration date. These DAGs were likely deleted.
  
          :param expiration_date: set inactive DAGs that were touched before this
              time
          :type expiration_date: datetime
          :return: None
          """
          for dag in (
              session.query(DagModel)
              .filter(DagModel.last_parsed_time < expiration_date, DagModel.is_active)
              .all()
          ):
              log.info(
                  "Deactivating DAG ID %s since it was last touched by the scheduler at %s",
                  dag.dag_id,
                  dag.last_parsed_time.isoformat(),
              )
              dag.is_active = False
              session.merge(dag)
              session.commit()
  
      @staticmethod
      @provide_session
      def get_num_task_instances(dag_id, task_ids=None, states=None, session=None):
          """
          Returns the number of task instances in the given DAG.
  
          :param session: ORM session
          :param dag_id: ID of the DAG to get the task concurrency of
          :type dag_id: unicode
          :param task_ids: A list of valid task IDs for the given DAG
          :type task_ids: list[unicode]
          :param states: A list of states to filter by if supplied
          :type states: list[state]
          :return: The number of running tasks
          :rtype: int
          """
          qry = session.query(func.count(TaskInstance.task_id)).filter(
              TaskInstance.dag_id == dag_id,
          )
          if task_ids:
              qry = qry.filter(
                  TaskInstance.task_id.in_(task_ids),
              )
  
          if states:
              if None in states:
                  if all(x is None for x in states):
                      qry = qry.filter(TaskInstance.state.is_(None))
                  else:
                      not_none_states = [state for state in states if state]
                      qry = qry.filter(
                          or_(TaskInstance.state.in_(not_none_states), TaskInstance.state.is_(None))
                      )
              else:
                  qry = qry.filter(TaskInstance.state.in_(states))
          return qry.scalar()
  
      @classmethod
      def get_serialized_fields(cls):
          """Stringified DAGs and operators contain exactly these fields."""
          if not cls.__serialized_fields:
              cls.__serialized_fields = frozenset(vars(DAG(dag_id='test')).keys()) - {
                  'parent_dag',
                  '_old_context_manager_dags',
                  'safe_dag_id',
                  'last_loaded',
                  '_full_filepath',
                  'user_defined_filters',
                  'user_defined_macros',
                  'partial',
                  '_pickle_id',
                  '_log',
                  'is_subdag',
                  'task_dict',
                  'template_searchpath',
                  'sla_miss_callback',
                  'on_success_callback',
                  'on_failure_callback',
                  'template_undefined',
                  'jinja_environment_kwargs',
                  # has_on_*_callback are only stored if the value is True, as the default is False
                  'has_on_success_callback',
                  'has_on_failure_callback',
              }
          return cls.__serialized_fields
  
      def get_edge_info(self, upstream_task_id: str, downstream_task_id: str) -> EdgeInfoType:
          """
          Returns edge information for the given pair of tasks if present, and
          None if there is no information.
          """
          # Note - older serialized DAGs may not have edge_info being a dict at all
          if self.edge_info:
              return self.edge_info.get(upstream_task_id, {}).get(downstream_task_id, {})
          else:
              return {}
  
      def set_edge_info(self, upstream_task_id: str, downstream_task_id: str, info: EdgeInfoType):
          """
          Sets the given edge information on the DAG. Note that this will overwrite,
          rather than merge with, existing info.
          """
          self.edge_info.setdefault(upstream_task_id, {})[downstream_task_id] = info
  
  
  class DagTag(Base):
      """A tag name per dag, to allow quick filtering in the DAG view."""
  
      __tablename__ = "dag_tag"
      name = Column(String(100), primary_key=True)
      dag_id = Column(String(ID_LEN), ForeignKey('dag.dag_id'), primary_key=True)
  
      def __repr__(self):
          return self.name
  
  
  class DagModel(Base):
      """Table containing DAG properties"""
  
      __tablename__ = "dag"
      """
      These items are stored in the database for state related information
      """
      dag_id = Column(String(ID_LEN), primary_key=True)
      root_dag_id = Column(String(ID_LEN))
      # A DAG can be paused from the UI / DB
      # Set this default value of is_paused based on a configuration value!
      is_paused_at_creation = conf.getboolean('core', 'dags_are_paused_at_creation')
      is_paused = Column(Boolean, default=is_paused_at_creation)
      # Whether the DAG is a subdag
      is_subdag = Column(Boolean, default=False)
      # Whether that DAG was seen on the last DagBag load
      is_active = Column(Boolean, default=False)
      # Last time the scheduler started
      last_parsed_time = Column(UtcDateTime)
      # Last time this DAG was pickled
      last_pickled = Column(UtcDateTime)
      # Time when the DAG last received a refresh signal
      # (e.g. the DAG's "refresh" button was clicked in the web UI)
      last_expired = Column(UtcDateTime)
      # Whether (one  of) the scheduler is scheduling this DAG at the moment
      scheduler_lock = Column(Boolean)
      # Foreign key to the latest pickle_id
      pickle_id = Column(Integer)
      # The location of the file containing the DAG object
      # Note: Do not depend on fileloc pointing to a file; in the case of a
      # packaged DAG, it will point to the subpath of the DAG within the
      # associated zip.
      fileloc = Column(String(2000))
      # String representing the owners
      owners = Column(String(2000))
      # Description of the dag
      description = Column(Text)
      # Default view of the inside the webserver
      default_view = Column(String(25))
      # Schedule interval
      schedule_interval = Column(Interval)
      # Tags for view filter
      tags = relationship('DagTag', cascade='all,delete-orphan', backref=backref('dag'))
  
      max_active_tasks = Column(Integer, nullable=False)
  
      has_task_concurrency_limits = Column(Boolean, nullable=False)
  
      # The execution_date of the next dag run
      next_dagrun = Column(UtcDateTime)
      # Earliest time at which this ``next_dagrun`` can be created
      next_dagrun_create_after = Column(UtcDateTime)
  
      __table_args__ = (
          Index('idx_root_dag_id', root_dag_id, unique=False),
          Index('idx_next_dagrun_create_after', next_dagrun_create_after, unique=False),
      )
  
      NUM_DAGS_PER_DAGRUN_QUERY = conf.getint('scheduler', 'max_dagruns_to_create_per_loop', fallback=10)
  
      def __init__(self, concurrency=None, **kwargs):
          super().__init__(**kwargs)
          if self.max_active_tasks is None:
              if concurrency:
                  warnings.warn(
                      "The 'DagModel.concurrency' parameter is deprecated. Please use 'max_active_tasks'.",
                      DeprecationWarning,
                      stacklevel=2,
                  )
                  self.max_active_tasks = concurrency
              else:
                  self.max_active_tasks = conf.getint('core', 'max_active_tasks_per_dag')
          if self.has_task_concurrency_limits is None:
              # Be safe -- this will be updated later once the DAG is parsed
              self.has_task_concurrency_limits = True
  
      def __repr__(self):
          return f"<DAG: {self.dag_id}>"
  
      @property
      def timezone(self):
          return settings.TIMEZONE
  
      @staticmethod
      @provide_session
      def get_dagmodel(dag_id, session=None):
          return session.query(DagModel).filter(DagModel.dag_id == dag_id).first()
  
      @classmethod
      @provide_session
      def get_current(cls, dag_id, session=None):
          return session.query(cls).filter(cls.dag_id == dag_id).first()
  
      @provide_session
      def get_last_dagrun(self, session=None, include_externally_triggered=False):
          return get_last_dagrun(
              self.dag_id, session=session, include_externally_triggered=include_externally_triggered
          )
  
      @staticmethod
      @provide_session
      def get_paused_dag_ids(dag_ids: List[str], session: Session = None) -> Set[str]:
          """
          Given a list of dag_ids, get a set of Paused Dag Ids
  
          :param dag_ids: List of Dag ids
          :param session: ORM Session
          :return: Paused Dag_ids
          """
          paused_dag_ids = (
              session.query(DagModel.dag_id)
              .filter(DagModel.is_paused == expression.true())
              .filter(DagModel.dag_id.in_(dag_ids))
              .all()
          )
  
          paused_dag_ids = {paused_dag_id for paused_dag_id, in paused_dag_ids}
          return paused_dag_ids
  
      def get_default_view(self) -> str:
          """
          Get the Default DAG View, returns the default config value if DagModel does not
          have a value
          """
          # This is for backwards-compatibility with old dags that don't have None as default_view
          return self.default_view or conf.get('webserver', 'dag_default_view').lower()
  
      @property
      def safe_dag_id(self):
          return self.dag_id.replace('.', '__dot__')
  
      @provide_session
      def set_is_paused(self, is_paused: bool, including_subdags: bool = True, session=None) -> None:
          """
          Pause/Un-pause a DAG.
  
          :param is_paused: Is the DAG paused
          :param including_subdags: whether to include the DAG's subdags
          :param session: session
          """
          filter_query = [
              DagModel.dag_id == self.dag_id,
          ]
          if including_subdags:
              filter_query.append(DagModel.root_dag_id == self.dag_id)
          session.query(DagModel).filter(or_(*filter_query)).update(
              {DagModel.is_paused: is_paused}, synchronize_session='fetch'
          )
          session.commit()
  
      @classmethod
      @provide_session
      def deactivate_deleted_dags(cls, alive_dag_filelocs: List[str], session=None):
          """
          Set ``is_active=False`` on the DAGs for which the DAG files have been removed.
          Additionally change ``is_active=False`` to ``True`` if the DAG file exists.
  
          :param alive_dag_filelocs: file paths of alive DAGs
          :param session: ORM Session
          """
          log.debug("Deactivating DAGs (for which DAG files are deleted) from %s table ", cls.__tablename__)
  
          dag_models = session.query(cls).all()
          for dag_model in dag_models:
              if dag_model.fileloc is not None:
                  if correct_maybe_zipped(dag_model.fileloc) not in alive_dag_filelocs:
                      dag_model.is_active = False
                  else:
                      # If is_active is set as False and the DAG File still exists
                      # Change is_active=True
                      if not dag_model.is_active:
                          dag_model.is_active = True
              else:
                  continue
  
      @classmethod
      def dags_needing_dagruns(cls, session: Session):
          """
          Return (and lock) a list of Dag objects that are due to create a new DagRun.
  
          This will return a resultset of rows  that is row-level-locked with a "SELECT ... FOR UPDATE" query,
          you should ensure that any scheduling decisions are made in a single transaction -- as soon as the
          transaction is committed it will be unlocked.
          """
          # TODO[HA]: Bake this query, it is run _A lot_
          # We limit so that _one_ scheduler doesn't try to do all the creation
          # of dag runs
          query = (
              session.query(cls)
              .filter(
                  cls.is_paused == expression.false(),
                  cls.is_active == expression.true(),
                  cls.next_dagrun_create_after <= func.now(),
              )
              .order_by(cls.next_dagrun_create_after)
              .limit(cls.NUM_DAGS_PER_DAGRUN_QUERY)
          )
  
          return with_row_locks(query, of=cls, session=session, **skip_locked(session=session))
  
      def calculate_dagrun_date_fields(
          self, dag: DAG, most_recent_dag_run: Optional[pendulum.DateTime], active_runs_of_dag: int
      ) -> None:
          """
          Calculate ``next_dagrun`` and `next_dagrun_create_after``
  
          :param dag: The DAG object
          :param most_recent_dag_run: DateTime of most recent run of this dag, or none if not yet scheduled.
          :param active_runs_of_dag: Number of currently active runs of this dag
          """
          self.next_dagrun, self.next_dagrun_create_after = dag.next_dagrun_info(most_recent_dag_run)
  
          if dag.max_active_runs and active_runs_of_dag >= dag.max_active_runs:
              # Since this happens every time the dag is parsed it would be quite spammy at info
              log.debug(
                  "DAG %s is at (or above) max_active_runs (%d of %d), not creating any more runs",
                  dag.dag_id,
                  active_runs_of_dag,
                  dag.max_active_runs,
              )
              self.next_dagrun_create_after = None
  
          log.info("Setting next_dagrun for %s to %s", dag.dag_id, self.next_dagrun)
  
  
  def dag(*dag_args, **dag_kwargs):
      """
      Python dag decorator. Wraps a function into an Airflow DAG.
      Accepts kwargs for operator kwarg. Can be used to parametrize DAGs.
  
      :param dag_args: Arguments for DAG object
      :type dag_args: Any
      :param dag_kwargs: Kwargs for DAG object.
      :type dag_kwargs: Any
      """
  
      def wrapper(f: Callable):
          # Get dag initializer signature and bind it to validate that dag_args, and dag_kwargs are correct
          dag_sig = signature(DAG.__init__)
          dag_bound_args = dag_sig.bind_partial(*dag_args, **dag_kwargs)
  
          @functools.wraps(f)
          def factory(*args, **kwargs):
              # Generate signature for decorated function and bind the arguments when called
              # we do this to extract parameters so we can annotate them on the DAG object.
              # In addition, this fails if we are missing any args/kwargs with TypeError as expected.
              f_sig = signature(f).bind(*args, **kwargs)
              # Apply defaults to capture default values if set.
              f_sig.apply_defaults()
  
              # Set function name as dag_id if not set
              dag_id = dag_bound_args.arguments.get('dag_id', f.__name__)
              dag_bound_args.arguments['dag_id'] = dag_id
  
              # Initialize DAG with bound arguments
              with DAG(*dag_bound_args.args, **dag_bound_args.kwargs) as dag_obj:
                  # Set DAG documentation from function documentation.
                  if f.__doc__:
                      dag_obj.doc_md = f.__doc__
  
                  # Generate DAGParam for each function arg/kwarg and replace it for calling the function.
                  # All args/kwargs for function will be DAGParam object and replaced on execution time.
                  f_kwargs = {}
                  for name, value in f_sig.arguments.items():
                      f_kwargs[name] = dag_obj.param(name, value)
  
                  # set file location to caller source path
                  back = sys._getframe().f_back
                  dag_obj.fileloc = back.f_code.co_filename if back else ""
  
                  # Invoke function to create operators in the DAG scope.
                  f(**f_kwargs)
  
              # Return dag object such that it's accessible in Globals.
              return dag_obj
  
          return factory
  
      return wrapper
  
  
  STATICA_HACK = True
  globals()['kcah_acitats'[::-1].upper()] = False
  if STATICA_HACK:  # pragma: no cover
  
      from airflow.models.serialized_dag import SerializedDagModel
  
      DagModel.serialized_dag = relationship(SerializedDagModel)
  
  
  class DagContext:
      """
      DAG context is used to keep the current DAG when DAG is used as ContextManager.
  
      You can use DAG as context:
  
      .. code-block:: python
  
          with DAG(
              dag_id="example_dag",
              default_args=default_args,
              schedule_interval="0 0 * * *",
              dagrun_timeout=timedelta(minutes=60),
          ) as dag:
              ...
  
      If you do this the context stores the DAG and whenever new task is created, it will use
      such stored DAG as the parent DAG.
  
      """
  
      _context_managed_dag: Optional[DAG] = None
      _previous_context_managed_dags: List[DAG] = []
  
      @classmethod
      def push_context_managed_dag(cls, dag: DAG):
          if cls._context_managed_dag:
              cls._previous_context_managed_dags.append(cls._context_managed_dag)
          cls._context_managed_dag = dag
  
      @classmethod
      def pop_context_managed_dag(cls) -> Optional[DAG]:
          old_dag = cls._context_managed_dag
          if cls._previous_context_managed_dags:
              cls._context_managed_dag = cls._previous_context_managed_dags.pop()
          else:
              cls._context_managed_dag = None
          return old_dag
  
      @classmethod
      def get_current_dag(cls) -> Optional[DAG]:
          return cls._context_managed_dag
  