text: # Copyright 2018 Iguazio
  #
  # Licensed 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 getpass
  import os
  import shlex
  import traceback
  import typing
  import uuid
  from abc import ABC, abstractmethod
  from ast import literal_eval
  from base64 import b64encode
  from copy import deepcopy
  from datetime import datetime, timedelta, timezone
  from os import environ
  from typing import Dict, List, Optional, Tuple, Union
  
  import IPython
  from kubernetes.client.rest import ApiException
  from nuclio.build import mlrun_footer
  from sqlalchemy.orm import Session
  
  import mlrun.errors
  import mlrun.utils.helpers
  import mlrun.utils.regex
  from mlrun.api import schemas
  from mlrun.api.constants import LogSources
  from mlrun.api.db.base import DBInterface
  from mlrun.utils.helpers import generate_object_uri, verify_field_regex
  
  from ..config import config
  from ..datastore import store_manager
  from ..db import RunDBError, get_or_set_dburl, get_run_db
  from ..execution import MLClientCtx
  from ..k8s_utils import get_k8s_helper
  from ..kfpops import mlrun_op, write_kfpmeta
  from ..lists import RunList
  from ..model import (
      BaseMetadata,
      HyperParamOptions,
      ImageBuilder,
      ModelObj,
      RunObject,
      RunTemplate,
  )
  from ..secrets import SecretsStore
  from ..utils import (
      dict_to_json,
      dict_to_yaml,
      enrich_image_url,
      get_in,
      get_parsed_docker_registry,
      get_ui_url,
      is_ipython,
      logger,
      now_date,
      update_in,
  )
  from .constants import PodPhases, RunStates
  from .funcdoc import update_function_entry_points
  from .generators import get_generator
  from .utils import RunError, calc_hash, results_to_iter
  
  run_modes = ["pass"]
  
  
  class FunctionStatus(ModelObj):
      def __init__(self, state=None, build_pod=None):
          self.state = state
          self.build_pod = build_pod
  
  
  class FunctionSpec(ModelObj):
      def __init__(
          self,
          command=None,
          args=None,
          image=None,
          mode=None,
          build=None,
          entry_points=None,
          description=None,
          workdir=None,
          default_handler=None,
          pythonpath=None,
          disable_auto_mount=False,
      ):
  
          self.command = command or ""
          self.image = image or ""
          self.mode = mode
          self.args = args or []
          self.rundb = None
          self.description = description or ""
          self.workdir = workdir
          self.pythonpath = pythonpath
  
          self._build = None
          self.build = build
          self.default_handler = default_handler
          # TODO: type verification (FunctionEntrypoint dict)
          self.entry_points = entry_points or {}
          self.disable_auto_mount = disable_auto_mount
  
      @property
      def build(self) -> ImageBuilder:
          return self._build
  
      @build.setter
      def build(self, build):
          self._build = self._verify_dict(build, "build", ImageBuilder)
  
  
  class BaseRuntime(ModelObj):
      kind = "base"
      _is_nested = False
      _is_remote = False
      _dict_fields = ["kind", "metadata", "spec", "status", "verbose"]
  
      def __init__(self, metadata=None, spec=None):
          self._metadata = None
          self.metadata = metadata
          self.kfp = None
          self._spec = None
          self.spec = spec
          self._db_conn = None
          self._secrets = None
          self._k8s = None
          self._is_built = False
          self.is_child = False
          self._status = None
          self.status = None
          self._is_api_server = False
          self.verbose = False
  
      def set_db_connection(self, conn, is_api=False):
          if not self._db_conn:
              self._db_conn = conn
          self._is_api_server = is_api
  
      @property
      def metadata(self) -> BaseMetadata:
          return self._metadata
  
      @metadata.setter
      def metadata(self, metadata):
          self._metadata = self._verify_dict(metadata, "metadata", BaseMetadata)
  
      @property
      def spec(self) -> FunctionSpec:
          return self._spec
  
      @spec.setter
      def spec(self, spec):
          self._spec = self._verify_dict(spec, "spec", FunctionSpec)
  
      @property
      def status(self) -> FunctionStatus:
          return self._status
  
      @status.setter
      def status(self, status):
          self._status = self._verify_dict(status, "status", FunctionStatus)
  
      def _get_k8s(self):
          return get_k8s_helper()
  
      def set_label(self, key, value):
          self.metadata.labels[key] = str(value)
          return self
  
      @property
      def uri(self):
          return self._function_uri()
  
      @property
      def is_deployed(self):
          return True
  
      def _is_remote_api(self):
          db = self._get_db()
          if db and db.kind == "http":
              return True
          return False
  
      def _use_remote_api(self):
          if (
              self._is_remote
              and not self._is_api_server
              and self._get_db()
              and self._get_db().kind == "http"
          ):
              return True
          return False
  
      def _function_uri(self, tag=None, hash_key=None):
          return generate_object_uri(
              self.metadata.project,
              self.metadata.name,
              tag=tag or self.metadata.tag,
              hash_key=hash_key,
          )
  
      def _ensure_run_db(self):
          self.spec.rundb = self.spec.rundb or get_or_set_dburl()
  
      def _get_db(self):
          self._ensure_run_db()
          if not self._db_conn:
              if self.spec.rundb:
                  self._db_conn = get_run_db(self.spec.rundb, secrets=self._secrets)
          return self._db_conn
  
      # This function is different than the auto_mount function, as it mounts to runtimes based on the configuration.
      # That's why it's named differently.
      def try_auto_mount_based_on_config(self):
          pass
  
      def validate_and_enrich_service_account(
          self, allowed_service_account, default_service_account
      ):
          pass
  
      def fill_credentials(self):
          if "MLRUN_AUTH_SESSION" in os.environ or "V3IO_ACCESS_KEY" in os.environ:
              self.metadata.credentials.access_key = os.environ.get(
                  "MLRUN_AUTH_SESSION"
              ) or os.environ.get("V3IO_ACCESS_KEY")
  
      def run(
          self,
          runspec: RunObject = None,
          handler=None,
          name: str = "",
          project: str = "",
          params: dict = None,
          inputs: Dict[str, str] = None,
          out_path: str = "",
          workdir: str = "",
          artifact_path: str = "",
          watch: bool = True,
          schedule: Union[str, schemas.ScheduleCronTrigger] = None,
          hyperparams: Dict[str, list] = None,
          hyper_param_options: HyperParamOptions = None,
          verbose=None,
          scrape_metrics: bool = None,
          local=False,
          local_code_path=None,
      ) -> RunObject:
          """Run a local or remote task.
  
          :param runspec:        run template object or dict (see RunTemplate)
          :param handler:        pointer or name of a function handler
          :param name:           execution name
          :param project:        project name
          :param params:         input parameters (dict)
          :param inputs:         input objects (dict of key: path)
          :param out_path:       default artifact output path
          :param artifact_path:  default artifact output path (will replace out_path)
          :param workdir:        default input artifacts path
          :param watch:          watch/follow run log
          :param schedule:       ScheduleCronTrigger class instance or a standard crontab expression string
                                 (which will be converted to the class using its `from_crontab` constructor.
                                 see this link for help:
                                 https://apscheduler.readthedocs.io/en/v3.6.3/modules/triggers/cron.html#module-apscheduler.triggers.cron
          :param hyperparams:    dict of param name and list of values to be enumerated e.g. {"p1": [1,2,3]}
                                 the default strategy is grid search, can specify strategy (grid, list, random)
                                 and other options in the hyper_param_options parameter
          :param hyper_param_options:  dict or :py:class:`~mlrun.model.HyperParamOptions` struct of
                                       hyper parameter options
          :param verbose:        add verbose prints/logs
          :param scrape_metrics: whether to add the `mlrun/scrape-metrics` label to this run's resources
          :param local:      run the function locally vs on the runtime/cluster
          :param local_code_path: path of the code for local runs & debug
  
          :return: run context object (RunObject) with run metadata, results and status
          """
          mlrun.utils.helpers.verify_dict_items_type("Inputs", inputs, [str], [str])
  
          if self.spec.mode and self.spec.mode not in run_modes:
              raise ValueError(f'run mode can only be {",".join(run_modes)}')
  
          # Perform auto-mount if necessary - make sure it only runs on client side (when using remote API)
          if self._use_remote_api():
              self.try_auto_mount_based_on_config()
              self.fill_credentials()
  
          if local:
  
              if schedule is not None:
                  raise mlrun.errors.MLRunInvalidArgumentError(
                      "local and schedule cannot be used together"
                  )
              # allow local run simulation with a flip of a flag
              command = self
              if local_code_path:
                  project = project or self.metadata.project
                  name = name or self.metadata.name
                  command = local_code_path
              return mlrun.run_local(
                  runspec,
                  command,
                  name,
                  self.spec.args,
                  workdir=workdir,
                  project=project,
                  handler=handler,
                  params=params,
                  inputs=inputs,
                  artifact_path=artifact_path,
                  mode=self.spec.mode,
              )
  
          if runspec:
              runspec = deepcopy(runspec)
              if isinstance(runspec, str):
                  runspec = literal_eval(runspec)
              if not isinstance(runspec, (dict, RunTemplate, RunObject)):
                  raise ValueError(
                      "task/runspec is not a valid task object," f" type={type(runspec)}"
                  )
  
          if isinstance(runspec, RunTemplate):
              runspec = RunObject.from_template(runspec)
          if isinstance(runspec, dict) or runspec is None:
              runspec = RunObject.from_dict(runspec)
  
          runspec.spec.handler = (
              handler or runspec.spec.handler or self.spec.default_handler or ""
          )
          if runspec.spec.handler and self.kind not in ["handler", "dask"]:
              runspec.spec.handler = runspec.spec.handler_name
  
          def_name = self.metadata.name
          if runspec.spec.handler_name:
              def_name += "-" + runspec.spec.handler_name
          runspec.metadata.name = name or runspec.metadata.name or def_name
          verify_field_regex(
              "run.metadata.name", runspec.metadata.name, mlrun.utils.regex.run_name
          )
          runspec.metadata.project = (
              project
              or runspec.metadata.project
              or self.metadata.project
              or config.default_project
          )
          runspec.spec.parameters = params or runspec.spec.parameters
          runspec.spec.inputs = inputs or runspec.spec.inputs
          runspec.spec.hyperparams = hyperparams or runspec.spec.hyperparams
          runspec.spec.hyper_param_options = (
              hyper_param_options or runspec.spec.hyper_param_options
          )
          runspec.spec.verbose = verbose or runspec.spec.verbose
          if scrape_metrics is None:
              if runspec.spec.scrape_metrics is None:
                  scrape_metrics = config.scrape_metrics
              else:
                  scrape_metrics = runspec.spec.scrape_metrics
          runspec.spec.scrape_metrics = scrape_metrics
          runspec.spec.output_path = out_path or artifact_path or runspec.spec.output_path
          runspec.spec.input_path = (
              workdir or runspec.spec.input_path or self.spec.workdir
          )
  
          spec = runspec.spec
          if spec.secret_sources:
              self._secrets = SecretsStore.from_list(spec.secret_sources)
  
          # update run metadata (uid, labels) and store in DB
          meta = runspec.metadata
          meta.uid = meta.uid or uuid.uuid4().hex
          runspec.spec.output_path = runspec.spec.output_path or config.artifact_path
          if runspec.spec.output_path:
              runspec.spec.output_path = runspec.spec.output_path.replace(
                  "{{run.uid}}", meta.uid
              )
              runspec.spec.output_path = mlrun.utils.helpers.fill_artifact_path_template(
                  runspec.spec.output_path, runspec.metadata.project
              )
          if is_local(runspec.spec.output_path):
              logger.warning(
                  "artifact path is not defined or is local,"
                  " artifacts will not be visible in the UI"
              )
              if self.kind not in ["", "local", "handler", "dask"]:
                  raise ValueError(
                      "absolute artifact_path must be specified"
                      " when running remote tasks"
                  )
          db = self._get_db()
  
          if not self.is_deployed:
              raise RunError(
                  "function image is not built/ready, use .deploy() method first"
              )
  
          if self.verbose:
              logger.info(f"runspec:\n{runspec.to_yaml()}")
  
          if "V3IO_USERNAME" in environ and "v3io_user" not in meta.labels:
              meta.labels["v3io_user"] = environ.get("V3IO_USERNAME")
  
          if not self.is_child:
              db_str = "self" if self._is_api_server else self.spec.rundb
              logger.info(f"starting run {meta.name} uid={meta.uid} DB={db_str}")
              meta.labels["kind"] = self.kind
              if "owner" not in meta.labels:
                  meta.labels["owner"] = environ.get("V3IO_USERNAME") or getpass.getuser()
              if runspec.spec.output_path:
                  runspec.spec.output_path = runspec.spec.output_path.replace(
                      "{{run.user}}", meta.labels["owner"]
                  )
  
              if db and self.kind != "handler":
                  struct = self.to_dict()
                  hash_key = db.store_function(
                      struct, self.metadata.name, self.metadata.project, versioned=True
                  )
                  runspec.spec.function = self._function_uri(hash_key=hash_key)
  
          # execute the job remotely (to a k8s cluster via the API service)
          if self._use_remote_api():
              if self._secrets:
                  runspec.spec.secret_sources = self._secrets.to_serial()
              try:
                  resp = db.submit_job(runspec, schedule=schedule)
                  if schedule:
                      logger.info(f"task scheduled, {resp}")
                      return
  
                  if resp:
                      txt = get_in(resp, "status.status_text")
                      if txt:
                          logger.info(txt)
                  if watch or self.kfp:
                      runspec.logs(True, self._get_db())
                      resp = self._get_db_run(runspec)
              except Exception as err:
                  logger.error(f"got remote run err, {err}")
                  result = None
                  # if we got a schedule no reason to do post_run stuff (it purposed to update the run status with error,
                  # but there's no run in case of schedule)
                  if not schedule:
                      result = self._update_run_state(task=runspec, err=err)
                  return self._wrap_run_result(
                      result, runspec, schedule=schedule, err=err
                  )
              return self._wrap_run_result(resp, runspec, schedule=schedule)
  
          elif self._is_remote and not self._is_api_server and not self.kfp:
              logger.warning(
                  "warning!, Api url not set, " "trying to exec remote runtime locally"
              )
  
          execution = MLClientCtx.from_dict(
              runspec.to_dict(), db, autocommit=False, is_api=self._is_api_server
          )
          self._pre_run(runspec, execution)  # hook for runtime specific prep
  
          # create task generator (for child runs) from spec
          task_generator = None
          if not self._is_nested:
              task_generator = get_generator(spec, execution)
  
          last_err = None
          if task_generator:
              # multiple runs (based on hyper params or params file)
              runner = self._run_many
              if hasattr(self, "_parallel_run_many") and task_generator.use_parallel():
                  runner = self._parallel_run_many
              results = runner(task_generator, execution, runspec)
              results_to_iter(results, runspec, execution)
              result = execution.to_dict()
  
          else:
              # single run
              try:
                  resp = self._run(runspec, execution)
                  if watch and self.kind not in ["", "handler", "local"]:
                      state = runspec.logs(True, self._get_db())
                      if state != "succeeded":
                          logger.warning(f"run ended with state {state}")
                  result = self._update_run_state(resp, task=runspec)
              except RunError as err:
                  last_err = err
                  result = self._update_run_state(task=runspec, err=err)
  
          self._post_run(result, execution)  # hook for runtime specific cleanup
  
          return self._wrap_run_result(result, runspec, schedule=schedule, err=last_err)
  
      def _wrap_run_result(
          self, result: dict, runspec: RunObject, schedule=None, err=None
      ):
          # if the purpose was to schedule (and not to run) nothing to wrap
          if schedule:
              return
  
          if result and self.kfp and err is None:
              write_kfpmeta(result)
  
          # show ipython/jupyter result table widget
          results_tbl = RunList()
          if result:
              results_tbl.append(result)
          else:
              logger.info("no returned result (job may still be in progress)")
              results_tbl.append(runspec.to_dict())
  
          uid = runspec.metadata.uid
          project = runspec.metadata.project
          if is_ipython and config.ipython_widget:
              results_tbl.show()
              print()
              ui_url = get_ui_url(project, uid)
              if ui_url:
                  ui_url = f' or <a href="{ui_url}" target="_blank">click here</a> to open in UI'
              IPython.display.display(
                  IPython.display.HTML(
                      f"<b> > to track results use the .show() or .logs() methods {ui_url}</b>"
                  )
              )
          elif not self.is_child:
              ui_url = get_ui_url(project, uid)
              ui_url = f"\nor click {ui_url} for UI" if ui_url else ""
              project_flag = f"-p {project}" if project else ""
              print(
                  f"to track results use the CLI:\n"
                  f"info: mlrun get run {uid} {project_flag}\nlogs: mlrun logs {uid} {project_flag}{ui_url}"
              )
  
          if result:
              run = RunObject.from_dict(result)
              logger.info(f"run executed, status={run.status.state}")
              if run.status.state == "error":
                  if self._is_remote and not self.is_child:
                      print(f"runtime error: {run.status.error}")
                  raise RunError(run.status.error)
              return run
  
          return None
  
      def _get_db_run(self, task: RunObject = None):
          if self._get_db() and task:
              project = task.metadata.project
              uid = task.metadata.uid
              iter = task.metadata.iteration
              try:
                  return self._get_db().read_run(uid, project, iter=iter)
              except RunDBError:
                  return None
          if task:
              return task.to_dict()
  
      def _generate_runtime_env(self, runobj: RunObject):
          runtime_env = {
              "MLRUN_EXEC_CONFIG": runobj.to_json(),
              "MLRUN_DEFAULT_PROJECT": runobj.metadata.project
              or self.metadata.project
              or config.default_project,
          }
          if runobj.spec.verbose:
              runtime_env["MLRUN_LOG_LEVEL"] = "DEBUG"
          if config.httpdb.api_url:
              runtime_env["MLRUN_DBPATH"] = config.httpdb.api_url
          if self.metadata.namespace or config.namespace:
              runtime_env["MLRUN_NAMESPACE"] = self.metadata.namespace or config.namespace
          return runtime_env
  
      def _get_cmd_args(self, runobj: RunObject):
          extra_env = self._generate_runtime_env(runobj)
          if self.spec.pythonpath:
              extra_env["PYTHONPATH"] = self.spec.pythonpath
          args = []
          command = self.spec.command
          code = (
              self.spec.build.functionSourceCode if hasattr(self.spec, "build") else None
          )
  
          if runobj.spec.handler and self.spec.mode == "pass":
              raise ValueError('cannot use "pass" mode with handler')
  
          if code:
              extra_env["MLRUN_EXEC_CODE"] = code
  
          load_archive = self.spec.build.load_source_on_run and self.spec.build.source
          need_mlrun = code or load_archive or self.spec.mode != "pass"
  
          if need_mlrun:
              args = ["run", "--name", runobj.metadata.name, "--from-env"]
              if runobj.spec.handler:
                  args += ["--handler", runobj.spec.handler]
              if self.spec.mode:
                  args += ["--mode", self.spec.mode]
              if self.spec.build.origin_filename:
                  args += ["--origin-file", self.spec.build.origin_filename]
  
              if load_archive:
                  if code:
                      raise ValueError("cannot specify both code and source archive")
                  args += ["--source", self.spec.build.source]
  
              if command:
                  args += [shlex.quote(command)]
              command = "mlrun"
              if self.spec.args:
                  args = args + self.spec.args
          else:
              command = command.format(**runobj.spec.parameters)
              if self.spec.args:
                  args = [
                      shlex.quote(arg.format(**runobj.spec.parameters))
                      for arg in self.spec.args
                  ]
  
          extra_env = [{"name": k, "value": v} for k, v in extra_env.items()]
          return command, args, extra_env
  
      def _pre_run(self, runspec: RunObject, execution):
          pass
  
      def _post_run(self, results, execution):
          pass
  
      def _run(self, runobj: RunObject, execution) -> dict:
          pass
  
      def _run_many(self, generator, execution, runobj: RunObject) -> RunList:
          results = RunList()
          num_errors = 0
          tasks = generator.generate(runobj)
          for task in tasks:
              try:
                  self.store_run(task)
                  resp = self._run(task, execution)
                  resp = self._update_run_state(resp, task=task)
                  run_results = resp["status"].get("results", {})
                  if generator.eval_stop_condition(run_results):
                      logger.info(
                          f"reached early stop condition ({generator.options.stop_condition}), stopping iterations!"
                      )
                      results.append(resp)
                      break
  
              except RunError as err:
                  task.status.state = "error"
                  task.status.error = str(err)
                  resp = self._update_run_state(task=task, err=err)
                  num_errors += 1
                  if num_errors > generator.max_errors:
                      logger.error("too many errors, stopping iterations!")
                      results.append(resp)
                      break
  
              results.append(resp)
  
          return results
  
      def store_run(self, runobj: RunObject):
          if self._get_db() and runobj:
              project = runobj.metadata.project
              uid = runobj.metadata.uid
              iter = runobj.metadata.iteration
              self._get_db().store_run(runobj.to_dict(), uid, project, iter=iter)
  
      def _store_run_dict(self, rundict: dict):
          if self._get_db() and rundict:
              project = get_in(rundict, "metadata.project", "")
              uid = get_in(rundict, "metadata.uid")
              iter = get_in(rundict, "metadata.iteration", 0)
              self._get_db().store_run(rundict, uid, project, iter=iter)
  
      def _update_run_state(
          self, resp: dict = None, task: RunObject = None, err=None
      ) -> dict:
          """update the task state in the DB"""
          was_none = False
          if resp is None and task:
              was_none = True
              resp = self._get_db_run(task)
  
              if not resp:
                  self.store_run(task)
                  return task.to_dict()
  
              if task.status.status_text:
                  update_in(resp, "status.status_text", task.status.status_text)
  
          if resp is None:
              return None
  
          if not isinstance(resp, dict):
              raise ValueError(f"post_run called with type {type(resp)}")
  
          updates = None
          last_state = get_in(resp, "status.state", "")
          if last_state == "error" or err:
              updates = {"status.last_update": now_date().isoformat()}
              updates["status.state"] = "error"
              update_in(resp, "status.state", "error")
              if err:
                  update_in(resp, "status.error", str(err))
              err = get_in(resp, "status.error")
              if err:
                  updates["status.error"] = str(err)
          elif not was_none and last_state != "completed":
              updates = {"status.last_update": now_date().isoformat()}
              updates["status.state"] = "completed"
              update_in(resp, "status.state", "completed")
  
          if self._get_db() and updates:
              project = get_in(resp, "metadata.project")
              uid = get_in(resp, "metadata.uid")
              iter = get_in(resp, "metadata.iteration", 0)
              self._get_db().update_run(updates, uid, project, iter=iter)
  
          return resp
  
      def _force_handler(self, handler):
          if not handler:
              raise RunError(f"handler must be provided for {self.kind} runtime")
  
      def full_image_path(self, image=None):
          image = image or self.spec.image or ""
  
          image = enrich_image_url(image)
          if not image.startswith("."):
              return image
          registry, _ = get_parsed_docker_registry()
          if registry:
              return f"{registry}/{image[1:]}"
          namespace_domain = environ.get("IGZ_NAMESPACE_DOMAIN", None)
          if namespace_domain is not None:
              return f"docker-registry.{namespace_domain}:80/{image[1:]}"
          raise RunError("local container registry is not defined")
  
      def as_step(
          self,
          runspec: RunObject = None,
          handler=None,
          name: str = "",
          project: str = "",
          params: dict = None,
          hyperparams=None,
          selector="",
          hyper_param_options: HyperParamOptions = None,
          inputs: dict = None,
          outputs: dict = None,
          workdir: str = "",
          artifact_path: str = "",
          image: str = "",
          labels: dict = None,
          use_db=True,
          verbose=None,
          scrape_metrics=False,
      ):
          """Run a local or remote task.
  
          :param runspec:         run template object or dict (see RunTemplate)
          :param handler:         name of the function handler
          :param name:            execution name
          :param project:         project name
          :param params:          input parameters (dict)
          :param hyperparams:     hyper parameters
          :param selector:        selection criteria for hyper params
          :param inputs:          input objects (dict of key: path)
          :param outputs:         list of outputs which can pass in the workflow
          :param artifact_path:   default artifact output path (replace out_path)
          :param workdir:         default input artifacts path
          :param image:           container image to use
          :param labels:          labels to tag the job/run with ({key:val, ..})
          :param use_db:          save function spec in the db (vs the workflow file)
          :param verbose:         add verbose prints/logs
          :param scrape_metrics:  whether to add the `mlrun/scrape-metrics` label to this run's resources
  
          :return: KubeFlow containerOp
          """
  
          # if self.spec.image and not image:
          #     image = self.full_image_path()
  
          if use_db:
              # if the same function is built as part of the pipeline we do not use the versioned function
              # rather the latest function w the same tag so we can pick up the updated image/status
              versioned = False if hasattr(self, "_build_in_pipeline") else False
              url = self.save(versioned=versioned, refresh=True)
          else:
              url = None
  
          if runspec is not None:
              verify_field_regex(
                  "run.metadata.name", runspec.metadata.name, mlrun.utils.regex.run_name
              )
  
          return mlrun_op(
              name,
              project,
              function=self,
              func_url=url,
              runobj=runspec,
              handler=handler,
              params=params,
              hyperparams=hyperparams,
              selector=selector,
              hyper_param_options=hyper_param_options,
              inputs=inputs,
              outputs=outputs,
              job_image=image,
              labels=labels,
              out_path=artifact_path,
              in_path=workdir,
              verbose=verbose,
              scrape_metrics=scrape_metrics,
          )
  
      def with_code(self, from_file="", body=None, with_doc=True):
          """Update the function code
          This function eliminates the need to build container images every time we edit the code
  
          :param from_file:   blank for current notebook, or path to .py/.ipynb file
          :param body:        will use the body as the function code
          :param with_doc:    update the document of the function parameters
  
          :return: function object
          """
          if body and from_file:
              raise mlrun.errors.MLRunInvalidArgumentError(
                  "must provide either body or from_file argument. not both"
              )
  
          if (not body and not from_file) or (from_file and from_file.endswith(".ipynb")):
              from nuclio import build_file
  
              _, _, body = build_file(from_file, name=self.metadata.name)
  
          else:
              if from_file:
                  with open(from_file) as fp:
                      body = fp.read()
              if self.kind == mlrun.runtimes.RuntimeKinds.serving:
                  body = body + mlrun_footer.format(
                      mlrun.runtimes.serving.serving_subkind
                  )
  
          self.spec.build.functionSourceCode = b64encode(body.encode("utf-8")).decode(
              "utf-8"
          )
          if with_doc:
              update_function_entry_points(self, body)
          return self
  
      def with_requirements(self, requirements: Union[str, List[str]]):
          """add package requirements from file or list to build spec.
  
          :param requirements:  python requirements file path or list of packages
  
          :return: function object
          """
          if isinstance(requirements, str):
              with open(requirements, "r") as fp:
                  requirements = fp.read().splitlines()
          commands = self.spec.build.commands or []
          commands.append("python -m pip install " + " ".join(requirements))
          self.spec.build.commands = commands
          return self
  
      def export(self, target="", format=".yaml", secrets=None, strip=True):
          """save function spec to a local/remote path (default to./function.yaml)
  
          :param target:   target path/url
          :param format:   `.yaml` (default) or `.json`
          :param secrets:  optional secrets dict/object for target path (e.g. s3)
          :param strip:    strip status data
  
          :returns: self
          """
          if self.kind == "handler":
              raise ValueError(
                  "cannot export local handler function, use "
                  + "code_to_function() to serialize your function"
              )
          calc_hash(self)
          struct = self.to_dict(strip=strip)
          if format == ".yaml":
              data = dict_to_yaml(struct)
          else:
              data = dict_to_json(struct)
          stores = store_manager.set(secrets)
          target = target or "function.yaml"
          datastore, subpath = stores.get_or_create_store(target)
          datastore.put(subpath, data)
          logger.info(f"function spec saved to path: {target}")
          return self
  
      def save(self, tag="", versioned=False, refresh=False) -> str:
          db = self._get_db()
          if not db:
              logger.error("database connection is not configured")
              return ""
  
          if refresh and self._is_remote_api():
              try:
                  meta = self.metadata
                  db_func = db.get_function(meta.name, meta.project, meta.tag)
                  if db_func and "status" in db_func:
                      self.status = db_func["status"]
                      if (
                          self.status.state
                          and self.status.state == "ready"
                          and "nuclio_name" not in self.status
                      ):
                          self.spec.image = get_in(db_func, "spec.image", self.spec.image)
              except Exception:
                  pass
  
          tag = tag or self.metadata.tag
  
          obj = self.to_dict()
          logger.debug(f"saving function: {self.metadata.name}, tag: {tag}")
          hash_key = db.store_function(
              obj, self.metadata.name, self.metadata.project, tag, versioned
          )
          hash_key = hash_key if versioned else None
          return "db://" + self._function_uri(hash_key=hash_key, tag=tag)
  
      def to_dict(self, fields=None, exclude=None, strip=False):
          struct = super().to_dict(fields, exclude=exclude)
          if strip:
              if "status" in struct:
                  del struct["status"]
          return struct
  
      def doc(self):
          print("function:", self.metadata.name)
          print(self.spec.description)
          if self.spec.default_handler:
              print("default handler:", self.spec.default_handler)
          if self.spec.entry_points:
              print("entry points:")
              for name, entry in self.spec.entry_points.items():
                  print(f"  {name}: {entry.get('doc', '')}")
                  params = entry.get("parameters")
                  if params:
                      for p in params:
                          line = p["name"]
                          if "type" in p:
                              line += f"({p['type']})"
                          line += "  - " + p.get("doc", "")
                          if "default" in p:
                              line += f", default={p['default']}"
                          print("    " + line)
  
  
  def is_local(url):
      if not url:
          return True
      return "://" not in url and not url.startswith("/")
  
  
  class BaseRuntimeHandler(ABC):
      # setting here to allow tests to override
      wait_for_deletion_interval = 10
  
      @staticmethod
      @abstractmethod
      def _get_object_label_selector(object_id: str) -> str:
          """
          Should return the label selector should be used to get only resources of a specific object (with id object_id)
          """
          pass
  
      @staticmethod
      @abstractmethod
      def _get_possible_mlrun_class_label_values() -> List[str]:
          """
          Should return the possible values of the mlrun/class label for runtime resources that are of this runtime
          handler kind
          """
          pass
  
      def list_resources(
          self,
          project: str,
          object_id: typing.Optional[str] = None,
          label_selector: str = None,
          group_by: Optional[mlrun.api.schemas.ListRuntimeResourcesGroupByField] = None,
      ) -> Union[
          mlrun.api.schemas.RuntimeResources,
          mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
          mlrun.api.schemas.GroupedByProjectRuntimeResourcesOutput,
      ]:
          # We currently don't support removing runtime resources in non k8s env
          if not mlrun.k8s_utils.get_k8s_helper(
              silent=True
          ).is_running_inside_kubernetes_cluster():
              return {}
          k8s_helper = get_k8s_helper()
          namespace = k8s_helper.resolve_namespace()
          label_selector = self._resolve_label_selector(
              project, object_id, label_selector
          )
          pods = self._list_pods(namespace, label_selector)
          pod_resources = self._build_pod_resources(pods)
          crd_objects = self._list_crd_objects(namespace, label_selector)
          crd_resources = self._build_crd_resources(crd_objects)
          response = self._build_list_resources_response(
              pod_resources, crd_resources, group_by
          )
          response = self._enrich_list_resources_response(
              response, namespace, label_selector, group_by
          )
          return response
  
      def build_output_from_runtime_resources(
          self,
          runtime_resources_list: List[mlrun.api.schemas.RuntimeResources],
          group_by: Optional[mlrun.api.schemas.ListRuntimeResourcesGroupByField] = None,
      ):
          pod_resources = []
          crd_resources = []
          for runtime_resources in runtime_resources_list:
              pod_resources += runtime_resources.pod_resources
              crd_resources += runtime_resources.crd_resources
          response = self._build_list_resources_response(
              pod_resources, crd_resources, group_by
          )
          response = self._build_output_from_runtime_resources(
              response, runtime_resources_list, group_by
          )
          return response
  
      def delete_resources(
          self,
          db: DBInterface,
          db_session: Session,
          label_selector: str = None,
          force: bool = False,
          grace_period: int = None,
      ):
          if grace_period is None:
              grace_period = config.runtime_resources_deletion_grace_period
          # We currently don't support removing runtime resources in non k8s env
          if not mlrun.k8s_utils.get_k8s_helper(
              silent=True
          ).is_running_inside_kubernetes_cluster():
              return
          k8s_helper = get_k8s_helper()
          namespace = k8s_helper.resolve_namespace()
          label_selector = self._resolve_label_selector(
              "*", label_selector=label_selector
          )
          crd_group, crd_version, crd_plural = self._get_crd_info()
          if crd_group and crd_version and crd_plural:
              deleted_resources = self._delete_crd_resources(
                  db, db_session, namespace, label_selector, force, grace_period,
              )
          else:
              deleted_resources = self._delete_pod_resources(
                  db, db_session, namespace, label_selector, force, grace_period,
              )
          self._delete_resources(
              db,
              db_session,
              namespace,
              deleted_resources,
              label_selector,
              force,
              grace_period,
          )
  
      def delete_runtime_object_resources(
          self,
          db: DBInterface,
          db_session: Session,
          object_id: str,
          label_selector: str = None,
          force: bool = False,
          grace_period: int = None,
      ):
          if grace_period is None:
              grace_period = config.runtime_resources_deletion_grace_period
          label_selector = self._add_object_label_selector_if_needed(
              object_id, label_selector
          )
          self.delete_resources(db, db_session, label_selector, force, grace_period)
  
      def monitor_runs(self, db: DBInterface, db_session: Session):
          k8s_helper = get_k8s_helper()
          namespace = k8s_helper.resolve_namespace()
          label_selector = self._get_default_label_selector()
          crd_group, crd_version, crd_plural = self._get_crd_info()
          runtime_resource_is_crd = False
          if crd_group and crd_version and crd_plural:
              runtime_resource_is_crd = True
              runtime_resources = self._list_crd_objects(namespace, label_selector)
          else:
              runtime_resources = self._list_pods(namespace, label_selector)
          project_run_uid_map = self._list_runs_for_monitoring(db, db_session)
          for runtime_resource in runtime_resources:
              try:
                  self._monitor_runtime_resource(
                      db,
                      db_session,
                      project_run_uid_map,
                      runtime_resource,
                      runtime_resource_is_crd,
                      namespace,
                  )
              except Exception as exc:
                  logger.warning(
                      "Failed monitoring runtime resource. Continuing",
                      runtime_resource_name=runtime_resource["metadata"]["name"],
                      namespace=namespace,
                      exc=str(exc),
                      traceback=traceback.format_exc(),
                  )
  
      def _add_object_label_selector_if_needed(
          self,
          object_id: typing.Optional[str] = None,
          label_selector: typing.Optional[str] = None,
      ):
          if object_id:
              object_label_selector = self._get_object_label_selector(object_id)
              if label_selector:
                  label_selector = ",".join([object_label_selector, label_selector])
              else:
                  label_selector = object_label_selector
          return label_selector
  
      def _enrich_list_resources_response(
          self,
          response: Union[
              mlrun.api.schemas.RuntimeResources,
              mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
              mlrun.api.schemas.GroupedByProjectRuntimeResourcesOutput,
          ],
          namespace: str,
          label_selector: str = None,
          group_by: Optional[mlrun.api.schemas.ListRuntimeResourcesGroupByField] = None,
      ) -> Union[
          mlrun.api.schemas.RuntimeResources,
          mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
          mlrun.api.schemas.GroupedByProjectRuntimeResourcesOutput,
      ]:
          """
          Override this to list resources other then pods or CRDs (which are handled by the base class)
          """
          return response
  
      def _build_output_from_runtime_resources(
          self,
          response: Union[
              mlrun.api.schemas.RuntimeResources,
              mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
              mlrun.api.schemas.GroupedByProjectRuntimeResourcesOutput,
          ],
          runtime_resources_list: List[mlrun.api.schemas.RuntimeResources],
          group_by: Optional[mlrun.api.schemas.ListRuntimeResourcesGroupByField] = None,
      ):
          """
          Override this to add runtime resources other then pods or CRDs (which are handled by the base class) to the
          output
          """
          return response
  
      def _delete_resources(
          self,
          db: DBInterface,
          db_session: Session,
          namespace: str,
          deleted_resources: List[Dict],
          label_selector: str = None,
          force: bool = False,
          grace_period: int = None,
      ):
          """
          Override this to handle deletion of resources other then pods or CRDs (which are handled by the base class)
          Note that this is happening before the deletion of the CRDs or the pods
          Note to add this at the beginning:
          if grace_period is None:
              grace_period = config.runtime_resources_deletion_grace_period
          """
          pass
  
      def _resolve_crd_object_status_info(
          self, db: DBInterface, db_session: Session, crd_object
      ) -> Tuple[bool, Optional[datetime], Optional[str]]:
          """
          Override this if the runtime has CRD resources.
          :return: Tuple with:
          1. bool determining whether the crd object is in terminal state
          2. datetime of when the crd object got into terminal state (only when the crd object in terminal state)
          3. the desired run state matching the crd object state
          """
          return False, None, None
  
      def _update_ui_url(
          self,
          db: DBInterface,
          db_session: Session,
          project: str,
          uid: str,
          crd_object,
          run: Dict = None,
      ):
          """
          Update the UI URL for relevant jobs.
          """
          pass
  
      def _resolve_pod_status_info(
          self, db: DBInterface, db_session: Session, pod: Dict
      ) -> Tuple[bool, Optional[datetime], Optional[str]]:
          """
          :return: Tuple with:
          1. bool determining whether the pod is in terminal state
          2. datetime of when the pod got into terminal state (only when the pod in terminal state)
          3. the run state matching the pod state
          """
          in_terminal_state = pod["status"]["phase"] in PodPhases.terminal_phases()
          run_state = PodPhases.pod_phase_to_run_state(pod["status"]["phase"])
          last_container_completion_time = None
          if in_terminal_state:
              for container_status in pod["status"].get("container_statuses", []):
                  if container_status.get("state", {}).get("terminated"):
                      container_completion_time = container_status["state"][
                          "terminated"
                      ].get("finished_at")
  
                      # take latest completion time
                      if (
                          not last_container_completion_time
                          or last_container_completion_time < container_completion_time
                      ):
                          last_container_completion_time = container_completion_time
  
          return in_terminal_state, last_container_completion_time, run_state
  
      def _get_default_label_selector(self) -> str:
          """
          Override this to add a default label selector
          """
          class_values = self._get_possible_mlrun_class_label_values()
          if not class_values:
              return ""
          if len(class_values) == 1:
              return f"mlrun/class={class_values[0]}"
          return f"mlrun/class in ({', '.join(class_values)})"
  
      @staticmethod
      def _get_crd_info() -> Tuple[str, str, str]:
          """
          Override this if the runtime has CRD resources. this should return the CRD info:
          crd group, crd version, crd plural
          """
          return "", "", ""
  
      @staticmethod
      def _are_resources_coupled_to_run_object() -> bool:
          """
          Some resources are tightly coupled to mlrun Run object, for example, for each Run of a Function of the job kind
          a kubernetes job is being generated, on the opposite a Function of the daskjob kind generates a dask cluster,
          and every Run is being executed using this cluster, i.e. no resources are created for the Run.
          This function should return true for runtimes in which Run are coupled to the underlying resources and therefore
          aspects of the Run (like its state) should be taken into consideration on resources deletion
          """
          return False
  
      @staticmethod
      def _expect_pods_without_uid() -> bool:
          return False
  
      def _list_pods(self, namespace: str, label_selector: str = None) -> List:
          k8s_helper = get_k8s_helper()
          pods = k8s_helper.list_pods(namespace, selector=label_selector)
          # when we work with custom objects (list_namespaced_custom_object) it's always a dict, to be able to generalize
          # code working on runtime resource (either a custom object or a pod) we're transforming to dicts
          pods = [pod.to_dict() for pod in pods]
          return pods
  
      def _list_crd_objects(self, namespace: str, label_selector: str = None) -> List:
          k8s_helper = get_k8s_helper()
          crd_group, crd_version, crd_plural = self._get_crd_info()
          crd_objects = []
          if crd_group and crd_version and crd_plural:
              try:
                  crd_objects = k8s_helper.crdapi.list_namespaced_custom_object(
                      crd_group,
                      crd_version,
                      namespace,
                      crd_plural,
                      label_selector=label_selector,
                  )
              except ApiException as exc:
                  # ignore error if crd is not defined
                  if exc.status != 404:
                      raise
              else:
                  crd_objects = crd_objects["items"]
          return crd_objects
  
      def _resolve_label_selector(
          self,
          project: str,
          object_id: typing.Optional[str] = None,
          label_selector: typing.Optional[str] = None,
      ) -> str:
          default_label_selector = self._get_default_label_selector()
  
          if label_selector:
              label_selector = ",".join([default_label_selector, label_selector])
          else:
              label_selector = default_label_selector
  
          if project and project != "*":
              label_selector = ",".join([label_selector, f"mlrun/project={project}"])
  
          label_selector = self._add_object_label_selector_if_needed(
              object_id, label_selector
          )
  
          return label_selector
  
      def _wait_for_pods_deletion(
          self, namespace: str, deleted_pods: List[Dict], label_selector: str = None,
      ):
          k8s_helper = get_k8s_helper()
          deleted_pod_names = [pod_dict["metadata"]["name"] for pod_dict in deleted_pods]
  
          def _verify_pods_removed():
              pods = k8s_helper.v1api.list_namespaced_pod(
                  namespace, label_selector=label_selector
              )
              existing_pod_names = [pod.metadata.name for pod in pods.items]
              still_in_deletion_pods = set(existing_pod_names).intersection(
                  deleted_pod_names
              )
              if still_in_deletion_pods:
                  raise RuntimeError(
                      f"Pods are still in deletion process: {still_in_deletion_pods}"
                  )
  
          if deleted_pod_names:
              timeout = 180
              logger.debug(
                  "Waiting for pods deletion",
                  timeout=timeout,
                  interval=self.wait_for_deletion_interval,
              )
              mlrun.utils.retry_until_successful(
                  self.wait_for_deletion_interval,
                  timeout,
                  logger,
                  True,
                  _verify_pods_removed,
              )
  
      def _wait_for_crds_underlying_pods_deletion(
          self, deleted_crds: List[Dict], label_selector: str = None,
      ):
          # we're using here the run identifier as the common ground to identify which pods are relevant to which CRD, so
          # if they are not coupled we are not able to wait - simply return
          # NOTE - there are surely smarter ways to do this, without depending on the run object, but as of writing this
          # none of the runtimes using CRDs are like that, so not handling it now
          if not self._are_resources_coupled_to_run_object():
              return
  
          def _verify_crds_underlying_pods_removed():
              project_uid_crd_map = {}
              for crd in deleted_crds:
                  project, uid = self._resolve_runtime_resource_run(crd)
                  if not uid or not project:
                      logger.warning(
                          "Could not resolve run uid from crd. Skipping waiting for pods deletion",
                          crd=crd,
                      )
                      continue
                  project_uid_crd_map.setdefault(project, {})[uid] = crd["metadata"][
                      "name"
                  ]
              still_in_deletion_crds_to_pod_names = {}
              jobs_runtime_resources: mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput = self.list_resources(
                  "*",
                  label_selector=label_selector,
                  group_by=mlrun.api.schemas.ListRuntimeResourcesGroupByField.job,
              )
              for project, project_jobs in jobs_runtime_resources.items():
                  if project not in project_uid_crd_map:
                      continue
                  for job_uid, job_runtime_resources in jobs_runtime_resources[
                      project
                  ].items():
                      if job_uid not in project_uid_crd_map[project]:
                          continue
                      if job_runtime_resources.pod_resources:
                          still_in_deletion_crds_to_pod_names[
                              project_uid_crd_map[project][job_uid]
                          ] = [
                              pod_resource.name
                              for pod_resource in job_runtime_resources.pod_resources
                          ]
              if still_in_deletion_crds_to_pod_names:
                  raise RuntimeError(
                      f"CRD underlying pods are still in deletion process: {still_in_deletion_crds_to_pod_names}"
                  )
  
          if deleted_crds:
              timeout = 180
              logger.debug(
                  "Waiting for CRDs underlying pods deletion",
                  timeout=timeout,
                  interval=self.wait_for_deletion_interval,
              )
              mlrun.utils.retry_until_successful(
                  self.wait_for_deletion_interval,
                  timeout,
                  logger,
                  True,
                  _verify_crds_underlying_pods_removed,
              )
  
      def _delete_pod_resources(
          self,
          db: DBInterface,
          db_session: Session,
          namespace: str,
          label_selector: str = None,
          force: bool = False,
          grace_period: int = None,
      ) -> List[Dict]:
          if grace_period is None:
              grace_period = config.runtime_resources_deletion_grace_period
          k8s_helper = get_k8s_helper()
          pods = k8s_helper.v1api.list_namespaced_pod(
              namespace, label_selector=label_selector
          )
          deleted_pods = []
          for pod in pods.items:
              pod_dict = pod.to_dict()
  
              # best effort - don't let one failure in pod deletion to cut the whole operation
              try:
                  (
                      in_terminal_state,
                      last_update,
                      run_state,
                  ) = self._resolve_pod_status_info(db, db_session, pod_dict)
                  if not force:
                      if not in_terminal_state:
                          continue
  
                      # give some grace period if we have last update time
                      now = datetime.now(timezone.utc)
                      if (
                          last_update is not None
                          and last_update + timedelta(seconds=float(grace_period)) > now
                      ):
                          continue
  
                  # if resources are tightly coupled to the run object - we want to perform some actions on the run object
                  # before deleting them
                  if self._are_resources_coupled_to_run_object():
                      try:
                          self._pre_deletion_runtime_resource_run_actions(
                              db, db_session, pod_dict, run_state
                          )
                      except Exception as exc:
                          # Don't prevent the deletion for failure in the pre deletion run actions
                          logger.warning(
                              "Failure in pod run pre-deletion actions. Continuing",
                              exc=repr(exc),
                              pod_name=pod.metadata.name,
                          )
  
                  self._delete_pod(namespace, pod)
                  deleted_pods.append(pod_dict)
              except Exception as exc:
                  logger.warning(
                      f"Cleanup failed processing pod {pod.metadata.name}: {repr(exc)}. Continuing"
                  )
          self._wait_for_pods_deletion(namespace, deleted_pods, label_selector)
          return deleted_pods
  
      def _delete_crd_resources(
          self,
          db: DBInterface,
          db_session: Session,
          namespace: str,
          label_selector: str = None,
          force: bool = False,
          grace_period: int = None,
      ) -> List[Dict]:
          if grace_period is None:
              grace_period = config.runtime_resources_deletion_grace_period
          k8s_helper = get_k8s_helper()
          crd_group, crd_version, crd_plural = self._get_crd_info()
          deleted_crds = []
          try:
              crd_objects = k8s_helper.crdapi.list_namespaced_custom_object(
                  crd_group,
                  crd_version,
                  namespace,
                  crd_plural,
                  label_selector=label_selector,
              )
          except ApiException as exc:
              # ignore error if crd is not defined
              if exc.status != 404:
                  raise
          else:
              for crd_object in crd_objects["items"]:
                  # best effort - don't let one failure in pod deletion to cut the whole operation
                  try:
                      (
                          in_terminal_state,
                          last_update,
                          desired_run_state,
                      ) = self._resolve_crd_object_status_info(db, db_session, crd_object)
                      if not force:
                          if not in_terminal_state:
                              continue
  
                          # give some grace period if we have last update time
                          now = datetime.now(timezone.utc)
                          if (
                              last_update is not None
                              and last_update + timedelta(seconds=float(grace_period))
                              > now
                          ):
                              continue
  
                      # if resources are tightly coupled to the run object - we want to perform some actions on the run
                      # object before deleting them
                      if self._are_resources_coupled_to_run_object():
  
                          try:
                              self._pre_deletion_runtime_resource_run_actions(
                                  db, db_session, crd_object, desired_run_state,
                              )
                          except Exception as exc:
                              # Don't prevent the deletion for failure in the pre deletion run actions
                              logger.warning(
                                  "Failure in crd object run pre-deletion actions. Continuing",
                                  exc=str(exc),
                                  crd_object_name=crd_object["metadata"]["name"],
                              )
  
                      self._delete_crd(
                          namespace, crd_group, crd_version, crd_plural, crd_object
                      )
                      deleted_crds.append(crd_object)
                  except Exception:
                      exc = traceback.format_exc()
                      crd_object_name = crd_object["metadata"]["name"]
                      logger.warning(
                          f"Cleanup failed processing CRD object {crd_object_name}: {exc}. Continuing"
                      )
          self._wait_for_crds_underlying_pods_deletion(deleted_crds, label_selector)
          return deleted_crds
  
      def _pre_deletion_runtime_resource_run_actions(
          self,
          db: DBInterface,
          db_session: Session,
          runtime_resource: Dict,
          run_state: str,
      ):
          project, uid = self._resolve_runtime_resource_run(runtime_resource)
  
          # if cannot resolve related run nothing to do
          if not uid:
              if not self._expect_pods_without_uid():
                  logger.warning(
                      "Could not resolve run uid from runtime resource. Skipping pre-deletion actions",
                      runtime_resource=runtime_resource,
                  )
                  raise ValueError("Could not resolve run uid from runtime resource")
              else:
                  return
  
          logger.info(
              "Performing pre-deletion actions before cleaning up runtime resources",
              project=project,
              uid=uid,
          )
  
          self._ensure_run_state(db, db_session, project, uid, run_state)
  
          self._ensure_run_logs_collected(db, db_session, project, uid)
  
      def _is_runtime_resource_run_in_terminal_state(
          self, db: DBInterface, db_session: Session, runtime_resource: Dict,
      ) -> Tuple[bool, Optional[datetime]]:
          """
          A runtime can have different underlying resources (like pods or CRDs) - to generalize we call it runtime
          resource. This function will verify whether the Run object related to this runtime resource is in transient
          state. This is useful in order to determine whether an object can be removed. for example, a kubejob's pod
          might be in completed state, but we would like to verify that the run is completed as well to verify the logs
          were collected before we're removing the pod.
  
          :returns: bool determining whether the run in terminal state, and the last update time if it exists
          """
          project, uid = self._resolve_runtime_resource_run(runtime_resource)
  
          # if no uid, assume in terminal state
          if not uid:
              return True, None
  
          run = db.read_run(db_session, uid, project)
          last_update = None
          last_update_str = run.get("status", {}).get("last_update")
          if last_update_str is not None:
              last_update = datetime.fromisoformat(last_update_str)
  
          if run.get("status", {}).get("state") not in RunStates.terminal_states():
              return False, last_update
  
          return True, last_update
  
      def _list_runs_for_monitoring(
          self, db: DBInterface, db_session: Session,
      ):
          runs = db.list_runs(db_session, project="*")
          project_run_uid_map = {}
          run_with_missing_data = []
          duplicated_runs = []
          for run in runs:
              project = run.get("metadata", {}).get("project")
              uid = run.get("metadata", {}).get("uid")
              if not uid or not project:
                  run_with_missing_data.append(run.get("metadata", {}))
                  continue
              current_run = project_run_uid_map.setdefault(project, {}).get(uid)
  
              # sanity
              if current_run:
                  duplicated_runs = {
                      "monitored_run": current_run.get(["metadata"]),
                      "duplicated_run": run.get(["metadata"]),
                  }
                  continue
  
              project_run_uid_map[project][uid] = run
  
          # If there are duplications or runs with missing data it probably won't be fixed
          # Monitoring is running periodically and we don't want to log on every problem we found which will spam the log
          # so we're aggregating the problems and logging only once per aggregation
          if duplicated_runs:
              logger.warning(
                  "Found duplicated runs (same uid). Heuristically monitoring the first one found",
                  duplicated_runs=duplicated_runs,
              )
  
          if run_with_missing_data:
              logger.warning(
                  "Found runs with missing data. They will not be monitored",
                  run_with_missing_data=run_with_missing_data,
              )
  
          return project_run_uid_map
  
      def _monitor_runtime_resource(
          self,
          db: DBInterface,
          db_session: Session,
          project_run_uid_map: Dict,
          runtime_resource: Dict,
          runtime_resource_is_crd: bool,
          namespace: str,
      ):
          project, uid = self._resolve_runtime_resource_run(runtime_resource)
          if not project or not uid:
              # Currently any build pod won't have UID and therefore will cause this log message to be printed which
              # spams the log
              # TODO: uncomment the log message when builder become a kind / starts having a UID
              # logger.warning(
              #     "Could not resolve run project or uid from runtime resource, can not monitor run. Continuing",
              #     project=project,
              #     uid=uid,
              #     runtime_resource_name=runtime_resource["metadata"]["name"],
              #     namespace=namespace,
              # )
              return
          run = project_run_uid_map.get(project, {}).get(uid)
          if runtime_resource_is_crd:
              (_, _, run_state,) = self._resolve_crd_object_status_info(
                  db, db_session, runtime_resource
              )
          else:
              (_, _, run_state,) = self._resolve_pod_status_info(
                  db, db_session, runtime_resource
              )
          self._update_ui_url(db, db_session, project, uid, runtime_resource, run)
          _, updated_run_state = self._ensure_run_state(
              db, db_session, project, uid, run_state, run, search_run=False,
          )
          if updated_run_state in RunStates.terminal_states():
              self._ensure_run_logs_collected(db, db_session, project, uid)
  
      def _build_list_resources_response(
          self,
          pod_resources: List[mlrun.api.schemas.RuntimeResource] = None,
          crd_resources: List[mlrun.api.schemas.RuntimeResource] = None,
          group_by: Optional[mlrun.api.schemas.ListRuntimeResourcesGroupByField] = None,
      ) -> Union[
          mlrun.api.schemas.RuntimeResources,
          mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
          mlrun.api.schemas.GroupedByProjectRuntimeResourcesOutput,
      ]:
          if crd_resources is None:
              crd_resources = []
          if pod_resources is None:
              pod_resources = []
  
          if group_by is None:
              return mlrun.api.schemas.RuntimeResources(
                  crd_resources=crd_resources, pod_resources=pod_resources
              )
          else:
              if group_by == mlrun.api.schemas.ListRuntimeResourcesGroupByField.job:
                  return self._build_grouped_by_job_list_resources_response(
                      pod_resources, crd_resources
                  )
              elif group_by == mlrun.api.schemas.ListRuntimeResourcesGroupByField.project:
                  return self._build_grouped_by_project_list_resources_response(
                      pod_resources, crd_resources
                  )
              else:
                  raise NotImplementedError(
                      f"Provided group by field is not supported. group_by={group_by}"
                  )
  
      def _build_grouped_by_project_list_resources_response(
          self,
          pod_resources: List[mlrun.api.schemas.RuntimeResource] = None,
          crd_resources: List[mlrun.api.schemas.RuntimeResource] = None,
      ) -> mlrun.api.schemas.GroupedByProjectRuntimeResourcesOutput:
          resources = {}
          for pod_resource in pod_resources:
              self._add_resource_to_grouped_by_project_resources_response(
                  resources, "pod_resources", pod_resource
              )
          for crd_resource in crd_resources:
              self._add_resource_to_grouped_by_project_resources_response(
                  resources, "crd_resources", crd_resource
              )
          return resources
  
      def _build_grouped_by_job_list_resources_response(
          self,
          pod_resources: List[mlrun.api.schemas.RuntimeResource] = None,
          crd_resources: List[mlrun.api.schemas.RuntimeResource] = None,
      ) -> mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput:
          resources = {}
          for pod_resource in pod_resources:
              self._add_resource_to_grouped_by_job_resources_response(
                  resources, "pod_resources", pod_resource
              )
          for crd_resource in crd_resources:
              self._add_resource_to_grouped_by_job_resources_response(
                  resources, "crd_resources", crd_resource
              )
          return resources
  
      def _add_resource_to_grouped_by_project_resources_response(
          self,
          resources: mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
          resource_field_name: str,
          resource: mlrun.api.schemas.RuntimeResource,
      ):
          if "mlrun/class" in resource.labels:
              project = resource.labels.get("mlrun/project", "")
              mlrun_class = resource.labels["mlrun/class"]
              kind = self._resolve_kind_from_class(mlrun_class)
              self._add_resource_to_grouped_by_field_resources_response(
                  project, kind, resources, resource_field_name, resource
              )
  
      def _add_resource_to_grouped_by_job_resources_response(
          self,
          resources: mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
          resource_field_name: str,
          resource: mlrun.api.schemas.RuntimeResource,
      ):
          if "mlrun/uid" in resource.labels:
              project = resource.labels.get("mlrun/project", config.default_project)
              uid = resource.labels["mlrun/uid"]
              self._add_resource_to_grouped_by_field_resources_response(
                  project, uid, resources, resource_field_name, resource
              )
  
      @staticmethod
      def _add_resource_to_grouped_by_field_resources_response(
          first_field_value: str,
          second_field_value: str,
          resources: mlrun.api.schemas.GroupedByJobRuntimeResourcesOutput,
          resource_field_name: str,
          resource: mlrun.api.schemas.RuntimeResource,
      ):
          if first_field_value not in resources:
              resources[first_field_value] = {}
          if second_field_value not in resources[first_field_value]:
              resources[first_field_value][
                  second_field_value
              ] = mlrun.api.schemas.RuntimeResources(pod_resources=[], crd_resources=[])
          if not getattr(
              resources[first_field_value][second_field_value], resource_field_name
          ):
              setattr(
                  resources[first_field_value][second_field_value],
                  resource_field_name,
                  [],
              )
          getattr(
              resources[first_field_value][second_field_value], resource_field_name
          ).append(resource)
  
      @staticmethod
      def _resolve_kind_from_class(mlrun_class: str) -> str:
          class_to_kind_map = {}
          for kind in mlrun.runtimes.RuntimeKinds.runtime_with_handlers():
              runtime_handler = mlrun.runtimes.get_runtime_handler(kind)
              class_values = runtime_handler._get_possible_mlrun_class_label_values()
              for value in class_values:
                  class_to_kind_map[value] = kind
          return class_to_kind_map[mlrun_class]
  
      @staticmethod
      def _get_run_label_selector(project: str, run_uid: str):
          return f"mlrun/project={project},mlrun/uid={run_uid}"
  
      @staticmethod
      def _ensure_run_logs_collected(
          db: DBInterface, db_session: Session, project: str, uid: str
      ):
          # import here to avoid circular imports
          import mlrun.api.crud as crud
  
          log_file_exists = crud.Logs().log_file_exists(project, uid)
          if not log_file_exists:
              _, logs_from_k8s = crud.Logs().get_logs(
                  db_session, project, uid, source=LogSources.K8S
              )
              if logs_from_k8s:
                  logger.info("Storing run logs", project=project, uid=uid)
                  crud.Logs().store_log(logs_from_k8s, project, uid, append=False)
  
      @staticmethod
      def _ensure_run_state(
          db: DBInterface,
          db_session: Session,
          project: str,
          uid: str,
          run_state: str,
          run: Dict = None,
          search_run: bool = True,
      ) -> Tuple[bool, str]:
          if run is None:
              run = {}
          if search_run:
              try:
                  run = db.read_run(db_session, uid, project)
              except mlrun.errors.MLRunNotFoundError:
                  run = {}
          if not run:
              logger.warning(
                  "Run not found. A new run will be created",
                  project=project,
                  uid=uid,
                  desired_run_state=run_state,
                  search_run=search_run,
              )
              run = {"metadata": {"project": project, "uid": uid}}
          db_run_state = run.get("status", {}).get("state")
          if db_run_state:
              if db_run_state == run_state:
                  return False, run_state
              # if the current run state is terminal and different than the desired - log
              if db_run_state in RunStates.terminal_states():
  
                  # This can happen when the SDK running in the user's Run updates the Run's state to terminal, but
                  # before it exits, when the runtime resource is still running, the API monitoring (here) is executed
                  if run_state not in RunStates.terminal_states():
                      now = datetime.now(timezone.utc)
                      last_update_str = run.get("status", {}).get("last_update")
                      if last_update_str is not None:
                          last_update = datetime.fromisoformat(last_update_str)
                          debounce_period = config.runs_monitoring_interval
                          if last_update > now - timedelta(
                              seconds=float(debounce_period)
                          ):
                              logger.warning(
                                  "Monitoring found non-terminal state on runtime resource but record has recently "
                                  "updated to terminal state. Debouncing",
                                  project=project,
                                  uid=uid,
                                  db_run_state=db_run_state,
                                  run_state=run_state,
                                  last_update=last_update,
                                  now=now,
                                  debounce_period=debounce_period,
                              )
                              return False, run_state
  
                  logger.warning(
                      "Run record has terminal state but monitoring found different state on runtime resource. Changing",
                      project=project,
                      uid=uid,
                      db_run_state=db_run_state,
                      run_state=run_state,
                  )
  
          logger.info("Updating run state", run_state=run_state)
          run.setdefault("status", {})["state"] = run_state
          run.setdefault("status", {})["last_update"] = now_date().isoformat()
          db.store_run(db_session, run, uid, project)
  
          return True, run_state
  
      @staticmethod
      def _resolve_runtime_resource_run(runtime_resource: Dict) -> Tuple[str, str]:
          project = (
              runtime_resource.get("metadata", {}).get("labels", {}).get("mlrun/project")
          )
          if not project:
              project = config.default_project
          uid = runtime_resource.get("metadata", {}).get("labels", {}).get("mlrun/uid")
          return project, uid
  
      @staticmethod
      def _delete_crd(namespace, crd_group, crd_version, crd_plural, crd_object):
          k8s_helper = get_k8s_helper()
          name = crd_object["metadata"]["name"]
          try:
              k8s_helper.crdapi.delete_namespaced_custom_object(
                  crd_group, crd_version, namespace, crd_plural, name,
              )
              logger.info(
                  "Deleted crd object",
                  name=name,
                  namespace=namespace,
                  crd_plural=crd_plural,
              )
          except ApiException as exc:
              # ignore error if crd object is already removed
              if exc.status != 404:
                  raise
  
      @staticmethod
      def _delete_pod(namespace, pod):
          k8s_helper = get_k8s_helper()
          try:
              k8s_helper.v1api.delete_namespaced_pod(pod.metadata.name, namespace)
              logger.info("Deleted pod", pod=pod.metadata.name)
          except ApiException as exc:
              # ignore error if pod is already removed
              if exc.status != 404:
                  raise
  
      @staticmethod
      def _build_pod_resources(pods) -> List[mlrun.api.schemas.RuntimeResource]:
          pod_resources = []
          for pod in pods:
              pod_resources.append(
                  mlrun.api.schemas.RuntimeResource(
                      name=pod["metadata"]["name"],
                      labels=pod["metadata"]["labels"],
                      status=pod["status"],
                  )
              )
          return pod_resources
  
      @staticmethod
      def _build_crd_resources(custom_objects) -> List[mlrun.api.schemas.RuntimeResource]:
          crd_resources = []
          for custom_object in custom_objects:
              crd_resources.append(
                  mlrun.api.schemas.RuntimeResource(
                      name=custom_object["metadata"]["name"],
                      labels=custom_object["metadata"]["labels"],
                      status=custom_object.get("status", {}),
                  )
              )
          return crd_resources
  