content: #  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
  #
  #  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:
  #
  #       https://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.
  """Kubeflow orchestrator flavor."""
  
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type, cast
  
  from pydantic import Field, model_validator
  
  from zenml.config.base_settings import BaseSettings
  from zenml.constants import KUBERNETES_CLUSTER_RESOURCE_TYPE
  from zenml.integrations.kubeflow import KUBEFLOW_ORCHESTRATOR_FLAVOR
  from zenml.integrations.kubernetes.pod_settings import KubernetesPodSettings
  from zenml.logger import get_logger
  from zenml.models import ServiceConnectorRequirements
  from zenml.orchestrators import BaseOrchestratorConfig, BaseOrchestratorFlavor
  from zenml.utils.pydantic_utils import before_validator_handler
  from zenml.utils.secret_utils import SecretField
  
  if TYPE_CHECKING:
      from zenml.integrations.kubeflow.orchestrators import KubeflowOrchestrator
  
  logger = get_logger(__name__)
  
  DEFAULT_KFP_UI_PORT = 8080
  
  
  class KubeflowOrchestratorSettings(BaseSettings):
      """Settings for the Kubeflow orchestrator."""
  
      synchronous: bool = Field(
          True,
          description="If `True`, the client running a pipeline using this "
          "orchestrator waits until all steps finish running. If `False`, "
          "the client returns immediately and the pipeline is executed "
          "asynchronously.",
      )
      timeout: int = Field(
          1200, description="How many seconds to wait for synchronous runs."
      )
  
      client_args: Dict[str, Any] = Field(
          default_factory=dict,
          description="Arguments to pass when initializing the KFP client. "
          "Example: {'host': 'https://kubeflow.example.com', 'client_id': 'kubeflow-oidc-authservice', 'existing_token': 'your-auth-token'}",
      )
      client_username: Optional[str] = SecretField(
          default=None,
          description="Username to generate a session cookie for the kubeflow client. "
          "Both `client_username` and `client_password` need to be set together.",
      )
      client_password: Optional[str] = SecretField(
          default=None,
          description="Password to generate a session cookie for the kubeflow client. "
          "Both `client_username` and `client_password` need to be set together.",
      )
      user_namespace: Optional[str] = Field(
          None,
          description="The user namespace to use when creating experiments and runs. "
          "Example: 'my-experiments' or 'team-alpha'",
      )
      pod_settings: Optional[KubernetesPodSettings] = Field(
          None,
          description="Pod settings to apply to the orchestrator and step pods.",
      )
  
      @model_validator(mode="before")
      @classmethod
      @before_validator_handler
      def _validate_and_migrate_pod_settings(
          cls, data: Dict[str, Any]
      ) -> Dict[str, Any]:
          """Validates settings and migrates pod settings from older version.
  
          Args:
              data: Dict representing user-specified runtime settings.
  
          Returns:
              Validated settings.
  
          Raises:
              ValueError: If username and password are not specified together.
          """
          node_selectors = cast(Dict[str, str], data.get("node_selectors") or {})
          node_affinity = cast(
              Dict[str, List[str]], data.get("node_affinity") or {}
          )
  
          affinity = {}
          if node_affinity:
              from kubernetes import client as k8s_client
  
              match_expressions = [
                  k8s_client.V1NodeSelectorRequirement(
                      key=key,
                      operator="In",
                      values=values,
                  )
                  for key, values in node_affinity.items()
              ]
  
              affinity = k8s_client.V1Affinity(
                  node_affinity=k8s_client.V1NodeAffinity(
                      required_during_scheduling_ignored_during_execution=k8s_client.V1NodeSelector(
                          node_selector_terms=[
                              k8s_client.V1NodeSelectorTerm(
                                  match_expressions=match_expressions
                              )
                          ]
                      )
                  )
              )
          pod_settings = KubernetesPodSettings(
              node_selectors=node_selectors, affinity=affinity
          )
          data["pod_settings"] = pod_settings
  
          # Validate username and password for auth cookie logic
          username = data.get("client_username")
          password = data.get("client_password")
          client_creds_error = "`client_username` and `client_password` both need to be set together."
          if username and password is None:
              raise ValueError(client_creds_error)
          if password and username is None:
              raise ValueError(client_creds_error)
  
          return data
  
  
  class KubeflowOrchestratorConfig(
      BaseOrchestratorConfig, KubeflowOrchestratorSettings
  ):
      """Configuration for the Kubeflow orchestrator."""
  
      kubeflow_hostname: Optional[str] = Field(
          None,
          description="The hostname to use to talk to the Kubeflow Pipelines API. "
          "If not set, the hostname will be derived from the Kubernetes API proxy. "
          "Mandatory when connecting to a multi-tenant Kubeflow Pipelines deployment. "
          "Example: 'https://kubeflow.example.com' or 'kubeflow.company.com'",
      )
      kubeflow_namespace: str = Field(
          "kubeflow",
          description="The Kubernetes namespace in which Kubeflow Pipelines is deployed.",
      )
      kubernetes_context: Optional[str] = Field(
          None,
          description="Name of a kubernetes context to run pipelines in. "
          "Not applicable when connecting to a multi-tenant Kubeflow Pipelines "
          "deployment (i.e. when `kubeflow_hostname` is set) or if the stack "
          "component is linked to a Kubernetes service connector. "
          "Example: 'my-cluster' or 'production-cluster'",
      )
  
      @model_validator(mode="before")
      @classmethod
      @before_validator_handler
      def _validate_deprecated_attrs(
          cls, data: Dict[str, Any]
      ) -> Dict[str, Any]:
          """Pydantic root_validator for deprecated attributes.
  
          This root validator is used for backwards compatibility purposes. E.g.
          it handles attributes that are no longer available or that have become
          mandatory in the meantime.
  
          Args:
              data: Values passed to the object constructor
  
          Returns:
              Values passed to the object constructor
          """
          provisioning_attrs = [
              "skip_cluster_provisioning",
              "skip_ui_daemon_provisioning",
              "kubeflow_pipelines_ui_port",
          ]
  
          # remove deprecated attributes from values dict
          for attr in provisioning_attrs:
              if attr in data:
                  del data[attr]
  
          return data
  
      @property
      def is_remote(self) -> bool:
          """Checks if this stack component is running remotely.
  
          This designation is used to determine if the stack component can be
          used with a local ZenML database or if it requires a remote ZenML
          server.
  
          Returns:
              True if this config is for a remote component, False otherwise.
          """
          return True
  
      @property
      def is_local(self) -> bool:
          """Checks if this stack component is running locally.
  
          Returns:
              True if this config is for a local component, False otherwise.
          """
          return False
  
      @property
      def is_synchronous(self) -> bool:
          """Whether the orchestrator runs synchronous or not.
  
          Returns:
              Whether the orchestrator runs synchronous or not.
          """
          return self.synchronous
  
      @property
      def is_schedulable(self) -> bool:
          """Whether the orchestrator is schedulable or not.
  
          Returns:
              Whether the orchestrator is schedulable or not.
          """
          return True
  
  
  class KubeflowOrchestratorFlavor(BaseOrchestratorFlavor):
      """Kubeflow orchestrator flavor."""
  
      @property
      def name(self) -> str:
          """Name of the flavor.
  
          Returns:
              The name of the flavor.
          """
          return KUBEFLOW_ORCHESTRATOR_FLAVOR
  
      @property
      def service_connector_requirements(
          self,
      ) -> Optional[ServiceConnectorRequirements]:
          """Service connector resource requirements for service connectors.
  
          Specifies resource requirements that are used to filter the available
          service connector types that are compatible with this flavor.
  
          Returns:
              Requirements for compatible service connectors, if a service
              connector is required for this flavor.
          """
          return ServiceConnectorRequirements(
              resource_type=KUBERNETES_CLUSTER_RESOURCE_TYPE,
          )
  
      @property
      def docs_url(self) -> Optional[str]:
          """A url to point at docs explaining this flavor.
  
          Returns:
              A flavor docs url.
          """
          return self.generate_default_docs_url()
  
      @property
      def sdk_docs_url(self) -> Optional[str]:
          """A url to point at SDK docs explaining this flavor.
  
          Returns:
              A flavor SDK docs url.
          """
          return self.generate_default_sdk_docs_url()
  
      @property
      def logo_url(self) -> str:
          """A url to represent the flavor in the dashboard.
  
          Returns:
              The flavor logo.
          """
          return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/orchestrator/kubeflow.png"
  
      @property
      def config_class(self) -> Type[KubeflowOrchestratorConfig]:
          """Returns `KubeflowOrchestratorConfig` config class.
  
          Returns:
                  The config class.
          """
          return KubeflowOrchestratorConfig
  
      @property
      def implementation_class(self) -> Type["KubeflowOrchestrator"]:
          """Implementation class for this flavor.
  
          Returns:
              The implementation class.
          """
          from zenml.integrations.kubeflow.orchestrators import (
              KubeflowOrchestrator,
          )
  
          return KubeflowOrchestrator
  