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  #
  #  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,
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  """Databricks orchestrator base config and settings."""
  
  from typing import TYPE_CHECKING, Dict, Optional, Tuple, Type
  
  from pydantic import Field
  
  from zenml.config.base_settings import BaseSettings
  from zenml.integrations.databricks import DATABRICKS_ORCHESTRATOR_FLAVOR
  from zenml.logger import get_logger
  from zenml.orchestrators import BaseOrchestratorConfig
  from zenml.orchestrators.base_orchestrator import BaseOrchestratorFlavor
  from zenml.utils.enum_utils import StrEnum
  from zenml.utils.secret_utils import SecretField
  
  if TYPE_CHECKING:
      from zenml.integrations.databricks.orchestrators import (
          DatabricksOrchestrator,
      )
  
  logger = get_logger(__name__)
  
  
  class DatabricksAvailabilityType(StrEnum):
      """Databricks availability type."""
  
      ON_DEMAND = "ON_DEMAND"
      SPOT = "SPOT"
      SPOT_WITH_FALLBACK = "SPOT_WITH_FALLBACK"
  
  
  class DatabricksOrchestratorSettings(BaseSettings):
      """Databricks orchestrator base settings.
  
      Configuration for Databricks cluster and Spark execution settings.
      Field descriptions are defined inline using Field() descriptors.
      """
  
      # Cluster Configuration
      spark_version: Optional[str] = Field(
          default=None,
          description="Apache Spark version for the Databricks cluster. "
          "Uses workspace default if not specified. Example: '16.4.x-scala2.12'",
      )
      num_workers: Optional[int] = Field(
          default=None,
          description="Fixed number of worker nodes. Cannot be used with autoscaling.",
      )
      node_type_id: Optional[str] = Field(
          default=None,
          description="Databricks node type identifier. "
          "Refer to Databricks documentation for available instance types. "
          "Example: 'i3.xlarge'",
      )
      policy_id: Optional[str] = Field(
          default=None,
          description="Databricks cluster policy ID for governance and cost control.",
      )
      autotermination_minutes: Optional[int] = Field(
          default=None,
          description="Minutes of inactivity before automatic cluster termination. "
          "Helps control costs by shutting down idle clusters.",
      )
      autoscale: Tuple[int, int] = Field(
          default=(0, 1),
          description="Cluster autoscaling bounds as (min_workers, max_workers). "
          "Automatically adjusts cluster size based on workload.",
      )
      single_user_name: Optional[str] = Field(
          default=None,
          description="Databricks username for single-user cluster access mode.",
      )
      spark_conf: Optional[Dict[str, str]] = Field(
          default=None,
          description="Custom Spark configuration properties as key-value pairs. "
          "Example: {'spark.sql.adaptive.enabled': 'true', 'spark.sql.adaptive.coalescePartitions.enabled': 'true'}",
      )
      spark_env_vars: Optional[Dict[str, str]] = Field(
          default=None,
          description="Environment variables for the Spark driver and executors. "
          "Example: {'SPARK_WORKER_MEMORY': '4g', 'SPARK_DRIVER_MEMORY': '2g'}",
      )
      schedule_timezone: Optional[str] = Field(
          default=None,
          description="Timezone for scheduled pipeline execution. "
          "Uses IANA timezone format (e.g., 'America/New_York').",
      )
      availability_type: Optional[DatabricksAvailabilityType] = Field(
          default=None,
          description="Instance availability type: ON_DEMAND (guaranteed), SPOT (cost-optimized), "
          "or SPOT_WITH_FALLBACK (spot with on-demand backup).",
      )
  
  
  class DatabricksOrchestratorConfig(
      BaseOrchestratorConfig, DatabricksOrchestratorSettings
  ):
      """Databricks orchestrator base config.
  
      Attributes:
          host: Databricks host.
          client_id: Databricks client id.
          client_secret: Databricks client secret.
      """
  
      host: str
      client_id: str = SecretField(default=None)
      client_secret: str = SecretField(default=None)
  
      @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_remote(self) -> bool:
          """Checks if this stack component is running remotely.
  
          Returns:
              True if this config is for a remote component, False otherwise.
          """
          return True
  
      @property
      def is_schedulable(self) -> bool:
          """Whether the orchestrator is schedulable or not.
  
          Returns:
              Whether the orchestrator is schedulable or not.
          """
          return True
  
  
  class DatabricksOrchestratorFlavor(BaseOrchestratorFlavor):
      """Databricks orchestrator flavor."""
  
      @property
      def name(self) -> str:
          """Name of the flavor.
  
          Returns:
              The name of the flavor.
          """
          return DATABRICKS_ORCHESTRATOR_FLAVOR
  
      @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/databricks.png"
  
      @property
      def config_class(self) -> Type[DatabricksOrchestratorConfig]:
          """Returns `KubeflowOrchestratorConfig` config class.
  
          Returns:
                  The config class.
          """
          return DatabricksOrchestratorConfig
  
      @property
      def implementation_class(self) -> Type["DatabricksOrchestrator"]:
          """Implementation class for this flavor.
  
          Returns:
              The implementation class.
          """
          from zenml.integrations.databricks.orchestrators import (
              DatabricksOrchestrator,
          )
  
          return DatabricksOrchestrator
  