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@file:Suppress("NAME_SHADOWING", "DEPRECATION")

package com.pulumi.gcp.vertex.kotlin

import com.pulumi.core.Output
import com.pulumi.kotlin.KotlinCustomResource
import com.pulumi.kotlin.PulumiTagMarker
import com.pulumi.kotlin.ResourceMapper
import com.pulumi.kotlin.options.CustomResourceOptions
import com.pulumi.kotlin.options.CustomResourceOptionsBuilder
import com.pulumi.resources.Resource
import kotlin.Boolean
import kotlin.String
import kotlin.Suppress
import kotlin.Unit
import kotlin.collections.Map

/**
 * Builder for [AiFeatureGroupFeature].
 */
@PulumiTagMarker
public class AiFeatureGroupFeatureResourceBuilder internal constructor() {
    public var name: String? = null

    public var args: AiFeatureGroupFeatureArgs = AiFeatureGroupFeatureArgs()

    public var opts: CustomResourceOptions = CustomResourceOptions()

    /**
     * @param name The _unique_ name of the resulting resource.
     */
    public fun name(`value`: String) {
        this.name = value
    }

    /**
     * @param block The arguments to use to populate this resource's properties.
     */
    public suspend fun args(block: suspend AiFeatureGroupFeatureArgsBuilder.() -> Unit) {
        val builder = AiFeatureGroupFeatureArgsBuilder()
        block(builder)
        this.args = builder.build()
    }

    /**
     * @param block A bag of options that control this resource's behavior.
     */
    public suspend fun opts(block: suspend CustomResourceOptionsBuilder.() -> Unit) {
        this.opts = com.pulumi.kotlin.options.CustomResourceOptions.opts(block)
    }

    internal fun build(): AiFeatureGroupFeature {
        val builtJavaResource = com.pulumi.gcp.vertex.AiFeatureGroupFeature(
            this.name,
            this.args.toJava(),
            this.opts.toJava(),
        )
        return AiFeatureGroupFeature(builtJavaResource)
    }
}

/**
 * Vertex AI Feature Group Feature is feature metadata information.
 * To get more information about FeatureGroupFeature, see:
 * * [API documentation](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.featureGroups.features)
 * * How-to Guides
 *     * [Creating a Feature](https://cloud.google.com/vertex-ai/docs/featurestore/latest/create-feature)
 * ## Example Usage
 * ### Vertex Ai Feature Group Feature
 * 
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as gcp from "@pulumi/gcp";
 * const sampleDataset = new gcp.bigquery.Dataset("sample_dataset", {
 *     datasetId: "job_load_dataset",
 *     friendlyName: "test",
 *     description: "This is a test description",
 *     location: "US",
 * });
 * const sampleTable = new gcp.bigquery.Table("sample_table", {
 *     deletionProtection: false,
 *     datasetId: sampleDataset.datasetId,
 *     tableId: "job_load_table",
 *     schema: `[
 *     {
 *         "name": "feature_id",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "example_feature",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "feature_timestamp",
 *         "type": "TIMESTAMP",
 *         "mode": "NULLABLE"
 *     }
 * ]
 * `,
 * });
 * const sampleFeatureGroup = new gcp.vertex.AiFeatureGroup("sample_feature_group", {
 *     name: "example_feature_group",
 *     description: "A sample feature group",
 *     region: "us-central1",
 *     labels: {
 *         "label-one": "value-one",
 *     },
 *     bigQuery: {
 *         bigQuerySource: {
 *             inputUri: pulumi.interpolate`bq://${sampleTable.project}.${sampleTable.datasetId}.${sampleTable.tableId}`,
 *         },
 *         entityIdColumns: ["feature_id"],
 *     },
 * });
 * const featureGroupFeature = new gcp.vertex.AiFeatureGroupFeature("feature_group_feature", {
 *     name: "example_feature",
 *     region: "us-central1",
 *     featureGroup: sampleFeatureGroup.name,
 *     description: "A sample feature",
 *     labels: {
 *         "label-one": "value-one",
 *     },
 * });
 * ```
 * ```python
 * import pulumi
 * import pulumi_gcp as gcp
 * sample_dataset = gcp.bigquery.Dataset("sample_dataset",
 *     dataset_id="job_load_dataset",
 *     friendly_name="test",
 *     description="This is a test description",
 *     location="US")
 * sample_table = gcp.bigquery.Table("sample_table",
 *     deletion_protection=False,
 *     dataset_id=sample_dataset.dataset_id,
 *     table_id="job_load_table",
 *     schema="""[
 *     {
 *         "name": "feature_id",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "example_feature",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "feature_timestamp",
 *         "type": "TIMESTAMP",
 *         "mode": "NULLABLE"
 *     }
 * ]
 * """)
 * sample_feature_group = gcp.vertex.AiFeatureGroup("sample_feature_group",
 *     name="example_feature_group",
 *     description="A sample feature group",
 *     region="us-central1",
 *     labels={
 *         "label-one": "value-one",
 *     },
 *     big_query=gcp.vertex.AiFeatureGroupBigQueryArgs(
 *         big_query_source=gcp.vertex.AiFeatureGroupBigQueryBigQuerySourceArgs(
 *             input_uri=pulumi.Output.all(sample_table.project, sample_table.dataset_id, sample_table.table_id).apply(lambda project, dataset_id, table_id: f"bq://{project}.{dataset_id}.{table_id}"),
 *         ),
 *         entity_id_columns=["feature_id"],
 *     ))
 * feature_group_feature = gcp.vertex.AiFeatureGroupFeature("feature_group_feature",
 *     name="example_feature",
 *     region="us-central1",
 *     feature_group=sample_feature_group.name,
 *     description="A sample feature",
 *     labels={
 *         "label-one": "value-one",
 *     })
 * ```
 * ```csharp
 * using System.Collections.Generic;
 * using System.Linq;
 * using Pulumi;
 * using Gcp = Pulumi.Gcp;
 * return await Deployment.RunAsync(() =>
 * {
 *     var sampleDataset = new Gcp.BigQuery.Dataset("sample_dataset", new()
 *     {
 *         DatasetId = "job_load_dataset",
 *         FriendlyName = "test",
 *         Description = "This is a test description",
 *         Location = "US",
 *     });
 *     var sampleTable = new Gcp.BigQuery.Table("sample_table", new()
 *     {
 *         DeletionProtection = false,
 *         DatasetId = sampleDataset.DatasetId,
 *         TableId = "job_load_table",
 *         Schema = @"[
 *     {
 *         ""name"": ""feature_id"",
 *         ""type"": ""STRING"",
 *         ""mode"": ""NULLABLE""
 *     },
 *     {
 *         ""name"": ""example_feature"",
 *         ""type"": ""STRING"",
 *         ""mode"": ""NULLABLE""
 *     },
 *     {
 *         ""name"": ""feature_timestamp"",
 *         ""type"": ""TIMESTAMP"",
 *         ""mode"": ""NULLABLE""
 *     }
 * ]
 * ",
 *     });
 *     var sampleFeatureGroup = new Gcp.Vertex.AiFeatureGroup("sample_feature_group", new()
 *     {
 *         Name = "example_feature_group",
 *         Description = "A sample feature group",
 *         Region = "us-central1",
 *         Labels =
 *         {
 *             { "label-one", "value-one" },
 *         },
 *         BigQuery = new Gcp.Vertex.Inputs.AiFeatureGroupBigQueryArgs
 *         {
 *             BigQuerySource = new Gcp.Vertex.Inputs.AiFeatureGroupBigQueryBigQuerySourceArgs
 *             {
 *                 InputUri = Output.Tuple(sampleTable.Project, sampleTable.DatasetId, sampleTable.TableId).Apply(values =>
 *                 {
 *                     var project = values.Item1;
 *                     var datasetId = values.Item2;
 *                     var tableId = values.Item3;
 *                     return $"bq://{project}.{datasetId}.{tableId}";
 *                 }),
 *             },
 *             EntityIdColumns = new[]
 *             {
 *                 "feature_id",
 *             },
 *         },
 *     });
 *     var featureGroupFeature = new Gcp.Vertex.AiFeatureGroupFeature("feature_group_feature", new()
 *     {
 *         Name = "example_feature",
 *         Region = "us-central1",
 *         FeatureGroup = sampleFeatureGroup.Name,
 *         Description = "A sample feature",
 *         Labels =
 *         {
 *             { "label-one", "value-one" },
 *         },
 *     });
 * });
 * ```
 * ```go
 * package main
 * import (
 * 	"fmt"
 * 	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/bigquery"
 * 	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/vertex"
 * 	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
 * )
 * func main() {
 * 	pulumi.Run(func(ctx *pulumi.Context) error {
 * 		sampleDataset, err := bigquery.NewDataset(ctx, "sample_dataset", &bigquery.DatasetArgs{
 * 			DatasetId:    pulumi.String("job_load_dataset"),
 * 			FriendlyName: pulumi.String("test"),
 * 			Description:  pulumi.String("This is a test description"),
 * 			Location:     pulumi.String("US"),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		sampleTable, err := bigquery.NewTable(ctx, "sample_table", &bigquery.TableArgs{
 * 			DeletionProtection: pulumi.Bool(false),
 * 			DatasetId:          sampleDataset.DatasetId,
 * 			TableId:            pulumi.String("job_load_table"),
 * 			Schema: pulumi.String(`[
 *     {
 *         "name": "feature_id",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "example_feature",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "feature_timestamp",
 *         "type": "TIMESTAMP",
 *         "mode": "NULLABLE"
 *     }
 * ]
 * `),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		sampleFeatureGroup, err := vertex.NewAiFeatureGroup(ctx, "sample_feature_group", &vertex.AiFeatureGroupArgs{
 * 			Name:        pulumi.String("example_feature_group"),
 * 			Description: pulumi.String("A sample feature group"),
 * 			Region:      pulumi.String("us-central1"),
 * 			Labels: pulumi.StringMap{
 * 				"label-one": pulumi.String("value-one"),
 * 			},
 * 			BigQuery: &vertex.AiFeatureGroupBigQueryArgs{
 * 				BigQuerySource: &vertex.AiFeatureGroupBigQueryBigQuerySourceArgs{
 * 					InputUri: pulumi.All(sampleTable.Project, sampleTable.DatasetId, sampleTable.TableId).ApplyT(func(_args []interface{}) (string, error) {
 * 						project := _args[0].(string)
 * 						datasetId := _args[1].(string)
 * 						tableId := _args[2].(string)
 * 						return fmt.Sprintf("bq://%v.%v.%v", project, datasetId, tableId), nil
 * 					}).(pulumi.StringOutput),
 * 				},
 * 				EntityIdColumns: pulumi.StringArray{
 * 					pulumi.String("feature_id"),
 * 				},
 * 			},
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		_, err = vertex.NewAiFeatureGroupFeature(ctx, "feature_group_feature", &vertex.AiFeatureGroupFeatureArgs{
 * 			Name:         pulumi.String("example_feature"),
 * 			Region:       pulumi.String("us-central1"),
 * 			FeatureGroup: sampleFeatureGroup.Name,
 * 			Description:  pulumi.String("A sample feature"),
 * 			Labels: pulumi.StringMap{
 * 				"label-one": pulumi.String("value-one"),
 * 			},
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		return nil
 * 	})
 * }
 * ```
 * ```java
 * package generated_program;
 * import com.pulumi.Context;
 * import com.pulumi.Pulumi;
 * import com.pulumi.core.Output;
 * import com.pulumi.gcp.bigquery.Dataset;
 * import com.pulumi.gcp.bigquery.DatasetArgs;
 * import com.pulumi.gcp.bigquery.Table;
 * import com.pulumi.gcp.bigquery.TableArgs;
 * import com.pulumi.gcp.vertex.AiFeatureGroup;
 * import com.pulumi.gcp.vertex.AiFeatureGroupArgs;
 * import com.pulumi.gcp.vertex.inputs.AiFeatureGroupBigQueryArgs;
 * import com.pulumi.gcp.vertex.inputs.AiFeatureGroupBigQueryBigQuerySourceArgs;
 * import com.pulumi.gcp.vertex.AiFeatureGroupFeature;
 * import com.pulumi.gcp.vertex.AiFeatureGroupFeatureArgs;
 * import java.util.List;
 * import java.util.ArrayList;
 * import java.util.Map;
 * import java.io.File;
 * import java.nio.file.Files;
 * import java.nio.file.Paths;
 * public class App {
 *     public static void main(String[] args) {
 *         Pulumi.run(App::stack);
 *     }
 *     public static void stack(Context ctx) {
 *         var sampleDataset = new Dataset("sampleDataset", DatasetArgs.builder()
 *             .datasetId("job_load_dataset")
 *             .friendlyName("test")
 *             .description("This is a test description")
 *             .location("US")
 *             .build());
 *         var sampleTable = new Table("sampleTable", TableArgs.builder()
 *             .deletionProtection(false)
 *             .datasetId(sampleDataset.datasetId())
 *             .tableId("job_load_table")
 *             .schema("""
 * [
 *     {
 *         "name": "feature_id",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "example_feature",
 *         "type": "STRING",
 *         "mode": "NULLABLE"
 *     },
 *     {
 *         "name": "feature_timestamp",
 *         "type": "TIMESTAMP",
 *         "mode": "NULLABLE"
 *     }
 * ]
 *             """)
 *             .build());
 *         var sampleFeatureGroup = new AiFeatureGroup("sampleFeatureGroup", AiFeatureGroupArgs.builder()
 *             .name("example_feature_group")
 *             .description("A sample feature group")
 *             .region("us-central1")
 *             .labels(Map.of("label-one", "value-one"))
 *             .bigQuery(AiFeatureGroupBigQueryArgs.builder()
 *                 .bigQuerySource(AiFeatureGroupBigQueryBigQuerySourceArgs.builder()
 *                     .inputUri(Output.tuple(sampleTable.project(), sampleTable.datasetId(), sampleTable.tableId()).applyValue(values -> {
 *                         var project = values.t1;
 *                         var datasetId = values.t2;
 *                         var tableId = values.t3;
 *                         return String.format("bq://%s.%s.%s", project,datasetId,tableId);
 *                     }))
 *                     .build())
 *                 .entityIdColumns("feature_id")
 *                 .build())
 *             .build());
 *         var featureGroupFeature = new AiFeatureGroupFeature("featureGroupFeature", AiFeatureGroupFeatureArgs.builder()
 *             .name("example_feature")
 *             .region("us-central1")
 *             .featureGroup(sampleFeatureGroup.name())
 *             .description("A sample feature")
 *             .labels(Map.of("label-one", "value-one"))
 *             .build());
 *     }
 * }
 * ```
 * ```yaml
 * resources:
 *   featureGroupFeature:
 *     type: gcp:vertex:AiFeatureGroupFeature
 *     name: feature_group_feature
 *     properties:
 *       name: example_feature
 *       region: us-central1
 *       featureGroup: ${sampleFeatureGroup.name}
 *       description: A sample feature
 *       labels:
 *         label-one: value-one
 *   sampleFeatureGroup:
 *     type: gcp:vertex:AiFeatureGroup
 *     name: sample_feature_group
 *     properties:
 *       name: example_feature_group
 *       description: A sample feature group
 *       region: us-central1
 *       labels:
 *         label-one: value-one
 *       bigQuery:
 *         bigQuerySource:
 *           inputUri: bq://${sampleTable.project}.${sampleTable.datasetId}.${sampleTable.tableId}
 *         entityIdColumns:
 *           - feature_id
 *   sampleDataset:
 *     type: gcp:bigquery:Dataset
 *     name: sample_dataset
 *     properties:
 *       datasetId: job_load_dataset
 *       friendlyName: test
 *       description: This is a test description
 *       location: US
 *   sampleTable:
 *     type: gcp:bigquery:Table
 *     name: sample_table
 *     properties:
 *       deletionProtection: false
 *       datasetId: ${sampleDataset.datasetId}
 *       tableId: job_load_table
 *       schema: |
 *         [
 *             {
 *                 "name": "feature_id",
 *                 "type": "STRING",
 *                 "mode": "NULLABLE"
 *             },
 *             {
 *                 "name": "example_feature",
 *                 "type": "STRING",
 *                 "mode": "NULLABLE"
 *             },
 *             {
 *                 "name": "feature_timestamp",
 *                 "type": "TIMESTAMP",
 *                 "mode": "NULLABLE"
 *             }
 *         ]
 * ```
 * 
 * ## Import
 * FeatureGroupFeature can be imported using any of these accepted formats:
 * * `projects/{{project}}/locations/{{region}}/featureGroups/{{feature_group}}/features/{{name}}`
 * * `{{project}}/{{region}}/{{feature_group}}/{{name}}`
 * * `{{region}}/{{feature_group}}/{{name}}`
 * * `{{feature_group}}/{{name}}`
 * When using the `pulumi import` command, FeatureGroupFeature can be imported using one of the formats above. For example:
 * ```sh
 * $ pulumi import gcp:vertex/aiFeatureGroupFeature:AiFeatureGroupFeature default projects/{{project}}/locations/{{region}}/featureGroups/{{feature_group}}/features/{{name}}
 * ```
 * ```sh
 * $ pulumi import gcp:vertex/aiFeatureGroupFeature:AiFeatureGroupFeature default {{project}}/{{region}}/{{feature_group}}/{{name}}
 * ```
 * ```sh
 * $ pulumi import gcp:vertex/aiFeatureGroupFeature:AiFeatureGroupFeature default {{region}}/{{feature_group}}/{{name}}
 * ```
 * ```sh
 * $ pulumi import gcp:vertex/aiFeatureGroupFeature:AiFeatureGroupFeature default {{feature_group}}/{{name}}
 * ```
 */
public class AiFeatureGroupFeature internal constructor(
    override val javaResource: com.pulumi.gcp.vertex.AiFeatureGroupFeature,
) : KotlinCustomResource(javaResource, AiFeatureGroupFeatureMapper) {
    /**
     * The timestamp of when the FeatureGroup was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
     */
    public val createTime: Output
        get() = javaResource.createTime().applyValue({ args0 -> args0 })

    /**
     * The description of the FeatureGroup.
     */
    public val description: Output?
        get() = javaResource.description().applyValue({ args0 ->
            args0.map({ args0 ->
                args0
            }).orElse(null)
        })

    /**
     * All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
     */
    public val effectiveLabels: Output>
        get() = javaResource.effectiveLabels().applyValue({ args0 ->
            args0.map({ args0 ->
                args0.key.to(args0.value)
            }).toMap()
        })

    /**
     * The name of the Feature Group.
     */
    public val featureGroup: Output
        get() = javaResource.featureGroup().applyValue({ args0 -> args0 })

    /**
     * The labels with user-defined metadata to organize your FeatureGroup.
     * **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
     * Please refer to the field `effective_labels` for all of the labels present on the resource.
     */
    public val labels: Output>?
        get() = javaResource.labels().applyValue({ args0 ->
            args0.map({ args0 ->
                args0.map({ args0 ->
                    args0.key.to(args0.value)
                }).toMap()
            }).orElse(null)
        })

    /**
     * The resource name of the Feature Group Feature.
     */
    public val name: Output
        get() = javaResource.name().applyValue({ args0 -> args0 })

    /**
     * The ID of the project in which the resource belongs.
     * If it is not provided, the provider project is used.
     */
    public val project: Output
        get() = javaResource.project().applyValue({ args0 -> args0 })

    /**
     * The combination of labels configured directly on the resource
     * and default labels configured on the provider.
     */
    public val pulumiLabels: Output>
        get() = javaResource.pulumiLabels().applyValue({ args0 ->
            args0.map({ args0 ->
                args0.key.to(args0.value)
            }).toMap()
        })

    /**
     * The region for the resource. It should be the same as the feature group's region.
     * - - -
     */
    public val region: Output
        get() = javaResource.region().applyValue({ args0 -> args0 })

    /**
     * The timestamp of when the FeatureGroup was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
     */
    public val updateTime: Output
        get() = javaResource.updateTime().applyValue({ args0 -> args0 })

    /**
     * The name of the BigQuery Table/View column hosting data for this version. If no value is provided, will use featureId.
     */
    public val versionColumnName: Output
        get() = javaResource.versionColumnName().applyValue({ args0 -> args0 })
}

public object AiFeatureGroupFeatureMapper : ResourceMapper {
    override fun supportsMappingOfType(javaResource: Resource): Boolean =
        com.pulumi.gcp.vertex.AiFeatureGroupFeature::class == javaResource::class

    override fun map(javaResource: Resource): AiFeatureGroupFeature =
        AiFeatureGroupFeature(javaResource as com.pulumi.gcp.vertex.AiFeatureGroupFeature)
}

/**
 * @see [AiFeatureGroupFeature].
 * @param name The _unique_ name of the resulting resource.
 * @param block Builder for [AiFeatureGroupFeature].
 */
public suspend fun aiFeatureGroupFeature(
    name: String,
    block: suspend AiFeatureGroupFeatureResourceBuilder.() -> Unit,
): AiFeatureGroupFeature {
    val builder = AiFeatureGroupFeatureResourceBuilder()
    builder.name(name)
    block(builder)
    return builder.build()
}

/**
 * @see [AiFeatureGroupFeature].
 * @param name The _unique_ name of the resulting resource.
 */
public fun aiFeatureGroupFeature(name: String): AiFeatureGroupFeature {
    val builder = AiFeatureGroupFeatureResourceBuilder()
    builder.name(name)
    return builder.build()
}




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