{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE DuplicateRecordFields #-}
{-# LANGUAGE NamedFieldPuns #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE RecordWildCards #-}
{-# LANGUAGE StrictData #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# OPTIONS_GHC -fno-warn-unused-binds #-}
{-# OPTIONS_GHC -fno-warn-unused-imports #-}
{-# OPTIONS_GHC -fno-warn-unused-matches #-}

-- Derived from AWS service descriptions, licensed under Apache 2.0.

-- |
-- Module      : Amazonka.SageMaker.CreateLabelingJob
-- Copyright   : (c) 2013-2023 Brendan Hay
-- License     : Mozilla Public License, v. 2.0.
-- Maintainer  : Brendan Hay
-- Stability   : auto-generated
-- Portability : non-portable (GHC extensions)
--
-- Creates a job that uses workers to label the data objects in your input
-- dataset. You can use the labeled data to train machine learning models.
--
-- You can select your workforce from one of three providers:
--
-- -   A private workforce that you create. It can include employees,
--     contractors, and outside experts. Use a private workforce when want
--     the data to stay within your organization or when a specific set of
--     skills is required.
--
-- -   One or more vendors that you select from the Amazon Web Services
--     Marketplace. Vendors provide expertise in specific areas.
--
-- -   The Amazon Mechanical Turk workforce. This is the largest workforce,
--     but it should only be used for public data or data that has been
--     stripped of any personally identifiable information.
--
-- You can also use /automated data labeling/ to reduce the number of data
-- objects that need to be labeled by a human. Automated data labeling uses
-- /active learning/ to determine if a data object can be labeled by
-- machine or if it needs to be sent to a human worker. For more
-- information, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html Using Automated Data Labeling>.
--
-- The data objects to be labeled are contained in an Amazon S3 bucket. You
-- create a /manifest file/ that describes the location of each object. For
-- more information, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-data.html Using Input and Output Data>.
--
-- The output can be used as the manifest file for another labeling job or
-- as training data for your machine learning models.
--
-- You can use this operation to create a static labeling job or a
-- streaming labeling job. A static labeling job stops if all data objects
-- in the input manifest file identified in @ManifestS3Uri@ have been
-- labeled. A streaming labeling job runs perpetually until it is manually
-- stopped, or remains idle for 10 days. You can send new data objects to
-- an active (@InProgress@) streaming labeling job in real time. To learn
-- how to create a static labeling job, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html Create a Labeling Job (API)>
-- in the Amazon SageMaker Developer Guide. To learn how to create a
-- streaming labeling job, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-create-job.html Create a Streaming Labeling Job>.
module Amazonka.SageMaker.CreateLabelingJob
  ( -- * Creating a Request
    CreateLabelingJob (..),
    newCreateLabelingJob,

    -- * Request Lenses
    createLabelingJob_labelCategoryConfigS3Uri,
    createLabelingJob_labelingJobAlgorithmsConfig,
    createLabelingJob_stoppingConditions,
    createLabelingJob_tags,
    createLabelingJob_labelingJobName,
    createLabelingJob_labelAttributeName,
    createLabelingJob_inputConfig,
    createLabelingJob_outputConfig,
    createLabelingJob_roleArn,
    createLabelingJob_humanTaskConfig,

    -- * Destructuring the Response
    CreateLabelingJobResponse (..),
    newCreateLabelingJobResponse,

    -- * Response Lenses
    createLabelingJobResponse_httpStatus,
    createLabelingJobResponse_labelingJobArn,
  )
where

import qualified Amazonka.Core as Core
import qualified Amazonka.Core.Lens.Internal as Lens
import qualified Amazonka.Data as Data
import qualified Amazonka.Prelude as Prelude
import qualified Amazonka.Request as Request
import qualified Amazonka.Response as Response
import Amazonka.SageMaker.Types

-- | /See:/ 'newCreateLabelingJob' smart constructor.
data CreateLabelingJob = CreateLabelingJob'
  { -- | The S3 URI of the file, referred to as a /label category configuration
    -- file/, that defines the categories used to label the data objects.
    --
    -- For 3D point cloud and video frame task types, you can add label
    -- category attributes and frame attributes to your label category
    -- configuration file. To learn how, see
    -- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs>.
    --
    -- For named entity recognition jobs, in addition to @\"labels\"@, you must
    -- provide worker instructions in the label category configuration file
    -- using the @\"instructions\"@ parameter:
    -- @\"instructions\": {\"shortInstruction\":\"\<h1>Add header\<\/h1>\<p>Add Instructions\<\/p>\", \"fullInstruction\":\"\<p>Add additional instructions.\<\/p>\"}@.
    -- For details and an example, see
    -- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api Create a Named Entity Recognition Labeling Job (API)>
    -- .
    --
    -- For all other
    -- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html built-in task types>
    -- and
    -- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html custom tasks>,
    -- your label category configuration file must be a JSON file in the
    -- following format. Identify the labels you want to use by replacing
    -- @label_1@, @label_2@,@...@,@label_n@ with your label categories.
    --
    -- @{ @
    --
    -- @\"document-version\": \"2018-11-28\",@
    --
    -- @\"labels\": [{\"label\": \"label_1\"},{\"label\": \"label_2\"},...{\"label\": \"label_n\"}]@
    --
    -- @}@
    --
    -- Note the following about the label category configuration file:
    --
    -- -   For image classification and text classification (single and
    --     multi-label) you must specify at least two label categories. For all
    --     other task types, the minimum number of label categories required is
    --     one.
    --
    -- -   Each label category must be unique, you cannot specify duplicate
    --     label categories.
    --
    -- -   If you create a 3D point cloud or video frame adjustment or
    --     verification labeling job, you must include
    --     @auditLabelAttributeName@ in the label category configuration. Use
    --     this parameter to enter the
    --     <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName LabelAttributeName>
    --     of the labeling job you want to adjust or verify annotations of.
    labelCategoryConfigS3Uri :: Prelude.Maybe Prelude.Text,
    -- | Configures the information required to perform automated data labeling.
    labelingJobAlgorithmsConfig :: Prelude.Maybe LabelingJobAlgorithmsConfig,
    -- | A set of conditions for stopping the labeling job. If any of the
    -- conditions are met, the job is automatically stopped. You can use these
    -- conditions to control the cost of data labeling.
    stoppingConditions :: Prelude.Maybe LabelingJobStoppingConditions,
    -- | An array of key\/value pairs. For more information, see
    -- <https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what Using Cost Allocation Tags>
    -- in the /Amazon Web Services Billing and Cost Management User Guide/.
    tags :: Prelude.Maybe [Tag],
    -- | The name of the labeling job. This name is used to identify the job in a
    -- list of labeling jobs. Labeling job names must be unique within an
    -- Amazon Web Services account and region. @LabelingJobName@ is not case
    -- sensitive. For example, Example-job and example-job are considered the
    -- same labeling job name by Ground Truth.
    labelingJobName :: Prelude.Text,
    -- | The attribute name to use for the label in the output manifest file.
    -- This is the key for the key\/value pair formed with the label that a
    -- worker assigns to the object. The @LabelAttributeName@ must meet the
    -- following requirements.
    --
    -- -   The name can\'t end with \"-metadata\".
    --
    -- -   If you are using one of the following
    --     <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html built-in task types>,
    --     the attribute name /must/ end with \"-ref\". If the task type you
    --     are using is not listed below, the attribute name /must not/ end
    --     with \"-ref\".
    --
    --     -   Image semantic segmentation (@SemanticSegmentation)@, and
    --         adjustment (@AdjustmentSemanticSegmentation@) and verification
    --         (@VerificationSemanticSegmentation@) labeling jobs for this task
    --         type.
    --
    --     -   Video frame object detection (@VideoObjectDetection@), and
    --         adjustment and verification (@AdjustmentVideoObjectDetection@)
    --         labeling jobs for this task type.
    --
    --     -   Video frame object tracking (@VideoObjectTracking@), and
    --         adjustment and verification (@AdjustmentVideoObjectTracking@)
    --         labeling jobs for this task type.
    --
    --     -   3D point cloud semantic segmentation
    --         (@3DPointCloudSemanticSegmentation@), and adjustment and
    --         verification (@Adjustment3DPointCloudSemanticSegmentation@)
    --         labeling jobs for this task type.
    --
    --     -   3D point cloud object tracking (@3DPointCloudObjectTracking@),
    --         and adjustment and verification
    --         (@Adjustment3DPointCloudObjectTracking@) labeling jobs for this
    --         task type.
    --
    -- If you are creating an adjustment or verification labeling job, you must
    -- use a /different/ @LabelAttributeName@ than the one used in the original
    -- labeling job. The original labeling job is the Ground Truth labeling job
    -- that produced the labels that you want verified or adjusted. To learn
    -- more about adjustment and verification labeling jobs, see
    -- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html Verify and Adjust Labels>.
    labelAttributeName :: Prelude.Text,
    -- | Input data for the labeling job, such as the Amazon S3 location of the
    -- data objects and the location of the manifest file that describes the
    -- data objects.
    --
    -- You must specify at least one of the following: @S3DataSource@ or
    -- @SnsDataSource@.
    --
    -- -   Use @SnsDataSource@ to specify an SNS input topic for a streaming
    --     labeling job. If you do not specify and SNS input topic ARN, Ground
    --     Truth will create a one-time labeling job that stops after all data
    --     objects in the input manifest file have been labeled.
    --
    -- -   Use @S3DataSource@ to specify an input manifest file for both
    --     streaming and one-time labeling jobs. Adding an @S3DataSource@ is
    --     optional if you use @SnsDataSource@ to create a streaming labeling
    --     job.
    --
    -- If you use the Amazon Mechanical Turk workforce, your input data should
    -- not include confidential information, personal information or protected
    -- health information. Use @ContentClassifiers@ to specify that your data
    -- is free of personally identifiable information and adult content.
    inputConfig :: LabelingJobInputConfig,
    -- | The location of the output data and the Amazon Web Services Key
    -- Management Service key ID for the key used to encrypt the output data,
    -- if any.
    outputConfig :: LabelingJobOutputConfig,
    -- | The Amazon Resource Number (ARN) that Amazon SageMaker assumes to
    -- perform tasks on your behalf during data labeling. You must grant this
    -- role the necessary permissions so that Amazon SageMaker can successfully
    -- complete data labeling.
    roleArn :: Prelude.Text,
    -- | Configures the labeling task and how it is presented to workers;
    -- including, but not limited to price, keywords, and batch size (task
    -- count).
    humanTaskConfig :: HumanTaskConfig
  }
  deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)

-- |
-- Create a value of 'CreateLabelingJob' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'labelCategoryConfigS3Uri', 'createLabelingJob_labelCategoryConfigS3Uri' - The S3 URI of the file, referred to as a /label category configuration
-- file/, that defines the categories used to label the data objects.
--
-- For 3D point cloud and video frame task types, you can add label
-- category attributes and frame attributes to your label category
-- configuration file. To learn how, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs>.
--
-- For named entity recognition jobs, in addition to @\"labels\"@, you must
-- provide worker instructions in the label category configuration file
-- using the @\"instructions\"@ parameter:
-- @\"instructions\": {\"shortInstruction\":\"\<h1>Add header\<\/h1>\<p>Add Instructions\<\/p>\", \"fullInstruction\":\"\<p>Add additional instructions.\<\/p>\"}@.
-- For details and an example, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api Create a Named Entity Recognition Labeling Job (API)>
-- .
--
-- For all other
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html built-in task types>
-- and
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html custom tasks>,
-- your label category configuration file must be a JSON file in the
-- following format. Identify the labels you want to use by replacing
-- @label_1@, @label_2@,@...@,@label_n@ with your label categories.
--
-- @{ @
--
-- @\"document-version\": \"2018-11-28\",@
--
-- @\"labels\": [{\"label\": \"label_1\"},{\"label\": \"label_2\"},...{\"label\": \"label_n\"}]@
--
-- @}@
--
-- Note the following about the label category configuration file:
--
-- -   For image classification and text classification (single and
--     multi-label) you must specify at least two label categories. For all
--     other task types, the minimum number of label categories required is
--     one.
--
-- -   Each label category must be unique, you cannot specify duplicate
--     label categories.
--
-- -   If you create a 3D point cloud or video frame adjustment or
--     verification labeling job, you must include
--     @auditLabelAttributeName@ in the label category configuration. Use
--     this parameter to enter the
--     <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName LabelAttributeName>
--     of the labeling job you want to adjust or verify annotations of.
--
-- 'labelingJobAlgorithmsConfig', 'createLabelingJob_labelingJobAlgorithmsConfig' - Configures the information required to perform automated data labeling.
--
-- 'stoppingConditions', 'createLabelingJob_stoppingConditions' - A set of conditions for stopping the labeling job. If any of the
-- conditions are met, the job is automatically stopped. You can use these
-- conditions to control the cost of data labeling.
--
-- 'tags', 'createLabelingJob_tags' - An array of key\/value pairs. For more information, see
-- <https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what Using Cost Allocation Tags>
-- in the /Amazon Web Services Billing and Cost Management User Guide/.
--
-- 'labelingJobName', 'createLabelingJob_labelingJobName' - The name of the labeling job. This name is used to identify the job in a
-- list of labeling jobs. Labeling job names must be unique within an
-- Amazon Web Services account and region. @LabelingJobName@ is not case
-- sensitive. For example, Example-job and example-job are considered the
-- same labeling job name by Ground Truth.
--
-- 'labelAttributeName', 'createLabelingJob_labelAttributeName' - The attribute name to use for the label in the output manifest file.
-- This is the key for the key\/value pair formed with the label that a
-- worker assigns to the object. The @LabelAttributeName@ must meet the
-- following requirements.
--
-- -   The name can\'t end with \"-metadata\".
--
-- -   If you are using one of the following
--     <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html built-in task types>,
--     the attribute name /must/ end with \"-ref\". If the task type you
--     are using is not listed below, the attribute name /must not/ end
--     with \"-ref\".
--
--     -   Image semantic segmentation (@SemanticSegmentation)@, and
--         adjustment (@AdjustmentSemanticSegmentation@) and verification
--         (@VerificationSemanticSegmentation@) labeling jobs for this task
--         type.
--
--     -   Video frame object detection (@VideoObjectDetection@), and
--         adjustment and verification (@AdjustmentVideoObjectDetection@)
--         labeling jobs for this task type.
--
--     -   Video frame object tracking (@VideoObjectTracking@), and
--         adjustment and verification (@AdjustmentVideoObjectTracking@)
--         labeling jobs for this task type.
--
--     -   3D point cloud semantic segmentation
--         (@3DPointCloudSemanticSegmentation@), and adjustment and
--         verification (@Adjustment3DPointCloudSemanticSegmentation@)
--         labeling jobs for this task type.
--
--     -   3D point cloud object tracking (@3DPointCloudObjectTracking@),
--         and adjustment and verification
--         (@Adjustment3DPointCloudObjectTracking@) labeling jobs for this
--         task type.
--
-- If you are creating an adjustment or verification labeling job, you must
-- use a /different/ @LabelAttributeName@ than the one used in the original
-- labeling job. The original labeling job is the Ground Truth labeling job
-- that produced the labels that you want verified or adjusted. To learn
-- more about adjustment and verification labeling jobs, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html Verify and Adjust Labels>.
--
-- 'inputConfig', 'createLabelingJob_inputConfig' - Input data for the labeling job, such as the Amazon S3 location of the
-- data objects and the location of the manifest file that describes the
-- data objects.
--
-- You must specify at least one of the following: @S3DataSource@ or
-- @SnsDataSource@.
--
-- -   Use @SnsDataSource@ to specify an SNS input topic for a streaming
--     labeling job. If you do not specify and SNS input topic ARN, Ground
--     Truth will create a one-time labeling job that stops after all data
--     objects in the input manifest file have been labeled.
--
-- -   Use @S3DataSource@ to specify an input manifest file for both
--     streaming and one-time labeling jobs. Adding an @S3DataSource@ is
--     optional if you use @SnsDataSource@ to create a streaming labeling
--     job.
--
-- If you use the Amazon Mechanical Turk workforce, your input data should
-- not include confidential information, personal information or protected
-- health information. Use @ContentClassifiers@ to specify that your data
-- is free of personally identifiable information and adult content.
--
-- 'outputConfig', 'createLabelingJob_outputConfig' - The location of the output data and the Amazon Web Services Key
-- Management Service key ID for the key used to encrypt the output data,
-- if any.
--
-- 'roleArn', 'createLabelingJob_roleArn' - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to
-- perform tasks on your behalf during data labeling. You must grant this
-- role the necessary permissions so that Amazon SageMaker can successfully
-- complete data labeling.
--
-- 'humanTaskConfig', 'createLabelingJob_humanTaskConfig' - Configures the labeling task and how it is presented to workers;
-- including, but not limited to price, keywords, and batch size (task
-- count).
newCreateLabelingJob ::
  -- | 'labelingJobName'
  Prelude.Text ->
  -- | 'labelAttributeName'
  Prelude.Text ->
  -- | 'inputConfig'
  LabelingJobInputConfig ->
  -- | 'outputConfig'
  LabelingJobOutputConfig ->
  -- | 'roleArn'
  Prelude.Text ->
  -- | 'humanTaskConfig'
  HumanTaskConfig ->
  CreateLabelingJob
newCreateLabelingJob
  pLabelingJobName_
  pLabelAttributeName_
  pInputConfig_
  pOutputConfig_
  pRoleArn_
  pHumanTaskConfig_ =
    CreateLabelingJob'
      { labelCategoryConfigS3Uri =
          Prelude.Nothing,
        labelingJobAlgorithmsConfig = Prelude.Nothing,
        stoppingConditions = Prelude.Nothing,
        tags = Prelude.Nothing,
        labelingJobName = pLabelingJobName_,
        labelAttributeName = pLabelAttributeName_,
        inputConfig = pInputConfig_,
        outputConfig = pOutputConfig_,
        roleArn = pRoleArn_,
        humanTaskConfig = pHumanTaskConfig_
      }

-- | The S3 URI of the file, referred to as a /label category configuration
-- file/, that defines the categories used to label the data objects.
--
-- For 3D point cloud and video frame task types, you can add label
-- category attributes and frame attributes to your label category
-- configuration file. To learn how, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs>.
--
-- For named entity recognition jobs, in addition to @\"labels\"@, you must
-- provide worker instructions in the label category configuration file
-- using the @\"instructions\"@ parameter:
-- @\"instructions\": {\"shortInstruction\":\"\<h1>Add header\<\/h1>\<p>Add Instructions\<\/p>\", \"fullInstruction\":\"\<p>Add additional instructions.\<\/p>\"}@.
-- For details and an example, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api Create a Named Entity Recognition Labeling Job (API)>
-- .
--
-- For all other
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html built-in task types>
-- and
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html custom tasks>,
-- your label category configuration file must be a JSON file in the
-- following format. Identify the labels you want to use by replacing
-- @label_1@, @label_2@,@...@,@label_n@ with your label categories.
--
-- @{ @
--
-- @\"document-version\": \"2018-11-28\",@
--
-- @\"labels\": [{\"label\": \"label_1\"},{\"label\": \"label_2\"},...{\"label\": \"label_n\"}]@
--
-- @}@
--
-- Note the following about the label category configuration file:
--
-- -   For image classification and text classification (single and
--     multi-label) you must specify at least two label categories. For all
--     other task types, the minimum number of label categories required is
--     one.
--
-- -   Each label category must be unique, you cannot specify duplicate
--     label categories.
--
-- -   If you create a 3D point cloud or video frame adjustment or
--     verification labeling job, you must include
--     @auditLabelAttributeName@ in the label category configuration. Use
--     this parameter to enter the
--     <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName LabelAttributeName>
--     of the labeling job you want to adjust or verify annotations of.
createLabelingJob_labelCategoryConfigS3Uri :: Lens.Lens' CreateLabelingJob (Prelude.Maybe Prelude.Text)
createLabelingJob_labelCategoryConfigS3Uri = Lens.lens (\CreateLabelingJob' {labelCategoryConfigS3Uri} -> labelCategoryConfigS3Uri) (\s@CreateLabelingJob' {} a -> s {labelCategoryConfigS3Uri = a} :: CreateLabelingJob)

-- | Configures the information required to perform automated data labeling.
createLabelingJob_labelingJobAlgorithmsConfig :: Lens.Lens' CreateLabelingJob (Prelude.Maybe LabelingJobAlgorithmsConfig)
createLabelingJob_labelingJobAlgorithmsConfig = Lens.lens (\CreateLabelingJob' {labelingJobAlgorithmsConfig} -> labelingJobAlgorithmsConfig) (\s@CreateLabelingJob' {} a -> s {labelingJobAlgorithmsConfig = a} :: CreateLabelingJob)

-- | A set of conditions for stopping the labeling job. If any of the
-- conditions are met, the job is automatically stopped. You can use these
-- conditions to control the cost of data labeling.
createLabelingJob_stoppingConditions :: Lens.Lens' CreateLabelingJob (Prelude.Maybe LabelingJobStoppingConditions)
createLabelingJob_stoppingConditions = Lens.lens (\CreateLabelingJob' {stoppingConditions} -> stoppingConditions) (\s@CreateLabelingJob' {} a -> s {stoppingConditions = a} :: CreateLabelingJob)

-- | An array of key\/value pairs. For more information, see
-- <https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what Using Cost Allocation Tags>
-- in the /Amazon Web Services Billing and Cost Management User Guide/.
createLabelingJob_tags :: Lens.Lens' CreateLabelingJob (Prelude.Maybe [Tag])
createLabelingJob_tags = Lens.lens (\CreateLabelingJob' {tags} -> tags) (\s@CreateLabelingJob' {} a -> s {tags = a} :: CreateLabelingJob) Prelude.. Lens.mapping Lens.coerced

-- | The name of the labeling job. This name is used to identify the job in a
-- list of labeling jobs. Labeling job names must be unique within an
-- Amazon Web Services account and region. @LabelingJobName@ is not case
-- sensitive. For example, Example-job and example-job are considered the
-- same labeling job name by Ground Truth.
createLabelingJob_labelingJobName :: Lens.Lens' CreateLabelingJob Prelude.Text
createLabelingJob_labelingJobName = Lens.lens (\CreateLabelingJob' {labelingJobName} -> labelingJobName) (\s@CreateLabelingJob' {} a -> s {labelingJobName = a} :: CreateLabelingJob)

-- | The attribute name to use for the label in the output manifest file.
-- This is the key for the key\/value pair formed with the label that a
-- worker assigns to the object. The @LabelAttributeName@ must meet the
-- following requirements.
--
-- -   The name can\'t end with \"-metadata\".
--
-- -   If you are using one of the following
--     <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html built-in task types>,
--     the attribute name /must/ end with \"-ref\". If the task type you
--     are using is not listed below, the attribute name /must not/ end
--     with \"-ref\".
--
--     -   Image semantic segmentation (@SemanticSegmentation)@, and
--         adjustment (@AdjustmentSemanticSegmentation@) and verification
--         (@VerificationSemanticSegmentation@) labeling jobs for this task
--         type.
--
--     -   Video frame object detection (@VideoObjectDetection@), and
--         adjustment and verification (@AdjustmentVideoObjectDetection@)
--         labeling jobs for this task type.
--
--     -   Video frame object tracking (@VideoObjectTracking@), and
--         adjustment and verification (@AdjustmentVideoObjectTracking@)
--         labeling jobs for this task type.
--
--     -   3D point cloud semantic segmentation
--         (@3DPointCloudSemanticSegmentation@), and adjustment and
--         verification (@Adjustment3DPointCloudSemanticSegmentation@)
--         labeling jobs for this task type.
--
--     -   3D point cloud object tracking (@3DPointCloudObjectTracking@),
--         and adjustment and verification
--         (@Adjustment3DPointCloudObjectTracking@) labeling jobs for this
--         task type.
--
-- If you are creating an adjustment or verification labeling job, you must
-- use a /different/ @LabelAttributeName@ than the one used in the original
-- labeling job. The original labeling job is the Ground Truth labeling job
-- that produced the labels that you want verified or adjusted. To learn
-- more about adjustment and verification labeling jobs, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html Verify and Adjust Labels>.
createLabelingJob_labelAttributeName :: Lens.Lens' CreateLabelingJob Prelude.Text
createLabelingJob_labelAttributeName = Lens.lens (\CreateLabelingJob' {labelAttributeName} -> labelAttributeName) (\s@CreateLabelingJob' {} a -> s {labelAttributeName = a} :: CreateLabelingJob)

-- | Input data for the labeling job, such as the Amazon S3 location of the
-- data objects and the location of the manifest file that describes the
-- data objects.
--
-- You must specify at least one of the following: @S3DataSource@ or
-- @SnsDataSource@.
--
-- -   Use @SnsDataSource@ to specify an SNS input topic for a streaming
--     labeling job. If you do not specify and SNS input topic ARN, Ground
--     Truth will create a one-time labeling job that stops after all data
--     objects in the input manifest file have been labeled.
--
-- -   Use @S3DataSource@ to specify an input manifest file for both
--     streaming and one-time labeling jobs. Adding an @S3DataSource@ is
--     optional if you use @SnsDataSource@ to create a streaming labeling
--     job.
--
-- If you use the Amazon Mechanical Turk workforce, your input data should
-- not include confidential information, personal information or protected
-- health information. Use @ContentClassifiers@ to specify that your data
-- is free of personally identifiable information and adult content.
createLabelingJob_inputConfig :: Lens.Lens' CreateLabelingJob LabelingJobInputConfig
createLabelingJob_inputConfig = Lens.lens (\CreateLabelingJob' {inputConfig} -> inputConfig) (\s@CreateLabelingJob' {} a -> s {inputConfig = a} :: CreateLabelingJob)

-- | The location of the output data and the Amazon Web Services Key
-- Management Service key ID for the key used to encrypt the output data,
-- if any.
createLabelingJob_outputConfig :: Lens.Lens' CreateLabelingJob LabelingJobOutputConfig
createLabelingJob_outputConfig = Lens.lens (\CreateLabelingJob' {outputConfig} -> outputConfig) (\s@CreateLabelingJob' {} a -> s {outputConfig = a} :: CreateLabelingJob)

-- | The Amazon Resource Number (ARN) that Amazon SageMaker assumes to
-- perform tasks on your behalf during data labeling. You must grant this
-- role the necessary permissions so that Amazon SageMaker can successfully
-- complete data labeling.
createLabelingJob_roleArn :: Lens.Lens' CreateLabelingJob Prelude.Text
createLabelingJob_roleArn = Lens.lens (\CreateLabelingJob' {roleArn} -> roleArn) (\s@CreateLabelingJob' {} a -> s {roleArn = a} :: CreateLabelingJob)

-- | Configures the labeling task and how it is presented to workers;
-- including, but not limited to price, keywords, and batch size (task
-- count).
createLabelingJob_humanTaskConfig :: Lens.Lens' CreateLabelingJob HumanTaskConfig
createLabelingJob_humanTaskConfig = Lens.lens (\CreateLabelingJob' {humanTaskConfig} -> humanTaskConfig) (\s@CreateLabelingJob' {} a -> s {humanTaskConfig = a} :: CreateLabelingJob)

instance Core.AWSRequest CreateLabelingJob where
  type
    AWSResponse CreateLabelingJob =
      CreateLabelingJobResponse
  request overrides =
    Request.postJSON (overrides defaultService)
  response =
    Response.receiveJSON
      ( \s h x ->
          CreateLabelingJobResponse'
            Prelude.<$> (Prelude.pure (Prelude.fromEnum s))
            Prelude.<*> (x Data..:> "LabelingJobArn")
      )

instance Prelude.Hashable CreateLabelingJob where
  hashWithSalt _salt CreateLabelingJob' {..} =
    _salt
      `Prelude.hashWithSalt` labelCategoryConfigS3Uri
      `Prelude.hashWithSalt` labelingJobAlgorithmsConfig
      `Prelude.hashWithSalt` stoppingConditions
      `Prelude.hashWithSalt` tags
      `Prelude.hashWithSalt` labelingJobName
      `Prelude.hashWithSalt` labelAttributeName
      `Prelude.hashWithSalt` inputConfig
      `Prelude.hashWithSalt` outputConfig
      `Prelude.hashWithSalt` roleArn
      `Prelude.hashWithSalt` humanTaskConfig

instance Prelude.NFData CreateLabelingJob where
  rnf CreateLabelingJob' {..} =
    Prelude.rnf labelCategoryConfigS3Uri
      `Prelude.seq` Prelude.rnf labelingJobAlgorithmsConfig
      `Prelude.seq` Prelude.rnf stoppingConditions
      `Prelude.seq` Prelude.rnf tags
      `Prelude.seq` Prelude.rnf labelingJobName
      `Prelude.seq` Prelude.rnf labelAttributeName
      `Prelude.seq` Prelude.rnf inputConfig
      `Prelude.seq` Prelude.rnf outputConfig
      `Prelude.seq` Prelude.rnf roleArn
      `Prelude.seq` Prelude.rnf humanTaskConfig

instance Data.ToHeaders CreateLabelingJob where
  toHeaders =
    Prelude.const
      ( Prelude.mconcat
          [ "X-Amz-Target"
              Data.=# ( "SageMaker.CreateLabelingJob" ::
                          Prelude.ByteString
                      ),
            "Content-Type"
              Data.=# ( "application/x-amz-json-1.1" ::
                          Prelude.ByteString
                      )
          ]
      )

instance Data.ToJSON CreateLabelingJob where
  toJSON CreateLabelingJob' {..} =
    Data.object
      ( Prelude.catMaybes
          [ ("LabelCategoryConfigS3Uri" Data..=)
              Prelude.<$> labelCategoryConfigS3Uri,
            ("LabelingJobAlgorithmsConfig" Data..=)
              Prelude.<$> labelingJobAlgorithmsConfig,
            ("StoppingConditions" Data..=)
              Prelude.<$> stoppingConditions,
            ("Tags" Data..=) Prelude.<$> tags,
            Prelude.Just
              ("LabelingJobName" Data..= labelingJobName),
            Prelude.Just
              ("LabelAttributeName" Data..= labelAttributeName),
            Prelude.Just ("InputConfig" Data..= inputConfig),
            Prelude.Just ("OutputConfig" Data..= outputConfig),
            Prelude.Just ("RoleArn" Data..= roleArn),
            Prelude.Just
              ("HumanTaskConfig" Data..= humanTaskConfig)
          ]
      )

instance Data.ToPath CreateLabelingJob where
  toPath = Prelude.const "/"

instance Data.ToQuery CreateLabelingJob where
  toQuery = Prelude.const Prelude.mempty

-- | /See:/ 'newCreateLabelingJobResponse' smart constructor.
data CreateLabelingJobResponse = CreateLabelingJobResponse'
  { -- | The response's http status code.
    httpStatus :: Prelude.Int,
    -- | The Amazon Resource Name (ARN) of the labeling job. You use this ARN to
    -- identify the labeling job.
    labelingJobArn :: Prelude.Text
  }
  deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)

-- |
-- Create a value of 'CreateLabelingJobResponse' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'httpStatus', 'createLabelingJobResponse_httpStatus' - The response's http status code.
--
-- 'labelingJobArn', 'createLabelingJobResponse_labelingJobArn' - The Amazon Resource Name (ARN) of the labeling job. You use this ARN to
-- identify the labeling job.
newCreateLabelingJobResponse ::
  -- | 'httpStatus'
  Prelude.Int ->
  -- | 'labelingJobArn'
  Prelude.Text ->
  CreateLabelingJobResponse
newCreateLabelingJobResponse
  pHttpStatus_
  pLabelingJobArn_ =
    CreateLabelingJobResponse'
      { httpStatus =
          pHttpStatus_,
        labelingJobArn = pLabelingJobArn_
      }

-- | The response's http status code.
createLabelingJobResponse_httpStatus :: Lens.Lens' CreateLabelingJobResponse Prelude.Int
createLabelingJobResponse_httpStatus = Lens.lens (\CreateLabelingJobResponse' {httpStatus} -> httpStatus) (\s@CreateLabelingJobResponse' {} a -> s {httpStatus = a} :: CreateLabelingJobResponse)

-- | The Amazon Resource Name (ARN) of the labeling job. You use this ARN to
-- identify the labeling job.
createLabelingJobResponse_labelingJobArn :: Lens.Lens' CreateLabelingJobResponse Prelude.Text
createLabelingJobResponse_labelingJobArn = Lens.lens (\CreateLabelingJobResponse' {labelingJobArn} -> labelingJobArn) (\s@CreateLabelingJobResponse' {} a -> s {labelingJobArn = a} :: CreateLabelingJobResponse)

instance Prelude.NFData CreateLabelingJobResponse where
  rnf CreateLabelingJobResponse' {..} =
    Prelude.rnf httpStatus
      `Prelude.seq` Prelude.rnf labelingJobArn