// Code generated by smithy-go-codegen DO NOT EDIT.

package sagemaker

import (
	"context"
	awsmiddleware "github.com/aws/aws-sdk-go-v2/aws/middleware"
	"github.com/aws/aws-sdk-go-v2/aws/signer/v4"
	"github.com/aws/aws-sdk-go-v2/service/sagemaker/types"
	"github.com/aws/smithy-go/middleware"
	smithyhttp "github.com/aws/smithy-go/transport/http"
)

// Creates an Autopilot job also referred to as Autopilot experiment or AutoML job
// V2. CreateAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html)
// and DescribeAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html)
// are new versions of CreateAutoMLJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html)
// and DescribeAutoMLJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html)
// which offer backward compatibility. CreateAutoMLJobV2 can manage tabular
// problem types identical to those of its previous version CreateAutoMLJob , as
// well as non-tabular problem types such as image or text classification. Find
// guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in
// Migrate a CreateAutoMLJob to CreateAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment-api.html#autopilot-create-experiment-api-migrate-v1-v2)
// . For the list of available problem types supported by CreateAutoMLJobV2 , see
// AutoMLProblemTypeConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLProblemTypeConfig.html)
// . You can find the best-performing model after you run an AutoML job V2 by
// calling DescribeAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html)
// .
func (c *Client) CreateAutoMLJobV2(ctx context.Context, params *CreateAutoMLJobV2Input, optFns ...func(*Options)) (*CreateAutoMLJobV2Output, error) {
	if params == nil {
		params = &CreateAutoMLJobV2Input{}
	}

	result, metadata, err := c.invokeOperation(ctx, "CreateAutoMLJobV2", params, optFns, c.addOperationCreateAutoMLJobV2Middlewares)
	if err != nil {
		return nil, err
	}

	out := result.(*CreateAutoMLJobV2Output)
	out.ResultMetadata = metadata
	return out, nil
}

type CreateAutoMLJobV2Input struct {

	// An array of channel objects describing the input data and their location. Each
	// channel is a named input source. Similar to the InputDataConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html#sagemaker-CreateAutoMLJob-request-InputDataConfig)
	// attribute in the CreateAutoMLJob input parameters. The supported formats depend
	// on the problem type:
	//   - For tabular problem types: S3Prefix , ManifestFile .
	//   - For image classification: S3Prefix , ManifestFile , AugmentedManifestFile .
	//   - For text classification: S3Prefix .
	//   - For time-series forecasting: S3Prefix .
	//
	// This member is required.
	AutoMLJobInputDataConfig []types.AutoMLJobChannel

	// Identifies an Autopilot job. The name must be unique to your account and is
	// case insensitive.
	//
	// This member is required.
	AutoMLJobName *string

	// Defines the configuration settings of one of the supported problem types.
	//
	// This member is required.
	AutoMLProblemTypeConfig types.AutoMLProblemTypeConfig

	// Provides information about encryption and the Amazon S3 output path needed to
	// store artifacts from an AutoML job.
	//
	// This member is required.
	OutputDataConfig *types.AutoMLOutputDataConfig

	// The ARN of the role that is used to access the data.
	//
	// This member is required.
	RoleArn *string

	// Specifies a metric to minimize or maximize as the objective of a job. If not
	// specified, the default objective metric depends on the problem type. For the
	// list of default values per problem type, see AutoMLJobObjective (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html)
	// . For tabular problem types, you must either provide both the AutoMLJobObjective
	// and indicate the type of supervised learning problem in AutoMLProblemTypeConfig
	// ( TabularJobConfig.ProblemType ), or none at all.
	AutoMLJobObjective *types.AutoMLJobObjective

	// This structure specifies how to split the data into train and validation
	// datasets. The validation and training datasets must contain the same headers.
	// For jobs created by calling CreateAutoMLJob , the validation dataset must be
	// less than 2 GB in size. This attribute must not be set for the time-series
	// forecasting problem type, as Autopilot automatically splits the input dataset
	// into training and validation sets.
	DataSplitConfig *types.AutoMLDataSplitConfig

	// Specifies how to generate the endpoint name for an automatic one-click
	// Autopilot model deployment.
	ModelDeployConfig *types.ModelDeployConfig

	// The security configuration for traffic encryption or Amazon VPC settings.
	SecurityConfig *types.AutoMLSecurityConfig

	// An array of key-value pairs. You can use tags to categorize your Amazon Web
	// Services resources in different ways, such as by purpose, owner, or environment.
	// For more information, see Tagging Amazon Web ServicesResources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html)
	// . Tag keys must be unique per resource.
	Tags []types.Tag

	noSmithyDocumentSerde
}

type CreateAutoMLJobV2Output struct {

	// The unique ARN assigned to the AutoMLJob when it is created.
	//
	// This member is required.
	AutoMLJobArn *string

	// Metadata pertaining to the operation's result.
	ResultMetadata middleware.Metadata

	noSmithyDocumentSerde
}

func (c *Client) addOperationCreateAutoMLJobV2Middlewares(stack *middleware.Stack, options Options) (err error) {
	err = stack.Serialize.Add(&awsAwsjson11_serializeOpCreateAutoMLJobV2{}, middleware.After)
	if err != nil {
		return err
	}
	err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpCreateAutoMLJobV2{}, middleware.After)
	if err != nil {
		return err
	}
	if err = addSetLoggerMiddleware(stack, options); err != nil {
		return err
	}
	if err = awsmiddleware.AddClientRequestIDMiddleware(stack); err != nil {
		return err
	}
	if err = smithyhttp.AddComputeContentLengthMiddleware(stack); err != nil {
		return err
	}
	if err = addResolveEndpointMiddleware(stack, options); err != nil {
		return err
	}
	if err = v4.AddComputePayloadSHA256Middleware(stack); err != nil {
		return err
	}
	if err = addRetryMiddlewares(stack, options); err != nil {
		return err
	}
	if err = addHTTPSignerV4Middleware(stack, options); err != nil {
		return err
	}
	if err = awsmiddleware.AddRawResponseToMetadata(stack); err != nil {
		return err
	}
	if err = awsmiddleware.AddRecordResponseTiming(stack); err != nil {
		return err
	}
	if err = addClientUserAgent(stack, options); err != nil {
		return err
	}
	if err = smithyhttp.AddErrorCloseResponseBodyMiddleware(stack); err != nil {
		return err
	}
	if err = smithyhttp.AddCloseResponseBodyMiddleware(stack); err != nil {
		return err
	}
	if err = addOpCreateAutoMLJobV2ValidationMiddleware(stack); err != nil {
		return err
	}
	if err = stack.Initialize.Add(newServiceMetadataMiddleware_opCreateAutoMLJobV2(options.Region), middleware.Before); err != nil {
		return err
	}
	if err = awsmiddleware.AddRecursionDetection(stack); err != nil {
		return err
	}
	if err = addRequestIDRetrieverMiddleware(stack); err != nil {
		return err
	}
	if err = addResponseErrorMiddleware(stack); err != nil {
		return err
	}
	if err = addRequestResponseLogging(stack, options); err != nil {
		return err
	}
	return nil
}

func newServiceMetadataMiddleware_opCreateAutoMLJobV2(region string) *awsmiddleware.RegisterServiceMetadata {
	return &awsmiddleware.RegisterServiceMetadata{
		Region:        region,
		ServiceID:     ServiceID,
		SigningName:   "sagemaker",
		OperationName: "CreateAutoMLJobV2",
	}
}