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

package machinelearning

import (
	"context"
	"fmt"
	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/machinelearning/types"
	"github.com/aws/smithy-go/middleware"
	smithytime "github.com/aws/smithy-go/time"
	smithyhttp "github.com/aws/smithy-go/transport/http"
	smithywaiter "github.com/aws/smithy-go/waiter"
	"github.com/jmespath/go-jmespath"
	"time"
)

// Returns a list of MLModel that match the search criteria in the request.
func (c *Client) DescribeMLModels(ctx context.Context, params *DescribeMLModelsInput, optFns ...func(*Options)) (*DescribeMLModelsOutput, error) {
	if params == nil {
		params = &DescribeMLModelsInput{}
	}

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

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

type DescribeMLModelsInput struct {

	// The equal to operator. The MLModel results will have FilterVariable values that
	// exactly match the value specified with EQ.
	EQ *string

	// Use one of the following variables to filter a list of MLModel:
	//
	// * CreatedAt -
	// Sets the search criteria to MLModel creation date.
	//
	// * Status - Sets the search
	// criteria to MLModel status.
	//
	// * Name - Sets the search criteria to the contents
	// of MLModelName.
	//
	// * IAMUser - Sets the search criteria to the user account that
	// invoked the MLModel creation.
	//
	// * TrainingDataSourceId - Sets the search criteria
	// to the DataSource used to train one or more MLModel.
	//
	// * RealtimeEndpointStatus -
	// Sets the search criteria to the MLModel real-time endpoint status.
	//
	// *
	// MLModelType - Sets the search criteria to MLModel type: binary, regression, or
	// multi-class.
	//
	// * Algorithm - Sets the search criteria to the algorithm that the
	// MLModel uses.
	//
	// * TrainingDataURI - Sets the search criteria to the data file(s)
	// used in training a MLModel. The URL can identify either a file or an Amazon
	// Simple Storage Service (Amazon S3) bucket or directory.
	FilterVariable types.MLModelFilterVariable

	// The greater than or equal to operator. The MLModel results will have
	// FilterVariable values that are greater than or equal to the value specified with
	// GE.
	GE *string

	// The greater than operator. The MLModel results will have FilterVariable values
	// that are greater than the value specified with GT.
	GT *string

	// The less than or equal to operator. The MLModel results will have FilterVariable
	// values that are less than or equal to the value specified with LE.
	LE *string

	// The less than operator. The MLModel results will have FilterVariable values that
	// are less than the value specified with LT.
	LT *string

	// The number of pages of information to include in the result. The range of
	// acceptable values is 1 through 100. The default value is 100.
	Limit *int32

	// The not equal to operator. The MLModel results will have FilterVariable values
	// not equal to the value specified with NE.
	NE *string

	// The ID of the page in the paginated results.
	NextToken *string

	// A string that is found at the beginning of a variable, such as Name or Id. For
	// example, an MLModel could have the Name2014-09-09-HolidayGiftMailer. To search
	// for this MLModel, select Name for the FilterVariable and any of the following
	// strings for the Prefix:
	//
	// * 2014-09
	//
	// * 2014-09-09
	//
	// * 2014-09-09-Holiday
	Prefix *string

	// A two-value parameter that determines the sequence of the resulting list of
	// MLModel.
	//
	// * asc - Arranges the list in ascending order (A-Z, 0-9).
	//
	// * dsc -
	// Arranges the list in descending order (Z-A, 9-0).
	//
	// Results are sorted by
	// FilterVariable.
	SortOrder types.SortOrder

	noSmithyDocumentSerde
}

// Represents the output of a DescribeMLModels operation. The content is
// essentially a list of MLModel.
type DescribeMLModelsOutput struct {

	// The ID of the next page in the paginated results that indicates at least one
	// more page follows.
	NextToken *string

	// A list of MLModel that meet the search criteria.
	Results []types.MLModel

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

	noSmithyDocumentSerde
}

func (c *Client) addOperationDescribeMLModelsMiddlewares(stack *middleware.Stack, options Options) (err error) {
	err = stack.Serialize.Add(&awsAwsjson11_serializeOpDescribeMLModels{}, middleware.After)
	if err != nil {
		return err
	}
	err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpDescribeMLModels{}, 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); err != nil {
		return err
	}
	if err = smithyhttp.AddErrorCloseResponseBodyMiddleware(stack); err != nil {
		return err
	}
	if err = smithyhttp.AddCloseResponseBodyMiddleware(stack); err != nil {
		return err
	}
	if err = stack.Initialize.Add(newServiceMetadataMiddleware_opDescribeMLModels(options.Region), middleware.Before); 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
}

// DescribeMLModelsAPIClient is a client that implements the DescribeMLModels
// operation.
type DescribeMLModelsAPIClient interface {
	DescribeMLModels(context.Context, *DescribeMLModelsInput, ...func(*Options)) (*DescribeMLModelsOutput, error)
}

var _ DescribeMLModelsAPIClient = (*Client)(nil)

// DescribeMLModelsPaginatorOptions is the paginator options for DescribeMLModels
type DescribeMLModelsPaginatorOptions struct {
	// The number of pages of information to include in the result. The range of
	// acceptable values is 1 through 100. The default value is 100.
	Limit int32

	// Set to true if pagination should stop if the service returns a pagination token
	// that matches the most recent token provided to the service.
	StopOnDuplicateToken bool
}

// DescribeMLModelsPaginator is a paginator for DescribeMLModels
type DescribeMLModelsPaginator struct {
	options   DescribeMLModelsPaginatorOptions
	client    DescribeMLModelsAPIClient
	params    *DescribeMLModelsInput
	nextToken *string
	firstPage bool
}

// NewDescribeMLModelsPaginator returns a new DescribeMLModelsPaginator
func NewDescribeMLModelsPaginator(client DescribeMLModelsAPIClient, params *DescribeMLModelsInput, optFns ...func(*DescribeMLModelsPaginatorOptions)) *DescribeMLModelsPaginator {
	if params == nil {
		params = &DescribeMLModelsInput{}
	}

	options := DescribeMLModelsPaginatorOptions{}
	if params.Limit != nil {
		options.Limit = *params.Limit
	}

	for _, fn := range optFns {
		fn(&options)
	}

	return &DescribeMLModelsPaginator{
		options:   options,
		client:    client,
		params:    params,
		firstPage: true,
		nextToken: params.NextToken,
	}
}

// HasMorePages returns a boolean indicating whether more pages are available
func (p *DescribeMLModelsPaginator) HasMorePages() bool {
	return p.firstPage || (p.nextToken != nil && len(*p.nextToken) != 0)
}

// NextPage retrieves the next DescribeMLModels page.
func (p *DescribeMLModelsPaginator) NextPage(ctx context.Context, optFns ...func(*Options)) (*DescribeMLModelsOutput, error) {
	if !p.HasMorePages() {
		return nil, fmt.Errorf("no more pages available")
	}

	params := *p.params
	params.NextToken = p.nextToken

	var limit *int32
	if p.options.Limit > 0 {
		limit = &p.options.Limit
	}
	params.Limit = limit

	result, err := p.client.DescribeMLModels(ctx, &params, optFns...)
	if err != nil {
		return nil, err
	}
	p.firstPage = false

	prevToken := p.nextToken
	p.nextToken = result.NextToken

	if p.options.StopOnDuplicateToken &&
		prevToken != nil &&
		p.nextToken != nil &&
		*prevToken == *p.nextToken {
		p.nextToken = nil
	}

	return result, nil
}

// MLModelAvailableWaiterOptions are waiter options for MLModelAvailableWaiter
type MLModelAvailableWaiterOptions struct {

	// Set of options to modify how an operation is invoked. These apply to all
	// operations invoked for this client. Use functional options on operation call to
	// modify this list for per operation behavior.
	APIOptions []func(*middleware.Stack) error

	// MinDelay is the minimum amount of time to delay between retries. If unset,
	// MLModelAvailableWaiter will use default minimum delay of 30 seconds. Note that
	// MinDelay must resolve to a value lesser than or equal to the MaxDelay.
	MinDelay time.Duration

	// MaxDelay is the maximum amount of time to delay between retries. If unset or set
	// to zero, MLModelAvailableWaiter will use default max delay of 120 seconds. Note
	// that MaxDelay must resolve to value greater than or equal to the MinDelay.
	MaxDelay time.Duration

	// LogWaitAttempts is used to enable logging for waiter retry attempts
	LogWaitAttempts bool

	// Retryable is function that can be used to override the service defined
	// waiter-behavior based on operation output, or returned error. This function is
	// used by the waiter to decide if a state is retryable or a terminal state. By
	// default service-modeled logic will populate this option. This option can thus be
	// used to define a custom waiter state with fall-back to service-modeled waiter
	// state mutators.The function returns an error in case of a failure state. In case
	// of retry state, this function returns a bool value of true and nil error, while
	// in case of success it returns a bool value of false and nil error.
	Retryable func(context.Context, *DescribeMLModelsInput, *DescribeMLModelsOutput, error) (bool, error)
}

// MLModelAvailableWaiter defines the waiters for MLModelAvailable
type MLModelAvailableWaiter struct {
	client DescribeMLModelsAPIClient

	options MLModelAvailableWaiterOptions
}

// NewMLModelAvailableWaiter constructs a MLModelAvailableWaiter.
func NewMLModelAvailableWaiter(client DescribeMLModelsAPIClient, optFns ...func(*MLModelAvailableWaiterOptions)) *MLModelAvailableWaiter {
	options := MLModelAvailableWaiterOptions{}
	options.MinDelay = 30 * time.Second
	options.MaxDelay = 120 * time.Second
	options.Retryable = mLModelAvailableStateRetryable

	for _, fn := range optFns {
		fn(&options)
	}
	return &MLModelAvailableWaiter{
		client:  client,
		options: options,
	}
}

// Wait calls the waiter function for MLModelAvailable waiter. The maxWaitDur is
// the maximum wait duration the waiter will wait. The maxWaitDur is required and
// must be greater than zero.
func (w *MLModelAvailableWaiter) Wait(ctx context.Context, params *DescribeMLModelsInput, maxWaitDur time.Duration, optFns ...func(*MLModelAvailableWaiterOptions)) error {
	_, err := w.WaitForOutput(ctx, params, maxWaitDur, optFns...)
	return err
}

// WaitForOutput calls the waiter function for MLModelAvailable waiter and returns
// the output of the successful operation. The maxWaitDur is the maximum wait
// duration the waiter will wait. The maxWaitDur is required and must be greater
// than zero.
func (w *MLModelAvailableWaiter) WaitForOutput(ctx context.Context, params *DescribeMLModelsInput, maxWaitDur time.Duration, optFns ...func(*MLModelAvailableWaiterOptions)) (*DescribeMLModelsOutput, error) {
	if maxWaitDur <= 0 {
		return nil, fmt.Errorf("maximum wait time for waiter must be greater than zero")
	}

	options := w.options
	for _, fn := range optFns {
		fn(&options)
	}

	if options.MaxDelay <= 0 {
		options.MaxDelay = 120 * time.Second
	}

	if options.MinDelay > options.MaxDelay {
		return nil, fmt.Errorf("minimum waiter delay %v must be lesser than or equal to maximum waiter delay of %v.", options.MinDelay, options.MaxDelay)
	}

	ctx, cancelFn := context.WithTimeout(ctx, maxWaitDur)
	defer cancelFn()

	logger := smithywaiter.Logger{}
	remainingTime := maxWaitDur

	var attempt int64
	for {

		attempt++
		apiOptions := options.APIOptions
		start := time.Now()

		if options.LogWaitAttempts {
			logger.Attempt = attempt
			apiOptions = append([]func(*middleware.Stack) error{}, options.APIOptions...)
			apiOptions = append(apiOptions, logger.AddLogger)
		}

		out, err := w.client.DescribeMLModels(ctx, params, func(o *Options) {
			o.APIOptions = append(o.APIOptions, apiOptions...)
		})

		retryable, err := options.Retryable(ctx, params, out, err)
		if err != nil {
			return nil, err
		}
		if !retryable {
			return out, nil
		}

		remainingTime -= time.Since(start)
		if remainingTime < options.MinDelay || remainingTime <= 0 {
			break
		}

		// compute exponential backoff between waiter retries
		delay, err := smithywaiter.ComputeDelay(
			attempt, options.MinDelay, options.MaxDelay, remainingTime,
		)
		if err != nil {
			return nil, fmt.Errorf("error computing waiter delay, %w", err)
		}

		remainingTime -= delay
		// sleep for the delay amount before invoking a request
		if err := smithytime.SleepWithContext(ctx, delay); err != nil {
			return nil, fmt.Errorf("request cancelled while waiting, %w", err)
		}
	}
	return nil, fmt.Errorf("exceeded max wait time for MLModelAvailable waiter")
}

func mLModelAvailableStateRetryable(ctx context.Context, input *DescribeMLModelsInput, output *DescribeMLModelsOutput, err error) (bool, error) {

	if err == nil {
		pathValue, err := jmespath.Search("Results[].Status", output)
		if err != nil {
			return false, fmt.Errorf("error evaluating waiter state: %w", err)
		}

		expectedValue := "COMPLETED"
		var match = true
		listOfValues, ok := pathValue.([]interface{})
		if !ok {
			return false, fmt.Errorf("waiter comparator expected list got %T", pathValue)
		}

		if len(listOfValues) == 0 {
			match = false
		}
		for _, v := range listOfValues {
			value, ok := v.(types.EntityStatus)
			if !ok {
				return false, fmt.Errorf("waiter comparator expected types.EntityStatus value, got %T", pathValue)
			}

			if string(value) != expectedValue {
				match = false
			}
		}

		if match {
			return false, nil
		}
	}

	if err == nil {
		pathValue, err := jmespath.Search("Results[].Status", output)
		if err != nil {
			return false, fmt.Errorf("error evaluating waiter state: %w", err)
		}

		expectedValue := "FAILED"
		listOfValues, ok := pathValue.([]interface{})
		if !ok {
			return false, fmt.Errorf("waiter comparator expected list got %T", pathValue)
		}

		for _, v := range listOfValues {
			value, ok := v.(types.EntityStatus)
			if !ok {
				return false, fmt.Errorf("waiter comparator expected types.EntityStatus value, got %T", pathValue)
			}

			if string(value) == expectedValue {
				return false, fmt.Errorf("waiter state transitioned to Failure")
			}
		}
	}

	return true, nil
}

func newServiceMetadataMiddleware_opDescribeMLModels(region string) *awsmiddleware.RegisterServiceMetadata {
	return &awsmiddleware.RegisterServiceMetadata{
		Region:        region,
		ServiceID:     ServiceID,
		SigningName:   "machinelearning",
		OperationName: "DescribeMLModels",
	}
}
