import { SENSITIVE_STRING } from "@aws-sdk/smithy-client" ; import { MetadataBearer as $MetadataBearer , SmithyException as __SmithyException } from "@aws-sdk/types" ; /**
 * <p>Your request caused an exception with an internal dependency. Contact customer support. </p>
 */ export interface InternalDependencyException extends __SmithyException , $MetadataBearer { name : "InternalDependencyException" ; $fault : "server" ; Message ? : string ; } /**
 * <p> An internal failure occurred. </p>
 */ export interface InternalFailure extends __SmithyException , $MetadataBearer { name : "InternalFailure" ; $fault : "server" ; Message ? : string ; } export interface InvokeEndpointInput { /**
     * <p>The name of the endpoint that you specified when you created the endpoint using the
     *                 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html">CreateEndpoint</a> API. </p>
     */ EndpointName : string | undefined ; /**
     * <p>Provides input data, in the format specified in the <code>ContentType</code>
     *             request header. Amazon SageMaker passes all of the data in the body to the model. </p>
     *         <p>For information about the format of the request body, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html">Common Data
     *                 Formats-Inference</a>.</p>
     */ Body : Uint8Array | undefined ; /**
     * <p>The MIME type of the input data in the request body.</p>
     */ ContentType ? : string ; /**
     * <p>The desired MIME type of the inference in the response.</p>
     */ Accept ? : string ; /**
     * <p>Provides additional information about a request for an inference submitted to a model
     *             hosted at an Amazon SageMaker endpoint. The information is an opaque value that is
     *             forwarded verbatim. You could use this value, for example, to provide an ID that you can
     *             use to track a request or to provide other metadata that a service endpoint was
     *             programmed to process. The value must consist of no more than 1024 visible US-ASCII
     *             characters as specified in <a href="https://tools.ietf.org/html/rfc7230#section-3.2.6">Section 3.3.6. Field Value
     *                 Components</a> of the Hypertext Transfer Protocol (HTTP/1.1). </p>
     *         <p>The code in your model is responsible for setting or updating any custom attributes in
     *             the response. If your code does not set this value in the response, an empty value is
     *             returned. For example, if a custom attribute represents the trace ID, your model can
     *             prepend the custom attribute with <code>Trace ID:</code> in your post-processing
     *             function.</p>
     *
     *         <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python
     *             SDK.</p>
     */ CustomAttributes ? : string ; /**
     * <p>The model to request for inference when invoking a multi-model endpoint.</p>
     */ TargetModel ? : string ; /**
     * <p>Specify the production variant to send the inference request to when invoking an
     *             endpoint that is running two or more variants. Note that this parameter overrides the
     *             default behavior for the endpoint, which is to distribute the invocation traffic based
     *             on the variant weights.</p>
     *         <p>For information about how to use variant targeting to perform a/b testing, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html">Test models in
     *                 production</a>
     *          </p>
     */ TargetVariant ? : string ; /**
     * <p>If the endpoint hosts multiple containers and is configured to use direct invocation,
     *             this parameter specifies the host name of the container to invoke.</p>
     */ TargetContainerHostname ? : string ; /**
     * <p>If you provide a value, it is added to the captured data when you enable data capture
     *             on the endpoint. For information about data capture, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html">Capture
     *             Data</a>.</p>
     */ InferenceId ? : string ; } export namespace InvokeEndpointInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , ... ( obj . Body && { Body : SENSITIVE_STRING } ) , ... ( obj . CustomAttributes && { CustomAttributes : SENSITIVE_STRING } ) , } ) ; } export interface InvokeEndpointOutput { /**
     * <p>Includes the inference provided by the model.</p>
     *         <p>For information about the format of the response body, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html">Common Data
     *                 Formats-Inference</a>.</p>
     */ Body : Uint8Array | undefined ; /**
     * <p>The MIME type of the inference returned in the response body.</p>
     */ ContentType ? : string ; /**
     * <p>Identifies the production variant that was invoked.</p>
     */ InvokedProductionVariant ? : string ; /**
     * <p>Provides additional information in the response about the inference returned by a
     *             model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is
     *             forwarded verbatim. You could use this value, for example, to return an ID received in
     *             the <code>CustomAttributes</code> header of a request or other metadata that a service
     *             endpoint was programmed to produce. The value must consist of no more than 1024 visible
     *             US-ASCII characters as specified in <a href="https://tools.ietf.org/html/rfc7230#section-3.2.6">Section 3.3.6. Field Value
     *                 Components</a> of the Hypertext Transfer Protocol (HTTP/1.1). If the customer
     *             wants the custom attribute returned, the model must set the custom attribute to be
     *             included on the way back. </p>
     *         <p>The code in your model is responsible for setting or updating any custom attributes in
     *             the response. If your code does not set this value in the response, an empty value is
     *             returned. For example, if a custom attribute represents the trace ID, your model can
     *             prepend the custom attribute with <code>Trace ID:</code> in your post-processing
     *             function.</p>
     *         <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python
     *             SDK.</p>
     */ CustomAttributes ? : string ; } export namespace InvokeEndpointOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , ... ( obj . Body && { Body : SENSITIVE_STRING } ) , ... ( obj . CustomAttributes && { CustomAttributes : SENSITIVE_STRING } ) , } ) ; } /**
 * <p> Model (owned by the customer in the container) returned 4xx or 5xx error code.
 *         </p>
 */ export interface ModelError extends __SmithyException , $MetadataBearer { name : "ModelError" ; $fault : "client" ; Message ? : string ; /**
     * <p> Original status code. </p>
     */ OriginalStatusCode ? : number ; /**
     * <p> Original message. </p>
     */ OriginalMessage ? : string ; /**
     * <p> The Amazon Resource Name (ARN) of the log stream. </p>
     */ LogStreamArn ? : string ; } /**
 * <p>Either a serverless endpoint variant's resources are still being provisioned, or a multi-model endpoint is still downloading or loading the target model. Wait and try your request again.</p>
 */ export interface ModelNotReadyException extends __SmithyException , $MetadataBearer { name : "ModelNotReadyException" ; $fault : "client" ; Message ? : string ; } /**
 * <p> The service is unavailable. Try your call again. </p>
 */ export interface ServiceUnavailable extends __SmithyException , $MetadataBearer { name : "ServiceUnavailable" ; $fault : "server" ; Message ? : string ; } /**
 * <p> Inspect your request and try again. </p>
 */ export interface ValidationError extends __SmithyException , $MetadataBearer { name : "ValidationError" ; $fault : "client" ; Message ? : string ; } export interface InvokeEndpointAsyncInput { /**
     * <p>The name of the endpoint that you specified when you created the endpoint using
     *             the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html">
     *                <code>CreateEndpoint</code>
     *             </a> API.</p>
     */ EndpointName : string | undefined ; /**
     * <p>The MIME type of the input data in the request body.</p>
     */ ContentType ? : string ; /**
     * <p>The desired MIME type of the inference in the response.</p>
     */ Accept ? : string ; /**
     * <p>Provides additional information about a request for an inference submitted to
     *             a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is
     *             forwarded verbatim. You could use this value, for example, to provide an ID that you
     *             can use to track a request or to provide other metadata that a service endpoint was
     *             programmed to process. The value must consist of no more than 1024
     *             visible US-ASCII characters as specified in
     *             <a href="https://datatracker.ietf.org/doc/html/rfc7230#section-3.2.6">Section 3.3.6.
     *                 Field Value Components</a> of the Hypertext Transfer Protocol (HTTP/1.1). </p>
     *
     *         <p>The code in your model is responsible for setting or updating any custom attributes
     *             in the response. If your code does not set this value in the response, an empty
     *             value is returned. For example, if a custom attribute represents the trace ID,
     *             your model can prepend the custom attribute with <code>Trace ID</code>: in your post-processing function. </p>
     *
     *         <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK. </p>
     */ CustomAttributes ? : string ; /**
     * <p>The identifier for the inference request. Amazon SageMaker will generate an identifier for you
     *             if none is specified. </p>
     */ InferenceId ? : string ; /**
     * <p>The Amazon S3 URI where the inference request payload is stored.</p>
     */ InputLocation : string | undefined ; /**
     * <p>Maximum age in seconds a request can be in the queue before it is marked as expired.</p>
     */ RequestTTLSeconds ? : number ; } export namespace InvokeEndpointAsyncInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , ... ( obj . CustomAttributes && { CustomAttributes : SENSITIVE_STRING } ) , } ) ; } export interface InvokeEndpointAsyncOutput { /**
     * <p>Identifier for an inference request. This will be the same as the <code>InferenceId</code> specified
     *             in the input. Amazon SageMaker will generate an identifier for you if you do not specify one.</p>
     */ InferenceId ? : string ; /**
     * <p>The Amazon S3 URI where the inference response payload is stored.</p>
     */ OutputLocation ? : string ; } export namespace InvokeEndpointAsyncOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; }