// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_inference_experiment::_create_inference_experiment_output::CreateInferenceExperimentOutputBuilder;

pub use crate::operation::create_inference_experiment::_create_inference_experiment_input::CreateInferenceExperimentInputBuilder;

/// Fluent builder constructing a request to `CreateInferenceExperiment`.
///
/// <p> Creates an inference experiment using the configurations specified in the request. </p>
/// <p> Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests.html">Shadow tests</a>. </p>
/// <p> Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration. </p>
/// <p> While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests-view-monitor-edit.html">View, monitor, and edit shadow tests</a>. </p>
#[derive(std::clone::Clone, std::fmt::Debug)]
pub struct CreateInferenceExperimentFluentBuilder {
    handle: std::sync::Arc<crate::client::Handle>,
                    inner: crate::operation::create_inference_experiment::builders::CreateInferenceExperimentInputBuilder,
}
impl CreateInferenceExperimentFluentBuilder {
    /// Creates a new `CreateInferenceExperiment`.
    pub(crate) fn new(handle: std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: Default::default(),
        }
    }
    /// Consume this builder, creating a customizable operation that can be modified before being
    /// sent. The operation's inner [http::Request] can be modified as well.
    pub async fn customize(
        self,
    ) -> std::result::Result<
        crate::client::customize::CustomizableOperation<
            crate::operation::create_inference_experiment::CreateInferenceExperiment,
            aws_http::retry::AwsResponseRetryClassifier,
        >,
        aws_smithy_http::result::SdkError<
            crate::operation::create_inference_experiment::CreateInferenceExperimentError,
        >,
    > {
        let handle = self.handle.clone();
        let operation = self
            .inner
            .build()
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?
            .make_operation(&handle.conf)
            .await
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?;
        Ok(crate::client::customize::CustomizableOperation { handle, operation })
    }

    /// Sends the request and returns the response.
    ///
    /// If an error occurs, an `SdkError` will be returned with additional details that
    /// can be matched against.
    ///
    /// By default, any retryable failures will be retried twice. Retry behavior
    /// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> std::result::Result<
        crate::operation::create_inference_experiment::CreateInferenceExperimentOutput,
        aws_smithy_http::result::SdkError<
            crate::operation::create_inference_experiment::CreateInferenceExperimentError,
        >,
    > {
        let op = self
            .inner
            .build()
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?
            .make_operation(&self.handle.conf)
            .await
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?;
        self.handle.client.call(op).await
    }
    /// <p>The name for the inference experiment.</p>
    pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
        self.inner = self.inner.name(input.into());
        self
    }
    /// <p>The name for the inference experiment.</p>
    pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.inner = self.inner.set_name(input);
        self
    }
    /// <p> The type of the inference experiment that you want to run. The following types of experiments are possible: </p>
    /// <ul>
    /// <li> <p> <code>ShadowMode</code>: You can use this type to validate a shadow variant. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests.html">Shadow tests</a>. </p> </li>
    /// </ul>
    pub fn r#type(mut self, input: crate::types::InferenceExperimentType) -> Self {
        self.inner = self.inner.r#type(input);
        self
    }
    /// <p> The type of the inference experiment that you want to run. The following types of experiments are possible: </p>
    /// <ul>
    /// <li> <p> <code>ShadowMode</code>: You can use this type to validate a shadow variant. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests.html">Shadow tests</a>. </p> </li>
    /// </ul>
    pub fn set_type(
        mut self,
        input: std::option::Option<crate::types::InferenceExperimentType>,
    ) -> Self {
        self.inner = self.inner.set_type(input);
        self
    }
    /// <p> The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days. </p>
    pub fn schedule(mut self, input: crate::types::InferenceExperimentSchedule) -> Self {
        self.inner = self.inner.schedule(input);
        self
    }
    /// <p> The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days. </p>
    pub fn set_schedule(
        mut self,
        input: std::option::Option<crate::types::InferenceExperimentSchedule>,
    ) -> Self {
        self.inner = self.inner.set_schedule(input);
        self
    }
    /// <p>A description for the inference experiment.</p>
    pub fn description(mut self, input: impl Into<std::string::String>) -> Self {
        self.inner = self.inner.description(input.into());
        self
    }
    /// <p>A description for the inference experiment.</p>
    pub fn set_description(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.inner = self.inner.set_description(input);
        self
    }
    /// <p> The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment. </p>
    pub fn role_arn(mut self, input: impl Into<std::string::String>) -> Self {
        self.inner = self.inner.role_arn(input.into());
        self
    }
    /// <p> The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment. </p>
    pub fn set_role_arn(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.inner = self.inner.set_role_arn(input);
        self
    }
    /// <p> The name of the Amazon SageMaker endpoint on which you want to run the inference experiment. </p>
    pub fn endpoint_name(mut self, input: impl Into<std::string::String>) -> Self {
        self.inner = self.inner.endpoint_name(input.into());
        self
    }
    /// <p> The name of the Amazon SageMaker endpoint on which you want to run the inference experiment. </p>
    pub fn set_endpoint_name(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.inner = self.inner.set_endpoint_name(input);
        self
    }
    /// Appends an item to `ModelVariants`.
    ///
    /// To override the contents of this collection use [`set_model_variants`](Self::set_model_variants).
    ///
    /// <p> An array of <code>ModelVariantConfig</code> objects. There is one for each variant in the inference experiment. Each <code>ModelVariantConfig</code> object in the array describes the infrastructure configuration for the corresponding variant. </p>
    pub fn model_variants(mut self, input: crate::types::ModelVariantConfig) -> Self {
        self.inner = self.inner.model_variants(input);
        self
    }
    /// <p> An array of <code>ModelVariantConfig</code> objects. There is one for each variant in the inference experiment. Each <code>ModelVariantConfig</code> object in the array describes the infrastructure configuration for the corresponding variant. </p>
    pub fn set_model_variants(
        mut self,
        input: std::option::Option<std::vec::Vec<crate::types::ModelVariantConfig>>,
    ) -> Self {
        self.inner = self.inner.set_model_variants(input);
        self
    }
    /// <p> The Amazon S3 location and configuration for storing inference request and response data. </p>
    /// <p> This is an optional parameter that you can use for data capture. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html">Capture data</a>. </p>
    pub fn data_storage_config(
        mut self,
        input: crate::types::InferenceExperimentDataStorageConfig,
    ) -> Self {
        self.inner = self.inner.data_storage_config(input);
        self
    }
    /// <p> The Amazon S3 location and configuration for storing inference request and response data. </p>
    /// <p> This is an optional parameter that you can use for data capture. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html">Capture data</a>. </p>
    pub fn set_data_storage_config(
        mut self,
        input: std::option::Option<crate::types::InferenceExperimentDataStorageConfig>,
    ) -> Self {
        self.inner = self.inner.set_data_storage_config(input);
        self
    }
    /// <p> The configuration of <code>ShadowMode</code> inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates. </p>
    pub fn shadow_mode_config(mut self, input: crate::types::ShadowModeConfig) -> Self {
        self.inner = self.inner.shadow_mode_config(input);
        self
    }
    /// <p> The configuration of <code>ShadowMode</code> inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates. </p>
    pub fn set_shadow_mode_config(
        mut self,
        input: std::option::Option<crate::types::ShadowModeConfig>,
    ) -> Self {
        self.inner = self.inner.set_shadow_mode_config(input);
        self
    }
    /// <p> The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The <code>KmsKey</code> can be any of the following formats: </p>
    /// <ul>
    /// <li> <p>KMS key ID</p> <p> <code>"1234abcd-12ab-34cd-56ef-1234567890ab"</code> </p> </li>
    /// <li> <p>Amazon Resource Name (ARN) of a KMS key</p> <p> <code>"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"</code> </p> </li>
    /// <li> <p>KMS key Alias</p> <p> <code>"alias/ExampleAlias"</code> </p> </li>
    /// <li> <p>Amazon Resource Name (ARN) of a KMS key Alias</p> <p> <code>"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"</code> </p> </li>
    /// </ul>
    /// <p> If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call <code>kms:Encrypt</code>. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for <code>OutputDataConfig</code>. If you use a bucket policy with an <code>s3:PutObject</code> permission that only allows objects with server-side encryption, set the condition key of <code>s3:x-amz-server-side-encryption</code> to <code>"aws:kms"</code>. For more information, see <a href="https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html">KMS managed Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i> </p>
    /// <p> The KMS key policy must grant permission to the IAM role that you specify in your <code>CreateEndpoint</code> and <code>UpdateEndpoint</code> requests. For more information, see <a href="https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html">Using Key Policies in Amazon Web Services KMS</a> in the <i>Amazon Web Services Key Management Service Developer Guide</i>. </p>
    pub fn kms_key(mut self, input: impl Into<std::string::String>) -> Self {
        self.inner = self.inner.kms_key(input.into());
        self
    }
    /// <p> The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The <code>KmsKey</code> can be any of the following formats: </p>
    /// <ul>
    /// <li> <p>KMS key ID</p> <p> <code>"1234abcd-12ab-34cd-56ef-1234567890ab"</code> </p> </li>
    /// <li> <p>Amazon Resource Name (ARN) of a KMS key</p> <p> <code>"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"</code> </p> </li>
    /// <li> <p>KMS key Alias</p> <p> <code>"alias/ExampleAlias"</code> </p> </li>
    /// <li> <p>Amazon Resource Name (ARN) of a KMS key Alias</p> <p> <code>"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"</code> </p> </li>
    /// </ul>
    /// <p> If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call <code>kms:Encrypt</code>. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for <code>OutputDataConfig</code>. If you use a bucket policy with an <code>s3:PutObject</code> permission that only allows objects with server-side encryption, set the condition key of <code>s3:x-amz-server-side-encryption</code> to <code>"aws:kms"</code>. For more information, see <a href="https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html">KMS managed Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i> </p>
    /// <p> The KMS key policy must grant permission to the IAM role that you specify in your <code>CreateEndpoint</code> and <code>UpdateEndpoint</code> requests. For more information, see <a href="https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html">Using Key Policies in Amazon Web Services KMS</a> in the <i>Amazon Web Services Key Management Service Developer Guide</i>. </p>
    pub fn set_kms_key(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.inner = self.inner.set_kms_key(input);
        self
    }
    /// Appends an item to `Tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p> Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see <a href="https://docs.aws.amazon.com/ARG/latest/userguide/tagging.html">Tagging your Amazon Web Services Resources</a>. </p>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p> Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see <a href="https://docs.aws.amazon.com/ARG/latest/userguide/tagging.html">Tagging your Amazon Web Services Resources</a>. </p>
    pub fn set_tags(
        mut self,
        input: std::option::Option<std::vec::Vec<crate::types::Tag>>,
    ) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
}