// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_model::_create_model_output::CreateModelOutputBuilder;

pub use crate::operation::create_model::_create_model_input::CreateModelInputBuilder;

impl CreateModelInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::create_model::CreateModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_model::CreateModelError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_model();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateModel`.
///
/// <p>Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.</p>
/// <p>Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.</p>
/// <p>To host your model, you create an endpoint configuration with the <code>CreateEndpointConfig</code> API, and then create an endpoint with the <code>CreateEndpoint</code> API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.</p>
/// <p>To run a batch transform using your model, you start a job with the <code>CreateTransformJob</code> API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.</p>
/// <p>In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateModelFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_model::builders::CreateModelInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_model::CreateModelOutput,
        crate::operation::create_model::CreateModelError,
    > for CreateModelFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_model::CreateModelOutput,
            crate::operation::create_model::CreateModelError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateModelFluentBuilder {
    /// Creates a new `CreateModel`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the CreateModel as a reference.
    pub fn as_input(&self) -> &crate::operation::create_model::builders::CreateModelInputBuilder {
        &self.inner
    }
    /// 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_model::CreateModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_model::CreateModelError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::create_model::CreateModel::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_model::CreateModel::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::create_model::CreateModelOutput,
        crate::operation::create_model::CreateModelError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl Into<crate::config::Builder>) -> Self {
        self.set_config_override(Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>The name of the new model.</p>
    pub fn model_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.model_name(input.into());
        self
    }
    /// <p>The name of the new model.</p>
    pub fn set_model_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_model_name(input);
        self
    }
    /// <p>The name of the new model.</p>
    pub fn get_model_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_model_name()
    }
    /// <p>The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.</p>
    pub fn primary_container(mut self, input: crate::types::ContainerDefinition) -> Self {
        self.inner = self.inner.primary_container(input);
        self
    }
    /// <p>The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.</p>
    pub fn set_primary_container(mut self, input: ::std::option::Option<crate::types::ContainerDefinition>) -> Self {
        self.inner = self.inner.set_primary_container(input);
        self
    }
    /// <p>The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.</p>
    pub fn get_primary_container(&self) -> &::std::option::Option<crate::types::ContainerDefinition> {
        self.inner.get_primary_container()
    }
    /// Appends an item to `Containers`.
    ///
    /// To override the contents of this collection use [`set_containers`](Self::set_containers).
    ///
    /// <p>Specifies the containers in the inference pipeline.</p>
    pub fn containers(mut self, input: crate::types::ContainerDefinition) -> Self {
        self.inner = self.inner.containers(input);
        self
    }
    /// <p>Specifies the containers in the inference pipeline.</p>
    pub fn set_containers(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::ContainerDefinition>>) -> Self {
        self.inner = self.inner.set_containers(input);
        self
    }
    /// <p>Specifies the containers in the inference pipeline.</p>
    pub fn get_containers(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::ContainerDefinition>> {
        self.inner.get_containers()
    }
    /// <p>Specifies details of how containers in a multi-container endpoint are called.</p>
    pub fn inference_execution_config(mut self, input: crate::types::InferenceExecutionConfig) -> Self {
        self.inner = self.inner.inference_execution_config(input);
        self
    }
    /// <p>Specifies details of how containers in a multi-container endpoint are called.</p>
    pub fn set_inference_execution_config(mut self, input: ::std::option::Option<crate::types::InferenceExecutionConfig>) -> Self {
        self.inner = self.inner.set_inference_execution_config(input);
        self
    }
    /// <p>Specifies details of how containers in a multi-container endpoint are called.</p>
    pub fn get_inference_execution_config(&self) -> &::std::option::Option<crate::types::InferenceExecutionConfig> {
        self.inner.get_inference_execution_config()
    }
    /// <p>The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">SageMaker Roles</a>.</p><note>
    /// <p>To be able to pass this role to SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission.</p>
    /// </note>
    pub fn execution_role_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.execution_role_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">SageMaker Roles</a>.</p><note>
    /// <p>To be able to pass this role to SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission.</p>
    /// </note>
    pub fn set_execution_role_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_execution_role_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">SageMaker Roles</a>.</p><note>
    /// <p>To be able to pass this role to SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission.</p>
    /// </note>
    pub fn get_execution_role_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_execution_role_arn()
    }
    /// Appends an item to `Tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>An 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/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services Resources</a>.</p>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p>An 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/general/latest/gr/aws_tagging.html">Tagging 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
    }
    /// <p>An 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/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services Resources</a>.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
    /// <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html">VpcConfig</a> object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. <code>VpcConfig</code> is used in hosting services and in batch transform. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html">Protect Endpoints by Using an Amazon Virtual Private Cloud</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html">Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud</a>.</p>
    pub fn vpc_config(mut self, input: crate::types::VpcConfig) -> Self {
        self.inner = self.inner.vpc_config(input);
        self
    }
    /// <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html">VpcConfig</a> object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. <code>VpcConfig</code> is used in hosting services and in batch transform. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html">Protect Endpoints by Using an Amazon Virtual Private Cloud</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html">Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud</a>.</p>
    pub fn set_vpc_config(mut self, input: ::std::option::Option<crate::types::VpcConfig>) -> Self {
        self.inner = self.inner.set_vpc_config(input);
        self
    }
    /// <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html">VpcConfig</a> object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. <code>VpcConfig</code> is used in hosting services and in batch transform. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html">Protect Endpoints by Using an Amazon Virtual Private Cloud</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html">Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud</a>.</p>
    pub fn get_vpc_config(&self) -> &::std::option::Option<crate::types::VpcConfig> {
        self.inner.get_vpc_config()
    }
    /// <p>Isolates the model container. No inbound or outbound network calls can be made to or from the model container.</p>
    pub fn enable_network_isolation(mut self, input: bool) -> Self {
        self.inner = self.inner.enable_network_isolation(input);
        self
    }
    /// <p>Isolates the model container. No inbound or outbound network calls can be made to or from the model container.</p>
    pub fn set_enable_network_isolation(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_enable_network_isolation(input);
        self
    }
    /// <p>Isolates the model container. No inbound or outbound network calls can be made to or from the model container.</p>
    pub fn get_enable_network_isolation(&self) -> &::std::option::Option<bool> {
        self.inner.get_enable_network_isolation()
    }
}