// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_solution::_create_solution_output::CreateSolutionOutputBuilder;

pub use crate::operation::create_solution::_create_solution_input::CreateSolutionInputBuilder;

impl CreateSolutionInputBuilder {
    /// 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_solution::CreateSolutionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_solution::CreateSolutionError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_solution();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateSolution`.
///
/// <p>Creates the configuration for training a model. A trained model is known as a solution version. After the configuration is created, you train the model (create a solution version) by calling the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html">CreateSolutionVersion</a> operation. Every time you call <code>CreateSolutionVersion</code>, a new version of the solution is created.</p>
/// <p>After creating a solution version, you check its accuracy by calling <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_GetSolutionMetrics.html">GetSolutionMetrics</a>. When you are satisfied with the version, you deploy it using <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html">CreateCampaign</a>. The campaign provides recommendations to a client through the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html">GetRecommendations</a> API.</p>
/// <p>To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. </p> <note>
/// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> for solution hyperparameter optimization at this time.</p>
/// </note>
/// <p> <b>Status</b> </p>
/// <p>A solution can be in one of the following states:</p>
/// <ul>
/// <li> <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p> </li>
/// <li> <p>DELETE PENDING &gt; DELETE IN_PROGRESS</p> </li>
/// </ul>
/// <p>To get the status of the solution, call <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html">DescribeSolution</a>. Wait until the status shows as ACTIVE before calling <code>CreateSolutionVersion</code>.</p>
/// <p class="title"> <b>Related APIs</b> </p>
/// <ul>
/// <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutions.html">ListSolutions</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html">CreateSolutionVersion</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html">DescribeSolution</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteSolution.html">DeleteSolution</a> </p> </li>
/// </ul>
/// <ul>
/// <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a> </p> </li>
/// </ul>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateSolutionFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_solution::builders::CreateSolutionInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_solution::CreateSolutionOutput,
        crate::operation::create_solution::CreateSolutionError,
    > for CreateSolutionFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_solution::CreateSolutionOutput,
            crate::operation::create_solution::CreateSolutionError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateSolutionFluentBuilder {
    /// Creates a new `CreateSolution`.
    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 CreateSolution as a reference.
    pub fn as_input(&self) -> &crate::operation::create_solution::builders::CreateSolutionInputBuilder {
        &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_solution::CreateSolutionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_solution::CreateSolutionError,
            ::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_solution::CreateSolution::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_solution::CreateSolution::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_solution::CreateSolutionOutput,
        crate::operation::create_solution::CreateSolutionError,
        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 for the solution.</p>
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.name(input.into());
        self
    }
    /// <p>The name for the solution.</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 name for the solution.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_name()
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn perform_hpo(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_hpo(input);
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn set_perform_hpo(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_hpo(input);
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn get_perform_hpo(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_hpo()
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a> </p>
    /// </important>
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn perform_auto_ml(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_auto_ml(input);
        self
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a> </p>
    /// </important>
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn set_perform_auto_ml(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_auto_ml(input);
        self
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a> </p>
    /// </important>
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn get_perform_auto_ml(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_auto_ml()
    }
    /// <p>The ARN of the recipe to use for model training. This is required when <code>performAutoML</code> is false.</p>
    pub fn recipe_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.recipe_arn(input.into());
        self
    }
    /// <p>The ARN of the recipe to use for model training. This is required when <code>performAutoML</code> is false.</p>
    pub fn set_recipe_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_recipe_arn(input);
        self
    }
    /// <p>The ARN of the recipe to use for model training. This is required when <code>performAutoML</code> is false.</p>
    pub fn get_recipe_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_recipe_arn()
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn dataset_group_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.dataset_group_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn set_dataset_group_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_dataset_group_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn get_dataset_group_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_dataset_group_arn()
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn event_type(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.event_type(input.into());
        self
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn set_event_type(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_event_type(input);
        self
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn get_event_type(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_event_type()
    }
    /// <p>The configuration to use with the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p> <note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn solution_config(mut self, input: crate::types::SolutionConfig) -> Self {
        self.inner = self.inner.solution_config(input);
        self
    }
    /// <p>The configuration to use with the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p> <note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn set_solution_config(mut self, input: ::std::option::Option<crate::types::SolutionConfig>) -> Self {
        self.inner = self.inner.set_solution_config(input);
        self
    }
    /// <p>The configuration to use with the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p> <note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn get_solution_config(&self) -> &::std::option::Option<crate::types::SolutionConfig> {
        self.inner.get_solution_config()
    }
    /// Appends an item to `tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution.</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>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
}