// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_auto_ml_job::_create_auto_ml_job_output::CreateAutoMlJobOutputBuilder;

pub use crate::operation::create_auto_ml_job::_create_auto_ml_job_input::CreateAutoMlJobInputBuilder;

impl CreateAutoMlJobInputBuilder {
    /// 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_auto_ml_job::CreateAutoMlJobOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_auto_ml_job::CreateAutoMLJobError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_auto_ml_job();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateAutoMLJob`.
///
/// <p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.</p> <note>
/// <p>We recommend using the new versions <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html">CreateAutoMLJobV2</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a>, which offer backward compatibility.</p>
/// <p> <code>CreateAutoMLJobV2</code> can manage tabular problem types identical to those of its previous version <code>CreateAutoMLJob</code>, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).</p>
/// <p>Find guidelines about how to migrate a <code>CreateAutoMLJob</code> to <code>CreateAutoMLJobV2</code> in <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment-api.html#autopilot-create-experiment-api-migrate-v1-v2">Migrate a CreateAutoMLJob to CreateAutoMLJobV2</a>.</p>
/// </note>
/// <p>You can find the best-performing model after you run an AutoML job by calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a> (recommended) or <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html">DescribeAutoMLJob</a>.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateAutoMLJobFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_auto_ml_job::builders::CreateAutoMlJobInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_auto_ml_job::CreateAutoMlJobOutput,
        crate::operation::create_auto_ml_job::CreateAutoMLJobError,
    > for CreateAutoMLJobFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_auto_ml_job::CreateAutoMlJobOutput,
            crate::operation::create_auto_ml_job::CreateAutoMLJobError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateAutoMLJobFluentBuilder {
    /// Creates a new `CreateAutoMLJob`.
    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 CreateAutoMLJob as a reference.
    pub fn as_input(&self) -> &crate::operation::create_auto_ml_job::builders::CreateAutoMlJobInputBuilder {
        &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_auto_ml_job::CreateAutoMlJobOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_auto_ml_job::CreateAutoMLJobError,
            ::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_auto_ml_job::CreateAutoMLJob::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_auto_ml_job::CreateAutoMLJob::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_auto_ml_job::CreateAutoMlJobOutput,
        crate::operation::create_auto_ml_job::CreateAutoMLJobError,
        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>Identifies an Autopilot job. The name must be unique to your account and is case insensitive.</p>
    pub fn auto_ml_job_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.auto_ml_job_name(input.into());
        self
    }
    /// <p>Identifies an Autopilot job. The name must be unique to your account and is case insensitive.</p>
    pub fn set_auto_ml_job_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_auto_ml_job_name(input);
        self
    }
    /// <p>Identifies an Autopilot job. The name must be unique to your account and is case insensitive.</p>
    pub fn get_auto_ml_job_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_auto_ml_job_name()
    }
    /// Appends an item to `InputDataConfig`.
    ///
    /// To override the contents of this collection use [`set_input_data_config`](Self::set_input_data_config).
    ///
    /// <p>An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to <code>InputDataConfig</code> supported by <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.</p>
    pub fn input_data_config(mut self, input: crate::types::AutoMlChannel) -> Self {
        self.inner = self.inner.input_data_config(input);
        self
    }
    /// <p>An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to <code>InputDataConfig</code> supported by <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.</p>
    pub fn set_input_data_config(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::AutoMlChannel>>) -> Self {
        self.inner = self.inner.set_input_data_config(input);
        self
    }
    /// <p>An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to <code>InputDataConfig</code> supported by <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.</p>
    pub fn get_input_data_config(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::AutoMlChannel>> {
        self.inner.get_input_data_config()
    }
    /// <p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.</p>
    pub fn output_data_config(mut self, input: crate::types::AutoMlOutputDataConfig) -> Self {
        self.inner = self.inner.output_data_config(input);
        self
    }
    /// <p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.</p>
    pub fn set_output_data_config(mut self, input: ::std::option::Option<crate::types::AutoMlOutputDataConfig>) -> Self {
        self.inner = self.inner.set_output_data_config(input);
        self
    }
    /// <p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.</p>
    pub fn get_output_data_config(&self) -> &::std::option::Option<crate::types::AutoMlOutputDataConfig> {
        self.inner.get_output_data_config()
    }
    /// <p>Defines the type of supervised learning problem available for the candidates. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types"> Amazon SageMaker Autopilot problem types</a>.</p>
    pub fn problem_type(mut self, input: crate::types::ProblemType) -> Self {
        self.inner = self.inner.problem_type(input);
        self
    }
    /// <p>Defines the type of supervised learning problem available for the candidates. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types"> Amazon SageMaker Autopilot problem types</a>.</p>
    pub fn set_problem_type(mut self, input: ::std::option::Option<crate::types::ProblemType>) -> Self {
        self.inner = self.inner.set_problem_type(input);
        self
    }
    /// <p>Defines the type of supervised learning problem available for the candidates. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types"> Amazon SageMaker Autopilot problem types</a>.</p>
    pub fn get_problem_type(&self) -> &::std::option::Option<crate::types::ProblemType> {
        self.inner.get_problem_type()
    }
    /// <p>Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a> for the default values.</p>
    pub fn auto_ml_job_objective(mut self, input: crate::types::AutoMlJobObjective) -> Self {
        self.inner = self.inner.auto_ml_job_objective(input);
        self
    }
    /// <p>Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a> for the default values.</p>
    pub fn set_auto_ml_job_objective(mut self, input: ::std::option::Option<crate::types::AutoMlJobObjective>) -> Self {
        self.inner = self.inner.set_auto_ml_job_objective(input);
        self
    }
    /// <p>Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a> for the default values.</p>
    pub fn get_auto_ml_job_objective(&self) -> &::std::option::Option<crate::types::AutoMlJobObjective> {
        self.inner.get_auto_ml_job_objective()
    }
    /// <p>A collection of settings used to configure an AutoML job.</p>
    pub fn auto_ml_job_config(mut self, input: crate::types::AutoMlJobConfig) -> Self {
        self.inner = self.inner.auto_ml_job_config(input);
        self
    }
    /// <p>A collection of settings used to configure an AutoML job.</p>
    pub fn set_auto_ml_job_config(mut self, input: ::std::option::Option<crate::types::AutoMlJobConfig>) -> Self {
        self.inner = self.inner.set_auto_ml_job_config(input);
        self
    }
    /// <p>A collection of settings used to configure an AutoML job.</p>
    pub fn get_auto_ml_job_config(&self) -> &::std::option::Option<crate::types::AutoMlJobConfig> {
        self.inner.get_auto_ml_job_config()
    }
    /// <p>The ARN of the role that is used to access the data.</p>
    pub fn role_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.role_arn(input.into());
        self
    }
    /// <p>The ARN of the role that is used to access the data.</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 ARN of the role that is used to access the data.</p>
    pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_role_arn()
    }
    /// <p>Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.</p>
    pub fn generate_candidate_definitions_only(mut self, input: bool) -> Self {
        self.inner = self.inner.generate_candidate_definitions_only(input);
        self
    }
    /// <p>Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.</p>
    pub fn set_generate_candidate_definitions_only(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_generate_candidate_definitions_only(input);
        self
    }
    /// <p>Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.</p>
    pub fn get_generate_candidate_definitions_only(&self) -> &::std::option::Option<bool> {
        self.inner.get_generate_candidate_definitions_only()
    }
    /// 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 ServicesResources</a>. Tag keys must be unique per resource.</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 ServicesResources</a>. Tag keys must be unique per resource.</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 ServicesResources</a>. Tag keys must be unique per resource.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
    /// <p>Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.</p>
    pub fn model_deploy_config(mut self, input: crate::types::ModelDeployConfig) -> Self {
        self.inner = self.inner.model_deploy_config(input);
        self
    }
    /// <p>Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.</p>
    pub fn set_model_deploy_config(mut self, input: ::std::option::Option<crate::types::ModelDeployConfig>) -> Self {
        self.inner = self.inner.set_model_deploy_config(input);
        self
    }
    /// <p>Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.</p>
    pub fn get_model_deploy_config(&self) -> &::std::option::Option<crate::types::ModelDeployConfig> {
        self.inner.get_model_deploy_config()
    }
}