// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::fmt::Debug)]
pub struct CreateSolutionInput {
    /// <p>The name for the solution.</p>
    pub name: ::std::option::Option<::std::string::String>,
    /// <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 perform_hpo: ::std::option::Option<bool>,
    /// <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 perform_auto_ml: ::std::option::Option<bool>,
    /// <p>The ARN of the recipe to use for model training. This is required when <code>performAutoML</code> is false.</p>
    pub recipe_arn: ::std::option::Option<::std::string::String>,
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub dataset_group_arn: ::std::option::Option<::std::string::String>,
    /// <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 event_type: ::std::option::Option<::std::string::String>,
    /// <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 solution_config: ::std::option::Option<crate::types::SolutionConfig>,
    /// <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 tags: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>,
}
impl CreateSolutionInput {
    /// <p>The name for the solution.</p>
    pub fn name(&self) -> ::std::option::Option<&str> {
        self.name.as_deref()
    }
    /// <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(&self) -> ::std::option::Option<bool> {
        self.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(&self) -> ::std::option::Option<bool> {
        self.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(&self) -> ::std::option::Option<&str> {
        self.recipe_arn.as_deref()
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn dataset_group_arn(&self) -> ::std::option::Option<&str> {
        self.dataset_group_arn.as_deref()
    }
    /// <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(&self) -> ::std::option::Option<&str> {
        self.event_type.as_deref()
    }
    /// <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(&self) -> ::std::option::Option<&crate::types::SolutionConfig> {
        self.solution_config.as_ref()
    }
    /// <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>
    ///
    /// If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use `.tags.is_none()`.
    pub fn tags(&self) -> &[crate::types::Tag] {
        self.tags.as_deref().unwrap_or_default()
    }
}
impl CreateSolutionInput {
    /// Creates a new builder-style object to manufacture [`CreateSolutionInput`](crate::operation::create_solution::CreateSolutionInput).
    pub fn builder() -> crate::operation::create_solution::builders::CreateSolutionInputBuilder {
        crate::operation::create_solution::builders::CreateSolutionInputBuilder::default()
    }
}

/// A builder for [`CreateSolutionInput`](crate::operation::create_solution::CreateSolutionInput).
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default, ::std::fmt::Debug)]
pub struct CreateSolutionInputBuilder {
    pub(crate) name: ::std::option::Option<::std::string::String>,
    pub(crate) perform_hpo: ::std::option::Option<bool>,
    pub(crate) perform_auto_ml: ::std::option::Option<bool>,
    pub(crate) recipe_arn: ::std::option::Option<::std::string::String>,
    pub(crate) dataset_group_arn: ::std::option::Option<::std::string::String>,
    pub(crate) event_type: ::std::option::Option<::std::string::String>,
    pub(crate) solution_config: ::std::option::Option<crate::types::SolutionConfig>,
    pub(crate) tags: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>,
}
impl CreateSolutionInputBuilder {
    /// <p>The name for the solution.</p>
    /// This field is required.
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.name = ::std::option::Option::Some(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.name = input;
        self
    }
    /// <p>The name for the solution.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        &self.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.perform_hpo = ::std::option::Option::Some(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.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.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.perform_auto_ml = ::std::option::Option::Some(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.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.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.recipe_arn = ::std::option::Option::Some(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.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.recipe_arn
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    /// This field is required.
    pub fn dataset_group_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.dataset_group_arn = ::std::option::Option::Some(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.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.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.event_type = ::std::option::Option::Some(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.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.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.solution_config = ::std::option::Option::Some(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.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.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 {
        let mut v = self.tags.unwrap_or_default();
        v.push(input);
        self.tags = ::std::option::Option::Some(v);
        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.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.tags
    }
    /// Consumes the builder and constructs a [`CreateSolutionInput`](crate::operation::create_solution::CreateSolutionInput).
    pub fn build(
        self,
    ) -> ::std::result::Result<crate::operation::create_solution::CreateSolutionInput, ::aws_smithy_types::error::operation::BuildError> {
        ::std::result::Result::Ok(crate::operation::create_solution::CreateSolutionInput {
            name: self.name,
            perform_hpo: self.perform_hpo,
            perform_auto_ml: self.perform_auto_ml,
            recipe_arn: self.recipe_arn,
            dataset_group_arn: self.dataset_group_arn,
            event_type: self.event_type,
            solution_config: self.solution_config,
            tags: self.tags,
        })
    }
}