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// 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 StartModelInput {
    /// <p>The name of the project that contains the model that you want to start.</p>
    pub project_name: ::std::option::Option<::std::string::String>,
    /// <p>The version of the model that you want to start.</p>
    pub model_version: ::std::option::Option<::std::string::String>,
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    pub min_inference_units: ::std::option::Option<i32>,
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub client_token: ::std::option::Option<::std::string::String>,
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub max_inference_units: ::std::option::Option<i32>,
}
impl StartModelInput {
    /// <p>The name of the project that contains the model that you want to start.</p>
    pub fn project_name(&self) -> ::std::option::Option<&str> {
        self.project_name.as_deref()
    }
    /// <p>The version of the model that you want to start.</p>
    pub fn model_version(&self) -> ::std::option::Option<&str> {
        self.model_version.as_deref()
    }
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    pub fn min_inference_units(&self) -> ::std::option::Option<i32> {
        self.min_inference_units
    }
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub fn client_token(&self) -> ::std::option::Option<&str> {
        self.client_token.as_deref()
    }
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub fn max_inference_units(&self) -> ::std::option::Option<i32> {
        self.max_inference_units
    }
}
impl StartModelInput {
    /// Creates a new builder-style object to manufacture [`StartModelInput`](crate::operation::start_model::StartModelInput).
    pub fn builder() -> crate::operation::start_model::builders::StartModelInputBuilder {
        crate::operation::start_model::builders::StartModelInputBuilder::default()
    }
}

/// A builder for [`StartModelInput`](crate::operation::start_model::StartModelInput).
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default, ::std::fmt::Debug)]
pub struct StartModelInputBuilder {
    pub(crate) project_name: ::std::option::Option<::std::string::String>,
    pub(crate) model_version: ::std::option::Option<::std::string::String>,
    pub(crate) min_inference_units: ::std::option::Option<i32>,
    pub(crate) client_token: ::std::option::Option<::std::string::String>,
    pub(crate) max_inference_units: ::std::option::Option<i32>,
}
impl StartModelInputBuilder {
    /// <p>The name of the project that contains the model that you want to start.</p>
    /// This field is required.
    pub fn project_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.project_name = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The name of the project that contains the model that you want to start.</p>
    pub fn set_project_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.project_name = input;
        self
    }
    /// <p>The name of the project that contains the model that you want to start.</p>
    pub fn get_project_name(&self) -> &::std::option::Option<::std::string::String> {
        &self.project_name
    }
    /// <p>The version of the model that you want to start.</p>
    /// This field is required.
    pub fn model_version(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.model_version = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The version of the model that you want to start.</p>
    pub fn set_model_version(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.model_version = input;
        self
    }
    /// <p>The version of the model that you want to start.</p>
    pub fn get_model_version(&self) -> &::std::option::Option<::std::string::String> {
        &self.model_version
    }
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    /// This field is required.
    pub fn min_inference_units(mut self, input: i32) -> Self {
        self.min_inference_units = ::std::option::Option::Some(input);
        self
    }
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    pub fn set_min_inference_units(mut self, input: ::std::option::Option<i32>) -> Self {
        self.min_inference_units = input;
        self
    }
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    pub fn get_min_inference_units(&self) -> &::std::option::Option<i32> {
        &self.min_inference_units
    }
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub fn client_token(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.client_token = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub fn set_client_token(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.client_token = input;
        self
    }
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub fn get_client_token(&self) -> &::std::option::Option<::std::string::String> {
        &self.client_token
    }
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub fn max_inference_units(mut self, input: i32) -> Self {
        self.max_inference_units = ::std::option::Option::Some(input);
        self
    }
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub fn set_max_inference_units(mut self, input: ::std::option::Option<i32>) -> Self {
        self.max_inference_units = input;
        self
    }
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub fn get_max_inference_units(&self) -> &::std::option::Option<i32> {
        &self.max_inference_units
    }
    /// Consumes the builder and constructs a [`StartModelInput`](crate::operation::start_model::StartModelInput).
    pub fn build(self) -> ::std::result::Result<crate::operation::start_model::StartModelInput, ::aws_smithy_types::error::operation::BuildError> {
        ::std::result::Result::Ok(crate::operation::start_model::StartModelInput {
            project_name: self.project_name,
            model_version: self.model_version,
            min_inference_units: self.min_inference_units,
            client_token: self.client_token,
            max_inference_units: self.max_inference_units,
        })
    }
}