// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_stream_processor::_create_stream_processor_output::CreateStreamProcessorOutputBuilder;

pub use crate::operation::create_stream_processor::_create_stream_processor_input::CreateStreamProcessorInputBuilder;

impl CreateStreamProcessorInputBuilder {
    /// 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_stream_processor::CreateStreamProcessorOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_stream_processor::CreateStreamProcessorError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_stream_processor();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateStreamProcessor`.
///
/// <p>Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.</p>
/// <p>Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.</p>
/// <ul>
/// <li> <p>If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (<code>Input</code>) and a Kinesis data stream (<code>Output</code>) stream for receiving the output. You must use the <code>FaceSearch</code> option in <code>Settings</code>, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, use <code>StopStreamProcessor</code> to stop processing.</p> </li>
/// <li> <p>If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (<code>Input</code>), Amazon S3 bucket information (<code>Output</code>), and an Amazon SNS topic ARN (<code>NotificationChannel</code>). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you want to detect by using the <code>ConnectedHome</code> option in settings, and selecting one of the following: <code>PERSON</code>, <code>PET</code>, <code>PACKAGE</code>, <code>ALL</code> You can also specify where in the frame you want Amazon Rekognition to monitor with <code>RegionsOfInterest</code>. When you run the <code>StartStreamProcessor</code> operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.</p> </li>
/// </ul>
/// <p> Use <code>Name</code> to assign an identifier for the stream processor. You use <code>Name</code> to manage the stream processor. For example, you can start processing the source video by calling <code>StartStreamProcessor</code> with the <code>Name</code> field. </p>
/// <p>This operation requires permissions to perform the <code>rekognition:CreateStreamProcessor</code> action. If you want to tag your stream processor, you also require permission to perform the <code>rekognition:TagResource</code> operation.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateStreamProcessorFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_stream_processor::builders::CreateStreamProcessorInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_stream_processor::CreateStreamProcessorOutput,
        crate::operation::create_stream_processor::CreateStreamProcessorError,
    > for CreateStreamProcessorFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_stream_processor::CreateStreamProcessorOutput,
            crate::operation::create_stream_processor::CreateStreamProcessorError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateStreamProcessorFluentBuilder {
    /// Creates a new `CreateStreamProcessor`.
    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 CreateStreamProcessor as a reference.
    pub fn as_input(&self) -> &crate::operation::create_stream_processor::builders::CreateStreamProcessorInputBuilder {
        &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_stream_processor::CreateStreamProcessorOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_stream_processor::CreateStreamProcessorError,
            ::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_stream_processor::CreateStreamProcessor::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_stream_processor::CreateStreamProcessor::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_stream_processor::CreateStreamProcessorOutput,
        crate::operation::create_stream_processor::CreateStreamProcessorError,
        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>Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is <code>StreamProcessorInput</code>. This is required for both face search and label detection stream processors.</p>
    pub fn input(mut self, input: crate::types::StreamProcessorInput) -> Self {
        self.inner = self.inner.input(input);
        self
    }
    /// <p>Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is <code>StreamProcessorInput</code>. This is required for both face search and label detection stream processors.</p>
    pub fn set_input(mut self, input: ::std::option::Option<crate::types::StreamProcessorInput>) -> Self {
        self.inner = self.inner.set_input(input);
        self
    }
    /// <p>Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is <code>StreamProcessorInput</code>. This is required for both face search and label detection stream processors.</p>
    pub fn get_input(&self) -> &::std::option::Option<crate::types::StreamProcessorInput> {
        self.inner.get_input()
    }
    /// <p>Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is <code>StreamProcessorOutput</code>. This must be a <code>S3Destination</code> of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.</p>
    pub fn output(mut self, input: crate::types::StreamProcessorOutput) -> Self {
        self.inner = self.inner.output(input);
        self
    }
    /// <p>Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is <code>StreamProcessorOutput</code>. This must be a <code>S3Destination</code> of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.</p>
    pub fn set_output(mut self, input: ::std::option::Option<crate::types::StreamProcessorOutput>) -> Self {
        self.inner = self.inner.set_output(input);
        self
    }
    /// <p>Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is <code>StreamProcessorOutput</code>. This must be a <code>S3Destination</code> of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.</p>
    pub fn get_output(&self) -> &::std::option::Option<crate::types::StreamProcessorOutput> {
        self.inner.get_output()
    }
    /// <p>An identifier you assign to the stream processor. You can use <code>Name</code> to manage the stream processor. For example, you can get the current status of the stream processor by calling <code>DescribeStreamProcessor</code>. <code>Name</code> is idempotent. This is required for both face search and label detection stream processors. </p>
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.name(input.into());
        self
    }
    /// <p>An identifier you assign to the stream processor. You can use <code>Name</code> to manage the stream processor. For example, you can get the current status of the stream processor by calling <code>DescribeStreamProcessor</code>. <code>Name</code> is idempotent. This is required for both face search and label detection stream processors. </p>
    pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_name(input);
        self
    }
    /// <p>An identifier you assign to the stream processor. You can use <code>Name</code> to manage the stream processor. For example, you can get the current status of the stream processor by calling <code>DescribeStreamProcessor</code>. <code>Name</code> is idempotent. This is required for both face search and label detection stream processors. </p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_name()
    }
    /// <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
    pub fn settings(mut self, input: crate::types::StreamProcessorSettings) -> Self {
        self.inner = self.inner.settings(input);
        self
    }
    /// <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
    pub fn set_settings(mut self, input: ::std::option::Option<crate::types::StreamProcessorSettings>) -> Self {
        self.inner = self.inner.set_settings(input);
        self
    }
    /// <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
    pub fn get_settings(&self) -> &::std::option::Option<crate::types::StreamProcessorSettings> {
        self.inner.get_settings()
    }
    /// <p>The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.</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 Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.</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 Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.</p>
    pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_role_arn()
    }
    /// Adds a key-value pair to `Tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p> A set of tags (key-value pairs) that you want to attach to the stream processor. </p>
    pub fn tags(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.tags(k.into(), v.into());
        self
    }
    /// <p> A set of tags (key-value pairs) that you want to attach to the stream processor. </p>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
    /// <p> A set of tags (key-value pairs) that you want to attach to the stream processor. </p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_tags()
    }
    /// <p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p>
    /// <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p>
    /// <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>
    pub fn notification_channel(mut self, input: crate::types::StreamProcessorNotificationChannel) -> Self {
        self.inner = self.inner.notification_channel(input);
        self
    }
    /// <p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p>
    /// <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p>
    /// <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>
    pub fn set_notification_channel(mut self, input: ::std::option::Option<crate::types::StreamProcessorNotificationChannel>) -> Self {
        self.inner = self.inner.set_notification_channel(input);
        self
    }
    /// <p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p>
    /// <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p>
    /// <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>
    pub fn get_notification_channel(&self) -> &::std::option::Option<crate::types::StreamProcessorNotificationChannel> {
        self.inner.get_notification_channel()
    }
    /// <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected. </p>
    /// <p> </p>
    pub fn kms_key_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.kms_key_id(input.into());
        self
    }
    /// <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected. </p>
    /// <p> </p>
    pub fn set_kms_key_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_kms_key_id(input);
        self
    }
    /// <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected. </p>
    /// <p> </p>
    pub fn get_kms_key_id(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_kms_key_id()
    }
    /// Appends an item to `RegionsOfInterest`.
    ///
    /// To override the contents of this collection use [`set_regions_of_interest`](Self::set_regions_of_interest).
    ///
    /// <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. </p>
    pub fn regions_of_interest(mut self, input: crate::types::RegionOfInterest) -> Self {
        self.inner = self.inner.regions_of_interest(input);
        self
    }
    /// <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. </p>
    pub fn set_regions_of_interest(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::RegionOfInterest>>) -> Self {
        self.inner = self.inner.set_regions_of_interest(input);
        self
    }
    /// <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. </p>
    pub fn get_regions_of_interest(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::RegionOfInterest>> {
        self.inner.get_regions_of_interest()
    }
    /// <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>
    pub fn data_sharing_preference(mut self, input: crate::types::StreamProcessorDataSharingPreference) -> Self {
        self.inner = self.inner.data_sharing_preference(input);
        self
    }
    /// <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>
    pub fn set_data_sharing_preference(mut self, input: ::std::option::Option<crate::types::StreamProcessorDataSharingPreference>) -> Self {
        self.inner = self.inner.set_data_sharing_preference(input);
        self
    }
    /// <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>
    pub fn get_data_sharing_preference(&self) -> &::std::option::Option<crate::types::StreamProcessorDataSharingPreference> {
        self.inner.get_data_sharing_preference()
    }
}