1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_language_model::_create_language_model_output::CreateLanguageModelOutputBuilder;

pub use crate::operation::create_language_model::_create_language_model_input::CreateLanguageModelInputBuilder;

/// Fluent builder constructing a request to `CreateLanguageModel`.
///
/// <p>Creates a new custom language model.</p>
/// <p>When creating a new custom language model, you must specify:</p>
/// <ul>
/// <li> <p>If you want a Wideband (audio sample rates over 16,000 Hz) or Narrowband (audio sample rates under 16,000 Hz) base model</p> </li>
/// <li> <p>The location of your training and tuning files (this must be an Amazon S3 URI)</p> </li>
/// <li> <p>The language of your model</p> </li>
/// <li> <p>A unique name for your model</p> </li>
/// </ul>
#[derive(std::clone::Clone, std::fmt::Debug)]
pub struct CreateLanguageModelFluentBuilder {
    handle: std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_language_model::builders::CreateLanguageModelInputBuilder,
}
impl CreateLanguageModelFluentBuilder {
    /// Creates a new `CreateLanguageModel`.
    pub(crate) fn new(handle: std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: Default::default(),
        }
    }
    /// Consume this builder, creating a customizable operation that can be modified before being
    /// sent. The operation's inner [http::Request] can be modified as well.
    pub async fn customize(
        self,
    ) -> std::result::Result<
        crate::client::customize::CustomizableOperation<
            crate::operation::create_language_model::CreateLanguageModel,
            aws_http::retry::AwsResponseRetryClassifier,
        >,
        aws_smithy_http::result::SdkError<
            crate::operation::create_language_model::CreateLanguageModelError,
        >,
    > {
        let handle = self.handle.clone();
        let operation = self
            .inner
            .build()
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?
            .make_operation(&handle.conf)
            .await
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?;
        Ok(crate::client::customize::CustomizableOperation { handle, operation })
    }

    /// 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_language_model::CreateLanguageModelOutput,
        aws_smithy_http::result::SdkError<
            crate::operation::create_language_model::CreateLanguageModelError,
        >,
    > {
        let op = self
            .inner
            .build()
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?
            .make_operation(&self.handle.conf)
            .await
            .map_err(aws_smithy_http::result::SdkError::construction_failure)?;
        self.handle.client.call(op).await
    }
    /// <p>The language code that represents the language of your model. Each custom language model must contain terms in only one language, and the language you select for your custom language model must match the language of your training and tuning data.</p>
    /// <p>For a list of supported languages and their associated language codes, refer to the <a href="https://docs.aws.amazon.com/transcribe/latest/dg/supported-languages.html">Supported languages</a> table. Note that US English (<code>en-US</code>) is the only language supported with Amazon Transcribe Medical.</p>
    /// <p>A custom language model can only be used to transcribe files in the same language as the model. For example, if you create a custom language model using US English (<code>en-US</code>), you can only apply this model to files that contain English audio.</p>
    pub fn language_code(mut self, input: crate::types::ClmLanguageCode) -> Self {
        self.inner = self.inner.language_code(input);
        self
    }
    /// <p>The language code that represents the language of your model. Each custom language model must contain terms in only one language, and the language you select for your custom language model must match the language of your training and tuning data.</p>
    /// <p>For a list of supported languages and their associated language codes, refer to the <a href="https://docs.aws.amazon.com/transcribe/latest/dg/supported-languages.html">Supported languages</a> table. Note that US English (<code>en-US</code>) is the only language supported with Amazon Transcribe Medical.</p>
    /// <p>A custom language model can only be used to transcribe files in the same language as the model. For example, if you create a custom language model using US English (<code>en-US</code>), you can only apply this model to files that contain English audio.</p>
    pub fn set_language_code(
        mut self,
        input: std::option::Option<crate::types::ClmLanguageCode>,
    ) -> Self {
        self.inner = self.inner.set_language_code(input);
        self
    }
    /// <p>The Amazon Transcribe standard language model, or base model, used to create your custom language model. Amazon Transcribe offers two options for base models: Wideband and Narrowband.</p>
    /// <p>If the audio you want to transcribe has a sample rate of 16,000 Hz or greater, choose <code>WideBand</code>. To transcribe audio with a sample rate less than 16,000 Hz, choose <code>NarrowBand</code>.</p>
    pub fn base_model_name(mut self, input: crate::types::BaseModelName) -> Self {
        self.inner = self.inner.base_model_name(input);
        self
    }
    /// <p>The Amazon Transcribe standard language model, or base model, used to create your custom language model. Amazon Transcribe offers two options for base models: Wideband and Narrowband.</p>
    /// <p>If the audio you want to transcribe has a sample rate of 16,000 Hz or greater, choose <code>WideBand</code>. To transcribe audio with a sample rate less than 16,000 Hz, choose <code>NarrowBand</code>.</p>
    pub fn set_base_model_name(
        mut self,
        input: std::option::Option<crate::types::BaseModelName>,
    ) -> Self {
        self.inner = self.inner.set_base_model_name(input);
        self
    }
    /// <p>A unique name, chosen by you, for your custom language model.</p>
    /// <p>This name is case sensitive, cannot contain spaces, and must be unique within an Amazon Web Services account. If you try to create a new custom language model with the same name as an existing custom language model, you get a <code>ConflictException</code> error.</p>
    pub fn model_name(mut self, input: impl Into<std::string::String>) -> Self {
        self.inner = self.inner.model_name(input.into());
        self
    }
    /// <p>A unique name, chosen by you, for your custom language model.</p>
    /// <p>This name is case sensitive, cannot contain spaces, and must be unique within an Amazon Web Services account. If you try to create a new custom language model with the same name as an existing custom language model, you get a <code>ConflictException</code> error.</p>
    pub fn set_model_name(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.inner = self.inner.set_model_name(input);
        self
    }
    /// <p>Contains the Amazon S3 location of the training data you want to use to create a new custom language model, and permissions to access this location.</p>
    /// <p>When using <code>InputDataConfig</code>, you must include these sub-parameters: <code>S3Uri</code>, which is the Amazon S3 location of your training data, and <code>DataAccessRoleArn</code>, which is the Amazon Resource Name (ARN) of the role that has permission to access your specified Amazon S3 location. You can optionally include <code>TuningDataS3Uri</code>, which is the Amazon S3 location of your tuning data. If you specify different Amazon S3 locations for training and tuning data, the ARN you use must have permissions to access both locations.</p>
    pub fn input_data_config(mut self, input: crate::types::InputDataConfig) -> Self {
        self.inner = self.inner.input_data_config(input);
        self
    }
    /// <p>Contains the Amazon S3 location of the training data you want to use to create a new custom language model, and permissions to access this location.</p>
    /// <p>When using <code>InputDataConfig</code>, you must include these sub-parameters: <code>S3Uri</code>, which is the Amazon S3 location of your training data, and <code>DataAccessRoleArn</code>, which is the Amazon Resource Name (ARN) of the role that has permission to access your specified Amazon S3 location. You can optionally include <code>TuningDataS3Uri</code>, which is the Amazon S3 location of your tuning data. If you specify different Amazon S3 locations for training and tuning data, the ARN you use must have permissions to access both locations.</p>
    pub fn set_input_data_config(
        mut self,
        input: std::option::Option<crate::types::InputDataConfig>,
    ) -> Self {
        self.inner = self.inner.set_input_data_config(input);
        self
    }
    /// Appends an item to `Tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>Adds one or more custom tags, each in the form of a key:value pair, to a new custom language model at the time you create this new model.</p>
    /// <p>To learn more about using tags with Amazon Transcribe, refer to <a href="https://docs.aws.amazon.com/transcribe/latest/dg/tagging.html">Tagging resources</a>.</p>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p>Adds one or more custom tags, each in the form of a key:value pair, to a new custom language model at the time you create this new model.</p>
    /// <p>To learn more about using tags with Amazon Transcribe, refer to <a href="https://docs.aws.amazon.com/transcribe/latest/dg/tagging.html">Tagging resources</a>.</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
    }
}