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Update autorest version to prerelease 3.0.0-alpha.20240428.6.x #43726

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Update autorest version to prerelease 3.0.0-alpha.20240428.6.x #43726

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54f50e1
Initialize repository for autorest build 20240428.6.x
azure-sdk Apr 28, 2024
1fb31ea
Update SDK code ba_co_1
azure-sdk Apr 28, 2024
cb4f268
Update SDK code ex_ke_4
azure-sdk Apr 28, 2024
09db2aa
Update SDK code ad_ba_0
azure-sdk Apr 28, 2024
05c45c9
Update SDK code da_ev_3
azure-sdk Apr 28, 2024
f5a7408
Update SDK code st_wo_9
azure-sdk Apr 28, 2024
f78f9dd
Update SDK code mi_pr_6
azure-sdk Apr 28, 2024
c8a38ea
Update SDK code co_da_2
azure-sdk Apr 28, 2024
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2 changes: 1 addition & 1 deletion eng/Packages.Data.props
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Expand Up @@ -223,7 +223,7 @@
All should have PrivateAssets="All" set so they don't become package dependencies
-->
<ItemGroup>
<PackageReference Update="Microsoft.Azure.AutoRest.CSharp" Version="3.0.0-beta.20240428.8" PrivateAssets="All" />
<PackageReference Update="Microsoft.Azure.AutoRest.CSharp" Version="3.0.0-alpha.20240428.6.x" PrivateAssets="All" />
<PackageReference Update="Azure.ClientSdk.Analyzers" Version="0.1.1-dev.20240214.2" PrivateAssets="All" />
<PackageReference Update="coverlet.collector" Version="3.2.0" PrivateAssets="All" />
<PackageReference Update="Microsoft.CodeAnalysis.NetAnalyzers" Version="7.0.4" PrivateAssets="All" />
Expand Down
37 changes: 19 additions & 18 deletions eng/emitter-package-lock.json
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13 changes: 8 additions & 5 deletions eng/emitter-package.json
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@@ -1,15 +1,18 @@
{
"main": "dist/src/index.js",
"dependencies": {
"@azure-tools/typespec-csharp": "0.2.0-beta.20240428.8"
"@azure-tools/typespec-csharp": "https://pkgs.dev.azure.com/azure-sdk/public/_packaging/azure-sdk-for-js-test-autorest/npm/registry/@azure-tools/typespec-csharp/-/typespec-csharp-0.2.0-alpha.20240428.6.x.tgz"
},
"devDependencies": {
"@typespec/versioning": "0.55.0",
"@typespec/rest": "0.55.0",
"@typespec/compiler": "0.55.0",
"@typespec/http": "0.55.0",
"@typespec/openapi": "0.55.0",
"@azure-tools/typespec-azure-core": "0.41.0",
"@azure-tools/typespec-client-generator-core": "0.41.5"
"@typespec/versioning": "0.55.0",
"@azure-tools/typespec-client-generator-core": "https://artprodcus3.artifacts.visualstudio.com/A0fb41ef4-5012-48a9-bf39-4ee3de03ee35/29ec6040-b234-4e31-b139-33dc4287b756/_apis/artifact/cGlwZWxpbmVhcnRpZmFjdDovL2F6dXJlLXNkay9wcm9qZWN0SWQvMjllYzYwNDAtYjIzNC00ZTMxLWIxMzktMzNkYzQyODdiNzU2L2J1aWxkSWQvMzc0MDkzNS9hcnRpZmFjdE5hbWUvcGFja2FnZXM1/content?format=file&subPath=%2Fazure-tools-typespec-client-generator-core-0.42.0-pr-753.20240427.2.tgz",
"@typespec/rest": "0.55.0",
"@typespec/openapi": "0.55.0"
},
"overrides": {
"@autorest/csharp": "https://pkgs.dev.azure.com/azure-sdk/public/_packaging/azure-sdk-for-js-test-autorest/npm/registry/@autorest/csharp/-/csharp-3.0.0-alpha.20240428.6.x.tgz"
}
}
48 changes: 24 additions & 24 deletions sdk/anomalydetector/Azure.AI.AnomalyDetector/src/Generated/Docs/Multivariate.xml
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Expand Up @@ -149,7 +149,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<AnomalyDetectionModel> response = await client.TrainMultivariateModelAsync(modelInfo);
]]></code>
This sample shows how to call TrainMultivariateModelAsync with all parameters.
Expand All @@ -158,7 +158,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"))
ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"))
{
DataSchema = DataSchema.OneTable,
DisplayName = "<displayName>",
Expand All @@ -181,7 +181,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<AnomalyDetectionModel> response = client.TrainMultivariateModel(modelInfo);
]]></code>
This sample shows how to call TrainMultivariateModel with all parameters.
Expand All @@ -190,7 +190,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"))
ModelInfo modelInfo = new ModelInfo(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"))
{
DataSchema = DataSchema.OneTable,
DisplayName = "<displayName>",
Expand All @@ -216,8 +216,8 @@ Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultiva
using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = await client.TrainMultivariateModelAsync(content);

Expand All @@ -236,8 +236,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
dataSchema = "OneTable",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
displayName = "<displayName>",
slidingWindow = 1234,
alignPolicy = new
Expand Down Expand Up @@ -287,8 +287,8 @@ Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultiva
using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = client.TrainMultivariateModel(content);

Expand All @@ -307,8 +307,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
dataSchema = "OneTable",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
displayName = "<displayName>",
slidingWindow = 1234,
alignPolicy = new
Expand Down Expand Up @@ -539,7 +539,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<MultivariateDetectionResult> response = await client.DetectMultivariateBatchAnomalyAsync("<modelId>", options);
]]></code>
This sample shows how to call DetectMultivariateBatchAnomalyAsync with all parameters.
Expand All @@ -548,7 +548,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"))
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"))
{
TopContributorCount = 1234,
};
Expand All @@ -563,7 +563,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<MultivariateDetectionResult> response = client.DetectMultivariateBatchAnomaly("<modelId>", options);
]]></code>
This sample shows how to call DetectMultivariateBatchAnomaly with all parameters.
Expand All @@ -572,7 +572,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"))
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"))
{
TopContributorCount = 1234,
};
Expand All @@ -590,8 +590,8 @@ Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultiva
using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = await client.DetectMultivariateBatchAnomalyAsync("<modelId>", content);

Expand All @@ -613,8 +613,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
topContributorCount = 1234,
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = await client.DetectMultivariateBatchAnomalyAsync("<modelId>", content);

Expand Down Expand Up @@ -654,8 +654,8 @@ Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultiva
using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = client.DetectMultivariateBatchAnomaly("<modelId>", content);

Expand All @@ -677,8 +677,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
topContributorCount = 1234,
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = client.DetectMultivariateBatchAnomaly("<modelId>", content);

Expand Down
20 changes: 10 additions & 10 deletions sdk/anomalydetector/Azure.AI.AnomalyDetector/src/Generated/Docs/Univariate.xml
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Expand Up @@ -42,7 +42,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -108,7 +108,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -157,7 +157,7 @@ UnivariateDetectionOptions options = new UnivariateDetectionOptions(new TimeSeri
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
})
{
Expand Down Expand Up @@ -196,7 +196,7 @@ UnivariateDetectionOptions options = new UnivariateDetectionOptions(new TimeSeri
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
})
{
Expand Down Expand Up @@ -253,7 +253,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -321,7 +321,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -371,7 +371,7 @@ UnivariateChangePointDetectionOptions options = new UnivariateChangePointDetecti
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
}, TimeGranularity.Yearly)
{
Expand Down Expand Up @@ -407,7 +407,7 @@ UnivariateChangePointDetectionOptions options = new UnivariateChangePointDetecti
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
}, TimeGranularity.Yearly)
{
Expand Down Expand Up @@ -455,7 +455,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -509,7 +509,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down