Update AutoRest C# version to 3.0.0-beta.20240506.1 (#43872)

* Update Generator Version 3.0.0-beta.20240506.1 * Update SDK codes de_he_2 * Update SDK codes he_mi_3 * Update SDK codes ad_co_0 * Update SDK codes st_wo_6 * Update SDK codes mi_pu_4 * Update SDK codes co_de_1 * update api signature --------- Co-authored-by: Mingzhe Huang (from Dev Box) <mingzhehuang@microsoft.com>
Azure · May 7, 2024 · fc4237a · fc4237a
1 parent 5499781
commit fc4237a
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diff --git a/eng/Packages.Data.props b/eng/Packages.Data.props
@@ -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.20240428.10" PrivateAssets="All" />
+    <PackageReference Update="Microsoft.Azure.AutoRest.CSharp" Version="3.0.0-beta.20240506.1" PrivateAssets="All" />
     <PackageReference Update="Azure.ClientSdk.Analyzers" Version="0.1.1-dev.20240429.1" PrivateAssets="All" />
     <PackageReference Update="coverlet.collector" Version="3.2.0" PrivateAssets="All" />
     <PackageReference Update="Microsoft.CodeAnalysis.NetAnalyzers" Version="7.0.4" PrivateAssets="All" />

diff --git a/eng/emitter-package-lock.json b/eng/emitter-package-lock.json
diff --git a/eng/emitter-package.json b/eng/emitter-package.json
@@ -1,15 +1,15 @@
 {
   "main": "dist/src/index.js",
   "dependencies": {
-    "@azure-tools/typespec-csharp": "0.2.0-beta.20240428.10"
+    "@azure-tools/typespec-csharp": "0.2.0-beta.20240506.1"
   },
   "devDependencies": {
+    "@azure-tools/typespec-client-generator-core": "0.41.8",
     "@typespec/openapi": "0.55.0",
+    "@typespec/http": "0.55.0",
+    "@typespec/rest": "0.55.0",
     "@azure-tools/typespec-azure-core": "0.41.0",
     "@typespec/compiler": "0.55.0",
-    "@azure-tools/typespec-client-generator-core": "0.41.5",
-    "@typespec/versioning": "0.55.0",
-    "@typespec/http": "0.55.0",
-    "@typespec/rest": "0.55.0"
+    "@typespec/versioning": "0.55.0"
   }
 }
diff --git a/sdk/anomalydetector/Azure.AI.AnomalyDetector/src/Generated/Docs/Multivariate.xml b/sdk/anomalydetector/Azure.AI.AnomalyDetector/src/Generated/Docs/Multivariate.xml
@@ -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.
@@ -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>",
@@ -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.
@@ -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>",
@@ -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);
 
@@ -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
@@ -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);
 
@@ -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
@@ -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.
@@ -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,
 };
@@ -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.
@@ -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,
 };
@@ -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);
 
@@ -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);
 
@@ -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);
 
@@ -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);
 

diff --git a/sdk/anomalydetector/Azure.AI.AnomalyDetector/src/Generated/Docs/Univariate.xml b/sdk/anomalydetector/Azure.AI.AnomalyDetector/src/Generated/Docs/Univariate.xml
@@ -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,
         }
     },
@@ -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,
         }
     },
@@ -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"),
     }
 })
 {
@@ -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"),
     }
 })
 {
@@ -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,
         }
     },
@@ -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,
         }
     },
@@ -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)
 {
@@ -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)
 {
@@ -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,
         }
     },
@@ -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,
         }
     },